Is Rational Design Good for Anything? - American Chemical Society

Chapter 22

Is Rational Design Good for Anything? Donald B. Boyd

Downloaded by UNIV OF CALIFORNIA SAN DIEGO on February 17, 2016 | http://pubs.acs.org Publication Date: July 7, 1999 | doi: 10.1021/bk-1999-0719.ch022

Department of Chemistry, Indiana University—Purdue University at Indianapolis, Indianapolis, IN 46202-3274

A n overview is given of some documentable examples where rational approaches have been significantly influential in the drug discovery process. The scope of rational design is defined to include not only computational chemistry and structure determination, but also other nonrandom approaches. In contrast to the enumerated successes, a case study is used to illustrate how quantitative analysis can be used to reveal when a strategy for finding a drug may be untenable. A n attempt is made to prognosticate the evolving role of rational design in the new era of combinatorial chemistry.

Prior to the last few decades, the discovery of medicines was mainly an empirical endeavor. Relatively little was known about medically relevant biological targets and three-dimensional molecular structures o f the receptors. Compounds were synthesized or obtained from natural sources such as plants or microorganisms found in soil and water samples. The compounds were tested for biological activity on whole organisms such as bacteria or laboratory animals. In those early days, it was not unheard of for a chemist to administer a newly synthesized compound to himself or his associates to learn what biological effects it had. Starting i n the 1960s, increased understanding of how compounds exerted their biological effects made possible the testing of compounds i n purified, isolated enzymes and other biochemical systems. The usual strategy was to use in vitro testing for preliminary evaluation of the compounds and then, on the more promising ones, use in vivo (animal) testing, which generally required more material to be synthesized. Most of the compounds being tested were laboriously synthesized one at a time. If sufficient compound was available, it could be broadly screened i n assays set up to look for various kinds of biological activity. Regarding microorganisms as a source of drugs, isolation of materials from fermentation broths turned up the same active compounds over and over again, so by the early 1980s it became clear that this strategy was giving diminishing returns. The sheer labor intensity of the organic chemical and biological effort led gradually to greater interest i n so-called rational design approaches such as analyzing quantitative structure-activity relationships ( Q S A R ) or considering the mechanism of

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© 1999 American Chemical Society

In Rational Drug Design; Parrill, Abby L., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1999.


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an enzymatic reaction. The hope of these new approaches was that the odds of finding a useful compound would be increased. Pharmaceutical researchers knew from experience that the odds were very low for finding a useful drug by random screening. A n often quoted figure, which was at least qualitatively correct, was that 10,000 compounds had to be evaluated to find one that would become a medicine. Random screening was inefficient. Increasing numbers of scientists became convinced that rational approaches would help them focus on those parts of "compound space" (a term not then widely used) that would most likely lead to a new drug. W i t h advancing computer technology and improved methods for solving structures by X - r a y crystallography and nuclear magnetic resonance ( N M R ) spectroscopy, the 1980s saw a growing interest i n rational approaches. B y the early 1990s, random screening appeared less and less'efficient, and the pendulum i n pharmaceutical discovery research was swinging toward modern molecular modeling techniques (1,2,3) and iterative structure-based ligand design ( S B L D ) (4,5) as the most promising ways to discover the medicines of the future. However, about five years ago, a new technology appeared on the scene. Robotic systems run by computers made possible combinatorial chemistry (6) and high-throughput screening (HTS). Suddenly, the random approach was economically far superior to designing one compound at a time. Combinatorial chemistry and H T S went hand-in-hand: combinatorial chemistry made possible the generation of huge numbers of new compounds to feed into the screening machines, and H T S could run through the new combinatorial compound libraries at a voracious rate. In addition, old corporate compound libraries consisting of hundreds of thousands of materials could be mined quickly and economically. Often little or no consideration was given to the fact that the old compounds had been setting i n bottles on shelves for years, i f not decades, and might no longer have the chemical structures marked on the labels. This does not matter in a random approach because the whole enterprise is founded on chance. In any case, hits from these old materials would have to be followed up by identification of the active component(s) and fresh syntheses. Looking at this brief history, we see that the pendulum was swinging toward rational design until a few years ago and now seems to have reversed course. W i l l rational design be abandoned i n favor of the random approach for finding useful new bioactive compounds? A r e rational design methods doomed to j o i n slide rules and I B M punch cards i n the technological scrap heap? Clearly, the answer to these questions is no, as evidenced by the work presented at the symposium on which these proceedings are based. Our goal in this brief space is to cite examples where rational approaches have been fruitful. W e concentrate on compounds that have made it all the way to the pharmaceutical market. In addition, we give an example o f a drug discovery project that had a rationale but lacked rationality, thereby dooming its prospect for success. Computer modeling detected the lack of rationality. W h a t E x a c t l y Is R a t i o n a l Design? When most researchers hear the term "rational drug design" they probably think i n terms of using considerations of the three-dimensional structure of the ligands and/or their receptor to discover medically useful compounds. However, this is not the whole story. Rational design is a collection of approaches that do not depend on chance alone to succeed. Clearly, one facet of rational design includes the computational chemistry (7) techniques of (a) molecular modeling, (b) property prediction, such as by using quantum mechanics (8) or group additivity (9), (c) statistical modeling, such as using Q S A R correlations involving molecular descriptors to predict bioactivities of new structures (10), and (d) other algorithms, such as docking methods based on energy minimization (77) or genetic algorithms (12). Rational design need not necessarily



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involve predicting binding affinities by molecular mechanics or free energy perturbation theory (75). Performing molecular dynamics simulations is not synonymous with rational drug design. A second facet o f rational design includes use o f experimental and computational techniques for determining the three-dimensional structure of receptors. Thus, X-ray crystallography and multidimensional N M R spectroscopy are used to solve the structures of proteins, nucleic acids, and other biomacromolecules. Computing plays an important role here too because the experimental structures are now usually refined with the molecular dynamics technique of simulated annealing (14,15). Distance geometry, another computational chemistry technique (76), is used to transform N M R - d e r i v e d distance constraints into three-dimensional atomic coordinates. When an experimental structure is unavailable, homology modeling is often used to build a tentative three-dimensional structure based on a known protein structure with a similar amino acid sequence. Yet another important approach of rational design is using compound databases (libraries) to find interesting compounds. The two- or three-dimensional structures i n these computer databases can be searched to retrieve, for instance, molecules that meet requirements for a particular pharmacophore, i.e., the minimal features of molecular structure that are essential for evoking an intended biological response. The libraries can also be analyzed by quantitative techniques to map the molecules i n compound space based on some set of Q S A R descriptors (77). B y knowing how the compounds are distributed in this space, the diversity or similarity of the structures can be ascertained. Compound libraries, rather than individual compounds, can be "designed." A long-standing approach to finding an enzyme inhibitor is to consider the biochemical mechanism of action of the enzyme; drugs that are transition state analogs may result (18). Another facet of rational design is to try to devise a ligand that w i l l resemble a natural substrate. Thus, a peptidomimetic approach has been quite productive (79). A relatively new facet of rational design is genomics. Here the goal is to find specific associations between gene products and disease states, thereby making it possible to design drugs that w i l l block or stimulate those targets as appropriate. Handling a l l the genetic sequences has given rise to another computer-based subdiscipline called bioinformatics. Thus, rational design encompasses many techniques and scientific disciplines including traditional medicinal chemistry (20). The main point of the term rational design is to differentiate its methods from blind screening, i.e., testing all compounds available or randomly selected ones. Unfortunately, the term "rational design" is upsetting to some traditional medicinal chemists because they think it implies that the research they have been doing was irrational. This implication is unintended. N . B . , many traditional medicinal chemistry studies have been entirely rational; they were based on a sound rationale and were carefully planned to test some hypothesis. Finally, it should not be forgotten that educated guesswork, intuition, trial and error, and plain old-fashioned luck have played a role i n both rational and random drug discovery i n the past and w i l l continue to do so in the future. W h a t H a v e Y o u Done for U s L a t e l y ? Because of the wide use of the term "computer-aided drug design," computational chemists are often challenged about whether their computer-based approaches have actually designed a drug. Shown i n Figure 1 are pharmaceutical products for which computing played a vital role i n their discovery. The earliest example of such a compound, to the author's knowledge, is the antibacterial agent norfloxacin. Structural modifications that led the chemists at K y o r i n Pharmaceutical Company to this compound were guided with the assistance of Q S A R (27). The compound has been on the market since 1983 under various brand names including Noroxin®. In Rational Drug Design; Parrill, Abby L., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1999.


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Spurred by this advance, the 6-fluoro-quinolones became a major class of antibacterial agents. Among drugs entering the market more recently is dorzolamide hydrochloride, which is sold under the brand name Trusopt® by Merck for treatment of glaucoma (22). Iterative S B L D and ab initio molecular orbital calculations helped yield this carbonic anhydrase inhibitor. Molecular modeling, Q S A R , molecular shape analysis, and docking played a role i n the discovery o f donepezil hydrochloride, an acetylcholinesterase inhibitor (23). Eisai markets this compound as Aricept® for use in patients with Alzheimer's disease. Yet another example is losartan sodium. This is an angiotensin II receptor antagonist that was discovered at DuPont and has been sold by M e r c k under the brand name Cozaar® since 1995 for control of hypertension. Molecular modeling of the octapeptide angiotensin II and a lead compound from the patent literature have been repeatedly acknowledged (e.g., 24,25) for helping set the original direction for the structure-activity relationship (SAR) that was pursued by the medicinal chemists to a successful conclusion. A drug for migraine, zolmitriptan, is a 5 - H T I D agonist; it was discovered at Wellcome and is marketed by Zeneca under the brand name Zomig®. Molecular modeling and the active analog approach helped define the pharmacophore (26).

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Figure 1. Chemical structures of compounds that reached the pharmaceutical market with the aid of rational approaches.

Iterative S B L D has been quite successful in predicting inhibitors of human immunodeficiency virus (HIV-1) protease. The receptor site of this enzyme is rather deep, and much crystallographic data are available. In Figure 2, some of the marketed H I V - 1 protease inhibitors are shown. Indinavir sulfate was designed with the help of X-ray crystallography and molecular mechanics calculations (27,28). Merck began marketing of this pharmaceutical in 1996 under the brand name Crixivan®. A second product is nelfinavir mesylate. Discovered at L i l l y and Agouron (29,30), the compound is being marketed as Viracept® by Agouron. Another rational design approach yielded the H I V - 1 protease inhibitor saquinavir. Considerations of the enzyme mechanism and the transition state of the substrate (31) led to a compound marketed by Roche first as Invirase® and more recently i n a more potent dosage form as Fortovase®. A fourth inhibitor for treating A I D S is ritonavir, which was designed using a peptidomimetic strategy and is marketed by Abbott as Norvir® (32,33). In Rational Drug Design; Parrill, Abby L., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 1999.


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People outside of the pharmaceutical field sometimes ask i f any drug has ever been designed "from the ground up" by computational methods. The answer is not yet, at least not to the author's knowledge. The question itself, while legitimate, may reveal a lack of familiarity with the difficulties of the drug discovery process, which almost always has been - and probably w i l l continue to be - iterative and multidisciplinary. The medicinal chemist makes a compound, and the biologist and other scientists test it. This cycle is repeated many, many times because there are so many properties that a compound must exhibit (34) before it can be introduced into medical practice. Perhaps one of the closest ways computational chemistry could come to designing a molecule from the ground up is with de novo methods (35,36). Although these methods for finding or designing ligands are becoming more powerful all the time, it would take serendipity i f one of the hypothetical ligands, when synthesized, turned out to possess all the properties required to become a pharmaceutical product.

Figure 2. Chemical structures of some marketed HIV-1 protease inhibitors that were discovered with the help of rational approaches. To keep an objective perspective of rational design, it is worth recalling that having good structural information for a target is no guarantee for success. Numerous investigators at pharmaceutical companies and elsewhere have applied S B L D for years to find new inhibitors that would block dihydrofolate reductase ( D H F R ) , a target related to oncolytic and anti-infective therapies. None of those many rational structural and modeling studies yielded a new medicine (37). Drug discovery is so challenging that no method works i n every case.


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Detecting the Lack of Rationality in a Project In this section, we present briefly an example of how the quantitative thinking of a computational chemist can detect that a medicinal chemistry project lacks rationality and therefore has little chance of success. This case study involved finding a drug for treatment of a central nervous system ( C N S ) disorder, whose exact nature is not germane to the principles we wish to illustrate. In some C N S projects, the scientists may have only a hypothesis about what effect a compound w i l l have on a human brain. W i l l the compound treat depression? B e hallucinogenic? B e an anxiolytic? Improve cognitive scores? Reduce appetite? Bluntly put, the old paradigm was often to find a compound hypothesized to have some C N S effect and meeting safety requirements for clinical testing, to test it i n humans, and then to see empirically what, i f any, desirable pharmacological activities it may exhibit. After such a C N S compound was approved for marketing, additional uses for it, with luck, might show up when prescribed to a wider patient population. The case study discussed here was not based on the o l d C N S paradigm. Rather, the specific disease for which the compound was being sought was known, and a well thought out approach was being followed to find an inhibitor of an enzyme hypothesized to be related to a pathology. Traditional medicinal chemistry was used, i.e., screening was used to find some low-level leads, and then a standard repertoire of chemistry explored the S A R around several leads. One-compound-at-a-time custom synthesis was used. The medicinal chemists decided what new compounds to make based on their many years of combined experience and on the activity (potency) data they were getting back from the biologists. One of the first things that was checked when a computational chemistry perspective was brought to bear on this project was the biological data. There were two assays operative. A primary one was based on in vitro inhibition of the target enzyme. A secondary assay evaluated compounds in a whole cell system to detect levels of the peptide thought to be related to the enzyme's action. Comparing data from the two assays gave the type of scatter shown in Figure 3. It was immediately apparent that a fundamental flaw existed in the project's screening tactics. There was no salutary correlation between the data from the two assays! If both assays were associated with the same target, then the data points should have shown more of a relationship. The biologists running the assays had convinced themselves, based on their earlier work, that a strong correlation existed, but apparently no one had actually checked this thoroughly. A quantitative approach thus provided fresh insight, albeit unwelcome news. Another factor besides potency that has to be considered in a drug discovery project is distribution of the compound to the site of action i n the body of the patient. For a compound to be CNS-active, it must be able to cross the blood-brain barrier ( B B B ) . There are some general ideas about what physical properties a compound must exhibit in order to be able to cross the B B B , but there are no hard and fast rules. One helpful clue, however, comes from Q S A R investigators who showed that many drugs that are able to reach the C N S are lipophilic and have an ionizable group such that the compounds exhibit an octanol-water distribution coefficient ( l o g D / w ) i n the range 1.5 - 2.5 (38). The compounds that had been synthesized and tested in this case study were checked to see i f they met the l o g D / w criterion. The C L O G P program (39,40) was used to compute octanol-water partition coefficients, which for these compounds were the same as distribution coefficients because the compounds had no groups that would be ionized near physiological p H . The results are shown i n Figure 4. A s is immediately obvious, the vast majority of the compounds lack a sufficiently low distribution coefficient. Less than 5% of the compounds are near the desirable range. 0

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Thus, most of the compounds are so lipophilic that their prospects of crossing the B B B and getting to their intended target is not good.

30 enzyme assay, IQ , microMolar 50 Figure 3. Distribution of biological data from primary (enzyme) and secondary (whole cell) assays used to evaluate compounds being synthesized. Data from the primary screen were reported as percent inhibition. Data from the secondary screen were reported as concentration of compound required for 50% inhibition of production of the supposed product of the enzyme.

This example shows how scientists can be so focused on their routine and their goal (primarily increasing potency) that they neglect to use simple quantitative tools at their disposal to learn i f their approach is rational. If it is not rational, then discovery research resources (personnel) can be redeployed i n directions with a greater chance of success i n finding a drug. The point is that a computational perspective can empower scientists working on a project by supplying important information.



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Figure 4. Histogram o f the distribution of computed l o g D / w values for compounds being synthesized to explore the S A R . Less than 5% o f the compound population had l o g D / w values less than 3 (unshaded bar). A t the peak of the histogram, more than 15% of the compound population had l o g D / w between 5.5 and 6.0. M a n y compounds were even above 6, i.e., they were extremely lipophilic. 0

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O u t l o o k for R a t i o n a l D e s i g n Predicting the future is treacherous. Several years before the dawning of the age of combinatorial chemistry and H T S , the well-respected former editor of the Journal of Medicinal Chemistry wrote (41): "The time of random structural variations i n molecular modification ... is past." It has not turned out like this. Combinatorial chemistry and H T S have carried the hit-or-miss approach to a technological pinnacle unimaginable a few years ago. T o put the combinatorial chemistry/HTS approach in perspective, it helps to use an analogy: trying to discover a drug is as difficult as looking for a needle i n a haystack. With combinatorial chemistry and H T S , the philosophy is to create a bigger "haystack" in hopes that a useful compound w i l l be somewhere in it. In contrast, the philosophy of rational design is that the odds of finding a useful compound can be beat, and the goal is to reduce the size of the haystack! It is not necessary to ask whether one philosophy is better; clearly both random and rational approaches can and should be used. A s mentioned, combinatorial chemistry need not be a completely random enterprise; compound libraries can be rationally designed using Q S A R techniques to produce structures in specific volumes of multidimensional molecular descriptor space so as to fill unexplored "voids" i n an S A R or i n a region around a lead compound. Because of the quantity of data that has to be tracked, stored, retrieved, and analyzed, information management underlies the modern random approach. So with either a random or rational approach, computing is vital. In fact, the term "computer-aided drug design" could take on a much broader meaning. Rational approaches, many o f w h i c h are based on modern computer technology, are helping to stimulate ideas and test hypotheses for new ligands. Computational and structural chemists can now point to specific examples where their approaches have proved useful in helping medicines reach patients. This chapter and these proceedings confirm that rational design is contributing not only good science, but also pharmaceutically interesting compounds. The answer to the question posed in the title of this chapter is clearly yes, rational design is good for finding those elusive needles i n the haystack. Looking toward the future, we see an increasing proportion of the newly approved pharmaceuticals being discovered with the aid of rational design approaches.


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Acknowledgments


The author thanks D r . M. R a m i Reddy, Dr. A b b y L . Parrill, and the sponsors of the A C S symposium for the opportunity to share these results. The author is grateful to M a x M. Marsh (Indiana University) for his invaluable advice. Communications to the author can be sent v i a the Internet ([email protected]) or W o r l d W i d e W e b (http://chem.iupui.edu/).

References

1.

Balbes, L . M.; Mascarella, S. W.; Boyd, D. B . In Reviews in Computational Chemistry, Lipkowitz, K. B.; Boyd, D. B., Eds.; V C H Publishers: New York, N Y , 1994, Vol. 5; pp 337-379. 2. Boyd D. B. In Encyclopedia of Computer Science and Technology; Kent, A . ; Williams, J. G., Eds.; Marcel Dekker: New York, 1995, Vol. 33 (Suppl. 18); pp 41-71. 3. Boyd, D. B . In Ullmann's Encyclopedia of Industrial Chemistry, Wiley-VCH, Weinheim, 1998, 6th Edition; in press. 4. Appelt, K.; Bacquet, R. J.; Bartlett, C. A . ; Booth, C. L . J.; Freer, S. T.; Fuhry, M. A . M.; Gehring, M. R.; Herrmann, S. M.; Howland, E. F.; Janson, C. A.; Jones, T. R.; Kan, C.-C.; Kathardekar, V . ; Lewis, K . K.; Marzoni, G. P.; Matthews, D. A.; Mohr, C.; Moomaw, E. W.; Morse, C. A.; Oatley, S. J.; Ogden, R. C.; Reddy, M. R.; Reich, S. H . ; Schoettlin, W. S.; Smith, W. W.; Varney, M. D.; Villafranca, J. E.; Ward, R. W.; Webber, S.; Webber, S. E.; Welsh, K . M.; White, J. J. Med. Chem. 1991, 34, 1925-1934. 5. Boyd, D. B.; Milosevich, S. A . F. Perspect. Drug Discovery Des. 1993, 1, 345-358. 6. Gordon, E. M.; Barrett, R. W.; Dower, W. J.; Fodor, S. P. A . ; Gallop, M. A . J. Med. Chem. 1994, 37, 1385-1401. 7. Lipkowitz, K . B.; Boyd, D. B., Eds. Reviews in Computational Chemistry; V C H Publishers: New York, 1990, Vol. 1; pp vii-xii. 8. Boyd, D. B . Drug Inf. J. 1983, 17, 121-131. 9. Carrupt, P.-A.; Testa, B.; Gaillard, P. In Reviews in Computational Chemistry; Lipkowitz, K . B.; Boyd, D. B., Eds.; Wiley-VCH: New York, 1997, Vol. 11; pp 241-315. 10. Plummer, E. L . In Reviews in Computational Chemistry; Lipkowitz, K . B . ; Boyd, D. B., Eds.; V C H Publishers: New York, 1990, Vol. 1; pp 119-168 11. Schlick, T. In Reviews in Computational Chemistry; Lipkowitz, K. B.; Boyd, D. B., Eds.; V C H Publishers: New York, 1992, Vol. 3; pp 1-71. 12. Judson, R. In Reviews in Computational Chemistry; Lipkowitz, K. B.; Boyd, D. B., Eds.; V C H Publishers: New York, 1997, Vol. 10; pp 1-73. 13. Straatsma, T. P. In Reviews in Computational Chemistry; Lipkowitz, K . B . ; Boyd, D. B., Eds.; V C H Publishers: New York, 1996, Vol. 9, pp 81-127. 14. Lybrand, T. P. In Reviews in Computational Chemistry; Lipkowitz, K . B.; Boyd, D. B., Eds.; V C H Publishers: New York, 1990, Vol. 1; pp 295-320.



355

15. Torda, A . E.; van Gunsteren, W. F. In Reviews in Computational Chemistry; Lipkowitz, K. B.; Boyd, D. B., Eds.; V C H Publishers: New York, 1992, Vol. 3; pp 143-172. 16. Blaney, J. M.; Dixon, J. S. In Reviews in Computational Chemistry; Lipkowitz, K. B.; Boyd, D. B., Eds.; V C H Publishers: New York, 1994, Vol. 5, pp 299-335. 17. Martin, E. J.; Spellmeyer, D. C.; Critchlow, R. E., Jr., Blaney, J. M. In Reviews in Computational Chemistry; Lipkowitz, K . B.; Boyd, D. B . , Eds.; V C H Publishers: New York, 1997, Vol. 10; pp 75-100. 18. Kalman, T. I., Ed. Drug Action and Design: Mechanism-Based Enzyme Inhibitors; Elsevier North Holland: New York, 1979. 19. Damewood, J. R., Jr. In Reviews in Computational Chemistry; Lipkowitz, K. B.; Boyd, D. B., Eds.; V C H Publishers: New York, 1996, Vol. 9; pp 1-79. 20. Boyd, D. B. In Rational Molecular Design in Drug Research, Proceedings of the Alfred Benzon Symposium No. 42; Liljefors, T.; Jørgensen, F. S.; Krogsgaard-Larsen, P., Eds.; Munksgaard: Copenhagen, 1998; pp 15-23. 21. Koga, H . ; Itoh, A . ; Murayama, S.; Suzue, S.; Irikura, T. J. Med. Chem. 1980, 23, 1358-1363. 22. Greer, J.; Erickson, J. W.; Baldwin, J. J.; Varney, M. D. J. Med. Chem. 1994, 37, 1035-1054. 23. Kawakami, Y.; Inoue, A.; Kawai, T.; Wakita, M.; Sugimoto, H.; Hopfinger, A. J. Bioorg. Med. Chem. 1996, 4, 1429-1446. 24. Duncia, J. V . ; Chiu, A . T.; Carini, D. J.; Gregory, G. B . ; Johnson, A . L . ; Price, W. A.; Wells, G. J.; Wong, P. C.; Calabrese, J. C.; Timmermans, P. B . M. W. M. J. Med. Chem. 1990, 33, 1312-1329. 25. Duncia, J. V.; Carini, D. J.; Chiu, A . T.; Johnson, A . L.; Price, W. A.; Wong, P. C.; Wexler, R. R.; Timmermans, P. B . M. W. M. Med. Res. Rev. 1992, 12, 149-191. 26. Glen, R. C.; Martin, G. R.; Hill, A . P.; Hyde, R. M.; Woollard, P. M.; Salmon, J. A . ; Buckingham, J.; Robertson, A . D. J. Med. Chem. 1995, 38, 3566-3580. 27. Dorsey, B . D.; Levin, R. B . ; McDaniel, S. L . ; Vacca, J. P.; Guare, J. P.; Darke, P. L.; Zugay, J. A.; Emini, E. A.; Schleif, W. A.; Quintero, J. C.; Lin, J. H . ; Chen, I. W.; Holloway, M. K . ; Fitzgerald, P. M. D.; Axel, M. G.; Ostovic, D.; Anderson, P. S.; Huff, J. R. J. Med. Chem. 1994, 37, 34433451. 28. Holloway, M. K . ; Wai, J. M. In Computer-Aided Molecular Design: Applications in Agrochemicals, Materials, and Pharmaceuticals; Reynolds, C. H.; Holloway, M. K.; Cox, H . K., Eds.; A C S Symp. Series 589; American Chemical Society: Washington, DC, 1995; pp 36-50. 29. Patick, A . K . ; Mo, H . ; Markowitz, M.; Appelt, K . ; Wu, B . ; Musick, L.; Kalish, V . ; Kaldor, S.; Reich, S.; Ho, D.; Webber, S. Antimicrob. Agents Chemother. 1996, 40, 292-297. 30. Kaldor, S. W.; Kalish, V . J.; Davies, J. F., II; Shetty, B . V . ; Fritz, J. E.; Appelt, K.; Burgess, J. A.; Campanale, K. M.; Chirgadze, N . Y.; Clawson, D. K.; Dressman, B . A.; Hatch, S. D.; Khalil, D. A.; Kosa, M. B.; Lubbehusen,


356

31.


32.

33.

34. 35. 36.

37.

38. 39. 40. 41.

P. P.; Muesing, M. A.; Patick, A . K.; Reich, S. H . ; Su, K . S.; Tatlock, J. H . J. Med. Chem. 1997, 40, 3979-3985. Roberts, N. A.; Martin, J. A.; Kinchington, D.; Broadhurst, A . V.; Craig, J. C.; Duncan, I. B.; Galpin, S. A.; Handa, B . K.; Kay, J.; Kröhn, A . ; Lambert, R. W.; Merrett, J. H.; Mills, J. S.; Parkes, K . E. B.; Redshaw, S.; Ritchie, A . J.; Taylor, D. L.; Thomas, G. J.; Machin, P. J. Science 1990, 248, 358-361. Kempf, D. J.; Marsh, K . C.; Denissen, J. F.; McDonald, E.; Vasavanonda, S.; Flentge, C. A . ; Green, B . E.; Fino, L . ; Park, C. H . ; Kong, X . P.; Wideburg, N. E.; Saldivar, A.; Ruiz, L.; Kati, W. M.; Sham, H . L.; Robins, T.; Stewart, K. D.; Hsu, A.; Plattner, J. J.; Leonard, J. M.; Norbeck, D. W. Proc. Natl. Acad. Sci. U. S. A. 1995, 92, 2484-2488. Kempf, D. J.; Sham, H . L.; Marsh, K . C.; Flentge, C. A . ; Betebenner, D.; Green, B . E.; McDonald, E.; Vasavanonda, S.; Saldivar, A.; Wideberg, N. E.; Kati, W. M.; Ruiz, L.; Zhao, C.; Fino, L.; Patterson, J.; Molla, A.; Plattner, J. J.; Norbeck, D. W. J. Med. Chem. 1998, 41, 602-617. Lipkowitz, K . B.; Boyd, D. B., Eds. Reviews in Computational Chemistry; Wiley-VCH: New York, 1997; Vol. 11; pp v-x. Murcko, M. A . In Reviews in Computational Chemistry; Lipkowitz, K . B . ; Boyd, D. B., Eds.; Wiley-VCH: New York, 1997, Vol. 11; pp 1-66. Clark, D. E.; Murray, C. W.; Li, J. In Reviews in Computational Chemistry; Lipkowitz, K . B.; Boyd, D. B., Eds.; Wiley-VCH: New York, 1997, Vol. 11; pp 67-125. Everett, A . J. In Topics in Medicinal Chemistry, Leeming, P. R., Ed.; Proc. 4th SCI-RSC Med. Chem. Symp., Cambridge, U.K., Sept. 6-9, 1987; R. Soc. Chem., London, 1988; pp 314-331. Hansch, C.; Bjoerkroth, J. P.; Leo, A . J. Pharm. Sci. 1987, 76, 663-687. Hansch, C.; Leo, A . Exploring QSAR: Fundamentals and Applications in Chemistry and Biology, American Chemical Society: Washington, DC, 1995. DayMenus Software Manual, Version 3.63, Daylight Chemical Information Systems, Inc.: Irvine, C A , 1991. Burger, A . Progr. Drug Res. 1991, 37, 287-371.


Is Rational Design Good for Anything? - American Chemical Society

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