The assessment of persistence (P), bioaccumulation (B), and toxicity (T) of a chemical is a crucial first step at ensuring
chemical safety and is a cornerstone of the European Union’s chemicals regulation REACH (Registration, Evaluation,
Authorization, and Restriction of Chemicals). Existing methods for PBT assessment are overly complex and cumbersome,
have produced incorrect conclusions, and rely heavily on animal-intensive testing. We explore how new-approach methodologies (NAMs) can overcome the limitations of current PBT assessment. We propose two innovative hazard indicators, termed cumulative toxicity equivalents (CTE) and persistent toxicity equivalents (PTE). Together they are intended to replace existing PBT indicators and can also accommodate the emerging concept of PMT (where M stands for mobility). The proposed “toxicity equivalents” can be measured with high throughput in vitro bioassays. CTE refers to the toxic effects measureddirectly in any given sample, including single chemicals, substitution products, or mixtures. PTE is the equivalent measureof cumulative toxicity equivalents measured after simulated environmental degradation of the sample. With an appropriate panel of animal-free or alternative in vitro bioassays, CTE and PTE comprise key environmental and human health hazard indicators. CTE and PTE do not require analytical identification of transformation products and mixture components but instead prompt two key questions: is the chemical or mixture toxic, and is this toxicity persistent or can it be attenuated by environmental degradation? Taken together, the proposed hazard indicators CTE and PTE have the potential to integrate P, B/M and T assessment into one high-throughput experimental workflow that sidesteps the need for analytical measurements and will support the Chemicals Strategy for Sustainability of the European Union.

Vulnerable windows in child development in utero and after birth are critical time points for uncovering the links between environment and health. Particular attention is paid to the first 1000 days of life from conception to the second year of life. The ELSPAC (European Longitudinal Study of Pregnancy and Childhood) birth cohort, launched in the early 1990s, is a rich source of longitudinal data about health and life events, based mainly in Brno, Czechia. There are currently no air quality concentration maps that can be used to assess exposure to air pollutants for this period of the 1990s in Central Europe. Simply transferring current models to the 1990’s is burdened with the error introduced by the temporal change in emission sources and land use of the area. Therefore, Czech air quality monitoring data were used to develop monthly land use regression (LUR) models, which combine collected spatial variables with monitoring data to predict the variation in exposures to pollutants. Monthly pollutant concentrations were regressed against the GIS-based potential predictor variables to develop LUR models, following a supervised forward linear regression, with several predefined constraints. We constructed 180 LUR monthly models for sulphur dioxide (SO2), nitrogen oxides (NOx) and suspended particulate matter (SPM) for 1990–1994, that completely cover the first 1000 days for all ELSPAC study participants. The final models showed, on average reasonably good performance (adjusted R2 = 0.59 with hold-out validation (HOV) R2 = 0.40 for SO2; adjusted R2 = 0.75 with HOV R2 = 0.35 for NOx; and adjusted R2 = 0.61 with HOV R2 = 0.31 for SPM; with a mean number of stations of 74, 38 and 41, respectively). For these models, roads and greenness were predominantly selected as the best predictors. The modelled exposures will serve in many subsequent ELSPAC epidemiological studies, but our models may be also used in other Czech and possibly other Central European cities in that period.

Exposure science is an emerging field focusing on all aspects concerning the contact between chemical, biological, physical or psycho-social stressors and human and ecological receptors. With that, exposure science is central in protecting human and ecosystem health and contributes to the global transition towards green and sustainable societies. Recent advancement in exposure science has been huge. Today, the coverage of exposure methodologies extends from local microenvironments to cities worldwide, and enables, for example, aggregated characterization of population exposure to indoor and outdoor sources, geospatial pollutant source-to-exposure analysis, or integrated assessments of urban and country level climate change abatement policies, In Europe, however, exposure science is not yet sufficiently recognized as a scientific field, resulting in disconnected scientific advancements, underrepresentation in academia, and ineffective uptake into policies and decision support. In response, the wider European exposure science community developed an overarching ‘European Exposure Science Strategy 2020–2030’, as a coordinated effort under the guidance of the ‘Europe Regional Chapter of the International Society of Exposure Science’ (ISES Europe). All strategy elements are published open access and are available to guide the wider European and global exposure science community on its journey to advance relevant exposure knowledge and help implementing it into related policies. Furthermore, the published strategy elements are the basis of the ongoing work in the ISES Europe Working Groups, which can build on the defined overarching goals and concrete action plans. With that, the strategy enables exposure science to develop its full potential as a key discipline in protecting human and ecosystem health, and facilitating a sustainable transition of our society in Europe and worldwide.

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.

Proteomics and metabolomics are essential in systems biology, and simultaneous proteo-metabolome liquid–liquid extraction (SPM-LLE) allows isolation of the metabolome and proteome from the same sample. Since the proteome is present as a pellet in SPM-LLE, it must be solubilized for quantitative proteomics. Solubilization and proteome extraction are critical factors in the information obtained at the proteome level. In this study, we investigated the performance of two surfactants (sodium deoxycholate (SDC), sodium dodecyl sulfate (SDS)) and urea in terms of proteome coverage and extraction efficiency of an interphase proteome pellet generated by methanol–chloroform based SPM-LLE. We also investigated how the performance differs when the proteome is extracted from the interphase pellet or by direct cell lysis. We quantified 12 lipids covering triglycerides and various phospholipid classes, and 25 polar metabolites covering central energy metabolism in chloroform and methanol extracts. Our study reveals that the proteome coverages between the two surfactants and urea for the SPM-LLE interphase pellet were similar, but the extraction efficiencies differed significantly. While SDS led to enrichment of basic proteins, which were mainly ribosomal and ribonuclear proteins, urea was the most efficient extraction agent for simultaneous proteo-metabolome analysis. The results of our study also show that the performance of surfactants for quantitative proteomics is better when the proteome is extracted through direct cell lysis rather than an interphase pellet. In contrast, the performance of urea for quantitative proteomics was significantly better when the proteome was extracted from an interphase pellet than by direct cell lysis. We demonstrated that urea is superior to surfactants for proteome extraction from SPM-LLE interphase pellets, with a particularly good performance for the extraction of proteins associated with metabolic pathways. Data are available via ProteomeXchange with identifier PXD027338.

The liver is the central metabolic organ of the body. The plethora of anabolic and catabolic pathways in the liver is tightly regulated by physiological signaling but may become imbalanced as a consequence of malnutrition or exposure to certain chemicals, so-called metabolic endocrine disrupters, or metabolism-disrupting chemicals (MDCs). Among different metabolism-related diseases, obesity and non-alcoholic fatty liver disease (NAFLD) constitute a growing health problem, which has been associated with a western lifestyle combining excessive caloric intake and reduced physical activity. In the past years, awareness of chemical exposure as an underlying cause of metabolic endocrine effects has continuously increased. Within this review, we have collected and summarized evidence that certain environmental MDCs are capable of contributing to metabolic diseases such as liver steatosis and cholestasis by different molecular mechanisms, thereby contributing to the metabolic syndrome. Despite the high relevance of metabolism-related diseases, standardized mechanistic assays for the identification and characterization of MDCs are missing. Therefore, the current state of candidate test systems to identify MDCs is presented, and their possible implementation into a testing strategy for MDCs is discussed.

Current approaches for the assessment of environmental and human health risks due to exposure to chemical substances have served their purpose reasonably well. Nevertheless, the systems in place for different uses of chemicals are faced with various challenges, ranging from a growing number of chemicals to changes in the types of chemicals and materials produced. This has triggered global awareness of the need for a paradigm shift, which in turn has led to the publication of new concepts for chemical risk assessment and explorations of how to translate these concepts into pragmatic approaches. As a result, next-generation risk assessment (NGRA) is generally seen as the way forward. However, incorporating new scientific insights and innovative approaches into hazard and exposure assessments in such a way that regulatory needs are adequately met has appeared to be challenging. The European Partnership for the Assessment of Risks from Chemicals (PARC) has been designed to address various challenges associated with innovating chemical risk assessment. Its overall goal is to consolidate and strengthen the European research and innovation capacity for chemical risk assessment to protect human health and the environment. With around 200 participating organisations from all over Europe, including three European agencies, and a total budget of over 400 million euro, PARC is one of the largest projects of its kind. It has a duration of seven years and is coordinated by ANSES, the French Agency for Food, Environmental and Occupational Health & Safety.

Degradation kinetics of pesticides in plants are crucial for modeling mechanism-based pesticide residual concentrations. However, due to complex open-field conditions that involve multiple pesticide plant uptake and elimination processes, it is difficult to directly measure degradation kinetics of pesticides in plants. To address this limitation, we proposed a modeling approach for estimating degradation rate constants of pesticides in plants, using potato as a model crop. An operational tool was developed to backward-estimate degradation rate constants, and three pesticides were selected to perform example simulations.
The simulation results of thiamethoxam indicated that the growth dynamics of the potato had a significant impact on the degradation kinetic estimates when the pesticide was applied during the early growth stage, as the size of the potato determined the uptake and elimination kinetics via diffusion. Using mepiquat, we demonstrated that geographical variations in weather conditions and soil properties led to significant differences in the dissipation kinetics in both potato plants and soil, which propagated the variability of the degradation rate constant. Simulation results of chlorpyrifos differed between two reported field studies, which is due to the effect of the vertical distribution of the residue concentration in the soil, which is not considered in the majority of recent studies.
Our proposed approach is adaptable to plant growth dynamics, preharvest intervals, and multiple pesticide application events. In future research, it is expected that the proposed method will enable region-specific inputs to improve the estimation of the degradation kinetics of pesticides in plants.