In Silico Identification of Proteins Associated with Drug-induced Liver Injury Based on the Prediction of Drug-target Interactions

2017 ◽  
Vol 36 (7) ◽  
pp. 1600142 ◽  
Author(s):  
Sergey Ivanov ◽  
Maxim Semin ◽  
Alexey Lagunin ◽  
Dmitry Filimonov ◽  
Vladimir Poroikov
Keyword(s):  
Liver Injury ◽  
In Silico ◽  
Drug Target ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
In Silico Identification

In silico assessment of drug-induced liver injury in humans

2016 ◽  
Vol 258 ◽  
pp. S118
Author(s):  
C. Yang ◽  
S. Thakkar ◽  
A. Mostrag ◽  
V. Gombar ◽  
B. Bienfait ◽  
...  
Keyword(s):  
Liver Injury ◽  
In Silico ◽  
Drug Induced ◽  
Drug Induced Liver Injury

scholarly journals The development and application of in silico models for drug induced liver injury

RSC Advances ◽  
2018 ◽  
Vol 8 (15) ◽  
pp. 8101-8111 ◽  
Author(s):  
Xiao Li ◽  
Yaojie Chen ◽  
Xinrui Song ◽  
Yuan Zhang ◽  
Huanhuan Li ◽  
...  
Keyword(s):  
Pharmaceutical Industry ◽  
Liver Injury ◽  
In Silico ◽  
Nutritional Supplements ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
In Silico Models

Drug-induced liver injury (DILI), caused by drugs, herbal agents or nutritional supplements, is a major issue for patients and the pharmaceutical industry.

In silico weight of evidence assessment of drug-induced liver injury in humans

2017 ◽  
Vol 280 ◽  
pp. S284-S285 ◽  
Author(s):  
Chihae Yang ◽  
James Rathman ◽  
Aleksandra Mostrag ◽  
Shraddha Thakkar ◽  
Weida Tong ◽  
...  
Keyword(s):  
Liver Injury ◽  
In Silico ◽  
Weight Of Evidence ◽  
Drug Induced ◽  
Drug Induced Liver Injury

The in silico identification of human bile salt export pump (ABCB11) inhibitors associated with cholestatic drug-induced liver injury

2017 ◽  
Vol 13 (2) ◽  
pp. 417-424 ◽  
Author(s):  
Lili Xi ◽  
Jia Yao ◽  
Yuhui Wei ◽  
Xin'an Wu ◽  
Xiaojun Yao ◽  
...  
Keyword(s):  
Liver Injury ◽  
Potential Risk ◽  
Screening Tools ◽  
Bile Salt Export Pump ◽  
Drug Induced ◽  
Human Bile ◽  
Drug Induced Liver Injury ◽  
In Silico Identification ◽  
Integrated Screening ◽  
Pharmacophore Models

The RF and pharmacophore models can be considered as integrated screening tools to indicate the potential risk of cholestatic DILI.

scholarly journals In silico modeling to optimize interpretation of liver safety biomarkers in clinical trials

2017 ◽  
Vol 243 (3) ◽  
pp. 300-307 ◽  
Author(s):  
Rachel J Church ◽  
Paul B Watkins
Keyword(s):  
Clinical Trials ◽  
Liver Injury ◽  
Alanine Aminotransferase ◽  
In Silico ◽  
Drug Induced ◽  
Serum Alanine Aminotransferase ◽  
Drug Induced Liver Injury ◽  
Drug Candidates ◽  
Liver Safety ◽  
In Silico Modeling

Current strategies to delineate the risk of serious drug-induced liver injury associated with drugs rely on assessment of serum biomarkers that have been utilized for many decades. In particular, serum alanine aminotransferase and total bilirubin levels are typically used to assess hepatic integrity and function, respectively. Parallel measurement of these biomarkers is utilized to identify patients with drug-induced hepatocellular jaundice (“Hy’s Law” cases) which carries at least a 10% risk of death or liver transplant. However, current guidelines regarding use of these biomarkers in clinical trials can put study subjects at risk for life-threatening drug-induced liver injury, or result in over estimation of risk that may halt development of safe drugs. In addition, pharmaceutical companies are increasingly being required to conduct large and expensive clinical trials to “defend” the safety of their new drug when results from smaller trials are inconclusive. Innovative approaches and some novel biomarkers are now being employed to maximize the value of traditional biochemical tests. DILIsym®, a product of the DILIsim Initiative, utilizes serial serum alanine aminotransferase values, along with serum biomarkers of apoptosis vs necrosis, to estimate percent hepatocyte loss and total bilirubin elevations resulting from loss of global liver function. The results from analyses conducted with DILIsym have been reported to the FDA to support the safety of entolimod and cimaglermin alfa after elevations in serum alanine aminotransferase and/or bilirubin halted clinical development. DILIsym can also be utilized to determine whether rises in serum conjugated and unconjugated bilirubin are consistent with mechanisms unrelated to toxicity ( i.e. inhibition of bilirubin transport or metabolism). In silico modeling of traditional and novel drug-induced liver injury biomarker data obtained in clinical trials may be the most efficient and accurate way to define the liver safety profile of new drug candidates. Impact statement Blood tests used in clinical trials to detect and monitor drug-induced liver injury (DILI) have not changed in half a century. These tests have several shortcomings: their use has not completely prevented clinical trial participants from risk of life-threatening DILI, they can give false positive results that halt the development of safe drug candidates, and they can create liver safety “concerns” that require large additional clinical trials to accurately define DILI risk. This review highlights the use of in silico modeling to improve interpretation of the blood tests currently available to detect DILI risk in new drug candidates. This approach is increasingly being applied in clinical trials to more precisely assess the degree of hepatocellular injury and its functional impact. This new approach holds the promise of more accurately defining DILI risk in smaller clinical trials.

DILI-Stk: An ensemble model for the prediction of drug-induced liver injury of drug candidates

2021 ◽  
Vol 17 ◽  
Author(s):  
Jingyu Lee ◽  
Myeong-Sang Yu ◽  
Dokyun Na
Keyword(s):  
Drug Discovery ◽  
Liver Injury ◽  
In Silico ◽  
Prediction Models ◽  
Drug Withdrawal ◽  
Learning Algorithm ◽  
Public Access ◽  
Ensemble Model ◽  
Drug Induced ◽  
Drug Induced Liver Injury

Background: Drug-induced liver injury (DILI) is a leading cause of drug failure, accounting for nearly 20% of drug withdrawal. Thus, there has been a great demand for in silico DILI prediction models for successful drug discovery. To date, various models have been developed for DILI prediction; however, building an accurate model for practical use in drug discovery remains challenging. Methods: We constructed an ensemble model composed of three high-performance DILI prediction models to utilize the unique advantage of each machine learning algorithm. Results: The ensemble model exhibited high predictive performance, with an area under the curve of 0.88, sensitivity of 0.83, specificity of 0.77, F1-score of 0.82, and accuracy of 0.80. When a test dataset collected from the literature was used to compare the performance of our model with publicly available DILI prediction models, our model achieved an accuracy of 0.77, sensitivity of 0.82, specificity of 0.72, and F1-score of 0.79, which were higher than those of the other DILI prediction models. As many published DILI prediction models are not available for public access, which hinders in silico drug discovery, we made our DILI prediction model publicly accessible (http://ssbio.cau.ac.kr/software/dili/). Conclusion: We expect that our ensemble model may facilitate advancements in drug discovery by providing a highly predictive model and reducing the drug withdrawal rate.

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