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Document Figure 1 An integrated technology for drug discovery combines the precision of structure-based design with the parallelism of combinatorial synthesis. Chemi-informatics systems track and integrate all data emerging from the discovery cycle. Page 526 (SAR). Chemi-informatics plays a key role in this integration by assuring that properties important in drug development are both factored into compound design and cumulatively tracked throughout the discovery process (Figure 1). The product of this approach is a permanently useful set of drug-design parameters. The integration of these technologies promises to produce an increase in the efficiency of drug discovery and may ultimately offer a means for reducing the aggregate failure rate of compounds selected for development. One paradigm for achieving the integration of these technologies is described below. II. Structure-Based Design Structure-based drug design has become a highly developed technology that is in active use in most major pharmaceutical companies. Structure-based design is an iterative process in which lead compounds identified by screening, de novo design, or mechanism-based features are systematically elaborated to improve potency and specificity [1,2]. The process involves successive rounds of structure determination of lead—target complexes, design of lead modifications using molecular modeling tools, synthesis of new drug leads, and measurement of the chemical and biological properties of the modified leads using screens for target http://legacy.netlibrary.com/nlreader/nlReader.dll?bookid=12640&filename=Page_526.html (1 of 2) [4/9/2004 12:47:32 AM]
Document Page 527 function. Iterative refinement and optimization of drug leads is an effective strategy for generating potent preclinical candidates. Structure-based design can also be used to design new chemical classes of compounds that present similar substituents to the target using a template or scaffold which is chemically distinct from previously characterized leads [3,4]. Structure determination typically relies on x-ray crystallography or high-field nuclear magnetic resonance to directly visualize the 3-dimensional structure of a molecular target and the structures of complexes of the target with drug leads. Alternately, many targets fall into identifiable classes that frequently enable the development of homology models of the 3-dimensional target structure or a mechanism-based strategy for drug-lead generation. Ongoing genome sequencing efforts have led to the identification of hundreds of potential therapeutic targets, many of which represent possible sources of crossover pharmacology. Homology modeling is a key feature of an integrated drug discovery effort because it allows this genomics information to be utilized early in the development of target ligands or in the engineering of ligand specificity. Although structure-based design is an effective technology, current limitations center on the inability to quantitatively predict how specific modifications of the lead will actually affect ligand binding affinity [5,6]. This reflects the complexity of the drug-binding process and our inability to accurately predict the conformational response of macromolecular structures to ligand binding. In addition, we have only limited ability to accurately calculate molecular energy parameters or to accurately estimate the effects of factors such as polarizability, solvation, and entropy that may have an important influence on drugbinding energetics. Although computational methods will continue to improve, most design work (and algorithms) still relies heavily on heuristic rules (Figure 2) that have been developed through experience and that guide the structural and medicinal chemists in the systematic modification of lead compounds [7]. As a practical consequence, many cycles of serial lead modification are required in order to produce molecules of suitable potency and specificity to be considered preclinical drug candidates. Structural information can increase the efficiency with which pharmacokinetic or toxicological liabilities in lead compounds are eliminated by suggesting where compounds can be modified so as to alter drug properties without affecting target potency. Structural data can also be used to direct de novo design of alternate and distinct chemical classes of lead compounds, each of which might be expected to have a different pharmacological profile [3,4]. New chemical compound classes can also be designed from existing lead compounds by recombining substituents and core regions (scaffolds) from existing lead compounds. Chemically distinct lead series can then be optimized in parallel so that when a preclinical candidate is found to have inadequate drug properties, a backup is immediately available for preclinical evaluation. As http://legacy.netlibrary.com/nlreader/nlReader.dll?bookid=12640&filename=Page_527.html [4/9/2004 12:47:45 AM]
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