Cruz-Monteagudo, Maykel published the artcileComputational chemistry approach for the early detection of drug-induced idiosyncratic liver toxicity, Name: 2-(2,3-Dichloro-4-(thiophene-2-carbonyl)phenoxy)acetic acid, the main research area is hepatotoxicity drug computational chem QSAR.
Idiosyncratic drug toxicity (IDT), considered as a toxic host-dependent event, with an apparent lack of dose response relationship, is usually not predictable from early phases of clin. trials, representing a particularly confounding complication in drug development. Albeit a rare event (usually <1/5000), IDT is often life threatening and is one of the major reasons new drugs never reach the market or are withdrawn post marketing. Computational methodologies, like the computer-based approach proposed in the present study, can play an important role in addressing IDT in early drug discovery. We report for the first time a systematic evaluation of classification models to predict idiosyncratic hepatotoxicity based on linear discriminant anal. (LDA), artificial neural networks (ANN), and machine learning algorithms (OneR) in conjunction with a 3D mol. structure representation and feature selection methods. These modeling techniques (LDA, feature selection to prevent over-fitting and multicollinearity, ANN to capture nonlinear relationships in the data, as well as the simple OneR classifier) were found to produce QSTR models with satisfactory internal cross-validation statistics and predictivity on an external subset of chems. More specifically, the models reached values of accuracy/sensitivity/specificity over 84%/78%/90%, resp. in the training series along with predictivity values ranging from ca. 78 to 86% of correctly classified drugs. An LDA-based desirability anal. was carried out in order to select the levels of the predictor variables needed to trigger the more desirable drug, i.e. the drug with lower potential for idiosyncratic hepatotoxicity. Finally, two external test sets were used to evaluate the ability of the models in discriminating toxic from nontoxic structurally and pharmacol. related drugs and the ability of the best model (LDA) in detecting potential idiosyncratic hepatotoxic drugs, resp. The computational approach proposed here can be considered as a useful tool in early IDT prognosis. Journal of Computational Chemistry published new progress about Algorithm. 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, Name: 2-(2,3-Dichloro-4-(thiophene-2-carbonyl)phenoxy)acetic acid.
Referemce:
Benzothiophene – Wikipedia,
Benzothiophene | C8H6S – PubChem