PBT assessment and prioritization of Contaminants of Emerging Concern: Pharmaceuticals

The strong and widespread use of pharmaceuticals, together with incorrect disposal procedures, has recently made these products contaminants of emerging concern (CEC). Unfortunately, little is known about pharmaceuticals' environmental behaviour and ecotoxicity, so that EMEA (European Medicines Agency) released guidelines for the pharmaceuticals' environmental risk assessment. In particular, there is a severe lack of information about persistence, bioaccumulation and toxicity (PBT) of the majority of the thousands of substances on the market. Computational tools, like QSAR (Quantitative Structure Activity Relationship) models, are the only way to screen large sets of chemicals in short time, with the aim of ranking, highlighting and prioritizing the most environmentally hazardous for focusing further experimental studies. In this work we propose a screening method to assess the potential persistence, bioaccumulation and toxicity of more than 1200 pharmaceutical ingredients, based on the application of two different QSAR models. We applied the Insubria-PBT Index, a MLR (Multiple Linear Regression) QSAR model based on four simple molecular descriptors, implemented in QSARINS software, and able to synthesize the PBT potential in a unique cumulative value and the US-EPA PBT Profiler that assesses the PBT behaviour evaluating separately P, B and T. Particular attention was given to the study of Applicability Domain in order to provide reliable predictions. An agreement of 86% was found between the two models and a priority list of 35 pharmaceuticals, highlighted as potential PBTs by consensus, was proposed for further experimental validation. Moreover, the results of this computational screening are in agreement with preliminary experimental data in the literature. This study shows how in silico models can be applied in the hazard assessment to perform preliminary screening and prioritization of chemicals, and how the identification of the structural features, mainly associated with the potential PBT behaviour of the prioritized pharmaceuticals, is particularly relevant to perform the rational a priori design of new, environmentally safer, pharmaceuticals

Development of human biotransformation QSARs and application for PBT assessment refinement

Toxicokinetics heavily influence chemical toxicity as the result of Absorption, Distribution, Metabolism (Biotransformation) and Elimination (ADME) processes. Biotransformation (metabolism) reactions can lead to detoxification or, in some cases, bioactivation of parent compounds to more toxic chemicals. Moreover, biotransformation has been recognized as a key process determining chemical half-life in an organism and is thus a key determinant for bioaccumulation assessment for many chemicals. This study addresses the development of QSAR models for the prediction of in vivo whole body human biotransformation (metabolism) half-lives measured or empirically-derived for over 1000 chemicals, mainly represented by pharmaceuticals. Models presented in this study meet regulatory standards for fitting, validation and applicability domain. These QSARs were used, in combination with literature models for the prediction of biotransformation half-lives in fish, to refine the screening of the potential PBT behaviour of over 1300 Pharmaceuticals and Personal Care Products (PPCPs). The refinement of the PBT screening allowed, among others, for the identification of PPCPs, which were predicted as PBTs on the basis of their chemical structure, but may be easily biotransformed. These compounds are of lower concern in comparison to potential PBTs characterized by large predicted biotransformation half-lives

Ecotoxicity interspecies QAAR models from <i>Daphnia</i> toxicity of pharmaceuticals and personal care products^$

<p>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. <i>In silico</i> methods may be helpful in filling data gaps for the toxicity of PPCPs towards various ecological indicator organisms. The good correlation between toxicity toward <i>Daphnia magna</i> and those on two fish species (<i>Pimephales promelas</i> and <i>Oncorhynchus mykiss</i>), 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.</p