[Cell-biology] Drug Discovery and Development and the Human Cytome Project - Update 21 May 2006

Peter Van Osta pvosta_rommel at rommel_cs.com
Sun May 21 09:04:01 EST 2006


As the on-line version of my article on the Human Cytome Project and the
application of cytomics in medicine and drug discovery (pharmaceutical
research) evolves, I put the updated version in this newsgroup for
reference. The original "question" on a Human Cytome Project was posted in
bionet.cellbiol on Monday 1 December 2003.

Source document:
Drug Discovery and Development and the Human Cytome Project

This article was created by Peter Van Osta, MD.


This article is dedicated to all the patients hoping and waiting for new
treatments of unmet medical needs and the improvement of existing therapies.
It is also dedicated to all the scientists working in basic and applied
research, working day and night to deliver these new drugs and treatments.

The breakthroughs in basic research have not resulted in the creation of
many new therapies for patients, which lead to the 'pipeline problem'.
Improving drug discovery and development is not a simple endeavour, as we
have seen in recent years. Although this article is critical about the
(evolution of the) overall drug discovery and development process it also
honours the individual contributions of scientists who have discovered and
developed drugs which save and improve the lives of many people. The purpose
of critical discussion is to advance the understanding of the field. While
many are spurred to criticize from competitive instincts, "a discussion
which you win but which fails to help ... clarify ... should be regarded as
a sheer loss." (Popper). Let us look at the present with the future in our
The problems with drug discovery and development are already leading to
international initiatives. See also Innovation and Stagnation: Challenge and
Opportunity on the Critical Path to New Medical Products - USA, Innovative
Medicines Initiative (IMI) - Europe EU, New Safe Medicines Faster Project -
Europe EU and the Priority Medicines for Europe and the World Project "A
Public Health Approach to Innovation" - WHO.

Drugs have both a humanitarian value and a financial value. Pharmaceutical
research and development contribute a major part of the research necessary
to move new science from the laboratory to the bedside. Through academic and
industry efforts, many new drugs and devices have been developed and
marketed, which save and improve the lives of many people. However, the
costs to bring new drugs have risen sharply in recent years and the output
of drug development and as such the Return On Investment (R.O.I.) has not
kept pace . Fewer drugs and biologics are making it from Phase I clinical
trials to the marketplace, which has dramatically increased the cost of drug
development (Crawford L.M., 2004). Late stage failure in Phase III clinical
trials and NDA disapproval has risen from 20% up to 50% (Crawford L.M.,
2004). From an economical perspective, the goal of improvements in drug
discovery and development is to increase the Net Present Value (NPV also
"fair value" or "time value") of pipeline molecules and to decrease the
costs associated with pursuing failed projects. Basicaly the Net Present
Value (NPV) is the worth of a good at the present moment and for investments
the Net Present Value is an important indicator. Only an investment, that
offers you a positive net present value, is considered to be worth to
pursue. This has not been the case in recent years for many drug development
projects, as up to 90% fail. The bottom line form an economical perspective
is that in the end any change in the process or its (scientific) content
should improve the Return on Capital Employed (ROCE).

This article aims at improving the probability of success in drug
development (reduce late stage clinical development attrition) by using
better disease models (higher predictive power) in drug discovery an
pre-clinical development. This improvement should lead to bringing better
drugs (more effective, less side effects) to the patients, both cheaper and

The challenges which the pharmaceutical industry is facing:
Increasing competition and ending patents.
R&D becoming more and more expensive and less productive.
Regulatory requirements becoming increasingly strict and heavy.
Challenges to keep a balanced product pipeline.
Changing demographics and disease profiles.

>From a business perspective, there are 2 sources of value creation by a more
productive discovery processes ("clinical" quality of molecules):
Increased Net Present Value (NPV) of pipeline molecules because of higher
likelihood of successfully reaching the market (more true positives).
Decreased costs associated with pursuing fewer failed projects (less false
There are 4 levers for creating value in (pre-)clinical drug development
(process improvement):
Increase the probability of success (POS) in drug development.
Decrease the time in drug development.
Decrease the cost of drug development.
Maximize the income potential per product in the pipeline.

What should we achieve for the overall drug development process:
Decrease late-stage attrition.
Reduce the time-to-market.
What should be the deliverables (metrics):
9 out of 10 drugs succeeding in drug development instead of failing.
50% reduction in time-to-market.

The pharmaceutical industry has a history of initial innovative
breakthroughs (first-in-class), followed by slower, stepwise improvements of
such initial successes (best-in-class). How can we improve the Probability
Of Success (POS) of the overall drug discovery and development process and
as such improve both the quality and quantity of new drugs, both for
innovative as well as stepwise improvements? Why do we need to learn more
about the human cytome to improve drug discovery and development? How can
cytome research help us to discover and develop better drugs with a higher
success rate in clinical development? What is wrong with the drug discovery
and development process as it is now, so its costs are soaring and its
R.O.I. is declining?

Everyone managing the discovery and development of drugs has to ask a few
questions about every new scientific idea or technology which pops up (and
they do, all the time). Every scientific idea or technology to be applied to
drug discovery and development must specify realistic and compatible goals
and expectations. When we want to introduce a new scientific idea into drug
discovery and development we must balance between good science and a
credible business plan. We must be critical about the promises made. Is the
claim or argument relevant to the overall subject? ('Subject matter'
relevance). Is the argument or claim relevant to proving or disproving the
conclusion at hand? ('Probative' relevance).

The pitfalls of applied research

The vulnerability of applied research, such as drug discovery and
development, is hidden in the basics of scientific reasoning. In traditional
Aristotelian logic, deductive reasoning is inference in which the conclusion
is of no greater generality than the premises, as opposed to abductive and
inductive reasoning, where the conclusion is of greater generality than the
premises. Other theories of logic define deductive reasoning as inference in
which the conclusion is just as certain as the premises, as opposed to
inductive reasoning, where the conclusion can have less certainty than the
premises. Scientific research is to a large extent based on inductive
reasoning and as such vulnerable to overenthusiastic generalizations and
simplifications. The discussion about the problems of the drug discovery and
development process is full of red herrings and other logical fallacies,
which distracts our attention from the real question: does the treatment
work in man (see also Organon from Aristotle). Ignoratio elenchi (also known
as irrelevant conclusion) is the logical fallacy of presenting an argument
that may in itself be valid, but which proves or supports a different
proposition than the one it is purporting to prove or support (The promise
to the pharmaceutical industry "do this" or "buy that" and you will deliver
more and better drugs to the market). The ignoratio elenchi fallacy is an
argument that may well have relevant premises, but does not have a relevant
conclusion. The Red Herring fallacy is the counterpart of the ignoratio
elenchi where the explicit conclusion is relevant but the premises are not,
because they actually support something else. The complex relation between
the input of the drug discovery and development process (manpower,
methodology and technology) and its output (drugs which succeed) is
underestimated, leading to unacceptable late stage attrition rates. However,
there are no simple answer to complex problems, such as how to create a
truly productive process, both effective and efficient. The truth is forced
upon us during the late stages of clinical development, when we fail because
of a lack of predictive power of discovery and preclinical development.

Like most opportunistic enterprises, pharmaceutical companies run the
managerial risk of succumbing to the enthusiastic optimism of a pragmatic
fallacy. Leading scientists and managers have to understand and
systematically manage ambiguity in an increasingly complex environment.
There is more ambiguity in clinical reality and economical reality than in
an Eppendorf tube. You have to assess risk and benefits of decisions and
anticipate the impact on drug discovery and development in the longer term,
far beyond the short-term quarterly goals. Those who take responibility for
strategic management have to grasp opportunities capable of generating new
opportunities for improving drug discovery and development in a productive
way. You have to scan the scientific, business and regulatory environment
and think well ahead to identify things which may get in the way of meeting
objectives - either obstacles or changes in the overall situation. Managers
and scientists have to develop complex strategies which take into account
the diverse interests across scientific domains, economics, rules and
regulations. It does not help that scientists prefer the scientific
excitement of reading Nature and Science, while managers prefer the Wall
Street Journal and the Financial Times. There is a lack of cross-discipline
understanding and colaboration throughout the entire process (not enough
silo busters). The true challenge is to appreciate that the discovery,
development, application, and regulation of the target to drugs pipeline has
to be viewed as integral processes with each element having important,
sometimes critical, implications on the other components with decisions
weighed accordingly.


This article in itself is not about Business Process Improvement or Business
Process Reengineering as this is outside the scope of Research Process
Improvement and Research Content Improvement. The processes surrounding the
drug discovery and development process require attention and optimisation
too, but in the end success in drug discovery and development depends on
bringing the right drug to the right patient. Both the drug discovery and
development process and its scientific content require optimisation beyond
their current state.

The scientific content of the drug discovery and development process, goes
beyond business management principles and is more difficult to optimise than
the process itself. It is the present state of basic (reductionistic) and
applied science and technology itself, in relation to the complexity of
biological systems, which still limits our chances for success. Inadequate
understanding of basic science for certain diseases and the identification
of targets amenable to manipulation is one of the major causes of failure in
drug development. The endpoint of discovery is understanding a complex
biological process, not just a pile of molecular data. The endpoint of
development is therapeutic success in man (a complex biological system), not
just a molecular interaction. The level of understanding at the end of drug
discovery (and preclinical development) should achieve a knowledge level
which is capable to predict success at the end of the pipeline much better
than we do now. No matter what is the origin of the compound under
evaluation, or how it came into being, a good description of its in vivo
pharmacological properties is necessary to assess its drug-like potential.
The sooner NCEs or NBEs evolve in a "rich" or lifelike biological
environment (resembling the situation in a human population) the sooner we
capture (un-)wanted phenomena.

There is a time-shift between the implementation of a new approach (linking
genes almost directly to clinical diseases) and finding out about its impact
on commercial success, which makes the feedback loop inefficient due to its
long delay in relation to the quarterly and annual business cycle. From a
business perspective, any process can be sped up and content can be
sacrificed or complexity reduced. In a stage-gate process, the stages
deliver the content for the decisions at the gates, so the stages should be
informative and predictive. Processes and portfolio management can be
optimised near perfection. This may be provide sufficient leverage for a
(albeit complex) 'nuts and bolts process' (e.g. automotive industry), but
not for processes in a biomedical context when our understanding of
pathogenesis and pathophysiology is still very patchy and incomplete. We
leave a large potential for improvement untapped. Improving a development
process which still fails for 90% of all developmental drugs, is not
optimised at all. With the current inefficient process we are, in most
cases, unable to serve smaller patient populations.

If we ever want to reach the goal of personalized medicine, which is in my
opinion is beter understood as succeeding in unraveling the molecular
diversity of clinicaly similar disease manifestations, some conditions need
to be fulfilled:
A complete understanding of the molecular pathophysiology of a disease.
Availability of higly reliable, highly predictive but cheap diagnostic
A very efficient and productive drug discovery and development process.
A lot of research and development will be needed to reach this goal, leaving
aside the ethical minefield.

The complexity of intermediary modulation of gene-disease (un-)coupling was
clearly underestimated in recent years. In the early stages of drug
discovery, the data tend to be reasonably black and white. As you get to
more multifactorial information and more complex systems later on in drug
discovery and development, that becomes less true. Managing this complexity
in a coherent way is a challenge we must deal with in order to be
successful. So how can we facilitate (and understand) the flow through the
pipeline, without generating empty downstream flow in clinical development?
How to plug accelerators into the drug discovery and (pre-)clinical
development pipeline which prove their value onto the end of the pipeline?
How do we create a true Pipeline Flow Facilitation (PFF) process?

The first part of this article shows the problems of the drug discovery and
development process. It shows the present problems of the pharmaceutical
The second part of this article looks for the best way to improve the drug
discovery and preclinical development process as these feed clinical
development with drug candidates which should make it to the right patients.
The third part of this article deals with the problems with disease models
in drug discovery and preclinical development and why they cause so much
late stage attrition later on in clinical development.
Overview of related articles on this website

Personal interest and background where I provide som information how the
idea for a Human Cytome Project (HCP) has grown over time.

References have been put together on one page.

Overview of problems and questions

Scientific background about the Human Cytome Project idea can be found here

The potential impact on the efficiency of drug discovery and development
where I give an analysis of the reasons for the unacceptable high attrition
rates in drug development which have now reached 9O%. Our preclinical
disease models are failing, they look back instead of forward towards the
clinical disease process in man.

Overview of solutions and suggestions

A proposal of how to explore the human cytome where I give an overview of
the deliverables and the scientific methods which are (already) avalable.

How to deal with the analysis of the cytome in order to improve our
understanding of disease processes is being dealth with in another article.
The first part deals with the problems of analyzing the cytome at the
appropriate level of biological organization.
The second part deals with the ways of exploring and analyzing the cytome at
the multiple levels of biological organization.

A concept for a software framework for exploring the human cytome is a
high-level concept for large scale exploration of space and time in cells
and organisms.

Drug discovery and development

Evolution of process and content performance

Figure 1: Evolution of sales for some big pharmaceutical companies.
Source: Yahoo finance and other sources
Figure 2: Evolution of earnings for some big pharmaceutical companies.
Source: Yahoo finance and other sources

Figure 3: Evolution of earnings as ratio of sales for some big
pharmaceutical companies.
Source: Yahoo finance and other sources
Figure 4: NYSE Pharma shares of Merck &Co. (NYSE:MRK), Pfizer (PFE),
Eli Lilly (LLY), GlaxoSmithkline (GSK) and Bristol-Myers Squibb (BMY).
Source: Yahoo finance

The graph in Figure 1 shows the evolution of sales for some big
pharmaceutical companies from 1999 until 2004. Pfizer, Johnson & Johnson and
GSK show an increase ahead of the others. Merck suffered from the problems
associated with Vioxx, which illustrates the fragility of commercial sucess.
The graph in Figure 2 shows the evolution of net earnings from 1999 to 2004.
Here there is less difference between the companies, no company is able to
outperform the others in a dramatic way. The graph in Figure 3 shows the
evolution of the percentage net earnings to sales. On average there is a
steady decline from 19.5% in 1999 down to 14.0% in 2004.
The graph in Figure 4 shows that in recent years the growth of the
pharmaceutical industry has slowed down. The pharmaceutical industry found
themselves in a tight spot in the beginning of the 21st century. The sector
has seen a decrease in financial performance following a boom period in the
1990s, fueled by a succession of drugs with sales over US$ 1 billion per
year (blockbusters).

How can the pharmaceutical industry get out of the current situation of
spiraling costs and reduced R.O.I? There is no simple answer to this
question, as a solution requires improvements in multiple domains. The
management of research must ensure that the resources are directed to
investigations consistent with the ultimate goal, the development of a
successful drug. The management of research is full of uncertainty and
complexity. Research has substantial elements of creativity and innovation
and predicting the outcome of research in full is therefore very difficult.
The costs and risks involved in developing, testing and bringing new drugs
to market continue to grow, pharmaceutical companies are coming under
increased pressure to make the discovery and development process more
manageable and efficient. Today we need both better processes as well as
better science to succeed in the "disease jungle" or the "pathogen
 minefield". Success will go to those who can manage the hybrid activities
between science, technology, and the market.

Although innovative and sound research is a prerequisite, it is ultimately
the therapeutic success of the drug which results in sales and profits. And
it is usually proprietary (patented) products which earn the highest
returns, because they produce sustainable competitive advantage over a
substantial period of time (e.g. patent lifecycle). We must keep in mind
that it is the ability to produce proprietary products, not just interesting
science, which leads to a profitable and sustainable pharmaceutical company.

The business process around the drug discovery and development process
itself can be improved as well as the R&D process management itself (e.g.
Business Process Improvement, process and portfolio management, .). Reducing
R&D costs and shortening product development cycles will certainly
contribute to an increase in profitability. But when the scientific
substrate of the R&D process itself is not optimised too, we leave a huge
potential for treating diseases cost-effectively and generating profit
untapped. Both the process and its content require our attention. The recent
gulf of mergers an acquisitions provides some short-term relief, but when we
combine two companies with each a 90% attrition rate in drug development, we
just get a bigger company with also a 90% attrition rate in drug
development. This high attrition rate leaves little margin for dramatic
improvement of overall productivity. At the moment the pharmaceutical
industry is trying to generate some leverage by working on improving the
development process for the 10% developmental drugs which make it through
the pipeline. Business process engineering as such is less riskier than
rethinking the overall discovery and preclinical development process.
Business process improvement can be modeled on what was done in the
automotive and aerospace industry when those sectors faced hard times. Due
to the success of its blockbusters in the 1990s, the pharmaceutical industry
only recently faced the same challenges.

I will focus on the content of the drug discovery and development process.
How can we scrutinize the R&D projects earlier in the preclinical
development process to help minimize the risks involved in clinical
development of new drugs (now down to 10% success rates)? We need a better
process content in relation to clinical reality, not only more content as

Drug discovery and development: an inefficient process

At the end of the drug discovery and development pipeline, there are
patients waiting for treatments, company presidents and shareholders waiting
for profit and governments trying to balance their health care budget. For
pharmaceutical and biotech companies, the critical issue is to select new
molecular entities (NME) for clinical development that have a high success
rate of moving through development to drug approval. Finding new drugs
(which can be patented to protect the enormous investments involved) and at
the same time reducing unwanted side effects is vital for the industry. We
must try to understand the reasons for failure in clinical development in
order to improve drug discovery and preclinical development.

Figure 5. Evolution of Total Sales and R&D Spending
Source: Pharmaceutical Research and Manufacturers of America (PhRMA)
Pharmaceutical Industry Profile 2004 (Washington, DC: PhRMA, 2004)
Figure 6. Evolution of Total Sales and percentage of R&D Spending
Source: Pharmaceutical Research and Manufacturers of America (PhRMA)
Pharmaceutical Industry Profile 2004 (Washington, DC: PhRMA, 2004)

Figure 7. Evolution of Research and Development Spending Domestic and Abroad
Source: Pharmaceutical Research and Manufacturers of America (PhRMA)
Pharmaceutical Industry Profile 2004 (Washington, DC: PhRMA, 2004)
Figure 8. Evolution of Research and Development Spending and NDAs submitted
Source: Pharmaceutical Research and Manufacturers of America (PhRMA)
Pharmaceutical Industry Profile 2004 (Washington, DC: PhRMA, 2004)
FDA CDER NDAs received per year

The demand for innovative medical treatments is constantly growing as people
in the wealthy developed world live longer with a concomitant increase in
the burden of chronic diseases. At the same time, patient expectations about
the quality of treatment and care they receive are rising and unmet medical
needs remain high. There still are significant pharmaceutical gaps, that is,
those diseases of public health importance for which pharmaceutical
treatments either do not exist or are inadequate. What can modern society
expect from its pharmaceutical industry to deal with the challenges arising?
Let us take a look at the evolution in income of the US pharmaceutical
industry and output over the last 30 years, from 1970 to 2003. The total
sales of the US pharmaceutical industry has risen almost exponentially over
the past 30 years (Figure 5). About 16% of sales income is spent on R&D
(Figure 6), which makes R&D, after marketing costs, the second biggest item
in the spending profile of large pharmaceutical companies. The percentage of
sale income spent on R&D has risen from 9.3% in 1970 to 15.6% on 2003, a
rise of 6.3%. The total amount of money spent on R&D has risen enormously
since 1970, mostly in the US (Figure 7). In 2003, almost half of all R&D
spending worldwide was made in the USA. However despite this almost
exponential rise in total R&D spending, the number of NDAs approved by the
FDA has not risen significantly (Figure 8). The money invested in R&D has
not lead to an equal rise in output of the R&D process. Due to the
increasing mismatch between rising R&D expenditure and decreasing R&D
efficiency (Figure 8) the overall profit margins of the pharmaceutical
industry are decreasing (Figure 3).

Whatever the phrmaceutical industry spends on R&D, it has a significant
overhead of additional manpower to sustain. In 2000 the US pharmaceutical
industry directly employed 247,000 people (down form 264,400 in 1993), with
51,588 of them working in R&D, which means only 21% of its workforce is
directly involved in the drug discovery and development process (Kermani F.,
2000; PhRMA 2002). In 2000 the European pharmaceutical industry employed
560,000 people of which only 88,200 worked in R&D, which is 16%. (EU source:
The European Federation of Pharmaceutical Industries and Associations
(EFPIA) and The Institute for Employment Studies (UK)). In 1990 the European
pharmaceutical industry directly employed 500,762 people (76,287 in R&D or
15,2%). It took 10 years to increase total employment to 540,106 people (of
which 87,834 in R&D or 16,3%, conflicting data), but then it took only 3
years to increase total employment up to 586,748 (of which 99,337 in R&D or
16,9%). The industry is not capable to reduce its overhead and to
significantly increase its new drug generating workforce in relation to its
total employment. From 1990 to 2003 the European pharmaceutical industry
icreased its workforce with 85,986 , but added only 23,050 for R&D. The
expenditures in R&D grow faster than its R&D workforce which indicates that
money is being spent mainly on equipment (e.g. for HTS), but which fails to
sustain the growth of productivity in the end (Figure 8). Ubiquity does not
equal overall process efficiency and effectiveness.

Two elements which are often overlooked in the discussions about the
increasing cost and duration of R&D: tax returns and the US Public Law
98-417 (the Hatch-Waxman Act) which was enacted in 1984. Pharmaceutical
companies in general spend a certain amount of the revenues on R&D because
of its impact on tax returns, so the cost is not the only driver. When sales
increase tax deductions are important incentives to spend part of the
revenue on investments in R&D (Figure 6). But in the end, the new
investments have to support further growth, which is not always the case.
The "Drug Price Competition and Patent Term Restoration Act" (1984) was
intended to balance two important public policy goals. First, drug
manufacturers need meaningful market protection incentives to encourage the
development of valuable new drugs. Second, once the statutory patent
protection and marketing exclusivity for these new drugs has expired, the
public benefits from the rapid availability of lower priced generic versions
of the innovator drug (Abbreviated New Drug Applications or ANDA). One
aspect of the "Drug Price Competition and Patent Term Restoration Act", the
"Patent Term Restoration" refers to the 17 years of legal protection given a
firm for each drug patent. Some of that time allowance is used while the
drug goes through the approval process, so this law allows restoration of up
to five years of lost patent time. Under the Hatch-Waxman Amendments, patent
protection can be extended (under certain conditions) for up to 28 years,
about 11 years of extra protection compared to the 17 years originally
granted by US law. The regulations governing the Patent Term Restoration
program are located in the Code of Federal Regulations (CFR), Title 21 CFR
Part 60.
The Uruguay Rounds Agreements Act (Public Law 103-465), which became
effective on June 8, 1995, changed the patent term in the United States.
Before June 8, 1995, patents typically had 17 years of patent life from the
date the patent was issued. Patents granted after the June 8, 1995 date now
have a 20-year patent life from the date of the first filing of the patent
application. Although pharmaceutical companies suffer from longer
development cycles, tax incentives and extended patent protection lessen the
impact on their business results. The patients are the true losers of the
game, because they have to wait longer for new drugs for unmet medical
needs. Instead of creating a long-winded and inefficient process, medicine
would be served better with a shorter and more productive process.

Figure 9. Evolution of R&D spending allocation
Source: Pharmaceutical Research and Manufacturers of America (PhRMA) and
Source: USA NSF Division of Science Resources Statistics (SRS)

Although overall R&D spending has increased over the years, there has been a
remarkable shift in the allocation of R&D spending. Clinical development
spending has increased significantly (Figure 9), while spending on applied
research (i.e. preclinical) has decreased. Basic research spending shows an
increase in recent years. The overall picture is an increased spending on
clinical development, while there is less spending on the processes feeding
clinical development with appropriate development candidates. Mainly the
investments in applied research, which is the bridge between basic research
and clinical development shows signs of neglect. The Early Development
Candidates (EDC) were expected to require less preclinical validation than

The cost to develop a single drug which reaches the market has increased
tremendously in recent years and only 3 out of 10 drugs which reached the
market in the nineties generated enough profit to pay for the investment
(DiMasi, J., 1994; Grabowski H, 2002; DiMasi JA, 2003). This is mainly due
to the low efficiency and high failure rate of the drug discovery and
development process. Pharmaceutical companies are always trying hard to
reduce this failure rate. Indirect losses in drug development caused by a
failure in drug discovery are among the most difficult to quantify but also
among the most compelling in the riskmitigation category. Pharmaceutical
companies want to find ways to bring down the enormous costs involved in
drug discovery and development (Dickson M, 2004; Rawlins MD., 2004).

Only about 1 out of 5,000 to 10,000 drugs makes it from early pre-clinical
research to the market, which is not an example of a highly efficient
process. The focus of the pharmaceutical industry on blockbuster drugs is a
consequence of the mismatch between the soaring costs and the profits
required to keep the drug discovery and development process going. The
blockbuster model now delivers just 5% return on investment and only one in
six new drug prospects will deliver returns above their cost of capital. The
"nichebuster" is now an emerging model for the post-blockbuster era.

Only diseases with patient populations large enough (and wealthy enough) to
pay back the costs for a full blown drug development are now worth while
working on. Research for new antibacterial drugs is being abandoned, due to
an insufficient return on investment (R.O.I.) to pay for the development
costs of new drugs (Lewis L, 1993; Projan SJ., 2003; Shlaes DM., 2003). If
the industry cannot bring the costs down, it may as well try to raise its
income by changing its price policy, but this shifts the solution for the
problem from in- to outside the company and places the burden on the
national health care systems.

Companies which were more successful in the past achieved a higher
efficiency even without the availability of extensive genomic and proteomic
data and new low-level disease models. The founder of Janssen Pharmaceutica,
Paul Janssen, PhD, MD (1926-2003), in his early days achieved a ratio of 1
drug for every 3,000 molecules screened. Over the years he and his teams
developed about 80 drugs (out of 80,000 molecules, so 1 drug for 1,000
molecules screened) of which 5 (6.3%) made it to the WHO Model List of
Essential Medicines. He worked in fields as diverse as gastroenterology,
psychiatry, neurology, mycology and parasitology, anaesthesia and allergy.
As a scientist he has been one of the most highly productive and widely
esteemed pharmacological researchers in the world for more than 45 years. He
had a deep understanding of both drug discovery and drug development. Dr.
Paul Janssen had always been the personification of a unique combination: on
the one hand the brilliant scientist, and on the other the very successful
manager. Let us take a look at his approach to active strategic management
which requires active information gathering and active problem solving. Dr.
Paul Janssen practiced Management By Walking Around (MBWA), which gave him
access to all the research going on and allowed him to orchestrate the
efforts of his scientists, from discovery up to Phase III, like a conductor
and thereby avoiding silo development.

A deep understanding of a wide range of issues is required to bring a drug
from early drug discovery to the market. Introducing new technology and
generating more data alone are not sufficient to improve the drug discovery
and development process (Drews J. 1999; Horrobin DF, 2003; Kubinyi H., 2003;
Omta S.W.F., 1995). We need better content and understanding, not just more
targets and data to be fed into the preclinical and clinical development
process. As such the present-day discovery process, suffers from molecular
myopia as it lacks the big picture understanding of disease mechanisms in
man. In contrast the more traditional physiology based process, suffered
from system-wide presbyopia as it lacked molecular resolution. The ideal
approach would be the combination of both, which has the potantial to
improve both the quantity as well as the quality of the process. Quantity
without a match in content quality (clinical relevance) leads to failure
later on in the drug development pipeline. We have to look at drug discovery
and preclinical development with clinical drug development and the patient
in mind. Look back from clinical reality into the drug discovery and
development process and analyse its failures. A process which in the end
fails to prove its value in man should be changed.

The drug discovery and development process

Let us now take a closer look at the evolution of the output of the drug
discovery and development over the years. How does the productivity of the
process evolves? What is the cost/benefit ratio of the investments made and
the overall outcome for drug discovery and development.

Figure 10. NDAs submitted over the years.
Source: FDA CDER NDAs received and NMEs approved.
Figure 11. Evolution of INDs and NDAs over the years.
NDAs left axis, INDs right axis.
Source: FDA.

The number of NDAs submitted does not show a significant increase in recent
years (Figure 10), compared to almost 20 and 30 years ago in the days of
physiology based drug discovery. The number of approved New Molecular
Entities (NME) shows a sharp decrease in the early sixties, due to the more
stringent regulations for drug safety testing because of the Thalidomide
scandal. About 66% of the NMEs did not make it anymore when better testing
was required by the FDA and the pre-Thalidomide productivity was never
reached again. The number of NMEs was at its lowest at the end of the
sixties (9 in 1969) and has slowly increased since the early seventies
(Figure 10). NMEs are about 25% of all NDAs, before halfway the eighties it
was on average less than 20%. The number of NDAs is not the only indicator
of success for the pharmaceutical industry. Blockbusters generate higher
sales per product, so both the number of NDAs as well as the sales per
marketed drug are important indicators. Depending on a blockbuster makes a
company vulnerable to problems (SAE) with a single drug and patent expiry of
a blockbuster has a bigger impact. In figure 7 we can see that the number of
INDs and NDAs submitted over the years, does not show a significant
improvement. The larger than average number of approvals in 1996 reflects
the implementation of the Prescription Drug User Fee Act (PDUFA). The number
of INDs does not show a significant increase over the years, so the overall
productivity of drug discovery and development has not improved, despite the
high investments in Research and Development (Figure 7 and Figure 11). The
number of active INDs shows an overall increase, but this only means that
the drug development pipelines are filling up because the clinical trials
take longer. The in- and outflow of drug development (INDs and NDAs) has not
changed in a way to explain the increase in active INDs. The pharmaceutical
industry itself expects that products will stay in phases longer than has
historically been the case, lowering the probability of a product moving
from one phase to another in a particular year. We have not seen a
proportional increase in NDA submissions to the FDA, compared to the number
of active INDs (Figure 10 and 11).
The main reasons for declining productivity of drug development are:
Tackling diseases with complex etiologies, which are not well understood.
Demands for safety, tolerability are much higher than before.
The proliferation of targets is diluting focus.
Genomics has been slow to influence day-to-day drug discovery.
A negative impact of mergers on R&D performance?

Figure 12. Sources: for 1976 Hansen, 1979; for 1987a Wiggins, 1987;
for 1987b Woltman, 1987c, for 1987c DiMasi, 1991;
for 1990a and b OTA pre-tax, for 2000 DiMasi, 2003.
Differences are also due to out-of-pocket versus capitalized costs.
Figure 13. Source: FDA CDER INDs received per year

Figure 14. Source: FDA CDER NDAs approved per year
Figure 15. Source: FDA CDER NDAs approved per year

Let us now take a closer look at the drug discovery and development process
(clinical trials). Although different sources give different outcomes, the
trend is one of increasing costs and reduced Return On Investment (R.O.I.).
In 2000 it took about US$ 500 to US$ 802 million to develop a new drug and
bring it to the market (DiMasi J.A., 2003), which is a significant rise
since 1976 when it cost about US$ 137 million (all numbers in year 2000 US$)
(Figure 12). These estimates include opportunity costs, which are lost
profits that could have been realized if the money tied up in an enterprise
had been invested elsewhere (DiMasi J.A., 2003). Almost half of the DiMasi
(Tufts) US$ 802-million figure - $399 million - is comprised of this "cost
of capital", leaving a figure of US$ 403 million for direct out-of-pocket
expenses, most of which is expended in clinical trials. Whether you favor
US$ 403 million or US$ 802 million, the cost of drug discovery and
development is far too high. When we look into the diferent stages of drug
discovery and development, the US$ 802M costs are divided over: Discovery
and preclinical testing US$ 335M, Phase I: US$ 141.7M, Phase II: US$ 137.2M
and Phase III: US$ 174M. The total cost for clinical development is US$
452.9M. The cost for FDA Review/Approval: US$ 13.8M.

The basic numbers for time spent and costs made in drug discovery and
development can be found in several documents published by institutes which
generate reports about the pharmaceutical industry (Boston Consulting Group,
Tufts Center for the Study of Drug Development, Pharmaceutical Research and
Manufacturers of America (PhRMA), the Institute for Regulatory Science
(RSI), CMR International, etc.). IMS is a source for pharmaceutical market
information. The Association of Clinical Research Professionals (ACRP) and
the Center for Information and Study on Clinical Research Participation
(CISCRP) provide information on clinical trials.

To be complete, there are alternative views which criticize the calculation
of the cost of drug discovery and development. Here are the Public Citizen
and TB Alliance reports. A discussion of these reports and the Tufts study
can be found here. Although an open and critical discussion is the only way
to understand complex issues such as research and development costs, the
discussion sometimes loses its focus and becomes tainted by sophisms to
support political and personal agendas. I leave it to the critical reader to
decide. The consequence of accepting the alternative views would be that the
pharmaceutical industry would be losing money due to costs outside its core
mission, which is even worse, because research and development can be
improved, but this would not help in this case. The result is in each case,
that drugs are only worth while to develop, if they have an enormous market
potential (large numbers of wealthy patients), require as little as possible
investments (me-too drugs and generics) and have the shortest development
cycle possible (less complex diseases). Otherwise they do not earn back the
money invested, when finally they reach the market. This leads to an
increasing focus on typical Western "diseases" such as obesity or
hypercholesterolemia, due to overintake of food and unhealthy living.
Tropical diseases, if they do not make it to the wealthy world, are to be
avoided. You cannot blame the pharmaceutical industry, because if they do
not live up to the expectations of their shareholders, they are punished by
a decreasing stock-value (see Figure 1).

The number of INDs coming out of drug discovery does not show a significant
improvement since 1992 although overall costs have risen sharply.(Figure 13
and 14). The non-inovative drugs get a standard review by the FDA instead of
a priority review and constitute about 75% of all NDA submissions (Figure
About 10-20 % of the total costs are due to the drug discovery process, the
rest is caused by drug development, production and marketing costs. Clinical
development costs, on average US$ 467 million, which makes up more than half
the total cost. The cost of a Phase I clinical trial is about US$ 15.2, for
Phase II it costs about US$ 16.7 and Phase III US$ 27.1 (in 2000 US$, DiMasi
J.A., 2003). The cost of a Phase III clinical trial ranges between US$ 4
million and US$ 20 million and you need at least two of them (Kittredge C,
2005). Study delays, such as slow patient recruitment, protocol amendments
and review processes, are contributing factors. Every day that a drug is
prevented from being on the market means a loss of sales, which in the case
of blockbuster drugs can be as much as US$ 4-5 million per day.

Since pharma's "big wave of innovation" during the early 1990s, peaking in
1996 when 131 new drug applications (NDA) were filed and 53 new molecular
entities (NME) were approved, R&D productivity has fallen by half. The
larger than average number of approvals in 1996 reflects the implementation
of the Prescription Drug User Fee Act (PDUFA). In 2003, 72 NDAs were filed
and 21 NMEs approved - a 45% and 60% decline, respectively, since 1996
(Figure 13). The FDA provides the CDER Drug and Biologic Approval Reports.
There is also a slow but steady increase in the relative number of drugs
which appear to have therapeutic qualities similar to those of one or more
already marketed drugs (FDA Standard Review procedure) in contrast to the
drugs which show a significant improvement compared to marketed products in
the treatment, diagnosis, or prevention of a disease (FDA Priority Review
procedure)(Figure 14). The approval of NDAs shows a time shift in relation
to the INDs submitted some years before, due to the time required for
reviewing the data (compare Figure 13 and 14). IND peaks translate into
smaller NDA approval peaks some years later, due to late stage attrition in
drug development. About 90% of INDs do not make it to an NDA approval years

>From about 8 years in the 1960s it now takes an average pharmaceutical
company about 10 to 15 years to bring one new drug to the market. Of these
15 years about 6.5 years or 43% of the total time is spent in pre-clinical
research. Development starts with candidate/target selection or the
selection of a promising compound for development. Pre-clinical and
non-clinical research involves necessary animal and bench testing before
administration to humans plus start of tests which run concurrently with
exposure to humans (e.g. two-year rodent carcinogenicity tests). About 7
years or 46 % of the total time is time spent in clinical research (1.5
years in Phase I, 2 years in Phase II and 3 years in Phase III). Phase I
(First Time In Man, FTIM) of a clinical trial deals with drug safety and
blood levels in healthy volunteers (pharmacology). Phase II (Proof of
concept, PoC) deals with basic efficacy of a new drug, which proves that it
has a therapeutic value in man (exploratory therapeutic). Finally Phase III
deals with the efficacy of the drug in large patient populations
(confirmatory therapeutic). It is easy to understand that the increase of
the population used to study the effect has a dramatic impact on the
complexity and the cost of the clinical trial.

To process a New Drug Application (NDA) takes the U.S. Food and Drug
Administration (FDA) on average 1.5 years based on the results and documents
provided by the pharmaceutical industry. The situation in Europe for the
European Medicines Evaluation Agency (EMEA) is probably of the same order of
magnitude. About 0.1 % of the original molecules screened in drug discovery
enter phase I (5 out of 5,000 to be optimistic) and 0.02 % of the original
molecules finally reach the FDA (1 out of 5,000). Of the 5 molecules
entering phase I, about 4 out of 5 or 80 % fail to make it to a NDA. After
approval by the FDA, the drug hits the market and enters phase IV of the
clinical study process.

In the 1990's about 38 % of the drugs which came out of discovery research
dropped out in phase I. Of those molecules which made it out of phase I, 60
% of those failed in phase II clinical studies. And now we get to the really
expensive phase III in which 40 % of the remaining candidates failed. Of
those drugs which made it out of phase III to the FDA 23 % of the ones that
made it through the clinical trials failed to be approved by the FDA. All
this translates to about 11 % overall success rates from starting the
clinical trials (Kola I., 2004).

Figure 16. Less than 10% of INDs make it to an NDA.
Source: FDA CDER NDAs approved per year and FDA CDER INDs received per year
Figure 17. Overall success of clinical development decreased from 18% to 9%,
worst decline in Phase II (effectiveness), from 46% to 28%
Source: Loew C.J., PhRMA, HHS Public Meeting, November 8, 2004

Figure 18. Evolution of attrition from 1995 to 2004.
Source: Pharmaceutical Research and Manufacturers of America (PhRMA)
Figure 19. Trends in probability of success from 'first human dose' to
market by therapeutic area.
Source: Pharmaceutical Research and Manufacturers of America (PhRMA)

As a rough indication of overall inefficiency we can compare FDA NDA and IND
data five years different. If we take on average 5 years from IND (IMP in
Europe) after 5 years of IND filing, less than 10% of INDs make it to an NDA
(Figure 16). The evolution of NDA approvals also shows a decline over the
years. In recent years overall success rates for clinical development
decreased from 18% to 9% (Figure 17). This is mainly due to an almost 40%
reduction of success in phase II clinical trials, which means a failure in
exploratory treatment or clinical activity. A Phase II clinical trial is
intended to determine activity, it does not yet determine efficacy, which is
the goal of a Phase III clinical trial. Thus the outcome of Phase II is a
decisive point in a drug's development. If we look at the evolution of
attrition rates from 1995 to 2004, we see an overall increase in development
candidates in preclinical development and an increase in Phase I and II
development (Figure 18). There is no significant increase in Phase III
clinical trials, as most developmental drugs increasingly fail in Phase II.
The drugs show an activity in drug discovery and preclinical development,
but no significant activity in a clinical situation on a real-life disease
process. The increase in attrition is not the same for every therapeutic
area (Figure 19). For alimentary and metabolic diseases the probability of
success (POS) is even increasing and is about then times as high as for the
nervous system (1999).

The significance of increasing Phase II failures is a new evolution, as in
the 1980s and early 1990s the failure rates remained relatively steady. The
failure rate of new clinical entities (NCEs) remained relatively steady
through the 1980s and early 1990s (DiMasi J.A., 2001). Among NCEs for which
an investigational new drug (IND) application was filed in 1981-1983,
approval success rates were 23.2%; 1984-1986, 20.5%; 1987-1989, 22.2%; and
1990-1992, 17.2%. This includes both self-originated and acquired NCEs.
According to the FDA historically 14% of drugs that entered Phase I clinical
trials eventually won approval, now 8% of these drugs make it to the
marketplace, and that half of products fail in the late stage of Phase III
trials, compared to one in five in the past (Crawford L.M., 2004). "...In
the past, we used to see a 20 % product failure in the late stages of the
Phase 3 trials. Currently, the failure ratio at this stage is 50 %. The
reason for this unpredictability, in our analysis, is the growing disconnect
between the dramatically advancing basic sciences that accelerate the drug
discovery process, and the lagging applied sciences that guide the drug
development along the critical path. ..." (Crawford L.M., 2004). Overall
late stage attrition is on the rise, but how should preclinical development
and Phase I clinical trials predict success or failure in Phase II or III,
when they are not conceived or designed to do this? Each stage from
discovery over preclinical development to clinical development is meant to
provide an answer for a particular question, not for the question arising in
the next stage of the discovery and development pipeline. Which elements or
markers in preclinical development would allow us to predict events in
clinical development. In order to achieve this we need a better
understanding of the critical issues in the clinical disease process. The
analysis of failures in Phase II should at least help us to understand the
mechanism of these failures in order to feed those lessons back into
preclinical development. The transition from preclinical development to
Phase I and Phase I itself deals with finding a appropriate dosing scheme to
start with (e.g. MTD Maximum Tolerated Dose), but not yet with clinical
activity, which comes into play at Phase II.

There are some practical considerations to determine the clinical activity
of a developmental drug, one of which is the sample size. The study design
(case/control, cohort study, RCT, etc.) is the first decision, but sample
size is a close second. An important issue is the power of the trial. Once
the level of activity that is of interest has been decided on, one should
design a trial that exposes the fewest possible patients to inactive
therapy, e.g. by appplying the method of Gehan and Schneiderman (Gehan E.A.,
1990). In general you need more patients when you want to find out about a
smaller therapeutic effect. This is an important cause of the overall
increase in patient numbers required, depending on what you want to prove.
When we cannot achieve a dramatic therapeutic breakthrough with a diseases,
a small improvment is what we want to prove. Instead of a revolutionary
breakthrough, quite often therapeutic improvements are only incremental. Let
me clarify this with an example.
When Louis Pasteur (1822 - 1895) developed a vaccine against rabies, the
shortterm outcome was clear, either you died or you survived. Rabies is a
viral disease with about 100% mortality, i.e. you almost always die when you
get the disease. So the therapeutic effect was very simple to assess, which
also made complicated analysis of the therapeutic results less necessary.
There was also less consideration about possible side effects, as dying from
rabies was a horrible disease process.
Let us now take a look at Alzheimer's Disease (AD) (named after Alois
Alzheimer), a debilitating degenerative disease of which the pathological
process is still not well understood. We cannot achieve a "restitutio in
integrum" (restoration to original condition) and regrow the brain cells
which are lost due to the disease process. So, now we can decide to wait
until we know all about the process and then start developing a cure. This
would mean that in the mean time we do nothing to help whatsoever. As you
can understand, this is not a valid option. In the mean time, therapy is
aimed at slowing down the process of mental deterioration. This however is a
more subtle outcome than the short-term live or die outcome in the case of
rabies. These less than 100% success rates make it harder to prove the
success of a new therapy. The need to prove a small improvement, makes
clinical trials more complex and much larger.

Figure 20. Sample size (N) for comparing two means.
In addition to ? and ?, N only depends on ?/?, or the effect size.
? = 0.05 and 1 - ? = power = 90% for a 2-sided test.
The graph shows N as a function of ?/? = difference in units of s.d.
Figure 21. Sample size (N) for comparing proportions (p).
In addition to ? and ?, N depends on p and ?.
Let ?=0.05, 1- ? = power = 90%, 2-sided testing, p=0.5 (conservative
estimate for variance).
The graph shows N as a function of ? = difference p1-p2, e.g. 0.2 = 0.6 -

Designing a clinical trial is not a trivial endeavour as the days of Louis
Pasteur are gone and the environment in which to develop new therapies has
changed dramaticaly. A clinical trial requires careful design in order to be
able to answer the research question (hypothesis) with some confidence in
the answer. You want to prove that a new therapy works in a reliable way.
Traditionally, H0 is the hypothesis that includes equality or the
expectation that nothing will happen and the alternative hypothesis H1 that
something significant will happen (Rosner B, 1995). A p-value is a measure
of how much evidence we have against the null hypotheses. The significance
test yields a p-value that gives the likelihood of the study effect, given
that the null hypothesis is true. A small p-value provides evidence against
the null hypothesis, because data have been observed that would be unlikely
if the null hypothesis were correct. Thus we reject the null hypothesis when
the p-value is sufficiently small. However life in clinical trilas is not
that simple. There are two type of statistical errors you can make in a
trial. A Type I error occurs when you reject H0 when H0 is true, i.e., you
declare a significant difference when the result happened by chance (false
positive - a drug will be used while it is not effective). A Type II error
occurs when you accept H0 when H1 is true, i.e., you say there is no
significant difference when there really is a difference (false negative - a
drug will not be used while it has an effect). How do we deal with these
issues? While we can't prevent the possibility of incorrect decisions, we
can try to minimize their probabilities. We will refer to alpha (?) and beta
(?) as the probabilities of Type I and Type II errors, respectively.
Alpha (?) is the probability of making a Type I error (rejecting the null
hypothesis when the null hypothesis is true).
Beta (?) is the probability of making a Type II error (accepting the null
hypothesis when the null hypothesis is false).
Significance level or ? = P[Type I error] = P[Reject H0 | H0 true] .
? = P[Type II error] = P[Accept H0 | H0 false]
Power = 1 - ?
An interesting element of a trial is the power of the trial. A study can
have too little power to find a meaningful difference, when the sample size
is too small. No significant difference is found and the treatment or method
is discarded when it may in fact be useful. The alternative Hypothesis (H1
or Ha) is that there will be a significant (therapeutic) effect. The P(Type
II error) = ? and ? depends on how large the effect really is. The power (P)
of a test is the probability that we reject the null hypothesis given a
particular alternative hypothesis is true and Power = 1 - ?. Summarized: ? =
Probability(missing the difference) and Power = Probability(detecting the
difference). All this comes down to the overall rule that in order to prove
a small decrease in disease progression we need a relatively large number of
a patients. It is because of this kind of effect, the size of patients in
clinical trials has risen dramaticaly in recent years. Also in the case of
rabies, there was no effective treatment to compare with, so the comparison
was straightforward and simple. In Figure 20 and Figure 21 you can see for
two different types of trials, the effect of sample size required to detect
an increasing difference. This is an important reason for having patient
populations of up to 5,000 patients in Phase III clinical trials. If we
could make a big difference with a treatment, then we would not need such
large numbers of patients to prove our case. With a chronic degenerative
disease, reducing the speed of progress of the disease with only 0.1%, could
mean that in 20 years thousands of people would benefit (longevity in the
Western world), but the problem is that you must prove this small difference
within the scope of a clinical trial. This is one of the most important
reasons for clinical trials to become increasingly global in nature and more
complex in protocol design. The difference with the 19th century is also
that we now have to compare with drugs which are already on the market and
have a proven therapeutic effect. The pharmaceutical industry is
increasingly challenging itself to improve against its own therapeutic
success of the past. As such the pharmaceutical industry itself is the
biggest problem for the pharmaceutical industry. There is a lot more to be
told on on clinical trial design, but this is not within the scope of this
article. The main issue is that in modern clinical development, the
situation is more complex to evaluate than before.

Figure 22. Despite a reduction in attrition due to pharmacokinetics issues,
efficacy has not improved, since 1991.
In 1991 40% of PK failures were caused by poorly bioavailable
when we remove these from the equation, then only 7% of failures in 1991
were caused by poor ADME.
Source: Pharmaceutical industry attrition profiles, evolution (Kennedy, T.,
1997; Prentis RA, 1988).
Figure 23. The major cause for failure, efficacy, only becomes apparent late
in development.
Source: KMR 1998 - 2000

What about the evolution of the basic reasons for attrition in drug
development? Attrition due to a lack of efficacy of drugs in development has
not improved since 1991 (Kennedy, T., 1997; Prentis RA, 1988) (Figure 22).
Attrition rates due to poor pharmacokinetical profiles (PK) have dropped
significantly, due to better preclinical in-vitro and in-vivo models.
However about 40% of failures in clinical development were due to
inappropriate pharmacokinetics of poorly bioavailable anti-infectives, if
those were removed from the equation then ADME was only responsible for 7%
of failures in 1991 (Kennedy, T., 1997). The basic numbers on attrition
causes explain why attrition rates in Phase I clinical trials have declined
less than those in Phase II. Drugs with unfavorable PK profiles are now
increasingly stopped before they reach clinical development, so the
ineffective ones now make it into Phase II in relatively larger numbers. The
clinical development attrition trends also show an unfavorable evolution
since 1991 (Figure 22). The disease models used in drug discovery and
preclinical development fail to predict failure in clinical development in
about 80 to 90% of the drugs which enter clinical development. And the
combined predictive power of all clinical trials (Phase I to III) fails to
predict failure in 1 out of four or 25% or even 50% of all drugs submitted
to the FDA for approval.
The major cause of attrition, efficacy, also shows up late in development,
as preclinical development and Phase I are unable to detect this failure.
Preclinical development lacks the proper predictive models and Phase I is
not designed to detect a failure in efficacy. Clinical safety issues
increase with the number of people taking the drug, after it is on the
market (Figure 23).

The reason for the up to 90% failure in clinical development is both related
to the target (lack of efficacy, mechanism related toxicology) and to the
compound (pharmacokinetics, chemistry related toxicology). A decade ago, the
number of drugs failing preclinically due to poor pharmacokinetics was
upwards of 40%, but improved in vitro and animal models have reduced that
rate to about 10%. Failures due to toxicology, however, are still in the 30%
to 40% range, making it the number one reason for preclinical attrition.
"...The main causes of failure in the clinic include safety problems and
lack of effectiveness: inability to predict these failures before human
testing or early in clinical trials dramatically escalates costs. ..." (
Innovation and Stagnation: Challenge and Opportunity on the Critical Path to
New Medical Products)

What can we learn out this numbers and what is being done in drug discovery?
The role of absorption, distribution, metabolism, excretion (ADME) and
toxicity (ADMET) is an important part of the drug discovery process as ADMET
is an important cause of failure in drug development (Yan Z, 2001; Lin J,
2003; Nassar AE, 2004). Pharmaceutical profiling assays provide an early
assessment of drug-like properties, such as solubility, permeability,
metabolism, stability and drug-drug interactions (Di L., 2005). The drug
discovery process (target identification, target validation, lead
identification/optimization .) and preclinical development such as ADMET
studies, fail to predict the failure of a drug in clinical development for 4
out of 5 or at least 80 % of the molecules which enter phase I. The rates of
failure in expensive Phase III trials in oncology are the worst in the
industry (Kamb A., 2005). Improving the predictive power of disease models
in drug discovery, preclinical development and ADMET is an important issue
to reduce the late stage attrition rate in drug development.

A new drug spends about 90 % or 13.5 years of his career within the
discovery and development process, before it reaches the FDA for the last 10
% or 1.5 years. So the FDA does not account for the majority of the time it
takes to bring a new drug to the market, nor does it account for the
majority of failures which is only 20-25 % or 1 out of 5 or 1 out of 4 drugs
which enter phase I or 1 out of 5,000 (0.02 %) if we start from the
beginning of the process. Although the investments in the early stages of
the drug discovery process have increased tremendously, this means nothing
compared to the cost of failure in phase III of a clinical trial.

The manufacturing process

After the drug discovery and development is finished for a particular drug,
the drug enters the market and is being manufactured. Making manufacturing
more efficient is also an imperative for the pharmaceutical industry. The 16
largest drug companies spend more than twice as much on manufacturing as
they do on R&D, according to a recent study by GlaxoSmithKline, Brentford,
UK. These large companies spent $90 billion, or 36% of their expenses, on
manufacturing in 2001, compared to some $40 billion or 16% on R&D. One of
the most important reasons for horizontal mergers in the pharmaceutical
industry is to reduce the operational costs of manufacturing.

Companies are under increased regulatory pressure for manufacturing, such as
the Good Manufacturing Practice Guide for Active Pharmaceutical Ingredients
(ICH Q7A), FDA Good Manufacturing Practice (GMP) and product labeling. The
Good Automated Manufacturing Practice (GAMP) organization was founded in
1991 by pharmaceutical experts to meet the evolving FDA expectations for GMP
compliance of manufacturing and related systems. Impending requirements
being imposed by the FDA in the U.S. and the EMEA in Europe require
companies to submit product labeling content in highly structured XML
formats (Structured Product Labeling (SPL) in the US and Product Information
Management (PIM) in Europe). Distribution of drugs is regulated by the Good
Distribution Practice (GDP) of Medicinal Products for Human Use. New
initiatives are being taken to improve the overall manufacturing process.
Process Analytical Technology (PAT) provides a framework for innovative
pharmaceutical manufacturing, control and product quality assurance. FDA
Process Analytical Technology Initiative (PAT). The EUFEPS Process
Analytical Technology Sciences.

The FDA wants to deal with the growing public health problem of counterfeit
prescription drugs in the United States. Counterfeit drugs are not only
illegal but are also inherently unsafe. A famous case of the withdrawal of a
drug due to deliberate product tampering was the Tylenol murder case. The
Tylenol murders occurred in the autumn of 1982, when seven people in the
Chicago, Illinois area in the United States died after ingesting Extra
Strength Tylenol medicine capsules which had been laced with cyanide poison.
This incident was the first known case of death caused by deliberate product
tampering. Johnson & Johnson was praised by the media at the time for its
handling of the incident, although it cost the company about US$ 100M in
lost revenues (see also Johnson & Johnson Credo). In the near future the FDA
will require that the industry to implement full-scale RFID serialization
(needed for closed-loop drug tracking) and electronic pedigree (ePedigree)
applications (needed to find and prosecute violators) The Radiofrequency
Identification Technology (RFID) is meant to monitor and protect the U.S.
drug dupply chain. Radio Frequency IDentification (RFID) is an automatic
identification method, relying on storing and remotely retrieving data using
devices called RFID tags or transponders. In general, authentication systems
that operate independently from the underlying data collection technology
will help the drug industry secure the drug supply, protect valuable brands,
and avoid legislation that will force costly compliance requirements that
add little business value.

Inspection by the FDA are not to be taken lightly. Pharmaceutical, Medical
Device, Biopharmaceutical and Generic Drug companies all face a common
dread. The FDA has called, and they are coming to audit their manufacturing
facility. At the "FDA's Electronic Freedom of Information Reading Room", the
FDA publishes the findings of its inspections on-line: Warning Letters and
Vulnerability in Phase IV

The pharmaceutical industry depends on a relatively small number of active
components. While there are around 10,300 FDA-approved drugs in the United
States today, most of these are made up of some combination of only 433
distinct molecules. Half of these 433 molecules were approved before 1938,
and at least 50 are "me too" drugs, a slightly modified form of a compound
already on the market. Finally, there are only eight major, chemical
"scaffolds" upon which all the 433 molecules are based.

Due to the difficulty and inefficiency of the drug discovery and development
process, pharmaceutical companies rely on only a few drugs for their income
and profit. This makes them extremely vulnerable for massive income loss
when one of the drugs encounters problems after it is on the market. Serious
problems with a drug after it has been on the market in general means
lawsuits against the company and a serious blow to its reputation (e.g.
pharmacovigilance or Phase IV trial). Each year about 17,200 Adverse Events
(AE) and 800 Serious Adverse Events (SAE) are typically reported to the FDA
for newly approved drugs (Source: FDA). Seven of the 303 (2.3%) new
molecular entities (NME) approved by the FDA between January 1994 and April
2004 were withdrawn from the market due to safety concerns. Although 97.7%
of NMEs do not cause such safety problems, the 2.3% which do, bring the
pharmaceutical industry in trouble. Older drugs can also be a major cause of
hospital admissions, such as with aspirin (Pirmohamed M, 2004). The
perception that new drugs are less safe than older ones is not always true.
However the accompanying harm to patients and the billions spent developing
and marketing the drugs are a big problem for the industry. No amount of
testing can guarantee to find all of the possible side-effects for every
person who may take a medicine. A reaction which occurs at a rate of 1 in
100,000 people or even at a higher rate of 1 in 10,000 for instance, may not
be seen until very large numbers of people use the medicine. Even with these
odds, no pharmaceutical company wants to be in the news with Serious Adverse
Events (SAE) about a drug already on the market. Being the CEO of a
pharmaceutical company is not something for the faint of heart. One day a
company is praised for a new breakthrough drug, the next day it has its name
in the news associated with lethal side effects of another drug. Some recent
events have shown that pharmacovigilance principles and procedures are in
need for improvement. The EU European Risk Management Strategy (ERMS) of
2002 is an example of such an initiative. It aims at strengthening the EU
Pharmacovigilance System (see also EudraVigilance).

Current methods in pharmacovigilance often use monitoring and simple
analysis of safety signals after they have been detected in the
postmarketing process. Sometimes Phase IV clinical trials (postmarketing)
reveal important side effects which were not discovered before. This was the
case for Vioxx according to the APPROVe study by Merck. The cost of missing
a safety signal or not detecting it before it affects the general population
is huge. The withdrawal of a drug from the market has serious consequences
both due to the loss in revenue for the company and the financial
consequences of lawsuits. The cost of an adverse drug reaction on an average
per patient basis is about ? 2800 (approximately US$ 3,360) in
hospitalization costs alone (Gautier, 2003). The total losses to a company
can reach billions of dollars from the loss of reputation and revenue and
from medical and litigation expenses.

Some examples of Serious Adverse Events (SAE) over the years give an
indication of the impact on the lives of people, society and the
pharmaceutical industry. An inadvertently toxic preparation of sulfanilamide
had a central influence on the US Food and Drug Administration (FDA). A
preparation called "Elixir Sulfanilamide" contained diethylene glycol as a
solvent, which is toxic. This preparation killed over one hundred people,
mostly children, and led to the passage of the 1938 Food, Drug, and Cosmetic
Act (the 1937 Elixir Sulfanilamide Incident). Thalidomide (Softenon) was
withdrawn from the market in the sixties when thousands of babies were born
with deformities as a result of their mothers taking Thalidomide during
pregnancy (McBride WG, 1961). Thalidomide never made it to the USA in the
sixties, mainly due to Dr. Frances Oldham Kelsey of the FDA, who refused to
authorize thalidomide for market when she had serious concerns about the
drug's safety. In the USA the Thalidomide case lead to the Kefauver-Harris
Drug Amendments (1962) to be applied retroactively to the Federal Food,
Drug, and Cosmetic Act (1938). In Europe the Thalidomide case lead to the
first European Community pharmaceutical directive issued in 1965, namely
Directive 65/65/EEC1. No medicinal product should ever again be marketed in
the EU without prior authorisation. On 16 July 1998, the FDA announced the
approval of Thalidomide for Hansen's Disease (Leprosy) for erythema nodosum
leprosum (ENL). This imposed unprecedented authority to restrict
distribution (Thalidomide Education and Prescribing Safety oversight
program- S.T.E.P.S).

Several notorious cases of adverse events have been widely publicized in
recent years. One is the case of cerivastatin (Baycol, a popular
cholesterol-lowering drug) from Bayer. In 2001 cerivastatin (Baycol) was
removed from European and USA markets because of the risk for rhabdomyolysis
(Bayer, 2001; Furberg CD, 2001; Davidson MH., 2002; Kind AH, 2002; Ravnan
SL, 2002; Staffa JA, 2002; Maggini M, 2004). In 2001 when the drug was
recalled, there were approximately 700,000 users of the drug. The initial
cost of the recall was US $20 million in refunds for active prescriptions.
(Eakin, 2003) An additional US $705 million in lost operating earnings and
more than US $150 million in out-of-court settlements magnified the negative
financial impact.

Prepulsid was withdrawn form the market due to cardiovascular adverse
effects (Griffin JP., 2000; Wilkinson JJ, 2004). In late 2003 there was the
SSRI case, concerning the antidepressant medicines known as selective
serotonin reuptake inhibitors (SSRI). The SSRIs were associated with an
increased risk of suicidal behavior (Fergusson D, 2004; Gunnell D, 2005). In
2004 the COX- 2 inhibitor rofecoxib (Vioxx) was withdrawn because of
cardiovascular adverse effects (Dyer C., 2004; Juni P, 2004).

The sales and marketing process

The sales and marketing of drugs is also highly regulated. The Federal Food,
Drug, and Cosmetic Act (the act) requires that all drug advertisements
contain (among other things) information in brief summary relating to side
effects, contraindications, and effectiveness. In the US, the FDA Office of
Medical Policy, Division of Drug Marketing, Advertising, and Communications
(DDMAC) takes care of this. One of the most important reasons for horizontal
mergers in the pharmaceutical industry is to reduce the operational costs of
Sales and Marketing.
Improving the process

"...If biomedical science is to deliver on its promise, scientific
creativity and effort must also be focused on improving the medical product
development process itself, with the explicit goal of robust development
pathways that are efficient and predictable and result in products that are
safe, effective, and available to patients. We must modernize the critical
development path that leads from scientific discovery to the patient..."
Innovation and Stagnation: Challenge and Opportunity on the Critical Path to
New Medical Products, FDA ( March 2004)

In 2000, EUFEPS established the New Safe Medicines Faster Project, the
ultimate goal of which would be to contribute to effective development of
medicines for the benefit of the European citizens. In a Workshop, held on
March 15-16, 2000, in Brussels, ideas and suggestions for research topics,
methodologies, techniques and other means of promoting the drug development
process were identified, put together and published in the Workshop I
Report. In the future, it was sugested, identifying new technologies,
capable of more effective selection, development and approval of new,
innovative and safe drugs; using such technologies to increase the capacity
and speed of the pharmaceutical development process; and cultivating a
pan-European interdisciplinary network to bridge the existing gap between
industry, academia, health care and regulatory authorities; would to be of
paramount importance.

Figure 24. Budget spending as a percentage of total R&D budget (US$ 935M)
Source: Life Science Insights, Ernst & Young, Tufts CSDD and Boston
Consulting Group, July 2004.
Figure 25. Time spending as a percentage of total R&D time (14.5 years)
Source: Life Science Insights, Ernst & Young, Tufts CSDD and Boston
Consulting Group, July 2004.

Figure 26. Budget spent and remaining as a percentage of total R&D budget
(US$ 935M)
Source: Life Science Insights, Ernst & Young, Tufts CSDD and Boston
Consulting Group, July 2004.
Figure 27. Time spent and remaining as a percentage of total R&D time (14.5
Source: Life Science Insights, Ernst & Young, Tufts CSDD and Boston
Consulting Group, July 2004.

Figure 28. Burnrate of budget for each individual phase.
Source: Life Science Insights, Ernst & Young, Tufts CSDD and Boston
Consulting Group, July 2004.
Figure 29. Cumulative burnrate of overall process. Additional impact of a
phase on overall burnrate.
Source: Life Science Insights, Ernst & Young, Tufts CSDD and Boston
Consulting Group, July 2004.

Every project or process has a time, cost and quality, which are important
parameters when we want to improve its performance. When we look at drug
discovery and development, we look at a process which is applied on
particular R&D projects. Do we apply the right process on our individual
projects? Let us now take a look at the cost and time of the overall R&D
process, which nowadays starts with target identification and target
validation. We already know that the output of the overall process is low
(90% attrition in clinical development). When we take a look at our budget
(US$ 935M), we spend about 18% on target identification, qualification and
prioritization, 22% on target validation and we spend about 22% on Phase III
clinical trials (Figure 24).
When we take a look at the most time consuming phases, we spend almost 21%
of our time on preclinical studies and about 21% on Phase III clinical
trials (Figure 25).
By the time we are finished with lead identification and optimization, we
have spent about 40% of the R&D budget. By the time we reach Phase III of
clinical development, we have spent about 80% of our budget. From target
identification to preclinical studies it takes about 66% of our total R&D
budget, which leaves us with 33% for clinical development (Figure 26).
When we take a look at the time, we spend about 60% of our time from target
identification to preclinical studies, which leaves us with 40% of our time
for clinical development (Figure 27).
Let us now take a look at the burnrate of our budget per unit of time. At
its start the process resembles a fighter jet taking of full throttle
forward, afterburners glowing and racing towards the sky. When we reach
preclinical development the process resembles a caravan of mice and men
crossing the (pre-)clinical-desert until we reach Phase III (Figure 28 and
Figure 29).
Compared to the "primitive" physiology based (empirical) process we have
added a target identification and validation step in-front of the process,
which consumes about 40% of our budget and 20% of our time, but we have
neglected to balance this investment with the quality of its predictive
power in relation to the clinical outcome of the process (75% failure due to
biological reasons). Improving a process requires a balance between cost,
time and quality. Target identification and validation should be done with
an in-depth patho-physiological understanding of the biological process at a
molecular level and not only the target on itself. Try to understand the
system of biology and the biology of the system, not only the mechanics of
target-drug interaction.

In order to improve the drug discovery and development process, where should
we try to optimize it? We have to balance time, cost and quality. Adding
more steps in front of the process as with target identification and
validation is not an issue anymore. Instead we should do things different
and improve the time, cost and quality of what we are doing in a balanced
way. A critical path defines the optimal sequencing and timing of
interventions by all stakeholders involved in a procedure (Coffey RJ, 1992;
Kost GJ., 1983; Kost GJ., 1986). Critical paths have to be developed through
collaborative efforts of basic and applied scientists, managers and others
to improve the quality and value of drug discovery and development.
Unbalanced changes in a project process (scope, time, cost, quality), lead
to a disproportionate decline in performance. Quality should be measured
against the impact on clinical success and not only on the next step in the
process. After about 7 or more years in pre-clinical research, a new drug is
ready for filing an initial new drug application (IND) after which the FDA's
Center for Drug Evaluation and Research (CDER) monitors the clinical
studies. The CDER monitors the study design and conduct of clinical trials
to ensure that people in the trials are not exposed to unnecessary risks.
The Center for Biologics Evaluation and Research (CBER) is the Center within
FDA that regulates biological products for human use under applicable
federal laws. Biologics, in contrast to drugs that are chemically
synthesized, are derived from living sources (such as humans, animals, and
micro-organisms). The FDA monitors the participants of clinical trials
(FDA/ORA Bioresearch Monitoring Information Page). In Europe the European
Medicines Agency (EMEA) is a decentralised body of the European Union with
headquarters in London. The Committee for Medicinal Products for Human Use
(CHMP), deals with medicinal products for human use. In Europe the EMEA is
the bridge between the pharmaceutical industry and the national "Competent
Authorities". In Europe an Investigational Medicinal Product (IMP) is the
name for a drug in clinical development. In Europe a Development Medicinal
Product (DMP) is a medicinal product under investigation in a clinical trial
in the EEA, which does not have marketing authorization in the European
Economic Area (EEA).

The clinical trials, from phase I to III are highly regulated and a company
can only optimize the flow of events, but up to a large part it cannot
decide freely what needs to be done in these stages of the process (e.g. ICH
E6 Good Clinical Practices). The ICH develops guidances for harmonisation of
drug development on Quality (Q), Safety (S), Efficacy (E) and
Multidisciplinary (M) topics. Once a drug hits a regulatory authority, such
as the FDA (CDER) or the EMEA strict rules need to be followed for the
approval and failure to comply will only delay this process. The European
legislation on pharmaceuticals can be found in EudraLex - The Rules
Governing Medicinal Products in the European Union

So it is by improving the quality and shortening the process in drug
discovery an preclinical development, a pharmaceutical company can make the
most significant difference. But this has proven to be a dauting challenge
up to now, as attrition rates in clinical trials remain high. A reduction of
more than 60 % in time and about 50 % of the costs could be achieved by
implementing a well-designed e-Clinical process (people, process, technology
and proper change management), buth this does not yet deal with the fact
that about 9 out of 10 INDs (USA) or IMPs (EU) do not belong in clinical
development at all.

A lot of money is being lost in drug development and clinical trials because
there are too many drugs in clinical trials which should have never reached
this stage. Every approved NDA carries the burden of all the other INDs
which failed and with 9 out of 10 INDs faling, this burden is very high.
This shows that the gatekeepers of (pre-)clinical drug development are
failing, which should not happen (in such high numbers) in a
well-established stage-gate process. The stages provide the information for
the gatekeepers to decide, but when the predictive power of stage-based data
is too low, the decisions at the gates are of limited power. The results in
drug discovery and preclinical development are biased towards overestimating
the chances of success in clinical development. Efficacy is overestimated
and adverse effects are underestimated. There is a need for a broader
strategy to support go-no go decisions at each stage-gate. The failure to
stop 90% of candidate drugs before IND filing, only becomes visible years
later in drug development. Late stage attrition in drug development is due
to early stage failure of disease models in drug discovery and preclinical
Improving drug development

Evolution of the overall process

Figure 30. Evolution of discovery and development process
A. 1950s and 1960s, B. 1980s, C. and D. 199Os and present.
Modified from Ratti E., 2001.

The drug discovery and development process has changed considerably over the
past 50 years (Figure 30). The discovery process had several steps added
in-front which were meant to reduce uncertainty and make the overall process
more predictable. Clinical development was divided in multiple stages, but
the true proof of therapeutic improvement for a given therapy compared to
either placebo or competing therapies is still at the end of the pipeline,
now in Phase III. The discovery and preclinical development stages cannot
answer the questions of clinical development. What happened up-front is that
we moved further away from man and moved down to the single molecular level.
We still cannot model the complexity of man, but we can model a molecule. We
reduced complexity and increasingly introduced false positives and poor data
quality. The latest developments are to bring man, the ultimat model
organism, back into the process in an earlier stage (e.g. Phase 0,
microdosing). Much work remains to be done to improve the predictive power
of those early stages. The early stage predictions of success and failure in
relation to late stage development should capture more of the complexity of
pathological processes in man into the models employed. What happens can be
compared to what is going on in the poem "The Blind Men and the Elephant" by
John Godfrey Saxe. A lot of detail, but no understanding of the complexity
of the overall behavior of the drug in relation to its place in the
"ecological" system of the "biotope" man. The focus on molecular targets in
recent years, now resembles the situation in the poem "Der Zauberlehrling"
from Johann Wolfgang Goethe. The molecular "Sorcerer's Apprentice" can no
longer control the spirits that he called and now needs help to master the
deluge of new and unvalidated targets.
We added inner resolution (molecular instead of system level), but at the
same time we reduced the outer resolution (molecular resolution instead of
system-wide overview). Man is not a pile of molecules, but a complex
Process performance

The process does not perform at the same historical success rates anymore as
attrition has now reached 92% Preclinical and clinical development is a
process driven endeavour where the improvements can be made by improving the
process management, both in management approach as well as with better
project management tools. Model improvement in preclinical development is a
crucial issue. The main reason for failure in clinical development is due to
the failure of preclinical models.
The current bottlenecks in drug development are:
Predictive pharmacology (PK/PD).
Predictive toxicology (Tox).
Lack of validated biomarkers.
New clinical trial designs.

Pharmacokinetics (PK) describes the kinetics of a drug, or how the body
handles a specific compound. Generally, it involves the absorption of the
compound, where the compound goes in the body, how the compound is changed,
and how it is eliminated: absorption, distribution, metabolism, excretion
(ADME) (Bohets H, 2001; Caldwell G.W., 2004; Parrott N, 2005)
Pharmacodynamics (PD) or drug metabolism (DM) describes the impact that the
drug has on the body, i.e. what are the drugs effects on the body?
Pharmacodynamics (PD) studies the relationship of the time course of a drug
(and metabolites) in the body and its effects, it describes the action of a
specific compound with regard to its uptake, movement, binding and
interactions at its site of activity. A general way to consider these is
pharmacokinetics (PK) is what the body does to the drug, and
pharmacodynamics (PD) is what the drug does to the body.

Reactions involved in drug metabolism (DM) are often classified as Phase I
(activation) and Phase II (detoxification) reactions. Enzymes catalyzing
Phase I reactions include cytochrome P450 enzymes. Enzymes catalyzing Phase
II reactions include the conjugation enzymes UDP-glucuronosyltransferases
(UGT), glutathione S-transferases (GST) as well as other enzymes that
protect the cell from toxic damage due to oxidative stress. Phase I and
Phase II enzymes acting in concert, convert hydrophobic compounds to more
hydrophilic compounds that can be readily eliminated in bile or urine.
Preclinical development

Once a chemical lead is discovered, it is subjected to preclinical testing
to assess biological activity. Preclinical studies are conducted both in
vitro- in cell cultures and tissues- and in vivo- on live animals such as
dogs, monkeys, and pigs. In addition to establishing the drug's
pharmacological effects, these studies also identify acute and subchronic
toxicology, teratogenicity, and carcinogenicity risks. How to find out if a
discovery lead has the physical and chemical, as well as the biological,
properties to be a valid drug development candidate? Many disciplines are
involved in hit-to-lead transition and lead development. From determining
Quantitative Structure Activity Relations (QSAR) to in-vivo assays in model
organisms. The process of lead optimization is an iterative process where
many scientific disciplines are involved of which I only mention a few. The
problems with late stage attrition in clinical development has its cause in
the decisions made at the transition from "model to man". We are unable to
predict clinical success from preclinical disease models in 90% of all drugs
in clinical development.
Six scientific disciplines are involved in preclinical compound
Analytical and bioanalytical methods
Pharmacology (e.g. therapeutic ratio, Mode Of Action)
Nonclinical formulation
Pharmacokinetics (PK, ADME)
Pharmacodynamics (PD, DM)
Pathology and toxicology (Path/Tox)

This is the traditional matrix of techniques involved in preclinical
assessment of a drug candidate (pharmacokinetics (PK) and pharmacodynamics
(PD) are of course also being studied in patients during clinical
development). The final decisions concerning the usefulness of a drug are
the domain of experimental and clinical pharmacology (Burger A., 1987).
Bioavaiability of a drug is an important issue, as so elegantly captured in
Lipinski's rule of five and can be used as a rule of thumb to indicate
whether a molecule is likely to be orally bioavailable (bioactive) (Lipinsky
CA, 1997). However this has not been able to reduce the late stage attrition
rate in clinical development. Most of the tools used for toxicology and
human safety testing are decades old and may fail to predict the specific
safety problem that ultimately halts development or that requires post
authorization withdrawal. Each aspect of preclinical safety studies
(pharmacological screening for unintended effects; pharmacokinetic
investigations in species used for toxicology testing; single- and
repeat-dose toxicity testing; and special toxicology testing (such as
mutagenicity) has not been rigorously tested by a robust analysis of its
predictive power. Preclinical development was never designed to make up for
the pathophysiological deficit of target-based drug discovery.
Physiologically unvalidated development candidates were mainly screened for
pharmacokinetic (PK) properties and pharmacodynamic (PD) properties, but
this does not validate their clinical therapeutic efficacy in a clinical
patho-physiological environment.

One reason for this stagnation of inovation in preclinical development is
the fact that many of these experiments are required and highly regulated by
regulatory authorities on IND and NDA filing (Hayashi M, 1994; Legler UF.,
1993; Spielmann H, 2001). The Organisation for Economic Co-operation and
Development (OECD) has provided many guidelines, such as the
Reproduction/Developmental Toxicity Screening Test (OECD Guideline 421).
With limited resources, a company must focus on those tests which it has to
perform to get the clinical development candidate accepted in the first
place. There is a trend to improve the preclinical evaluation of drugs, such
as performing Phase 0 tests. Regulatory authorities are also aware of the
fact that something has to happen to reduce attrition rates in clinical
development. The focus is on optimising the interface between late
preclinical development and early clinical drug development by utilising
modern in vitro - in vivo extrapolation techniques. The industry has to
improve the current wasteful and uninformative system for testing drug
candidates, and shift to research methods that use biomarkers to predict
drug side effects and benefits (derived from a speech given by FDA acting
deputy director Janet Woodcock). Biomarkers could help us to to improve the
predictive power of drug discovery and early drug development (Fowler BA.,
2005; Kola I, 2005). Verification that a biomarker assay is specific for its
intended purpose poses a formidable challenge.
We need (validated) biomarkers for preclinical and clinical development in
order to:
Treat diseases more effectively:
We currently lack predictive biomarkers to stratify patients with similar
diseases as well as accurately measure disease susceptibility, presence and
Verify the impact of novel drugs on targets/pathways:
We currently lack the ability to determine the ability of a novel drug to
bind the desired target and whether this binding actually leads to changes
in the desired pathway.
Avoid Serious Adverse Events (SAE):
About 1.5 million people are hospitalized each year due to adverse effects
of prescription drugs.
Increase drug development predictability:
Only 1 in 10 drugs entering Phase I ever reach the market with the great
majority of compounds failing in Phase II.

We really need drug candidates (NCEs, NBEs) which make it to the market at
much higher rates than with the current 10% overall success rate from IND to
NDA. This will require a significant paradigm change in the assessment of
potential investigational drug candidates (Apic G., 2005; Caldwell GW, 2001;
Schadt EE, 2005; Shaffer C., 2005).

Highly sensitive techniques, such as Accelerator Mass Spectrometry (AMS) and
PET allow for the detection of biomarkers. Accelerator mass spectrometry
(AMS) is a mass spectrometric method for quantifying rare isotopes, which is
being applied to biomedical and toxicological research (Barker J, 1999;
Brown K, 2006; MacGregor JT, 1995; Turteltaub KW, 1990). AMS can be used to
study long-term pharmacokinetics and to identify biomolecular interactions
in neurotoxicology and neuroscience (Palmblad M, 2005). AMS enables
compounds and metabolites to be measured in human urine and plasma after
administration of low pharmacologically or toxicologically relevant doses of
labelled chemicals and drugs (White IN, 2004).
Clinical development

For many scientists working in drug discovery and preclinical development,
the environment in which clinical trials happen is still something
mysterious. I want to clarify some of these issues, mainly the regulatory
(and scientific) framework in which these complex and expensive trials have
to be performed. Drug discovery and preclinical development should be done
while keeping an eye on clinical development. Maybe this helps to understand
the fears and nightmares of those scientists and their collaborators, every
time they start a development track and face the fact that up to 90% of
their efforts are in vain.
A clinical trial (also clinical research) is a research study in human
volunteers to answer specific health questions. Carefully conducted clinical
trials are the fastest and safest way to find treatments that work in people
and ways to improve health. Interventional trials determine whether
experimental treatments or new ways of using known therapies are safe and
effective under controlled environments. Observational trials address health
issues in large groups of people or populations in natural settings. Each
clinical trial starts with the definition of a primary question (study
hypothesis). From that hypothesis the best way to confirm or reject it, can
be designed. There are several designs possible, which can be found in the
literature on clinical trials. A randomised, double-blind, crossover or
factorial, multi-site Phase III clinical trial is a complex endeavour and a
drug which fails at this stage has cost an enormous amount of money (Figure
31). Not to mention the shattered hopes of the patients it was intended to
provide with new hope. A Phase III clinical trial may involve up to 5,000
patients distributed over numerous clinical trial sites, which gives you an
idea of the logistic complexity to manage such a trial. It is important to
keep in mind that a Phase I trial does not deal with the outcome of a Phase
II trial and so on. Each trial type is designed to answer a different type
of question and it only provides an answer to this question(s), not to the
ones being dealt with in another type of clinical trial. Clinical trials are
becoming more expensive and even more regulated. A Phase I trial costs about
US$ 8,000-15,000/subject, a Phase II costs about US$8,000-15,000/patient and
finally a Phase III trial costs about US$4,000-7,500/patient. Improving a
proces is a matter of methodology and technology.

The overall profile of therapeutic indications and adverse events, leads to
the labeling of the proposed drug. The "sponsor" of a new drug must obtain
approval from the FDA by specifying both the medical conditions the drug is
effective against and the patients groups for whom the drug has been shown
to be effective. This information is contained in the proposed "label"
submitted by the developer or sponsor. It is the sponsor's responsibility to
assemble all the evidence that would support the uses proposed in the label
(preclinical and clinical development). With the wide gap between molecular
targets and clinical diseases, this has become increasingly complex and
risky. This also explains the problems with "first-in-class" drugs and the
reduced risk once knowledge and understanding build after years of
widespread use. There is also the potential safety problem of "off-label"
use of a drug, besides the problems with reimbursement.

Figure 31. Clinical trials from Phase I to Phase IV. Phase II can consist of
a Phase IIa and IIb.
Phase III can also consist of a Phase IIIa and IIIb.

A basic clinical trial process consists of several stages:
Hypothesis formulation, primary and secondary endpoints
Protocol development
Investigator/site selection and trial preparation
Subject identification and enrollment (causes most of the delays)
Collection, monitoring and processing of data (CRF, PRO, Lab)
Clinical trial management
Data analysis and reporting of results (SAS)
Submission for review by a regulatory agency (FDA, EMEA, ...)

A "ram-it-through paradigm" in clinical trials readily produced
beta-blockers, H2 blockers, nonsedating antihistamines, and other big
classes of drugs. The development of predictive models to assist the
decision process to enter the next Phase of clinical development is an
interesting path taken to reduce late stage attrition (Albert JM, 1994; De
Ridder F., 2005; Hale M, 1996; Holford NH., 2000). There is a transition
going on from empirical to causal models for deriving evidence of
effectiveness. There is also a transition from empirical to causal models
for deriving evidence of safety. Clinical development is changing from a
reactive empirical model to a proactive Model Based Drug Development (MBDD)
process (pharmacometrics). Instead of rushing through clinical development,
a learn-and-confirm approach allows for a more dynamic and adaptive process
(Sheiner LB., 1997). Phase I and IIa are the learning phases and Phase IIb
and III are the confirming phases. There is a clear trend to learn more
earlier in the drug development process. Phase I is no longer just for
establishing safety and dosing levels, Phase I research is playing an
increasingly important role obtaining more data about the potential success
of a drug. The emphasis is increasingly on mechanistic early phase clinical
trials to maximise the chances of obtaining clinical data to make sound
go/nogo decisions. Model-based drug development includes exposure/response
assessments in the form of pharmacokinetic/pharmacodynamic (PK/PD) modeling.
Pharmacokinetic (PK) and pharmacodynamic (PD) modeling and simulation (M&S)
are powerful tools that enable effective implementation of the
learn-and-confirm paradigm in drug development (Chien JY, 2005; Gobburu JV,
2001; Grasela TH, 2005). One of the major prerequisites for the successful
application of PK/PD-modeling, however, is the availability of response
measures such as biomarkers that provide an immediately accessible link
between pharmacotherapeutic intervention and clinical outcome and allow to
easily assess variations in desired and/or undesired drug effects in
response to changes in dose, dosage regimen, dosage formulation,
administration pathway, or external factors affecting drug response.
Biomarker-based PK/PD modeling can become the basis for a scientifically
driven, evidence-based, streamlined drug development process.

The problems with drug discovery and development are not limited to
scientific and technical issues alone. Discovery and preclinical research
may be a scientific and technical minefield, but with clinical development
we in addition enter a moral and ethical minefield. Life in the lab may not
be easy, but life at the bedside isn't either. Improving the clinical
development process is not easy, as we have to operate in a highly regulated
environment, which limits the freedom to change the process. In a highly
regulated environment you cannot do different things, but you have to do
(regulated) things differently, e.g. a new statistical approach (e.g.
Bayesian methods), modeling and simulation, learning and confirming (Maurer
W., 2005). In the early stages of drug development we should be able to
extract more predictive information from our research, to reduce the late
stage attrition in Phase II and III. More knowledge and understanding of
complex processes earlier on, would allow for better predictions and less
failure later on in the process. Applied science within regulatory
constraints is the only way to bring basic science into clinical reality.
Just to give you an idea about all the regulatory issues involved, I will
give an overview of some guidelines (Grunfeld GB., 1992). As clinical trials
involve human experimentation, they are to be conducted according to high
ethical standards. Historical events lead to the adoption of ethical
guidelines for the conduct of research on human subjects. The Nuremberg
Doctors' trial, officially United States of America versus Karl Brandt, et
al., lead to the Nuremberg Code. The Nuremberg Code (1947) laid down 10
standards to which physicians must conform when carrying out experiments on
human subjects.
In summary, the Nuremberg Code includes the following guidlines for
Informed consent is essential.
Research should be based on prior animal work.
The risks should be justified by the anticipated benefits.
Research must be conducted by qualified scientists.
Physical and mental suffering must be avoided.
Research in which death or disabling injury is expected should not be
A physician participating in a clinical trial is bound by the Physician's
Oath put forward in the Declaration of Geneva (Adopted by the 2nd General
Assembly of the World Medical Association, Geneva, Switzerland, September
1948). The ethical dimension of human trials is guided by the Declaration of
Helsinki (first version adopted by the 18th WMA General Assembly, Helsinki,
Finland, June 1964). The World Medical Association (WMA) has developed the
Declaration of Helsinki as a statement of ethical principles to provide
guidance to physicians and other participants in medical research involving
human subjects. Some examples of extreme abuse lead to new legislation in
the US: the Tuskegee Syphilis Study (1932-1972, Public Health Service
Syphilis Study), Jewish Chronic Disease Hospital Study (1963) and trials
performed at the Holmesburg Prison in Philadelphia (from mid-1950s to
mid-1970s). The US National Commission for the Protection of Human Subjects
of Biomedical and Behavioral Research, published the Belmont report on 18
April 1979. The Belmont report deals with Ethical Principles and Guidelines
for the Protection of Human Subjects of Research. In 1987 the IND Rewrite
Regulations (FDA Title 21 CFR Parts 312, 314, 511, and 514) were created, to
ensure FDA's ability to monitor carefully the safety of patients
participating in clinical investigations, while also facilitating the
development of new beneficial drug therapies.

Government agencies take care of enacting the laws and regulations, such as
the US Food and Drug Administration (FDA) (e.g. CBER Guidances), The
European European Medicines Agency (EMEA) and the Japanese Ministry of
Health, Labor and Welfare. On 1 April, 2004, Japan's Pharmaceuticals and
Medical Devices Agency (PMDA) began conducting NDA reviews for the Ministry
of Health, Labor and Welfare. The Council for International Organizations Of
Medical Sciences (CIOMS) develops principles and proposals for the
collection, evaluation, and reporting of safety information obtained during
clinical trials to all appropriate stakeholders. The CIOMS I workgroup
(1990) dealt with international reporting of Adverse Drug Reactions (ADR)
and created the "CIOMS I reporting form" for standardized international
reporting of individual cases of serious, unexpected adverse drug reactions.
As regulatory authorities increase their standards on drug development, they
put the hurdle higher for all the players in the field. So, all companies
face the same challenge to improve their process. One of the positive
consequences has been that the US pharmaceutical industry is now the most
competitive in the world, as they had to adher to increasing quality
standards. The US pharmaceutical market is still the most profitable of all,
but also one the most regulated.

As clinical trials in Phase III are large-scale projects, streamlining data
exchange procedures can make a significant contribution to cost reduction
and shortening the duration of a trial, e.g. for a Computer Assisted New
Drug Application (CANDA) of the FDA. Standardization of data formats is one
aspect of process improvement for which many attempts have been made over
the years, e.g. Drug Application Methodology with Optical Storage (DAMOS),
Multi Agency Electronic Regulatory Submission (MERS), Market Authorization
by Network Submission and Evaluation (MANSEV) and Soumission Electronique
des Dossiers d'Autorisation de Mise sur le Marché (SEDAMM). The need for
international standardization in clinical trials is being dealth with by
organizations such as the International Conference on Harmonisation of
Technical Requirements for Registration of Pharmaceuticals for Human Use
(ICH), e.g. Common Technical Document (e-CTD for e-submission). There is the
ICH E3 guideline on the Structure and Content of Clinical Study Reports. The
Clinical Data Interchange Standards Consortium (CDISC) develops XML-based
standards for data exchange in clinical trials (e.g. ODM, SDTM,...).
eSignatures should allow for exchanging digital data in a secure way within
the pharmaceutical industry. The Secure Access For Everyone (SAFE)
initiative, is a set of standards for digitally signed transactions that
create a trusted community for legally enforceable data exchange.

Clinical trials are process driven and as such amenable to process
management improvement. Improving clinical trials is an ongoing effort,
driven by the tendency to new clinical trial designs and e-Clinical trials.
The traditional Randomized Controlled Trial (RCT) is still the standard, but
new designs are being applied. Besides a new design, new technology can help
to improve the clinical trial process. The Internet provides a means to
improve the process for large multicenter clinical trials (Paul J, 2005).
However clinical trials from Phase I to III (and IV) are a highly regulated
process, (FDA Title 21 CFR part 11, HIPAA, GCP, GMP,...) which limits the
freedom for improvement (e.g. Computer System Validation). Clinical trials
gain speed by using Cinical Trials Management Software (CTMS), Clinical Data
Management Software (CDMS) and automating the process of data capturing (RDC
or Remote Data Capture and EDC or Electronic Data Capturing), such as for
the Patient-Reported Outcome (PRO) and Case Report Forms (CRF). The usage of
computer systems in clincal trials is guided by the FDA Guidance for
Industry on Computerized Systems Used in Clinical Trials.
The Biomedical Research Integrated Domain Group (BRIDG) Model is a
comprehensive domain analysis model representing biomedical/clinical
research. It was developed to provide an overarching model that could
readily be comprehended by domain experts and would provide the basis for
harmonization among standards within the clinical research domain and
between biomedical/clinical research and healthcare. Work is going on to
develop a Clinical Trials Object Model (CTOM) and a Structured Protocol
Representation, from which e-Clinical trials could be designed, developed
and managed.
The procedures for data submisson are being improved by Electronic
Regulatory Submissions and Review. The review process is being regulated by
Good Review Practices (GRPs). The FDA JANUS project will combine information
from numerous clinical trials into a single data warehouse. JANUS will give
the FDA the ability to cross-analyze data from multiple trials, identify
systemic deficiencies across similar products, and detect potentially
dangerous drug interactions. So, a lot of improvements are going on in the
drug review process, but this will be of limited success if the drugs (NCE,
NBE) themselves do not show better quality profiles.

Process improvement is not only a matter of using ICT, but also requires a
change in project management principles in order to succeed. Clinical trial
process improvement is for 80% a matter of people and process and for 20% a
matter of technology (ICT, biomarkers,...). An overall improvement from
protocol (e.g. eProtocol) to NDA submission is required, for instance by
shortening the time from protocol completion to First Patient First Visit
(FPFV), First Patient In (FPI) to Last Patient Out (LPO), Last Patient Last
Visit (LPLV) to DB lock and finally from DB lock to NDA. A reduction of more
than 60 % in time and about 50 % of the costs could be achieved by
implementing a well-designed e-Clinical process (people, process, technology
and proper change management). An e-Clinical trail also allows for better
process monitoring (performance metrics system).
Improving drug discovery and preclinical development

The modern pharmaceutical industry faces an increasingly widening gap
between, on the one hand, growing numbers of potential drug targets and lead
compounds and, on the other hand, a lack of reliable methods to identify
those molecular targets unequivocally linked to disease pathophysiology and
lead compounds with the best chance of success in (pre)clinical development.
How should we proceed to improve drug discovery and preclinical development?
We have seen an enormous investment in research at the infra-cellular level,
such as High Throughput Screening (HTS), genome based and proteome based
disease models in the past ten years and at the same moment have witnessed a
disproportional decline in the productivity of research and development in
drug discovery (Horrobin DF, 2000; Horrobin DF, 2001; Noble D., 2003;
Bleicher KH, 2003, Betz UA, 2005; Bilello JA., 2005). The pharmaceutical
industry has yet to find a way to reduce its high attrition rates (Kola I.,
2004). Late stage attrition rates in oncology are among the highest in the
industry, alhough the need for better cancer treatment is high (Kola I.,
2004; Kamb A., 2005; Saijo N., 2004; Suggitt M, 2005).
The consolidation in the pharmaceutical industry will not solve this problem
in the long run, as it only reduces the costs (mainly of sales and marketing
and manufacturing) but does not improve scientific productivity; it only
postpones the moment of truth. The scientists themselves will have to find
new ways to improve their productivity; management cannot do this in their
place. Society tries to protect itself against the adverse effects of new
drugs, such as with Thalidomide in the sixties (McBride WG, 1961). This is
done by increasingly stringent regulations but the currently used methods in
the discovery process for new drugs cannot keep pace with these new
requirements. However, as we can see, increasingly strict regulations do not
explain all the problems pharmaceutical research is facing today.

We can summarize the (r)evolution in the overall drug discovery and
development process as such:
Traditionally we have a "trial and error" approach:
Blind trust on high-throughput technologies.
Limited success rates of new biological targets.
Separated scientific disciplines, functional orientation ("silos").
Sequential approaches in "new" biology and chemistry.
Low degree of specialization in chemistry and biology ("generalists").
This will have to evolve into a "cognitive" chemical biology approach:
Focus on selected families and a systems biology or biology of systems
Accumulation of knowledge on chemical and biological structure spaces,
learning curves.
Interdisciplinary problem solving.
Parallel processes, information driven and integrated technology,
Teams across scientific disciplines.
Technology platforms and demand for more specialized skill sets.
Networks of knowledge & partnering.

Disease models in drug discovery and preclinical development

Scientific progress from basic science to its applications

Basic and applied research achieve results through brilliant ideas and hard
work. "Adde parvum parvo magnus acervus erit" (Ovidius, By adding little to
little there will be a great heap). You cannot get much done in a short
time, but once you start to string together work for several years or
decades, you have the start of a body of work that puts a mark on the world.
The concern and goal of these articles is to clarify the problems with
bringing basic research to clinical applications, not to criticise basic
research as such. When we look at scientific methodology not from the
perspective of understanding fundamental biological processes, but for their
predictive power to generate results which facilitate the application of
basic science to clinical reality, a different picture emerges. I want to
look at scientific methodology with its impact on treating the pathological
process in man as a reference.
Keep in mind Occam's Razor: "Numquam ponendo est pluritas sine necessitate",
which we should translate as "One should use the explanation that is simple
enough to explain all there is to explain, but nothing simpler". Quite often
our explanations fail to capture the entire biological phenomenon, which is
why we fail in almost 90% of all drugs in clinical development. 75% Of all
failures in clinical development are caused by lack of efficacy (30%),
adverse efects in man (13%), animal toxicity (20%) and pharmacokinetics
I will take a look at several aspects of the discovery and development
process to provide wide ranging information to those who are interested in
improving the pharmaceutical R&D process. I want to avoid "stovepiping" by
skipping information levels. In 2003 Seymour Hersh wrote an article in the
New Yorker titled, "The Stovepipe". Hersh defines "stovepiping" as taking a
request for action arising from intelligence "directly to higher authorities
without the information on which it is based having been subjected to
rigorous scrutiny." Stovepiping results in intelligence failures when
conclusions are allowed to pass rapidly from the lowest levels of the
intelligence gathering apparatus, the ones with their hands directly on new
information as it comes in at ground level, up to the decision making
authorities many levels above without passing through the normal
many-layered time-intensive vetting and checking processes in-between. This
can also be applied to a request for improvement of the "war against
Failing disease models in drug discovery and preclinical development

Anyone working in science must realize that theories, models and
approximations are powerful tools for understanding and achieving research
and development goals. The price of having such powerful tools is that not
all of them are perfect. This may not be an ideal situation, but it is the
best that the scientific community has to offer. Each individual must try to
attain an understanding of the nature of our descriptions of the physical
world and what results can be trusted to any given degree of accuracy (see
also Models, Approximations and Reality).
Currently used disease models fail to predict the outcome of clinical
development and are incapable to reduce attrition rates in drug development.
The reasons for failure can be summarized as follows. Drugs fail in
development and beyond due to:
Man - understanding of pathophysiology is faulty.
Efficacy - no significant effect on a clinical disease process.
Toxicity - long term safety is still totally unpredictable.
Bioavailability and half life - half life cannot be predicted, only guessed.
Metabolism - drug/drug interactions; parent or metabolite.
What should we do to improve this?
Improve our ability to explore and understand human disease processes.
Better target identification and validation.
Improve the predictive power of toxicology.
Achieve a more precise drug metabolism and pharmacokinetics (DMPK)

There is no single disease model which allows us to predict clinical success
in all cases. Bridging the gap between molecule an man is a delicate
process, which requires careful consideration and design. Quite often there
is not enough consideration of building and validating the path from clinic
to model and back. In the end it is clinical reality which decides on the
fate of new drugs and not the technology or disease models used to create
them. The successful identification of drug targets requires an
understanding of the high-level functional interactions between the key
components of cells, organs and systems, and how these interactions change
in disease states (Butcher EC, 2004; Noble D, 2003; Stumm G, 2002).
Pre-clinical studies and early clinical trials should pay more attention to
both the pharmacology of the drug as well as the (in-vivo) biology of the
target (Newell DR., 2005). Both basic and applied research have have made
truly enormous contributions to the health of mankind, but the challenges
ahead are no less than they were in the past. Only with an open mind and a
thorough analysis of the present situation we will be able to analyze the
problems of present day research and development.

Validating the predictive power of our disease models is not an easy task as
we operate in a complex pathophysiological environment.
"...One must rely heavily on statistics in formulating a quantitative model
but, at each critical step in constructing the model, one must set aside
statistics and ask questions. ... without a qualitative perspective one is
apt to generate statistical unicorns, beasts that exist on paper but not in
reality. ... it has recently become all too clear that one can correlate a
set of dependent variables using random numbers as dependent variables. Such
correlations meet the usual criteria of high significance. ..." (Hansch C,
1973). We fail to predict clinical success with our current disease models,
which translates itself in a high (up to 90%) attrition rate in clinical
drug development.
The problem of prediction in relation to a model system (van Drie JH., 2003)
(free from the "Kubinyi Paradox"):
Inside the model: trivial
Outside the model: wrong
At the edge: 50/50

There are two approaches to drug discovery, historically a physiology based
approach was used, while nowadays a target based approach has become more
popular. Most of the problems of the drug discovery and development process
can be traced back to its early stages, namely target validation and lead
selection. With respect to the targets this is, among other factors, due to
a lack of definite clues with regard to the (dis)regulation of cellular
pathways that underlie diseases. This gap in basic knowledge is even further
widened by the lack of adequate animal test models and disease markers to
monitor the particular disease process as well as the outcome of therapeutic
interventions. With respect to lead selection there is a poor insight in the
fundamental relations between the physico-chemical characteristics and the
biological properties of the drug candidates, including mechanisms of
cellular action and toxicity, processes of drug disposition (ADME) and the
aspect of drug safety on a longterm basis. In modern drug discovery the
early stages of drug discovery involve the identification and early
validation of a disease-modifying target (Lindsay MA., 2003; Schneider M,
2004). Failing to make the right decision at the important step of hit to
lead transition has costly time and resource implications in downstream drug
development (Alanine A., 2003; Kuhlmann J, 1999). Why do the early stages of
drug discovery fail so often and why are they the cause of a huge efficiency
deficit later on in the drug discovery process?
Assessing the drugability of a target is only one of the important criteria
to consider. Drugability of a target is the feasibility of a target to be
effectively modulated by a small molecule ligand that has appropriate
bio-physicochemical and absorption, distribution, metabolism and excretion
properties (ADME) to be developed into a drug candidate with appropriate
properties for the desired therapeutic use. The weak spot is the definition
of therapeutic use, which quite often does not mean we are working with a
clinicaly validated target.

Since the early successes of compound screening against isolated molecular
targets in the 1970s, the industry moved away from physiological based
screening to a target-based screening (Luyten W, 1993; Herz JM, 1997). In
the beginning Target-based screening was initially used to improve the
drug-like properties and selectivity of pharmacologically active products.
Target-based drug discovery has been very successful when applied to already
physiologicaly validated targets of existing drug. Later on the hope was
that sequencing the human genome would generate a wealth of new targets, and
the hope of almost directly linking 'genes-to-drugs' was embraced by the
industry in the 1990s. When the drug discovery process moved beyond
historical targets, however, it became apparent that the target-directed
approach was flawed: without solid biological validation, target-based drug
discovery has proven very disappointing (Williams M., 2003; Butcher E.,
2005). Improving target identification requires a dramatic improvement of
our understanding of cellular pathways underlying pathogenesis and/or
pathophysiology. Improving lead selection requires an hypothesis-driven
approach in which, out of the multitude of potential targets, a rigid
selection of 'druggable' targets is made.

There is a fundamental problem with studying disease-relevant mechanisms in
the current disease models as the pharmaceutical industry has been investing
heavily in studying the 'bricks', instead of looking at the 'building'
(cytome, organism) as a dynamic unified pathophysiological system. Finding a
gene or a target does not equal understanding a clinical disease process in
man. The genome has yielded a series of novel molecules that do not have 20
or 40 years of biology behind them for us to understand exactly what they do
and where to apply them. The emphasis in recent years has been on increasing
quantity while at the same moment sacrificing the quality of correlation
with clinical reality. Accepting the perceived truth that ubiquity equals
utility, organisations often initiate efforts to automate processes,
assuming that improvement will be a natural consequence of automation. This
avalanche of data does not lead to an improvement of understanding. "Le
savant doit ordonner; on fait la Science avec des faits comme une maison
avec des pierres; mais une accumulation de faits n'est pas plus une science
qu'un tas de pierres n'est une maison." Henri Poincaré (1854-1912). We
increased our capacity in genomics and proteomics, but we did not improve
the quality of the preclinical physiological disease process evaluation in
the same way. Technologies such as genomics and proteomics have produced an
explosion of new poorly validated targets that actually increase the rate of
compound attrition and the costs of R&D.
You could also think of it as a pointillist painting, of which we have been
looking at the individual dots, instead of looking at the entire painting.
Another analogy is that we are trying to explain the tidal patterns of the
oceans, by studying a water molecule and ignoring the moon. We have to look
for the picture that is in the puzzle early on, not just giving the 'pieces'
to the next process down the line. We have to look at biological phenomena
at several scales of integration and from a functional point of view in
order to get a grip on the development of pathological processes.
Crosslinking and crossreferencing biological organisational levels in order
to understand the 'web' of biological interactions in a pathophysiological
process. We should try to understand the dynamics of disease processes also
at a higher level of biological integration, closer to the clinical reality,
than only the genome or proteome. An integrated cellular and organism-level
approach is needed to study disease processes (Lewis W., 2003).

When we modify a gene, e.g. by creating transgenic animals, we must try to
understand the dynamics of the pathways we are modifying. The gene products
are part of a delicate web of intertwined pathways where subtle changes can
have unpredictable effects. Quite often transgenic animals or animals with
gene knock-outs do not show the expected phenotype, because of a different
genetic background and the highly dynamic interplay of metabolic pathways
and environmental influences on the final phenotype (Sanford LP, 2001;
Pearson H. 2002).

The (early stage) disease models we use don't work as they should do and do
not provide enough overall predictive power in relation to clinical reality.
One can study cellular components, like DNA and protein as such, but this
will not reveal the complex interactions going on at the cellular level of
biological integration or in other words, the cytome. Both medicine and
pharmaceutical research would benefit from using more (primary) cell
oriented disease models (in-vivo and in-vitro) and even higher-order models,
instead of using infra-cellular models to try to describe complex
pathological processes at a molecular level and getting lost in the maze of
molecules which are the building blocks of cells. Keep in mind that
cytome-oriented research is not the same as cell-based research.

An important moment in the drug discovery and development pipeline is the
transition from discovery research to clinical development. Different
approaches to develop gatekeepers have been proposed to reduce the failure
rate in drug development on both sides of the transition (Lappin G., 2003;
Nicholson J.K., 2002; Pritchard J.F., 2003). Translational medicine is
emerging as a gatekeeper for evaluating drugs when they traverse the "great
divide" between "bench and bedside" (Fitzgerald GA., 2005). Translational
Medicine can be defined as an interactive process between preclinical and
clinical investigation. Translational biomarkers and molecular profiling
should assist in increasing success in clinical development. However,
translational medicine alone will only bring bad news earlier in the
process, so it should be combined with a concomitant improvement of disease
models in drug discovery and preclinical development.

Drug discovery and preclinical development should improve the quality of
drugs they allow to enter clinical development and clinical development
should be able to protect itself from drugs likely to fail in phases I to
III. A better quality of drugs entering drug development is needed, not just
more quantity (drastic reduction of false positives). Failing in larger
numbers will not bring the solution to create a better process from
discovery to phase III an IV. If we just drive more drugs into clinical
development, but keep failing at a rate of up to 90%, we are not helping

A highly defined oligo-parametric infra-cellular disease model used in High
Throughput Screening (HTS) which in its setup ignores the complexity of
higher order cellular phenomena, may produce beautiful results in the
laboratory, but fails to generate results of sufficient predictive power to
avoid considerable financial losses later on in the drug discovery pipeline
(Bleicher KH, 2003). A living (primary) cell may be a less well defined
experimental environment for the biochemist, but it will provide us with the
additional modulating influences on our disease models which are lost in
lower-order disease models. The cytome can be analyzed either in-vitro in
well designed cell-based assays or in-vivo by using for instance molecular

Metabolic variation in disease models

Preclinical models should help to identify factors that are important
determinants of intersubject variability in man. We need to make a clear
distinction between our inability to elucidate the overall molecular
mechanism of a disease process and population variability in metabolic
activity. Genetic variability is only one part of the picture, as there are
multiple determinants of intra- and interindividual variation:
Demographic: age, body weight or surface area, gender, race
Genetic: target variability and metabolism, e.g. CYP2D6, CYP2C19
Environmental: smoking, diet
Physiological/Pathophysiological: renal (Creatinine Clearance) or hepatic
impairment, disease state
Concomitant Drugs
Other Factors: meals, circadian variation, formulations

Real life variability should be incorporated in preclinical disease models
and not excluded. Nowadays the first stages of drug discovery and
development use genetically homogeneous disease models, which as a result do
not show the same metabolic heterogeneity of patient populations. In-vivo
variation is not an artefact of life, but a fact of life. Variability in
drug response is a complex and multifactorial phenomenon, of which genetics
is only a part. Genetic and metabolic heterogeneity is now seen as reason to
exclude potential patients from treatment, not as a consequence of the
failure of drug development. We need to make a clear distinction between our
inability to elucidate the true molecular mechanism of a disease process and
population variability. Molecular diversity in a clinical disease process
leads to treatment failure because of the wrong action is taken to modify a
disease process which we do not understand. Metabolic variation in drug
metabolism is another reason for treatment failure, but is more easy to deal
with as these problems involve shared properties of drugs.

If we cannot develop drugs which will work in a genetically and
metabolically heterogeneous environment, we try to reduce the patient
population until it fits our abilities. However this micro-management of
patient populations leads to a level of complexity in disease treatments the
pharmaceutical industry, physicians and society cannot deal with in the end.
Also, excluding people from treatment, because we are unable to develop
drugs which benefit a large population of people poses ethical problems.
Dose optimisation however is an interesting path to minimize side-effects.
At present there are still too many roadblocks to achieve the goal of a
better and finely tuned disease treatment. Personalized medicine will remain
a distant dream, if we do not succeed in achieving a much better
understanding of molecular pathophysiology and at the same time dramaticaly
improve the drug discovery and development process. Pharmacogenomics is
being used to explain differences in drug metabolism during drug
development, such as with Cytochrome P450 (Dracopoli NC., 2003; Halapi E,
2004; Kalow W., 2004). Toxicogenomics and genotyping are used as a tool to
identify safer drugs, worthwhile to enter clinical development (Guzey C,
2004; Koch WH., 2004; Yang Y, 2004).

There are many causes for variability in drug metabolism. this variability
can lead to an Induced Error Of Metabolism (IEOM), just as an Inborn Error
Of Metabolism caused by a genetic defect. Variability of a metabolic change
due to a drug can have many causes:

?Metabolic change = ?Uptake + ?Disease state + ?Host state + ?Elimination

In the case of a pathogen, the variation in virulence is a cause of
variation, as well as the host defense system status (e.g. the Varicella
Zoster virus which causes Varicella or chicken pox, and herpes zoster or
shingles). Metabolic variation due to variability in drug uptake and
elimination can be a serious cause of trouble. The Cytochrome P450 enzymes
are an important cause of metabolic variation in the metabolism of drugs
(Slaughter RL, 1995). Many drug interactions are a result of inhibition or
induction of cytochrome P450 enzymes (CYP450). The CYP3A subfamily is
involved in many clinically significant drug interactions, including those
involving nonsedating antihistamines and cisapride, that may result in
cardiac dysrhythmias. CYP3A4 and CYP1A2 enzymes are involved in drug
interactions involving theophylline. CYP2D6 is involved in the metabolism of
many psychotherapeutic agents.
Variability in drug metabolism can also be caused by food. Grapefruit juice
affects the pharmacokinetics of various kinds of drugs, the major mechanism
being considered to be inactivation of intestinal cytochrome P450 3A4, a
so-called mechanism-based inhibition (Bailey DG, 1991; Fuhr U., 1998; Saito
M, 2005).

The EMEA road map and the FDA Critical Path identify pharmacogenomics (PGx)
as the emerging technology that will enable efficient and successful drug
development. Pharmacogenomics is not yet used to design or use early stage
disease models with sufficient genetic heterogeneity to select drug
molecules which will hold their activity in a metabolic heterogeneous
environment. Genetic heterogeneity, epigenetic modulation and metabolic
variation are not taken into account in the first stages of the drug
discovery process. Optimizing a drug molecule for binding to one particular
genetic variant, imminently leads to failure in a genetically heterogeneous
patient population. Randomization in experimental design to counteract a
systematic bias in one's results involves more than sample unit
randomization patterns.

Biological variation in heterogeneous cell or animal populations may be an
unpleasant fact of life, but it correlates better to the real conditions of
the genetically and metabolically heterogeneous patient populations.
Ignoring biological variation in drug discovery will cause failure in drug
development. Using pharmaco-genomics only to exclude slow metabolizers,
etc., from clinical trials and thereby homogenizing the trial population can
lead to a dramatic reduction in potential patient population and a decline
in profit generation potential. Finding sites to manipulate metabolism which
are less sensitive to genetic variation would improve our overall success
rates. The important phase of a drug life cycle starts when it hits the
market and we better take care that it will spend its full life cycle to
generate enough revenue to fuel the company.

Hypo- or Subcellular disease models

When molecular biology entered drug discovery in the 1980s and 1990s the
dominant view on the relation between genotype and phenotype was derived
from the simple dynamics of prokaryote genetics. The preeminent French
scientist and 1965 Nobel laureate Jacques Monod, said in 1972 "Tout ce qui
est vrai pour le Colibacille est vrai pour l'éléphant" ("What is true for
Escherichia coli is also true of the elephant"). However, the outcome of the
Human Genome Project has revealed that the processing of our genetic
information is much more complex than in Prokaryotes. We have seen an
increase in capacity of DNA and RNA expression techniques, but their
information still delivers data up to the level of the expressed protein,
but not beyond. The quantitative chain of functional causation stops at the
protein level. Higher order spatial and temporal dimensions of cellular
dynamics are beyond the reach of these techniques. Gene expression studies
do not tell you about the functional outcome of protein dynamics and
enzymatic activity in the different cellular compartments. Up and
down-regulation of gene expression, does not inform you about the functional
interrelation of the encoded proteins and their spatial and temporal
dynamics in the cell. Molecular pathways do not exist as parallelized
unrelated up-and down regulating patterns, but are highly dynamic and
intertwined modular networks (Sauer U., 2004).

Gleevec or imatinib or STI571 (signal transduction inhibitor number 571) is
a good example of a drug aiming at a molecular target for which the entire
chain of models, from molecule to man was thoroughly explored. The disease
mechanism was well understood and the molecular biological mechanism was
well-embedded in a pathophysiological understanding (Jones RL, 2005; Mauro
MJ, 2001). This endeavor required the integration of a number of
disciplines, including structural biology, computational chemistry,
structurally directed medicinal chemistry, array screening assays, and
molecular and cellular biology (Druker BJ, 2000).

Publications on the association of the Philadelphia chromosome and leukemia
can be traced back to the early sixities of the twentieth century (Benson
ES, 1961; Tjio JH, 1966). In 1982 it was found that in chronic myelogenous
leukemia (CML), c-abl sequences are translocated from chromosome 9 to
chromosome 22q- (de Klein A, 1982). During the eighties of the twentieth
century the molecular mechanism of the gene product, a tyrosine kinase was
investigated (Pendergast AM, 1987; Maxwell SA, 1987; Lugo TG, 1990). More
than 15 years of work by scientists from all over the world was needed to
understand the molecular mechanism of the disease (this costs far more than
800M US $). Once the target was identified and the disease process
understood, the selection for drug candidates could start (Druker BJ, 1996).
Finding the gene is not enough, the hard work is to find out about the
molecular mechanism of the disease and this has not changed even now the
Human Genome Project has been completed. The work on the c-abl tyrosine
kinase predates the Human Genome Project with more than 20 years (the same
goes for Herceptin). With the current state of technology and science we may
expect to see the results from the new targets coming out of the Human
Genome Project in 10 to 20 years.

Let us now look at some methods used in molecular biology, not for their
value in basic research, but for their predictive value for preclinical
development (a bit unfair, I know). We must be aware that studying a basic
molecular mechanism is still far away from understanding the clinical disese
process as such. Southern, Northern and Western blots may show the
quantitative sequence of gene expression up to protein concentration (Alwine
JC, 1977; Alwine JC, 1979; Howe JG, 1981; Hinshelwood MM, 1993). DNA
microarrays give a quantitative indication of gene expression (Barbieri RL,
1994; Schena M, 1995; DeRisi J, 1996; Jeong JG, 2004; Kawasaki ES., 2004).
However finding a positive correlation between the pattern of gene
expression and a given disease state is not the same as finding a causative
relationship between (a) gene(s) and the causation matrix of a disease
(Miklos GL, 2004). Moving up to the level of the dynamics of protein
expression already demands a higher degree of sophistication in both assay
design and data analysis (Kumble KD., 2003). However, without a functional
assay on in-vivo dynamics of protein function and studying its spatial and
temporal expression patterns (process flux) in the cell (compartments) and
tissue, the functional impact on the cell remains unclear (Kriete A, 2003;
Young MB, 2003; Egner, A., 2004).

Studying subcomponents of cellular pathways ignores the functional unity of
the biological processes in the cell and the functional interactions between
pathways. Studying an isolated drug target ignores important off-target
interactions which become a cause of failure too late in drug development.
Studying proteins in isolation and uncoupled from the intracellular
molecular oscillating clocks, ignores the importance of temporal patterns
(Okamura H., 2004). Without a better understanding of the phenotypic and
functional outcome in the cell, the failure rate of the drug discovery
process will remain high and very costly. There is a predictive deficit in
the current oligo-parametric disease models used in pharmaceutical research
which necessitates complex and expensive studies later on in the drug
development pipeline to make up for the predictive deficit.

A simple homogeneous binding assay, will fail to capture important aspects
of functional protein heterogeneity. G-protein-coupled receptors (GPCRs)
represent by far the largest class of targets for modern drugs, but we have
not yet unraveled the subtle dynamics of their function (George SR, 2002;
Kenakin T., 2002; Kimple RJ, 2002; Ellis C., 2004; Kristiansen K., 2004).
Heterodimerization enhances the complexity of ligand recognition and
diversity of signaling responses of heterotrimeric guanine
nucleotide-binding protein-coupled receptors (GPCRs) (Foord SM., 2003;
Liebmann C., 2004). Heterogeneity of protein interactions that underlie both
cell-surface receptor expression and the exhibited phenotype are caused by
interactions with proteins which modify the activity profile of the GPCR,
such as activity modifying proteins (RAMPs) (Christopoulos A, 2003; Fischer
JA, 2002; Morfis M, 2003;  Sexton PM, 2001; Tilakaratne N, 2000; Udawela M,
2004). These protein functions and interactions in different cells and cell
types which we do not take into account in our subcellular models pop up
rather unpleasant in the drug development process.

The popular techniques to explore and analyze low-dimensional data at high
speed are based on the idea that this would provide all the data with
sufficient predictive power to allow for a bottom-up approach to drug
discovery. The current High Throughput Screening (HTS) and other early stage
methods allow gathering low-dimensional data at high speed and volume, but
their predictive power is too low as they lack depth of descriptive power
(Perlin MW, 2002; Entzeroth M, 2003). We are just clogging the drug
development pipeline with under-correlating data in relation to clinical
reality. A bigger flow of unmanageable data does not equal a higher
correlation to clinical reality.

The knowledge gathered at the infra-cellular level has to be viewed in its
relation to the (living) cell in its native environment and the biological
and non-biological processes influencing its function and health, which
requires a top-down functional and phenotypical approach rather than a
bottom-up descriptive approach. Complex disease processes cannot be
explained by simple oligo-parametric low-level models. A high-speed
oligo-parametric disease model does not equal high predictive power. It is
not the ability to study a simplified disease model at high speed which will
allow us to succeed, but we must study and verify the functional outcome of
the in-vivo disease process itself.

A game of chess is not described by naming its pieces, but by the spatial
and temporal interaction of both players or in other words the flow of
actions and reactions, described in a space-time continuum and if we add the
color it is a spatio-spectro-temporal flow of events. The individual pieces
or moves do not explain the final outcome of the game, only when the entire
process is analyzed from a positional and functional point of view we can
understand and predict the reason why one player wins or loses. You have to
study a game of chess at the appropriate organizational level in order to
understand it or you will fail to find an explanation for the outcome of the

Isocellular disease models

Cellular disease models have already allowed us to study disease processes
in geat detail, but they need to be dealth with carefully. Validation of a
cellular model and a thorough understanding of its strengths and weaknesses
is required. Using cellular disease models in more detail is not a trivial
endeavor. Cellular disease models need to be related to at least the in vivo
cellular disease process we want to study, so a validation of this
correlation is very important (Gattei V, 1993; Thornhill MH, 1993; Lidington
EA, 1999; Dimitrova D. S., 2002).

We now know that metabolic pathways show complex interactions and that gross
genetic rearrangements can impair entire parts of cellular metabolism. The
cellular models used in research should be validated for their functional
and phenotypical representation of in vivo, in-organism processes. However
many popular cell lines are not selected for their close linkage to clinical
reality, but for their maintainability in the laboratory, lack of
phenotypical variation, ease of transfectability, etc. . It is assumed that
those cellular models are a valid representative of the disease process, but
almost never a thorough assessment is being done. The phenotypic background
of a cell has an important impact on the structure and function of cellular
proteins (Tilakaratne N, 2000; Kenakin, 2003).

Primary cell lines cells in general require a more complex tissue culture
medium than most popular cell lines. Cancer cells (and transformed cells)
can usually grow on much simpler culture medium. Replicative senescence and
varying behavior at each passage (which may necessitate a change of cell
lines for long term experiments) also make primary cell lines less popular,
as they necessitate a change of cell lines and variability in experimental
data. Reduction of unpleasant variability in experiments by choosing a
specific disease model may create 'nice' results, but of a reduced
predictive value. Quite often results obtained with one cell line, cannot be
confirmed by using another cell line, without even talking about primary
cells (Kenakin T, 2003).

CHO cells (Chinese Hamster Ovary, Cricetulus griseus) are used in many
assays, but they are not derived from a human cell and are aneuploid (Tjio,
J. H., 1958). HeLa cells are derived from an aggressive cervical cancer;
they have been transformed by human papillomavirus 18 (HPV18) and have
different properties from normal cervical cells (Gey, G.O., 1952). The U-2
OS osteosarcoma cell line is easy to maintain and transfect (Ponten J,
1967). The PC12 cell line which responds reversibly to nerve growth factor
(NGF) has been established from a rat adrenal pheochromocytoma, it has a
homogeneous and near-diploid chromosome number of 40 (Greene LA, 1967). HEC
cells are derived of a human endometrial adenocarcinoma cell line and are
also very popular (Kuramoto H., 1972). Two cell lines are very popular for
epithelial barrier studies: Caco-2 cells and Madin Darby canine kidney
(MDCK) cells. Caco-2 cells are the most popular cellular model in studies on
passage and transport, they were derived from a human colorectal
adenocarcinoma (Kirkland SC, 1986; Hidalgo IJ, 1989). Caco-2 cells are being
used as a model to evaluate small intestine transport. The interpretation of
Caco-2 transport data is often confusing, and is not always in agreement
with in vivo observations, even when P-glycoprotein (P-gp) is blocked by
specific inhibitors (Hu, M., 1999; Hunter, J., 1993; Lennernas H, 1994).
Heterogeneity in the Caco -2 cell line, depending on passage number and
origin of the cells, leads to differences in transepithelial transport
(Walter E., 1996). Madin Darby canine kidney (MDCK) cells were isolated from
a dog kidney (Gaush, C.R., 1966). They are currently used to study the
regulation of cell growth, drug metabolism, toxicity and transport at the
distal renal tubule epithelial level. MDCK cells are also used as a cellular
barrier model for assessing intestinal epithelial drug transport (Cho, M.J.,
1989). We quite often use the models which will grow in vitro, just to have
at least something, although we know that this is a highly uncertain
approach. Availability of a particular cellular model system does not equal
predictability of the system.

Some popular cell lines may correlate with themselves and not with the
complex dynamics of the physiological process in man they are supposed to
represent. Studying the dynamics of the involvement of a protein in a
disease in patients and transforming this knowledge into a disease model in
a particular cell line requires a careful assessment before embarking on a
drug discovery process. Functional cell model drift should be verified at
regular intervals and taken into account.

Even within individual cell lines there is not always homogeneity in
phenotype and function. Cancer cells show genetical and chromosomal
instability as they tend to lose parts of chromosomes (Duesberg P., 1998;
Lengauer C, 1998; Duesberg P, 2004). Using cell lines derived from cancers
poses a correlation risk in relation to clinical reality on research done by
using these types of cell lines. Continuous sub-cultivation of cells and an
increase in the number of passages may lead to chromosome rearrangements and
loss of functional reactivity (Dzhambazov B, 2003). Loss of function
destabilises a cell when critical parts of pathways are lost, although cell
cycling may continue in parts of the cell culture, but this will cause a
drift and on experimental results.

Many of the most popular cell lines lack parts or even entire chromosomes
and therefore large chunks of metabolic pathways. A drug molecule can not
interact with the proteins which are not present in the cell line and an
adverse or even positive effect will go unnoticed. Functional loss of
proteins and enzymes in cancer cell makes them unresponsive to drugs if the
protein(s) which are the target of a drug are lost without killing the cell
as such.

Even when a protein is successfully expressed in a cell as shown on a
Western blot, this does not equal functional success. Western blotting tells
you how much protein has accumulated in cells. Even knowing the rate of
synthesis of a protein by Radio-Immune Precipitation (RIP) does not predict
the functional outcome of protein expression. Protein function is also
depending on the metabolic background of the cell in which the protein is
expressed and its spatial and temporal organisation. If the enzymatic and
structural background of the cell does not meet the prerequisites to put a
functional protein in the right location, embedded in the right functional
environment, nothing appropriate will happen. An appropriate functional
assay is required to validate proper function of the expressed protein.

Conclusions drawn from phenotypically uniform and simplified cell lines do
not show a reliable or constant correlation to "in-organism" cellular
dynamics. A functional comparison between isolated native cardiac myocytes
and cloned hERG demonstrates the advantages of cardiac myocytes over
heterologously expressed hERG channels in predicting QT interval
prolongation and TdP in man (Davie C, 2004). The involvement of
heterotrimeric G proteins in cell division was only discovered by looking at
the native protein in its natural environment and not by using a trasfected
system where the physiological gene regulation is disabled (Zwaal RR, 1996;
Kimple RJ, 2002).

The location of glycosyltransferases involved in N- and O-glycan chain
elongation was traditionally found to be confined to the Golgi-apparatus in
phenotypically simple cells, such as HeLa cells. However, localization
studies conducted in primary cell cultures often reveal ectopic
localizations of glycosyltransferases usually at post-Golgi sites, including
the plasma membrane (Berger EG., 2002). This shows that the prototypical or
average cell does not exist in drug discovery as a valid broadrange cellular
model. Cells are not just interchangeable containers with molecules in which
we can pour reagents but highly dynamic environments.

In vivo enzymatic reactions are not linearly correlated to protein
concentration or of "zero order". The intracellular environment causes a
more complex functional pattern for a given protein, such as bell shaped
relation between protein concentration and function. A "blunt" on/off
expression in a transfected cell does not correlate well to the
physiological condition in a primary cell. When the appropriate metabolic
environment is not present when studying a protein in a cellular disease
model, predictivity of the disease model may be low compared to
physiological conditions.

A traditional (homogeneous) cell culture in the laboratory may not yet mimic
the physiological conditions in an entire organism, so our approach to
cell-based research (and beyond) requires some redesign also. Creating a
virtual organism, by differential screening of a multitude of cell type
representing the main cell types in the human body (cardiomyocytes,
hepatocytes .) could help us to improve the predictive value of cellular
disease models. We need to study cell-to-cell and cell-type-specific pathway
dynamics in more detail, as is the case for nuclear factor-kappaB
(NF-KappaB) (Schooley K, 2003). Studying Biologically Multiplexed Activity
Profiles (BioMAP) directly in primary cells provides us with results which
are closer to the situation in man. BioMAP profiling can allow integration
of meaningful human biology into drug development programs (Kunkel EJ,

Metabolic pathways in cells do not exist in a void, but are interconnected
and highly dynamic processes. Blocking a pathway has far-reaching
consequences for the intracellular environment. The upstream metabolites
will either find their way through other metabolic pathways or pile-up. Some
inborn errors of metabolism are an example of this principle (PKU .). Drugs
blocking pathways also cause a distortion of the delicate balance in
metabolic processes and may cause upstream effects by metabolites which are
normally metabolized before they can cause any harm. The kinetics of the
"pharmakon" may be documented, but the change in cellular metabolism and
pathway-network distortion are less well understood. Upstream metabolites
may become processed by other pathways and unexpected adverse effects may
show up. Adverse effects on cellular metabolism are only present in those
cells which have an intact metabolic pathway and not even all cell types
activate the same pathways at all times.

Cell cycling and circadian oscillators are a source of periodic variation in
our cell based experiments (Okamura H., 2004). Oscillations of cellular
functions are to be explored and taken into account, as they act as a
dynamic background or reference level representing a dynamic reference for
studying cellular structure and function. Oscillations which are not
accounted for act as periodic noise, disturbing our measurements and masking
subtle, but possibly important events in our experiments.

Differential multiplexing in (high-content) cell based screening could help
us to acquire more information about the spatial and temporal dynamics of
cellular processes. Developments are going on towards experimental
multiplexing and up-scaling of the capacity of quantitative cellular
research (Perlman ZE, 2004). Techniques such as High-Content Screening (HCS)
or multiplexed quantification can be applied to cellular systems to study
intra-cellular events on a large scale (Van Osta P., 2000; Taylor DL, 2001;
Van Osta P., 2002b; Abraham VC, 2004; Van Osta P., 2004). Subcellular
differential phenotyping is already possible on a large scale by using human
cell arrays. We can use multiplexed molecular profiling in cells to obtain
more information (Stoughton RB, 2005). Light-microscope technology is used
to explore the spatial and temporal dynamics in cell arrays in great detail
(Ziauddin J, 2001; Bailey SN, 2002; Baghdoyan S, 2004; Conrad C, 2004;
Hartman JL 4th, 2004).

Analyzing a large number of tissues for candidate gene expression is now
greatly facilitated by using Tissue MicroArray (TMA) technology (Kononen J,
1998; Simon R, 2002; Braunschweig T, 2004).

>From individual cell to cytome

Studying cell function and drug impact at the level of the individual cell
is called cellomics (Russo E., 2000). However, the concept of cellomics does
not take into account the supra-cellular heterogeneity which is present in
every cellular system, such as a cell culture or an organism. By studying
cells while ignoring their diversity we make the same mistake as the
statistician who drowned crossing a river that on average was just three
feet deep.

Due to the heterogeneity of cell types and differences between cells in a
healthy and disease state, we need to take this heterogeneity into account.
Cytomes can be defined as cellular systems and the subsystems and functional
components of the body. Cytomics is the study of the heterogeneity of
cytomes or more precisely the study of molecular single cell phenotypes
resulting from genotype and exposure in combination with exhaustive
bioinformatics knowledge extraction (Davies E, 2001; Ecker RC, 2004b; Valet
G, 2003; Valet G, 2004).

In order to get the broader view on pathological processes, we should move
on to the phenotypical and functional study of the cellular level or the
cytome in order to understand what is really going on in important disease
processes. Although the genome and proteome level have their predictive
value in order to understand the processes involved in disease (and health),
the cytome level allows for an understanding of pathological phenotypes at a
higher level. By integrating the knowledge from the genome and proteome, we
could give guidance to the exploration of the cytome, which was not possible
before this knowledge was available.

The cytome level will also provide guidance to focus the research at the
genome and proteome level and so creating a better cross-level understanding
of what is going on in cells (Gong JP, 2003; Valet G, 2004; Valet G, 2004b).
Some would see this as taking a step back from the current structural and
systematic descriptive approach, but it is mainly a matter of integrating
research at another level of biological integration and looking in a
different way to the web of interactions going on at the cellular level.
Biological processes do not exist in a void, but they are a part of a web of
interactions in space and time, rather than being an island on their own. A
cell is a multidimensional physical structure (3D and time) with a finite
size, not a dimensionless quantity. We cannot ignore the spatial and
temporal distribution of events, without losing too much information.

In recent years the tools have matured to start studying the cellular level
of biological integration, but the tools are still used in the same way as
if they were derived from low-content high-throughput phenomena as this is
still the dominant research model. The tools to generate and explore a
high-dimensional feature space are still scattered and not brought into line
with the exploration of the cytome.

Functional processing in cellular pathways

The interconnection of genome, proteome and cytome data will be necessary in
order to allow for an in-depth understanding of the processes and pathways
interacting at the cellular level. A monocausal approach will have to be
replaced with a poly- and pluricausal approach in order to understand and
explain the phenomena going on at the cellular level. Pluricausal means
causal contributions at different levels, such as genes, other cells and
environmental influences. Polycausal means multiple causal contributions at
the same biological level, such as polygenic diseases or multiple agonistic
and antagonistic environmental influences. The concept of a multithreaded,
multidimensional, weighed causality is needed in order to study the web of
interactions at the cellular level. A drug modulates cellular function, but
changes can be studied at different levels of biological integration:

Disease outcome = drug x (a x clinicaln + b x physiologicalp + c x cellularq
+ d x geneticr )

Disease models should incorporate mixed and nonlinear effects. Diagnosis and
drug discovery merge if we take parallel models for both. The clinical
diagnosis or para-clinical diagnosis of a disease should show a high
correlation with the disease models used to study its possible treatment. A
cause (e.g. a single gene defect, a bacteria) can have multiple consequences
and as such be poly-consequential, which is the mirror situation of a single
consequence being caused by multiple causes (co-causality or co-modulation)
acting either synergistic or antagonistic (e.g. a disease with both a
genetic an environmental component). In reality, a pathological condition is
a mixture of those extremes (e.g. a bacterial or viral infection and the
host's immune system) and as such a simple approach is not likely to succeed
in unraveling the mechanism of a disease. With the current systematic and
descriptive approach however, we get lost in the maze of molecular
interactions. We are looking at too low a level of biological integration
and we get lost in a maze of structures and interactions. The cell is the
lowest acceptable target, not its single components, like DNA or proteins.

We are looking at the alphabet, not even words or sentences, nature is not a
dictionary, but it is a novel. We should study the flow of events in a cell
with more power, not only the building blocks. As an example, Mendel did not
need to know about DNA in order to formulate his laws of inheritance and he
did not know that the discovery of the physical carrier of inheritance, DNA,
would confirm his views later on, but his laws are still valid as such.
Certainly physics was not at the stage it was in the 20th century when
Newton formulated the law of gravity, but his observations and conclusions
were valid. When Einstein formulated his relativity theory, he did not have
modern physics at his disposal. His theory does not fit well to the quantum
level, but does explain phenomena at a higher level of functional
integration and as such is an appropriate model.

The value of a scientific model does not lie in the scale of phenomena it
describes, but in its predictive correlation to the reality it tries to
capture. The more we may try to exclude elements from reality, the better we
may be able to build a model which holds in a tightly controlled situation
in our laboratory, but fails when challenged by full-blown reality in the
outside world.

What we find should not be in contradiction to what lower level structural
descriptive research discovers, but we should not wait for its completion to
start working on the problems we are facing in medicine and health care

Epicellular disease models

Epicellular models are of great importance for both efficacy testing as well
as for toxicity testing. Organoids, parts of organs, isolated organs and
animals are being used as epicellular disease models. The advantages of in
vitro systems in toxicity testing are numerous. In vitro tests are usually
quicker and less expensive. Experimental conditions can be highly controlled
and the results are easily quantified. However, the relative simplicity of
nonwhole-animal testing results in limitations as well. Cells or tissues in
culture cannot predict the effect of a toxin on a living organism with its
complex interaction of nervous, endocrine, immune, and hematopoietic
systems. In vitro systems can predict the cellular and molecular effects of
a drug or toxin, but only a human or animal can exhibit the complex
physiological response of the whole organism, including signs and symptoms
of injury.

Rats, mice, dogs, (non-human) primates, rabbits, cats, guinea pigs,
hamsters, miniature swine, goats, farm pigs, etc. are some of the species
which are available for preclinical and non-clinical evaluation. The main
reasons for wanting to predict human sensitivity are that most failures in
the clinic are due to safety problems and lack of effectiveness. The
inability of animal models to predict these failures before human testing or
early in clinical trials dramatically escalates costs. The choice of the
relevant animal species for preclinical studies is an important
consideration. The goal of pre-clinical safety assessment studies is
ultimately an estimate of safety in humans. A critical assumption is that
the toxicity responses seen in various animal models will be reflective of
those in humans (e.g. comparative toxicogenomics). Assumptions require
in-depth validation, some of which we will discuss in this section.

Animal models are an important part of the drug discovery an development
process and they have made a significant contribution to our understanding
of disease processes. The correlation of the animal model to the actual
process (efficacy, toxicity) in man is an important issue to consider
(Hondeghem LM, 2002; Huskey SE, 2003). Inter-species differences, can have a
significant impact on the interpretation of results in an animal model for a
human disease. Earlier successes do not relieve us from continuing critical
assessment of every model. With the increasing complexity of the diseases
being studied, subtle interspecies differences become increasingly
important. In the past twenty years a lot has changed in the use of animal
models to study human disease processes and develop new drugs.

An historical example of a drug discovered by using an animal model for a
disease, is the discovery of the first successful oral antibiotic, Prontosil
(Sulfamidochrysoidine). Prontosil was developed by Gerhard Domagk, working
in the Bayer sector of I. G. Farbenindustrie. Sulfamidochrysoidine seemed to
have no effect on bacteria in vitro, but Domagk went ahead and tested it on
26 mice injected with streptococci. Fourteen were kept as controls, and 12
were treated with Prontosil. All of the controls died within a few days,
while all of the treated mice survived. Later Gerard Domagk received the
Nobel Price for his discovery, which saved the lives of many people.

Much has changed since Gerard Domagk's discovery of Prontosil. Animal models
are being used for evaluation of efficacy and toxicity. We can now use
genetically modified animals to study gene regulation and cell
differentiation in a mammalian system (Gordon JW, 1980; Isola LM, 1991;
Brusa R., 1999). Transgenic and gene-deleted (knockout) mice are used
extensively in drug discovery (Rudmann DG, 1999). The pioneering work of
Mario Capecchi, Martin Evans, Oliver Smithies and others has enabled the
construction of increasingly sofisticated animal models (Smithies O, 1984,
Evans MJ., 1989). Homologous recombination between DNA sequences residing in
the chromosome and newly introduced, cloned DNA sequences (gene targeting)
allows the transfer of any modification of the cloned gene into the genome
of a living cell (Capecchi MR., 1989). The challenges now are to model the
complex multifactorial diseases, instead of simple monogenic diseases
(Smithies O., 1993; Smithies O., 2005). Simple knockouts are usually
designed to lead to loss of protein function, whereas a subset of
cancer-causing mutations clearly results in gain of function. Mored dynamic
transgenic mouse systems are now avaialable (Sauer B., 1998; Maddison K,
2005). The original Cre and FLP recombinases have demonstrated their utility
in developing conditional gene targeting, and now other analogous
recombinases are also ready to be used, in the same way or in combined
strategies, to achieve more sophisticated experimental schemes for
addressing complex biological questions (Garcia-Otin AL, 2006).

Although knockout technology is highly advantageous for both biomedical
research and drug development, it also contains a number of limitations. For
example, because of developmental defects, many knockout mice die while they
are still embryos before the researcher has a chance to use the model for
experimentation. Even if a mouse survives, several mouse models have
somewhat different physical and physiological (or phenotypic) traits than
their human counterparts. An example of this phenomenon is the p53 knockout.
Gene p53 has been implicated in as many as half of all human cancers.
However p53 knockout mice develop a completely different range of tumors
than do humans. In particular, mice develop lymphomas and sarcomas, whereas
humans tend to develop epithelial cell-derived cancers. Because such
differences exist it cannot be assumed that a particular gene will exhibit
identical function in both mouse and human, and thus limits the utility of
knockout mice as models of human disease (Pray L., 2002).

There is not only interspecies variation, but also intraspecies variation.
The biological variability is also present in genetically modified animals.
We expect a specific phenotype from a specific genetical modification. In
genetically modified mice however, the observed phenotype is not always the
direct result of the genetic alteration (Linder CC., 2001; Schulhof J,
2001). The effect of the genetic modification is not completely
straightforward, due to variations in the genetic background of the animals
(Crusio WE., 2004). Transgenic mice containing the same genetic manipulation
exhibit profoundly different phenotypes due to diverse genetic backgrounds
(Sigmund CD., 2000; Sanford LP, 2001; Holmes A, 2003; Thyagarajan T, 2003;
Bothe GW, 2004).

The relevance of a particular model is linked to the similarity of a process
in the model animal and man. There is however considerable variation between
species which complicates the use and evaluation of animal models (Hucker
HB., 1970; Smith CC., 1967; Smith RL., 1974; Hengstler JG, 1999; Chiu SH,
1998; Nelson SD., 1982; Fry JR., 1982). A required part of drug development
is the "Chronic Bioassay". Hundreds of pharmaceuticals have been reported to
give a positive result in the standard "Chronic Bioassay", which consists of
an 18 to 24 month daily administration of the test compound in mice and
rats. This is in contrast with 20 pharmaceuticals, which are known to be
carcinogenic to humans (Van Deun K, 1997). Interspecies differences are an
important issue and require in-depth validation (e.g. comparative
toxicogenomics). Animals are popular models for studying G.I. absorption for
oral drug uptake, but in addition to metabolic differences, the anatomical,
physiological, and biochemical differences in the gastrointestinal (G.I.)
tract of the human and common laboratory animals can cause significant
variation in drug absorption from the oral route (Kararli TT., 1995).

A classical example of inter- and intraspecies variability is Thalidomide.
The mouse and rat were resistant, the rabbit and hamster variably responded,
and certain strains of primates were sensitive to thalidomide developmental
toxicity. Different strains of the same species of animals were also found
to have variable sensitivity to thalidomide (Neubert D, 1988; Bila V, 1989).
Although the drug was marketed in 1957, reproductive studies on thalidomide
in animals were not started until 1961, after the drug's effects on human
fetuses had begun to be suspected (MacBride, 1961). Initial studies on rats
and mice revealed some reproductive abnormalities, notably reduction in
litter size due to resorption of fetuses; however, only when the compound
was tested in the New Zealand white rabbit did abnormalities similar to
those noticed in human babies occur (Cozens DD., 1965). Studies on monkeys
revealed that they were almost as sensitive as humans to the deformative
effects of the drug (Delahunt CS, 1965).

Mouse models require careful evaluation and validation. Selection of mouse
models of cancer is often based simply on availability of a mouse strain and
a known compatible tumor. As a consequence cancer models in mice quite often
fail to predict success in clinical development later on (Kelland LR., 2004;
Kerbel RS., 2003; Peterson JK, 2004; Schuh JC., 2004; Voskoglou-Nomikos T,

Using inbred mouse strains reduces variation in genetic background, but also
reduces the correlation of the disease model to real-world genetic and
metabolic variation encountered in human populations. Finding a strong
correlation in an inbred laboratory animal population is no guarantee that
the correlation will hold in an out bred natural population (Mackay TF.,
1996; Macdonald SJ, 2004). Do we want nice results with a low standard
deviation (SD), or do we need results highly correlating with clinical
reality?  If one wishes to obtain the optimal mouse model for a human
disease, one needs to choose the correct genetic background as well as the
correct mutation (Erickson RP., 1996). Careful validation of a transgenic
mouse model is required, such as with models for Alzheimer's Disease, for
example by using biomarkers and PET (Bacskai BJ, 2003; Klunk WE, 2004; Klunk
WE, 2005). Validation of a model for a complex disease requires a thorough

We still do not have an in-depth understanding of the delicate spatial and
temporal interplay in metabolic pathways in cells, organs or entire
organisms in transgenic animals. Introducing or removing a gene without a
clear understanding of its spatial and temporal expression pattern, leaves
us with a correlation deficit in relation to the disease process in man.

When we modify a gene, we modify a pathway-web with upstream and downstream
consequences for cellular metabolism in different cellular compartments
(nucleus, Golgi .). The metabolites which (dis-) appear due to the
modification will modify a highly dynamic network of metabolic interactions.
In-vivo spatial and temporal variation in protein structure and activity
profiles will add to the complexity of unravelling the functional impact of
modified gene expression.

When we want to bridge the gap from "model to man" we must identify and
verify the process in its native environment the cell and the entire
organism (e.g. with a biomarker) and compare and validate what happens in a
mouse to what happens in man (Lee JW, 2006). The use of biomarkers can be
added to preclinical studies to help justify the choice of animal species.
The ultimate animal model for compound selection is still man himself and
with the recent introduction of extremely sensitive detection methods such
as accelerator mass spectrometry (AMS) and Positron Emission Tomography
(PET) it is now possible to evaluate the absorption, metabolism and
elimination of compounds in man as part of the pre-clinical selection
process (e.g. microdosing).

When Biology meets Chemistry

Figure 32. Biology and Chemistry meet at the point of minimal system
The bandwidth of system selection pressure is minimal at the moment of
Figure 33. Proportion of understanding achievable within chemical and
biological system space.
Biology is more complex than molecular chemistry, we do not master each one

Figure 34. Evolution of system selection pressure during the R&D process.
Selection pressure evolves in a non-linear way.
Figure 35. Bandwidth of uncertainty during the R&D process.
What is not present at the moment of decision, does not contribute to the

Working with models at different levels of biological complexity, has its
consequences. We arrive at the point of confrontation between biology and
chemistry at a moment when we have reduced system complexity to its bare
minimum (Figure 32). The long an winding road towards unraveling the disese
process and arriving at a screenable target has made us strip away man from
the target molecule. Almost nothing is left of the intrahuman ecosystem at
the moment when we confront chemistry with biology. Less than 0.01 % of the
full blown selection pressure is present at the moment of confrontation
between biology and chemistry. We are as such 99.99 % blind for interfering
phenomena. The molecule surviving the initial selection process evolves in
an almost pristine environment compared to the metabolic jungle of man.
Unable to increase our understanding of biological processe in the intact
human ecosystem, fast enough to keep productivity of the R&D process
sustainable, we retreated into simplified models in the hope to catch up
later on in the process.

When we slowly retreat back to the confrontation with the full blown
intrahuman ecosystem, we make our decisons at each stage gate within an
environment which is only partly representative for the ultimate situation
(Figure 33). Instead of symplifying at each step (as is the case during
unraveling a disease, left side of Figure 32), we are confronted with a
system of higher complexity after passing a stage gate, for which the
previous step provides less than ideal predictive power. Even if we extract
all information before arriving at a stage gate, our predictive power is
flawed in relation to increased complexity awaiting us after the gate. We
see what we observe, but not what we need to know. The retreat is also not
following the same trajectory as we used for approaching the
biological-chemical confrontation, but a twisted path biased by regulations
and interspaced with gaps, caused by model discontinuities.

The relative power of our understanding in relation to the system we are
working in, is higher in the early chemical environment than in the later
biological environment (Figure 34). As we can witness in the high late stage
attrition rates, the overall chance of succes of Phase III now equals about
50 %, or it is as predictive as tossing a coin.

The selection pressure, both positive and negative reaches a high level only
at the end of the pharmaceutical R&D process, because it is only then the
full blown complexity of man and human population is able to exert its
effect (Figure 34). The consequence is that the power of a decision at a
stage gate is highly uncertain during most of the process (Figure 35).
Increasing the capacity of under-predicting processes early on in the
pipeline, will not bring down attrition rates.

Less questions, better answers

Figure 36. A drug discovery and development process with a right choice of
93% at each step still only performs at about 50% overall. By Phase III we
50% of our efforts on failures.
Figure 37. Searching for needles in a haystack leads to very high false
positive rates.

The overall drug discovery and development process is Data Rich, but
Information Poor (DRIP), which explains its low overall efficiency. Within
major pharmaceutical companies, there are numerous functional silos
generating different data from different technologies with different levels
of error. A process as complex as the present-day drug discovery and
development process requires a success rate (true positives and true
negatives) of more than 93% at each step to perform above 50% overall
(Figure 36). Even if we make the right choice in 93% of all cases at each
stage-gate and make a false positive or false negative choice in only 7%,
this means that by the end we will spend 50% of our efforts on failures in
Phase III. The wrong choices we make in the beginning we take with us
through the entire process. At the moment the choice of target and lead
candidate is flawed, due to a lack of knowledge about the disease process
and the interaction of the chemical substance with the human biological
system. We fail at the beginning, but pay the price at the end. As we were
incapable to understand molecular processes in complex biosystems, we added
some simplified, but high-speed environments upfront to the process.

False positives are a problem in any kind of test: no test is perfect, and
every the test will incorrectly report a positive result in certain cases.
The problem of false positives lies, however, not just in the chance of a
false positive prior to testing, but determining the chance that a positive
result is in fact a false positive. Using Bayes' theorem, if a condition is
rare and only a minority of molecules show activity in a High Throughput
Screening (HTS) campaign, then the majority of positive results will be
false positives, even if the assay for that condition is (otherwise)
reasonably accurate (Figure 37).
Blind screening of a random chemical library (e.g. generated by
combinatorial chemistry) against a biological target leads to an excessive
number of false positives, putting more pressure on the secondary screens
and beyond to weed out these false positives. A simple example, based on
Bayesian inference illustrates the phenomenon. An assay is being used which
has a 99.99% true positive rate. The graph shows the percentage of false
positives for three different true negative rates 95%, 97% and 99%. As you
can calculate yourself (see Bayesian inference), the true negative rate has
a more dramatic impact on the number of false positives when the population
frequency (hit rate) is very low. By using a chemical library whose chemical
space is badly matched to the biological space of the target we create an
high proportion of false positive results. As the high attrition rates in
clinical development show (e.g. Phaze II) show, we do not get rid of these
false positives until very late in the overall process. However when the
population frequency of chemical structures matching with the validated
target increases, the rate of false positives decreases exponentialy (Figure
37). Designing molecules with an understanding of their potential
interaction with a truly validated biological target leads to an increase in
process efficiency which surpasses all other attempts made to improve the
drug discovery and development process. Mistakes made at the beginning are
harder to compensate for later on in the process. It is at the start of the
process when the path towards a succesful drug in man heads in the right
direction or not. We first need to understand what happens in the cytome of
man, not only in an Eppendorf. Now the tools are becoming available to
achieve this goal:
Investigate the molecular physiology of a disease process in individual
cells and in man.
Design chemical structures which match the validated drug target(s).

The idea of a Human Cytome Project is about the improving ability to
understand system-wide phenomena at a (sub-)cellular resolution (molecular
physiology). This leads to being able to ask the question about
effectiveness and toxicity in one step in a complex biological environment,
in model organisms and in man (ask less questions, but get better answers).
Molecular observation and answers to system-wide questions leading to better
models generated from improved observations.

Human Cytome Project - the way to go

Practical issues on how to to explore the human cytome and a concept for a
software architecture can be found on these pages:
The article "How to explore and find new directions for research", discusses
ways to explore the cytome, ranging from digital microscopy, High Content
Screening (HCS) to Molecular Imaging. The cytomic microcosm, both in-vivo
ans well as in-vitro is accessible for highly detailed detection and
analysis. New experimental techniques allow for an in-depth exploration of
the (patho-)physiology of the human cyctome. The human cytome is now more
accessible than ever for exploration and as a consequence a leap forward in
our understanding of the complex machinery of the cytome in health and
The article "A framework concept to explore the human cytome on a large
scale", presents some concepts on the way towards large scale exploration of
the human cytome system.
Final remarks

The war against human diseases is not an easy one to win. It spans the globe
and stretches through the ages. The trenches of our war against disease
reach from bench to bedside and require a strong and united effort in order
to succeed.

Innovation is much more than discovery of new drug targets. There is no
substitute for knowledge and understanding. More rational, more efficient
and more informative preclinical and clinical drug development is required.
We have to get away from a trial and error approach to a "cognitive"
chemical biology approach, matching biological and chemical space. We have
to do a better effort to build up understanding about the target (system)
and active compounds.
Drug discovery and development show a decreasing efficiency in relation to
the amount of money being invested. The level of understanding at the end of
discovery (and preclinical development) should achieve a knowledge level
which is capable to predict success at the end of the pipeline much better
than we do now. We need to improve our preclinical disease models and our
understanding of clinical reality. The quantity of what we can achieve with
automation of drug discovery needs to be matched with the predictive quality
of the science underlying the automated procedure. There is an urgent need
to improve the productivity of drug discovery and development, but this time
we should evaluate the underlying process better than we did with the advent
of target-based drug discovery and High Throughput Screening (HTS).

Our disease models should capture more of the high-order complexity of
biology, beyond the genes and proteins which are the current focus of
research. Higher-order disease models should place genetic and protein
research results in a broader perspective and complement them with
information about high-order interations in space and time. We should study
cells with taking into account their in-cytome differentiation and their
real-life behavior. The sooner NCEs or NBEs evolve in a "rich" or lifelike
biological environment (background, population variability) the earlier we
capture (un-)wanted phenomena. It is all about improving the Probability Of
Success (POS) for the overall process. There is no escape from the demands
for better treatments from patients, society and shareholders. Scientists
are working day and night to develop new treatments for unmet medical needs,
but their effort should become more efficient and effective, so more of the
candidate drugs reach the patients waiting for new and improved treatments.
The weight of higher-order biological exploration should increase in the
overall discovery and development process. More of man's diseases should be
captured in our (pre-)clinical disease models.

In the end process improvement should lead to a dramatic decrease in false
positive results in preclinical development, while at the same time avoiding
an increase in false negatives. This should lead to a better performance of
the overall drug discovery and development pipeline in which more and better
drugs reach the patients. I want to end this article with a quote from Dr.
Paul Janssen: "For many sick people, there are still no drugs, and it is our
job to develop good medicines".

I am indebted, for their pioneering work in automated digital microscopy, to
Frans Cornelissen, Hugo Geerts, Jan-Mark Geusebroek, Roger Nuyens, Rony
Nuydens, Luk Ver Donck and their colleagues. Many thanks also to the
pioneers of Nanovid microscopy, Marc De Brabander, Jan De Mey, Hugo Geerts,
Marc Moeremans, Rony Nuydens and their colleagues. I also want to thank all
those scientists who have helped me with general information and articles, I
hope I always quoted everyone properly when possible.


References can be found here


Towards a Human Cytome Project
Draft: Human Cytome Project
Innovation and Stagnation: Challenge and Opportunity on the Critical Path to
New Medical Products
Innovative Medicines Initiative (IMI)
European Medicines Agency (EMEA) Road Map to 2010
New Safe Medicines Faster Project
Priority Medicines for Europe and the World Project "A Public Health
Approach to Innovation"
Moving Medical Innovations Forward? New Initiatives from HHS
OECD -Innovation and Technology Policy
Biomedical Structural Research
E-Cell Project
Physiome Project
EuroPhysiome - STEP
IUPS Physiome Project
GIOME Project
Functional genomics
National Resource for Cell Analysis and Modeling - NRCAM
Prediction in Cell-based Systems (Predictive Cytomics)
Human Genome Project
Post-Human Genome Project Progress & Resources
How Many Genes Are in the Human Genome?
History of Human Cytome Project set of articles

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The author of this webpage is Peter Van Osta, MD.

A first draft was published on Monday, 1 December 2003 in the
bionet.cellbiol newsgroup. I plan to post regular updates of this text to
the bionet.cellbiol newsgroup.

Latest revision on 21 May 2006

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