title: eCounterscreening: using QSAR predictions to prioritize testing for off-target activities and setting the balance between benefit and risk.
abstract: During drug development, compounds are tested against counterscreens, a panel of off-target activities that would be undesirable for a drug to have. Testing every compound against every counterscreen is generally too costly in terms of time and money, and we need to find a rational way of prioritizing counterscreen testing. Here we present the eCounterscreening paradigm, wherein predictions from QSAR models for counterscreen activity are used to generate a recommendation as to whether a specific compound in a specific project should be tested against a specific counterscreen. The rules behind the recommendations, which can be summarized in a risk-benefit plot specific for a counterscreen/project combination, are based on a previously assembled database of prospective QSAR predictions. The recommendations require two user-defined cutoffs: the level of activity in a specific counterscreen that is considered undesirable and the level of risk the chemist is willing to accept that an undesired counterscreen activity will go undetected. We demonstrate in a simulated prospective experiment that eCounterscreening can be used to postpone a large fraction of counterscreen testing and still have an acceptably low risk of undetected counterscreen activity.
authors.affiliation: Structural Chemistry, Merck Research Laboratories , P.O. Box 2000, Rahway, New Jersey 07065, United States.
authors.forename: Robert P
authors.initials: RP
authors.lastname: Sheridan
authors.forename: Daniel R
authors.initials: DR
authors.lastname: McMasters
authors.forename: Johannes H
authors.initials: JH
authors.lastname: Voigt
authors.forename: Mary Jo
authors.initials: MJ
authors.lastname: Wildey
mesh_terms: D000465:Algorithms; D057225:Data Mining; D016208:Databases, Factual; D055808:Drug Discovery; D064420:Drug-Related Side Effects and Adverse Reactions; D057166:High-Throughput Screening Assays / Q000379:methods*; D008956:Models, Chemical; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship; D018570:Risk Assessment