A tool to utilize adverse effect profles to identify brain-active medications for repurposing

Abstract

Background: To shorten the time required to bring new treatments to clinics, recent efforts have focused on repurposing existing Food and Drug Administration (FDA)-approved drugs with established safety data for new indications. We hypothesized that adverse effect profles might aid in prioritizing compounds for investigation in central nervous system (CNS) applications by providing an indication of their abilities to cross the blood-brain barrier. Methods: Data were drawn from an investigation of similarity of adverse effect profles, utilizing pre- and post-marketing data. A panel of known CNS-active drugs was utilized to estimate aggregate similarity profles for all other FDA drugs in the database. Permutations were used to test whether similarities for any given drug exceeded that expected under the null hypothesis. To estimate the performance of algorithms using such profles, manually-curated lists of known CNS-active and -inactive medications were classified using logistic regression. Algorithms with and without this similarity data were compared for prediction of CNS penetrance. Results: Models incorporating adverse effect similarity data exhibited greater discrimination of brain-penetrant and nonpenetrant drugs than models without this data. A visualization tool was developed to allow any medication to be evaluated for adverse effect similarity to the CNS panel or a custom panel. Conclusions: Consideration of adverse effect profles allows in silico prioritization of compounds for follow-up investigation for CNS indications. In concert with chemical screening approaches, this may accelerate repurposing efforts for putative CNS-active medications.