Abstract A021: Improving cardiotoxicity prediction for oncology drugs via domain-specific adaptation
Daniel Nguyen, Dong Nguyen, Richard Zhang, Jesse T. Chao · Clinical Cancer Research · 2025
Abstract Background: Antineoplastic drugs have significantly improved outcomes for cancer patients. However, the cardiotoxicity of antineoplastic drugs, such as anthracyclines and anti-HER2 agents, poses serious challenges in cancer care, increasing patients’ susceptibility to cardiac complications, including heart failure, hypertension, arrhythmias and coronary disease. While predictive models have recently been developed for predicting drug-induced cardiotoxicity (DICT), performance on antineoplastic drugs is less sensitive and specific than on general drugs. Methods: We developed a domain adaptation approach to improve DICT prediction, using data from the recently released FDA DICTrank dataset and the CancerDrug_DB database to identify relevant antineoplastic drugs. We used the latest publicly available cardiotoxicity models as baselines, and constructed oncology-specific models that incorporated novel domain-relevant features, meta-modelling and adaptive strategies to enhance predictive performance on antineoplastic agents, leveraging public structural, physicochemical, mechanism of action, molecular target and morphological (Cell Painting) data. Results: Preliminary results demonstrate that our domain-specific adaptation approach conferred significant increases in performance on antineoplastic drugs (holdout AUC = 0.861), compared to the best performing general DICT predictors (holdout AUC = 0.735). Domain-specific adaptation from structural features alone performed better (holdout AUC = 0.975) on antineoplastic drugs compared to the general model (holdout AUC = 0.548). Model interpretability analyses revealed unique structural, physicochemical and protein targets as key features for predicting antineoplastic DICT. Conclusion: Domain-specific adaptation is a promising approach to DICT prediction for antineoplastic drugs. The strong performance of DICT predictors developed from both in-silico and biological data can enhance the safety and efficiency of the oncology drug development process, especially in early-stage compound screening. Citation Format: Daniel Nguyen, David L. Nguyen, Richard Zhang, Jesse T. Chao. Improving cardiotoxicity prediction for oncology drugs via domain-specific adaptation [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A021.