In silico methods for predictive toxicological and pharmacological modelling
Abstract
This thesis is concerned with the development and improvement of in silico toxicological models and will capitalise on the latest advances in predictive toxicological models at all levels of the toxicological QSAR pipeline. A review of data from in vitro and in vivo assays used for the prediction of mutagenicity and carcinogenicity is presented, collating over 10,000 molecules. The results of this review supported the subsequent development of an in silico model to predict the in vivo carcinogenicity assay. The overall model found 69.3% accuracy and 0.700 ROC AUC. This work was followed with an investigation into the use of quantum mechanical methods to predict lipophilicity (LogP). Solvated free energies in water and in 1-octanol were calculated using the M06-2X hybrid density functional and the def2-SVP basis set. The resulting model performed well in the SAMPL6 LogP Prediction Challenge where the model was placed seventh overall and notably superior to conventional methods. Finally, a larger investigation was conducted with skin sensitisation as the prediction target. Skin sensitisation is a toxicological outcome with scarce data available for the assays used in its prediction. The use of quantum mechanical calculations in this study enabled direct quantitative characterisation of electronic effects highly relevant to the skin sensitisation adverse outcome pathway. Ames mutagenicity models were also used for predicting skin sensitisation due to the importance of…
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Evidence Hub reads this as a thesis, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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