Chemical Safety Assessment Using Read-Across: Assessing the Use of Novel Testing Methods to Strengthen the Evidence Base for Decision Making

Towards a more effective REACH legislation in protecting human health

There is growing evidence indicating the substantial contribution of man-made products to an increase in the risk of diseases of civilization. In this article, the Belgian Scientific Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) Committee gives a critical view on the working of REACH. The current regulatory framework needs to further evolve taking into account data generated using modern science and technology. There is a need for improved assessment process not only before but also after entering the market. Objectivity, transparency, and the follow-up after market access can be optimized. Additionally, no guidance documents exist for regulation of mixture effects. Further, the lengthiness before regulatory action is a big concern. Decision-making often takes several years leading to uncertainties for both producers and end users. A first proposed improvement is the implementation of independent toxicity testing, to assure objectivity, transparency, and check and improve compliance. A "no data, no market" principle could prevent access of hazardous chemicals to the market. Additionally, the introduction of novel testing could improve information on endpoints such as endocrine disrupting abilities, neurotoxicity, and immunotoxicity. An adapted regulatory framework that integrates data from different sources and comparing the outputs with estimates of exposure is required. Fast toxicology battery testing and toxicokinetic testing could improve speed of decision-making. Hereby, several improvements have been proposed that could improve the current REACH legislation.

Zooplankton-based adverse outcome pathways: A tool for assessing endocrine disrupting compounds in aquatic environments

Endocrine disrupting compounds (EDCs) pose a significant ecological risk, particularly in aquatic ecosystems. EDCs have become a focal point in ecotoxicology, and their identification and regulation have become a priority. Zooplankton have gained global recognition as bioindicators, benefiting from rigorous standardization and regulatory validation processes. This review aims to provide a comprehensive summary of zooplankton-based adverse outcome pathways (AOPs) with a focus on EDCs as toxicants and the utilisation of freshwater zooplankton as bioindicators in ecotoxicological assessments. This review presents case studies in which zooplankton have been used in the development of AOPs, emphasizing the identification of molecular initiating events (MIEs) and key events (KEs) specific to zooplankton exposed to EDCs. Zooplankton-based AOPs may become an important resource for understanding the intricate processes by which EDCs impair the endocrine system. Furthermore, the data sources, experimental approaches, advantages, and challenges associated with zooplankton-based AOPs are discussed. Zooplankton-based AOPs framework can provide vital tools for consolidating toxicological knowledge into a structured toxicity pathway of EDCs, offering a transformative platform for facilitating enhanced risk assessment and chemical regulation.

Application of machine learning-based read-across structure-property relationship (RASPR) as a new tool for predictive modelling: Prediction of power conversion efficiency (PCE) for selected classes of organic dyes in dye-sensitized solar cells (DSSCs)

The application of various in-silico-based approaches for the prediction of various properties of materials has been an effective alternative to experimental methods. Recently, the concepts of Quantitative structure-property relationship (QSPR) and read-across (RA) methods were merged to develop a new emerging chemoinformatic tool: read-across structure-property relationship (RASPR). The RASPR method can be applicable to both large and small datasets as it uses various similarity and error-based measures. It has also been observed that RASPR models tend to have an increased external predictivity compared to the corresponding QSPR models. In this study, we have modeled the power conversion efficiency (PCE) of organic dyes used in dye-sensitized solar cells (DSSCs) by using the quantitative RASPR (q-RASPR) method. We have used relatively larger classes of organic dyes-Phenothiazines (n=207), Porphyrins (n=281), and Triphenylamines (n=229) for the modelling purpose. We have divided each of the datasets into training and test sets in 3 different combinations, and with the training sets we have developed three different QSPR models with structural and physicochemical descriptors and validated them with the corresponding test sets. These corresponding modeled descriptors were used to calculate the RASPR descriptors using a Java-based tool RASAR Descriptor Calculator v2.0 (https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home), and then data fusion was performed by pooling the previously selected structural and physicochemical descriptors with the calculated RASPR descriptors. Further feature selection algorithm was employed to develop the final RASPR PLS models. Here, we also developed different machine learning (ML) models with the descriptors selected in the QSPR PLS and RASPR PLS models, and it was found that models with RASPR descriptors superseded in external predictivity the models with only structural and physicochemical descriptors: RMSEP reduced for phenothiazines from 1.16-1.25 to 1.07-1.18, for porphyrins from 1.60-1.79 to 1.45-1.53, for triphenylamines from 1.27-1.54 to 1.20-1.47.

First report on pesticide sub-chronic and chronic toxicities against dogs using QSAR and chemical read-across

Excessive use of chemicals is the outcome of the industrialization of agricultural sectors which leads to disturbance of ecological balance. Various agrochemicals are widely used in agricultural fields, urban green areas, and to protect from various pest-associated diseases. Due to their long-term health and environmental hazards, chronic toxicity assessment is crucial. Since in vivo and in vitro toxicity assessments are costly, lengthy, and require a large number of animal experiments, in silico toxicity approaches are better alternatives to save time, cost, and animal experimentation. We have developed the first regression-based 2D-QSAR models using different sub-chronic and chronic toxicity data of pesticides against dogs employing 2D descriptors. From the statistical results (n train = 53 - 62 ,   r 2  = 0.614 to 0.754, Q L O O 2  = 0.501 to 0.703 and   Q F 1 2  = 0.531 to 0.718, Q F 2 2 = 0.523 - 0.713 ), it was concluded that the models are robust, reliable, interpretable, and predictive. Similarity-based read-across algorithm was also used to improve the predictivity (Q F 1 2 = 0.595 - 0.813 , Q F 2 2 = 0.573 - 0.809 ) of the models. 5132 chemicals obtained from the CPDat and 1694 pesticides obtained from the PPDB database were also screened using the developed models, and their predictivity and reliability were checked. Thus, these models will be helpful for eco-toxicological data-gap filling, toxicity prediction of untested pesticides, and development of novel, safer & eco-friendly pesticides.

Ten years of using key characteristics of human carcinogens to organize and evaluate mechanistic evidence in IARC Monographs on the identification of carcinogenic hazards to humans: Patterns and associations

Systematic review and evaluation of mechanistic evidence using the Key Characteristics approach was proposed by the International Agency for Research on Cancer (IARC) in 2012 and used by the IARC Monographs Working Groups since 2015. Key Characteristics are 10 features of agents known to cause cancer in humans. From 2015 to 2022, a total of 19 Monographs (73 agents combined) used Key Characteristics for cancer hazard classification. We hypothesized that a retrospective analysis of applications of the Key Characteristics approach to cancer hazard classification using heterogenous mechanistic data on diverse agents would be informative for systematic reviews in decision-making. We extracted information on the conclusions, data types, and the role mechanistic data played in the cancer hazard classification from each Monograph. Statistical analyses identified patterns in the use of Key Characteristics, as well as trends and correlations among Key Characteristics, data types, and ultimate decisions. Despite gaps in data for many agents and Key Characteristics, several significant results emerged. Mechanistic data from in vivo animal, in vitro animal, and in vitro human studies were most impactful in concluding that an agent could cause cancer via a Key Characteristic. To exclude the involvement of a Key Characteristic, data from large-scale systematic in vitro testing programs such as ToxCast, were most informative. Overall, increased availability of systemized data streams, such as human in vitro data, would provide the basis for more confident and informed conclusions about both positive and negative associations and inform expert judgments on cancer hazard.

Biocidal products: Opportunities in risk assessment, management, and communication

In the coronavirus disease 2019 era, biocidal products are increasingly used for controlling harmful organisms, including microorganisms. However, assuring safety against adverse health effects is a critical issue from a public health standpoint. This study aimed to provide an overview of key aspects of risk assessment, management, and communication that ensure the safety of biocidal active ingredients and products. The inherent characteristics of biocidal products make them effective against pests and pathogens; however, they also possess potential toxicities. Therefore, public awareness regarding both the beneficial and potential adverse effects of biocidal products needs to be increased. Biocidal active ingredients and products are regulated under specific laws: the Federal Insecticide, Fungicide, and Rodenticide Act for the United States; the European Union (EU) Biocidal Products Regulation for the EU; and the Consumer Chemical Products and Biocide Safety Management Act for the Republic of Korea. Risk management also needs to consider the evidence of enhanced sensitivity to toxicities in individuals with chronic diseases, given the increased prevalence of these conditions in the population. This is particularly important for post-marketing safety assessments of biocidal products. Risk communication conveys information, including potential risks and risk-reduction measures, aimed at managing or controlling health or environmental risks. Taken together, the collaborative effort of stakeholders in risk assessment, management, and communication strategies is critical to ensuring the safety of biocidal products sold in the market as these strategies are constantly evolving.

Application of read-across methods as a framework for the estimation of emissions from chemical processes

The read-across method is a popular data gap filling technique with developed application for multiple purposes, including regulatory. Within the US Environmental Protection Agency's (US EPA) New Chemicals Program under Toxic Substances Control Act (TSCA), read-across has been widely used, as well as within technical guidance published by the Organization for Economic Co-operation and Development, the European Chemicals Agency, and the European Center for Ecotoxicology and Toxicology of Chemicals for filling chemical toxicity data gaps. Under the TSCA New Chemicals Review Program, US EPA is tasked with reviewing proposed new chemical applications prior to commencing commercial manufacturing within or importing into the United States. The primary goal of this review is to identify any unreasonable human health and environmental risks, arising from environmental releases/emissions during manufacturing and the resulting exposure from these environmental releases. The authors propose the application of read-across techniques for the development and use of a framework for estimating the emissions arising during the chemical manufacturing process. This methodology is to utilize available emissions data from a structurally similar analogue chemical or a group of structurally similar chemicals in a chemical family taking into consideration their physicochemical properties under specified chemical process unit operations and conditions. This framework is also designed to apply existing knowledge of read-across principles previously utilized in toxicity estimation for an analogue or category of chemicals and introduced and extended with a concurrent case study.

Harmonisation of read-across methodology for drug substance extractables and leachables (E&Ls)

Health-based exposure limits (HBELs) are derived for leachables from polymeric components that interact with the drug substance which exceed a safety concern threshold (SCT). However, given the nature of leachables, there is not always chemical-specific toxicology data. Read-across methodology specific to extractables and leachables (E&Ls) was developed based on survey data collected from 11 pharmaceutical companies and methodology used in other industries. One additional challenge for E&L read-across is most toxicology data is from the oral route of administration, whereas the parenteral route is very common for the leachable HBEL derivation. A conservative framework was developed to estimate oral bioavailability and the corresponding oral to parenteral extrapolation factor using physical chemical data. When this conservative framework was tested against 73 compounds with oral bioavailability data, it was found that the predicted bioavailability based on physico-chemical properties was conservatively greater than or equal to the experimental bioavailability 79% of the time. In conclusion, an E&L read-across methodology has been developed to provide a consistent, health protective framework for deriving HBELs when toxicology data is limited.

Ten Years of Using Key Characteristics of Human Carcinogens to Organize and Evaluate Mechanistic Evidence in IARC Monographs on the Identification of Carcinogenic Hazards to Humans: Patterns and Associations

Systematic review and evaluation of the mechanistic evidence only recently been instituted in cancer hazard identification step of decision-making. One example of organizing and evaluating mechanistic evidence is the Key Characteristics approach of the International Agency for Research on Cancer (IARC) Monographs on the Identification of Carcinogenic Hazards to Humans. The Key Characteristics of Human Carcinogens were proposed almost 10 years ago and have been used in every IARC Monograph since 2015. We investigated the patterns and associations in the use of Key Characteristics by the independent expert Working Groups. We examined 19 Monographs (2015-2022) that evaluated 73 agents. We extracted information on the conclusions by each Working Group on the strength of evidence for agent-Key Characteristic combinations, data types that were available for decisions, and the role mechanistic data played in the final cancer hazard classification. We conducted both descriptive and association analyses within and across data types. We found that IARC Working Groups were cautious when evaluating mechanistic evidence: for only ∼13% of the agents was strong evidence assigned for any Key Characteristic. Genotoxicity and cell proliferation were most data-rich, while little evidence was available for DNA repair and immortalization Key Characteristics. Analysis of the associations among Key Characteristics revealed that only chemical's metabolic activation was significantly co-occurring with genotoxicity and cell proliferation/death. Evidence from exposed humans was limited, while mechanistic evidence from rodent studies in vivo was often available. Only genotoxicity and cell proliferation/death were strongly associated with decisions on whether mechanistic data was impactful on the final cancer hazard classification. The practice of using the Key Characteristics approach is now well-established at IARC Monographs and other government agencies and the analyses presented herein will inform the future use of mechanistic evidence in regulatory decision-making.

Risk assessment in the 21st century: where are we heading?

Reliance on animal tests for chemical safety assessment is increasingly being challenged, not only because of ethical reasons, but also because they procrastinate regulatory decisions and because of concerns over the transferability of results to humans. New approach methodologies (NAMs) need to be fit for purpose and new thinking is required to reconsider chemical legislation, validation of NAMs and opportunities to move away from animal tests. This article summarizes the presentations from a symposium at the 2022 Annual Congress of the British Toxicology Society on the topic of the future of chemical risk assessment in the 21st century. The symposium included three case-studies where NAMs have been used in safety assessments. The first case illustrated how read-across augmented with some in vitro tests could be used reliably to perform the risk assessment of analogues lacking data. The second case showed how specific bioactivity assays could identify an NAM point of departure (PoD) and how this could be translated through physiologically based kinetic modelling in an in vivo PoD for the risk assessment. The third case showed how adverse-outcome pathway (AOP) information, including molecular-initiating event and key events with their underlying data, established for certain chemicals could be used to produce an in silico model that is able to associate chemical features of an unstudied substance with specific AOPs or AOP networks. The manuscript presents the discussions that took place regarding the limitations and benefits of these new approaches, and what are the barriers and the opportunities for their increased use in regulatory decision making.

Advancing the science on chemical classes

BACKGROUND

Hazard identification, risk assessment, regulatory, and policy activity are usually conducted on a chemical-by-chemical basis. Grouping chemicals into categories or classes is an underutilized approach that could make risk assessment and management of chemicals more efficient for regulators.

OBJECTIVE AND METHODS

While there are some available methods and regulatory frameworks that include the grouping of chemicals (e.g.,same molecular mechanism or similar chemical structure) there has not been a comprehensive evaluation of these different approaches nor a recommended course of action to better consider chemical classes in decision-making. This manuscript: 1) reviews current national and international approaches to grouping; 2) describes how groups could be defined based on the decision context (e.g., hazard/risk assessment, restrictions, prioritization, product development) and scientific considerations (e.g., intrinsic physical-chemical properties); 3) discusses advantages of developing a decision tree approach for grouping; 4) uses ortho-phthalates as a case study to identify and organize frameworks that could be used across agencies; and 5) discusses opportunities to advance the class concept within various regulatory decision-making scenarios.

RESULTS

Structural similarity was the most common grouping approach for risk assessment among regulatory agencies (national and state level) and non-regulatory organizations, albeit with some variations in its definition. Toxicity to the same target organ or to the same biological function was also used in a few cases. The phthalates case study showed that a decision tree approach for grouping should include questions about uses regulated by other agencies to encourage more efficient, coherent, and protective chemical risk management.

DISCUSSION AND CONCLUSION

Our evaluation of how classes of chemicals are defined and used identified commonalities and differences based on regulatory frameworks, risk assessments, and business strategies. We also identified that using a class-based approach could result in a more efficient process to reduce exposures to multiple hazardous chemicals and, ultimately, reduce health risks. We concluded that, in the absence of a prescribed method, a decision tree approach could facilitate the selection of chemicals belonging to a pre-defined class (e.g., chemicals with endocrine-disrupting activity; organohalogen flame retardants [OFR]) based on the decision-making context (e.g., regulatory risk management).

EU’s next generation risk assessment: hurdles and opportunities for new approach methodologies

The EU’s decision to ban animal testing for toxicity testing, has positively influenced the pace of developing New Approach Methodologies (NAMs). This development also supports replacing animal methods in other forms of risk assessment (RA), such as for oral-toxicity testing. This study aims to identify the hurdles and opportunities for validation and implementation of NAMs in the current EU’s chemical RA. Through conducting semi-structured interviews with 14 stakeholders, experiences and perspectives about the validation and implementation of NAMs in RA for orally ingested chemicals were analyzed. Stakeholders considered the use of NAMs for RA processes both a cultural and generational issue. Both were perceived as hurdles for reaching the next generation RA approach. The differing views on NAMs originated from experience and stakeholder positions, but communication and collaboration on developing future RA approaches could support overcoming this skepticism. Irrespectively of their background, all interviewees were generally optimistic that NAMs will support the development of more accurate and sustainable RA. This research highlights the need for the EU to adjust legislation and guidance documents to shift in testing requirements from the traditional overexposure approach to more predictive, mechanistic testing in RA, which will take time. This study, however, shows that—when all stakeholders engage in communication and confidence building—NAMs can already play an important role in reducing and refining animal testing.

Grouping of UVCB substances with dose-response transcriptomics data from human cell-based assays

The application of in vitro biological assays as new approach methodologies (NAMs) to support grouping of UVCB (unknown or variable composition, complex reaction products, and biological materials) substances has recently been demonstrated. In addition to cell-based phenotyping as NAMs, in vitro transcriptomic profiling is used to gain deeper mechanistic understanding of biological responses to chemicals and to support grouping and read-across. However, the value of gene expression profiling for characterizing complex substances like UVCBs has not been explored. Using 141 petroleum substance extracts, we performed dose-response transcriptomic profiling in human induced pluripotent stem cell (iPSC)-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, as well as cell lines MCF7 and A375. The goal was to determine whether transcriptomic data can be used to group these UVCBs and to further characterize the molecular basis for in vitro biological responses. We found distinct transcriptional responses for petroleum substances by manufacturing class. Pathway enrichment informed interpretation of effects of substances and UVCB petroleum-class. Transcriptional activity was strongly correlated with concentration of polycyclic aromatic compounds (PAC), especially in iPSC-derived hepatocytes. Supervised analysis using transcriptomics, alone or in combination with bioactivity data collected on these same substances/cells, suggest that transcriptomics data provide useful mechanistic information, but only modest additional value for grouping. Overall, these results further demonstrate the value of NAMs for grouping of UVCBs, identify informative cell lines, and provide data that could be used for justifying selection of substances for further testing that may be required for registration.

RAID: Regression Analysis–Based Inductive DNA Microarray for Precise Read-Across

Chemical structure-based read-across represents a promising method for chemical toxicity evaluation without the need for animal testing; however, a chemical structure is not necessarily related to toxicity. Therefore, in vitro studies were often used for read-across reliability refinement; however, their external validity has been hindered by the gap between in vitro and in vivo conditions. Thus, we developed a virtual DNA microarray, regression analysis–based inductive DNA microarray (RAID), which quantitatively predicts in vivo gene expression profiles based on the chemical structure and/or in vitro transcriptome data. For each gene, elastic-net models were constructed using chemical descriptors and in vitro transcriptome data to predict in vivo data from in vitro data (in vitro to in vivo extrapolation; IVIVE). In feature selection, useful genes for assessing the quantitative structure–activity relationship (QSAR) and IVIVE were identified. Predicted transcriptome data derived from the RAID system reflected the in vivo gene expression profiles of characteristic hepatotoxic substances. Moreover, gene ontology and pathway analysis indicated that nuclear receptor-mediated xenobiotic response and metabolic activation are related to these gene expressions. The identified IVIVE-related genes were associated with fatty acid, xenobiotic, and drug metabolisms, indicating that in vitro studies were effective in evaluating these key events. Furthermore, validation studies revealed that chemical substances associated with these key events could be detected as hepatotoxic biosimilar substances. These results indicated that the RAID system could represent an alternative screening test for a repeated-dose toxicity test and toxicogenomics analyses. Our technology provides a critical solution for IVIVE-based read-across by considering the mode of action and chemical structures.

eTRANSAFE: Building a sustainable framework to share reproducible drug safety knowledge with the public domain

Integrative drug safety research in translational health informatics has rapidly evolved and included data that are drawn in from many resources, combining diverse data that are either reused from (curated) repositories, or newly generated at source. Each resource is mandated by different sets of metadata rules that are imposed on the incoming data. Combination of the data cannot be readily achieved without interference of data stewardship and the top-down policy guidelines that supervise and inform the process for data combination to aid meaningful interpretation and analysis of such data.

The eTRANSAFE Consortium's effort to drive integrative drug safety research at a large scale hereby present the lessons learnt and the proposal of solution at the guidelines in practice at this Innovative Medicines Initiative (IMI) project. Recommendations in these guidelines were compiled from feedback received from key stakeholders in regulatory agencies, EFPIA companies, and academic partners. The research reproducibility guidelines presented in this study lay the foundation for a comprehensive data sharing and knowledge management plans accounting for research data management in the drug safety space - FAIR data sharing guidelines, and the model verification guidelines as generic deliverables that best practices that can be reused by other scientific community members at large.

FAIR data sharing is a dynamic landscape that rapidly evolves with fast-paced technology advancements. The research reproducibility in drug safety guidelines introduced in this study provides a reusable framework that can be adopted by other research communities that aim to integrate public and private data in biomedical research space.

A 10-step framework for use of read-across (RAX) in next generation risk assessment (NGRA) for cosmetics safety assessment

This paper presents a 10-step read-across (RAX) framework for use in cases where a threshold of toxicological concern (TTC) approach to cosmetics safety assessment is not possible. RAX builds on established approaches that have existed for more than two decades using chemical properties and in silico toxicology predictions, by further substantiating hypotheses on toxicological similarity of substances, and integrating new approach methodologies (NAM) in the biological and kinetic domains. NAM include new types of data on biological observations from, for example, in vitro assays, toxicogenomics, metabolomics, receptor binding screens and uses physiologically-based kinetic (PBK) modelling to inform about systemic exposure. NAM data can help to substantiate a mode/mechanism of action (MoA), and if similar chemicals can be shown to work by a similar MoA, a next generation risk assessment (NGRA) may be performed with acceptable confidence for a data-poor target substance with no or inadequate safety data, based on RAX approaches using data-rich analogue(s), and taking account of potency or kinetic/dynamic differences.

Ecological and human health risks of manure-borne steroid estrogens: A 20-year global synthesis study

Estrone (E1), 17α-estradiol (17α-E2), 17β-estradiol (17β-E2), and estriol (E3) are persistent in livestock manure and present serious pollution concerns because they can trigger endocrine disruption at part-per-trillion levels. This study conducted a global analysis of estrogen occurrence in manure using all literature data over the past 20 years. Besides, predicted environmental concentration (PEC) in soil and water was estimated using fate models, and risk/harm quotient (RQ/HQ) methods were applied to screen risks on children as well as on sensitive aquatic and soil species. The estradiol equivalent values ranged from 6.6 to 4.78 × 10⁴ ng/g and 12.4 to 9.46 × 10⁴ ng/L in the solid and liquid fraction. The estrogenic potency ranking in both fractions were 17β-E2> E1>17α-E2>E3. RQs of measured environmental concentration in the liquid fraction pose medium (E3) to high risk (E1, 17α-E2 & 17β-E2) to fish but are lower than risks posed by xenoestrogens. However, the RQ of PECs on both soil organisms and aquatic species were insignificant (RQ < 0.01), and HQs of contaminated water and soil ingestion were within acceptable limits. Nevertheless, meticulous toxicity studies are still required to confirm (or deny) the findings because endocrine disruption potency from mixtures of these classes of compounds cannot be ignored.

Chemical characterization of anemia-inducing aniline-related substances and their application to the construction of a decision tree-based anemia prediction model

Anemia is a well-observed toxicity of chemical substances, and aniline is a typical anemia-inducing substance. However, it remains unclear whether all aniline-like substances with various substituents could induce anemia. We thus investigated the physicochemical characteristics of anemia-inducing substances by decision tree analyses. Training and validation substances were selected from a publicly available database of rat repeated-dose toxicity studies, and discrimination models were constructed by decision tree and bootstrapping methods with molecular descriptors as explanatory variables. To improve the accuracy of discrimination, we individually evaluated the explanatory variables to modify them, established "prerules" that were applied before subjecting a substance to a decision tree by considering metabolism, such as azo reduction and N-dealkylation, and introduced the idea of "partly negative" evaluation for substances having multiple aniline-like substructures. The final model obtained showed 79.2% and 77.5% accuracy for the training and validation dataset, respectively. In addition, we identified some chemical properties that reduce the anemia inducibility of aniline-like substances, including the addition of a sulfonate or carboxy functional group and/or a bulky multiring structure to anilines. In conclusion, the present findings will provide a novel insight into the mechanistic understanding of chemically induced anemia and help to develop a prediction system.

Determination of in vitro metabolic hepatic clearance of valproic acid (VPA) and five analogues by UPLC-MS-QTOF, applicable in alternatives to animal testing

Laboratory measurements of intrinsic clearance support the development of TK models, with potential relevance to weight of evidence toxicity assessments of xenobiotics, including read-across, the concept of predictive estimation by data extrapolation between chemicals of similar structure (analogues). In this work a procedure with analytical method for determination of in vitro hepatic metabolic clearance, relevant to biotransformation toxicokinetic (TK) modelling, is presented. Cryopreserved primary human hepatocytes represent a suitable cells, due to their biological characteristics, for providing an in vitro model for simulating in vivo metabolic clearance. The experimental part considered an adequate sequential time-frame for collecting samples and controls for all chemicals tested, including centrifugation and aliquoting of the corresponding fractions until the instrumental session. For the first time, in vitro hepatocyte intrinsic clearance was measured for six analogue test chemicals: valproic acid, 2-ethyl caproic acid, octanoic acid, valeric acid, 2-methyl butyric acid and 2-trans pentenoic acid, during incubated cell culture exposure up to 2 h or 3.5 h. The time dependence of any metabolism was determined from analysis of the supernatant at intervals using a new developed analytical method for UPLC coupled with QTOF mass spectrometer. The chemicals could then be ranked by their relative intrinsic clearance. The analyses were reproducible, with coherence of the calculated in vitro intrinsic clearance between experiments.

In Vitro Bioavailability of the Hydrocarbon Fractions of Dimethyl Sulfoxide Extracts of Petroleum Substances

Determining the in vitro bioavailable concentration is a critical, yet unmet need to refine in vitro-to-in vivo extrapolation for unknown or variable composition, complex reaction product or biological material (UVCB) substances. UVCBs such as petroleum substances are commonly subjected to dimethyl sulfoxide (DMSO) extraction in order to retrieve the bioactive polycyclic aromatic compound (PAC) portion for in vitro testing. In addition to DMSO extraction, protein binding in cell culture media and dilution can all influence in vitro bioavailable concentrations of aliphatic and aromatic compounds in petroleum substances. However, these in vitro factors have not been fully characterized. In this study, we aimed to fill in these data gaps by characterizing the effects of these processes using both a defined mixture of analytical standards containing aliphatic and aromatic hydrocarbons, as well as 4 refined petroleum products as prototypical examples of UVCBs. Each substance was extracted with DMSO, and the protein binding in cell culture media was measured by using solid-phase microextraction. Semiquantitative analysis for aliphatic and aromatic compounds was achieved via gas chromatography-mass spectrometry. Our results showed that DMSO selectively extracted PACs from test substances, and that chemical profiles of PACs across molecular classes remained consistent after extraction. With respect to protein binding, chemical profiles were retained at a lower dilution (higher concentration), but a greater dilution factor (ie, lower concentration) resulted in higher protein binding in cell medium, which in turn altered the ultimate chemical profile of bioavailable PACs. Overall, this case study demonstrates that extraction procedures, protein binding in cell culture media, and dilution factors prior to in vitro testing can all contribute to determining the final bioavailable concentrations of bioactive constituents of UVCBs in vitro. Thus, in vitro-to-in vivo extrapolation for UVCBs may require greater attention to the concentration-dependent and compound-specific differences in recovery and bioavailability.

Current opinion on risk assessment of cosmetics

Risk assessment of cosmetic ingredients is a useful scientific method to characterize potential adverse effects resulting from using cosmetics. The process of risk assessment consists of four steps: hazard identification, dose-response assessment, exposure assessment, and risk characterization. Hazard identification of chemicals refers to the initial stage of risk assessment and generally utilizes animal studies to evaluate toxicity. Since 2013, however, toxicity studies of cosmetic ingredients using animals have not been permitted in the EU and alternative toxicity test methods for animal studies have momentum to be developed for cosmetic ingredients. In this paper, we briefly review the alternative test methods that are available for cosmetic ingredients including read-across, in silico, in chemico, and invitro methods. In addition, new technologies such as omics and artificial intelligence (AI) have been discussed to expand or improve the knowledge and hazard identification of cosmetic ingredients. Aggregate exposure of cosmetic ingredients is another safety issue and methods for its improvement were reviewed. There have been concerns over the safety of nano-cosmetics for a long time, but the risk of nano-cosmetics remains unclear. Therefore, current issues of cosmetic risk assessment are discussed and expert opinion will be provided for the safety of cosmetics.

Integrated approaches to testing and assessment as a tool for the hazard assessment and risk characterization of cosmetic preservatives

The safety assessment of cosmetic products is based on the safety of the ingredients, which requires information on chemical structures, toxicological profiles, and exposure data. Approximately 6% of the population is sensitized to cosmetic ingredients, especially preservatives and fragrances. In this context, the aim of this study was to perform a hazard assessment and risk characterization of benzalkonium chloride (BAC), benzyl alcohol (BA), caprylyl glycol (CG), ethylhexylglycerin (EG), chlorphenesin (CP), dehydroacetic acid (DHA), sodium dehydroacetate (SDH), iodopropynyl butylcarbamate (IPBC), methylchloroisothiazolinone and methylisothiazolinone (MCI/MIT), methylisothiazolinone (MIT), phenoxyethanol (PE), potassium sorbate (PS), and sodium benzoate (SB). Considering the integrated approaches to testing and assessment (IATA) and weight of evidence (WoE) as a decision tree, based on published safety reports. The hazard assessment was composed of a toxicological matrix correlating the toxicity level, defined as low (L), moderate (M), or high (H) and local or systemic exposure, considering the endpoints of skin sensitization, skin irritation, eye irritation, phototoxicity, acute oral toxicity, carcinogenicity, mutagenicity/genotoxicity, and endocrine activity. In a risk assessment approach, most preservatives had a margin of safety (MoS) above 100, except for DHA, SDH, and EG, considering the worst-case scenario (100% dermal absorption). However, isolated data do not set up a safety assessment. It is necessary to carry out a rational risk characterization considering hazard and exposure assessment to estimate the level of risk of an adverse health outcome, based on the concentration in a product, frequency of use, type of product, route of exposure, body surface location, and target population.

Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor

Endocrine-disrupting chemicals (EDCs) can interact with nuclear receptors, including estrogen receptor α (ERα) and androgen receptor (AR), to affect the normal endocrine system function, causing severe symptoms. Limited studies queried the EDC mechanisms, focusing on limited chemicals or a set of structurally similar compounds. It remained uncertain how hundreds of diverse EDCs could bind to ERα and AR and cause distinct functional consequences. Here, we employed a series of computational methodologies to investigate the structural features of EDCs that bind to and activate ERα and AR based on more than 4000 compounds. We used molecular docking and molecular dynamics simulations to elucidate the functional consequences and validated structure-function correlations experimentally using a time-resolved fluorescence resonance energy-transfer assay. We found that EDCs share three levels of key fragments. Primary (20 for ERα and 18 for AR) and secondary fragments (38 for ERα and 29 for AR) are responsible for the binding to receptors, and tertiary fragments determine the activity type (agonist, antagonist, or mixed). In summary, our study provides a general mechanism for the EDC function. Discovering the three levels of key fragments may drive fast screening and evaluation of potential EDCs from large sets of commercially used synthetic compounds.

Application of Biomarker Tools Using Bivalve Models Toward the Development of Adverse Outcome Pathways for Contaminants of Emerging Concern

As contaminant exposures in aquatic ecosystems continue to increase, the need for streamlining research efforts in environmental toxicology using predictive frameworks also grows. One such framework is the adverse outcome pathway (AOP). An AOP framework organizes and utilizes toxicological information to connect measurable molecular endpoints to an adverse outcome of regulatory relevance via a series of events at different levels of biological organization. Molecular endpoints or biomarkers are essential to develop AOPs and are valuable early warning signs of the toxicity of pollutants, including contaminants of emerging concern. Ecological risk-assessment approaches using tools such as biomarkers and AOPs benefit from identification of molecular targets conserved across species. Bivalve models are useful in such approaches and integral to our understanding of ecological and human health risks associated with contaminant exposures. We discuss the value of using biomarker approaches in bivalve models to meet the demands of twenty-first-century toxicology. Environ Toxicol Chem 2020;39:1472-1484. © 2020 SETAC.

Rapid hazard characterization of environmental chemicals using a compendium of human cell lines from different organs

The lack of adequate toxicity data for the vast majority of chemicals in the environment has spurred the development of new approach methodologies (NAMs). This study aimed to develop a practical high-throughput in vitro model for rapidly evaluating potential hazards of chemicals using a small number of human cells. Forty-two compounds were tested using human induced pluripotent stem cell (iPSC)-derived cells (hepatocytes, neurons, cardiomyocytes and endothelial cells), and a primary endothelial cell line. Both functional and cytotoxicity endpoints were evaluated using high-content imaging. Concentration-response was used to derive points-of-departure (POD). PODs were integrated with ToxPi and used as surrogate NAM-based PODs for risk characterization. We found chemical class-specific similarity among the chemicals tested; metal salts exhibited the highest overall bioactivity. We also observed cell type-specific patterns among classes of chemicals, indicating the ability of the proposed in vitro model to recognize effects on different cell types. Compared to available NAM datasets, such as ToxCast/Tox21 and chemical structure-based descriptors, we found that the data from the five-cell-type model was as good or even better in assigning compounds to chemical classes. Additionally, the PODs from this model performed well as a conservative surrogate for regulatory in vivo PODs and were less likely to underestimate in vivo potency and potential risk compared to other NAM-based PODs. In summary, we demonstrate the potential of this in vitro screening model to inform rapid risk-based decision-making through ranking, clustering, and assessment of both hazard and risks of diverse environmental chemicals.

Ten years post-GAO assessment, FDA remains uninformed of potentially harmful GRAS substances in foods

It has been approximately 10 years since the Government Accountability Office (GAO) published its report to Congress entitled, FDA Should Strengthen Its Oversight of Food Ingredients Determined to be Generally Recognized as Safe (GRAS), which strongly criticized FDA noting that its "oversight process does not help ensure the safety of all new GRAS determinations." Congress requested GAO to undertake this audit as a result of concerns that GRAS substances added to foods did not require FDA approval. Since 2010, FDA has addressed only a few of the criticisms regarding its process for establishing a food substance as GRAS. However, several of the most important GAO recommendations remain unaddressed, and most critically, FDA has chosen to remain uninformed about food substances self-determined as GRAS by manufacturers. In its 2016 final rule Substances Generally Recognized as Safe, FDA did not take the opportunity to include a provision for creation of a master list of all GRAS chemicals used in food, nor did the FDA request the authority to do so from Congress. FDA cannot fulfill its statutory obligation for ensuring the chemical safety of the U.S. food supply if it does not know which substances, in which quantities, have been added to foods.

New ideas for non-animal approaches to predict repeated-dose systemic toxicity: Report from an EPAA Blue Sky Workshop

The European Partnership for Alternative Approaches to Animal Testing (EPAA) convened a 'Blue Sky Workshop' on new ideas for non-animal approaches to predict repeated-dose systemic toxicity. The aim of the Workshop was to formulate strategic ideas to improve and increase the applicability, implementation and acceptance of modern non-animal methods to determine systemic toxicity. The Workshop concluded that good progress is being made to assess repeated dose toxicity without animals taking advantage of existing knowledge in toxicology, thresholds of toxicological concern, adverse outcome pathways and read-across workflows. These approaches can be supported by New Approach Methodologies (NAMs) utilising modern molecular technologies and computational methods. Recommendations from the Workshop were based around the needs for better chemical safety assessment: how to strengthen the evidence base for decision making; to develop, standardise and harmonise NAMs for human toxicity; and the improvement in the applicability and acceptance of novel techniques. "Disruptive thinking" is required to reconsider chemical legislation, validation of NAMs and the opportunities to move away from reliance on animal tests. Case study practices and data sharing, ensuring reproducibility of NAMs, were viewed as crucial to the improvement of non-animal test approaches for systemic toxicity.

21st Century Approaches for Evaluating Exposures, Biological Activity, and Risks of Complex Substances: Workshop highlights

The June 2019 workshop 21st Century Approaches for Evaluating Exposures, Biological Activity, and Risks of Complex Substances, co-organised by the International Council of Chemical Association's Long-Range Research Initiative and the European Commission's Joint Research Centre, is summarised. Focus was the need for improved approaches to evaluate the safety of complex substances. Approximately 10% and 20% of substances registered under the EU chemicals legislation are 'multi-constituent substances' and 'substances of unknown or variable compositions, complex reaction products and biological substances' (UVCBs), respectively, and UVCBs comprise approximately 25% of the U.S. Toxic Substances Control Act Inventory. Workshop participants were asked to consider how the full promise of new approach methodologies (NAMs) could be brought to bear to evaluate complex substances. Sessions focused on using NAMs for screening, biological profiling, and in complex risk evaluations; improving read-across approaches employing new data streams; and methods to evaluate exposure and dosimetry. The workshop concluded with facilitated discussions to explore actionable steps forward. Given the diversity of complex substances, no single 'correct' approach was seen as workable. The path forward should focus on 'learning by doing' by developing and openly sharing NAM-based fit-for-purpose case examples for evaluating biological activity, exposures and risks of complex substances.

QSAR/QSPR models based on quantum chemistry for risk assessment of pesticides according to current European legislation

In Europe, agencies and official organizations involved in the pesticide control such as the EFSA, ECHA, JRC and ECETOC or even the OECD are pointing out that the software tools based on quantitative structure relationship models, i.e. QSAR and QSPR, have a huge potential to improve the pesticide risk assessment process. In this sense, these non-animal test methods can promote the competitiveness of agriculture in this region: the consumer safety is increased with them due to the possibility of perform an overall better risk assessment of the degradation products and metabolites from pesticides. However, the use of theses computational-based (in silico) tools must be much more systematised and harmonised, improving their validation and including case studies to test them. To open databases, incorporating critical data in an orderly manner for building the models, becomes also necessary. Moreover, quantum chemistry through the Density Functional Theory should be promoted as tool for calculation of quantum descriptors, especially for the study of similar compounds with the same carbon skeleton but differing substitution patterns, e.g. isomers.

Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy

In silico methods and models are increasingly used for predicting properties of chemicals for hazard identification and hazard characterisation in the absence of experimental toxicity data. Many in silico models are available and can be used individually or in an integrated fashion. Whilst such models offer major benefits to toxicologists, risk assessors and the global scientific community, the lack of a consistent framework for the integration of in silico results can lead to uncertainty and even contradictions across models and users, even for the same chemicals. In this context, a range of methods for integrating in silico results have been proposed on a statistical or case-specific basis. Read-across constitutes another strategy for deriving reference points or points of departure for hazard characterisation of untested chemicals, from the available experimental data for structurally-similar compounds, mostly using expert judgment. Recently a number of software systems have been developed to support experts in this task providing a formalised and structured procedure. Such a procedure could also facilitate further integration of the results generated from in silico models and read-across. This article discusses a framework on weight of evidence published by EFSA to identify the stepwise approach for systematic integration of results or values obtained from these "non-testing methods". Key criteria and best practices for selecting and evaluating individual in silico models are also described, together with the means to combining the results, taking into account any limitations, and identifying strategies that are likely to provide consistent results.

Unlocking the potential of in silico chemical safety assessment - A report on a cross-sector symposium on current opportunities and future challenges

In silico chemical safety assessment can support the evaluation of hazard and risk following potential exposure to a substance. A symposium identified a number of opportunities and challenges to implement in silico methods, such as quantitative structure-activity relationships (QSARs) and read-across, to assess the potential harm of a substance in a variety of exposure scenarios, e.g. pharmaceuticals, personal care products, and industrial chemicals. To initiate the process of in silico safety assessment, clear and unambiguous problem formulation is required to provide the context for these methods. These approaches must be built on data of defined quality, while acknowledging the possibility of novel data resources tapping into on-going progress with data sharing. Models need to be developed that cover appropriate toxicity and kinetic endpoints, and that are documented appropriately with defined uncertainties. The application and implementation of in silico models in chemical safety requires a flexible technological framework that enables the integration of multiple strands of data and evidence. The findings of the symposium allowed for the identification of priorities to progress in silico chemical safety assessment towards the animal-free assessment of chemicals.

Linking Bisphenol S to Adverse Outcome Pathways Using a Combined Text Mining and Systems Biology Approach

BACKGROUND

Available toxicity data can be optimally interpreted if they are integrated using computational approaches such as systems biology modeling. Such approaches are particularly warranted in cases where regulatory decisions have to be made rapidly.

OBJECTIVES

The study aims at developing and applying a new integrative computational strategy to identify associations between bisphenol S (BPS), a substitute for bisphenol A (BPA), and components of adverse outcome pathways (AOPs).

METHODS

The proposed approach combines a text mining (TM) procedure and integrative systems biology to comprehensively analyze the scientific literature to enrich AOPs related to environmental stressors. First, to identify relevant associations between BPS and different AOP components, a list of abstracts was screened using the developed text-mining tool AOP-helpFinder, which calculates scores based on the graph theory to prioritize the findings. Then, to fill gaps between BPS, biological events, and adverse outcomes (AOs), a systems biology approach was used to integrate information from the AOP-Wiki and ToxCast databases, followed by manual curation of the relevant publications.

RESULTS

Links between BPS and 48 AOP key events (KEs) were identified and scored via 31 references. The main outcomes were related to reproductive health, endocrine disruption, impairments of metabolism, and obesity. We then explicitly analyzed co-mention of the terms BPS and obesity by data integration and manual curation of the full text of the publications. Several molecular and cellular pathways were identified, which allowed the proposal of a biological explanation for the association between BPS and obesity.

CONCLUSIONS

By analyzing dispersed information from the literature and databases, our novel approach can identify links between stressors and AOP KEs. The findings associating BPS and obesity illustrate the use of computational tools in predictive toxicology and highlight the relevance of the approach to decision makers assessing substituents to toxic chemicals. https://doi.org/10.1289/EHP4200.

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity

In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by utilizing novel testing approaches that reduce the testing of vertebrate animals. Central to this mission is the advancement of computational toxicology and artificial intelligence approaches to implementing innovative testing methods. In the current big data era, the terms volume (amount of data), velocity (growth of data), and variety (the diversity of sources) have been used to characterize the currently available chemical, in vitro, and in vivo data for toxicity modeling purposes. Furthermore, as suggested by various scientists, the variability (internal consistency or lack thereof) of publicly available data pools, such as PubChem, also presents significant computational challenges. The development of novel artificial intelligence approaches based on public massive toxicity data is urgently needed to generate new predictive models for chemical toxicity evaluations and make the developed models applicable as alternatives for evaluating untested compounds. In this procedure, traditional approaches (e.g., QSAR) purely based on chemical structures have been replaced by newly designed data-driven and mechanism-driven modeling. The resulting models realize the concept of adverse outcome pathway (AOP), which can not only directly evaluate toxicity potentials of new compounds, but also illustrate relevant toxicity mechanisms. The recent advancement of computational toxicology in the big data era has paved the road to future toxicity testing, which will significantly impact on the public health.

Mixtures of Aluminum and Indium Induce More than Additive Phenotypic and Toxicogenomic Responses in Daphnia magna

Aquatic systems are contaminated by many metals but their effects as mixtures on organisms are not well understood. Here, we assessed effects of aluminum with fairly well-known modes of actions and indium, an understudied emerging contaminant from electronics, followed by studying equi-effective mixtures thereof. We report acute and adverse phenotypic effects in Daphnia magna adults and global transcriptomic effects employing RNA sequencing in neonates. The mixture induced more than additive activity in mortality and in physiological effects, including growth and reproduction. Similarly, transcriptomic effects were more than additive, as indicated by a markedly higher number of 463 differentially expressed transcripts in the mixture and by distinct classes of genes assigned to several biological functions, including metabolic processes, suggesting depleted energy reserves, which may be responsible for the observed impaired reproduction and growth. A gene set enrichment analysis (GSEA) of a priori known response pathways for aluminum confirmed activation of distinct molecular pathways by indium. Our study is highlighting more than additive effects at the transcriptional and physiological level and is providing a state-of-the art approach to mixture analysis, which is important for risk assessment of these metals and metal mixtures.

The Impact of Novel Assessment Methodologies in Toxicology on Green Chemistry and Chemical Alternatives

The field of experimental toxicology is rapidly advancing by incorporating novel techniques and methods that provide a much more granular view into the mechanisms of potential adverse effects of chemical exposures on human health. The data from various in vitro assays and computational models are useful not only for increasing confidence in hazard and risk decisions, but also are enabling better, faster and cheaper assessment of a greater number of compounds, mixtures, and complex products. This is of special value to the field of green chemistry where design of new materials or alternative uses of existing ones is driven, at least in part, by considerations of safety. This article reviews the state of the science and decision-making in scenarios when little to no data may be available to draw conclusions about which choice in green chemistry is "safer." It is clear that there is no "one size fits all" solution and multiple data streams need to be weighed in making a decision. Moreover, the overall level of familiarity of the decision-makers and scientists alike with new assessment methodologies, their validity, value and limitations is evolving. Thus, while the "impact" of the new developments in toxicology on the field of green chemistry is great already, it is premature to conclude that the data from new assessment methodologies have been widely accepted yet.

INSIdE NANO: a systems biology framework to contextualize the mechanism-of-action of engineered nanomaterials

Engineered nanomaterials (ENMs) are widely present in our daily lives. Despite the efforts to characterize their mechanism of action in multiple species, their possible implications in human pathologies are still not fully understood. Here we performed an integrated analysis of the effects of ENMs on human health by contextualizing their transcriptional mechanism-of-action with respect to drugs, chemicals and diseases. We built a network of interactions of over 3,000 biological entities and developed a novel computational tool, INSIdE NANO, to infer new knowledge about ENM behavior. We highlight striking association of metal and metal-oxide nanoparticles and major neurodegenerative disorders. Our novel strategy opens possibilities to achieve fast and accurate read-across evaluation of ENMs and other chemicals based on their biosignatures.

Multi-dimensional in vitro bioactivity profiling for grouping of glycol ethers

High-content screening data derived from physiologically-relevant in vitro models promise to improve confidence in data-integrative groupings for read-across in human health safety assessments. The biological data-based read-across concept is especially applicable to bioactive chemicals with defined mechanisms of toxicity; however, the challenge of data-derived groupings for chemicals that are associated with little or no bioactivity has not been explored. In this study, we apply a suite of organotypic and population-based in vitro models for comprehensive bioactivity profiling of twenty E-Series and P-Series glycol ethers, solvents with a broad variation in toxicity ranging from relatively non-toxic to reproductive and hematopoetic system toxicants. Both E-Series and P-Series glycol ethers elicited cytotoxicity only at high concentrations (mM range) in induced pluripotent stem cell-derived hepatocytes and cardiomyocytes. Population-variability assessment comprised a study of cytotoxicity in 94 human lymphoblast cell lines from 9 populations and revealed differences in inter-individual variability across glycol ethers, but did not indicate population-specific effects. Data derived from various phenotypic and transcriptomic assays revealed consistent bioactivity trends between both cardiomyocytes and hepatocytes, indicating a more universal, rather than cell-type specific mode-of-action for the tested glycol ethers in vitro. In vitro bioactivity-based similarity assessment using Toxicological Priority Index (ToxPi) showed that glycol ethers group according to their alcohol chain length, longer chains were associated with increased bioactivity. While overall in vitro bioactivity profiles did not correlate with in vivo toxicity data on glycol ethers, in vitro bioactivity of E-series glycol ethers were indicative of and correlated with in vivo irritation scores.

Big-data and machine learning to revamp computational toxicology and its use in risk assessment

The creation of large toxicological databases and advances in machine-learning techniques have empowered computational approaches in toxicology. Work with these large databases based on regulatory data has allowed reproducibility assessment of animal models, which highlight weaknesses in traditional in vivo methods. This should lower the bars for the introduction of new approaches and represents a benchmark that is achievable for any alternative method validated against these methods. Quantitative Structure Activity Relationships (QSAR) models for skin sensitization, eye irritation, and other human health hazards based on these big databases, however, also have made apparent some of the challenges facing computational modeling, including validation challenges, model interpretation issues, and model selection issues. A first implementation of machine learning-based predictions termed REACHacross achieved unprecedented sensitivities of >80% with specificities >70% in predicting the six most common acute and topical hazards covering about two thirds of the chemical universe. While this is awaiting formal validation, it demonstrates the new quality introduced by big data and modern data-mining technologies. The rapid increase in the diversity and number of computational models, as well as the data they are based on, create challenges and opportunities for the use of computational methods.

One science-driven approach for the regulatory implementation of alternative methods: A multi-sector perspective

EU regulations call for the use of alternative methods to animal testing. During the last decade, an increasing number of alternative approaches have been formally adopted. In parallel, new 3Rs-relevant technologies and mechanistic approaches have increasingly contributed to hazard identification and risk assessment evolution. In this changing landscape, an EPAA meeting reviewed the challenges that different industry sectors face in the implementation of alternative methods following a science-driven approach. Although clear progress was acknowledged in animal testing reduction and refinement thanks to an integration of scientifically robust approaches, the following challenges were identified: i) further characterization of toxicity pathways; ii) development of assays covering current scientific gaps, iii) better characterization of links between in vitro readouts and outcome in the target species; iv) better definition of alternative method applicability domains, and v) appropriate implementation of the available approaches. For areas having regulatory adopted alternative methods (e.g., vaccine batch testing), harmonised acceptance across geographical regions was considered critical for broader application. Overall, the main constraints to the application of non-animal alternatives are the still existing gaps in scientific knowledge and technological limitations. The science-driven identification of most appropriate methods is key for furthering a multi-sectorial decrease in animal testing.

Relevance and Application of Read-Across - Mini Review of European Consensus Platform for Alternatives and Scandinavian Society for Cell Toxicology 2017 Workshop Session

Structure activity relationship (SAR)-based read-across is an effective approach for addressing data gaps in human health risk assessment for 'data-poor' chemicals. In read-across, available data on chemical structural analogues are used to predict the toxicity potential of the data-poor chemical. This approach has long been recognized by regulatory agencies and used by industry to evaluate the hazards of chemicals for which there are limited direct data. Construction of a scientifically robust SAR-based read-across hazard assessment is a complex and iterative process involving multiple considerations in each step. Traditional in vivo data generated using regulatory guideline compliant study designs typically forms the basis for read-across assessments. Recently, however, new data streams have been explored and incorporated to enhance read-across predictivity. These in vitro and omics data streams may be used in different ways involving identification of hazards or to provide insight into modes of action for observed toxicological responses. These 'new approach methods' can also enable comparison of biological responses across analogues or a category of structurally related chemicals in order to establish a pattern of biological similarity in addition chemical similarity and/or to help address potency differences across a category. The purpose of this workshop session was to inform on practical considerations in conducting SAR-based read-across assessments and to review recent activities related to application of new approach methods to the practice of read-across.

Application of the key characteristics of carcinogens in cancer hazard identification

Smith et al. (Env. Health Perspect. 124: 713, 2016) identified 10 key characteristics (KCs), one or more of which are commonly exhibited by established human carcinogens. The KCs reflect the properties of a cancer-causing agent, such as 'is genotoxic,' 'is immunosuppressive' or 'modulates receptor-mediated effects,' and are distinct from the hallmarks of cancer, which are the properties of tumors. To assess feasibility and limitations of applying the KCs to diverse agents, methods and results of mechanistic data evaluations were compiled from eight recent IARC Monograph meetings. A systematic search, screening and evaluation procedure identified a broad literature encompassing multiple KCs for most (12/16) IARC Group 1 or 2A carcinogens identified in these meetings. Five carcinogens are genotoxic and induce oxidative stress, of which pentachlorophenol, hydrazine and malathion also showed additional KCs. Four others, including welding fumes, are immunosuppressive. The overall evaluation was upgraded to Group 2A based on mechanistic data for only two agents, tetrabromobisphenol A and tetrachloroazobenzene. Both carcinogens modulate receptor-mediated effects in combination with other KCs. Fewer studies were identified for Group 2B or 3 agents, with the vast majority (17/18) showing only one or no KCs. Thus, an objective approach to identify and evaluate mechanistic studies pertinent to cancer revealed strong evidence for multiple KCs for most Group 1 or 2A carcinogens but also identified opportunities for improvement. Further development and mapping of toxicological and biomarker endpoints and pathways relevant to the KCs can advance the systematic search and evaluation of mechanistic data in carcinogen hazard identification.