scholarly journals Joint Decision-Making Model Based on Consensus Modeling Technology for the Prediction of Drug-Induced Liver Injury

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yukun Wang ◽  
Xuebo Chen

Drug-induced liver injury (DILI) is the major cause of clinical trial failure and postmarketing withdrawals of approved drugs. It is very expensive and time-consuming to evaluate hepatotoxicity using animal or cell-based experiments in the early stage of drug development. In this study, an in silico model based on the joint decision-making strategy was developed for DILI assessment using a relatively large dataset of 2608 compounds. Five consensus models were developed with PaDEL descriptors and PubChem, Substructure, Estate, and Klekota–Roth fingerprints, respectively. Submodels for each consensus model were obtained through joint optimization. The parameters and features of each submodel were optimized jointly based on the hybrid quantum particle swarm optimization (HQPSO) algorithm. The application domain (AD) based on the frequency-weighted and distance (FWD)-based method and Tanimoto similarity index showed the wide AD of the qualified consensus models. A joint decision-making model was integrated by the qualified consensus models, and the overwhelming majority principle was used to improve the performance of consensus models. The application scope narrowing caused by the overwhelming majority principle was successfully solved by joint decision-making. The proposed model successfully predicted 99.2% of the compounds in the test set, with an accuracy of 80.0%, a sensitivity of 83.9, and a specificity of 73.3%. For an external validation set containing 390 compounds collected from DILIrank, 98.2% of the compounds were successfully predicted with an accuracy of 79.9%, a sensitivity of 97.1%, and a specificity of 66.0%. Furthermore, 25 privileged substructures responsible for DILI were identified from Substructure, PubChem, and Klekota–Roth fingerprints. These privileged substructures can be regarded as structural alerts in hepatotoxicity evaluation. Compared with the main published studies, our method exhibits certain advantage in data size, transparency, and standardization of the modeling process and accuracy and credibility of prediction results. It is a promising tool for virtual screening in the early stage of drug development.

2018 ◽  
Vol 7 (3) ◽  
pp. 358-370 ◽  
Author(s):  
Rosa Chan ◽  
Leslie Z. Benet

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.


Author(s):  
Samantha Korver ◽  
Joanne Bowen ◽  
Kara Pearson ◽  
Raymond J. Gonzalez ◽  
Neil French ◽  
...  

AbstractDrug-induced liver injury (DILI) is a frequent and dangerous adverse effect faced during preclinical and clinical drug therapy. DILI is a leading cause of candidate drug attrition, withdrawal and in clinic, is the primary cause of acute liver failure. Traditional diagnostic markers for DILI include alanine aminotransferase (ALT), aspartate aminotransferase (AST) and alkaline phosphatase (ALP). Yet, these routinely used diagnostic markers have several noteworthy limitations, restricting their sensitivity, specificity and accuracy in diagnosing DILI. Consequently, new biomarkers for DILI need to be identified.A potential biomarker for DILI is cytokeratin-18 (CK18), an intermediate filament protein highly abundant in hepatocytes and cholangiocytes. Extensively researched in a variety of clinical settings, both full length and cleaved forms of CK18 can diagnose early-stage DILI and provide insight into the mechanism of hepatocellular injury compared to traditionally used diagnostic markers. However, relatively little research has been conducted on CK18 in preclinical models of DILI. In particular, CK18 and its relationship with DILI is yet to be characterised in an in vivo rat model. Such characterization of CK18 and ccCK18 responses may enable their use as translational biomarkers for hepatotoxicity and facilitate management of clinical DILI risk in drug development. The aim of this review is to discuss the application of CK18 as a biomarker for DILI. Specifically, this review will highlight the properties of CK18, summarise clinical research that utilised CK18 to diagnose DILI and examine the current challenges preventing the characterisation of CK18 in an in vivo rat model of DILI.


Author(s):  
C. Goldring ◽  
R. Weaver ◽  
B. Kramer ◽  
U. Klingmueller ◽  
A. Oppelt ◽  
...  

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253855
Author(s):  
Yuki Shimizu ◽  
Takamitsu Sasaki ◽  
Jun-ichi Takeshita ◽  
Michiko Watanabe ◽  
Ryota Shizu ◽  
...  

Drug-induced liver injury (DILI) is one of major causes of discontinuing drug development and withdrawing drugs from the market. In this study, we investigated chemical properties associated with DILI using in silico methods, to identify a physicochemical property useful for DILI screening at the early stages of drug development. Total of 652 drugs, including 432 DILI-positive drugs (DILI drugs) and 220 DILI-negative drugs (no-DILI drugs) were selected from Liver Toxicity Knowledge Base of US Food and Drug Administration. Decision tree models were constructed using 2,473 descriptors as explanatory variables. In the final model, the descriptor AMW, representing average molecular weight, was found to be at the first node and showed the highest importance value. With AMW alone, 276 DILI drugs (64%) and 156 no-DILI drugs (71%) were correctly classified. Discrimination with AMW was then performed using therapeutic category information. The performance of discrimination depended on the category and significantly high performance (>0.8 balanced accuracy) was obtained in some categories. Taken together, the present results suggest AMW as a novel descriptor useful for detecting drugs with DILI risk. The information presented may be valuable for the safety assessment of drug candidates at the early stage of drug development.


RSC Advances ◽  
2018 ◽  
Vol 8 (39) ◽  
pp. 22062-22068 ◽  
Author(s):  
Bailing Ma ◽  
Mi Lu ◽  
Bo-Yang Yu ◽  
Jiangwei Tian

A galactose-mediated targeting nanoprobe has been developed for the accurate imaging of ˙OH to predict drug-induced hepatotoxicity at an early stage.


Praxis ◽  
2010 ◽  
Vol 99 (21) ◽  
pp. 1259-1265 ◽  
Author(s):  
Bruggisser ◽  
Terraciano ◽  
Rätz Bravo ◽  
Haschke

Ein 71-jähriger Patient stellt sich mit Epistaxis und ikterischen Skleren auf der Notfallstation vor. Der Patient steht unter einer Therapie mit Phenprocoumon, Atorvastatin und Perindopril. Anamnestisch besteht ein langjähriger Alkoholabusus. Laborchemisch werden massiv erhöhte Leberwerte (ALAT, Bilirubin) gesehen. Der INR ist unter oraler Antikoagulation und bei akuter Leberinsuffizienz >12. Die weiterführenden Abklärungen schliessen eine Virushepatitis und eine Autoimmunhepatitis aus. Nachdem eine Leberbiopsie durchgeführt werden kann, wird eine medikamentös-toxische Hepatitis, ausgelöst durch die Komedikation von Atorvastatin, Phenprocoumon und Perindopril bei durch Alkohol bereits vorgeschädigter Leber diagnostiziert. Epidemiologie, Pathophysiologie und Klink der medikamentös induzierten Leberschäden (drug induced liver injury, DILI), speziell von Coumarinen, Statinen und ACE-Hemmern werden im Anschluss an den Fallbericht diskutiert.


Hepatology ◽  
2004 ◽  
Vol 40 (4) ◽  
pp. 773-773 ◽  
Author(s):  
Jay H. Hoofnagle

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