Alessandro Sangion

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.

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.

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

Abstract 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.

Synthesis, photodynamic activity, and quantitative structure-activity relationship modelling of a series of BODIPYs

Here we report the synthesis of eleven new BODIPYs (14-24) characterized by the presence of an aromatic ring on the 8 (meso) position and of iodine atoms on the pyrrolic 2,6 positions. These molecules, together with twelve BODIPYs already reported by us (1-12), represent a large panel of BODIPYs showing different atoms or groups as substituent of the aromatic moiety. Two physico-chemical features (1O2 generation rate and lipophilicity), which can play a fundamental role in the outcome as photosensitizers, have been studied. The in vitro photo-induced cell-killing efficacy of 23 PSs was studied on the SKOV3 cell line treating the cells for 24h in the dark then irradiating for 2h with a green LED device (fluence 25.2J/cm2). The cell-killing efficacy was assessed with the MTT test and compared with that one of meso un-substituted compound (13). In order to understand the possible effect of the substituents, a predictive quantitative structure-activity relationship (QSAR) regression model, based on theoretical holistic molecular descriptors, was developed. The results clearly indicate that the presence of an aromatic ring is fundamental for an excellent photodynamic response, whereas the electronic effects and the position of the substituents on the aromatic ring do not influence the photodynamic efficacy.

In Silico Approaches for the Prediction of In Vivo Biotransformation Rates

The assessment of chemical bioaccumulation is a required procedure under several regulatory frameworks. However, since the experimental quantification of bioaccumulation and related metrics (such as the Bioconcentration Factor, BCF) is resource intensive (money, animals) and time consuming, several computational approaches have been proposed as an alternative. Most bioaccumulation model estimates based on the octanol water partition coefficient (KOW) alone can be inaccurate, if they do not take into account additional processes that influence chemical partitioning, chemical uptake and elimination rates. In particular, the biotransformation rate constant (k B) can play a significant role in mitigating the bioaccumulation potential of hydrophobic chemicals. Bioaccumulation model (e.g., BCF) estimates can be refined when experimental or predicted k Bvalues are available. The aim of this chapter is to illustrate the development and the application of in silico models for in vivo biotransformation rates, for the cost-effective estimation of k Bfor screening assessment. The chapter includes several examples of quantitative structure-activity relationships (QSARs), which predict k B or the associated half-life from the chemical structure. Furthermore, the chapter describes the complementary role of in vitro biotransformation rate estimation and the subsequent in vitro-to-in vivo extrapolation (IVIVE) calculations for refining bioaccumulation model predictions.