title: Extraction of tacit knowledge from large ADME data sets via pairwise analysis.
abstract: Pharmaceutical companies routinely collect data across multiple projects for common ADME endpoints. Although at the time of collection the data is intended for use in decision making within a specific project, knowledge can be gained by data mining the entire cross-project data set for patterns of structure-activity relationships (SAR) that may be applied to any project. One such data mining method is pairwise analysis. This method has the advantage of being able to identify small structural changes that lead to significant changes in activity. In this paper, we describe the process for full pairwise analysis of our high-throughput ADME assays routinely used for compound discovery efforts at Pfizer (microsomal clearance, passive membrane permeability, P-gp efflux, and lipophilicity). We also describe multiple strategies for the application of these transforms in a prospective manner during compound design. Finally, a detailed analysis of the activity patterns in pairs of compounds that share the same molecular transformation reveals multiple types of transforms from an SAR perspective. These include bioisosteres, additives, multiplicatives, and a type we call switches as they act to either turn on or turn off an activity.
authors: Computational ADME Group, Department of Pharmacokinetics, Dynamics, and Drug Metabolism, Pfizer Inc., Groton, CT 06340, USA. christopher.keefer@pfizer.com
authors: Christopher E
authors: CE
authors: Keefer
authors: George
authors: G
authors: Chang
authors: Gregory W
authors: GW
authors: Kauffman