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

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.