AI/ML Models to Predict the Severity of Drug-Induced Liver Injury for Small Molecules.

Humans Artificial Intelligence Bayes Theorem Chemical and Drug Induced Liver Injury Machine Learning Databases, Factual Organic Anion Transporters Organic Cation Transport Proteins

Journal

Chemical research in toxicology
ISSN: 1520-5010
Titre abrégé: Chem Res Toxicol
Pays: United States
ID NLM: 8807448

Informations de publication

Date de publication:
17 07 2023
Historique:
medline: 18 7 2023
pubmed: 9 6 2023
entrez: 9 6 2023
Statut: ppublish

Résumé

Drug-induced liver injury (DILI), believed to be a multifactorial toxicity, has been a leading cause of attrition of small molecules during discovery, clinical development, and postmarketing. Identification of DILI risk early reduces the costs and cycle times associated with drug development. In recent years, several groups have reported predictive models that use physicochemical properties or

Identifiants

DOI: 10.1021/acs.chemrestox.3c00098 PMID: 37294641
pubmed: 37294641
doi: 10.1021/acs.chemrestox.3c00098
doi:

Substances chimiques

SLC22A12 protein, human 0
Organic Anion Transporters 0
Organic Cation Transport Proteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1129-1139

Auteurs

Mohan Rao (M)

Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States.
ORCID: 0000-0001-7395-5260

Vahid Nassiri (V)

Open Analytics, Jupiterstraat 20, 2600 Antwerpen, Belgium.

Cristóbal Alhambra (C)

Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States.

Jan Snoeys (J)

Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States.

Freddy Van Goethem (F)

Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States.

Onyi Irrechukwu (O)

Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States.

Michael D Aleo (MD)

TOXinsights LLC, Boiling Springs, Pennsylvania 17007, United States.
ORCID: 0000-0003-1549-2629

Helena Geys (H)

Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States.

Kaushik Mitra (K)

Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States.
ORCID: 0000-0003-4308-8236

Yvonne Will (Y)

Discovery, Product Development and Supply (DPDS), Preclinical Sciences and Translational Safety (PSTS), Predictive Investigative and Translational Toxicology (PITT), Janssen Pharmaceutical Companies of Johnson and Johnson, La Jolla, California 92121, United States.

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › AI/ML Models to Predict the Severity of...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › AI/ML Models to Predict the Severity of...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Probabilité › Théorème de Bayes › AI/ML Models to Predict the Severity of...
questionsmedicales.fr › Troubles dus à des produits chimiques › Intoxication › Lésions hépatiques dues aux substances › AI/ML Models to Predict the Severity of...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › AI/ML Models to Predict the Severity of...
questionsmedicales.fr › Sciences de l'information › Mémorisation et recherche des informations › Bases de données comme sujet › Bases de données factuelles › AI/ML Models to Predict the Severity of...
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Protéines membranaires › Protéines de transport membranaire › Pompes ioniques › Transporteurs d'anions › Transporteurs d'anions organiques › AI/ML Models to Predict the Severity of...
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Protéines membranaires › Protéines de transport membranaire › Protéines transporteurs de solutés › Transporteurs de cations organiques › AI/ML Models to Predict the Severity of...