Abstract LB-106: Using a data-driven Bayesian approach to predict the targets of orphan small molecules and ways to overcome drug resistance

Abstract For natural products and phenotypic screen derived small molecules, one of the greatest bottlenecks is target identification. Since most experimental approaches are failure-prone and require a large investment of time and resources, computational methods have the potential to substantially improve target identification efforts. Recently there has been an explosion of genomic, chemical, clinical, and pharmacological datasets that characterize a small molecule's mechanism of action, but no method has been invented to integrate the multiple, independent pieces of evidence provided by each data type into a cohesive prediction framework To address this problem we developed BANDIT: a Bayesian ANalysis to determine Drug Interaction Targets. BANDIT integrates over 20,000,000 pieces of unique information from seven distinct data types to predict drug targets. By integrating these data types within a Bayesian network we outperformed other target prediction methods and achieved a target prediction accuracy of over 90%. Furthermore, we observed that BANDIT was able to predict the precise targeting mechanism as well as the protein target. We further validated BANDIT's accuracy by using it to reproduce the results of a previously published experimental kinase inhibitor screen without any additional data. We observed that our target predictions for the tested small molecules were significantly more likely to be the targets with the highest levels of inhibition in the experimental screen (P = 3e-06). We next used BANDIT to predict targets and mechanisms for over 50,000 small molecules with no known targets. We identified 24 molecules with varying structures and efficacies that we predicted to inhibit microtubule polymerization. Using immunofluorescence and tubulin binding assays we were experimentally validated microtubules as targets for 18 of these compounds - a success rate much higher than expected by random chance. Moreover, one of the greatest challenges with current microtubule chemotherapy is the development of drug resistance, which is lethal for the patients. Remarkably, we identified a subset of our compounds to be active against tumor cells resistant to Eribulin, a microtubule depolymerizing agent FDA approved for breast cancer, and cross-resistant to Vincristine and Colchicine, known depolymerizing drugs. This demonstrated that BANDIT could not only rapidly identify drug mechanisms and targets, but also determine novel molecules with the potential to act on refractory tumors. Finally we used BANDIT to observe how different drug types could interact with one another. We observed many unexpected and exciting results. For instance we observed a tight clustering of the seemingly unrelated classes of beta-blockers with anti-Parkinson's medications. This interaction potentially indicates a shared protein target interaction and provides support to studies proposing the clinical use of beta-blockers in the treatment of tremors in Parkinson's patients. Examples such as this indicate the validity of BANDIT's clustering approach and reveal how previously unknown shared target interactions could cause the unexplained phenotypic effects. This approach could further be expanded to determine undiscovered relationships between classes of drugs, discover which drugs could be repositioned for new uses, and predict clinical drug synergies. Citation Format: Neel S. Madhukar, Prashant Khade, Linda Huang, Kaitlyn Gayvert, Giuseppe Galletti, Paraskevi Giannakakou, Olivier Elemento. Using a data-driven Bayesian approach to predict the targets of orphan small molecules and ways to overcome drug resistance. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-106.