scholarly journals Puromycin-based purification of cells with high expression of the cytochrome P450 CYP3A4 gene from a patient with drug-induced liver injury (DILI)

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Shoko Miyata ◽  
Noriaki Saku ◽  
Saeko Akiyama ◽  
Palaksha Kanive Javaregowda ◽  
Kenta Ite ◽  
...  
Keyword(s):  
Cytochrome P450 ◽  
Liver Injury ◽  
High Expression ◽  
Drug Metabolizing Enzymes ◽  
Drug Induced ◽  
Metabolizing Enzymes ◽  
P450 Gene ◽  
Drug Induced Liver Injury ◽  
Cyp3a4 Gene

Abstract Background Many drugs have the potential to induce the expression of drug-metabolizing enzymes, particularly cytochrome P450 3A4 (CYP3A4), in hepatocytes. Hepatocytes can be accurately evaluated for drug-mediated CYP3A4 induction; this is the gold standard for in vitro hepatic toxicology testing. However, the variation from lot to lot is an issue that needs to be addressed. Only a limited number of immortalized hepatocyte cell lines have been reported. In this study, immortalized cells expressing CYP3A4 were generated from a patient with drug-induced liver injury (DILI). Methods To generate DILI-derived cells with high expression of CYP3A4, a three-step approach was employed: (1) Differentiation of DILI-induced pluripotent stem cells (DILI-iPSCs); (2) Immortalization of the differentiated cells; (3) Selection of the cells by puromycin. It was hypothesized that cells with high cytochrome P450 gene expression would be able to survive exposure to cytotoxic antibiotics because of their increased drug-metabolizing activity. Puromycin, a cytotoxic antibiotic, was used in this study because of its rapid cytocidal effect at low concentrations. Results The hepatocyte-like cells differentiated from DILI-iPSCs were purified by exposure to puromycin. The puromycin-selected cells (HepaSM or SI cells) constitutively expressed the CYP3A4 gene at extremely high levels and exhibited hepatocytic features over time. However, unlike primary hepatocytes, the established cells did not produce bile or accumulate glycogen. Conclusions iPSC-derived hepatocyte-like cells with intrinsic drug-metabolizing enzymes can be purified from non-hepatocytes and undifferentiated iPSCs using the cytocidal antibiotic puromycin. The puromycin-selected hepatocyte-like cells exhibited characteristics of hepatocytes after immortalization and may serve as another useful source for in vitro hepatotoxicity testing of low molecular weight drugs.

scholarly journals Puromycin selection of cells with a high expression of the cytochrome P450 CYP3A4 gene activity from a patient with drug-induced liver injury (DILI) and their lifespan prolongation using a combination of CDK4R24C, cyclin D1 and TERT

2020 ◽  
Author(s):  
Shoko Miyata ◽  
Noriaki Saku ◽  
Palaksha Kanive Javaregowda ◽  
Kenta Ite ◽  
Masashi Toyoda ◽  
...  
Keyword(s):  
Cytochrome P450 ◽  
Liver Injury ◽  
High Expression ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Cyp3a4 Gene ◽  
Population Doublings ◽  
Cyp3a4 Induction ◽  
Selection Of

ABSTRACTMany drugs have the potential to induce the expression of drug-metabolizing enzymes, particularly cytochrome P450 3A4 (CYP3A4), in hepatocytes. Hepatocytes can accurately evaluate drug-mediated CYP3A4 induction as the gold standard for in vitro hepatic toxicology test, but their lot variation is an issue to be solved. Only a limited number of immortalized hepatocyte cells have been reported. In this study, we generated an immortalized cell expressing CYP3A4 from a patient with drug-induced liver injury (DILI). To generate DILI-derived cells with a high expression of CYP3A4, we employed a three-step approach: 1. Differentiation of DILI-induced pluripotent stem cells (DILI-iPSCs); 2. Immortalization of the differentiated cells; 3. Selection of the cells with puromycin. We hypothesize that cells with a high expression of cytochrome P450 genes can survive even after exposure to cytotoxic antibiotics because of high drug-metabolism activity. Puromycin, one of the cytotoxic antibiotics, was used in this study because of its rapid cytocidal effect at a low concentration. Phenotypic studies in vitro revealed that the puromycin-selected cells (HepaSM or SI cells) constitutively expressed the CYP3A4 gene at an extremely high level, and continued to proliferate at least up to 34 population doublings for more than 250 days. The expression profiles were independent of population doublings. Drug-mediated induction test revealed that the cells significantly increased CYP3A4 after exposure to rifampicin, suggesting that the immortalized cells would serve as another useful source for in vitro examination of drug metabolism and CYP3A4 induction.

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.

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