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In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review.
Shin, Hyun Kil; Huang, Ruili; Chen, Minjun.
Afiliación
  • Shin HK; Department of Predictive Toxicology, Korea Institute of Toxicology (KIT), 34114, Daejeon, Republic of Korea.
  • Huang R; Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA. Electronic address: ruili.huang@nih.gov.
  • Chen M; Division of Bioinformatics and Biostatistics, National Center for Toxicological Research (NCTR), U.S. Food and Drug Administration, 3900 NCTR Rd., Jefferson, AR, 72079, USA. Electronic address: minjun.chen@fda.hhs.gov.
Food Chem Toxicol ; 179: 113948, 2023 Sep.
Article en En | MEDLINE | ID: mdl-37460037
New approach methods (NAMs) have been developed to predict a wide range of toxicities through innovative technologies. Liver injury is one of the most extensively studied endpoints due to its severity and frequency, occurring among populations that consume drugs or dietary supplements. In this review, we focus on recent developments of in silico modeling for liver injury prediction using deep learning and in vitro data based on adverse outcome pathways (AOPs). Despite these models being mainly developed using datasets generated from drug-like molecules, they were also applied to the prediction of hepatotoxicity caused by herbal products. As deep learning has achieved great success in many different fields, advanced machine learning algorithms have been actively applied to improve the accuracy of in silico models. Additionally, the development of liver AOPs, combined with big data in toxicology, has been valuable in developing in silico models with enhanced predictive performance and interpretability. Specifically, one approach involves developing structure-based models for predicting molecular initiating events of liver AOPs, while others use in vitro data with structure information as model inputs for making predictions. Even though liver injury remains a difficult endpoint to predict, advancements in machine learning algorithms and the expansion of in vitro databases with relevant biological knowledge have made a huge impact on improving in silico modeling for drug-induced liver injury prediction.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos / Enfermedad Hepática Inducida por Sustancias y Drogas / Enfermedad Hepática Crónica Inducida por Sustancias y Drogas Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Food Chem Toxicol Año: 2023 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos / Enfermedad Hepática Inducida por Sustancias y Drogas / Enfermedad Hepática Crónica Inducida por Sustancias y Drogas Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Food Chem Toxicol Año: 2023 Tipo del documento: Article Pais de publicación: Reino Unido