eCounterscreening: Using QSAR Predictions to Prioritize Testing for Off-Target Activities and Setting the Balance between Benefit and Risk

作者：Sheridan Robert P^*; McMasters Daniel R; Voigt Johannes H; Wildey Mary Jo

来源：Journal of Chemical Information and Modeling, 2015, 55(2): 231-238.

DOI：10.1021/ci500666m

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.

• 出版日期2015-2