Collection and Evaluation of (Q)SAR Models for Mutagenicity and Carcinogenicity

This evaluation of the non-commercial (Q)SARs for mutagenicity and carcinogenicity consisted of a preliminary survey (Phase I), and then of a more detailed analysis of short listed models (Phase II). In Phase I, the models were collected from the literature, and then assessed according to the OECD principles based on the information provided by the authors-. Phase I provided the support for short listing a number of promising models, that were analyzed more in depth in Phase II. In Phase II, the information provided by the authors was completed and complemented with a series of analyses aimed at generating an overall profile of each of the short listed models. The models can be divided into two families based on their target: a) congeneric; and b) non-congeneric sets of chemicals. The QSARs for congeneric chemicals include most of the chemical classes top ranking in the EU High Production Volume list, with the notable exception of the halogenated aliphatics. They almost exclusively aim at modeling Salmonella mutagenicity and rodent carcinogenicity, which are crucial toxicological endpoints in the regulatory context. The lack of models for in vivo genotoxicity should be remarked. Overall the short listed models can be interpreted mechanistically, and agree with, and/or support the available scientific knowledge, and most of the models have good statistics. Based on external prediction tests, the QSARs for the potency of congeneric chemicals are 30 to 70 % correct, whereas the models for discriminating between active and inactive chemicals have considerably higher accuracy (63 to 100 %), thus indicating that predicting intervals is more reliable than predicting individual data points. The internal validation procedures (e.g., cross-validation, etc...) did not seem to be a reliable measure of external predictivity. Among the non-local, or global approaches for non-congeneric data sets, four models based on the use of Structural Alerts (SA) were short listed and investigated in more depth. The four sets did not differ to a large extent in their performance. In the general databases of chemicals the SAs appear to agree around 65% with rodent carcinogenicity data, and 75% with Salmonella mutagenicity data. The SAs based models do not seem to work equally efficiently in the discrimination between active and inactive chemicals within individual chemical classes. Thus, their main role is that of preliminary, or large-scale screenings. A priority for future research on the SAs is their expansion to include alerts for nongenotoxic carcinogens. A general indication of this study, valid for both congeneric and noncongeneric models, is that there is uncertainty associated with (Q)SARs; the level of uncertainty has to be considered when using (Q)SAR in a regulatory context. However, (Q)SARs are not meant to be black-box machines for predictions, but have a much larger scope including organization and rationalization of data, contribution to highlight mechanisms of action, complementation of other data from different sources (e.g., experiments). Using only non-testing methods, the larger the evidence from QSARs (several different models, if available) and other approaches (e.g. chemical categories, read across) the higher the confidence in the prediction.JRC.I.3-Toxicology and chemical substance

Investigating azoreductases and NAD(P)H dependent quinone oxidoreductases in 'Pseudomonas aeruginosa'

'Psedomonas aeruginosa' is a prevalent nosocmial pathogen predominantly associated with infections in immune compromised individuals and long term colonisation and pathogenesis in the lungs of Cystic Fibrosis patients. With multi-drug resistant strains increasingly common, the discovery of novel targets for antimicrobial chemotherapy is of utmost importance and expansion of data on 'P. aeruginosa's' complex genome could facilitate this. Azoreductases are a group of enzymes mainly noted for their reductive capacity against azo and quinone compounds. Ubiquitous amongst many classes of organism including prokaryotes and eukaryotes, the primary physiological role of azoreductases remians unclear. This study characterises azoreductase-like enzymes from 'P. aeruginosa' in terms of biochemical properties, substrate specificity and structural analysis. The effect of these enzymes on bacterial physiology in 'P. aeruginosa' is also explored in relation to antibiotic susceptibility. Three azoreductase-like genes from 'P. aeruginosa' (pa1224, pa1225 and pa4975) were overexpressed in 'E. coli' strains following molecular cloning. Recombinant proteins were biochemically characterised by means of Thin Layer Chromatography, Differential Scanning Fluorimetry and ezymatic assays. All enzymes were noted to be selective for FAD as the flavin cofactor and NADPH as the preferred reductant. All three enzymes were confirmed as NAD(P)H dependent quinone oxidoreductases (NQOs) with PA1224 also catalysing reduction of the azo substrate methyl red, albeit at a rate an order of magnitude lower than that observed for the quinone compounds. The preferred flavin cofactor for four previously characterised azoreductase and NQO enzymes (PA2280, PA2580, PA1204 and PA0949) was also explored and PA2280 and PA0949 were observed to select for FMN while PA2580 and PA1204 were selective for FAD. The crystal structure of PA2580 was solved with the nicotinamide group of NADPH bound and was noted to form a homodimer with the same short flavodoxin-like fold as previously described for other members of this enzyme family. Complemented strains of azoreductase-like gene deletion mutants of 'P. aeruginosa' PAO1 were generated via molecular cloning and used to monitor the effects of these enzymes on antibiotic susceptibility. Antimicrobial sensitivity assays were carried out and although the knockout strains displayed increased sensitivity to fluroquinolones, they did not revert to the wild type phenotype upon reinsertion of the genes of interest. This study has for the first time characterised three new NQO's from 'P. aeruginosa' PAO1 and solved the crystal structure of an azoreductase/NQO with nicotinamide bound. With these findings and a library of complemented strains generated, this original study offers a platform for the continued research into the physiological role of these enzymes

Machine learning approach in pharmacokinetics and toxicity prediction

Ph.DDOCTOR OF PHILOSOPH