A comparative survey of chemistry-driven in silico methods to identify hazardous substances under REACH

Prioritization of risks posed by synthetic chemicals manufactured in China toward humans and the environment via persistence, bioaccumulation, mobility and toxicity properties

Over a third of the global chemical production and sales occurred in China, which make effective assessment and management for chemicals produced by China's chemical industry essential not just for China but for the world. Here, we systematical assessed the persistence (P), bioaccumulation (B), mobility (M) and toxicity (T) potency properties for the chemicals listed in Inventory of Existing Chemical Substances of China (IECSC) via experimental data retrieved from large scale databases and in silico data generated with well-established models. Potential PBT, PMT and PB&MT substances were identified. High risk potentials were highlighted for groups of synthetic intermediates, raw materials, as well as a series of biocides. The potential PBT and PMT synthetic intermediates and/or raw materials unique to the IECSC were dominated with organofluorines, for example, the intermediates used as electronic light-emitting materials. Meanwhile, the biocides unique to the IECSC were mainly organochlorines. Some conventional classes of insecticides, such as organochlorines and pyrethroids, were classified as being of high concern. We further identified a group of PB&MT substances that were considered to be both "bioaccumulative" and "mobile". Their properties and common substructures for several major clusters were characterized. The present results prioritized groups of substances with high potentials to cause adverse effects to the environment and humans, many of which have not yet been fully recognized.

Evaluation of toxicity of Personal Care Products (PCPs) in freshwaters: Zebrafish as a model

Personal care products (PCPs) are part of the large and growing family of emerging contaminants (ECs). Many daily products such as sunscreens, toothpaste, make-up products, perfume, and others, fall under this definition, and their use is increasing exponentially. Furthermore, the degradation of some components of these products is limited. Indeed, they are able to easily reach and accumulate in aquatic systems, representing a new class of contaminants. Moreover, due to their chemical properties, they can interfere at different biological levels, and for this reason, they need to be thoroughly investigated. We have reviewed the literature on PCPs, with a special focus on the adverse effects on the freshwater zebrafish (Danio rerio). The aim of this work is to provide a careful assessment of the toxicity of these compounds, in order to raise awareness for more conscious and responsible use.

Use of In Silico Methods for Regulatory Toxicological Assessment of Pharmaceutical Impurities

The use of novel non-testing methodologies to support the toxicological assessment of drug impurities is having a growing impact in the regulatory framework for pharmaceutical development and marketed products. For DNA reactive (mutagenic) impurities specific recommendations for the use of in silico structure-based approaches (namely (Q)SAR methodologies) are provided in the ICH M7 guideline. In 2018 a draft reflection paper has been published by EMA addressing open issues in the qualification approach of non-genotoxic impurities (NGI) according to the ICH Q3A/Q3B guidelines, and proposing the use of alternative testing strategies, including TTC, (Q)SAR, read-across, and in vitro approaches, to gather impurity-specific safety information.In the present chapter we describe a workflow to perform the safety assessment of drug impurities based on non-testing in silico methodologies. The proposed approach consists of a stepwise decision scheme including three key phases: PHASE 1: assessment of bacterial mutagenicity and consequent classification of impurities according to ICH M7; PHASE 2: risk characterization of mutagenic impurities (Classes 1, 2 or 3); PHASE 3: qualification of non-mutagenic impurities (Classes 4 or 5). The proposed decision scheme offers the possibility to acquire impurity-specific data, also if testing is not feasible, and to decide on further in vitro testing, besides meeting 3R's principle.

A risk classification strategy for migrants of food contact material combined with three (Q)SAR tools in silico

The chemical constituents in food contact materials (FCMs) may transfer into food during the contact, which may pose potential risk to humans. So, it is important to evaluate the safety of FCMs. Due to the advantages of cost-effectiveness and high throughput, (Q)SAR tools have been gradually used for risk assessment. In this work, a risk classification strategy for migrants of food contact materials combined with three (Q)SAR tools was developed based on a single endpoint (Mutagenicity) assessment and risk matrix approach, respectively. 419 migrants existing in a self-built toxicology database beneficial from Python crawler technology were evaluated. 5 toxic hazard ranks and 4 risk ranks were obtained for single endpoint assessment and risk matrix respectively, with 21 substances assigned as Toxic hazard Class I and 43 substances assigned as RISK Ⅰ which need the highest safety concern. Besides, for the Toxic hazard Class I substances assessed by the single endpoint, 19 of them were confirmed experimentally, and all of them were overlapped in the RISK Ⅰ substances, which suggests the effectiveness and reliability of this strategy.

New QSAR models to predict chromosome damaging potential based on the in vivo micronucleus test

A large number of computer-based prediction methods to determine the potential of chemicals to induce mutations at the gene level has been developed over the last decades. Conversely, only few such methods are currently available to predict potential structural and numerical chromosome aberrations. Even fewer of these are based on the preferred testing method for this endpoint, i.e. the micronucleus test. For the present work, in vivo micronucleus test results of 718 structurally diverse compounds were collected and applied for the construction of new models by means of the freely available SARpy in silico model building software. Multiple QSAR models were created using parameter variation and manual verification of (non-) alerting structures. To this extent, the original set of 718 compounds was split into a training (80 %) and a test (20 %) set. SARpy was applied on the training set to automatically extract sets of rules by generating and selecting substructures based on their prediction performance whereas the test set was used to evaluate model performance. Five different splits were made randomly, each of which had a similar balance between positive and negative substances compared to the full dataset. All generated models were characterised by an overall better performance than existing free and commercial models for the same endpoint, while demonstrating high coverage.

The experimental determination of reliable biodegradation rates for mono-aromatics towards evaluating QSBR models

Quantitative Structure Biodegradation Relationships (QSBRs) are a tool to predict the biodegradability of chemicals. The objective of this work was to generate reliable biodegradation data for mono-aromatic chemicals in order to evaluate and verify previously developed QSBRs models. A robust biodegradation test method was developed to estimate specific substrate utilization rates, which were used as a proxy for biodegradation rates of chemicals in pure culture. Five representative mono-aromatic chemicals were selected that spanned a wide range of biodegradability. Aerobic biodegradation experiments were performed for each chemical in batch reactors seeded with known degraders. Chemical removal, degrader growth and CO₂ production were monitored over time. Experimental data were interpreted using a full carbon mass balance model, and Monod kinetic parameters (Y, K_s, q_max and μ_max) for each chemical were determined. In addition, stoichiometric equations for aerobic mineralization of the test chemicals were developed. The theoretically estimated biomass and CO₂ yields were similar to those experimentally observed; 35% (s.d ± 8%) of the recovered substrate carbon was converted to biomass, and 65% (s.d ± 8%) was mineralised to CO₂. Significant correlations were observed between the experimentally determined specific substrate utilization rates, as represented by q_max and q_max/K_s, at high and low substrate concentrations, respectively, and the first order biodegradation rate constants predicted by a previous QSBR study. Similarly, the correlation between q_max and selected molecular descriptors characterizing the chemicals structure in a previous QSBR study was also significant. These results suggest that QSBR models can be reliable and robust in prioritising chemical half-lives for regulatory screening purposes.

Semi-correlations combined with the index of ideality of correlation: a tool to build up model of mutagenic potential

Mutagenicity is the ability of a substance to induce mutations. This hazardous ability of a substance is decisive from point of view of ecotoxicology. The number of substances, which are used for practical needs, grows every year. Consequently, methods for at least preliminary estimation of mutagenic potential of new substances are necessary. Semi-correlations are a special case of traditional correlations. These correlations can be named as "correlations along two parallel lines." This kind of correlation has been tested as a tool to predict selected endpoints, which are represented by only two values: "inactive/active" (0/1). Here this approach is used to build up predictive models for mutagenicity of large dataset (n = 3979). The so-called index of ideality of correlation (IIC) has been tested as a statistical criterion to estimate the semi-correlation. Three random splits of experimental data into the training, invisible-training, calibration, and validation sets were analyzed. Two models were built up for each split: the first model based on optimization without the IIC and the second model based on optimization where IIC is involved in the Monte Carlo optimization. The statistical characteristics of the best model (calculated with taking into account the IIC) n = 969; sensitivity = 0.8050; specificity = 0.9069; accuracy = 0.8648; Matthews's correlation coefficient = 0.7196 (using IIC). Thus, the use of IIC improves the statistical quality of the binary classification models of mutagenic potentials (Ames test) of organic compounds.

PBT assessment under REACH: Screening for low aquatic bioaccumulation with QSAR classifications based on physicochemical properties to replace BCF in vivo testing on fish

Aquatic bioconcentration factors (BCFs) are critical in PBT (persistent, bioaccumulative, toxic) and risk assessment of chemicals. High costs and use of more than 100 fish per standard BCF study (OECD 305) call for alternative methods to replace as much in vivo testing as possible. The BCF waiving scheme is a screening tool combining QSAR classifications based on physicochemical properties related to the distribution (hydrophobicity, ionisation), persistence (biodegradability, hydrolysis), solubility and volatility (Henry's law constant) of substances in water bodies and aquatic biota to predict substances with low aquatic bioaccumulation (nonB, BCF<2000). The BCF waiving scheme was developed with a dataset of reliable BCFs for 998 compounds and externally validated with another 181 substances. It performs with 100% sensitivity (no false negatives), >50% efficacy (waiving potential), and complies with the OECD principles for valid QSARs. The chemical applicability domain of the BCF waiving scheme is given by the structures of the training set, with some compound classes explicitly excluded like organometallics, poly- and perfluorinated compounds, aromatic triphenylphosphates, surfactants. The prediction confidence of the BCF waiving scheme is based on applicability domain compliance, consensus modelling, and the structural similarity with known nonB and B/vB substances. Compounds classified as nonB by the BCF waiving scheme are candidates for waiving of BCF in vivo testing on fish due to low concern with regard to the B criterion. The BCF waiving scheme supports the 3Rs with a possible reduction of >50% of BCF in vivo testing on fish. If the target chemical is outside the applicability domain of the BCF waiving scheme or not classified as nonB, further assessments with in silico, in vitro or in vivo methods are necessary to either confirm or reject bioaccumulative behaviour.

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.

The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury

Drug-induced liver injury (DILI) is challenging for drug development, clinical practice and regulation. The Liver Toxicity Knowledge Base (LTKB) provides essential data for DILI study. Areas covered: The LTKB provided various types of data that can be used to assess and predict DILI. Among much information available, several reference drug lists with annotated human DILI risk are of important. The LTKB DILI classification data include DILI severity concern determined by the FDA drug labeling, DILI severity score from the NIH LiverTox database, and other DILI classification schemes from the literature. Overall, ~1000 drugs were annotated with at least one classification scheme, of which around 750 drugs were flagged for some degree of DILI risk. Expert commentary: The LTKB provides a centralized repository of information for DILI study and predictive model development. The DILI classification data in LTKB could be a useful resource for developing biomarkers, predictive models and assessing data from emerging technologies such as in silico, high-throughput and high-content screening methodologies. In coming years, streamlining the prediction process by including DILI predictive models for both DILI severity and types in LTKB would enhance the identification of compounds with the DILI potential earlier in drug development and risk assessment.

Grouping of CoMPARA data with respect to compounds from the carcinogenic potency database

Methods for clustering and measures of similarity of chemical structures have become an important supporting tool in chemoinformatics. They represent the basis for categorization of chemicals and read-across, where a molecular property is estimated from 'similar molecules'. This study proposes a clustering scheme within the given dataset with respect to a reference dataset. The scheme was applied on two datasets ToxCast_AR_Agonist and ToxCast_AR_Antagonists with 1654 and 1522 compounds, respectively. The compounds are tested to androgen receptor activity (AR) in 11 high throughput screening assays. The carcinogenic dataset was used as the reference set.

Integration of in silico methods and computational systems biology to explore endocrine-disrupting chemical binding with nuclear hormone receptors

Thousands of potential endocrine-disrupting chemicals present difficult regulatory challenges. Endocrine-disrupting chemicals can interfere with several nuclear hormone receptors associated with a variety of adverse health effects. The U.S. Environmental Protection Agency (U.S. EPA) has released its reviews of Tier 1 screening assay results for a set of pesticides in the Endocrine Disruptor Screening Program (EDSP), and recently, the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP) data. In this study, the predictive ability of QSAR and docking approaches is evaluated using these data sets. This study also presents a computational systems biology approach using carbaryl (1-naphthyl methylcarbamate) as a case study. For estrogen receptor and androgen receptor binding predictions, two commercial and two open source QSAR tools were used, as was the publicly available docking tool Endocrine Disruptome. For estrogen receptor binding predictions, the ADMET Predictor, VEGA, and OCHEM models (specificity: 0.88, 0.88, and 0.86, and accuracy: 0.81, 0.84, and 0.88, respectively) were each more reliable than the MetaDrug™ model (specificity 0.81 and accuracy 0.77). For androgen receptor binding predictions, the Endocrine Disruptome and ADMET Predictor models (specificity: 0.94 and 0.8, and accuracy: 0.78 and 0.71, respectively) were more reliable than the MetaDrug™ model (specificity 0.33 and accuracy 0.4). A consensus approach is proposed that reaches general agreement among the models (specificity 0.94 and accuracy 0.89). This study integrates QSAR, docking, and systems biology approaches as a virtual screening tool for use in risk assessment. As such, this systems biology pathways and network analysis approach provides a means to more critically assess the potential effects of endocrine-disrupting chemicals.

VOC exposures in California early childhood education environments

Little information exists about exposures to volatile organic compounds (VOCs) in early childhood education (ECE) environments. We measured 38 VOCs in single-day air samples collected in 2010-2011 from 34 ECE facilities serving California children and evaluated potential health risks. We also examined unknown peaks in the GC/MS chromatographs for indoor samples and identified 119 of these compounds using mass spectral libraries. VOCs found in cleaning and personal care products had the highest indoor concentrations (d-limonene and decamethylcyclopentasiloxane [D5] medians: 33.1 and 51.4 μg/m³, respectively). If reflective of long-term averages, child exposures to benzene, chloroform, ethylbenzene, and naphthalene exceeded age-adjusted "safe harbor levels" based on California's Proposition 65 guidelines (10^-5 lifetime cancer risk) in 71%, 38%, 56%, and 97% of facilities, respectively. For VOCs without health benchmarks, we used information from toxicological databases and quantitative structure-activity relationship models to assess potential health concerns and identified 12 VOCs that warrant additional evaluation, including a number of terpenes and fragrance compounds. While VOC levels in ECE facilities resemble those in school and home environments, mitigation strategies are warranted to reduce exposures. More research is needed to identify sources and health risks of many VOCs and to support outreach to improve air quality in ECE facilities.

A review of quantitative structure-property relationships for the fate of ionizable organic chemicals in water matrices and identification of knowledge gaps

Many organic chemicals are ionizable by nature. After use and release into the environment, various fate processes determine their concentrations, and hence exposure to aquatic organisms. In the absence of suitable data, such fate processes can be estimated using Quantitative Structure-Property Relationships (QSPRs). In this review we compiled available QSPRs from the open literature and assessed their applicability towards ionizable organic chemicals. Using quantitative and qualitative criteria we selected the 'best' QSPRs for sorption, (a)biotic degradation, and bioconcentration. The results indicate that many suitable QSPRs exist, but some critical knowledge gaps remain. Specifically, future focus should be directed towards the development of QSPR models for biodegradation in wastewater and sediment systems, direct photolysis and reaction with singlet oxygen, as well as additional reactive intermediates. Adequate QSPRs for bioconcentration in fish exist, but more accurate assessments can be achieved using pharmacologically based toxicokinetic (PBTK) models. No adequate QSPRs exist for bioconcentration in non-fish species. Due to the high variability of chemical and biological species as well as environmental conditions in QSPR datasets, accurate predictions for specific systems and inter-dataset conversions are problematic, for which standardization is needed. For all QSPR endpoints, additional data requirements involve supplementing the current chemical space covered and accurately characterizing the test systems used.

Classification of baseline toxicants for QSAR predictions to replace fish acute toxicity studies

Fish acute toxicity studies are required for environmental hazard and risk assessment of chemicals by national and international legislations such as REACH, the regulations of plant protection products and biocidal products, or the GHS (globally harmonised system) for classification and labelling of chemicals. Alternative methods like QSARs (quantitative structure-activity relationships) can replace many ecotoxicity tests. However, complete substitution of in vivo animal tests by in silico methods may not be realistic. For the so-called baseline toxicants, it is possible to predict the fish acute toxicity with sufficient accuracy from log K_ow and, hence, valid QSARs can replace in vivo testing. In contrast, excess toxicants and chemicals not reliably classified as baseline toxicants require further in silico, in vitro or in vivo assessments. Thus, the critical task is to discriminate between baseline and excess toxicants. For fish acute toxicity, we derived a scheme based on structural alerts and physicochemical property thresholds to classify chemicals as either baseline toxicants (=predictable by QSARs) or as potential excess toxicants (=not predictable by baseline QSARs). The step-wise approach identifies baseline toxicants (true negatives) in a precautionary way to avoid false negative predictions. Therefore, a certain fraction of false positives can be tolerated, i.e. baseline toxicants without specific effects that may be tested instead of predicted. Application of the classification scheme to a new heterogeneous dataset for diverse fish species results in 40% baseline toxicants, 24% excess toxicants and 36% compounds not classified. Thus, we can conclude that replacing about half of the fish acute toxicity tests by QSAR predictions is realistic to be achieved in the short-term. The long-term goals are classification criteria also for further groups of toxicants and to replace as many in vivo fish acute toxicity tests as possible with valid QSAR predictions.

Effect-directed analysis supporting monitoring of aquatic environments--An in-depth overview

Aquatic environments are often contaminated with complex mixtures of chemicals that may pose a risk to ecosystems and human health. This contamination cannot be addressed with target analysis alone but tools are required to reduce this complexity and identify those chemicals that might cause adverse effects. Effect-directed analysis (EDA) is designed to meet this challenge and faces increasing interest in water and sediment quality monitoring. Thus, the present paper summarizes current experience with the EDA approach and the tools required, and provides practical advice on their application. The paper highlights the need for proper problem formulation and gives general advice for study design. As the EDA approach is directed by toxicity, basic principles for the selection of bioassays are given as well as a comprehensive compilation of appropriate assays, including their strengths and weaknesses. A specific focus is given to strategies for sampling, extraction and bioassay dosing since they strongly impact prioritization of toxicants in EDA. Reduction of sample complexity mainly relies on fractionation procedures, which are discussed in this paper, including quality assurance and quality control. Automated combinations of fractionation, biotesting and chemical analysis using so-called hyphenated tools can enhance the throughput and might reduce the risk of artifacts in laboratory work. The key to determining the chemical structures causing effects is analytical toxicant identification. The latest approaches, tools, software and databases for target-, suspect and non-target screening as well as unknown identification are discussed together with analytical and toxicological confirmation approaches. A better understanding of optimal use and combination of EDA tools will help to design efficient and successful toxicant identification studies in the context of quality monitoring in multiply stressed environments.

The Consultancy Activity on In Silico Models for Genotoxic Prediction of Pharmaceutical Impurities

The toxicological assessment of DNA-reactive/mutagenic or clastogenic impurities plays an important role in the regulatory process for pharmaceuticals; in this context, in silico structure-based approaches are applied as primary tools for the evaluation of the mutagenic potential of the drug impurities. The general recommendations regarding such use of in silico methods are provided in the recent ICH M7 guideline stating that computational (in silico) toxicology assessment should be performed using two (Q)SAR prediction methodologies complementing each other: a statistical-based method and an expert rule-based method.Based on our consultant experience, we describe here a framework for in silico assessment of mutagenic potential of drug impurities. Two main applications of in silico methods are presented: (1) support and optimization of drug synthesis processes by providing early indication of potential genotoxic impurities and (2) regulatory evaluation of genotoxic potential of impurities in compliance with the ICH M7 guideline. Some critical case studies are also discussed.

Prediction of mutagenicity and carcinogenicity using in silico modelling: A case study of polychlorinated biphenyls

In silico modelling is an important alternative method for the evaluation of properties of chemical compounds. Basically, two concepts are used in its applications: QSAR modelling for endpoint predictions, and grouping (categorization) of large groups of chemicals. In the presented report we address both of these concepts. As a case study we present the results on a set of polychlorinated biphenyls (PCBs) and some of their metabolites. Their mutagenicity and carcinogenic potency were evaluated with CAESAR and T.E.S.T. models, which are freely available over the internet. We discuss the value and reliability of the predictions, the applicability domain of models and the ability to create prioritized groupings of PCBs as a category of chemicals.

Changes in mutagenicity and acute toxicity of solutions of iodinated X-ray contrast media during chlorination

In the present study, the effects of chlorination on the mutagenicity (assessed via the Ames assay) and acute toxicity (assessed via a bioluminescence inhibition assay) of solutions containing one of five commonly used iodinated X-ray contrast media (ICM) (iopamidol, iohexol, iopromide, iomeprol, and diatrizoate) were investigated. Of the five ICM tested, only iopamidol was degraded by chlorine. Chlorination of the iopamidol-containing solution induced both mutagenicity and acute toxicity, which increased with chlorination time (up to 96 h). The areas of five out of 54 peaks detected on the LC/MS total ion chromatogram had good correlation (r(2)>0.90) between peak area and observed mutagenicity. To identify possible contributors to the observed mutagenicity, the Ames assay and LC/MS analysis were conducted on samples collected at 48-h chlorination time and extracted under different pH conditions. Of the five peaks, one peak was detected in the sample extracted at pH 7, but this sample was not mutagenic, indicating that the peak was not related to the observed mutagenicity. MS/MS analysis with an orbitrap mass spectrometer of the remaining four peaks revealed that two of the peaks represented the same TP (detected in negative and positive ion modes). Finally, three TPs were identified as suspected contributors to the mutagenicity induced by the iopamidol-containing solution after chlorination: 5-[(1,3-dihydroxypropan-2-yl)carbamoyl]-3-[(3-hydroxypropanoyl)oxy]-2,4-diiodobenzoic acid; N-(1,3-dihydroxypropan-2-yl)-3-(2,3-dioxopropyl)-2,4,6-triiodobenzamide; and 3-[(1,3-dihydroxypropan-2-yl)carbamoyl]-5-[(3-hydroxybutanoyl)oxy]-2,4,6-triiodobenzoic acid. Prediction of the mutagenicity potential of these three TPs with a battery of four quantitative structure-activity relationship models did not contradict our conclusion that these TPs contributed to the observed mutagenicity.

Evaluation of toxic endpoints for a set of cosmetic ingredients with CAESAR models

The randomly selected set of 558 chemicals from Cosmetic inventory was studied with internet accessible program package CAESAR. Four toxic endpoints were considered: mutagenicity, carcinogenicity, developmental toxicity and skin sensitization. The CAESAR program provides beside the predictions comprehensive information on applicability domain and the similarity between the considered compound and the compounds from model's training set. This information was used to implement for clustering and classification of chemicals. As the technique the Self Organizing Maps was applied. This technique also enables us to define to each cluster the cluster indicator, i.e., the characteristic compound, which is considered as a representative for a cluster.

Optimizing the aquatic toxicity assessment under REACH through an integrated testing strategy (ITS)

To satisfy REACH requirements a high number of data on chemical of interest should be supplied to the European Chemicals Agency. To organize the various kinds of information and help the registrants to choose the best strategy to obtain the needed information limiting at the minimum the use of animal testing, integrated testing strategies (ITSs) schemes can be used. The present work deals with regulatory data requirements for assessing the hazards of chemicals to the aquatic pelagic environment. We present an ITS scheme for organizing and using the complex existing data available for aquatic toxicity assessment. An ITS to optimize the choice of the correct prediction strategy for aquatic pelagic toxicity is described. All existing information (like physico-chemical information), and all the alternative methods (like in silico, in vitro or the acute-to-chronic ratio) are considered. Moreover the weight of evidence approach to combine the available data is included.

Biobased building blocks for the rational design of renewable block polymers

Block polymers (BPs) derived from biomass (biobased) are necessary components of a sustainable future that relies minimally on petroleum-based plastics for applications ranging from thermoplastic elastomers and pressure-sensitive adhesives to blend compatibilizers. To facilitate their adoption, renewable BPs must be affordable, durable, processable, versatile, and reasonably benign. Their desirability further depends on the relative sustainability of the renewable resources and the methods employed in the monomer and polymer syntheses. Various strategies allow these BPs' characteristics to be tuned and enhanced for commercial applications, and many of these techniques also can be applied to manipulate the wide-ranging mechanical and thermal properties of biobased and self-assembling block polymers. From feedstock to application, this review article highlights promising renewable BPs, plus their material and assembly properties, in support of de novo design strategies that could revolutionize material sustainability.

Integrated testing strategy (ITS) for bioaccumulation assessment under REACH

REACH (registration, evaluation, authorisation and restriction of chemicals) regulation requires that all the chemicals produced or imported in Europe above 1 tonne/year are registered. To register a chemical, physicochemical, toxicological and ecotoxicological information needs to be reported in a dossier. REACH promotes the use of alternative methods to replace, refine and reduce the use of animal (eco)toxicity testing. Within the EU OSIRIS project, integrated testing strategies (ITSs) have been developed for the rational use of non-animal testing approaches in chemical hazard assessment. Here we present an ITS for evaluating the bioaccumulation potential of organic chemicals. The scheme includes the use of all available data (also the non-optimal ones), waiving schemes, analysis of physicochemical properties related to the end point and alternative methods (both in silico and in vitro). In vivo methods are used only as last resort. Using the ITS, in vivo testing could be waived for about 67% of the examined compounds, but bioaccumulation potential could be estimated on the basis of non-animal methods. The presented ITS is freely available through a web tool.

Sorption determination of phenols and polycyclic aromatic hydrocarbons in a multiphase constructed wetland system by solid phase microextraction

Non-degradative sorption of organic pollutants onto plant roots during phytoremediation is an essential retardation mechanism. To determine the extent of the attenuation processes the freely dissolved concentrations of organic solutes must be determined rather than their total concentrations. Thus, the assessment of attenuation caused by sorption onto plant compartments can be biased when using traditional methods. This bias holds especially true in cases of complex multiphase systems characterized by high concentrations of dissolved and suspended organic matter, both of them contributing to a reduction in concentration of the free solutes. A relatively new approach based on solid phase microextraction (SPME) circumvents these obstacles. SPME measures the true freely dissolved concentration of organic solutes without affecting the sorption equilibrium, thus allowing non-biased conclusions about ecotoxicological hazards of organic sorbates. Herein, sorption of phenols (including alkylated phenols) along with polycyclic aromatic hydrocarbons (PAHs) onto roots of Juncus effusus was investigated. Sorption coefficients in the complex system measured with SPME were significantly higher compared to those measured by traditional methods such as phase separation and solvent extraction. A concept based on Hildebrand solubility parameters was applied to interpret and predict sorption onto helophyte roots. The solubility parameter of the root sorbent was calculated as 26.1 (Jcm(-3))(0.5), which is between that of lignin and cellulose/hemicellulose.

Discriminating toxicant classes by mode of action: 4. Baseline and excess toxicity

Functional similarity of chemicals combines toxicological knowledge (which toxicity pathways can happen in which species under which exposure conditions) with chemical expertise (which parts of the chemical structures and physico-chemical properties are involved in which interactions) to discriminate between baseline and excess toxicants. The objective is to identify as many baseline toxicants as possible because their acute fish toxicities can be predicted with sufficient accuracy from their log Kow. Established tools like structural alerts are used to indicate modes of action (MOAs) that are typical causes of excess toxicity. Verhaar classifications are supplemented with additional chemical attributes and physico-chemical property thresholds to cover a larger range of compounds within the baseline toxicity domain. Our approach is precautionary to avoid false negatives with a sensitivity of 96.3%. It classifies 57.1% of the compounds of the EPA Fathead Minnow Acute Toxicity Database (EPAFHM) as baseline toxicants and suggests that more than 50% of acute fish toxicity testing could be replaced by reliable QSAR predictions. Furthermore, functional similarity can support the MOA classification of chemicals in different species. Toxicity profiles with fish, Daphnia and algae reveal specific targets for the compounds and, particularly for chemicals with multiple MOA, identify the most sensitive species.

Perspectives for integrating human and environmental risk assessment and synergies with socio-economic analysis

For more than a decade, the integration of human and environmental risk assessment (RA) has become an attractive vision. At the same time, existing European regulations of chemical substances such as REACH (EC Regulation No. 1907/2006), the Plant Protection Products Regulation (EC regulation 1107/2009) and Biocide Regulation (EC Regulation 528/2012) continue to ask for sector-specific RAs, each of which have their individual information requirements regarding exposure and hazard data, and also use different methodologies for the ultimate risk quantification. In response to this difference between the vision for integration and the current scientific and regulatory practice, the present paper outlines five medium-term opportunities for integrating human and environmental RA, followed by detailed discussions of the associated major components and their state of the art. Current hazard assessment approaches are analyzed in terms of data availability and quality, and covering non-test tools, the integrated testing strategy (ITS) approach, the adverse outcome pathway (AOP) concept, methods for assessing uncertainty, and the issue of explicitly treating mixture toxicity. With respect to exposure, opportunities for integrating exposure assessment are discussed, taking into account the uncertainty, standardization and validation of exposure modeling as well as the availability of exposure data. A further focus is on ways to complement RA by a socio-economic assessment (SEA) in order to better inform about risk management options. In this way, the present analysis, developed as part of the EU FP7 project HEROIC, may contribute to paving the way for integrating, where useful and possible, human and environmental RA in a manner suitable for its coupling with SEA.