Drug Discovery and Development - Human Cytome Project
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Introduction
A human cytome project aims at creating a better understanding of a cellular level of biological complexity in order to allow us to close the gap between (our) molecules and the intrahuman ecosystem. Understanding the (heterogeneous) cellular level of biological organisation and complexity is (almost) within reach of present day science, which makes such a project ambitious but achievable. A human cytome project is about creating a solid translational science, not from bench to bedside, but from molecule to man.
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. It deals with process (technology, biology)
and model deficits in drug discovery and development.
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 mind. Although this article may seem
wide ranging and to some shows lack of focus, it is meant to be comprehensive and also show the lack of
focus of many solutions which attempt to solve the pipeline problem by revolving around the core problem.
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 faster.
The challenges which the pharmaceutical industry is facing:
From a business perspective, there are 2 sources of value creation by a more productive discovery processes
("clinical" quality of molecules):
There are 4 levers for creating value in (pre-)clinical drug development (process improvement):
What should we achieve for the overall drug development process in order to restore its productivity:
What should be the deliverables (metrics), however ambitious they may seem:
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).
Personal note
My personal interest in cytomics, grew out of my own work on High Content Screening, as you can see in: Application of linear scale space and the spatial color model in light microscopy, Automated Tiled Multi-mode Image Acquisition and Processing Applied to Pharmaceutical Research and The M5 framework for exploring the cytome.
Scope of the article
This article deals
with the analyis of several apects of the drug discovery and development process
and the the weak spots and flaws within this process, which cause the high late stage
attrition in the pipeline. The pharmaceutical industry has passed the threshold
where only slight adaptive changes can restore its productivity and profitability.
Reducing costs is not enough to restore the health of the industry, a paradigm shift is needed,
but this will require a new vision on the fundamentals of the drug creating process and the way true
knowledge and understanding is being built on the foundations of scientific discovery and through
applied research.
However important business processes are, 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 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 industry.
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.
Som thoughts on 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.
There is a lot more to say on the philosophy of science and its impact on research, but this is outside the scope of this article.
In addition 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.
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). _DJI (Dow Jones Ind.), _IXIC (Nasdaq). 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). As all drug companies improved their stock value, the cause is common
to all of them and not to one company outperforming the other companies. Of all the knowledge required to develop
a new drug, the most important component is a true understanding of the molecular basis of the disease process.
This knowledge is mainly in the public domain and available for all companies, so no company is capable of
outperforming its peers in the long run. Although it may take up to 15 years to develop a new drug,
it may take up to 20-30 years to unravel the mechanism of a disease and this requires an effort on
a global scale, not just of a single pharmaceutical or biotech company. Almost 90% of all science required
to create a drug for a given disease, resides outside the pharmaceutical industry, but 90% of the risk is
hidden within the inefficient process used within the pharmaceutical industry (90% failure in drug development).
The industry faces the final challenge of proving that the ideas about a disease process truly work
in the complex biosystem of man.
The drug discovery and development process suffers from "particularism" and lack of "generalism"
as the success with one drug does not lead to a consistent increase of performance. Working on improving the process as it
exists today within the pharmaceutical and biotech industry is only leveraging 10% of the required knowledge-base.
A failure rate of 90% for 10 or 50 drugs in the development pipeline, is not an example of process improvement,
although the bigger pipeline will lead to a five-fold increase of drugs reaching the market.
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 such.
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 before?
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 potential 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 15).
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.
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
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 (
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 high success rate of anti-infectives is also caused by the fact that we are capable to make a valid representation of
the entire target system (bacteria) in a "test-tube" or "petri-dish" early on in the process,
and not only a billion-fold reduced representation of the human biosystem. As long as the dominant view on
applied science remains that a set of molecules in a "test-tube" can represent the complexity of
system (reductionism) our models will disappoint us at the end of the process when there is no
escape from the complexity and variability of man and human population. Leaving too much of the original
system out of an experiment brings too much flaws into the experiment. Elimination without confirmation
of validity against the original condition gives flawed results (and late stage attrition).
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 α and β, N only depends on Δ/σ, or the effect size. α = 0.05 and 1 - β = power = 90% for a 2-sided test. The graph shows N as a function of Δ/σ = difference in units of s.d. |
Figure 21. Sample size (N) for comparing proportions (p). In addition to α and β, N depends on p and Δ. Let α=0.05, 1- β = power = 90%, 2-sided testing, p=0.5 (conservative estimate for variance). The graph shows N as a function of Δ = difference p1-p2, e.g. 0.2 = 0.6 - 0.4 |
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 (α) and beta (β) as the probabilities of Type I and
Type II errors, respectively.
or
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) = β and β 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 - β. Summarized:
β = 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.
In inductive research, applying statistics has to be done with care. Expanding a trial population beyond
the boundaries of statistical relevance, may lead to spurious statistical significance but will not improve
the correlation to clinical relevance. By doing this we increasingly feed the process with false positives
and increase the pressure towards the end of the pipeline. The basic principles of probability (significance)
and induction (relevance) should be taken into account when designing and performing experiments.
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 anti-infectives, 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).
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,
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 Responses.
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 the largest clinical trials are underpowered to detect rare events
before a drug hits the human population at large. The increasing attention to chronic diseases for which there is no
"restitutio ad integrum" possible, but only long-term treatment also increases the
exposure of individual patients to the drug, which we cannot foresee during clinical trials before the
drug hits the market. 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 years) 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 development.
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 ecosystem.
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 characterization:
- 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 progression. - 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. Acc