2 research outputs found
An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential
Mutagenicity presents a challenging problem in modeling despite the addition of modern prediction methods and easier access to data. Previous studies have focused mainly on treatment of all mutagenic compound classes, while some have focused on a very small sets of alerting compounds such as aromatic amines with quantitative mutagenicity data. Data currently available at Novartis provide a large, unique set of mutagenicity data originating from one laboratory, providing compounds connected to the discovery and production of medicinal compounds rather than compounds associated with agricultural, environmental and foodstuff concern. We were especially interested in predicting the The performance of commonly used descriptors and methods performed quite poorly for the set of aromatic amines, while using the reaction energy to produce a reactive intermediate provides a prediction accuracy of at least 70% across multiple data sets. Whereas many papers have represented the study of aromatic amines and Ames results as a platform for study of prediction methods with good performance, we show that the set is quite difficult. The performance of methods in our hands will be compared with previously compiled datasets and reported models
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A Knowledge Based Approach of Toxicity Prediction for Drug Formulation. Modelling Drug Vehicle Relationships Using Soft Computing Techniques
This multidisciplinary thesis is concerned with the prediction of drug formulations for the reduction of drug toxicity. Both scientific and computational approaches are utilised to make original contributions to the field of predictive toxicology.
The first part of this thesis provides a detailed scientific discussion on all aspects of drug formulation and toxicity. Discussions are focused around the principal mechanisms of drug toxicity and how drug toxicity is studied and reported in the literature. Furthermore, a review of the current technologies available for formulating drugs for toxicity reduction is provided. Examples of studies reported in the literature that have used these technologies to reduce drug toxicity are also reported. The thesis also provides an overview of the computational approaches currently employed in the field of in silico predictive toxicology. This overview focuses on the machine learning approaches used to build predictive QSAR classification models, with examples discovered from the literature provided.
Two methodologies have been developed as part of the main work of this thesis. The first is focused on use of directed bipartite graphs and Venn diagrams for the visualisation and extraction of drug-vehicle relationships from large un-curated datasets which show changes in the patterns of toxicity. These relationships can be rapidly extracted and visualised using the methodology proposed in chapter 4.
The second methodology proposed, involves mining large datasets for the extraction of drug-vehicle toxicity data. The methodology uses an area-under-the-curve principle to make pairwise comparisons of vehicles which are classified according to the toxicity protection they offer, from which predictive classification models based on random forests and decisions trees are built. The results of this methodology are reported in chapter 6