The lack of predictive in vitro models for behavioral phenotypes impedes rapid advancement in neuropharmacology and psychopharmacology. In vivo behavioral assays are more predictive of activity in human disorders, but such assays are often highly resource-intensive. Here we describe the successful application of a computer vision-enabled system to identify potential neuropharmacological activity of two new mechanisms. The analytical system was trained using multiple drugs that are used clinically to treat depression, schizophrenia, anxiety, and other psychiatric or behavioral disorders. During blinded testing the PDE10 inhibitor TP-10 produced a signature of activity suggesting potential antipsychotic activity. This finding is consistent with TP-10's activity in multiple rodent models that is similar to that of clinically used antipsychotic drugs. The CK1ε inhibitor PF-670462 produced a signature consistent with anxiolytic activity and, at the highest dose tested, behavioral effects similar to that of opiate analgesics. Neither TP-10 nor PF-670462 was included in the training set. Thus, computer vision-based behavioral analysis can facilitate drug discovery by identifying neuropharmacological effects of compounds acting through new mechanisms.
Keywords: antipsychotic; anxiolytic; behavioral models; computer vision; drug development; high-throughput screening; machine learning; mouse.