Improvement of carbon tetrachloride drug-induced liver injury model in vitro

2012 ◽  
Author(s):  
WU Hai-jian
Keyword(s):  
Carbon Tetrachloride ◽  
Liver Injury ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Injury Model

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

2014 ◽  
Vol 2 (4) ◽  
pp. 63-70 ◽  
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

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

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.

scholarly journals 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

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

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

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.

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

2018 ◽  
Vol 7 (3) ◽  
pp. 358-370 ◽  
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.

scholarly journals The evolution of strategies to minimise the risk of human drug-induced liver injury (DILI) in drug discovery and development

2020 ◽  
Vol 94 (8) ◽  
pp. 2559-2585 ◽  
Author(s):  
Paul A. Walker ◽  
Stephanie Ryder ◽  
Andrea Lavado ◽  
Clive Dilworth ◽  
Robert J. Riley
Keyword(s):  
Drug Discovery ◽  
Liver Injury ◽  
Drug Uptake ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
New Chemical Entities ◽  
Preclinical Animal Models ◽  
Uptake Transporters

Abstract Early identification of toxicity associated with new chemical entities (NCEs) is critical in preventing late-stage drug development attrition. Liver injury remains a leading cause of drug failures in clinical trials and post-approval withdrawals reflecting the poor translation between traditional preclinical animal models and human clinical outcomes. For this reason, preclinical strategies have evolved over recent years to incorporate more sophisticated human in vitro cell-based models with multi-parametric endpoints. This review aims to highlight the evolution of the strategies adopted to improve human hepatotoxicity prediction in drug discovery and compares/contrasts these with recent activities in our lab. The key role of human exposure and hepatic drug uptake transporters (e.g. OATPs, OAT2) is also elaborated.

Development and Application of a Transcriptomic Signature of Bioactivation in an Advanced In Vitro Liver Model to Reduce Drug-induced Liver Injury Risk Early in the Pharmaceutical Pipeline

2020 ◽  
Vol 177 (1) ◽  
pp. 121-139 ◽  
Author(s):  
Wen Kang ◽  
Alexei A Podtelezhnikov ◽  
Keith Q Tanis ◽  
Stephen Pacchione ◽  
Ming Su ◽  
...  
Keyword(s):  
Liver Injury ◽  
Human Model ◽  
Reactive Metabolites ◽  
In Vitro Test ◽  
Drug Induced ◽  
Vitro Assay ◽  
Drug Induced Liver Injury ◽  
Drug Candidates ◽  
Transcriptomic Signature

Abstract Early risk assessment of drug-induced liver injury (DILI) potential for drug candidates remains a major challenge for pharmaceutical development. We have previously developed a set of rat liver transcriptional biomarkers in short-term toxicity studies to inform the potential of drug candidates to generate a high burden of chemically reactive metabolites that presents higher risk for human DILI. Here, we describe translation of those NRF1-/NRF2-mediated liver tissue biomarkers to an in vitro assay using an advanced micropatterned coculture system (HEPATOPAC) with primary hepatocytes from male Wistar Han rats. A 9-day, resource-sparing and higher throughput approach designed to identify new chemical entities with lower reactive metabolite-forming potential was qualified for internal decision making using 93 DILI-positive and -negative drugs. This assay provides 81% sensitivity and 90% specificity in detecting hepatotoxicants when a positive test outcome is defined as the bioactivation signature score of a test drug exceeding the threshold value at an in vitro test concentration that falls within 3-fold of the estimated maximum drug concentration at the human liver inlet following highest recommended clinical dose administrations. Using paired examples of compounds from distinct chemical series and close structural analogs, we demonstrate that this assay can differentiate drugs with lower DILI risk. The utility of this in vitro transcriptomic approach was also examined using human HEPATOPAC from a single donor, yielding 68% sensitivity and 86% specificity when the aforementioned criteria are applied to the same 93-drug test set. Routine use of the rat model has been adopted with deployment of the human model as warranted on a case-by-case basis. This in vitro transcriptomic signature-based strategy can be used early in drug discovery to derisk DILI potential from chemically reactive metabolites by guiding structure-activity relationship hypotheses and candidate selection.

scholarly journals Current Perspective: 3D Spheroid Models Utilizing Human-Based Cells for Investigating Metabolism-Dependent Drug-Induced Liver Injury

2020 ◽  
Vol 2 ◽  
Author(s):  
Christopher R. Cox ◽  
Stephen Lynch ◽  
Christopher Goldring ◽  
Parveen Sharma
Keyword(s):  
Liver Injury ◽  
Pharmaceutical Companies ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Current Perspective ◽  
Dosing Regimens ◽  
Underlying Mechanisms ◽  
Approved Drugs

Drug-induced liver injury (DILI) remains a leading cause for the withdrawal of approved drugs. This has significant financial implications for pharmaceutical companies, places increasing strain on global health services, and causes harm to patients. For these reasons, it is essential that in-vitro liver models are capable of detecting DILI-positive compounds and their underlying mechanisms, prior to their approval and administration to patients or volunteers in clinical trials. Metabolism-dependent DILI is an important mechanism of drug-induced toxicity, which often involves the CYP450 family of enzymes, and is associated with the production of a chemically reactive metabolite and/or inefficient removal and accumulation of potentially toxic compounds. Unfortunately, many of the traditional in-vitro liver models fall short of their in-vivo counterparts, failing to recapitulate the mature hepatocyte phenotype, becoming metabolically incompetent, and lacking the longevity to investigate and detect metabolism-dependent DILI and those associated with chronic and repeat dosing regimens. Nevertheless, evidence is gathering to indicate that growing cells in 3D formats can increase the complexity of these models, promoting a more mature-hepatocyte phenotype and increasing their longevity, in vitro. This review will discuss the use of 3D in vitro models, namely spheroids, organoids, and perfusion-based systems to establish suitable liver models to investigate metabolism-dependent DILI.

scholarly journals TAMH: A Useful In Vitro Model for Assessing Hepatotoxic Mechanisms

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Madison Davis ◽  
Brendan D. Stamper
Keyword(s):  
Liver Injury ◽  
Transforming Growth Factor ◽  
Chemical Exposure ◽  
Health Concern ◽  
Hepatocellular Injury ◽  
Drug Induced ◽  
Endogenous Factors ◽  
Drug Induced Liver Injury

In vitro models for hepatotoxicity can be useful tools to predict in vivo responses. In this review, we discuss the use of the transforming growth factor-αtransgenic mouse hepatocyte (TAMH) cell line, which is an attractive model to study drug-induced liver injury due to its ability to retain a stable phenotype and express drug-metabolizing enzymes. Hepatotoxicity involves damage to the liver and is often associated with chemical exposure. Since the liver is a major site for drug metabolism, drug-induced liver injury is a serious health concern for certain agents. At the molecular level, various mechanisms may protect or harm the liver during drug-induced hepatocellular injury including signaling pathways and endogenous factors (e.g., Bcl-2, GSH, Nrf2, or MAPK). The interplay between these and other pathways in the hepatocyte can change upon drug or drug metabolite exposure leading to intracellular stress and eventually cell death and liver injury. This review focuses on mechanistic studies investigating drug-induced toxicity in the TAMH line and how alterations to hepatotoxic mechanisms in this model relate to the in vivo situation. The agents discussed herein include acetaminophen (APAP), tetrafluoroethylcysteine (TFEC), flutamide, PD0325901, lapatinib, and flupirtine.

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