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.

Fecha de publicación: 01/05/2024 Fecha Ahead of Print: 02/04/2024

Autores de IIS La Fe

• 

  Marta Moreno Torres

  Autor

• Guillermo Quintas Soriano

  Autor

• 

  Ramiro Jover Atienza

  Autor

Participantes ajenos a IIS La Fe

• López-Pascual E
• Rapisarda A
• Drees A
• Steffensen IL
• Luechtefeld T
• Serrano-Candelas E
• de Lomana MG
• Gadaleta D
• Dirven H
• Vinken M

Grupos

• Hepatología experimental y Trasplante hepático

  Investigador Principal

  Laia Tolosa Pardo

• Unidad Mixta de Investigación en Hepatología Experimental

  

  Investigador Principal

  Ramiro Jover Atienza

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.

Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.

Keywords

• Drug risk assessment; Drug-induced cholestasis; Machine-learning assisted literature review; Prognosis model; Toxic cholestasis phenotypes