# predicTox: an integrated database of clinical risk frequencies and human gene expression signatures for cardiotoxic drugs

## TL;DR

The paper introduces predicTox, a database that combines gene expression and clinical data to help understand and predict cardiotoxic effects of certain drugs.

## Contribution

The novel contribution is the creation of an integrated database combining clinical risk frequencies and gene expression data for cardiotoxic drugs.

## Key findings

- predicTox includes drug-induced transcriptomic responses and genomic variants linked to cardiotoxicity.
- The database provides pathway analyses and downloadable gene expression data for cardiotoxic drugs.
- Mathematical models and statistical metrics are included to simulate drug effects on heart physiology.

## Abstract

We recently used drug-induced transcriptomic responses and whole-genome sequences in healthy human induced pluripotent stem cell (iPSC)-derived cardiomyocytes to identify cellular pathways and genomic variants potentially associated with the cardiotoxic effects of tyrosine kinase inhibitors (TKIs) and anthracyclines. Here, we describe predicTox (www.predictox.org), an interactive website that organizes our data and its integration with knowledge from cell pathways and genomic databases. DrugTox summary cards give results of these analyses and metadata for each drug. Fields include cardiotoxicity risk scores curated from the FDA Adverse Event Reporting System, cell pathways, and genomic variants potentially associated with drug-induced cardiotoxicity. At a detailed level, predicTox provides a ranked list of up- and downregulated pathways that are predominantly induced by cardiotoxic TKIs as well as lists of their pathway genes and the specific cardiotoxic TKIs inducing those pathways. predicTox provides downloadable lists of drug-induced differentially expressed genes and pathways as well as drug-related genomic variants associated with cardiotoxicity. Statistical metrics are given. Mathematical models allow simulation of drug effects on heart physiology. Building on the results of our algorithm for independent reidentification of the well-known rs2229774 variant for anthracycline-induced cardiotoxicity, we describe how our data can be queried to identify potential variants associated with drug-induced cardiotoxicity by affecting a drug’s pharmacodynamics and pharmacokinetics.