In utero exposure to a mixture of the perfluoroalkyl-isopropyl pesticide pyrifluquinazon with dibutyl phthalate cumulatively disrupts male rat reproductive development via different mechanisms of action

Mechanistic insight into the neurodevelopmental toxicity of the novel pesticide pyrifluquinazon (PFQ) and its major metabolite in early-life stage zebrafish

Pyrifluquinazon (PFQ), a novel insecticide containing a heptafluoroisopropyl moiety, has seen increasing use. However, limited research has been conducted on the toxicological effects and mechanisms of PFQ in aquatic organisms. To investigate the toxicity and underlying mechanisms of PFQ and its primary metabolite dPFQ in aquatic organisms, morphological, behavioral, hormonal, multi-omics analyses, and molecular docking studies were conducted on zebrafish larvae after exposure. The results showed that both PFQ and dPFQ induced developmental abnormalities, behavioral impairment, hormonal disruptions, and alterations in neurologically related metabolites and gene expression in early-stage zebrafish. Notably, delayed retinal vascular development was observed, which is also likely linked to the neurodevelopmental toxicity. Subsequently, identification and relative quantification of PFQ metabolites suggested that its toxicity might be primarily attributed to dPFQ. Finally, an Adverse Outcome Pathway (AOP) was proposed, initiating with the binding of dPFQ to the TRPV4 protein and ultimately leading to neurodevelopmental toxicity. This study delineated the neurodevelopmental toxicity of PFQ and its toxicological mechanisms in zebrafish, emphasizing the hazards posed by pesticide metabolites to non-target organisms and highlighting inherent limitations of extrapolating in vitro toxicity experiments.

Systematic approaches to machine learning models for predicting pesticide toxicity

Pesticides play an important role in modern agriculture by protecting crops from pests and diseases. However, the negative consequences of pesticides, such as environmental contamination and adverse effects on human and ecological health, underscore the importance of accurate toxicity predictions. To address this issue, artificial intelligence models have emerged as valuable methods for predicting the toxicity of organic compounds. In this review article, we explore the application of machine learning (ML) for pesticide toxicity prediction. This review provides a detailed summary of recent developments, prediction models, and datasets used for pesticide toxicity prediction. In this analysis, we compared the results of several algorithms that predict the harmfulness of various classes of pesticides. Furthermore, this review article identified emerging trends and areas for future direction, showcasing the transformative potential of machine learning in promoting safer pesticide usage and sustainable agriculture.

The adverse role of endocrine disrupting chemicals in the reproductive system

Reproductive system diseases pose prominent threats to human physical and mental well-being. Besides being influenced by genetic material regulation and changes in lifestyle, the occurrence of these diseases is closely connected to exposure to harmful substances in the environment. Endocrine disrupting chemicals (EDCs), characterized by hormone-like effects, have a wide range of influences on the reproductive system. EDCs are ubiquitous in the natural environment and are present in a wide range of industrial and everyday products. Currently, thousands of chemicals have been reported to exhibit endocrine effects, and this number is likely to increase as the testing for potential EDCs has not been consistently required, and obtaining data has been limited, partly due to the long latency of many diseases. The ability to avoid exposure to EDCs, especially those of artificially synthesized origin, is increasingly challenging. While EDCs can be divided into persistent and non-persistent depending on their degree of degradation, due to the recent uptick in research studies in this area, we have chosen to focus on the research pertaining to the detrimental effects on reproductive health of exposure to several EDCs that are widely encountered in daily life over the past six years, specifically bisphenol A (BPA), phthalates (PAEs), polychlorinated biphenyls (PCBs), parabens, pesticides, heavy metals, and so on. By focusing on the impact of EDCs on the hypothalamic-pituitary-gonadal (HPG) axis, which leads to the occurrence and development of reproductive system diseases, this review aims to provide new insights into the molecular mechanisms of EDCs' damage to human health and to encourage further in-depth research to clarify the potentially harmful effects of EDC exposure through various other mechanisms. Ultimately, it offers a scientific basis to enhance EDCs risk management, an endeavor of significant scientific and societal importance for safeguarding reproductive health.

Dose Addition Models Accurately Predict the Subacute Effects of a Mixture of Perfluorooctane Sulfonate and Perfluorooctanoic Acid on Japanese Quail (Coturnix japonica) Chick Mortality

Biomonitoring data have consistently demonstrated that fish, wildlife, and humans are exposed to multiple per- and polyfluoroalkyl substances (PFAS) in drinking water and foods. Despite ubiquitous exposure to mixtures of PFAS, there is a lack of in vivo PFAS mixture research that addresses whether these chemicals act in a cumulative, dose-additive (DA) manner or whether they behave independently. For this reason, there is a critical need for mixtures studies designed to evaluate the cumulative toxicity and potential chemical interactions to support the assessment of human and ecological risks and also to define appropriate regulatory actions. Our primary objective was to evaluate the previously published Japanese quail chick mortality concentration-response data for perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), and the mixture of PFOS + PFOA and to use statistical modeling to determine whether the effects of the mixtures were accurately predicted by either DA or response addition modeling. In addition, we wanted to compare different DA models to determine whether one model produced more accurate predictions than the others. Our results support the hypothesis of cumulative effects on shared endpoints from PFOA and PFOS co-exposure and DA approaches for predictive estimates of cumulative effects. Given the limited number of in vivo studies that have been executed with enough individual PFAS and PFAS mixture concentration-response data to test the hypothesis of DA for PFAS mixtures, this re-analysis of the data is an important contribution to our understanding of how PFAS mixtures act. The analysis will provide support for regulatory agencies as they begin to implement PFAS cumulative hazard assessments in higher vertebrates. Environ Toxicol Chem 2024;43:97-104. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Dose additive maternal and offspring effects of oral maternal exposure to a mixture of three PFAS (HFPO-DA, NBP2, PFOS) during pregnancy in the Sprague-Dawley rat

Simultaneous exposure to multiple per- and polyfluoroalkyl substances (PFAS) is common in humans across the globe. Individual PFAS are associated with adverse health effects, yet the nature of mixture effects after exposure to two or more PFAS remains unclear. Previously we reported that oral administration of hexafluoropropylene oxide-dimer acid (HFPO-DA, or GenX), Nafion byproduct 2 (NBP2), or perfluorooctane sulfonate (PFOS) individually during pregnancy produced maternal and F1 effects. Here, we hypothesized that responses to the combined exposure to these three PFAS would be dose additive. Pregnant Sprague-Dawley rats were exposed to a fixed-ratio equipotent mixture where the top dose contained each PFAS at their ED50 for neonatal mortality (100 % dose = PFOS 3 mg/kg; NBP2 10 mg/kg; HFPO-DA 110 mg/kg), followed by a dilution series (33.3, 10, 3.3, and 1 %) and vehicle controls (0 % dose). Consistent with the single chemical studies, dams were exposed from gestation day (GD)14-18 or from GD8-postnatal day (PND2). Fetal and maternal livers on GD18 displayed multiple significantly upregulated genes associated with lipid and carbohydrate metabolism at all dose levels, while dams displayed significantly increased liver weight (≥3.3 % dose) and reduced serum thyroid hormones (≥33.3 % dose). Maternal exposure from GD8-PND2 significantly reduced pup bodyweights at birth (≥33.3 % dose) and PND2 (all doses), increased neonatal liver weights (≥3.3 % dose), increased pup mortality (≥3.3 % dose), and reduced maternal bodyweights and weight gain at the top dose. Echocardiography of adult F1 males and females identified significantly increased left ventricular anterior wall thickness (~10 % increase), whereas other cardiac morphological, functional, and transcriptomic measures were unaffected. Mixture effects in maternal and neonatal animals conformed to dose addition using a relative potency factor (RPF) analysis. Results support dose addition-based cumulative assessment approaches for estimating combined effects of PFAS co-exposure.

Cumulative Risk Evaluation of Phthalates Under TSCA

The U.S. Environmental Protection Agency (EPA) is currently conducting separate Toxic Substances Control Act (TSCA) risk evaluations for seven phthalates: dibutyl phthalate (DBP), butyl benzyl phthalate (BBP), di(2-ethylhexyl) phthalate (DEHP), diisobutyl phthalate (DIBP), dicyclohexyl phthalate (DCHP), di-isodecyl phthalate (DIDP), and diisononyl phthalate (DINP). Phthalates are highly abundant plastic additives used primarily to soften materials and make them flexible, and biomonitoring shows widespread human exposure to a mixture of phthalates. Evidence supports biological additivity of phthalate mixture exposures, including the enhancement of toxicity affecting common biological targets. Risk estimates based on individual phthalate exposure may not be protective of public health. Thus, a cumulative risk approach is warranted. While EPA initially did not signal that it would incorporate cumulative risk assessment (CRA) as part of its current risk evaluation for the seven phthalates, the agency recently announced that it is reconsidering if CRA for phthalates would be appropriate. Based on our review of existing chemical mixtures risk assessment guidance, current TSCA scoping documents for the seven phthalates, and pertinent peer-reviewed literature, we delineate a CRA approach that EPA can easily implement for phthalates. The strategy for using CRA to inform TSCA risk evaluation for existing chemicals is based upon integrative physiology and a common adverse health outcome algorithm for identifying and grouping relevant nonchemical and chemical stressors. We recommend adjustments for how hazard indices (HIs) or margins of exposure (MOEs) based on CRA are interpreted for determining "unreasonable risk" under TSCA.