QSAR models for the screening, prediction and refinement of PBT Properties of Contaminants of Emerging Concern.

The prompt identification of the adverse effects of Contaminant of Emerging Concern (CEC) is fundamental to ensure high protection level for human health and the environment. Persistent, Bioaccumulative and Toxic (PBT) compounds are chemicals of high concern and should be readily identified. The aim of this thesis is to propose an approach based on Quantitative Structure Activity Relationship (QSAR) models for the evaluation of the intrinsic environmental hazard of CECs. First, a screening of the potential PBT behavior of pharmaceuticals is performed by consensus approach. Results demonstrate a high agreement (i.e.86%) between the different QSAR models. Then, QSARs are developed to estimate acute toxicity of pharmaceuticals in aquatic species. All models have good fitting (R2>0.75) and predictivity (Q2EXT>0.68). An Aquatic Toxicity Index is proposed and modelled. Moreover, interspecies correlation models are also developed. Finally, QSARs for the prediction of whole-body human biotransformation Half-Lives are developed for organic chemicals. Predictions for the biotransformation potential are integrated in a mechanistic mass-balance multimedia environmental fate food-web model to estimate the Biomagnification Factor (BMF) in human in a tiered approach. The introduction of biotransformation strongly affects the calculation of BMF and the elimination processes related to biotransformation are predominant in the overall bioaccumulation

Ecotoxicity interspecies QAAR models from Daphnia toxicity of pharmaceuticals and personal care products

Pharmaceutical and Personal Care Products (PPCPs) became a class of contaminants of emerging concern because are ubiquitously detected in surface water and soil, where they can affect wildlife. Ecotoxicological data are only available for a few PPCPs, thus modelling approaches are essential tools to maximize the information contained in the existing data. In silico methods may be helpful in filling data gaps for the toxicity of PPCPs towards various ecological indicator organisms. The good correlation between toxicity toward Daphnia magna and those on two fish species (Pimephales promelas and Oncorhynchus mykiss), improved by the addition of one theoretical molecular descriptor, allowed us to develop predictive models to investigate the relationship between toxicities in different species. The aim of this work is to propose quantitative activity-activity relationship (QAAR) models, developed in QSARINS and validated for their external predictivity. Such models can be used to predict the toxicity of PPCPs to a particular species using available experimental toxicity data from a different species, thus reducing the tests on organisms of higher trophic level. Similarly, good QAAR models, implemented by molecular descriptors to improve the quality, are proposed here for fish interspecies. We also comment on the relevance of autocorrelation descriptors in improving all studied interspecies correlations

A Historical Excursus on the Statistical Validation Parameters for QSAR Models: A Clarification Concerning Metrics and Terminology

In the last years, external validation of QSAR models was the subject of intensive debate in the scientific literature. Different groups have proposed different metrics to find "the best" parameter to characterize the external predictivity of a QSAR model. This editorial summarizes the history of parameter development for the external QSAR model validation and suggests, once again, the concurrent use of several different metrics to assess the real predictive capability of QSAR models

Hazard of pharmaceuticals for aquatic environment: Prioritization by structural approaches and prediction of ecotoxicity

Active Pharmaceutical Ingredients (APIs) are recognized as Contaminants of Emerging Concern (CEC) since they are detected in the environment in increasing amount, mainly in aquatic compartment, where they may be hazardous for wildlife. The huge lack of experimental data for a large number of end-points requires tools able to quickly highlight the potentially most hazardous and toxic pharmaceuticals, focusing experiments on the prioritized compounds. In silico tools, like QSAR (Quantitative Structure-Activity Relationship) models based on structural molecular descriptors, can predict missing data for toxic end-points necessary to prioritize existing, or even not yet synthesized chemicals for their potential hazard. In the present study, new externally validated QSAR models, specific to predict acute toxicity of APIs in key organisms of the three main aquatic trophic levels, i.e. algae, Daphnia and two species of fish, were developed using the QSARINS software. These Multiple Linear regressions - Ordinary Least Squares (MLR-OLS) models are based on theoretical molecular descriptors calculated by free PaDEL-Descriptor software and selected by Genetic Algorithm. The models are statistically robust, externally predictive and characterized by a wide structural applicability domain. They were applied to predict acute toxicity for a large set of APIs without experimental data. Then predictions were processed by Principal Component Analysis (PCA) and a trend, driven by the combination of toxicities for all the studied organisms, was highlighted. This trend, named Aquatic Toxicity Index (ATI), allowed the raking of pharmaceuticals according to their potential toxicity upon the whole aquatic environment. Finally a QSAR model for the prediction of this Aquatic Toxicity Index (ATI) was proposed to be applicable in QSARINS for the screening of existing APIs for their potential hazard and the a priori chemical design of not environmentally hazardous APIs

Are some \u201csafer alternatives\u201d hazardous as PBTs? The case study of new flame retardants

tSome brominated flame retardants (BFRs), as PBDEs, are persistent, bioaccumulative, toxic (PBT) andare restricted/prohibited under various legislations. They are replaced by \u201csafer\u201d flame retardants (FRs),such as new BFRs or organophosphorous compounds. However, informations on the PBT behaviour ofthese substitutes are often lacking. The PBT assessment is required by the REACH regulation and the PBTchemicals should be subjected to authorization. Several new FRs, proposed and already used as saferalternatives to PBDEs, are here screened by the cumulative PBT Index model, implemented in QSARINS(QSAR-Insubria), new software for the development/validation of QSAR models. The results, obtaineddirectly from the chemical structure for the three studied characteristics altogether, were compared withthose from the US-EPA PBT Profiler: the two different approaches are in good agreement, supporting theutility of a consensus approach in these screenings. A priority list of the most harmful FRs, predictedin agreement by the two modelling tools, has been proposed, highlighting that some supposed \u201csaferalternatives\u201d are detected as intrinsically hazardous for their PBT properties. This study also shows thatthe PBT Index could be a valid tool to evaluate appropriate and safer substitutes, a priori from the chemicaldesign, in a benign by design approach, avoiding unnecessary synthesis and tests

Aquatic ecotoxicity of personal care products: QSAR models and ranking for prioritization and safer alternatives' design

Personal Care Product (PCP) ingredients, widely used all over the world, over the last few years have become chemicals of increasing environmental concern, mainly because they are detected in water and may harm wildlife. Due to their high structural heterogeneity, the big number of end-points and the huge lack of experimental data, it is very important to have tools able to quickly highlight the most hazardous and toxic compounds, focusing the experiments on the prioritized chemicals. In silico tools, like QSAR models based on structural molecular descriptors, can predict the missing data for activities and properties necessary to prioritize the existing or even not yet synthesized chemicals for their potential hazard. In the present study, new externally validated QSAR models, specific to predict acute PCPs' toxicity in three key organisms of aquatic trophic levels, i.e. algae, crustacean and fish, were developed according to the OECD principles for the validation of QSARs, using the QSARINS software. These OLS models based on theoretical molecular descriptors calculated by PaDEL-Descriptor software, selected by genetic algorithm, are statistically robust, externally predictive and characterized by a wide structural applicability domain. They were applied to predict acute toxicity for over 500 PCPs without experimental data; a trend of acute aquatic toxicity was highlighted by PCA allowing the ranking of inherently more toxic compounds, using also a MCDM approach for prioritization purposes. Additionally, a QSAR model for the prediction of this aquatic toxicity index (ATI) was proposed to be applicable in QSARINS for the a priori chemical design of non environmentally hazardous PCPs

QSAR modeling of cumulative environmental end-points for the prioritization of hazardous chemicals

The hazard of chemicals in the environment is inherently related to the molecular structure and derives simultaneously from various chemical properties/activities/reactivities. Models based on Quantitative Structure Activity Relationships (QSARs) are useful to screen, rank and prioritize chemicals that may have an adverse impact on humans and the environment. This paper reviews a selection of QSAR models (based on theoretical molecular descriptors) developed for cumulative multivariate endpoints, which were derived by mathematical combination of multiple effects and properties. The cumulative end-points provide an integrated holistic point of view to address environmentally relevant properties of chemicals

PBT assessment and prioritization by PBT Index and consensus modeling: Comparison of screening results from structural models

The limited availability of comprehensive data for Persistence, Bioaccumulation and Toxicity (PBT) of chemicals is a serious hindrance to the assignment of compounds to the categories of PBT and vPvB; REACH regulation requires authorization for the use of such chemicals, and additionally plans for safer alternatives. In the context of screening and priority-setting tools for PBT-assessment, the cumulative PBT Index model, implemented in QSARINS (QSAR-INSUBRIA), new software for the development and validation of multiple linear regression QSAR models, offers a new holistic approach to identify chemicals with cumulative PBT properties directly from their molecular structure. In this study the Insubria PBT Index in QSARINS was applied to screen and prioritize various datasets, containing a large variety of chemicals of heterogeneous molecular structure previously screened by various authors by different methods, for their potential PBT behaviour. Particular attention was devoted to the model Applicability Domain, using different approaches such as Descriptors Range, Leverage, and Principal Component Analysis (PCA) of the modeling molecular descriptors, in order to discriminate between interpolated and extrapolated predictions. The results of this screening, which is based only on the molecular structure features and is not dependent on single threshold values for P, B and T, were compared with those obtained by the on-line US-EPA PBT Profiler. Good agreement between the various approaches was found, supporting the utility of a consensus approach in priority-setting studies. The main discrepancies have been highlighted and commented on. Moreover, a priority list containing the most hazardous compounds identified in agreement between the two tools has been drafted. The PBT Index, implemented in QSARINS, which has been demonstrated to be a practical, precautionary and reliable screening tool for PBT-behaviour immediately from the molecular structure, can be usefully applied for focusing experimental studies, and even before chemical synthesis, in a \u201cbenign by design\u201d approach of safer alternatives