Transcriptome-driven inference of adverse drug interactions

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ABSTRACT Ototoxicity is a debilitating side effect of over 150 medications, many of which are prescribed as part of multi- drug regimens to treat a broad range of conditions including cancer and recalcitrant infections. Adverse drug- drug interactions (DDIs) that potentiate ototoxicity complicate the implementation of multi-drug regimens, particularly to treat multiple concurrent conditions. In most cases, DDIs are currently detected only after the drugs are on the market, so effective preclinical methods to identify potential adverse interactions would facilitate safer co-prescriptions. The astronomical number of combinations renders measuring all possible drug interactions infeasible, so predicting how ototoxic drugs interact from data of individual compounds is necessary. While the current understanding of mechanisms underlying ototoxicity of specific drug classes has helped to explain clinical observations of specific adverse ototoxicity DDIs, and aided rational design of candidate otoprotective adjuvants, this strategy cannot anticipate adverse ototoxicity DDIs or develop otoprotectants for other lesser studied drug classes and first-in-class drugs under clinical development. To survey more broadly for potential ototoxicity DDIs, we will adapt INDIGO (Inferring Drug Interactions using chemo-Genomics and Orthology), a machine learning tool that currently can predict synergy/antagonism of antimicrobial drug activity in multiple bacterial species without requiring specific drug target information. We hypothesize that we can harness the underlying approach to predict potentially adverse (synergistic) or protective (antagonistic) ototoxic DDIs in humans, by building an “INDIGO-Tox” model based on data generated from an appropriate animal system. We will measure transcriptional profiles elicited by 15 drugs known to convey ototoxicity or otoprotection, as well as corresponding pairwise ototoxicity DDI phenotypes in zebrafish, a well-established in vivo model system for studying ototoxicity. We will use these data to train INDIGO-Tox model. We will then use INDIGO-Tox to predict DDIs between 10 additional drugs, using their zebrafish transcriptome response profiles as input data. We will validate predictions in zebrafish, and will test translation of top validated predictions in a well-established mouse ex vivo model of ototoxicity. We will also use the model to generate predictions for novel genes that influence ototoxicity, which we will then test in zebrafish. Successful completion will generate hypotheses for translation into humans, facilitate model expansion to assessing possible ototoxic interactions for a broader library of drugs, and will establish a path to predict interactions between ototoxicity and other organ toxicities.