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THE JOURNAL OF PHARMACOLOGY AND EXPERIMENTAL THERAPEUTICS
Copyright © 2011 by The American Society for Pharmacology and Experimental Therapeutics
JPET 336:3–8, 2011
Vol. 336, No. 1
171751/3638165
Printed in U.S.A.
Perspectives in Pharmacology
Productivity Shortfalls in Drug Discovery: Contributions from
the Preclinical Sciences?
Michael Williams
Department of Molecular Pharmacology and Biological Chemistry, Feinberg School of Medicine, Northwestern University,
Chicago, Illinois
ABSTRACT
An inverse relationship between human and financial investment and productivity, in the form of new drug approvals, has
been a consistent theme in drug discovery for more than a
decade. There appear to be many causes and solutions for this,
but few tangible outcomes. Although Food and Drug Administration regulators, the constraints resulting from short-term business decisions, and the harvesting of all “low-hanging fruit,”
have been cited as the major causes for the decreased
productivity, a change in the preclinical research culture is
equally culpable. Current trends in biomedical research have
led to a decreased emphasis on the null hypothesis/data-driven
approach; a trend toward qualitative rather than quantitative
science; an implicit assumption that all targets represent a
Introduction
In the second decade of the 21st century, rarely a week has
passed without a review or an article in the popular press
lamenting the inverse relationship between the investment
in the drug research and development process and the continued shortfall in productivity, the latter being assessed in the
lack of robustness of clinical pipelines and the reduced number
of new drug approvals (Munos, 2009; Carmichael and Begley,
2010). Despite optimistic declarations that a “golden age” in
drug discovery now exists (http://www.bloomberg.com/apps/
news?pid⫽newsarchive&sid⫽avss9uE1DgWM#), there is little
to objectively support such claims, especially when approximately 35,000 jobs have been eliminated in the pharmaceutical
industry in the first half of 2010 (http://www.fiercepharma.com/
node/22199). And as the research and development environArticle, publication date, and citation information can be found at
http://jpet.aspetjournals.org.
doi:10.1124/jpet.110.171751.
viable starting point for drug discovery efforts; and the replacement of the creativity, objectivity, passion, and logic characteristic of the drug hunter with consensus-dependent, technologydriven research cultures. In addition, the euphoria following the
mapping of the human genome and its implicit potential as a
source for new drug targets has given way to disillusionment as
the relevance, tractability, and complexity of novel diseaseassociated targets have become recognized as significant
challenges. Biomedical research efforts directed toward drug
discovery, both in academia and industry, must prioritize genuine innovation over technology and thus allow efforts in preclinical research to play a key role in the solution to the shortfall in
new drug applications.
ment becomes increasingly unstable because of mergers and/or
downsizing, the ability to develop medications for treating diseases that threaten to undermine the economic viability of
healthcare systems worldwide, e.g., Alzheimer’s disease (AD),
becomes ever more difficult to address.
This lack of productivity has been ascribed to increasing
stringent regulations from regulatory authorities, including
the FDA (Carpenter, 2010); the overt, “crippling” influence of
business decisions on basic science because of short-term
profitability goals (Weisbach and Moos, 1995; Cuatrecasas,
2006), which has resulted in pharma research and development being viewed as an expense rather than an opportunity—“a
drain on the balance sheet” (Carr, 2010); and the postulate
that drugs for all the “easy” targets, the “low-hanging fruit,”
have been discovered, with those remaining being difficult to
reduce to practice/products. Although these are undeniably important factors that affect success in drug discovery, there are
also shortcomings in the preclinical space that result from a
change in the culture of biomedical science that originated in
ABBREVIATIONS: AD, Alzheimer’s disease; HTS, high-throughput screening; NCEs, new chemical entities; FDA, Food and Drug Administration;
SAB, Scientific Advisory Board.
3
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Received July 6, 2010; accepted August 23, 2010
4
Williams
the era of molecular biology, which is a discipline conspicuously
lacking a quantitative approach (Maddox, 1992) and that has
inadvertently contributed to a dearth of biomedical scientists
trained in pharmacology (http://www.fda.gov/ScienceResearch/
SpecialTopics/CriticalPathInitiative/CriticalPathOpportunities
Reports/ucm077262.htm). As a result, drug discovery has been
viewed as being disconnected from the real world (Horrobin,
2003) and has also been increasingly characterized as a “turn on
the computer, turn off the brain” discipline (Kubinyi, 2003) with
technologies, metrics, and group approval, both institutional
and disciplinary, taking precedence over individual creativity
and innovation.1
Biomedical Research in the Commercial
Setting
Effect of the Biotech Revolution on Drug Discovery
1
Innovation is typically defined as the “introduction of new things or
methods,” by which metric pharma can certainly be considered to have been
very innovative. Beyond the noun, however, is a complex discipline that more
accurately describes innovation as “a change in the thought process for doing
something, or the useful application of new inventions or discoveries” (Barras,
1984). The introduction of “new things” without considering how they may fit
into and improve existing “things” is where there has been a shortfall in what
pharma has subscribed to as innovation. Instead, the latter has been largely
treated as an obligatory sound bite, with the key follow-ons of ownership,
integration, and value generation being largely overlooked. In general, failures
in innovation have been ascribed at the organizational level to poor leadership,
organization, communication, empowerment, and knowledge management,
and at the individual level to poor goal definition, alignment of actions to goals,
team participation, results monitoring, communication, and information access (O’Sullivan, 2002), all of which may resonate with those familiar with the
endless reorganizations in pharma. From a technical perspective, the real
innovation resulting from combinatorial chemistry, parallel chemical synthesis, or library synthesis occurred long after the practical utility of the former
was questioned by bench scientists. Similarly, the innovation in Apple’s iPod
was not in the basic idea of an mp3 player but in the strategy for its successful
creation and application (Williams, 2005; Goodwin, 2010)—the technology also
fulfilled a need, rather than looking to create one.
Technology Imperatives
Rather than creating synergies by using multiple complementary technologies to find answers to discrete questions in
a focused and coherent manner (Williams, 2005), technologydriven drug discovery has become a discipline that justifies
its existence by searching for questions. An example of this is
the proteomics approach to target validation, where the intrinsic complexity of the protein component of a cell or tissue
necessitates a reductionistic approach where experimental
samples must be separated into bins to facilitate analysis
with timelines for data generation that can stretch into
months or years.
To those with a technology bent, new iterations on a technology, regardless of its utility, inevitably become “must
haves,” with acquisition and implementation becoming ends
unto themselves. Each new software update, as well as each
2
The null hypothesis, originally proposed by the statistician Ronald Fisher,
is often considered by biomedical scientists as an arcane concept applicable
only to statisticians. The basic premise of the null hypothesis is simply stated
in that “There is no relationship between two quantities.” Thus in evaluating
a hypothesis, the experimenter must design experiments with a “null” approach that assumes that any difference observed between two data sets
(control versus test compound effects) is due purely to chance. When a data set
that is designed to show no difference actually demonstrates a statistically
significant difference, the null hypothesis can be refuted. As a practical example, an experiment that is limited to showing that Compound A is an antagonist of Target A can lead to possible spurious data when the effects of Compound A have not been evaluated at Target B. The null hypothesis in this
instance would be that Compound A does not block Target B, which should be
tested. Many studies defining the role of a kinase-signaling pathway in a
target response are often limited to the use of a single kinase inhibitor of
dubious selectivity at a single, micromolar concentration that provides, more
often than not, far from convincing evidence, especially when the “n” value is
1 and statistics are absent.
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The biotech revolution began in the mid-1980s with the
founding of Genentech and attracted venture capitalists to
drug discovery as a value proposition on par with the thensuccessful Silicon Valley dot coms. More than 3000 biotech
companies were founded, the great majority of which failed,
both in terms of successfully using their core “technology
platform” and compounds to find viable clinical drug candidates and/or in generating the necessary revenues to survive
(Pisano, 2006). This, however, did not prevent many investors, founders, and company employees from becoming
wealthy, regardless of the companies’ fates and/or the science, with money frequently being a driver for the basic
science. This situation was the antithesis of the pharma
industry view in the mid-20th century, when George W.
Merck, the president of Merck and Co., noted that “We try
never to forget that medicine is for the people. It is not for the
profits. The profits follow, and if we have remembered that,
they have never failed to appear. The better we have remembered it, the larger they have been” (Merck, 1950).
The biotech revolution also engendered a change in the
culture of biomedical science that was far more entrepreneurial and risk-oriented than that practiced in big pharma.
This resulted in a new type of research and development
leader— energetic and visionary, committed to operating
“outside the box” with “a bias for action and a winning attitude” (Douglas et al., 2010) and, perhaps most importantly,
facile in translating the often-abstract complexity of science
to the business world. Although such changes were critical to
the new business model at the strategic and leadership levels, they inevitably led to an oversimplification or “dumbing
down” of the basic science, its practice as well as its presentation, such that sound bites and PowerPoint slides, rather
than peer-reviewed publications, increasingly came to reflect
the intrinsic scientific content and intent of a drug discovery
effort. At times, this unfortunately led to marked disconnects
between the enthusiasm of the vision and the data, doing
little to enhance credibility (Prud’homme, 2004).
As a result, the rigor and transparency integral to the null
hypothesis approach (Caldwell, 2009)2 were gradually replaced with an overtly reductionistic approach to data generation and interpretation that has increasingly relied on
qualitative rather than quantitative science. Gone were the
replicate pA2 and IC50 value determinations that had historically underpinned hypothesis generation. Instead, key decisions became focused on the “statistics-free” effects of a single,
often arbitrarily selected dose/concentration of a compound,
evaluated at a single time point using a transfected target or
transgenic animal, with the experimental readout often being
based on the “⫹/⫺, ⫹, ⫹/⫹” readout of the density of a gel
band. This negated the basic premise of pharmacology, that
of the Law of Mass Action, with “all-or-none” responses frequently being described as concentration/dose-response
curves and with little consideration for either the structureactivity relationship of a compound as an integral component
of the concentration/dose-response effect or its ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity)
properties.
Productivity Shortfalls in Drug Discovery
Target Tractability
Implicit in the HTS/combinatorial chemistry paradigm
was/is that each target was equally facile as a starting point
for a drug discovery project. The mapping of the human
genome (International Human Genome Sequencing Consortium, 2004) provided an apparently infinite potential for
identifying a host of new targets against which to screen
compound libraries. The more targets, the more compounds,
the more data sets, the greater the opportunity for success
(Fryburg, 2010; Miller, 2010). However, the initial euphoria
for the human gene map and its myriad solutions to the
treatment of human disease (Collins et al., 2003) has gradually given way to disillusionment and frustration as it became apparent that the disease association of many of the
newly identified targets became less robust as additional
data became available. Craig Venter, who led the private
sector initiative to map the human genome, has recently been
quoted as saying that “the medical benefits derived from the
human genome [are] close to zero” (Boyer, 2010).
As an example, the Val(158)Met polymorphism in catecholO-methyl transferase (EC 2.1.1.6) that represented a new
approach to antipsychotic drug research is also associated
with gender-related nociception, anxiety, cognition, hypertension, as well as breast, endometrial, bladder, and prostate
cancer and Parkinson’s disease (Marino et al., 2008). This is
truly an example of reductionism being “humbled by nature’s
complexity” (Kenakin, 2009). Similarly, the relationship between identified genetic variants and disease causality—and
by definition mechanisms of drug action— has been equally
perplexing. Of 85 genetic variants identified as susceptibility
factors for acute coronary syndrome, none could be confirmed
in the genotyping of a cohort of some 800 patients with acute
coronary syndrome (Morgan et al., 2007), such that the use of
(single-nucleotide polymorphisms associated with cardiovascular disease risk has proven to be less useful in disease
diagnosis than “self-reported family history” (Paynter et al.,
2010). Given this and the polypharmic nature of many drugs
in current use (Roth et al., 2004), the focus of drug discovery
on discrete targets (Sams-Dodd, 2005), termed targephilia
(Enna and Williams, 2009a), to the exclusion of a more hierarchically integrated, pharmacologically based approach
(Williams, 2005), has become increasingly problematic. It has
been further compounded by NCEs that, although active
against the in vitro target construct, gave less than stellar
results in the clinic in terms of efficacy (none) or safety
issues. This has led to a major conundrum. Could some
targets be less tractable than others? And in the context of
finding second- or third-generation, “best in class” NCEs, are
some known targets intractable using current approaches to
drug discovery?
Tractability is typically viewed as the ability to identify
novel small-molecule hits and chemically iterate these into
drug-like molecules. The latter are usually defined in terms
of the “Rule of Five” (Lipinski et al., 2001). Thus, patentable
NCEs that interact potently and selectively with their selected targets, have acceptable physiochemical and pharmacokinetic properties, and have an acceptable safety margin in
preclinical testing can advance to the clinic but do not always
work once there. As repeated efforts yield repeated failures,
there are two conclusions. The first, the “sunk cost” approach, e.g., that the next compound or the one after that, or
the one after, and so on, will validate what has sometimes
become a decade or more of effort, yielding hundreds of tool
compounds and numerous publications but no breakthroughs
in the form of new drug candidates. The second was that the
target, for whatever reason, was intractable. Consider the
following examples.
Something Old
Opioid Receptor Agonists. Drugs active at opioid receptors remain the gold standard of analgesic care and include
morphine, codeine, and oxycodone. With the discovery of the
␮, ␦, and ␬ receptor subtypes in the 1970s, it was anticipated
that development of selective agonists for these receptors
would result in drugs that had a reduced liability for the
respiratory depression, tolerance, constipation, and addiction
associated with classical opioids. Some 40 years later, despite
considerable efforts in medicinal chemistry and molecular
biology to refine/define the structural characteristics of receptor-selective NCEs, the ”holy grail” of side effect-free opioids appears as elusive as ever, with a multitude of compounds showing compelling preclinical data but failing to
demonstrate these properties in the clinic (Corbett et al.,
2006).
Muscarinic Receptor Agonists, Antagonists, and
Modulators. With the cloning of the M1-M5 receptor family
in 1989, it was also anticipated that new drugs with greater
therapeutic efficacy and/or reduced side effect liabilities
would be found to replace drugs like scopolamine, atropine,
oxybutynin, and others that have major side effect liabilities.
This would occur by tweaking the selectivity and efficacy of
NCEs for the various muscarinic receptor subtypes, an un-
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iteration in high-throughput screening (HTS) and combinatorial/parallel chemistry while enhancing the number of compounds that could be made/screened, has led to a focus on
productivity metrics that counts identified hits rather than
drug-like lead molecules (Ullman and Boutellier, 2008).
Screening a compound library containing half a million compounds or new chemical entities (NCEs) at “x” targets in 1 to
2 weeks provided more data in a week than some scientists
had seen in an entire career and frequently represented a
self-contained task. If no drug-like hits resulted, there was
always another target to take the place of the one that failed,
thus justifying the strategy. However, as noted by Shaywitz
and Taleb (2008), “spreadsheets are easy; science is hard.”
Technologies did not always generate endpoints that added
real value to the drug discovery process. An example of this
was the solving of the x-ray structure of a target-optimized
lead interaction concomitantly with the entry of the lead into
clinical trials. This was elegant science temporally disconnected from real-time need.
A compound library acquisition/synthesis and HTS approach is integral to the Molecular Libraries Initiative component of the National Institutes of Health’s Roadmap (Austin et al., 2004), which has as its mission “to facilitate the use
of HTS to identify small molecules…by rapidly and efficiently
screening a large number of compounds that encompasses a
broad range of novel targets and activities….” Although well
intentioned, this approach has been viewed as having questionable success by experts in pharma (Kaiser, 2008) and has
been further limited by a lack of funding to assess the druglike properties of a compound that “turn a discovery into a
drug” (Carmichael and Begley, 2010).
5
6
Williams
Something New
The human kinome consists of approximately 518 mammalian protein kinases (Manning et al., 2002) and collectively represents a major set of potential disease targets.
From a drug discovery perspective, however, the kinome was
considered by many to be an intractable target, as competitive inhibitors, which act by blocking access of the substrate
ATP, would by definition lack selectivity, leading to promiscuous interactions with all kinases, resulting in an unacceptable side effect profile. In addition, in many instances, the
discrete protein substrate(s) of a given kinase, its endogenous
activity (often dependent on the ATP level in the microenvironment), and the redundancies in kinases for substrates
and substrates for kinases were unknown. The seminal kinase inhibitor, the naturally occurring bisindole alkaloid,
staurosporine, was notably nonselective in its kinase specificity (Karaman et al., 2008) (not that medicinal chemists
were not able to improve on its promiscuity) but proved to be
benign in vivo, which may be attributed to its inherently poor
solubility. However, molecular profiling of 20 other kinase
inhibitors, 16 of which were either approved drugs or in
clinical development, showed that they had markedly different kinase inhibitory specificities that could not be correlated
with either their structure or their activity at the intended
target (Fabian et al., 2005). This suggests that in disease
states in which aberrant kinase activity plays a key role,
specificity may be dictated by the target enzyme being induced, abnormally expressed, or hyperactive. Alternatively,
3
Those who have spent a major part of their career making and testing
opioid and muscarinic ligands in hopes of replacing morphine or finding an
effective drug for AD, may take issue with why, given the numerous other
receptor families that have yet to deliver a new therapeutic, e.g., neurokinin,
galanin, CCK, ␤-secretase, and so on, opioids and muscarinics have been
singled out. The answer is in the numbers. Searching Google Scholar back to
its 1991 initial archive date gave 81,200 hits for opioids and 101,000 for
muscarinics. The hits for neurokinin, galanin, CCK, or ␥-secretase did not
individually exceed 30,000.
it has been repeatedly suggested, albeit with less-than-convincing data, that excess ATP may be causal in tumor
growth, and in this context, inhibition of ATP synthase has
been shown to modulate breast carcinoma growth (Huang et
al., 2008).
Compounds active at the supposedly intractable kinome
target, including imatinib, sorafinib, and erlotinib, have revolutionized cancer treatment (Zhang et al., 2009) even
though their kinase selectivity is far from absolute. The BRaf kinase inhibitor, sorafinib, has only modest activity at its
stated target, compared with other kinases (Fabian et al.,
2005), a reflection of an inability at the time of its discovery to
access assays that could facilely interrogate other members of
the kinome. Second-generation kinase inhibitors, including the
B-Raf (V600E) kinase inhibitor, PLX4032, thought to be related
to PLX4720 (Tsai et al., 2008) for the treatment of metastatic
melanoma, and AC220, a Flt3 kinase inhibitor (Zarrinkar et al.,
2009) for the treatment of acute myeloid leukemia, have been
developed and are considerably more selective for their targets
than the earlier kinase inhibitors. Additional approaches to
address kinase inhibition include allosteric/noncompetitive inhibition (Karginov et al., 2010; Weisberg et al., 2010) and the
modulation of kinase-signaling pathways via traditional cell
surface receptor modulation. Thus the kinome, considered by
many as too promiscuous to yield useful drugs, has, at least in
the area of oncology, provided NCEs that represent a major
advance in cancer therapy.
And the Future…?
The intent of the present perspective is obviously to highlight the need for data and individual contributions from the
innovative “true believers” (Douglas et al., 2010) rather than
technology, prejudice, bureaucracy, and defeatism (“all the
easy data-independent targets have been done”) to drive the
preclinical drug discovery process. The current trend in ignoring the null hypothesis approach (and statistics), combined with the move toward qualitative rather than quantitative data sets, can contribute equally to the reduced
number of new drug approvals, as the infamously short-term
business decisions, FDA regulations, and the newer targets
become more “difficult” to reduce to practice. The interrogation of targets almost exclusively in transfected systems,
where the physiology of the target and its ancillary proteins
are usually lacking, has led to some embarrassing surprises
in both animal models and the clinic, where antagonists were
found to behave as full or partial agonists because of their
incomplete evaluation in the preclinical setting. Thus the
imperative in addressing pipeline deficits should be to focus
on an iterative, data-driven approach involving both chemists and pharmacologists, coupled with the creativity, objectivity, passion, and logic characteristic of the drug hunter
persona (Uitdehaag, 2007). Accordingly, a few areas that
could potentially “revitalize the industry” at the preclinical
level are outlined in Table 1.
For the present time, as long as the FDA, business interests, and the targets are judged as being causal rather than
the current scientific mindset, the considerable effort necessary to put better targets with improved compounds into the
translational medicine interface (Enna and Williams, 2009b;
Wehling, 2009) will not occur in a timely and productive
manner but will, instead, sustain the disconnect between the
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dertaking facilitated by the many hundreds of NCEs synthesized for this receptor family as it was used to generically
understand G-protein coupled receptor function at the molecular level (Wess et al., 2007). As with the newer opioid
NCEs, drug-like muscarinic NCEs with robust selectivity
attributes at the molecular level have failed to live up to
expectations in terms of the anticipated clinical outcomes.
Xanomeline, an M1/M4 agonist targeted for AD (Mirza et al.,
2003), had side effects sufficient to result in high dropout
rates in pivotal clinical trials, in addition to also precluding
dosing to efficacious levels. Similarly, the M1 allosteric modulator, desmethylclozapine, which was targeted for schizophrenia (Mendoza and Lindenmayer, 2009), failed to show
robust human efficacy.
Thus, for both of these G protein-coupled receptor families,
a major question is whether their function is so critical,
nuanced, and complex as to preclude advances based on the
molecular approaches currently being used that may lack the
necessary heuristic relationship to the complexity/redundancies of the systems present in a more physiological or diseaserelated milieu. Based on progress over the past 40 years, it
may well be concluded that the opioid and muscarinic receptor families represent intractable targets in the search for
improved small-molecule therapeutics. But maybe the next
NCE….???3
Productivity Shortfalls in Drug Discovery
7
TABLE 1
Revitalizing preclinical contributions to drug discovery—a data-independent assessment
This table was generated at the request of one of the reviewers. It could no doubt be enhanced and refined with additional input from the collective wisdom of those active
in preclinical research.
bench and the clinic (Horrobin, 2003) and further the “toxic
mix of science and economics” that leaves the pharma industry “ripe for disruption” (Carr, 2010).
Authorship Contributions
Wrote the manuscript: Williams.
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Innovation
Individual
The published literature is not the only source of new ideas for first-in-class drugs—some of the most successful ideas came from within the industry
(e.g., fluoxetine and cimetidine).
Get the data to support a new concept that you believe in before you try to sell it—ideas are a dime a dozen, and many people are unable to reduce a
concept to a peer-reviewable data set.
Data are golden.
When working on a best-in-class generation project, make sure you understand what “improved” looks like, how it stacks up to the initial drugs in the
class (always benchmark to the competition), and whether the postulated improvements can be realistically tested and proven in the clinic.
Management
Allow a scientist who is really passionate about a concept the time to gather the necessary data to convince his/her peers and you. A minimal investment allows the separation of the true drug hunters from the dilettantes, the misguided, and the politically motivated.
Mentor such people and recruit more of them.
Tolerate dissent and avoid group-think. Always look for the “devil’s advocate” viewpoint, and try not to shoot the messenger.
The grass is infrequently greener. An expert consultant is not necessarily objective, and may not know the correct solution to, or even understand, your
problem. Your experts should be in house.
To paraphrase Orson Welles’ famous ad for Paul Masson wine, “Serve no drug before its time.” If the compound is first in class, make sure it is the very
best it can be to avoid whiny post mortems along the lines of “right target, wrong compound,” which again will convince no one.
Data generation/experimentation
Individual
Use the null-hypothesis approach.
Apply appropriate statistical analysis to the data. An sample size of 1 convinces no one.
Avoid qualitative science. Apply the law of mass action, the concentration/dose response effect (Maddox, 1992), in using compounds to define target
pharmacology and pathway involvement. An antagonist used at a single concentration of 1 ␮M to invoke a response mediated at a target where it is active
at 2 nM may result in an erroneous conclusion.
Run appropriate controls. Do not rely on historical data or literature data obtained 15 to 20 years ago in a laboratory that no longer exists.
Do not ignore data because it does not conform to what was anticipated and/or wanted. The unexpected often represents a paradigm shift (e.g., if you
expect a decrease and get an increase). Mother Nature may also be telling you something about whether you should be running experiments.
The null-hypothesis culture
Make decisions based on data.
Apply the concept of the null hypothesis to the project portfolio—assume that none of them are on target to deliver and be pleasantly surprised when
you find they are.
Use a SWOT (strengths, weaknesses, opportunities, and threats) analysis approach to encourage transparency and dissenting viewpoints and to avoid
complacency and the status quo.
Develop and maintain a balanced risk, diverse target portfolio with a 3- to 5-yr timeframe (not 18–24 months).
Review projects regularly but not in a timeframe that precludes the time and effort necessary to generate key data.
Integrate absorption, distribution, metabolism, excretion and pharmaceutics in the lead optimization paradigm.
Although Scientific Advisory Board (SAB) meetings are usually events where everyone has a win-win agenda, remember that 90% of what is done in
drug discovery, the events that occur between project initiation and selection of a compound as an investigational new drug do not build on either the
experience or interest of an SAB.
Use retired pharmaceutical researchers as part of your SAB. Their prior experience can be very helpful, and at the very least, they will commiserate
with you.
Avoid:
- dumbing down the scientific focus of a meeting to accommodate nonscientist participants.
- sunk-cost scenarios.
- not making decisions.
Prohibit the use of wireless Internet devices at any and all meetings where you expect informed decision making.
Building a robust pipeline
Make the translational medicine paradigm a reality in your organization, but first make sure everyone agrees to precisely what it is (Enna and Williams, 2009b; Wehling, 2009).
Attempt to develop a seamless preclinical/clinical interface through phase IIa involving preclinical champions.
Involve clinical in the drug-discovery process, but diminish the impact of risk-averse clinicians who prefer working on “derisked” phase IIb in-licensing
candidates.
8
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Address correspondence to: Dr. Michael Williams, Department of Molecular Pharmacology and Biological Chemistry, Feinberg School of Medicine,
Northwestern University, Chicago, IL 60611. E-mail: [email protected]
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