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

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 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.

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

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 Machine-Learning Prediction of Oral Drug-Induced Liver Injury (DILI) via Multiple Features and Endpoints

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xiaobin Liu ◽  
Danhua Zheng ◽  
Yi Zhong ◽  
Zhaofan Xia ◽  
Heng Luo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Liver Injury ◽  
Computational Models ◽  
Characteristic Curve ◽  
Prediction Performance ◽  
Oral Drug ◽  
Good Prediction ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Drug discovery is a costly process which usually takes more than 10 years and billions of dollars for one successful drug to enter the market. Despite all the safety tests, drugs may still cause adverse reactions and be restricted in use or even withdrawn from the market. Drug-induced liver injury (DILI) is one of the major adverse drug reactions, and computational models may be used to predict and reduce it. To assess the computational prediction performance of DILI, we curated DILI endpoints from three databases and prepared drug features including chemical descriptors, therapeutic classifications, gene expressions, and binding proteins. We trained machine-learning models to predict the various DILI endpoints using different drug features. Using the optimal feature sets, the top-performing models obtained areas under the receiver operating characteristic curve (AUC) around 0.8 for some DILI endpoints. We found that some features, including therapeutic classifications and proteins, have good prediction performance towards DILI. We also discovered that the severity of DILI endpoints as well as the selection of negative samples may significantly affect the prediction results. Overall, our study provided a comprehensive collection, curation, and prediction of DILI endpoints using various drug features, which may help the drug researchers to better understand and prevent DILI during the drug discovery process.

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