Computational ADME/Tox Modeling: Aiding Understanding and Enhancing Decision Making in Drug Design

Robert   K.   Delisle; Jeffery   F.   Lowrie; Doug   W.   Hobbs; David   J.   Diller

Abstract

With recent estimates of drug development costs on the order of $800 million and increased pressure to reduce consumer drug costs, it is not surprising that the pharmaceutical industry is keenly interested in reducing the overall expense associated with drug development. An analysis of the reasons for attrition during the drug development process found that over half of all failures can be attributed to problems with human or animal pharmacokinetics and toxicity. Discovering pharmacokinetics and toxicity liabilities late within the drug development process results in wasted resource expenditures. This argues dramatically for evaluation of these properties as early as possible, leading to the concept of "Fail Early". Computational models provide a low cost, flexible evaluation of compound properties that can be implemented and used prior to chemical synthesis thereby creating an alternative philosophy of "Design for Success". Here we review the history and current trends within ADME/Tox modeling and discuss important issues related to development of computational models. In addition, we review some of the commercially available tools to achieve this goal as well as methods developed internally to address these issues from the design stage through development and optimization of drug candidates. In particular, we highlight those features that we feel best exemplify the Design for Success philosophy.

Keywords: qsar, neural network models, descriptor, logp, blood-brain barrier, cypa isoforms