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`42738. Special thanks to D. Vosburg for preparation
`of artwork.
`VIEWPOINT
`Organic Chemistry in Drug Discovery
`Malcolm MacCoss 1* and Thomas A. Baillie2
`The role played by organic chemistry in the pharmaceutical industry continues to be
`one of the main drivers in the drug discovery process. However, the precise nature
`of that role is undergoing a visible change, not only because of the new synthetic
`methods and technologies now available to the synthetic and medicinal chemist, but
`also in several key areas, particularly in drug metabolism and chemical toxicology, as
`chemists deal with the ever more rapid turnaround of testing data that influences
`their day-to-day decisions.
`Numerous changes are now occurring in the
`pharmaceutical industry, not just in the way
`that the industry is perceived, but also in the
`rapid expansion of biomedical and scientific
`knowledge, which affects the way science is
`practiced in the industry. The recent changes
`in the way that synthetic chemistry is prac-
`ticed in this environment center around new
`scientific advances in synthetic techniques
`and new technologies for rational drug de-
`sign, combinatorial chemistry, automated
`synthesis, and compound purification and
`identification. In addition, with the advent of
`high-throughput screening (HTS), we are
`now faced with many targets being screened
`and many hits being evaluated. However,
`success in this arena still requires skilled
`medicinal chemists making the correct choic-
`es, often with insight gleaned from interac-
`tions with computational chemists and struc-
`tural biologists, about which “hits” (1) are
`likely to play out as true “lead” ( 1) structures
`that will meet the plethora of hurdles that any
`drug candidate must surmount.
`In the recent past, the usual flow of informa-
`tion that was generated regarding any new com-
`pound prepared in the laboratory of a drug dis-
`covery company followed a paradigm similar to
`that shown in Fig. 1. This scheme was driven by
`the need to get the initial information on a com-
`pound first, before deciding whether its proper-
`ties met appropriate criteria before moving onto
`the next evaluation step. Such a linear sequence
`of events, although sparing of the number of
`compounds taken down the pathway, often
`meant that a considerable amount of time passed
`(several weeks) before it was known whether a
`particular change in a molecule was in fact a use-
`ful transformation, or whether it was a potency-
`enhancing change in the primary in vitro assay
`but was perhaps a liability in a downstream
`evaluation. Thus, the delay in getting appropriate
`feedback to the synthetic chemist meant that
`decisions about which molecules to prepare in
`the next round of synthesis were not guided by
`input from downstream data. With the advent of
`faster synthetic technologies, including advances
`in nuclear magnetic resonance (NMR) methods,
`1 DepartmentofBasicChemistry,MerckResearchLab-
`oratories, 126 East Lincoln Avenue, Rahway, NJ
`07065,USA.2DepartmentofDrugMetabolism,Merck
`Research Laboratories, Sumneytown Pike, West Point,
`PA 19486, USA.
`*To whom correspondence should be addressed. E-
`mail: malcolm_maccoss@merck.com
`D RUG D ISCOVERY
`19 MARCH 2004 VOL 303 SCIENCE www.sciencemag.org1810 SPECIALSECTION
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`IPR2025-00948
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`rapid separations, and automated syntheses, the
`cycle time for synthetic manipulation of analogs
`has decreased dramatically. In addition, in the
`same time frame, advances have been made
`in the ability to assay compounds, both in
`vitro and in vivo, at a much greater speed
`than was previously possible, and so the
`current paradigm has shifted toward that
`shown in Fig. 2, where it is now feasible to
`generate a tremendous amount of relevant
`data on a newly synthesized compound
`within 1 week of its initial preparation. This
`process allows for a much better-informed
`set of decisions, as one considers the next
`round of molecules that need to be prepared.
`It should be stressed that an awareness of
`the potential downstream obstacles to suc-
`cessful drug development is an important
`consideration in the chemist’s decision-
`making process. Based on a rationalization of
`experimental and computational approaches,
`Lipinski et al. presented the “rule of five” in
`the mid 1990s, which is an excellent working
`hypothesis for predicting good druglike prop-
`erties in new compounds (2, 3). Thus, close
`attention needs to be paid to molecular
`weights, as well as to the physicochemical
`properties of lead molecules, such as lipo-
`philicity (logP) and aqueous solubility (which
`will affect oral bioavailability and the feasi-
`bility of generating a parenteral formulation),
`together with animal pharmacokinetics,
`which can be extrapolated with caution to
`predict corresponding behavior in humans.
`The latter is particularly important in provid-
`ing some assurance that the candidate drug
`molecule will exhibit linear pharmacokinetics
`in humans, with appropriate dose size and
`elimination characteristics for the intended
`route and frequency of drug administration.
`Preliminary absorption, distribution, me-
`tabolism, and excretion (ADME) studies of
`lead compounds in animal species also pro-
`vide information on routes of clearance (such
`as renal, biliary, or metabolic), which is help-
`ful in guiding the selection of compounds that
`exhibit a balance between elimination path-
`ways and thus would not be unduly depen-
`dent on a single organ for excretion. At the
`same time, in vitro data are provided on the
`interaction of drug candidates with human
`cytochrome P-450 (CYP) enzymes, so that
`CYP inhibitors and inducers are identified at
`an early stage, and due consideration is given
`to the attendant risk that such candidates may
`cause drug-drug interactions in the clinic. In
`cases where oxidative metabolism by CYP
`enzymes is likely to be an important mecha-
`nism of drug clearance in humans, it is pref-
`erable to have contributions from multiple
`isoforms, as opposed to a single CYP (again,
`to minimize the potential for drug-drug inter-
`actions), whereas it is particularly undesir-
`able for metabolism to be catalyzed solely by
`an enzyme, such as CYP2D6 or CYP2C19,
`that exhibits genetic polymorphism (poten-
`tially leading to large individual variability in
`drug pharmacokinetics and clinical response
`where metabolism is the major route of clear-
`ance). Moreover, if the therapeutic target re-
`sides within the central nervous system
`(CNS), it becomes important to determine
`whether the structural series of interest serve
`as substrates for the efflux transporter P-
`glycoprotein and thereby are denied access to
`brain tissue in vivo. By obtaining such infor-
`mation in the discovery phase, potentially
`serious liabilities in a given structural se-
`ries become evident at the outset, and in-
`formed decisions can be made accordingly
`to redirect chemistry efforts.
`The chemist also needs to be conversant
`with issues of toxicology, given
`that the primary cause of failure
`of drug candidates in early de-
`velopment continues to be pre-
`clinical toxicity. Although the
`potential for genotoxicity can be
`assessed directly through a num-
`ber of in vitro assays, the same
`does not hold true for end-organ
`toxicities (such as drug-induced
`liver damage) or immune-
`mediated toxicities (idiosyncrat-
`ic reactions) (4 ). However,
`based on the premise that some
`(but certainly not all) drug-
`related adverse events appear to
`be mediated by a chemically re-
`active, electrophilic metabolite
`or metabolites, as opposed to the
`parent drug itself, it may be ar-
`gued that the generation of such
`electrophiles is an undesirable
`feature of any drug candidate.
`By means of appropriate in vitro
`“ trapping” experiments and as-
`sessments of covalent binding of
`lead drug candidates to protein,
`both in vitro and in vivo, it is
`usually possible for the medici-
`nal chemist, working closely
`with colleagues in drug metabo-
`lism, to identify routes of meta-
`bolic activation and, through
`appropriate structural modifica-
`tion, to minimize this potential
`liability (5 ). Moreover, before
`selection of a lead compound for
`development, information also
`will be available from in vivo
`studies in animals aimed at as-
`sessing selected off-target phar-
`macological activities of the compound of in-
`terest, including effects on the CNS and cardio-
`vascular systems. It is true that different phar-
`maceutical companies generate and weigh the
`above types of information to different extents
`in selecting lead candidates for progression into
`development. AtMerck, all of the above charac-
`teristics are taken into account in arriving at this
`key decision,which requires considerable ex-
`perience and sound judgment on the part of
`the group of senior scientists collectively
`charged with this responsibility.
`This new paradigm has led to a different
`type of decision-making by chemistry group
`leaders. As noted above, the results from a
`preliminary evaluation of the pharmacologi-
`cal, pharmacokinetic, metabolic, and toxico-
`logical profile of a series of molecules usu-
`ally will expose any serious deficits that
`would hinder or even preclude successful
`development of a drug candidate. As a result,
`such “flawed” compounds, or sometimes en-
`tire structural series, are dropped from further
`consideration, and development resources are
`conserved as a result.
`In the majority of cases, how-
`ever, there is no single factor that
`would lead to the exclusion of a
`molecule from further consider-
`ation, and the decision to advance
`a given compound needs to be
`based on a critical assessment of
`the relative attributes and poten-
`tial liabilities of that molecule.
`Admittedly, the availability of
`more, rather than less, informa-
`tion on each drug candidate can
`introduce an element of ambigu-
`ity into the chemist’ s decision-
`making process. For instance, if a
`structural change leads to in-
`creased potency in the lead bio-
`chemical assay, but the com-
`pound is less orally bioavailable
`in a rodent, has more activity on a
`biochemical counterscreen, and is
`less potent in a toxicity assay,
`then the decision to continue ex-
`ploring that avenue is less clear.
`Of course, all knowledge is
`useful and so the ongoing de-
`tailed compilation of structure-
`activity relationships (SARs)
`across many assays is already
`helping our understanding of
`what types of functionality are
`responsible for binding to various
`CYPs, cardiac ion channels,
`transporters, nuclear receptors re-
`sponsible for CYP induction, etc.
`In fact, the medicinal chemist has
`always had to make judgments
`regarding such data, but in the
`current environment the task is to
`make such decisions rapidly and
`to know how to weigh the data as they come in
`from different sources.These decisions can, of
`course, also be influenced by the nature of the
`target itself, because a tolerance for a particu-
`lar toxicity might well be different for diseases
`with such different profiles as obesity versus a
`particular cancer.
`Fig. 1.Linear path to drug
`candidate, with numerous
`feedback loops designed
`to provide information for
`target selection in the
`next round of synthesis.
`D RUG D ISCOVERY
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`SPECIALSECTION
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`At Merck, as at several other pharmaceuti-
`cal
`companies, we have found that the most
`fruitful approach to the selection of new drug
`candidates is to identify the key issues of a lead
`compound, based on early screening data, and
`then to focus on minimizing these deficiencies
`by informed chemical intervention, bearing in
`mind SAR data for the pharmacological target.
`For example, potent CYP inhibition in a lead
`compound may be localized to a single func-
`tional group, which may then be replaced by a
`noninhibitory substituent. Likewise, CYP in-
`duction (for example, through activation of
`the nuclear transcription factor PXR), metab-
`olism to a reactive electrophile, or unwanted
`cardiovascular activities (for example, ion
`channel activity that may lead to adverse
`cardiac effects in vivo, such as that reflected
`by prolongation of the QT interval on an
`electrocardiogram) (6 ) may be traced to spe-
`cific structural motifs that can be successfully
`engineered out of the lead structure. This
`multidisciplinary approach to drug discovery,
`with organic chemistry serving as the corner-
`stone of the process, is far removed from the
`linear paradigm of former years (Fig. 1).
`Thus, while many new technologies such as
`combinatorial chemistry, rapid analog synthesis,
`automated synthesis, open access liquid chroma-
`tography mass spectrometry, and high-speed au-
`tomated high-performance liquid chromatogra-
`phy (to name but a few) are now affecting
`medicinal chemistry, their main effect has been
`to shorten the cycle time of synthetic operations.
`This, in turn, has led to a profound difference in
`the way in which a medicinal chemistry project
`progresses through the system. Different compa-
`nies have embraced these new technologies in
`different ways (7– 10). For instance, some invest-
`ed heavily in the mid-1990s in combinatorial
`chemistry and made this technology a key driver
`of their efforts to discover new leads and to
`expand their existing sample collections, partic-
`ularly when traditional sources of compounds
`failed to deliver new leads. Others have used
`these technologies in appropriate projects and
`have forged alliances with smaller companies
`that specialize in such efforts, thus freeing up
`their internal operations to use their historical
`institutional knowledge of medicinal chemistry,
`but now guided by more information, as
`depicted in Fig. 2. This approach has led to more
`outsourcing of research medicinal chemistry
`than was common practice a few years
`ago (11– 13).
`It should be noted that pharmaceutical com-
`panies have sample collections filled with mol-
`ecules that were prepared many years ago for old
`discovery programs. Even if those molecules did
`not advance the program for which they were
`initially made, they were designed at the time by
`medicinal chemists in the hope of interacting
`with some type of proteinaceous domain (such
`as an enzyme, heterotrimeric G protein–coupled
`receptor, ion channel, etc). It is not unusual,
`therefore, for these molecules to be the starting
`point of new medicinal chemistry programs
`when they show up as hits in a new HTS screen.
`Thus, because of the rapid synthetic cycle times,
`a medium-sized group of medicinal chemists can
`now advance several different lead classes at the
`same time and thus potentially shorten the time-
`lines for developing a hit or lead into a true drug
`candidate. Usually, it is not clear at the start of
`a project what the downstream toxicological,
`metabolic, or off-target liabilities of a particular
`lead class are likely to be, and so different structural
`classes can now be investigated simultaneously to
`allow for data-driven decisions.
`When experienced medicinal chemists are
`asked to reflect on why various
`programs were advanced more
`quickly than others, they will
`invariably agree that it was be-
`cause of the nature and quality
`of the starting hit or lead. One
`of the most difficult properties
`to build into a newly discov-
`ered lead molecule is the de-
`sired pharmacokinetic (PK)
`profile, particularly in the case
`of orally dosed compounds. In
`recent years, the resources
`available for early PK evalua-
`tions in rodents have been in-
`creased, both for single com-
`pounds and, where appropriate,
`with the use of cassette dosing
`methods (14). Such rapidly obtained information
`on newly synthesized compounds is one of the
`most important factors in the quest to shorten the
`times from lead molecules to drug candidates.
`One must constantly be aware that the rapid
`synthesis of large numbers of molecules that are
`laden with ADME, physical property, or toxico-
`logical shortcomings may provide intriguing hits
`or leads, but they may not shorten the time to the
`elaboration of such a hit into a drug candidate. In
`fact, as noted above, most experienced medicinal
`chemists would prefer to start in a structural
`series that has inherently good ADME proper-
`ties, albeit with poor potency on the target
`receptor, and then set about improving the po-
`tency on the target, rather than working in the
`other direction (starting with a potent molecule
`that requires modification to optimize ADME
`and toxicological properties, which requires op-
`timization of several, often opposing, structural
`parameters within the predefined tight structure-
`activity boundaries required for potency), al-
`though the history of drug discovery is replete
`with examples of both. A good recent example
`of this situation from our laboratories has been
`the development of the orally active substance P
`antagonist EMEND (aprepitant) (15) (Fig. 3).
`Merck and many other companies have
`worked in this area for many years. The field
`was stimulated in 1991 by the discovery of CP-
`96,345 by Pfizer scientists, which showed that a
`potent subnanomolar small molecule could se-
`lectively antagonize substance P at the NK-1
`receptor (16). However, because of the difficulty
`in advancing structurally related molecules
`through the drug development process, presum-
`ably due largely to off-target activities, metabo-
`lism issues, and the need to penetrate the CNS, it
`took more than a decade before a small molecule
`was identified that had the appropriate properties
`to be a drug, and EMEND was launched by
`Merck in 2003 for the treatment of both acute
`and delayed-phase chemotherapy-induced nau-
`sea and vomiting. Based on our experiences and
`knowing the large number of other companies
`working in this area, it is very likely that tens of
`thousands of molecules have been prepared in
`Fig. 2.Nonlinear time-optimized path to drug candidate, with numerous feedback loops designed
`to provide optimal information on the next round of synthesis.
`Fig. 3.Structures of CP-96,345 and EMEND (aprepitant).
`D RUG D ISCOVERY
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`the past decade as substance P antagonists, a
`large percentage of which likely exhibited sub-
`nanomolar potency at the NK-1 receptor, but
`only one has made it to market. This example
`highlights the difficulty of and resources needed
`to optimize the ancillary properties of potent
`inhibitors/antagonists so that they can become
`safe, viable medicines.
`In the drug discovery process, we must also
`be cognizant of the interrelatedness of academic,
`government, and industrial research in the devel-
`opment of new drug entities. Despite large re-
`search budgets, the biomedical research carried
`out by pharmaceutical companies still represents
`only a small percentage of the overall worldwide
`research effort on diseases and approaches to
`their treatment. Academic and government lab-
`oratories, funded with public monies, often pro-
`vide much basic research and fundamental in-
`sight into diseases that can direct researchers
`toward novel ways of attacking diseases. How-
`ever, they are rarely organized (nor is it their
`mission) to embrace the drug discovery process
`in the multidisciplinary fashion outlined above
`that is the modern paradigm by which new hits
`or leads are first identified and then get trans-
`formed into new viable medicines. All of the
`above discussion speaks to one of the most
`important issues facing discovery medicinal
`chemistry today: the continuing need for excel-
`lent synthetic chemists. In large pharmaceutical
`companies, the drug discovery process is driven
`by multidisciplinary teams made up of the very
`best experts in each discipline, and chemistry is
`one key element in this. These teams have ready
`access to experts in other areas of biomedical
`science, and although chemists often end up as
`group leaders of discovery efforts, that usually
`occurs after much experience has been gained in
`the drug discovery process. Therecent advances
`discussed above have put more tools in the chem-
`ist’s toolkit, but in order to use these tools effec-
`tively, it invariably comes down to the ability to
`make the absolutely“ correct” molecule in a timely
`and cost-effective manner. This process requires
`the very best organic chemistry skills, and we must
`continue to provide funding in the university sys-
`tem for training in these core skill sets to chemists
`in their graduate and postdoctoral studies if we are
`to continue to provide the very best in medicines
`for what is becoming an aging population.
`References and Notes
`1. In this discussion, a “hit” is defined as a nonoptimized
`structure obtained from some screening process on a
`targetprotein.Itisoftenaveryweakbinderandislikely
`to have a nonoptimized pharmacokinetic profile. A
`“lead” is defined as a structure that has been derived
`from an early “hit” and, although still not fully opti-
`mized, has been shown to have some appropriate char-
`acteristics to be a precursor of a drug entity. Often a
`good lead will have shown some proof-of-concept ac-
`tivityinaninvivopharmacologicalmodel,butwilllikely
`not have been fully optimized for pharmacokinetic
`properties or undesirable off-target activities.
`2. C.A.Lipinski,F.Lombardo,B.W.Dominy,P.J.Feeney,
`Adv. Drug Delivery Rev.23, 3 (1997).
`3. In the discovery setting, the rule of five (2) predicts
`that poor absorption or permeation of drugs is more
`likely when a drug molecule possesses either (i) more
`than 5 hydrogen bond donors, (ii) 10 hydrogen bond
`acceptors, (iii) a molecular weight greater than 500,
`or (iv) a calculated logP greater than 5.
`4. J. Uetrecht,Drug Discov. Today8, 832 (2003).
`5. D. C. Evans, A. P. Watt, D. A. Nicoll-Griffith, T. A.
`Baillie,Chem. Res. Toxicol.17, 3 (2004).
`6. See (17) for an excellent review of the cardiovascular
`effects manifested by QT interval prolongation and
`the evaluation of drug candidates for this parameter.
`7. SeethecoverstoryinDrug Discov. Dev.6,30(2003).
`8. T. Koppal,Drug Discov. Dev.6, 59 (2003).
`9. A. DePalma,Drug Discov. Dev.5, 50 (2002).
`10. A. DePalma,Drug Discov. Dev.6, 51 (2003).
`11. M.McCoy,J.-F.Tremblay,Chem. Eng. News81,15(2003).
`12. A.M. Rouhi,Chem. Eng. News81, 75 (2003).
`13. T. Koppal,Drug Discov. Dev.6, 22 (2003).
`14. W. A. Korfmacheret al.,Rapid Commun. Mass Spec-
`trom.15, 335 (2001).
`15. J. J. Haleet al.,J. Med. Chem.41, 4607 (1998).
`16. R. M. Snideret al.,Science251, 435 (1991).
`17. R. Netzer, A. Ebneth, U. Bischoff, O. Pongs,Drug
`Discov. Today6, 78 (2001).
`REVIEW
`The Many Roles of Computation in
`Drug Discovery
`William L. Jorgensen
`An overview is given on the diverse uses of computational chemistry in drug discovery.
`Particular emphasis is placed on virtual screening, de novo design, evaluation of drug-
`likeness, and advanced methods for determining protein-ligand binding.
`“ Is there really a case where a drug that ’s
`on the market was designed by a comput-
`er?” When asked this, I invoke the profes-
`sorial mantra (“ All questions are good
`questions.” ), while sensing that the desired
`answer is “ no” . Then, the inquisitor could
`go back to the lab with the reassurance that
`his or her choice to avoid learning about
`computational chemistry remains wise. The
`reality is that the use of computers and
`computational methods permeates all as-
`pects of drug discovery today. Those who
`are most proficient with the computational
`tools have the advantage for delivering new
`drug candidates more quickly and at lower
`cost than their competitors.
`However, the phrasing of the question
`suggests misunderstanding and oversimpli-
`fication of the drug discovery process.
`First, it is the rare case today when an
`unmodified natural product like taxol be-
`comes a drug. It is also inconceivable that a
`human with or without computational tools
`could propose a single chemical structure
`that ends up as a drug; there are far too
`many hurdles and subtleties along the way.
`Most drugs now arise through discovery
`programs that begin with identification of
`a biomolecular target of potential thera-
`peutic value through biological studies in-
`cluding, for example, analysis of mice
`with gene knockouts. A multidisciplinary
`project team is then assembled with the
`goal of finding clinical candidates, i.e.,
`druglike compounds that are ready for hu-
`man clinical trials, which typically selec-
`tively bind to the molecular target and in-
`terfere either with its activity as a recep-
`tor or enzyme. Molecular libraries are
`screened, and the resulting leads are opti-
`mized in a cycle that features design, syn-
`thesis and assaying of numerous analogs,
`and animal studies. Crystal structure deter-
`mination for complexes of some analogs
`with the biomolecular target is often possi-
`ble, which enables “ structure-based drug
`design” (SBDD) and the efficient optimi-
`zation of leads. The success of SBDD is well
`documented (1, 2); it has contributed to the
`introduction of /H1101150 compounds into clinical
`trials and to numerous drug approvals. Min-
`imally, the role of computation here is in the
`structure refinement using simulated anneal-
`ing (3), development of the underlying molec-
`ular mechanics (MM) force fields, structure
`display, and building and MM evaluation of
`analogs. All top pharmaceutical companies
`have substantial structural biology and com-
`putational chemistry groups that are inter-
`twined and participate on the project teams.
`There is usually much “ tweaking” to-
`ward the end of the preclinical period of
`drug discovery when a series of compounds
`Department of Chemistry, Yale University, New
`Haven,
`CT 06520-8107, USA. E-mail: william.
`jorgensen@yale.edu
`D RUG D ISCOVERY
`www.sciencemag.org SCIENCE VOL 303 19 MARCH 2004 1813
`SPECIALSECTION
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