QSAR modeling of adipose/blood partition coefficients of Alcohols, PCBs, PBDEs, PCDDs and PAHs: A data gap filling approach

Evaluating the impact of polycyclic aromatic hydrocarbon bioaccumulation in adipose tissue of obese women

Polycyclic aromatic hydrocarbons (PAHs) are widespread across the environment and humans are unavoidably and constantly exposed to them. As lipophilic contaminants, these substances tend to accumulate in fatty tissues as adipose tissue and exposure to these endocrine disruptors has been associated with severe health hazards including prevalence and incidence of obesity. Previous studies have shown significantly higher concentrations of PAHs in adipose tissue compared to other human samples, such as urine and plasma, which are typically used for PAHs assessment. Therefore, conducting biomonitoring studies in adipose tissue is essential, although such studies are currently limited. In this study, the concentrations of 18 PAHs were measured in subcutaneous (scAT) and visceral adipose tissue (vAT) of 188 Portuguese obese females by high performance liquid chromatography (HPLC). The obtained results were then associated with the patient's data namely: 13 clinical, 4 social, and 42 biochemical parameters. Seventeen PAHs were present, at least, in one sample of both scAT and vAT, most of them with detection frequencies higher than 80%. Indeno [1,2,3-cd]pyrene (InP) was the only PAH never detected. Overall higher concentrations of PAHs were observed in scAT. Median concentrations of ∑PAHs were 32.2 ± 10.0 ng/g in scAT and 24.6 ± 10.0 ng/g in vAT. Thirty-six significant associations (7 with social, 18 with clinical, and 11 with biochemical parameters), including 21 Spearman's correlations were identified (12 positive and 9 negative correlations). Indicating the potential effects of PAHs on various parameters such as obesity evolution, body fat, number of adipocytes, total cholesterol, alkaline phosphatase, macrominerals, uric acid, sedimentation velocity, and luteinizing hormone. This study underscores the significance of biomonitoring PAH levels in adipose tissue and their potential effects on metabolic health. Further research is essential to fully comprehend the metabolic implications of PAHs in the human body and to develop strategies for obesity prevention and treatment.

Concentration identification and endpoint-oriented health risk assessments on a broad-spectrum of organic compounds in atmospheric fine particles: A sampling experimental study in Beijing, China

Understanding the complicate chemical components in atmospheric fine particulate matter (PM2.5) helps policy makers for pollutants control track progress and identify disparities in overall health risks. However, till now, information on accurate component detection, source identification, and effect-oriented risk assessment is scarce, especially for the simultaneous analysis of a broad-spectrum of compounds. In this study, a high-throughput target method was employed to distinguish the occurrence and characteristics of 152 chemicals: phthalate esters (PAEs), organophosphate esters (OPEs), carboxylic acid esters (CAEs), nitrophenols (NPs), nitrogen heterocyclic compounds (NHCs), per- and poly-fluoroalkyl substances (PFASs), triclosan and its derivatives (TCSs), and organosulfates (OSs) in ambient PM2.5 collected from Beijing, China. Detection frequencies of 77 targeted compounds were >50 %. Total concentrations of all compounds ranged from 33.1 to 745 ng/m3. The median concentration of ∑PAEs (108 ng/m3) was the highest, followed by ∑CAEs (12.2 ng/m3) and ∑NPs (10.1 ng/m3). Organophosphate diesters (di-OPEs) and TCSs were reported for the first time in ambient PM2.5. The pollutants mainly originated from the local industrial production, release of building materials, and environmental degradation of parent compounds. Based on absorption, distribution, metabolism, excretion, and toxicity (ADMET)-oriented risk evaluations, we found that bis (2-ethylhexyl) phthalate, diisobutyl phthalate, dibutyl phthalate, and di (2-ethylhexyl) adipate have high health risks. Additionally, the high oxidative stress potential of 4-nitrocatechol and the strong blood-brain barrier penetration potential of triclosan cannot be ignored. Our study will facilitate the evaluations of specific health outcomes and mechanisms of pollutants, and suggestion of pollutants priority control to reduce human health hazards caused by atmospheric particles.

Enhanced prediction of internal concentrations of phenolic endocrine disrupting chemicals and their metabolites in fish by a physiologically based toxicokinetic incorporating metabolism (PBTK-MT) model

Bisphenol A (BPA), 4-nonylphenol (4-NP), and triclosan (TCS) are phenolic endocrine disrupting chemicals (EDCs), which are widely detected in aquatic environments and further bioaccumulated and metabolized in fish. Physiologically based toxicokinetic (PBTK) models have been used to describe the absorption, distribution, metabolism, and excretion (ADME) of parent compounds in fish, whereas the metabolites are less explored. In this study, a PBTK incorporating metabolism (PBTK-MT) model for BPA, 4-NP, and TCS was established to enhance the performance of the traditional PBTK model. The PBTK-MT model comprised 16 compartments, showing great accuracy in predicting the internal concentrations of three compounds and their glucuronidated and sulfated conjugates in fish. The impact of typical hepatic metabolism on the PBTK-MT model was successfully resolved by optimizing the mechanism for deriving the partition coefficients between the blood and liver. The PBTK-MT model exhibited a potential data gap-filling capacity for unknown parameters through a backward extrapolation approach of parameters. Model sensitivity analysis suggested that only five parameters were sensitive in at least two PBTK-MT models, while most parameters were insensitive. The PBTK-MT model will contribute to a well understanding of the environmental behavior and risks of pollutants in aquatic biota.

Prediction of the Partition Coefficient between Adipose Tissue and Blood for Environmental Chemicals: From Single QSAR Models to an Integrated Approach

The adipose tissue:blood partition coefficient is a key-endpoint to predict the pharmacokinetics of chemicals in humans and animals, since other organ:blood affinities can be estimated as a function of this parameter. We performed a search in the literature to select all the available rat in vivo data. This approach resulted into two improvements to existing models: a homogeneous definition of the endpoint and an expanded data collection. The resulting dataset was used to develop QSAR models as a function of linear and non-linear algorithms. Several applicability domain definitions were assessed and the definition corresponding to a good balance between performance and coverage was retained. We assessed the pertinence of combining single models into integrated approaches to increase the accuracy in predictions. The best integrated model outperformed the single models and it was characterized by an external mean absolute error (MAE) equal to 0.26, while preserving an adequate coverage (84 %). This performance is comparable to experimental variability and it highlights the pertinence of the integrated model.

Current Approaches and Techniques in Physiologically Based Pharmacokinetic (PBPK) Modelling of Nanomaterials

There have been efforts to develop physiologically based pharmacokinetic (PBPK) models for nanomaterials (NMs). Since NMs have quite different kinetic behaviors, the applicability of the approaches and techniques that are utilized in current PBPK models for NMs is warranted. Most PBPK models simulate a size-independent endocytosis from tissues or blood. In the lungs, dosimetry and the air-liquid interface (ALI) models have sometimes been used to estimate NM deposition and translocation into the circulatory system. In the gastrointestinal (GI) tract, kinetics data are needed for mechanistic understanding of NM behavior as well as their absorption through GI mucus and their subsequent hepatobiliary excretion into feces. Following absorption, permeability (P_t) and partition coefficients (PCs) are needed to simulate partitioning from the circulatory system into various organs. Furthermore, mechanistic modelling of organ- and species-specific NM corona formation is in its infancy. More recently, some PBPK models have included the mononuclear phagocyte system (MPS). Most notably, dissolution, a key elimination process for NMs, is only empirically added in some PBPK models. Nevertheless, despite the many challenges still present, there have been great advances in the development and application of PBPK models for hazard assessment and risk assessment of NMs.

Inclusion of molecular descriptors in predictive models improves pesticide soil-air partitioning estimates

The soil-air exchange of pesticides is one potential fate and exposure pathways, and this process is generally thought to be governed by soil properties and environmental conditions. The experimental determination of soil-air partitioning coefficient (Ksa) is laborious and costly and typically, Ksa's are predicted from a semiempirical or a simple linear regression approach with soil and environmental variables. Here we developed a model that combined linear regression of soil, environmental and molecular parameters with the quantitative structural-property relationship (QSPR) to predict Ksa for pesticides. The values of theoretical descriptors of pesticides were calculated and the best descriptors selected using the Boruta Algorithm. Seventy-six experimental logKsa values for 17 pesticides were used in model development. Multiple linear regression (MLR) with a soil (organic carbon fraction), physicochemical (octanol-air partitioning coefficient), environmental (temperature and humidity) and molecular descriptor (Gmin, a 2D E-state molecular parameter), called as MLR-QSPR combined model exhibited better predictability (adj. r² = 0.95) of logKsa compared to MLR (adj. r² = 0.87) or QSPR (adj. r² = 0.82) itself. MLR-QSPR also showed the best performance in five-fold cross-validation (adj. r² = 0.94) and test set verification (adj. r² = 0.96). The developed model was validated and characterized by the applicability domain. Results showed that the proposed MLR-QSPR approach is highly predictive and statistically robust with >95% of predictions within ±0.5 log unit of the measured Ksa. Therefore, this approach can be used in estimating the soil-air partitioning of pesticides to better predict it's fate and transport in environments.

Levels and congener profiles of halogenated persistent organic pollutants in human serum and semen at an e-waste area in South China

Typical halogenated persistent organic pollutants (Hal-POPs), including polybrominated diphenyl ethers (PBDEs), polybrominated biphenyls (PBBs), polychlorinated biphenyls (PCBs), and dichlorodiphenyltrichloroethane (DDT), are a group of ubiquitous organic pollutants with an endocrine disrupting effect. This study evaluated the accumulation and congener profiles of Hal-POPs in the bodies of men who live/work in areas of South China where electronic wastes are collected and managed, especially in their semen samples. The results show that the detection frequency and serum concentrations of Hal-POP congeners within the high-exposure group (HEG) were higher than those of the low-exposure group (LEG). Furthermore, an identical trend was observed for the seminal plasma concentrations of Hal-POPs. The distribution characteristics, such as their mean, median, and discrete values, of PBDE congeners in serum and semen samples from the same subjects were consistent with each other. However, the distribution characteristics of PCB congeners in serum samples were different from those in semen samples. BDE153 was one of the most abundant congeners found in the serum and semen samples; hence, it can be identified as an indicator PBDE congener. Further research is needed to explore the mechanism of Hal-POPs distribution in human semen and serum samples.

The role of temperature in phenanthrene transfer and accumulation in crop leaves

Polycyclic aromatic hydrocarbons (PAHs) pollution has become a worldwide environmental problem, and the spread of PAHs can cause carcinogenicity, mutagenicity, and toxicity to humans. However, the transfer and accumulation of PAHs in crop leaves has not been clearly understood. In this study, we first reported that the environmental temperature could induce phenanthrene transfer and accumulation in hydrocultured wheat, corn and soybean leaves via vacuum-infiltration-centrifugation method. Phenanthrene accumulation rises significantly (p < 0.05) in the first 8 h and reaches the maximum accumulation rate at the 4th h. Then the accumulation turns stable in both apoplast and sympalst of wheat, soybean and corn leaves. Temperature is positively correlated with phenanthrene accumulation in apoplast and sympalst of soybean and corn leaves, and phenanthrene accumulation increases under lower temperature in apoplast and sympalst of wheat leaves. Temperature also displays a positive correlation with the phenanthrene accumulation under gradient phenanthrene treatments in both apoplast and sympalst. In addition, the wheat, corn and soybean leaves have the same phenanthrene accumulation pathways and symplast pathway is major for phenanthrene accumulation with the contribution over 55% in total phenanthrene accumulation. Meanwhile, based on the Elovich equation, the symplast and apoplast processes of phenanthrene accumulation are endothermic. In the end, our findings will offer a new understanding for phenanthrene transfer and accumulation pathway in plant leaves and put forward a new biological reference of PAHs transfer in environmental science.

Practices and Trends of Machine Learning Application in Nanotoxicology

Machine Learning (ML) techniques have been applied in the field of nanotoxicology with very encouraging results. Adverse effects of nanoforms are affected by multiple features described by theoretical descriptors, nano-specific measured properties, and experimental conditions. ML has been proven very helpful in this field in order to gain an insight into features effecting toxicity, predicting possible adverse effects as part of proactive risk analysis, and informing safe design. At this juncture, it is important to document and categorize the work that has been carried out. This study investigates and bookmarks ML methodologies used to predict nano (eco)-toxicological outcomes in nanotoxicology during the last decade. It provides a review of the sequenced steps involved in implementing an ML model, from data pre-processing, to model implementation, model validation, and applicability domain. The review gathers and presents the step-wise information on techniques and procedures of existing models that can be used readily to assemble new nanotoxicological in silico studies and accelerates the regulation of in silico tools in nanotoxicology. ML applications in nanotoxicology comprise an active and diverse collection of ongoing efforts, although it is still in their early steps toward a scientific accord, subsequent guidelines, and regulation adoption. This study is an important bookend to a decade of ML applications to nanotoxicology and serves as a useful guide to further in silico applications.