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Bisphenol A alternatives are manufactured as potentially less harmful substitutes of bisphenol A (BPA) that offer similar functionality. These alternatives are already in the market, entering the environment and thus raising ecological concerns. However, it can be expected that levels of BPA alternatives will dominate in the future, they are limited information on their environmental safety. The EU PARC project highlights BPA alternatives as priority chemicals and consolidates information on BPA alternatives, with a focus on environmental relevance and on the identification of the research gaps. The review highlighted aspects and future perspectives. In brief, an extension of environmental monitoring is crucial, extending it to cover BPA alternatives to track their levels and facilitate the timely implementation of mitigation measures. The biological activity has been studied for BPA alternatives, but in a non-systematic way and prioritized a limited number of chemicals. For several BPA alternatives, the data has already provided substantial evidence regarding their potential harm to the environment. We stress the importance of conducting more comprehensive assessments that go beyond the traditional reproductive studies and focus on overlooked relevant endpoints. Future research should also consider mixture effects, realistic environmental concentrations, and the long-term consequences on biota and ecosystems.

In recent years, an increasing number of diverse Engineered Nano-Materials (ENMs), such as nanoparticles and nanotubes, have been included in many technological applications and consumer products. The desirable and unique properties of ENMs are accompanied by potential hazards whose impacts are difficult to predict either qualitatively or in a quantitative and predictive manner. Alongside established methods for experimental and computational characterisation, physics-based modelling tools like molecular dynamics are increasingly considered in Safe and Sustainability-by-design (SSbD) strategies that put user health and environmental impact at the centre of the design and development of new products. Hence, the further development of such tools can support safe and sustainable innovation and its regulation. This paper stems from a community effort and presents the outcome of a four-year-long discussion on the benefits, capabilities and limitations of adopting physics-based modelling for computing suitable features of nanomaterials that can be used for toxicity assessment of nanomaterials in combination with data-based models and experimental assessment of toxicity endpoints. We review modern multiscale physics-based models that generate advanced system-dependent (intrinsic) or time- and environment-dependent (extrinsic) descriptors/features of ENMs (primarily, but not limited to nanoparticles, NPs), with the former being related to the bare NPs and the latter to their dynamic fingerprinting upon entering biological media. The focus is on (i) effectively representing all nanoparticle attributes for multicomponent nanomaterials, (ii) generation and inclusion of intrinsic nanoform properties, (iii) inclusion of selected extrinsic properties, (iv) the necessity of considering distributions of structural advanced features rather than only averages. This review enables us to identify and highlight a number of key challenges associated with ENMs’ data generation, curation

In recent years, an increasing number of diverse Engineered Nano-Materials (ENMs), such as nanoparticles and nanotubes, have been included in many technological applications and consumer products. The desirable and unique properties of ENMs are accompanied by potential hazards whose impacts are difficult to predict either qualitatively or in a quantitative and predictive manner. Alongside established methods for experimental and computational characterisation, physics-based modelling tools like molecular dynamics are increasingly considered in Safe and Sustainability-by-design (SSbD) strategies that put user health and environmental impact at the centre of the design and development of new products. Hence, the further development of such tools can support safe and sustainable innovation and its regulation. This paper stems from a community effort and presents the outcome of a four-year-long discussion on the benefits, capabilities and limitations of adopting physics-based modelling for computing suitable features of nanomaterials that can be used for toxicity assessment of nanomaterials in combination with data-based models and experimental assessment of toxicity endpoints. We review modern multiscale physics-based models that generate advanced system-dependent (intrinsic) or time- and environment-dependent (extrinsic) descriptors/features of ENMs (primarily, but not limited to nanoparticles, NPs), with the former being related to the bare NPs and the latter to their dynamic fingerprinting upon entering biological media. The focus is on (i) effectively representing all nanoparticle attributes for multicomponent nanomaterials, (ii) generation and inclusion of intrinsic nanoform properties, (iii) inclusion of selected extrinsic properties, (iv) the necessity of considerin

Societies worldwide are investing considerable resources into the safe development and use of nanomaterials. Although each of these protective efforts is crucial for governing the risks of nanomaterials, they are insufficient in isolation. What is missing is a more integrative governance approach that goes beyond legislation. Development of this approach must be evidence based and involve key stakeholders to ensure acceptance by end users. The challenge is to develop a framework that coordinates the variety of actors involved in nanotechnology and civil society to facilitate consideration of the complex issues that occur in this rapidly evolving research and development area. Here, we propose three sets of essential elements required to generate an effective risk governance framework for nanomaterials. (1) Advanced tools to facilitate risk-based decision making, including an assessment of the needs of users regarding risk assessment, mitigation, and transfer. (2) An integrated model of predicted human behavior and decision making concerning nanomaterial risks. (3) Legal and other (nano-specific and general) regulatory requirements to ensure compliance and to stimulate proactive approaches to safety. The implementation of such an approach should facilitate and motivate good practice for the various stakeholders to allow the safe and sustainable future development of nanotechnology.

Nanotechnology and the production of nanomaterials have been expanding rapidly in recent years. Since many types of engineered nanoparticles are suspected to be toxic to living organisms and to have a negative impact on the environment, the process of designing new nanoparticles and their applications must be accompanied by a thorough risk analysis. (Quantitative) Structure-Activity Relationship ([Q]SAR) modelling creates promising options among the available methods for the risk assessment. These in silico models can be used to predict a variety of properties, including the toxicity of newly designed nanoparticles. However, (Q)SAR models must be appropriately validated to ensure the clarity, consistency and reliability of predictions. This paper is a joint initiative from recently completed European research projects focused on developing (Q)SAR methodology for nanomaterials. The aim was to interpret and expand the guidance for the well-known "OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models", with reference to nano-(Q)SAR, and present our opinions on the criteria to be fulfilled for models developed for nanoparticles.

Societies worldwide are investing considerable resources into the safe development and use of nanomaterials. Although each of these protective efforts is crucial for governing the risks of nanomaterials, they are insufficient in isolation. What is missing is a more integrative governance approach that goes beyond legislation. Development of this approach must be

Despite the number of available chemicals growing exponentially, testing of their toxicological and environmental behavior is often a critical issue and alternative strategies are required. Additionally, there is the need to predict properties of not yet synthesized compounds to reduce the costs of synthesis, selecting only those that have the maximal potential to be active and nontoxic compounds. In order to evaluate chemical properties avoiding chemical synthesis and reducing expensive and time-demanding laboratory testing, it is necessary to build in silico models establishing a mathematical relationship between the structures of molecules and the considered properties (quantitative structure-activity relationships, QSARs). Molecular descriptors play a fundamental role in QSAR and other in silico models since they formally are the numerical representation of a molecular structure. Molecular descriptors can be classified using different criteria. Among them, there are two main categories, experimental and theoretical descriptors. The basis to understand and perform molecular descriptor calculation, the different theoretical descriptor categories together with their perspectives are described in this chapter.

Nanotechnology is one of the most important technological developments of the 21st century. In silico methods to predict toxicity, such as quantitative structure-activity relationships (QSARs), promote the safe-by-design approach for the development of new materials, including nanomaterials. In this study, a set of cytotoxicity experimental data corresponding to 19 data points for silica nanomaterials were investigated, to compare the widely employed CORAL and Random Forest approaches in terms of their usefulness for developing so-called 'nano-QSAR' models. 'External' leave-one-out cross-validation (LOO) analysis was performed, to validate the two different approaches. An analysis of variable importance measures and signed feature contributions for both algorithms was undertaken, in order to interpret the models developed. CORAL showed a more pronounced difference between the average coefficient of determination (R²) for training and for LOO (0.83 and 0.65 for training and LOO, respectively), compared to Random Forest (0.87 and 0.78 without bootstrap sampling, 0.90 and 0.78 with bootstrap sampling), which may be due to overfitting. With regard to the physicochemical properties of the nanomaterials, the aspect ratio and zeta potential were found to be the two most important variables for Random Forest, and the average feature contributions calculated for the corresponding descriptors were consistent with the clear trends observed in the data set: less negative zeta potential values and lower aspect ratio values were associated with higher cytotoxicity. In contrast, CORAL failed to capture these trends.

In the last decades, several scientific researches have been focused on studying how to encompass and convert – by a theoretical pathway – the information encoded in the molecular structure into one or more numbers used to establish quantitative relationships between structures and properties, biological activities, or other experimental properties. Molecular descriptors are formally mathematical representations of a molecule obtained by a well-specified algorithm applied to a defined molecular representation or a well-specified experimental procedure. They play a fundamental role in chemistry, pharmaceutical sciences, environmental protection policy, toxicology, ecotoxicology, health research, and quality control. Evidence of the interest of the scientific community in the molecular descriptors is provided by the huge number of descriptors proposed up today: more than 5000 descriptors derived from different theories and approaches are defined in the literature and most of them can be calculated by means of dedicated software applications. Molecular descriptors are of outstanding importance in the research fields of quantitative structure–activity relationships (QSARs) and quantitative structure–property relationships (QSPRs), where they are the independent chemical information used to predict the properties of interest. Along with the definition of appropriate molecular descriptors, the molecular structure representation and the mathematical tools for deriving and assessing models are other fundamental components of the QSAR/QSPR approach. The remarkable progress during the last few years in chemometrics and chemoinformatics has led to new strategies for finding mathematical meaningful relationships between the molecular structure and biological activities, physico-chemical, toxicological, and environmental properties of chemicals. Different approaches for deriving molecular descriptors here reviewed and some of the most relevant descriptors are presented in detail

Concentrations, composition profiles and biomagnification of polychlorinated biphenyls non-ortho (IUPAC nos 77, 126, 169), mono-ortho (105, 114, 118, 123, 156, 157, 167, 189) and total PCBs were investigated in black cormorants (Phalacrocorax carbo sinensis) breeding at the coast of the Gulf of Gdansk, Baltic Sea. Total PCB concentrations in birds were in the range from 750 to 5200 ng/g wet wt ( from 19000 to 110000 ng/g lipid wt) in the breast muscles and from 440 to 4200 ng/g, wet wt (13000 to 69000 ng/g lipid wt) in liver. Penta-, hexa and hepta-CBs were dominating homologue classes both in the muscle tissue and liver. PCB congeners nos. 201, 209, 172/192. 194 and 195 were characterized by the greatest bioaccumulation factors (between 100 and 300). Principal Component Analysis (PCA) has been used to analyse accumulation pattern and interdependences among CB congeners.

Concentrations and composition profiles of polychlorinated biphenyls (PCBs) were investigated in composite samples of 10 species of edible fish from the Gulf of Gda sk, in the southern part of the Baltic Sea, Poland, to understand the status of contamination and possible human exposure risk. Apart from the total PCBs, planar non-ortho (IUPAC nos 77, 126, 169) and mono-ortho (nos 105, 114, 118, 123, 156, 157, 167, 189) chlorobiphenyls were also quantified and their dioxin-like toxicity assessed. The absolute total PCB concentrations in fish ranged from 43 to 490 ng g-1 wet wt (910-11000 ng g-1 lipids), while of TCDD TEQs of planar members were from 0.15 to 3.1 pg g-1 wet wt (8.1-81 pg g-1 lipids). The penta- and hexa-CBs usually comprised 70-80% of the total PCBs and were followed by hepta-, tetra- and tri-CBs, and for a specific site tri- and tetra-CBs comprised as much as 22%. Among the individual CB congeners, nos 118, 153 (+132) and 138 (+160 +163 +164) were the most abundant, while no. 110 comprised between 6.8 and 9.3% of the total PCBs in some species. Principal component analysis (PCA) was applied to examine the interdependences among CB congeners in the factor space. The PCA model and cluster analyses were further used to examine site- and species-specific differences and similarities of PCB composition, and the results are discussed. An assessed daily intake rate of TCDD TEQ of planar PCBs with the fishmeal of the Gulf of Gda sk in the 1990s was between 78 and 96 pg per capita or between 1.3 and 1.6 pg kg-1 body weight.

The concentration, pattern, bioaccumulation and biomagnification features of many chlorobiphenyl congeners, including non- and mono-ortho chlorine substituted members have been determined in a pelagic food chain including mixed phyto- and zooplankton-1 herring (Clupea harengus), harbour porpoise (Phocoena phocoena) and black cormorants (Phalacrocorax carbo sinensis) collected from the southern part of the Baltic, Proper. TCDD (tetrachlorodibenzo-p-dioxin) toxic equivalents (TEQs) in plankton, herring, harbour porpoise and cormorants. were 0.42, 5.3, 79 and 2700 pg g(-1) lipid weight, respectively. Concentrations of total, polychlorinated biphehyls (PCBs) in plankton, herring harbour porpoise and cormorants were 1.9, 120, 8700 and 2100 ng g(-1) wet weight (210, 1300, 10000 and 42000 ng g(-1) lipid weight) respectively. Herring, harbour porpoise and black cormorant apparently bioaccumulate many PCBs found in their food, and the values of the bioaccumulation factor's (BAFs) for PCBs were approximately 10 in herring, 35 in harbour porpoise and up to 300 in breast muscle of, cormorants. Harbour porpoise clearly is able to metabolize. the most toxic, non-ortho PCBs (no. 77, 126 and 169) and a few mono-ortho PCBs (no. 114, 123 and 156), while all non- and mono-ortho PCBs are bioaccumulated by herring. Penta-, hexa- and hepta-CBs were the dominant homologue classes both in the muscle tissue and liver. PCB congeners nos. 201, 209, 172/192, 194 and 195 were characterized by the largest.. BAFs (between 100 and 300). Principal component analysis has been used to analyse the interdependences. and differences in the CB congener accumulation patterns between the, components of the investigated pelagic food web.