Optimizing Predictive Performance of CASE Ultra Expert System Models Using the Applicability Domains of Individual Toxicity Alerts

Abstract

Fragment based expert system models of toxicological end points are primarily comprised of a set of substructures that are statistically related to the toxic property in question. These special substructures are often referred to as toxicity alerts, toxicophores, or biophores. They are the main building blocks/classifying units of the model, and it is important to define the chemical structural space within which the alerts are expected to produce reliable predictions. Furthermore, defining an appropriate applicability domain is required as part of the OECD guidelines for the validation of quantitative structure-activity relationships (QSARs). In this respect, this paper describes a method to construct applicability domains for individual toxicity alerts that are part of the CASE Ultra expert system models. Defining applicability domain for individual alerts was necessary because each CASE Ultra model is comprised of multiple alerts, and different alerts of a model usually represent different toxicity mechanisms and cover different structural space; the use of an applicability domain for the overall model is often not adequate. The domain for each alert was constructed using a set of fragments that were found to be statistically related to the end point in question as opposed to using overall structural similarity or physicochemical properties. Use of the applicability domains in reducing false positive predictions is demonstrated. It is now possible to obtain ROC (receiver operating characteristic) profiles of CASE Ultra models by applying domain adherence cutoffs on the alerts identified in test chemicals. This helps in optimizing the performance of a model based on their true positive-false positive prediction trade-offs and reduce drastic effects on the predictive performance caused by the active/inactive ratio of the model's training set. None of the major currently available commercial expert systems for toxicity prediction offer the possibility to explore a model's full range of sensitivity-specificity spectrum, and therefore, the methodology developed in this study can be of benefit in improving the predictive ability of the alert based expert systems.

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

“…The maximal common substructures (MCS) 46 method provided a second set of structural alerts for comparison with those obtained by co-occurring fragments (Method 1). MCS extracted the largest substructures being more frequently associated with actives than with inactives.…”

Environmental fate and effect assessment of thioridazine and its transformation products formed by photodegradation

“…A combination of statistical and rule-based systems as recommended by the ICH M7 guideline (International Conference on Harmonization (ICH), 2014) was used to predict mutagenic activities of THI and TPs. This included the models GT1_A7B, GT1_AT_ECOLI, GT_EXPERT, PHAR-M_ECOLI, and PHARM_SALM (Chakravarti et al, 2012;Saiakhov et al, 2013Saiakhov et al, , 2014 of the CASE Ultra software (v. 1.5.2.0 MultiCASE Inc.). In addition, bacterial mutagenicity (Salmonella typhimurium) with training sets from 2012 SAR Genetox Database provided by Leadscope ® (v. 3.2.3-1) was applied as well (Roberts et al, 2000).…”

UV-photodegradation of desipramine: Impact of concentration, pH and temperature on formation of products including their biodegradability and toxicity

“…The in silico software programs used here were: Case Ultra V 1.5.2.0 (MultiCASE Inc.) (Chakravarti et al, 2012;Saiakhov et al, 2013), EPI (Estimation Programs Interface) Suite (EPIWEB 4.1) from the U.S. Environmental Protection Agency (EPI Suite software (EPIWEB 4.1)), and Oasis Catalogic (V.5.11.6TB) from the Laboratory of Mathematical Chemistry, University Bourgas, Bulgaria (Laboratory of Mathematical Chemistry, 2012). Structure illustrations were done using Marvin Sketch 15.6.15.0, 2015, ChemAxon (http://www.chemaxon.com).…”