Innovative screening for functional improved aromatic amine derivatives: Toxicokinetics, free radical oxidation pathway and carcinogenic adverse outcome pathway

Exploring the application of machine learning to identify the correlations between phthalate esters and disease: enhancing nursing assessments

Background

Health risks associated with phthalate esters depend on exposure level, individual sensitivities, and other contributing factors.

Purpose

This study employed artificial intelligence algorithms while applying data mining techniques to identify correlations between phthalate esters [di(2-ethylhexyl) phthalate, DEHP], lifestyle factors, and disease outcomes.

Methods

We conducted exploratory analysis using demographic and laboratory data collected from the Taiwan Biobank. The study developed a prediction model to examine the relationship between phthalate esters and the risk of developing certain diseases based on various artificial intelligence algorithms, including logistic regression, artificial neural networks, and Bayesian networks.

Results

The results indicate that phthalate esters exhibited a greater impact on bone and joint issues than heart problems. We observed that DEHP metabolites, such as mono(2-carboxymethylhexyl) phthalate, mono-n-butyl phthalate, and monoethylphthalate, leave higher residue in females than in males, with statistically significant differences. Monoethylphthalate levels were lower in individuals who exercised regularly than those who did not, indicating statistically significant differences.

Conclusions

This study's findings can serve as a valuable reference for clinical nursing assessments regarding diseases related to osteoporosis, arthritis, and musculoskeletal pain. Medical professionals can enhance care quality by considering factors beyond patients' essential physical assessment items.Trial Registration: This study was registered under NCT05892029 on May 5, 2023, retrospectively.

Synergistic effect of horizontal transfer of antibiotic resistance genes between bacteria exposed to microplastics and per/polyfluoroalkyl substances: An explanation from theoretical methods

Microplastics (MPs) and per/polyfluoroalkyl substances (PFASs), as emerging pollutants widely present in aquatic environments, pose a significant threat to human health through the horizontal gene transfer (HGT) of antibiotic resistance genes (ARGs). Molecular dynamics simulations and machine learning can accurately capture the complex interactions between molecules. This study utilized them to identify the HGT risk between bacteria under MPs and PFASs stress. This study found that MPs and PFASs significantly increase the HGT risk between bacteria, up to 1.57 and 1.59 times, respectively. Notably, long-chain PFASs and perfluoroalkyl carboxylic acids increased the HGT risk by 1.38 and 1.40 times, respectively. Additionally, MPs primarily increase the HGT risk by enhancing hydrogen bonding interaction between key proteins in the HGT pathway and "active codons". The electronegativity and polarizability of PFASs critically influence the HGT risk, acting inversely and directly proportional, respectively. The HGT risk between bacteria under the combined stress from PP-MPs and PFASs exhibits a significant synergistic effect (synergistic effect value of 27.6), which markedly increases the HGT risk. Further analysis revealed that a smaller minimum distance and sharper RDF curve peaks between key proteins and "active codons" indicate higher HGT risk. This indicates that stronger interactions lead to higher HGT risk. This study identifies the characteristics of HGT risks between bacteria in aquatic environments under the individual and combined stresses from MPs and PFASs at the molecular level. It provides a theoretical basis for mitigating ARG transfer and comprehensively assessing the health risks posed by these emerging pollutants.

Hepatotoxicity, developmental toxicity, and neurotoxicity risks associated with co-exposure of zebrafish to fluoroquinolone antibiotics and tire microplastics: An in silico study

This study aimed to investigate the differences in the mechanisms of microscopic hepatotoxicity, developmental toxicity, and neurotoxicity in aquatic organisms co-exposed to styrene-butadiene rubber tire microplastics (SBR TMPs) and fluoroquinolone antibiotics (FQs). We found that hepatotoxicity in zebrafish induced by SBR TMPs and FQs was significantly higher than developmental toxicity and neurotoxicity. Furthermore, the main effects of the FQs primarily manifested as synergistic toxicity, whereas the low- and high-order interactions of the FQs mainly exhibited synergistic and antagonistic effects, respectively. Factorial analysis and the mixture toxicity index revealed that the synergistic effects of lomefloxacin × moxifloxacin and ciprofloxacin × lomefloxacin × enrofloxacin interactions significantly contributed to hepatotoxicity in zebrafish exposed to SBR TMP. SBR TMPs and antibiotics primarily induced hepatotoxicity, developmental toxicity, and neurotoxicity in zebrafish by affecting the activities of Cyp1a, Acox1, TRα, and mAChR. The observed toxicities were closely linked to the hydrophilic/hydrophobic groups, electronegativity, group mass, and structural complexity of the FQ molecules. This study provides new insights regarding the toxicological risks to aquatic organisms from co-exposure to SBR TMPs and FQs from a microscopic perspective. Future studies should include a broader range of antibiotics and tire microplastics and consider their long-term adverse effects on aquatic life.

Development of hybrid models by the integration of the read-across hypothesis with the QSAR framework for the assessment of developmental and reproductive toxicity (DART) tested according to OECD TG 414

The governing laws mandate animal testing guidelines (TG) to assess the developmental and reproductive toxicity (DART) potential of new and current chemical compounds for the categorization, hazard identification, and labeling. In silico modeling has evolved as a promising, economical, and animal-friendly technique for assessing a chemical's potential for DART testing. The complexity of the endpoint has presented a problem for Quantitative Structure-Activity Relationship (QSAR) model developers as various facets of the chemical have to be appropriately analyzed to predict the DART. For the next-generation risk assessment (NGRA) studies, researchers and governing bodies are exploring various new approach methodologies (NAMs) integrated to address complex endpoints like repeated dose toxicity and DART. We have developed four hybrid computational models for DART studies of rodents and rabbits for their adult and fetal life stages separately. The hybrid models were created by integrating QSAR features with similarities-derived features (obtained from read-across hypotheses). This analysis has identified that this integrated method gives a better statistical quality compared to the traditional QSAR models, and the predictivity and transferability of the model are also enhanced in this new approach.

Toxicological mechanisms and molecular impacts of tire particles and antibiotics on zebrafish

Tire microplastics (TMPs) and antibiotics are emerging pollutants that widely exist in water environments. The coexistence of these pollutants poses severe threats to aquatic organisms. However, the toxicity characteristics and key molecular factors of the combined exposure to TMPs in aquatic organisms remain unknown. Therefore, the joint toxicity of styrene-butadiene rubber TMPs (SBR-TMPs) and 32 antibiotics (macrolides, fluoroquinolones, β-lactams, sulfonamides, tetracyclines, nitroimidazoles, highly toxic antibiotics, high-content antibiotics, and common antibiotics) in zebrafish was investigated using a full factorial design, molecular docking, and molecular dynamics simulation. Sixty-four combinations of antibiotics were designed to investigate the hepatotoxicity of the coexistence of SBR-TMPs additives and antibiotics in zebrafish. Results indicated that low-order effects of antibiotics (e.g., enoxacin-lomefloxacin and ofloxacin-enoxacin-lomefloxacin) had relatively notable toxicity. The van der Waals interaction between additives and zebrafish cytochrome P450 enzymes primarily affected zebrafish hepatotoxicity. Zebrafish hepatotoxicity was also affected by the ability of SBR-TMPs to adsorb antibiotics, the relation between antibiotics, the affinity of antibiotics docking to zebrafish cytochrome P450 enzymes, electronegativity, atomic mass, and the hydrophobicity of the antibiotic molecules. This study aimed to eliminate the joint toxicity of TMPs and antibiotics and provide more environmentally friendly instructions for using different chemicals.

Tire additives: Evaluation of joint toxicity, design of new derivatives and mechanism analysis of free radical oxidation

N-(1,3-Dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD) is one of the most widely used antioxidant agents in tire additives. Its ozonation by-product 6PPD-quinone has recently been recognized as inducing acute mortality in aquatic organisms such as coho salmon. In this study, we aimed to develop an in-silico method to design environmentally friendly 6PPD derivatives and evaluate the joint toxicity of 6PPD with other commonly used tire additives on coho salmon through full factorial design-molecular docking and molecular dynamic simulation. The toxicity mentioned in this study is represented by the binding energy of chemical(s) binding to the coho salmon growth hormone. The recommended formula for tire additives with relatively low toxicity was then proposed. To further reduce the toxicity of 6PPD, 129 6PPD derivatives were designed based on the N-H bond dissociation reaction, and three of these derivatives showed improved antioxidant activity and 6PPD-106 was finally screened as the optimum alternative with lower toxicity to coho salmon. Besides, the mechanism of free radical oxidation (i.e., antioxidation and ozonation metabolic pathway) for 6PPD-106 was also analyzed and found that after ozonation, the toxicity of 6PPD-106's by-products is much lower than that of 6PPD's by-products. This study provided a molecular modelling-based examination of 6PPD, which comprehensively advanced the understanding of 6PPD's environmental behaviors and provided more environmentally friendly 6PPD alternatives with desired functional property and lower ecological risks.

Human Endocrine-Disrupting Effects of Phthalate Esters through Adverse Outcome Pathways: A Comprehensive Mechanism Analysis

Phthalate esters (PAEs) are widely exposed in the environment as plasticizers in plastics, and they have been found to cause significant environmental and health hazards, especially in terms of endocrine disruption in humans. In order to investigate the processes underlying the endocrine disruption effects of PAEs, three machine learning techniques were used in this study to build an adverse outcome pathway (AOP) for those effects on people. According to the results of the three machine learning techniques, the random forest and XGBoost models performed well in terms of prediction. Subsequently, sensitivity analysis was conducted to identify the initial events, key events, and key features influencing the endocrine disruption effects of PAEs on humans. Key features, such as Mol.Wt, Q+, QH+, ELUMO, minHCsats, MEDC-33, and EG, were found to be closely related to the molecular structure. Therefore, a 3D-QSAR model for PAEs was constructed, and, based on the three-dimensional potential energy surface information, it was discovered that the hydrophobic, steric, and electrostatic fields of PAEs significantly influence their endocrine disruption effects on humans. Lastly, an analysis of the contributions of amino acid residues and binding energy (BE) was performed, identifying and confirming that hydrogen bonding, hydrophobic interactions, and van der Waals forces are important factors affecting the AOP of PAEs' molecular endocrine disruption effects. This study defined and constructed a comprehensive AOP for the endocrine disruption effects of PAEs on humans and developed a method based on theoretical simulation to characterize the AOP, providing theoretical guidance for studying the mechanisms of toxicity caused by other pollutants.