Predictive, integrative, and regulatory aspects of AI-driven computational toxicology – Highlights of the German Pharm-Tox Summit (GPTS) 2024

IF 4.8 3区医学 Q1 PHARMACOLOGY & PHARMACY

Toxicology Pub Date : 2024-10-18 DOI:10.1016/j.tox.2024.153975

Ute Haßmann , Sigrid Amann , Nelly Babayan , Simone Fankhauser , Tina Hofmaier , Thomas Jakl , Monika Nendza , Helga Stopper , Sven Marcel Stefan , Robert Landsiedel

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Abstract

The 9th German Pharm-Tox Summit (GPTS) and the 90th Annual Meeting of the German Society for Experimental and Clinical Pharmacology and Toxicology (DGPT) took place in Munich from March 13–15, 2024. The event brought together over 700 participants from around the world to discuss cutting-edge developments in the fields of pharmacology and toxicology as well as scientific innovations and novel insights. A key focus of the conference was on the rapidly increasing role of computational toxicology, artificial intelligence (AI), and machine learning (ML) into the field, marking a shift away from traditional methods and allowing the reduction of animal testing as primary tool for toxicological risk assessment. Tools such as Toxometris.ai showcased the potential of AI-based risk assessments for predicting carcinogenicity, offering more ethical and efficient alternatives. Additionally, computer-driven models like computer-aided pattern analysis (C@PA) for drug toxicity prediction were presented, emphasizing the growing role of chem- and bioinformatic applications in computational sciences. Throughout the summit, there was a strong focus on the need for regulatory innovation to support the adoption of these advanced technologies and ensure the safety and sustainability of chemical substances and drugs.

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人工智能驱动的计算毒理学的预测、整合和监管方面--2024 年德国药物毒理学峰会（GPTS）亮点。

第九届德国药理毒理峰会（GPTS）暨德国实验与临床药理学和毒理学学会（DGPT）第90届年会于2024年3月13日至15日在慕尼黑举行。来自世界各地的 700 多名与会者汇聚一堂，共同探讨药理学和毒理学领域的前沿发展以及科学创新和新见解。本次会议的一个重点是计算毒理学、人工智能（AI）和机器学习（ML）在该领域中迅速增强的作用，这标志着传统方法的转变，并允许减少动物试验作为毒理学风险评估的主要工具。Toxometris.ai 等工具展示了基于人工智能的风险评估在预测致癌性方面的潜力，提供了更合乎道德和更高效的替代方法。此外，会议还介绍了用于药物毒性预测的计算机辅助模式分析（C@PA）等计算机驱动模型，强调了化学和生物信息学应用在计算科学中日益重要的作用。在整个峰会期间，与会者强烈关注监管创新的必要性，以支持这些先进技术的采用，并确保化学物质和药物的安全性和可持续性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Toxicology 医学-毒理学

CiteScore

7.80

自引率

4.40%

发文量

222

审稿时长

23 days

期刊介绍： Toxicology is an international, peer-reviewed journal that publishes only the highest quality original scientific research and critical reviews describing hypothesis-based investigations into mechanisms of toxicity associated with exposures to xenobiotic chemicals, particularly as it relates to human health. In this respect "mechanisms" is defined on both the macro (e.g. physiological, biological, kinetic, species, sex, etc.) and molecular (genomic, transcriptomic, metabolic, etc.) scale. Emphasis is placed on findings that identify novel hazards and that can be extrapolated to exposures and mechanisms that are relevant to estimating human risk. Toxicology also publishes brief communications, personal commentaries and opinion articles, as well as concise expert reviews on contemporary topics. All research and review articles published in Toxicology are subject to rigorous peer review. Authors are asked to contact the Editor-in-Chief prior to submitting review articles or commentaries for consideration for publication in Toxicology.