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Novel clinical phenotypes, drug categorization, and outcome prediction in drug-induced cholestasis: Analysis of a database of 432 patients developed by literature review and machine learning support.
Moreno-Torres, Marta; López-Pascual, Ernesto; Rapisarda, Anna; Quintás, Guillermo; Drees, Annika; Steffensen, Inger-Lise; Luechtefeld, Thomas; Serrano-Candelas, Eva; de Lomana, Marina Garcia; Gadaleta, Domenico; Dirven, Hubert; Vinken, Mathieu; Jover, Ramiro.
Afiliação
  • Moreno-Torres M; Joint Research Unit in Experimental Hepatology, Dep. Biochemistry and Molecular Biology, University of Valencia, Health Research Institute Hospital La Fe & CIBER of Hepatic and Digestive Diseases, Spain. Electronic address: marta.moreno.torres@uv.es.
  • López-Pascual E; Joint Research Unit in Experimental Hepatology, Dep. Biochemistry and Molecular Biology, University of Valencia, Health Research Institute Hospital La Fe & CIBER of Hepatic and Digestive Diseases, Spain.
  • Rapisarda A; Joint Research Unit in Experimental Hepatology, Dep. Biochemistry and Molecular Biology, University of Valencia, Health Research Institute Hospital La Fe & CIBER of Hepatic and Digestive Diseases, Spain.
  • Quintás G; Health and Biomedicine, LEITAT Technological Center, Barcelona, Spain.
  • Drees A; Entity of In Vitro Toxicology and Dermato-Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Belgium.
  • Steffensen IL; Department of Chemical Toxicology, Norwegian Institute of Public Health, Oslo, Norway.
  • Luechtefeld T; ToxTrack, Baltimore, MD, USA.
  • Serrano-Candelas E; ProtoQSAR S.L., CEEI-Technology Park of Valencia, Paterna 46980, Spain.
  • de Lomana MG; Bayer AG, Machine Learning Research, Research & Development, Pharmaceuticals, Berlin 13353, Germany.
  • Gadaleta D; Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, Milano 20156, Italy.
  • Dirven H; Department of Chemical Toxicology, Norwegian Institute of Public Health, Oslo, Norway.
  • Vinken M; Entity of In Vitro Toxicology and Dermato-Cosmetology, Department of Pharmaceutical and Pharmacological Sciences, Vrije Universiteit Brussel, Belgium.
  • Jover R; Joint Research Unit in Experimental Hepatology, Dep. Biochemistry and Molecular Biology, University of Valencia, Health Research Institute Hospital La Fe & CIBER of Hepatic and Digestive Diseases, Spain. Electronic address: ramiro.jover@uv.es.
Biomed Pharmacother ; 174: 116530, 2024 May.
Article em En | MEDLINE | ID: mdl-38574623
ABSTRACT

BACKGROUND:

Serum transaminases, alkaline phosphatase and bilirubin are common parameters used for DILI diagnosis, classification, and prognosis. However, the relevance of clinical examination, histopathology and drug chemical properties have not been fully investigated. As cholestasis is a frequent and complex DILI manifestation, our goal was to investigate the relevance of clinical features and drug properties to stratify drug-induced cholestasis (DIC) patients, and to develop a prognosis model to identify patients at risk and high-concern drugs.

METHODS:

DIC-related articles were searched by keywords and Boolean operators in seven databases. Relevant articles were uploaded onto Sysrev, a machine-learning based platform for article review and data extraction. Demographic, clinical, biochemical, and liver histopathological data were collected. Drug properties were obtained from databases or QSAR modelling. Statistical analyses and logistic regressions were performed.

RESULTS:

Data from 432 DIC patients associated with 52 drugs were collected. Fibrosis strongly associated with fatality, whereas canalicular paucity and ALP associated with chronicity. Drugs causing cholestasis clustered in three major groups. The pure cholestatic pattern divided into two subphenotypes with differences in prognosis, canalicular paucity, fibrosis, ALP and bilirubin. A predictive model of DIC outcome based on non-invasive parameters and drug properties was developed. Results demonstrate that physicochemical (pKa-a) and pharmacokinetic (bioavailability, CYP2C9) attributes impinged on the DIC phenotype and allowed the identification of high-concern drugs.

CONCLUSIONS:

We identified novel associations among DIC manifestations and disclosed novel DIC subphenotypes with specific clinical and chemical traits. The developed predictive DIC outcome model could facilitate DIC prognosis in clinical practice and drug categorization.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Fenótipo / Colestase / Aprendizado de Máquina Limite: Humans Idioma: En Revista: Biomed Pharmacother Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Fenótipo / Colestase / Aprendizado de Máquina Limite: Humans Idioma: En Revista: Biomed Pharmacother Ano de publicação: 2024 Tipo de documento: Article