In-vitro mutagenicity assays have traditionally been used for first line identification of potential genotoxic hazard, purporting to chemical carcinogenesis and heritable genetic damage. The recent advances m combinatorial chemistry and high throughput screening technologies have led to a massive explosion in numbers of possible therapeutic candidates being produced at the early stages of drug discovery. This rapid increase in the number of chemicals to be classified results in a greater need for to acquire alternative methods for the prediction of toxicity. Quantitative StructureActivity Relationships (QSAR) can till this need for early hazard identifications by elucidating the physicochemical basis of biological activity. The assumption with predictive QSARs for toxicity is that "biological activity may be described as a function of chemical constitution". This thesis focuses on the Ames mutagenicity assay data for two compound sets; one of 90 compounds, with limited structural flexibility, comprising a range of chemical classes (non-congeneric series), the second, a set of 30 flavonoid compounds. Three physicochemical descriptor sets were generated: EV A, a theoretical molecular descriptor based on the normal co-ordinate modes of vibration; WHIM, derived from weighting functions applied to the 3D-structural molecular co-ordinates; and TSAR, a series of hydrophobic, electronic and steric parameters traditionally associated with the production of biological QSARs. Various "unsupervised" data pre-treatment methods were adopted, to reduce the level of degeneracy within the individual descriptor sets, prior to the calculation of stepwise linear discriminant classification functions. Good predictive models for Ames mutagenicity, as determined by leave-one-out (jackknife) cross-validation, were obtained with each of the three physicochemical descriptor sets. An increase in the predictive ability was observed following the combination of variables from the individual descriptor sets, inferring that some unique information associated with mutagenic activity is contained within each descriptor set. The predictive stability of the models produced was assessed via independent compound predictions, with a poor overall success rate determined. This failure in external prediction was investigated and fundamental differences in physicochemical data space occupancy revealed. Conclusions on training set composition and general model applicability are made with consideration to individual model physicochemical data space coverage.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:343333 |
| Date | January 1999 |
| Creators | Smith, Mark David |
| Publisher | University of Portsmouth |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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