EDITORIAL article
Front. Pharmacol.
Sec. Predictive Toxicology
Volume 16 - 2025 | doi: 10.3389/fphar.2025.1721364
This article is part of the Research TopicAdvances and Applications of Predictive Toxicology in Knowledge Discovery, Risk Assessment, and Drug DevelopmentView all 13 articles
Editorial: Advances in Predictive Toxicology for Knowledge Discovery, Risk Assessment, and Drug Development
Provisionally accepted
- 1Beijing University of Chinese Medicine, Beijing, China
- 2Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- 3University of California, Riverside, Armenia
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Many studies have indicated advancements in applying computational frameworks, AI , and interdisciplinary technologies to drug toxicity prediction and assessment. Through approaches like AI modeling, multi-omics analysis, and integration of mechanistic information, research outcomes in predicting drug-induced toxicities, interpreting their mechanisms, and conducting risk evaluations emphasize the critical role of these tools. They enhance the accuracy of toxicity forecasts, deliver mechanistic insights, and optimize drug safety assessments. For example, Zhao et al. developed an artificial neural network (ANN) model to predict linezolid-induced thrombocytopenia, achieving 96.32% accuracy, which significantly exceeded traditional logistic regression. This highlights of ANN capacity This is a provisional file, not the typeset article to handle complex nonlinear in toxicity data.
Keywords: predictive toxicology, Mechanistic studies, Drug Toxicity, Risk Assessment, New drug development
Received: 09 Oct 2025; Accepted: 13 Oct 2025.
Copyright: © 2025 Zhang, Li, Tan, Zhang, Han, Liao and Zhai. 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:
Yong-Long Han, yonglongh@126.com
Jiayu Liao, jiayu.liao@ucr.edu
Huaqiang Zhai, jz711@qq.com
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.