# Transcriptome-driven inference of adverse drug interactions

> **NIH NIH R21** · SEATTLE CHILDREN'S HOSPITAL · 2021 · $186,000

## Abstract

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.

## Key facts

- **NIH application ID:** 9880239
- **Project number:** 1R21DC018341-01
- **Recipient organization:** SEATTLE CHILDREN'S HOSPITAL
- **Principal Investigator:** Shuyi Ma
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $186,000
- **Award type:** 1
- **Project period:** 2021-01-02 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9880239

## Citation

> US National Institutes of Health, RePORTER application 9880239, Transcriptome-driven inference of adverse drug interactions (1R21DC018341-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9880239. Licensed CC0.

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