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

Blue, green and yellow carbon dots derived from pyrogenic carbon: Structure and fluorescence behaviour

Fluorescence lifetimes and quantum yields featuring polycyclic aromatic hydrocarbons (PAHs) and other organics constituting pyrogenic carbon particulate matter (PM) are seldom measured. In this work, PM sampled in a fuel-rich ethylene flame was firstly separated in organic carbon (OC), soluble in dichloromethane, and refractory organic carbon (ROC), soluble in N-methyl pyrrolidinone, and then analyzed by size exclusion chromatography (SEC) coupled with online UV and fluorescence detection, and by offline fluorescence spectroscopy and mass spectrometry. It was found that three classes of differently light emitting carbon dots (CDs) could be bottom-up synthesized in the same flame system by selecting appropriately the residence time. Actually, OC presented blue fluorescence regardless the residence time, whereas ROC sampled at low and high residence time emitted fluorescence in the green (green CDs) and in the yellow (yellow CDs) region, respectively. The SEC molecular weight of all CDs presented similar trimodal distributions, centered around 300, 1000 and 10,000 u. For the first time fluorescence lifetimes and quantum yields of pyrogenic CD fractions were measured as additional parameters useful for discriminating the fluorescent components and inferring their structural properties, with the support of mass spectrometry. The different spectroscopic features of CDs could be associated to different compositional characteristics as the polydispersity of molecular components featuring blue CDs, opposed to the oligomer-like nature of green and yellow CDs. Pyrogenic CDs showed different fluorescence emission ranges, quantum yield and lifetimes, appealing for their possible applications in the fields of imaging, electronics and sensors

Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies

© 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio

Modeling toxic endpoints for improving human health risk assessment

Risk assessment procedures for mixtures of polycyclic aromatic hydrocarbons (PAHs) present a problem due to the lack of available potency and toxicity data on mixtures and individual compounds. This study examines the toxicity of parent compound PAHs and binary mixtures of PAHs in order to bridge the gap between component assessment and mixture assessment. Seven pure parent compound PAHs and four binary mixtures of PAHs were examined in the Salmonella/Microsome Mutagenicity Assay, a Gap Junction Intercellular Communication (GJIC) assay and the 7-ethoxyresorufin-O-deethylase assay (EROD). These assays were chosen for their ability to measure specific toxic endpoints related to the carcinogenic process (i.e. initiation, promotion, progression). Data from these assays was used in further studies to build Quantitative Structure-Activity Relationships (QSARs) to estimate toxic endpoints and to test the additive assumption in PAH mixtures. These QSAR models will allow for the development of bioassay based potential potencies (PPB) or toxic equivalency factors (TEFs) that are derived not only from bioassay data, but also from structure, activity, and physical/chemical properties. These models can be extended to any environmental media to evaluate risk to human health from exposures to PAHs