A novel cell-based assay for the evaluation of immune- and inflammatory-related gene expression as biomarkers for the risk assessment of drug-induced liver injury

2016 ◽  
Vol 241 ◽  
pp. 60-70 ◽  
Author(s):  
Shingo Oda ◽  
Kentaro Matsuo ◽  
Akira Nakajima ◽  
Tsuyoshi Yokoi
Keyword(s):  
Gene Expression ◽  
Risk Assessment ◽  
Liver Injury ◽  
Related Gene ◽  
Drug Induced ◽  
Drug Induced Liver Injury

New Perspectives on Drug-Induced Liver Injury Risk Assessment of Acyl Glucuronides

2020 ◽  
Vol 33 (7) ◽  
pp. 1551-1560 ◽  
Author(s):  
Markus Walles ◽  
Alan P. Brown ◽  
Alfred Zimmerlin ◽  
Peter End
Keyword(s):  
Risk Assessment ◽  
Liver Injury ◽  
Injury Risk ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Acyl Glucuronides

scholarly journals Diverse approaches to predicting drug-induced liver injury using gene-expression profiles

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
G. Rex Sumsion ◽  
Michael S. Bradshaw ◽  
Jeremy T. Beales ◽  
Emi Ford ◽  
Griffin R. G. Caryotakis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Injury ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Drug Induced ◽  
Drug Induced Liver Injury

scholarly journals Drug-induced liver injury: recent advances in diagnosis and risk assessment

Gut ◽  
2017 ◽  
Vol 66 (6) ◽  
pp. 1154-1164 ◽  
Author(s):  
Gerd A Kullak-Ublick ◽  
Raul J Andrade ◽  
Michael Merz ◽  
Peter End ◽  
Andreas Benesic ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Liver Injury ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Recent Advances

scholarly journals Utilization of Causal Reasoning of Hepatic Gene Expression in Rats to Identify Molecular Pathways of Idiosyncratic Drug-Induced Liver Injury

2013 ◽  
Vol 137 (1) ◽  
pp. 234-248 ◽  
Author(s):  
Daphna Laifenfeld ◽  
Luping Qiu ◽  
Rachel Swiss ◽  
Jennifer Park ◽  
Michael Macoritto ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Injury ◽  
Causal Reasoning ◽  
Molecular Pathways ◽  
Hepatic Gene Expression ◽  
Drug Induced ◽  
Drug Induced Liver Injury

scholarly journals Prediction of Alternative Drug-Induced Liver Injury Classifications Using Molecular Descriptors, Gene Expression Perturbation, and Toxicology Reports

2021 ◽  
Vol 12 ◽  
Author(s):  
Wojciech Lesiński ◽  
Krzysztof Mnich ◽  
Witold R. Rudnicki
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Liver Injury ◽  
Cell Lines ◽  
Predictive Models ◽  
Molecular Descriptors ◽  
Chemical Properties ◽  
Human Cell Lines ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Motivation: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI, based on the chemical properties of substances and experiments performed on cell lines, would bring a significant reduction in the cost of clinical trials and faster development of drugs. The current study aims to build predictive models of risk of DILI for chemical compounds using multiple sources of information.Methods: Using several supervised machine learning algorithms, we built predictive models for several alternative splits of compounds between DILI and non-DILI classes. To this end, we used chemical properties of the given compounds, their effects on gene expression levels in six human cell lines treated with them, as well as their toxicological profiles. First, we identified the most informative variables in all data sets. Then, these variables were used to build machine learning models. Finally, composite models were built with the Super Learner approach. All modeling was performed using multiple repeats of cross-validation for unbiased and precise estimates of performance.Results: With one exception, gene expression profiles of human cell lines were non-informative and resulted in random models. Toxicological reports were not useful for prediction of DILI. The best results were obtained for models discerning between harmless compounds and those for which any level of DILI was observed (AUC = 0.75). These models were built with Random Forest algorithm that used molecular descriptors.

Current conservative treatment of uterine fibroids. Ulipristal acetate and liver damage: contrived tragedy?

GYNECOLOGY ◽  
2018 ◽  
Vol 20 (6) ◽  
pp. 4-7
Author(s):  
A L Tikhomirov
Keyword(s):  
Risk Assessment ◽  
Liver Injury ◽  
Liver Damage ◽  
Uterine Fibroids ◽  
Tumor Formation ◽  
Drug Induced ◽  
Ulipristal Acetate ◽  
Drug Induced Liver Injury ◽  
Pharmacovigilance Risk Assessment Committee ◽  
Assessment Committee

Uterine fibroids are the most common pelvic tumor formation in women and the most common indication for hysterectomy. The effectiveness of long-term intermittent use of ulipristal acetate (UA) in patients with uterine myoma has been proven earlier. In May 2018, the ability of UA to cause a drug-induced liver injury (drug-induced liver injury, DILI) was disproved, and the European Commission approved a positive decision. According to the conclusion Expertise of the Pharmacovigilance Risk Assessment Committee (Pharmacovigilance Risk Assessment Committee - PRAC) the benefit/risk ratio remains favorable. Published recommendations are aimed at reducing the risk of liver damage. UA remains the 1st line of treatment for most myomas.

scholarly journals Integration of human cell lines gene expression and chemical properties of drugs for Drug Induced Liver Injury prediction

2020 ◽  
Author(s):  
Wojciech Lesiński ◽  
Krzysztof Mnich ◽  
Agnieszka Kitlas Golińska ◽  
Witold R. Rudnicki
Keyword(s):  
Gene Expression ◽  
Liver Injury ◽  
Cell Lines ◽  
Human Cell ◽  
Chemical Properties ◽  
Human Cell Lines ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Super Learner ◽  
And Chemical Properties

Abstract Motivation: Drug-induced liver injury (DILI) is one of the primary problems in drug development. Early prediction of DILI can bring a significant reduction in the cost of clinical trials. In this work we examined whether occurrence of DILI can be predicted using gene expression profile in cancer cell lines and chemical properties of drugs. Methods: We used gene expression profiles from 13 human cell lines, as well as molecular properties of drugs to build Machine Learning models of DILI. To this end, we have used a robust cross-validated protocol based on feature selection and Random Forest algorithm. In this protocol we first identify the most informative variables and then use them to build predictive models. The models are first built using data from single cell lines, and chemical properties. Then they are integrated using Super Learner method with several underlying methods for integration. The entire modelling process is performed using nested cross-validation. Results: We have obtained weakly predictive ML models when using either molecular descriptors, or some individual cell lines (AUC: 0.55-0.61). Models obtained with the Super Learner approach have a significantly improved accuracy (AUC=0.73), which allows to divide substances in two categories: low-risk and high-risk.

Test systems in drug discovery for hazard identification and risk assessment of human drug-induced liver injury

2017 ◽  
Vol 13 (7) ◽  
pp. 767-782 ◽  
Author(s):  
Richard J. Weaver ◽  
Catherine Betts ◽  
Eric A.G. Blomme ◽  
Helga H.J. Gerets ◽  
Klaus Gjervig Jensen ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Drug Discovery ◽  
Liver Injury ◽  
Hazard Identification ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Test Systems
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