Abstract 5039: A data driven approach to predicting tissue-specific adverse events

Adverse events are currently one of the main causes of failure in drug development and withdrawal after approval. As a result, predicting drug side effects is an incredibly important part of drug discovery and development. With the emergence of precision medicine there has been a surge in interest on creating drugs for specific protein targets, however we lack accurate ways to connect drug targets and mechanisms to specific side effects. Here we take a target-centric approach to in-silico drug side effect prediction. We have mined drug side effect databases and grouped sets of side effects to the originating human tissue. For each of 30+ tissues, we defined a set of “toxic targets”- proteins that are only targeted by drugs with toxicity in that tissue - and “safe targets” - proteins only targeted by drugs with no related tissue toxicities. We found that toxic targets are consistently more highly expressed than safe targets, indicating that their mechanisms may be more crucial in their respective tissue. Furthermore we found that toxic targets have higher network connectivity. Using published gene knockdown screens, we also found that toxic targets for each tissue are significantly more likely to be essential than safe targets and are more likely to be enriched for GO terms related to cell death. These pieces of information all reinforce the proposed relationship between the identified toxic targets and drug induced tissue toxicities. We next leveraged this information to draw insights into unexpected drug toxicity events. We applied the BANDIT drug target prediction tool to drugs misclassified by the PrOCTOR toxicity prediction method and drugs with a specific type of tissue toxicity that were not known to hit any of our identified toxic targets. We found that new drug-target predictions explained a large number of these toxicities, correctly classifying approximately five times as many side effects as would have been expected by random chance. These results all supported our target-centric hypothesis of drug side effect prediction. Therefore we built a set of machine-learning models that would integrate drug targets with tissue-wide expression patterns and gene-specific features to predict specific side effects for a given drug. We found that these methods could significantly outperform other prediction techniques and random chance. For instance, our method for predicting drug induced liver injury (DILI) had ~70% accuracy at pinpointing specific drugs known to cause DILI and its likelihood score correlated with the FDA’s reported DILI severity score. Overall these findings show how a target-centric approach to drug development can not only help us understand the relation between targets and specific phenotypic effects, but can help drug developers predict side-effects before costly and time-consuming clinical studies. Our hope is that adoption of these methods will lead to overall increase in drug development efficiency and bring safer drugs to the market quicker. Note: This abstract was not presented at the meeting. Citation Format: Kaitlyn M. Gayvert, Neel Madhukar, Coryandar Gilvary, Olivier Elemento. A data driven approach to predicting tissue-specific adverse events [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5039. doi:10.1158/1538-7445.AM2017-5039