# Characterizing clinical toxicity in cancer combination therapies

## TL;DR

This paper explores how to better predict cancer drug combinations that are both effective and less toxic.

## Contribution

The study evaluates the correlation between synergy scores and toxicity metrics in drug combinations, revealing limitations in current toxicity penalties.

## Key findings

- Some synergy metrics show trends with toxicity levels, but are not reliable as toxicity penalties.
- Current models lack validation against known drug-drug interactions for toxicity.
- More comprehensive toxicity data is needed to improve synergy prediction frameworks.

## Abstract

Predicting synergistic cancer drug combinations through computational methods offers a scalable approach to creating therapies that are more effective and less toxic. However, most algorithms focus solely on synergy without considering toxicity when selecting optimal drug combinations. In the absence of combinatorial toxicity assays, a few models use toxicity penalties to balance high synergy with lower toxicity. Still, these penalties have not been explicitly validated against known drug-drug interactions.

In this study, we examine whether synergy scores and toxicity metrics correlate with known adverse drug interactions. While some metrics show trends with toxicity levels, our results reveal significant limitations in using them as penalties. These findings highlight the challenges of incorporating toxicity into synergy prediction frameworks and suggest that advancing the field requires more comprehensive combination toxicity data.

The code written for this project is available at https://github.com/amw14/toxicity-cancer-drug-combination.