Abstract
Development of predictive in vitro surrogate methods for traditional approaches assessing bioavailability and pharmacokinetics of lead compounds must be made to both keep pace with highthroughput (HT) lead identification and to mitigate the high costs associated with progression of compounds with poor chances of developmental success. Indeed opportunities for improvement still exist in the lead optimization phase versus the lead identification phase, where HT methodologies have been nearly optimized. Review of examples, limitations, and development of high-throughput microtiterplate-based assays for evaluating metabolic liabilities, such as in vitro radiometric and fluorometric assays for inhibition of cytochrome P450 (CYP) activity, determination of stability of a compound in liver microsomes, or cloned CYPs coupled to reconstituting systems are described. Parallel approaches to improve speed, resolution, sample preparation, as well as data analysis using LC/MS and LC/MS/MS approaches and technologies to assess compound integrity and biotransformation by automation and multiplexing are also discussed. Realization of the benefits in automation of cell-based models for determining drug permeability to predict drug absorption are still hampered by bottlenecks in analytical analysis of compounds. The implementation and limitations of surrogate physiochemical methods for passive adsorption such as immobilized artificial membranes (IAM) and parallel artificial membrane permeation assays (PAMPA), and compound solubility by laser nephelometry are reviewed as well. Additionally, data from a high-throughput 96-well equilibrium dialysis device, showing good correlation to classical methods, is presented. Finally, the impact of improvements in these downstream bottlenecks in lead optimization and preclinical drug discovery are discussed in this review.
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Current Computer-Aided Drug Design
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