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Document Page 536 synthesis methods allows for the first time an extensive and systematic investigation of structure-activity relationships related to drug properties. Small sublibraries can be selected, synthesized, and screened in high-throughput assays with the goal of developing predictive SAR models for individual components of the biological processes responsible for pharmacokinetic and toxicological properties of drug candidates. Eventually, it will be possible to assemble the predictive SAR models for various component processes to produce predictive models for bioavailability and toxicology. At this point it will be possible to use these models to guide the selection of sublibraries directed against therapeutic targets so that library members with the most “drug like” properties and the fewest liabilities are chosen. Steering away from compounds with undesired properties will focus the selection process on molecules with an improved probability of successful development. This strategy permits the simultaneous refinement of multiple chemical properties in addition to target efficacy and will shorten the drug-discovery process. Developing a system capable of collecting multivariate SAR data and exploiting the data to produce predictive SAR models is a major systems integration task. However, recent advances in computers, operating systems, and computational chemical tools now enables the implementation of a system that can track compounds, store chemical property data in a comprehensive relational database, and operate on virtual libraries in an iterative fashion to develop SAR models and refine chemical properties [28]. Tools for the production of virtual libraries have been developed by several groups and large virtual libraries can be produced within a few days using high-power computer workstations. A variety of tools also exist for selecting compounds based on criteria such as the ability to fit a target receptor [23], similarity or diversity [26,27], or any number of other properties that might be important for interaction with the target receptor. Statistical programs also exist that are up to the task of developing SAR models [20]. To obtain SAR models that have utility in the drug-discovery process it will be necessary to collect data on the properties of large numbers of compounds so that the models are predictive across diverse chemical classes of lead molecules. This requires implementation of a comprehensive relational database to collect and correlate SAR data. The challenge is to integrate these tools into a system that can collect and operate on vast arrays of chemical-property data in an intelligent fashion to direct and refine the properties of robotically synthesizable compounds. One approach that has been used successfully is to create a hierarchical clientserver system with a powerful central selection algorithm (Figure 4) that chooses compounds from virtual libraries based on their computed properties, supervises data analysis, and integrates results from experimental measurements [28,35]. This system, which has been termed DirectedDiversity in the author's laborato- http://legacy.netlibrary.com/nlreader/nlReader.dll?bookid=12640&filename=Page_536.html [4/9/2004 12:50:32 AM]
Document Page 537 ries, holds the promise of a major reduction in the time required to produce new preclinical compounds with enhanced development potential. VI. Conclusion The preceding outlines a new drug-discovery paradigm that integrates structure-based design, directed strategies for combinatorial chemical synthesis, and a comprehensive chemi-informatics system for accumulating and analyzing information regarding chemical properties. Three-dimensional structures provide the information required to most efficiently direct the design and optimization of new lead compounds. High-throughput automated methods of chemical synthesis produce new classes of lead compounds and provide for the rapid generation of structure—activity data. Chemical informatics systems track chemical compounds, store chemical property data, develop predictive SAR models, and provide a means for intelligently directing the drug-discovery process. By applying this approach not only to the therapeutic target, but also to molecules involved in absorption, clearance, metabolism, or toxicology it will be possible to develop predictive models for bioavailability and toxicology. Ultimately this approach will greatly increase the cost effectiveness and efficiency of drug discovery by reducing the aggregate failure rate for development candidates. References 1. Appelt K, Bacquet RJ, Bartlett CA, Booth CL, Freer ST, Fuhry MAM, Gehring MR, Herrmann SM, Howland EF, Janson CA, Jones TR, Ka C-C, Kathardekar V, Lewis KK, Marzoni GP, Matthews DA, Mohr C, Moomaw EW, Morse CA, Oatley SJ, Ogden RC, Reddy MR, Reich SH, Schoettlin WS, Smith WW, Varney MD, Villafranca JE, Ward RW, Webber S, Welsh KM, White J. Design of enzyme inhibitors using iterative protein crystallographic analysis. J Med Chem 1991; 34:1925–1934. 2. Ealick SE, Babu YS, Bugg CE, Erion MD, Guida WC, Montgomery JA, Secrist III JA. Application of crystallographic and modeling methods in the design of purine nucleoside phosphorylase inhibitors. Proc Natl Acad Sci USA 1991; 88:11540–11544. 3. Varney MD, Marzoni GP, Palmer CL, Deal JG, Webber S, Welsh KM, Bacquet RJ, Bartlett CA, Morse CA, Booth CLJ, Herrmann SM, Howland EF, Ward RW, White J. Crystal-structure-based design of Benz[cd]indole-containing inhibitors of thymidylate synthase. J Med Chem 1992; 35:663–676. 4. Reich SH, Fuhry MAM, Nguyen D, Pino MJ, Welsh KM, Webber S, Janson CA, Jordan SR, Matthews DA, Smith WA, Bartlett CA, Booth CLJ, Herrmann SM, Howland EF, Morse CA, Ward RW, White J. Design and synthesis of novel 6,7- http://legacy.netlibrary.com/nlreader/nlReader.dll?bookid=12640&filename=Page_537.html [4/9/2004 12:50:39 AM]
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Document [Interleukin-1] homology w
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Document Kininogen, 119 high molecu
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Document Matrix-metalloproteinase (
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Document RT inhibitor, 41, 56 Nonpe
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Document Phosphoglycerate kinase (P
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Document [Protease targets] serinyl
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