Williams, Dominic P. published the artcilePredicting Drug-Induced Liver Injury with Bayesian Machine Learning, Related Products of benzothiophene, the main research area is machine learning drug liver toxicity prediction risk assessment.
Drug induced liver injury (DILI) can require significant risk management in drug development, and on occasion can cause morbidity or mortality, leading to drug attrition. Optimizing candidates preclinically can minimise hepatotoxicity risk but it is difficult to predict due to multiple etiologies encompassing DILI, often with multifactorial and overlapping mechanisms. In addition to epidemiol. risk factors, physicochem. properties, dose, disposition, lipophilicity, and hepatic metabolic function are also relevant for DILI risk. Better human relevant, predictive models are required to improve hepatotoxicity risk assessment in drug discovery. The authors’ hypothesis is that integrating mechanistically relevant hepatic safety assays with Bayesian machine learning will improve hepatic safety risk prediction. The authors present a quant. and mechanistic risk assessment for candidate nomination using data from in vitro assays (hepatic spheroids, BSEP, mitochondrial toxicity and bioactivation), together with physicochem. (cLogP) and exposure (Cmaxtotal) variables from a chem. diverse compound set (33 no/low-, 40 medium- and 23 high-severity DILI compounds). The Bayesian model predicts the continuous underlying DILI severity and uses a data-driven prior distribution over the parameters to prevent overfitting. The model quantifies the probability that a compound falls into either no/low, medium, or high-severity categories, with a balanced accuracy of 63% on held-out samples, and a continuous prediction of DILI severity along with uncertainty in the prediction. For a binary yes/no DILI prediction, the model has a balanced accuracy of 86%, a sensitivity of 87%, a specificity of 85%, a pos. predictive value of 92%, and a neg. predictive value of 78%. Combining physiol. relevant assays, improved alignment with FDA recommendations, and optimal statistical integration of assay data, leads to improved DILI risk prediction.
Chemical Research in Toxicology published new progress about Biotransformation. 40180-04-9 belongs to class benzothiophene, name is 2-(2,3-Dichloro-4-(thiophene-2-carbonyl)phenoxy)acetic acid, and the molecular formula is C13H8Cl2O4S, Related Products of benzothiophene.
Referemce:
Benzothiophene – Wikipedia,
Benzothiophene | C8H6S – PubChem