Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation : From Public to Proprietary Data
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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2023-08-21
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en
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Chemical Research in Toxicology, Volume 36, issue 8, pp. 1238−1247
Abstract
Drug-induced liver injury (DILI) is an important safety concern and a major reason to remove a drug from the market. Advancements in recent machine learning methods have led to a wide range of in silico models for DILI predictive methods based on molecule chemical structures (fingerprints). Existing publicly available DILI data sets used for model building are based on the interpretation of drug labels or patient case reports, resulting in a typical binary clinical DILI annotation. We developed a novel phenotype-based annotation to process hepatotoxicity information extracted from repeated dose in vivo preclinical toxicology studies using INHAND annotation to provide a more informative and reliable data set for machine learning algorithms. This work resulted in a data set of 430 unique compounds covering diverse liver pathology findings which were utilized to develop multiple DILI prediction models trained on the publicly available data (TG-GATEs) using the compound’s fingerprint. We demonstrate that the TG-GATEs compounds DILI labels can be predicted well and how the differences between TG-GATEs and the external test compounds (Johnson & Johnson) impact the model generalization performance.Description
Funding Information: This work was funded by Janssen Pharmaceutica (LiToTIC Janssen, 310254) and supported by the Academy of Finland (Flagship programme: Finnish Center for Artificial Intelligence FCAI), ELISE Networks of Excellence Centres (EU Horizon:2020 grant agreement 951847), and UKRI Turing AI World-Leading Researcher 521 Fellowship (EP/W002973/1). We also acknowledge Johnson & Johnson, who funded the work in providing the data set and computational resources and Aalto Science-IT Project from Computer Science IT. The table of Contents (TOC) figure was created with BioRender.com. | openaire: EC/H2020/951847/EU//ELISE
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Moein, M, Heinonen, M, Mesens, N, Chamanza, R, Amuzie, C, Will, Y, Ceulemans, H, Kaski, S & Herman, D 2023, ' Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation : From Public to Proprietary Data ', Chemical Research in Toxicology, vol. 36, no. 8, pp. 1238−1247 . https://doi.org/10.1021/acs.chemrestox.2c00378