Rationale: In psychiatric drug discovery, a critical step is predicting the psychopharmacological effect and therapeutic potential of novel (or repurposed) compounds early in the development process. This process is hampered by the need to utilize multiple disorder-specific and labor-intensive behavioral assays.
Objectives: This study aims to investigate the feasibility of a single high-throughput behavioral assay to classify psychiatric drugs into multiple psychopharmacological classes.
Methods: Using Pattern Array, a procedure for data mining exploratory behavior in mice, we mined ~100,000 complex movement patterns for those that best predict psychopharmacological class and dose. The best patterns were integrated into a classification model that assigns psychopharmacological compounds to one of six clinically relevant classes--antipsychotic, antidepressant, opioids, psychotomimetic, psychomotor stimulant, and α-adrenergic.
Results: Surprisingly, only a small number of well-chosen behaviors were required for successful class prediction. One of them, a behavior termed "universal drug detector", was dose-dependently decreased by drugs from all classes, thus providing a sensitive index of psychopharmacological activity. In independent validation in a blind fashion, simulating the process of in vivo pre-clinical drug screening, the classification model correctly classified nine out of 11 "unknown" compounds. Interestingly, even "misclassifications" match known alternate therapeutic indications, illustrating drug "repurposing" potential.
Conclusions: Unlike standard animal models, the discovered classification model can be systematically updated to improve its predictive power and add therapeutic classes and subclasses with each additional diversification of the database. Our study demonstrates the power of data mining approaches for behavior analysis, using multiple measures in parallel for drug screening and behavioral phenotyping.