An integrative translational framework for chemical induced neurotoxicity – a systematic review

Neurotoxic Effects of Mixtures of Perfluoroalkyl Substances (PFAS) at Environmental and Human Blood Concentrations

Per- and polyfluoroalkyl substances (PFAS) may cause various deleterious health effects. Epidemiological studies have demonstrated associations between PFAS exposure and adverse neurodevelopmental outcomes. The cytotoxicity, neurotoxicity, and mitochondrial toxicity of up to 12 PFAS including perfluoroalkyl carboxylates, perfluoroalkyl sulfonates, 6:2 fluorotelomer sulfonic acid (6:2 FTSA), and hexafluoropropylene oxide-dimer acid (HPFO-DA) were tested at concentrations typically observed in the environment (e.g., wastewater, biosolids) and in human blood using high-throughput in vitro assays. The cytotoxicity of all individual PFAS was classified as baseline toxicity, for which prediction models based on partition constants of PFAS between biomembrane lipids and water exist. No inhibition of the mitochondrial membrane potential and activation of oxidative stress response were observed below the cytotoxic concentrations of any PFAS tested. All mixture components and the designed mixtures inhibited the neurite outgrowth in differentiated neuronal cells derived from the SH-SY5Y cell line at concentrations around or below cytotoxicity. All designed mixtures acted according to concentration addition at low effect and concentration levels for cytotoxicity and neurotoxicity. The mixture effects were predictable from the experimental single compounds' concentration-response curves. These findings have important implications for the mixture risk assessment of PFAS.

NeuTox: A weighted ensemble model for screening potential neuronal cytotoxicity of chemicals based on various types of molecular representations

Chemical-induced neurotoxicity has been widely brought into focus in the risk assessment of chemical safety. However, the traditional in vivo animal models to evaluate neurotoxicity are time-consuming and expensive, which cannot completely represent the pathophysiology of neurotoxicity in humans. Cytotoxicity to human neuroblastoma cell line (SH-SY5Y) is commonly used as an alternative to animal testing for the assessment of neurotoxicity, yet it is still not appropriate for high throughput screening of potential neuronal cytotoxicity of chemicals. In this study, we constructed an ensemble prediction model, termed NeuTox, by combining multiple machine learning algorithms with molecular representations based on the weighted score of Particle Swarm Optimization. For the test set, NeuTox shows excellent performance with an accuracy of 0.9064, which are superior to the top-performing individual models. The subsequent experimental verifications reveal that 5,5'-isopropylidenedi-2-biphenylol and 4,4'-cyclo-hexylidenebisphenol exhibited stronger SH-SY5Y-based cytotoxicity compared to bisphenol A, suggesting that NeuTox has good generalization ability in the first-tier assessment of neuronal cytotoxicity of BPA analogs. For ease of use, NeuTox is presented as an online web server that can be freely accessed via http://www.iehneutox-predictor.cn/NeuToxPredict/Predict.

Early clues and molecular mechanism involved in neurodegenerative diseases induced in immature mice by combined exposure to polypropylene microplastics and DEHP

Studies have shown that exposure to either microplastics (MPs) or di-(2-ethylhexyl) phthalic acid (DEHP) alone can cause neurotoxicity in animals, but it remains uncertain whether and to what extent co-exposure to these two substances, which often occur together in reality, can also induce neurotoxicity. This study aimed to investigate the neurotoxicity and molecular mechanisms of combined exposure to DEHP and polypropylene microplastics (synthetic PP-MPs were used), the microplastics most commonly encountered by young children, in immature mice. The results showed that exposure to PP-MPs and/or DEHP did cause neurotoxic effects in immature mice, including induction of neurocognitive and memory deficits, damage to the CA3 region of the hippocampus, increased oxidative stress, and decreased AChE activity in the brain. The severity of the neurotoxicity increased with increasing concentrations of PP-MPs, combined exposure to PP-MPs and DEHP exhibited additive or synergistic effects. Transcriptomic analyses revealed that the PP-MPs and/or DEHP exposure altered the expression profiles of gene clusters involved in the stress response, and in protein processing in endoplasmic reticulum. Quantitative analyses further indicated that PP-MPs and/or DEHP exposure inhibited the activity of the heat shock response mediated by heat shock transcription factor 1, while chronically activated the unfolded protein response, consequently inducing neurotoxicity through neuronal apoptosis and neuroinflammation in the immature mice. As a pioneer study to highlight the neurotoxicity induced by combined exposure to PP-MPs and DEHP in immature mice, this research provides new insights into mitigating the health risks of PP-MPs and DEHP exposure in young children.

The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment

Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

Unravelling sex-specific BPA toxicokinetics in children using a pediatric PBPK model

Bisphenol A (BPA) is a widely known endocrine disruptor (ED) found in many children's products such as toys, feeding utensils, and teething rings. Recent epidemiology association studies have shown postnatal BPA exposure resulted in developing various diseases such as diabetes, obesity, and neurodegeneration, etc., later in their lives. However, little is known about its sex-specific metabolism and consequently internal exposure. The aim of this study was to develop a sex-specific pediatric physiologically based pharmacokinetic model (PBPK) for BPA to compare their toxicokinetic differences. First, the published adult PBPK model was re-validated, and then this model was extended by interpolation to incorporate pediatric sex specific physiological and biochemical parameters. We used both the classical body weight and ontogeny-based scaling approach to interpolate the metabolic process. Then, the pharmacokinetic attributes of the models using the two-scaling approach mentioned above were compared with adult model. Further, a sex-specific PBPK model with an ontogeny scaling approach was preferred to evaluate the pharmacokinetic differences. Moreover, this model was used to reconstruct the BPA exposure from two cohorts (Helix and PBAT Cohort) from 7 EU countries. The half-life of BPA was found to be almost the same in boys and girls at the same exposure levels. Our model estimated BPA children's exposure to be about 1500 times higher than the tolerable daily intake (TDI) recently set by European Food Safety Authority (EFSA) i.e., 0.04 ng/kg BW/day. The model demonstrated feasibility of extending the adult PBPK to sex-specific pediatric, thus investigate a gender-specific health risk assessment.

Pharmacophore Modeling Using Machine Learning for Screening the Blood-Brain Barrier Permeation of Xenobiotics

Daily exposure to xenobiotics affects human health, especially the nervous system, causing neurodegenerative diseases. The nervous system is protected by tight junctions present at the blood-brain barrier (BBB), but only molecules with desirable physicochemical properties can permeate it. This is why permeation is a decisive step in avoiding unwanted brain toxicity and also in developing neuronal drugs. In silico methods are being implemented as an initial step to reduce animal testing and the time complexity of the in vitro screening process. However, most in silico methods are ligand based, and consider only the physiochemical properties of ligands. However, these ligand-based methods have their own limitations and sometimes fail to predict the BBB permeation of xenobiotics. The objective of this work was to investigate the influence of the pharmacophoric features of protein-ligand interactions on BBB permeation. For these purposes, receptor-based pharmacophore and ligand-based pharmacophore fingerprints were developed using docking and Rdkit, respectively. Then, these fingerprints were trained on classical machine-learning models and compared with classical fingerprints. Among the tested footprints, the ligand-based pharmacophore fingerprint achieved slightly better (77% accuracy) performance compared to the classical fingerprint method. In contrast, receptor-based pharmacophores did not lead to much improvement compared to classical descriptors. The performance can be further improved by considering efflux proteins such as BCRP (breast cancer resistance protein), as well as P-gp (P-glycoprotein). However, the limited data availability for other proteins regarding their pharmacophoric interactions is a bottleneck to its improvement. Nonetheless, the developed models and exploratory analysis provide a path to extend the same framework for environmental chemicals, which, like drugs, are also xenobiotics. This research can help in human health risk assessment by a priori screening for neurotoxicity-causing agents.

Framework for risk assessment of PFAS utilizing experimental studies and in-silico models

Perfluoroalkyl substances (PFAS), especially PFOS and PFOA, are two widely used synthetic chemicals that can impact human health based on evidence from animal and epidemiologic studies. In this paper, we have reviewed and summarized the influence of PFAS exposure on health, pointing the quality of evidence, and applied translational techniques to integrate evidence for PFAS policy making. This is the first review where highly referred articles on PFAS used for policymaking by several regulatory agencies were collected and evaluated based on the review guidelines developed by the US National Toxicology Program's Office of Health Assessment and Translation (OHAT) review guidelines. Several limitations were observed, including co-exposure to multiple chemicals and limited measurement of primary and secondary outcomes related to specific toxicity. However, data from all the studies provided a moderate to strong level of confidence for link between PFAS exposure and different adverse outcomes. Secondly, for translating the risk to humans, an in-silico model and scaling approach was utilized. Physiologically based pharmacokinetic model (PBPK) was used to calculate the human equivalent dose (HED) from two widely accepted studies and compared with tolerable daily intakes (TDIs) established by various regulatory agencies. Inter-species dose extrapolation was done to compare with human the relevance of dosing scenarios used in animals. Overall, a framework for translation of risk was proposed based on the conclusions of this review with the goal of improving policymaking. The current paper can improve the methodological protocols for PFAS experimental studies and encourage the utilization of in-silico models for translating risk.

EDC-induced mechanisms of immunotoxicity: a systematic review

Endocrine-disrupting chemicals (EDCs) refer to a group of chemicals that cause adverse effects in human health, impairing hormone production and regulation, resulting in alteration of homeostasis, reproductive, and developmental, and immune system impairments. The immunotoxicity of EDCs involves many mechanisms altering gene expression that depend on the activation of nuclear receptors such as the aryl hydrocarbon receptor (AHR), the estrogen receptor (ER), and the peroxisome proliferator-activated receptor (PPAR), which also results in skin and intestinal disorders, microbiota alterations and inflammatory diseases. This systematic review aims to review different mechanisms of immunotoxicity and immunomodulation of T cells, focusing on T regulatory (Treg) and Th17 subsets, B cells, and dendritic cells (DCs) caused by specific EDCs such as 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), bisphenols (BPs) and polyfluoroalkyl substances (PFASs). To achieve this objective, a systematic study was conducted searching various databases including PubMed and Scopus to find in-vitro, in-vivo, and biomonitoring studies that examine EDC-dependent mechanisms of immunotoxicity. While doing the systematic review, we found species- and cell-specific outcomes and a translational gap between in-vitro and in-vivo experiments. Finally, an adverse outcome pathway (AOP) framework is proposed, which explains mechanistically toxicity endpoints emerging from different EDCs having similar key events and can help to improve our understanding of EDCs mechanisms of immunotoxicity. In conclusion, this review provides insights into the mechanisms of immunotoxicity mediated by EDCs and will help to improve human health risk assessment.

Risk Assessment of Perfluorooctane Sulfonate (PFOS) using Dynamic Age Dependent Physiologically based Pharmacokinetic Model (PBPK) across Human Lifetime

The widespread use of Perfluorooctane sulfonate (PFOS) in everyday life, its long half-life, and the lipophilicity that makes it easily accumulate in the body, raises the question of its safe exposure among different population groups. There are currently enough epidemiological studies showing evidence of PFOS exposure and its associated adverse effects on humans. Moreover, it is already known that physiological changes along with age e.g. organ volume, renal blood flow, cardiac output and albumin concentrations affect chemicals body burden. Human biomonitoring cohort studies have reported PFOS concentrations in blood and autopsy tissue data with PFOS present in sensitive organs across all human lifespan. However, to interpret such biomonitoring data in the context of chemical risk assessment, it is necessary to have a mechanistic framework that explains show the physiological changes across age affects the concentration of chemical inside different tissues of the human body. PBPK model is widely and successfully used in the field of risk assessment. The objective of this manuscript is to develop a dynamic age-dependent PBPK model as an extension of the previously published adult PFOS model and utilize this model to predict and compare the PFOS tissue distribution and plasma concentration across different age groups. Different cohort study data were used for exposure dose reconstruction and evaluation of time-dependent concentration in sensitive organs. Predicted plasma concentration followed trends observed in biomonitoring data and model predictions showed the increased disposition of PFOS in the geriatric population. PFOS model is sensitive to parameters governing renal resorption and elimination across all ages, which is related to PFOS half-life in humans. This model provides an effective framework for improving the quantitative risk assessment of PFOS throughout the human lifetime, particularly in susceptible age groups. The dynamic age-dependent PBPK model provides a step forward for developing such kind of dynamic model for other perfluoroalkyl substances.

Tris(1,3-dichloro-2-propyl)phosphate Reduces Growth Hormone Expression via Binding to Growth Hormone Releasing Hormone Receptors and Inhibits the Growth of Crucian Carp

Tris(1,3-dichloro-2-propyl)phosphate (TDCIPP) has commonly been used as an additive flame retardant and frequently detected in the aquatic environment and in biological samples worldwide. Recently, it was found that exposure to TDCIPP inhibited the growth of zebrafish, but the relevant molecular mechanisms remained unclear. In this study, 5 day-old crucian carp (Carassius auratus) larvae were treated with 0.5, 5, or 50 μg/L TDCIPP for 90 days; the effect on growth was evaluated; and related molecular mechanisms were explored. Results demonstrated that 5 or 50 μg/L TDCIPP treatment significantly inhibited the growth of crucian carp and downregulated the expression of growth hormones (ghs), growth hormone receptor (ghr), and insulin-like growth factor 1 (igf1). Molecular docking, dual-luciferase reporter gene assay, and in vitro experiments demonstrated that TDCIPP could bind to the growth hormone releasing hormone receptor protein of crucian carp and disturb the stimulation of growth hormone releasing hormone to the expression of ghs, resulting in the decrease of the mRNA level of gh1 and gh2 in pituitary cells. Our findings provide new perceptions into the molecular mechanisms of developmental toxicity of TDCIPP in fish.

ANN-Based Integrated Risk Ranking Approach: A Case Study of Contaminants of Emerging Concern of Fish and Seafood in Europe

Seafood, one of the most important food commodities consumed worldwide, is considered a high-quality, healthy, and safe food option. However, marine ecosystems are the ultimate destination for a large group of chemicals, including contaminants of emerging concern, and seafood consumption is a major pathway of human exposure. With growing awareness of food safety and food quality, and increased demand for information on the risk of contaminants of emerging concern, there is a need to assess food safety issues related to harmful contaminants in seafood and ensure the safety of marine food resources. In this study, the risks of emerging compounds (endocrine disruptors, brominated flame retardants, pharmaceuticals and personal care products, and toxic elements) in fish and seafood were analyzed according to their PBT (persistence, bioaccumulation, toxicity) properties as well as in terms of their concentration levels in seafood. A hazard index (HI) was estimated for each compound by applying an artificial neural network (ANN) approach known as Self-Organizing-Maps. Subsequently, an integrated risk rank (IRI) was developed considering the values of HI and the concentrations of emerging compounds in seafood species gathered from the scientific literature. Current results identified HHCB, MeHg, NP, AHTN and PBDE209 as the top five highest ranked compounds present in seafood, according to the 50th percentile (mean) of the IRI. However, this ranking slightly changed when taking into account the 99th percentile of the IRI, showing toxic elements, methylmercury and inorganic arsenic, as having the highest risk. The outcome of this study identified the priority contaminants and should help in regulatory decision-making and scientific panels to design screening programs as well as to take the appropriate safety measures.

Performance of Chlorella Vulgaris Exposed to Heavy Metal Mixtures: Linking Measured Endpoints and Mechanisms

Microalgae growth inhibition assays are candidates for referent ecotoxicology as a fundamental part of the strategy to reduce the use of fish and other animal models in aquatic toxicology. In the present work, the performance of Chlorella vulgaris exposed to heavy metals following standardized growth and photosynthesis inhibition assays was assessed in two different scenarios: (1) dilutions of single heavy metals and (2) an artificial mixture of heavy metals at similar levels as those found in natural rivers. Chemical speciation of heavy metals was estimated with Visual MINTEQ software; free heavy metal ion concentrations were used as input data, together with microalgae growth and photosynthesis inhibition, to compare different effects and explain possible toxicity mechanisms. The final goal was to assess the suitability of the ecotoxicological test based on the growth and photosynthesis inhibition of microalgae cultures, supported by mathematic models for regulatory and decision-making purposes. The C. vulgaris algae growth inhibition test was more sensitive for As, Zn, and Pb exposure whereas the photosynthesis inhibition test was more sensitive for Cu and Ni exposure. The effects on growth and photosynthesis were not related. C. vulgaris evidenced the formation of mucilaginous aggregations at lower copper concentrations. We found that the toxicity of a given heavy metal is not only determined by its chemical speciation; other chemical compounds (as nutrient loads) and biological interactions play an important role in the final toxicity. Predictive mixture effect models tend to overestimate the effects of metal mixtures in C. vulgaris for both growth and photosynthesis inhibition tests. Growth and photosynthesis inhibition tests give complementary information, and both are a fast, cheap, and sensitive alternative to animal testing. More research is needed to solve the challenge of complex pollutant mixtures as they are present in natural environments, where microalgae-based assays can be suitable monitoring tools for pollution management and regulatory purposes.