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

被引：1

作者：

Hassmann, Ute ^{[1
]}

Amann, Sigrid ^{[1
]}

Babayan, Nelly ^{[2
,4
]}

Fankhauser, Simone ^{[3
]}

Hofmaier, Tina ^{[4
]}

Jakl, Thomas ^{[3
]}

Nendza, Monika ^{[5
]}

Stopper, Helga ^{[6
]}

Stefan, Sven Marcel ^{[7
,8
]}

Landsiedel, Robert ^{[9
]}

机构：

[1] Toxlicon GmbH, Obwaldener Zeile 23, D-12205 Berlin, Germany

[2] Toxometris ai Inc, Glendale, CA USA

[3] Austrian Environm Minist, Spittelauer Lande 5, A-1090 Vienna, Austria

[4] Osterreich Agentur Gesundheit & Ernahrungssicherhe, Spargelfeldstr 191, A-1220 Vienna, Austria

[5] Analyt Lab, Bahnhofstr 1, D-24816 Luhnstedt, Germany

[6] Univ Wurzburg, Inst Pharmacol & Toxicol, Versbacher Str 9, D-97078 Wurzburg, Germany

[7] Univ Lubeck UzL, Univ Med Ctr Schleswig Holstein UKSH, Lubeck Inst Expt Dermatol LIED, Med Chem & Syst Pharmacol ,Med Syst Biol Div, Ratzeburger Allee 160, D-23538 Lubeck, Germany

[8] Med Univ Lublin, Dept Biopharm, Chodzki 4a, PL-20093 Lublin, Poland

[9] BASF SE Expt Toxikol & Okol, Carl Bosch Str, D-67056 Ludwigshafen Am Rhein, Germany

来源：

TOXICOLOGY | 2024年 / 509卷

关键词：

German Pharma-Tox Summit (GPTS); Computational Toxicology; Artificial intelligence (AI) In silico methods; Computer-aided pattern analysis; Generic Approach to Risk Management (GRA); AIDED PATTERN-ANALYSIS;

D O I：

10.1016/j.tox.2024.153975

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

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.

引用

页数：4