Evaluation of the fish acute toxicity test for pesticide registration.

The U.S. Environmental Protection Agency (USEPA) uses the in vivo fish acute toxicity test to assess potential risk of substances to non-target aquatic vertebrates. The test is typically conducted on a cold and a warm freshwater species and a saltwater species for a conventional pesticide registration, potentially requiring upwards of 200 or more fish. A retrospective data evaluation was conducted to explore the potential for using fewer fish species to support conventional pesticide risk assessments. Lethal concentration 50% (LC50) values and experimental details were extracted and curated from 718 studies on fish acute toxicity submitted to USEPA. The LC50 data were analysed to determine, when possible, the relative sensitivity of the tested species to each pesticide. One of the tested freshwater species was most sensitive in 85% of those cases. The tested cold freshwater species was the most sensitive overall among cases with established relative sensitivity and was within 3X of the LC50 value of the most sensitive species tested in 98% of those cases. The results support potentially using fewer than three fish species to conduct ecological risk assessments for the registration of conventional pesticides.

Current ecotoxicity testing needs among selected U.S. federal agencies.

U.S. regulatory and research agencies use ecotoxicity test data to assess the hazards associated with substances that may be released into the environment, including but not limited to industrial chemicals, pharmaceuticals, pesticides, food additives, and color additives. These data are used to conduct hazard assessments and evaluate potential risks to aquatic life (e.g., invertebrates, fish), birds, wildlife species, or the environment. To identify opportunities for regulatory uses of non-animal replacements for ecotoxicity tests, the needs and uses for data from tests utilizing animals must first be clarified. Accordingly, the objective of this review was to identify the ecotoxicity test data relied upon by U.S. federal agencies. The standards, test guidelines, guidance documents, and/or endpoints that are used to address each of the agencies' regulatory and research needs regarding ecotoxicity testing are described in the context of their application to decision-making. Testing and information use, needs, and/or requirements relevant to the regulatory or programmatic mandates of the agencies taking part in the Interagency Coordinating Committee on the Validation of Alternative Methods Ecotoxicology Workgroup are captured. This information will be useful for coordinating efforts to develop and implement alternative test methods to reduce, refine, or replace animal use in chemical safety evaluations.

Open-source QSAR models for pKa prediction using multiple machine learning approaches.

BACKGROUND: The logarithmic acid dissociation constant pKa reflects the ionization of a chemical, which affects lipophilicity, solubility, protein binding, and ability to pass through the plasma membrane. Thus, pKa affects chemical absorption, distribution, metabolism, excretion, and toxicity properties. Multiple proprietary software packages exist for the prediction of pKa, but to the best of our knowledge no free and open-source programs exist for this purpose. Using a freely available data set and three machine learning approaches, we developed open-source models for pKa prediction. METHODS: The experimental strongest acidic and strongest basic pKa values in water for 7912 chemicals were obtained from DataWarrior, a freely available software package. Chemical structures were curated and standardized for quantitative structure-activity relationship (QSAR) modeling using KNIME, and a subset comprising 79% of the initial set was used for modeling. To evaluate different approaches to modeling, several datasets were constructed based on different processing of chemical structures with acidic and/or basic pKas. Continuous molecular descriptors, binary fingerprints, and fragment counts were generated using PaDEL, and pKa prediction models were created using three machine learning methods, (1) support vector machines (SVM) combined with k-nearest neighbors (kNN), (2) extreme gradient boosting (XGB) and (3) deep neural networks (DNN). RESULTS: The three methods delivered comparable performances on the training and test sets with a root-mean-squared error (RMSE) around 1.5 and a coefficient of determination (R2) around 0.80. Two commercial pKa predictors from ACD/Labs and ChemAxon were used to benchmark the three best models developed in this work, and performance of our models compared favorably to the commercial products. CONCLUSIONS: This work provides multiple QSAR models to predict the strongest acidic and strongest basic pKas of chemicals, built using publicly available data, and provided as free and open-source software on GitHub.

Evaluation and Optimization of Pharmacokinetic Models for in Vitro to in Vivo Extrapolation of Estrogenic Activity for Environmental Chemicals.

BACKGROUND: To effectively incorporate in vitro data into regulatory use, confidence must be established in the quantitative extrapolation of in vitro activity to relevant end points in animals or humans. OBJECTIVE: Our goal was to evaluate and optimize in vitro to in vivo extrapolation (IVIVE) approaches using in vitro estrogen receptor (ER) activity to predict estrogenic effects measured in rodent uterotrophic studies. METHODS: We evaluated three pharmacokinetic (PK) models with varying complexities to extrapolate in vitro to in vivo dosimetry for a group of 29 ER agonists, using data from validated in vitro [U.S. Environmental Protection Agency (U.S. EPA) ToxCast™ ER model] and in vivo (uterotrophic) methods. In vitro activity values were adjusted using mass-balance equations to estimate intracellular exposure via an enrichment factor (EF), and steady-state model calculations were adjusted using fraction of unbound chemical in the plasma ([Formula: see text]) to approximate bioavailability. Accuracy of each model-adjustment combination was assessed by comparing model predictions with lowest effect levels (LELs) from guideline uterotrophic studies. RESULTS: We found little difference in model predictive performance based on complexity or route-specific modifications. Simple adjustments, applied to account for in vitro intracellular exposure (EF) or chemical bioavailability ([Formula: see text]), resulted in significant improvements in the predictive performance of all models. CONCLUSION: Computational IVIVE approaches accurately estimate chemical exposure levels that elicit positive responses in the rodent uterotrophic bioassay. The simplest model had the best overall performance for predicting both oral (PPK_EF) and injection (PPK_[Formula: see text]) LELs from guideline uterotrophic studies, is freely available, and can be parameterized entirely using freely available in silico tools. https://doi.org/10.1289/EHP1655.

In vitro to in vivo extrapolation for high throughput prioritization and decision making.

In vitro chemical safety testing methods offer the potential for efficient and economical tools to provide relevant assessments of human health risk. To realize this potential, methods are needed to relate in vitro effects to in vivo responses, i.e., in vitro to in vivo extrapolation (IVIVE). Currently available IVIVE approaches need to be refined before they can be utilized for regulatory decision-making. To explore the capabilities and limitations of IVIVE within this context, the U.S. Environmental Protection Agency Office of Research and Development and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods co-organized a workshop and webinar series. Here, we integrate content from the webinars and workshop to discuss activities and resources that would promote inclusion of IVIVE in regulatory decision-making. We discuss properties of models that successfully generate predictions of in vivo doses from effective in vitro concentration, including the experimental systems that provide input parameters for these models, areas of success, and areas for improvement to reduce model uncertainty. Finally, we provide case studies on the uses of IVIVE in safety assessments, which highlight the respective differences, information requirements, and outcomes across various approaches when applied for decision-making.

A coding polymorphism in the CYSLT2 receptor with reduced affinity to LTD4 is associated with asthma.

BACKGROUND: Cysteinyl leukotrienes (CYSLTR) are potent biological mediators in the pathophysiology of asthma for which two receptors have been characterized, CYSLTR1 and CYSLTR2. The leukotriene modifying agents currently used to control bronchoconstriction and inflammation in asthmatic patients are CYSLTR1-specific leukotriene receptor antagonists. In this report, we investigated a possible role for therapeutic modulation of CYSLTR2 in asthma by investigating genetic association with asthma and further characterization of the pharmacology of a coding polymorphism. METHODS: The association of CYSLTR2 polymorphisms with asthma was assessed by transmission disequilibrium test in two family-based collections (359 families from Denmark and Minnesota, USA and 384 families from the Genetics of Asthma International Network). RESULTS: A significant association of the coding polymorphism, 601A>G, with asthma was observed (P = 0.003). We replicated these findings in a collection of 384 families from the Genetics of Asthma International Network (P = 0.04). The G allele is significantly under-transmitted to asthmatics, indicating a possible role for this receptor in resistance to asthma. The potency of cysteinyl leukotrienes at the wild-type CYSLTR2 and the coding polymorphism 601A>G were assessed using a calcium mobilization assay. The potency of LTC4 and LTE4 was similar for both forms of the receptor and LTB4 was inactive, however, LTD4 was approximately five-fold less potent on 601A>G compared to wild-type CYSLTR2. CONCLUSIONS: Since 601A>G alters the potency of LTD4 and this variant allele may be associated with resistance to asthma, it is possible that modulation of the CYSLTR2 may be useful in asthma pharmacotherapy.