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ORIGINAL RESEARCH article
Front. Pharmacol.
Sec. Experimental Pharmacology and Drug Discovery
Volume 16 - 2025 | doi: 10.3389/fphar.2025.1448106
Prediction of Adverse Drug Reactions Based on Pharmacogenomics Combination Features: A Preliminary Study
Provisionally accepted- 1 Guangdong Pharmaceutical University, Guangzhou, China
- 2 Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
- 3 NMPA Key Laboratory for Technology Research and Evaluation of Pharmacovigilance, Guangzhou, China
- 4 Gangdong Provincial Traditional Chinese Medicine Precision Medicine Big Data Engineering Technology Research Center, Guangzhou, China
Adverse Drug Reactions (ADRs), a widespread phenomenon in clinical drug treatment, are often associated with a high risk of morbidity and even death. Drugs and changes in gene expression are the two important factors that affect whether and how adverse reactions occur. Notably, pharmacogenomics data have recently become more available and could be used to predict ADR occurrence. However, there is a challenge in effectively analyzing the massive data lacking guidance on mutual relationship for ADRs prediction. Herein, we constructed separate similarity features for drugs and ADRs using pharmacogenomics data from the Comparative Toxicogenomics Database [CTD, including Chemical-Gene Interactions (CGIs) and Gene-Disease Associations (GDAs)]. We proposed a novel deep learning architecture, DGANet, based on the constructed features for ADR prediction. The algorithm uses Convolutional Neural Networks (CNN) and cross-features to learn the latent drug-gene-ADR associations for ADRs prediction. The performance of DGANet was compared to three state-of-the-art algorithms with different genomic features. According to the results, GDANet outperformed the benchmark algorithms (AUROC=92.76%, AUPRC=92.49%), demonstrating a 3.36% AUROC and 4.05% accuracy improvement over the cutting-edge algorithms. We further proposed new genomic features that improved DGANet's predictive capability. Moreover, case studies on top-ranked candidates confirmed DGANet's ability to predict new ADRs.
Keywords: adverse drug reactions, Comparative toxicogenomics database, Chemical-gene interactions, Gene-disease associations, Convolutional Neural Networks
Received: 12 Jun 2024; Accepted: 24 Feb 2025.
Copyright: © 2025 He, Shi, Han and Yongming. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Fangfang Han, Guangdong Pharmaceutical University, Guangzhou, China
Cai Yongming, Guangdong Pharmaceutical University, Guangzhou, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.