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dc.contributor.authorGonzález-Jiménez, Andrés
dc.contributor.authorSuzuki, Ayako
dc.contributor.authorChen, Minjun
dc.contributor.authorAshby, Kristin
dc.contributor.authorAlvarez-Alvarez, Ismael
dc.contributor.authorAndrade-Bellido, Raúl Jesús 
dc.contributor.authorLucena-González, María Isabel 
dc.date.accessioned2022-01-17T12:51:52Z
dc.date.available2022-01-17T12:51:52Z
dc.date.created2021-12-20
dc.date.issued2021-03-05
dc.identifier.citationGonzalez-Jimenez, A., Suzuki, A., Chen, M. et al. Drug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approach. Arch Toxicol 95, 1793–1803 (2021). https://doi.org/10.1007/s00204-021-03013-3es_ES
dc.identifier.urihttps://hdl.handle.net/10630/23628
dc.description.abstractDrug-induced liver injury (DILI) presentation varies biochemically and histologically. Certain drugs present quite consistent injury patterns, i.e., DILI signatures. In contrast, others are manifested as broader types of liver injury. The variety of DILI presentations by a single drug suggests that both drugs and host factors may contribute to the phenotype. However, factors determining the DILI types have not been yet elucidated. Identifying such factors may help to accurately predict the injury types based on drugs and host information and assist the clinical diagnosis of DILI. Using prospective DILI registry datasets, we sought to explore and validate the associations of biochemical injury types at the time of DILI recognition with comprehensive information on drug properties and host factors. Random forest models identified a set of drug properties and host factors that differentiate hepatocellular from cholestatic damage with reasonable accuracy (69-84%). A simplified logistic regression model developed for practical use, consisting of patient’s age, drug’s lipoaffinity, and hybridization ratio, achieved a fair prediction (68%-74%), but suggested potential clinical usability, computing the likelihood of liver injury type based on two properties of drugs taken by a patient and patient’s age. In summary, considering both drug and host factors in evaluating DILI risk and phenotypes open an avenue for future DILI research and aid in the refinement of causality assessment.es_ES
dc.description.sponsorshipThe present study has been supported by grants of Instituto de Salud Carlos III cofounded by Fondo Europeo de Desarrollo Regional – FEDER (contract numbers: PI 18/01804; PT20/00127) and Agencia Española del Medicamento. Plataforma ISCiii de Investigación Clínica and CIBERehd are funded by Instituto de Salud Carlos III. IAA holds a Sara Borrell research contract from the National Health System, Instituto de Salud Carlos III (CD20/00083).es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectHígado - Enfermedadeses_ES
dc.subject.otherhepatotoxicityes_ES
dc.subject.otherphenotypees_ES
dc.subject.otherhepatocellulares_ES
dc.subject.othercholestatices_ES
dc.subject.otherinteractionses_ES
dc.subject.othermachine learninges_ES
dc.subject.otherbioinformaticses_ES
dc.titleDrug properties and host factors contribute to biochemical presentation of drug-induced liver injury: a prediction model from a machine learning approaches_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroFacultad de Medicinaes_ES
dc.identifier.doi10.1007/s00204-021-03013-3
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones_ES


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