Do In Vitro Assays Predict Drug Candidate Idiosyncratic Drug-Induced Liver Injury Risk?

Background: In order to avoid drug-induced liver injury (DILI), in vitro assays, which enable the assessment of both metabolic activation and immune reaction processes that ultimately result in DILI, are needed. Objective: In this study, the recent progress in the application of in vitro assays using cell culture systems is reviewed for potential DILI-causing drugs/xenobiotics and a mechanistic study on DILI, as well as for the limitations of in vitro cell culture systems for DILI research. Methods: Information related to DILI was collected through a literature search of the PubMed database. Results: The initial biological event for the onset of DILI is the formation of cellular protein adducts after drugs have been metabolically activated by drug metabolizing enzymes. The damaged peptides derived from protein adducts lead to the activation of CD4+ helper T lymphocytes and recognition by CD8+ cytotoxic T lymphocytes, which destroy hepatocytes through immunological reactions. Because DILI is a major cause of drug attrition and drug withdrawal, numerous in vitro systems consisting of hepatocytes and immune/inflammatory cells, or spheroids of human primary hepatocytes containing non-parenchymal cells have been developed. These cellular-based systems have identified DILIinducing drugs with approximately 50% sensitivity and 90% specificity. Conclusion: Different co-culture systems consisting of human hepatocyte-derived cells and other immune/inflammatory cells have enabled the identification of DILI-causing drugs and of the actual mechanisms of action.

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Drug-induced liver injury classification model based on in vitro human transcriptomics and in vivo rat clinical chemistry data

Systems Biomedicine ◽

10.4161/sysb.29400 ◽

2014 ◽

Vol 2 (4) ◽

pp. 63-70 ◽

Cited By ~ 10

Author(s):

Danyel Jennen ◽

Jan Polman ◽

Mark Bessem ◽

Maarten Coonen ◽

Joost van Delft ◽

...

Keyword(s):

Liver Injury ◽

Clinical Chemistry ◽

Classification Model ◽

Drug Induced ◽

Drug Induced Liver Injury ◽

Chemistry Data ◽

Model Based ◽

Injury Classification

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Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

10.20944/preprints202002.0178.v1 ◽

2020 ◽

Author(s):

Robert Ancuceanu ◽

Marilena Viorica Hovanet ◽

Adriana Iuliana Anghel ◽

Florentina Furtunescu ◽

Monica Neagu ◽

...

Keyword(s):

Machine Learning ◽

Liver Injury ◽

Computational Models ◽

Liver Toxicity ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Drug Induced ◽

Reference Drug ◽

Drug Induced Liver Injury

Drug induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized drugs and candidate drugs and predicting hepatotoxicity from the chemical structure of a substance remains a challenge worth pursuing, being also coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016 a group of researchers from FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans”, DILIrank. This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A number of 78 models with reasonable performance have been selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

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A case report of a drug‐induced liver injury (DILI) caused by multiple antidepressants with causality established by the updated Roussel Uclaf causality assessment method (RUCAM) and in vitro testing

Clinical Case Reports ◽

10.1002/ccr3.3348 ◽

2020 ◽

Vol 8 (12) ◽

pp. 3105-3109

Author(s):

Miguel González‐Muñoz ◽

Jaime Monserrat Villatoro ◽

Eva Marín‐Serrano ◽

Stefan Stewart ◽

Belén Bardón Rivera ◽

...

Keyword(s):

Case Report ◽

Liver Injury ◽

Causality Assessment ◽

Assessment Method ◽

In Vitro Testing ◽

Drug Induced ◽

Drug Induced Liver Injury

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Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

International Journal of Molecular Sciences ◽

10.3390/ijms21062114 ◽

2020 ◽

Vol 21 (6) ◽

pp. 2114

Author(s):

Robert Ancuceanu ◽

Marilena Viorica Hovanet ◽

Adriana Iuliana Anghel ◽

Florentina Furtunescu ◽

Monica Neagu ◽

...

Keyword(s):

Machine Learning ◽

Liver Injury ◽

Computational Models ◽

Liver Toxicity ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Drug Induced ◽

Reference Drug ◽

Drug Induced Liver Injury

Drug-induced liver injury (DILI) remains one of the challenges in the safety profile of both authorized and candidate drugs, and predicting hepatotoxicity from the chemical structure of a substance remains a task worth pursuing. Such an approach is coherent with the current tendency for replacing non-clinical tests with in vitro or in silico alternatives. In 2016, a group of researchers from the FDA published an improved annotated list of drugs with respect to their DILI risk, constituting “the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans” (DILIrank). This paper is one of the few attempting to predict liver toxicity using the DILIrank dataset. Molecular descriptors were computed with the Dragon 7.0 software, and a variety of feature selection and machine learning algorithms were implemented in the R computing environment. Nested (double) cross-validation was used to externally validate the models selected. A total of 78 models with reasonable performance were selected and stacked through several approaches, including the building of multiple meta-models. The performance of the stacked models was slightly superior to other models published. The models were applied in a virtual screening exercise on over 100,000 compounds from the ZINC database and about 20% of them were predicted to be non-hepatotoxic.

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Evaluation of the relevance of DILI predictive hypotheses in early drug development: review of in vitro methodologies vs. BDDCS classification

Toxicology Research ◽

10.1039/c8tx00016f ◽

2018 ◽

Vol 7 (3) ◽

pp. 358-370 ◽

Cited By ~ 11

Author(s):

Rosa Chan ◽

Leslie Z. Benet

Keyword(s):

Liver Injury ◽

Drug Development ◽

Safety Concern ◽

Drug Induced ◽

Development Review ◽

Drug Induced Liver Injury ◽

Underlying Mechanisms ◽

Early Drug

Drug-induced liver injury (DILI) is a major safety concern; it occurs frequently; it is idiosyncratic; it cannot be adequately predicted; and a multitude of underlying mechanisms has been postulated.

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