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 - 27/04/24
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.
Le texte complet de cet article est disponible en PDF.Graphical Abstract |
Highlights |
• | 35–45% of DILI patients exhibit cholestasis, i.e. a reduction in bile secretion and flow. |
• | Drug induced-cholestasis (DIC) needs characterization, and diagnosis/prognosis tools. |
• | Novel DIC phenotypes and their associated clinical features have been identified. |
• | Different drugs and drug properties associated with the different DIC phenotypes. |
• | A DIC prognosis model, based on easy clinical traits and drug properties, was developed. |
Abbreviations : AAS, ALF, ALP, ALT, AST, BBB, BP, DIC, DILI, F20, F30, GGT, HCA, Hcount, LogPapp, ML, MP, NSAIDS, PCA, pKa-a, QSAR, ROC, ST, Topo Pol, ULN, VIP
Keywords : Drug-induced cholestasis, Machine-learning assisted literature review, Toxic cholestasis phenotypes, Prognosis model, Drug risk assessment
Plan
Vol 174
Article 116530- mai 2024 Retour au numéroBienvenue sur EM-consulte, la référence des professionnels de santé.
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