Human exposure to toxic heavy metals is a global challenge. Concurrent exposure of heavy metals, such as lead (Pb), cadmium (Cd), arsenic (As) and methylmercury (MeHg) are particularly important due to their long lasting effects on the brain. The exact toxicological mechanisms invoked by exposure to mixtures of the metals Pb, Cd, As and MeHg are still unclear, however they share many common pathways for causing cognitive dysfunction. The combination of metals may produce additive/synergetic effects due to their common binding affinity with NMDA receptor (Pb, As, MeHg), Na+ - K+ ATP-ase pump (Cd, MeHg), biological Ca+2 (Pb, Cd, MeHg), Glu neurotransmitter (Pb, MeHg), which can lead to imbalance between the pro-oxidant elements (ROS) and the antioxidants (reducing elements). In this process, ROS dominates the antioxidants factors such as GPx, GS, GSH, MT-III, Catalase, SOD, BDNF, and CERB, and finally leads to cognitive dysfunction. The present review illustrates an account of the current knowledge about the individual metal induced cognitive dysfunction mechanisms and analyse common Mode of Actions (MOAs) of quaternary metal mixture (Pb, Cd, As, MeHg). This review aims to help advancement in mixture toxicology and development of next generation predictive model (such as PBPK/PD) combining both kinetic and dynamic interactions of metals.

Bisphenol A (BPA) and Di-(2-ethylhexyl) phthalate (DEHP) are two wide spread chemicals classified as endocrine disruptors (ED). The present study aims to estimate the non-dietary (dermal, non-dietary ingestion and inhalation) exposure to BPA and DEHP for a pregnant women cohort. In addition, to assess the prenatal exposure for the fetus, a physiologically based pharmacokinetic (PBPK) model was used. It was adapted for pregnancy in order to assess the internal dosimetry levels of EDs (BPA and DEHP) in the fetus. Estimates of exposure to BPA and DEHP from all pathways along with their relative importance were provided in order to establish which proportion of the total exposure came from diet and which came from non-dietary exposures. In this study, the different oral dosing scenarios (dietary and non-dietary) were considered keeping inhalation as a continuous exposure case. Total non-dietary mean values were 0.002 µg/kgbw/day (0.000; 0.004 µg/kgbw/day for 5th and 95th percentile, respectively) for BPA and 0.597 µg/kgbw/day (0.116 µg/kgbw/day and 1.506 µg/kgbw/day for 5th and 95th percentile, respectively) for DEHP. Indoor environments and especially dust ingestion were the main non-dietary contributors to the total exposure of BPA and DEHP with 60% and 81%. However, as expected, diet showed the higher contribution to total exposure with > 99.9% for BPA and 63% for DEHP. Although diet was considered the primary source of exposure to BPA and phthalates, it must be taken into account that with non-dietary sources the first-pass metabolism is lacking, so these may be of equal or even higher toxicological relevance than dietary sources. The present study is in the framework of "Health and environmental-wide associations based on large population surveys" (HEALS) project (FP7-603946).

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.

Over the last years, research has focused on microbiota to establish a missing link between neuronal health and intestine imbalance. Many studies have considered microbiota as critical regulators of the gut–brain axis. The crosstalk between microbiota and the central nervous system is mainly explained through three different pathways: the neural, endocrine, and immune pathways, intricately interconnected with each other. In day-to-day life, human beings are exposed to a wide variety of contaminants that affect our intestinal microbiota and alter the bidirectional communication between the gut and brain, causing neuronal disorders. The interplay between xenobiotics, microbiota and neurotoxicity is still not fully explored, especially for susceptible populations such as pregnant women, neonates, and developing children. Precisely, early exposure to contaminants can trigger neurodevelopmental toxicity and long-term diseases. There is growing but limited research on the specific mechanisms of the microbiota–gut–brain axis (MGBA), making it challenging to understand the effect of environmental pollutants. In this review, we discuss the biological interplay between microbiota–gut–brain and analyse the role of endocrine-disrupting chemicals: Bisphenol A (BPA), Chlorpyrifos (CPF), Diethylhexyl phthalate (DEHP), and Per- and polyfluoroalkyl substances (PFAS) in MGBA perturbations and subsequent neurotoxicity. The complexity of the MGBA and the changing nature of the gut microbiota pose significant challenges for future research. However, emerging in-silico models able to analyse and interpret meta-omics data are a promising option for understanding the processes in this axis and can help prevent neurotoxicity.