Review of QSAR Models and Software Tools for Predicting of Genotoxicity and Carcinogenicity

This review of QSARs for genotoxicity and carcinogenicity was performed in a broad sense, considering both models available in software tools and models that are published in the literature. The review considered the potential applicability of diverse models to pesticides as well as to other types of regulated chemicals and pharmaceuticals. The availability of models and information on their applicability is summarised in tables, and a range of illustrative or informative examples are described in more detail in the text. In many cases, promising models were identified but they are still at the research stage. For routine application in a regulatory setting, further efforts will be needed to explore the applicability of such models for specific purposes, and to implement them in a practically useful form (i.e. user-friendly software). It is also noted that a range of software tools are research tools suitable for model development, and these require more specialised expertise than other tools that are aimed primarily at end-users such as risk assessors. It is concluded that the most useful models are those which are implemented in software tools and associated with transparent documentation on the model development and validation process. However, it is emphasised that the assessment of model predictions requires a reasonable amount of QSAR knowledge, even if it is not necessary to be a QSAR practitioner.JRC.DG.I.6-Systems toxicolog

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

Statistical learning approaches for predicting pharmacological properties of pharmaceutical agents

Ph.DDOCTOR OF PHILOSOPH

Database development and machine learning classification of medicinal chemicals and biomolecules

Ph.DDOCTOR OF PHILOSOPH

Application of Benchmark Dose Analysis to in vitro Genotoxicity Data for Compound Risk Characterisation

Genotoxic risk from exposure to pharmaceutical compounds has historically been focussed on dichotomous hazard characterisation, with little regulatory acceptance of risk assessment paradigms. The regulations focus on testing novel compounds with outdated genotoxicity test systems. Recent overwhelming support of the Benchmark Dose (BMD) methodology provides the baseline for advanced exposure risk assessments. Novel flow cytometric in vitro DNA damage response assays (MultiFlow and ToxTracker) have been developed that provide quantitative dose-response information that can be used in a high-throughput screening environment. In the following work, BMD modelling is applied to the MultiFlow and ToxTracker biomarker dose-response datasets. This work demonstrates that the MultiFlow dose-response biomarker datasets are amenable to BMD analysis for a set of clastogens and aneugens, and that the biomarker dose-responses correlate with dose-responses from the gold-standard in vitro micronucleus assay. A detailed appraisal of BMD confidence intervals (CIs) is provided for a selection of 10 clastogens requiring metabolic activation (with S9), demonstrating the criticality of using BMD uncertainty measures in comparative potency analysis. A comparative potency algorithm is developed and utilised in machine learning to distinguish four S9-dependent groupings: high and low-level potentiation, no effect, and diminution. A deep dive case study is presented for MultiFlow and ToxTracker analysis of Topoisomerase II Poisons, where BMD CI potency ranks are shown to correlate broadly with compound structural information. The Adverse Outcome Pathway (AOP) for Topoisomerase-II Poisoning is developed upon, and the Lhasa Derek Nexus alerts are mapped to the AOP. A Quantitative Structural Activity Relationship model is developed using Topoisomerase-II Poison molecular descriptors and BMD measurements from MultiFlow and ToxTracker biomarkers that correspond to Key Events relative to the Topoisomerase-II Poison AOP. This thesis provides an all-encompassing report of in vitro DNA damage response biomarker BMD analysis for compound potency ranking and read across