A case study on the application of an expert-driven read-across approach in support of quantitative risk assessment of p,p'-dichlorodiphenyldichloroethane

Effects of organochlorine pesticides on human and rat 17β-hydroxysteroid dehydrogenase 1 activity: Structure-activity relationship and in silico docking analysis

The objective of this study was to examine the effect of 11 organochlorine pesticides on human and rat 17β-Hydroxysteroid dehydrogenase 1 (17β-HSD1) in human placental and rat ovarian microsome and on estradiol production in BeWo cells. The results showed that the IC50 values for endosulfan, fenhexamid, chlordecone, and rhothane on human 17β-HSD1 were 21.37, 73.25, 92.80, and 117.69 μM. Kinetic analysis revealed that endosulfan acts as a competitive inhibitor, fenhexamid as a mixed/competitive inhibitor, chlordecone and rhothane as a mixed/uncompetitive inhibitor. In BeWo cells, all insecticides except endosulfan significantly decreased estradiol production at 100 μM. For rats, the IC50 values for dimethomorph, fenhexamid, and chlordecone were 11.98, 36.92, and 109.14 μM. Dimethomorph acts as a mixed inhibitor, while fenhexamid acts as a mixed/competitive inhibitor. Docking analysis revealed that endosulfan and fenhexamid bind to the steroid-binding site of human 17β-HSD1. On the other hand, chlordecone and rhothane binds to a different site other than the steroid and NADPH-binding site. Dimethomorph binds to the steroid/NADPH binding site, and fenhexamid binds to the steroid binding site of rat 17β-HSD1. Bivariate correlation analysis showed a positive correlation between IC50 values and LogP for human 17β-HSD1, while a slight negative correlation was observed between IC50 values and the number of HBA. ADMET analysis provided insights into the toxicokinetics and toxicity of organochlorine pesticides. In conclusion, this study identified the inhibitory effects of 3-4 organochlorine pesticides and binding mechanisms on human and rat 17β-HSD1, as well as their impact on hormone production.

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis

Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency's ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some-but not all-cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals.

Advancing the science of a read-across framework for evaluation of data-poor chemicals incorporating systematic and new approach methods

The assessment of human health hazards posed by chemicals traditionally relies on toxicity studies in experimental animals. However, most chemicals currently in commerce do not meet the minimum data requirements for hazard identification and dose-response analysis in human health risk assessment. Previously, we introduced a read-across framework designed to address data gaps for screening-level assessment of chemicals with insufficient in vivo toxicity information (Wang et al., 2012). It relies on inference by analogy from suitably tested source analogues to a target chemical, based on structural, toxicokinetic, and toxicodynamic similarity. This approach has been used for dose-response assessment of data-poor chemicals relevant to the U.S. EPA's Superfund program. We present herein, case studies of the application of this framework, highlighting specific examples of the use of biological similarity for chemical grouping and quantitative read-across. Based on practical knowledge and technological advances in the fields of read-across and predictive toxicology, we propose a revised framework. It includes important considerations for problem formulation, systematic review, target chemical analysis, analogue identification, analogue evaluation, and incorporation of new approach methods. This work emphasizes the integration of systematic methods and alternative toxicity testing data and tools in chemical risk assessment to inform regulatory decision-making.

Read-across approaches: current applications and regulatory acceptance in Korea, Japan, and China

The aim of this paper was to investigate the current status of read-across approaches in the Republic of Korea, Japan, and China in terms of applications and regulatory acceptance. In the Republic of Korea, over the last 6 years, approximately 8% of safety data records used for chemical registrations were based upon read-across, and a guideline published on the use of read-across results in 2017. In Japan, read-across is generally accepted for screening hazard classification of toxicological endpoints according to the Chemical Substances Control Law (CSCL). In China, read-across data, along with data from other animal alternatives are accepted as a data source for chemical registrations, but could be only considered when testing is not technically feasible. At present, read-across is not widely used for chemical registrations and regulatory acceptance of read-across may differ among countries in Asia. With consideration of the advantages and limitations of read-across, it is expected that read-across may soon gradually be employed in Asian countries. Thus, regulatory agencies need to prepare for this progression.

Clearly weighing the evidence in read-across can improve assessments of data-poor chemicals

This paper provides a systematic weight-of-evidence method for read-across analyses of data-poor chemicals. The read-across technique extrapolates toxicity from analogous chemicals for which suitable test data are available to a target chemical. To determine that a candidate analogue is the 'best' and is sufficiently similar, the evidence for similarity of each candidate analogue to the target is weighed. We present a systematic weight of evidence method that provides transparency and imposes a consistent and rigorous inferential process. The method assembles relevant information concerning structure, physicochemical attributes, toxicokinetics, and toxicodynamics of the target and analogues. The information is then organized by evidence types and subtypes and weighted in terms of properties: relevance, strength, and reliability into weight levels, expressed as symbols. After evidence types are weighted, the bodies of evidence are weighted for collective properties: number, diversity, and coherence. Finally, the weights for the types and bodies of evidence are weighed for each analogue, and, if the overall weight of evidence is sufficient for one or more analogues, the analogue with the greatest weight is used to estimate the endpoint effect. We illustrate this WoE approach with a read-across analysis for screening the organochlorine contaminant, p,p'-dichlorodiphenyldichloroethane (DDD), for noncancer oral toxicity.

Grouping of chemicals based on the potential mechanisms of hepatotoxicity of naphthalene and structurally similar chemicals using in vitro testing for read-across and its validation

Integrated Approaches to Testing and Assessment provides a framework to improve the reliability of read-across for chemical risk assessment of systemic toxicity without animal testing. However, the availability of only a few case studies hinders the use of this concept for regulatory purposes. Thus, we compared the biological similarity of structurally similar chemicals using in vitro testing to demonstrate the validity of this concept for grouping chemicals and to extract key considerations in read-across. We analyzed the hepatotoxicity of naphthalene and three chemicals structurally similar to naphthalene (2,7-naphthalenediol, 1,5-naphthalenediol, and 1-naphthol) for which 90-day repeated dose toxicity data are available. To elucidate and compare their potential mechanisms, we conducted in vitro microarray analysis using rat primary hepatocytes and validated the results using a biomarker and metabolic activation analysis. We observed that 2,7-naphthalenediol, 1,5-naphthalenediol, and 1-naphthol had similar potential mechanisms, namely, induction of oxidative stress by their metabolic activation. Conversely, naphthalene did not show a similar toxicity effect. The existing in vivo data confirmed our grouping of chemicals based on this potential mechanism. Thus, our findings suggest that in vitro toxicogenomics and related biochemical assays are useful for comparing biological similarities and grouping chemicals based on their toxicodynamics for read-across.

Integration of read-across and artificial neural network-based QSAR models for predicting systemic toxicity: A case study for valproic acid

We present a systematic, comprehensive and reproducible weight-of-evidence approach for predicting the no-observed-adverse-effect level (NOAEL) for systemic toxicity by using read-across and quantitative structure-activity relationship (QSAR) models to fill gaps in rat repeated-dose and developmental toxicity data. As a case study, we chose valproic acid, a developmental toxicant in humans and animals. High-quality in vivo oral rat repeated-dose and developmental toxicity data were available for five and nine analogues, respectively, and showed qualitative consistency, especially for developmental toxicity. Similarity between the target and analogues is readily defined computationally, and data uncertainties associated with the similarities in structural, physico-chemical and toxicological properties, including toxicophores, were low. Uncertainty associated with metabolic similarity is low-to-moderate, largely because the approach was limited to in silico prediction to enable systematic and objective data collection. Uncertainty associated with completeness of read-across was reduced by including in vitro and in silico metabolic data and expanding the experimental animal database. Taking the "worst-case" approach, the smallest NOAEL values among the analogs (i.e., 200 and 100 mg/kg/day for repeated-dose and developmental toxicity, respectively) were read-across to valproic acid. Our previous QSAR models predict repeated-dose NOAEL of 148 (males) and 228 (females) mg/kg/day, and developmental toxicity NOAEL of 390 mg/kg/day for valproic acid. Based on read-across and QSAR, the conservatively predicted NOAEL is 148 mg/kg/day for repeated-dose toxicity, and 100 mg/kg/day for developmental toxicity. Experimental values are 341 mg/kg/day and 100 mg/kg/day, respectively. The present approach appears promising for quantitative and qualitative in silico systemic toxicity prediction of untested chemicals.

Potential of ToxCast Data in the Safety Assessment of Food Chemicals

Tox21 and ToxCast are high-throughput in vitro screening programs coordinated by the U.S. National Toxicology Program and the U.S. Environmental Protection Agency, respectively, with the goal of forecasting biological effects in vivo based on bioactivity profiling. The present study investigated whether mechanistic insights in the biological targets of food-relevant chemicals can be obtained from ToxCast results when the chemicals are grouped according to structural similarity. Starting from the 556 direct additives that have been identified in the ToxCast database by Karmaus et al. [Karmaus, A. L., Trautman, T. D., Krishan, M., Filer, D. L., and Fix, L. A. (2017). Curation of food-relevant chemicals in ToxCast. Food Chem. Toxicol. 103, 174-182.], the results showed that, despite the limited number of assays in which the chemical groups have been tested, sufficient results are available within so-called "DNA binding" and "nuclear receptor" target families to profile the biological activities of the defined chemical groups for these targets. The most obvious activity identified was the estrogen receptor-mediated actions of the chemical group containing parabens and structurally related gallates, as well the chemical group containing genistein and daidzein (the latter 2 being particularly active toward estrogen receptor β as a potential health benefit). These group effects, as well as the biological activities of other chemical groups, were evaluated in a series of case studies. Overall, the results of the present study suggest that high-throughput screening data could add to the evidence considered for regulatory risk assessment of food chemicals and to the evaluation of desirable effects of nutrients and phytonutrients. The data will be particularly useful for providing mechanistic information and to fill data gaps with read-across.