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Ionic liquids (ILs) have been proven to be an effective technology for enhancing drug transdermal absorption. However, due to the unique structural components of ILs, the design of efficient ILs and elucidation of action mechanisms remain to be explored. In this review, basic design principles of ideal ILs for transdermal drug delivery system (TDDS) are discussed considering melting point, skin permeability, and toxicity, which depend on the molar ratios, types, functional groups of ions and inter-ionic interactions. Secondly, the contributions of ILs to the development of TDDS through different roles are described: as novel skin penetration enhancers for enhancing transdermal absorption of drugs; as novel solvents for improving the solubility of drugs in carriers; as novel active pharmaceutical ingredients (API-ILs) for regulating skin permeability, solubility, release, and pharmacokinetic behaviors of drugs; and as novel polymers for the development of smart medical materials. Moreover, diverse action mechanisms, mainly including the interactions among ILs, drugs, polymers, and skin components, are summarized. Finally, future challenges related to ILs are discussed, including underlying quantitative structure-activity relationships, complex interaction forces between anions, drugs, polymers and skin microenvironment, long-term stability, and in vivo safety issues. In summary, this article will promote the development of TDDS based on ILs.

Quantitative structure-activity relationship (QSAR) is a cost-effective solution to directly and accurately estimating the environmental safety thresholds (ESTs) of pollutants in the ecological risk assessment due to the lack of toxicity data. In this study, QSAR models were developed for estimating the Predicted No-Effect Concentrations (PNECs) of petroleum hydrocarbons and their derivatives (PHDs) under dietary exposure, based on the quantified molecular descriptors and the obtained PNECs of 51 PHDs with given acute or chronic toxicity concentrations. Three high-reliable QSAR models were respectively developed for PHDs, aromatic hydrocarbons and their derivatives (AHDs), and alkanes, alkenes and their derivatives (ALKDs), with excellent fitting performance evidenced by high correlation coefficient (0.89–0.95) and low root mean square error (0.13–0.2 mg/kg), and high stability and predictive performance reflected by high internal and external verification coefficient (Q2LOO, 0.66–0.89; Q2F1, 0.62–0.78; Q2F2, 0.60–0.73). The investigated quantitative relationships between molecular structure and PNECs indicated that 18 autocorrelation descriptors, 3 information index descriptors, 4 barysz matrix descriptors, 6 burden modified eigenvalues descriptors, and 1 BCUT descriptor were important molecular descriptors affecting the PNECs of PHDs. The obtained results supported that PNECs of PHDs can be accurately estimated by the influencing molecular descriptors and the quantitative relationship from the developed QSAR models, that provided a new feasible solution for ESTs derivation in the ecological risk assessment.

Synthetic surfactant products are continuously released into the aquatic environment in large quantities, posing a burden on ecosystems as a “pseudo–persistent” organic pollutant. Threshold derivation for protecting aquatic ecosystems is challenging due to the various homologous components of surfactants. In this study, five commercially available products were chosen as representative major types of surfactants. Corresponding quantitative structure–activity relationships (QSAR) were screened and subsequently combined with interspecific correlation estimation (ICE) to develop species sensitivity distributions (SSDs) for each component. Then, the 5th percentile hazard concentrations (HC5s) were calculated. The results indicated that the developed QSAR–ICE models demonstrated good toxicity prediction performance. The HC5 of each component showed a negatively correlation with alkyl chain length and a positive correlation with the amount of ethylene oxide. The HC5s of surfactants correlate with variations in their charged properties. Quaternary ammonium compounds (QAC) exhibited the lowest HC5s (8.5 ± 18.3 μg/L), followed by alcohol ethoxylates (AE), linear alkylbenzene sulfonates (LAS), and alcohol ethoxylated sulfates (AES); and alkyl oxide (AO) exhibited the highest HC5s (15784.2 ± 21552.6 μg/L). For cationic surfactants, the HC5s in the invertebrates were significantly lower than those in the fish; conversely, for anionic surfactants, the opposite was true, indicating a difference in the toxic mechanisms of surfactants with different charged properties across species taxa. Additionally, among invertebrates, shellfish demonstrated heightened sensitivity to surfactants, owing to their high accumulation and low metabolism of pollutants. Salmoniformes were the most sensitive among all species, indicating the necessity of prioritizing these species for aquatic ecological conservation in surfactant-contaminated waters.

Candida albicans, an opportunistic pathogen, is the most common type of fungus and represents a substantial source of human invasive disease (nosocomial infection). This category of fungi are part of our microbiota, and given the appropriate environmental conditions, it has the potential to cause both superficial and systemic infections. There is a soaring resistance against the available anticandidal agents. The purpose of this research is to investigate the activity of certain previously synthesized benzo[g]quinazolines against C. albicans in vitro by using the cup-plate diffusion method. There was a marked difference in the effectiveness of the target compounds 1–6 against the sample of C. albicans that was tested. Benzo[g]quinazolines 1 (inhibition zone = 20 mm) and 2 (inhibition zone = 22 mm) had good effects in comparison to fluconazole (inhibition zone = 26 mm). A docking study was conducted between benzo[g]quinazolines 1–6 and Candida spp. CYP51 to establish the binding mode compared with fluconazole and VT-1161 (oteseconazole) as reference medicines, and it was determined that binding at the active site of Candida spp. CYP51 occurred in the same manner. Quantitative structure–activity relationship (QSAR) investigation was performed to further characterize the identified anticandidal agents and recognize the major regulatory components governing such activity. In future studies, the benzo[g]quinazoline scaffold could serve as a model for the design and development of novel derivatives with antifungal potential.

Identification of estrogen receptor (ER) agonists among environmental toxicants is essential for assessing the potential impact of toxicants on human health. Using 2D autocorrelation descriptors as predictor variables, two binary logistic regression models were developed to identify active ER agonists among hydroxylated polychlorinated biphenyls (OH-PCBs). The classifications made by the two models on the training set compounds resulted in accuracy, sensitivity and specificity of 95.9 %, 93.9 % and 97.6 % for ERα dataset and 91.9 %, 90.9 % and 92.7 % for ERβ dataset. The areas under the ROC curves, constructed with the training set data, were found to be 0.985 and 0.987 for the two models. Predictions made by models I and II correctly classified 84.0 % and 88.0 % of the test set compounds and 89.8 % and 85.8% of the cross-validation set compounds respectively. The two classification-based QSAR models proposed in this paper are considered robust and reliable for rapid identification of ERα and ERβ agonists among OH-PCB congeners.

Phenylurea herbicides (PUHs) present one of the most important herbicides, which have cause serious effects on ecological environment and humans. Nowadays enzyme strategy shows great advantages in degradation of PUHs. Here density functional theory (DFT), quantitative structure − activity relationship (QSAR) and quantum mechanics/molecular mechanics (QM/MM) approaches are used to investigate the degradation mechanism of PUHs catalyzed by P450 enzymes. Two successive N-demethylation pathways are identified and two hydrogen abstraction (H-abstraction) reaction pathways are identified as the rate-determining step through high-throughput DFT calculations. The Boltzmann-weighted average energy barrier of the second H-abstraction pathway (19.95 kcal/mol) is higher than that of the first H-abstraction pathway (16.80 kcal/mol). Two QSAR models are established to predict the energy barriers of the two H-abstraction pathways based on the quantum chemical descriptors and mordred molecular descriptors. The determination coefficient (R2) values of QSAR models are > 0.9, which reveal that the established QSAR models have great predictive capability. QM/MM calculations indicate that human P450 enzymes are more efficient in degradation of PUHs than crop and weed P450 enzymes. Correlations between energy barriers and key structural/charge parameters are revealed and key parameters that have influence on degradation efficiency of PUHs are identified. This study provides lateral insights into the biodegradation strategy and removal method of PUHs and valuable information for designing or engineering of highly efficient degradation enzymes and genetically modified crops.

Empirical and in silico data on the aquatic ecotoxicology of 2697 organic chemicals were collected in order to compile a dataset for assessing the predictive power of current Quantitative Structure Activity Relationship (QSAR) models and software platforms. This document presents the dataset and the data pipeline for its creation. Empirical data were collected from the US EPA ECOTOX Knowledgebase (ECOTOX) and the EFSA (European Food Safety Authority) report “Completion of data entry of pesticide ecotoxicology Tier 1 study endpoints in a XML schema – database”. Only data for OECD recommended algae, daphnia and fish species were retained. QSAR toxicity predictions were calculated for each chemical and each of six endpoints using ECOSAR, VEGA and the Toxicity Estimation Software Tool (T.E.S.T.) platforms. Finally, the dataset was amended with SMILES, InChIKey, pKa and logP collected from webchem and PubChem.

Natural bile acids, a class of steroids with a valeric acid side chain at the C-17 position, present significant challenges in separation and analysis due to structural similarities, isomerism, and large polarity differences. Therefore, advanced analytical methods are essential for the accurate identification and quantification of bile acids. This study conducted a comprehensive qualitative analysis of bile acids by integrating liquid chromatography-tandem mass spectrometry (LC-MS/MS), hydrogen-deuterium exchange tandem mass spectrometry (HDX-MS/MS), and quantitative structure-retention relationship (QSRR) methods. Firstly, LC-MS/MS conditions were optimized to enhance chromatographic separation and improve the reliability of characteristic fragment ions. MS/MS fragmentation rules for bile acids were derived from the mass spectral data of bile acid standards and validated through HDX-MS/MS experiments. Secondly, potential bile acids in snake bile were identified based on these validated fragmentation rules, and a QSRR model was established to predict the retention times of the proposed structures. Thirdly, HDX-MS/MS was applied to assist in identifying bile acid isomers. Finally, a total of 150 bile acids, including 11 free bile acids (free BA), 5 glyco-bile acids (GBA) and 134 tauro-bile acids (TBA), were detected in snake bile. Thirteen bile acids were accurately characterized by comparing their retention time and MS/MS spectra with standards. Forty-nine bile acids were reasonably annotated using the QSRR model and HDX-MS/MS. This study is notable for being the first to utilize the QSRR and HDX-MS/MS techniques for the annotation of bile acids in snake bile, providing a robust framework for the structural elucidation of these compounds., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Antibiotics usually induce the hormetic effects on bacteria, featured by low-dose stimulation and high-dose inhibition, which challenges the central belief in toxicity assessment and environmental risk assessment of antibiotics. However, there are currently no ideal parameters to quantitatively characterize hormesis. In this study, an effective area in hormesis (A H ) was developed to quantify the biphasic dose-responses of single antibiotics (sulfonamides (SAs), sulfonamides potentiators (SAPs), and tetracyclines (TCs)) and binary mixtures (SAs-SAPs, SAs-TCs, and SAs-SAs) to the bioluminescence of Aliivibrio fischeri. Using E bind (the lowest interaction energy between antibiotic and target protein) and K ow (octanol-water partition coefficient) as the structural descriptors, the reliable quantitative structure-activity relationship (QSAR) models were constructed for the A H values of test antibiotics and mixtures. Furthermore, a novel method based on A H was established to judge the joint toxic actions of binary antibiotics, which mainly exhibited synergism. The results also indicated that SAPs (or TCs) contributed more than SAs in the hormetic effects of antibiotic mixtures. This study proposes a new quantitative parameter for characterizing and predicting antibiotic hormesis, and considers hormesis as an integrated whole to reveal the combined effects of antibiotics, which will promote the development of risk evaluation for antibiotics and their mixtures., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In this study, we utilized an innovative quantitative read-across (RA) structure-activity relationship (q-RASAR) approach to predict the bioconcentration factor (BCF) values of a diverse range of organic compounds, based on a dataset of 575 compounds tested using Organisation for Economic Co-operation and Development Test Guideline 305 for bioaccumulation in fish. Initially, we constructed the q-RASAR model using the partial least squares regression method, yielding promising statistical results for the training set (R 2 =0.71, Q 2 LOO =0.68, mean absolute error [MAE] training =0.54). The model was further validated using the test set (Q 2 F1 =0.77, Q 2 F2 =0.75, MAE test =0.51). Subsequently, we explored the q-RASAR method using other regression-based supervised machine-learning algorithms, demonstrating favourable results for the training and test sets. All models exhibited R 2 and Q 2 F1 values exceeding 0.7, Q 2 LOO values greater than 0.6, and low MAE values, indicating high model quality and predictive capability for new, unidentified chemical substances. These findings represent the significance of the RASAR method in enhancing predictivity for new unknown chemicals due to the incorporation of similarity functions in the RASAR descriptors, independent of a specific algorithm., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In the field of microplastics (MPs) toxicity prediction, machine learning (ML) computer simulation techniques are showing great potential. In this study, six ML algorithms were utilized to predict the toxicity of MPs on BEAS-2B cells based on quantitative structure-activity relationship (QSAR) models. Comparing the models of different algorithms, the extreme gradient boosting model showed the best fit and prediction performance (R 2 tra = 0.9876, R 2 test = 0.9286). Additionally, Williams plot analysis showed that the six models developed were able to predict stably within their applicability domain, with few outliers. Finally, the three feature importance methods-Embedded Feature Importance (EFI), Recursive Feature Elimination (RFE), and SHapley Additive exPlanations (SHAP)-consistently identified particle size as the most critical feature affecting toxicity prediction. The proposed QSAR model can be utilized for preliminary environmental exposure assessments of MPs and to better understand the associated health risks., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

The risk assessment of an expanding array of emerging contaminants in aquatic ecosystems and the establishment of water quality criteria rely on species sensitivity distribution (SSD), necessitating ample multi-trophic toxicity data. Computational methods, such as quantitative structure-activity relationship (QSAR), enable the prediction of specific toxicity data, thus mitigating the need for costly experimental testing and exposure risk assessment. In this study, robust QSAR models for four aquatic species (Rana pipiens, Crassostrea virginica, Asellus aquaticus, and Lepomis macrochirus) were developed using leave-one-out (LOO) screening variables and the partial least squares algorithm to predict toxicity data for paraquat, bisphenol A, and carbamazepine. These predicted data can be integrated with experimental data to construct SSD models and derive hazardous concentration for 5 % of species (HC 5 ) for the criterion maximum concentration. The chronic water quality criterion for paraquat, bisphenol A, and carbamazepine were determined at 6.7, 11.1, and 3.5 μg/L, respectively. The QSAR-SSD approach presents a viable and cost-effective method for deriving water quality criteria for other emerging contaminants., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

β-lactam antibiotics, extensively used worldwide, pose significant risks to human health and ecological safety due to their accumulation in the environment. Recent studies have demonstrated the efficacy of transition metal-activated sulfite systems, like Fe(Ⅲ)/HSO 3 - , in removing PPCPs from water. However, research on their capability to degrade β-lactam antibiotics remains sparse. This paper evaluates the degradation of 14 types of β-lactam antibiotics in Fe(Ⅲ)/HSO 3 - system and establishes a QSAR model correlating molecular descriptors with degradation rates using the MLR method. Using cefazolin as a case study, this research predicts degradation pathways through NPA charge and Fukui function analysis, corroborated by UPLC-MS product analysis. The investigation further explores the influence of variables such as HSO 3 - dosage, substrate concentration, Fe(Ⅲ) dosage, initial pH and the presence of common seen water matrices including humic acid and bicarbonate on the degradation efficiency. Optimal conditions for cefazolin degradation in Fe(Ⅲ)/HSO 3 - system were determined to be 93.3 μM HSO 3 - , 8.12 μM Fe(Ⅲ) and an initial pH of 3.61, under which the interaction of Fe(Ⅲ) dosage with initial pH was found to significantly affect the degradation efficiency. This study not only provides a novel degradation approach for β-lactam antibiotics but also expands the theoretical application horizon of the Fe(Ⅲ)/HSO 3 - system., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Although uncertainties expressed in texts within QSAR studies can guide quantitative uncertainty estimations, they are often overlooked during uncertainty analysis. Using neurotoxicity as an example, this study developed a method to support analysis of implicitly and explicitly expressed uncertainties in QSAR modeling studies. Text content analysis was employed to identify implicit and explicit uncertainty indicators, whereafter uncertainties within the indicator-containing sentences were identified and systematically categorized according to 20 uncertainty sources. Our results show that implicit uncertainty was more frequent within most uncertainty sources (13/20), while explicit uncertainty was more frequent in only three sources, indicating that uncertainty is predominantly expressed implicitly in the field. The most highly cited sources included Mechanistic plausibility, Model relevance and Model performance, suggesting they constitute sources of most concern. The fact that other sources like Data balance were not mentioned, although it is recognized in the broader QSAR literature as an area of concern, demonstrates that the output from the type of analysis conducted here must be interpreted in the context of the broader QSAR literature before conclusions are drawn. Overall, the method established here can be applied in other QSAR modeling contexts and ultimately guide efforts targeted towards addressing the identified uncertainty sources., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Brown rot of stone fruit, a disease caused by the ascomycete fungus Monilinia fructicola, has caused significant losses to the agricultural industry. In order to explore and discover potential fungicides against M. fructicola, thirty-one novel mandelic acid derivatives containing piperazine moieties were designed and synthesized based on the amide skeleton. Among them, target compound Z 31 exhibited obvious in vitro antifungal activity with the EC 50 value of 11.8 mg/L, and significant effects for the postharvest pears (79.4 % protective activity and 70.5 % curative activity) at a concentration of 200 mg/L. Antifungal activity for the target compounds was found to be significantly improved by the large steric hindrance of the R 1 groups and the electronegative of the piperazines in the molecular structure, according to a three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis. Further mechanism studies have demonstrated that the compound Z 31 can disrupt cell membrane integrity, resulting in increased membrane permeability, release of intracellular electrolytes, and affect the normal growth of hyphae. Additional, morphological study also indicated that Z 31 may disrupt the integrity of the membrane by inducing generate excess endogenous reactive oxygen species (ROS) and resulting in the peroxidation of cellular lipids, which was further verified by the detection of malondialdehyde (MDA) content. These studies have provided the basis for the creation of novel fungicides to prevent brown rot in stone fruits., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Human glutaminyl cyclase (hQC) inhibitors have great potential to be used as anti- Alzheimer's disease (AD) agents by reducing the toxic pyroform of β-amyloid in the brains of AD patients. The four-dimensional quantitative structure activity relationship (4D-QSAR) model of N-substituted urea/thioureas was established with satisfying predictive ability and statistical reliability (Q 2 = 0.521, R 2 = 0.933, R 2 prep = 0.619). By utilizing the developed 4D-QSAR model, a set of new N-substituted urea/thioureas was designed and evaluated for their Absorption Distribution Metabolism Excretion and Toxicity (ADMET) properties. The results of molecular dynamics (MD) simulations, Principal component analysis (PCA), free energy landscape (FEL), dynamic cross-correlation matrix (DCCM) and molecular mechanics generalized Born Poisson-Boltzmann surface area (MM-PBSA) free energy calculations, revealed that the designed compounds were remained stable in protein binding pocket and compounds b ∼ f (-35.1 to -44.55 kcal/mol) showed higher binding free energy than that of compound 14 (-33.51 kcal/mol). The findings of this work will be a theoretical foundation for further research and experimental validation of urea/thiourea derivatives as hQC inhibitors., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The use of salicylates as flavoring agents in food and beverages is common, but their potential to disrupt the endocrine system remains unclear. Human placental 3β-hydroxysteroid dehydrogenase 1 (h3β-HSD1) plays a role in progesterone synthesis and is the potential target. This study evaluated the inhibition of 13 salicylates on h3β-HSD1, structure-activity relationship (SAR) and compared with rat placental homolog r3β-HSD4. Salicylates inhibited h3β-HSD1, depending on carbon chain number in the alcohol moiety and the IC 50 values for hexyl, ethylhexyl, homomenthyl, and menthyl salicylates were 53.27, 15.78, 2.35, and 2.31 μM, as mixed inhibitors, respectively, while methyl to benzyl salicylates were ineffective at 100 μM. Interestingly, only hexyl salicylate inhibited r3β-HSD4 with IC 50 of 31.05 μM. Bivariate analysis revealed a negative correlation between IC 50 and hydrophobicity (LogP), molecular weight, heavy atoms, and carbon number in the alcohol moiety against h3β-HSD1. Docking analysis demonstrated that these salicylates bind to cofactor binding sites or between the steroid and cofactor binding sites. Additionally, 3D-QSAR showed distinct binding via hydrogen bond donors and hydrophobic regions. In conclusion, the inhibition of h3β-HSD1 by salicylates appears to be dependent on factors such as LogP, molecular weight, heavy atoms, and carbon-chain length and there is species-dependent inhibition sensitivity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Tuberculosis (TB) is an infectious disease that primarily affects the lungs of humans and accounts for Mycobacterium tuberculosis (Mtb) bacteria as the etiologic agent. In this study, we introduce a computational framework designed to identify the important chemical features crucial for the effective inhibition of Mtb β-CAs. Through applying a mechanistic model, we elucidated the essential features pivotal for robust inhibition. Using this model, we engineered molecules that exhibit potent inhibitory activity and introduce relevant novel chemistry. The designed molecules were prioritized for synthesis based on their predicted pKi values via the QSAR (Quantitative Structure-Activity Relationship) model. All the rationally designed and synthesized compounds were evaluated in vitro against different carbonic anhydrase isoforms expressed from the pathogen Mtb; moreover, the off-target and widely human-expressed CA I and II were also evaluated. Among the reported derivatives, 2, 4, and 5 demonstrated the most valuable in vitro activity, resulting in promising candidates for the treatment of TB infection. All the synthesized molecules exhibited favorable pharmacokinetic and toxicological profiles based on in silico predictions. Docking analysis confirmed that the zinc-binding groups bind effectively into the catalytic triad of the Mtb β-Cas, supporting the in vitro outcomes with these binding interactions. Furthermore, molecules with good prediction accuracies according to previously established mechanistic and QSAR models were utilized to delve deeper into the realm of systems biology to understand their mechanism in combating tuberculotic pathogenesis. The results pointed to the key involvement of the compounds in modulating immune responses via NF-κβ1, SRC kinase, and TNF-α to modulate granuloma formation and clearance via T cells. This dual action, in which the pathogen's enzyme is inhibited while modulating the human immune machinery, represents a paradigm shift toward more effective and comprehensive treatment approaches for combating tuberculosis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Within the realm of pharmacological strategies for cardiovascular diseases (CVD) like hypertension, stroke, and heart failure, targeting the angiotensin-converting enzyme I (ACE-I) stands out as a significant treatment approach. This study employs QSAR modeling using Monte Carlo optimization techniques to investigate a range of compounds known for their ACE-I inhibiting properties. The modeling process involved leveraging local molecular graph invariants and SMILES notation as descriptors to develop conformation-independent QSAR models. The dataset was segmented into distinct sets for training, calibration, and testing to ensure model accuracy. Through the application of various statistical analyses, the efficacy, reliability, and predictive capability of the models were evaluated, showcasing promising outcomes. Additionally, molecular fragments derived from SMILES notation descriptors were identified to elucidate the activity changes observed in the compounds. The validation of the QSAR model and designed inhibitors was carried out via molecular docking, aligning well with the QSAR results. To ascertain the drug-worthiness of the designed molecules, their physicochemical properties were computed, aiding in the prediction of ADME parameters, pharmacokinetic attributes, drug-likeness, and medicinal chemistry compatibility., Competing Interests: Declaration of Competing Interest We have no conflicts of interest to disclose., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Salicylic esters (SEs), the widely used ultraviolet (UV) absorbers in sunscreen products, have been found to have health risks such as skin sensitization and estrogenic effects. This study aims to design SE substitutes that maintain high UV absorbance while reducing estrogenicity. Using molecular docking and Gaussian09 software for initial assessments and further application of a combination of two-dimensional and three-dimensional quantitative structure-activity relationships (2D-QSAR and 3D-QSAR, respectively) models, we designed 73 substitutes. The best-performing molecules, ethylhexyl salicylate (EHS)-5 and EHS-15, significantly reduced estrogenicity (44.54 % and 17.60 %, respectively) and enhanced UV absorbance (249.56 % and 46.94 %, respectively). Through screening for human health risks, we found that EHS-5 and EHS-15 were free from skin sensitivity and eye irritation and exhibited reduced skin permeability compared with EHS. Furthermore, the photolysis and synthetic pathways of EHS-5 and EHS-15 were deduced, demonstrating their good photodegradability and potential synthesizability. In addition, we analyzed the mechanisms underlying the changes in estrogenic effects and UV absorption properties. We identified covalent hydrogen bond basicity and acidity Propgen value for atomic molecular properties and the highest occupied molecular orbital eigenvalue as the main factors affecting the estrogenic effect and UV absorbance of SEs, respectively. This study focuses on the design and screening of SEs, exhibiting enhanced functionality, reduced health risks, and synthetic feasibility., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Polycystic kidney disease is the most prevalent hereditary kidney disease globally and is mainly linked to the overexpression of a gene called PKD1. To date, there is no effective treatment available for polycystic kidney disease, and the practicing treatments only provide symptomatic relief. Discovery of the compounds targeting the PKD1 gene by inhibiting its expression under the disease condition could be crucial for effective drug development. In this study, a molecular docking and molecular dynamic simulation, QSAR, and MM/GBSA-based approaches were used to determine the putative inhibitors of the Pkd1 enzyme from a library of 1379 compounds. Initially, fourteen compounds were selected based on their binding affinities with the Pkd1 enzyme using MOE and AutoDock tools. The selected drugs were further investigated to explore their properties as drug candidates and the stability of their complex formation with the Pkd1 enzyme. Based on the physicochemical and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, and toxicity profiling, two compounds including olsalazine and diosmetin were selected for the downstream analysis as they demonstrated the best drug-likeness properties and highest binding affinity with Pkd1 in the docking experiment. Molecular dynamic simulation using Gromacs further confirmed the stability of olsalazine and diosmetin complexes with Pkd1 and establishing interaction through strong bonding with specific residues of protein. High biological activity and binding free energies of two complexes calculated using 3D QSAR and Schrodinger module, respectively further validated our results. Therefore, the molecular docking and dynamics simulation-based in-silico approach used in this study revealed olsalazine and diosmetin as potential drug candidates to combat polycystic kidney disease by targeting Pkd1 enzyme., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Salmons are crucial to ecosystems and economic activities like commercial fishing and aquaculture, while also serving as an important source of nutrients, underscoring their ecological significance and the need for sustainable management. To better understand the toxicity and biological interactions between the salmon and industrial chemicals in the aquatic environment, we utilized the ToxValDB database to develop first ever computational toxicity models for six salmon subspecies (covering Atlantic and Pacific salmon) across two genera, employing Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) methods. For three smaller datasets (Oncorhynchus nerka, Oncorhynchus keta, and Oncorhynchus gorbuscha), we created mathematical models using the entire datasets where QSAR models demonstrated superior statistical quality compared to q-RASAR. Conversely, the three larger datasets (Oncorhynchus kisutch, Oncorhynchus tshawytscha, and Salmon salar) were divided into training and test sets, the q-RASAR models yielded better results compared to QSAR models. Mechanistic interpretations of these models revealed that descriptors such as Burden eigenvalues (BCUT), autocorrelation of topological structure (ATSC), and molecular polarizability were significant predictors of toxicity. For instance, higher polarizability and certain topological features were associated with increased toxicity as per the developed models. Statistically superior models for each subspecies were used to predict the aquatic toxicity of 1085 untested organic chemicals for toxicity data gap filling and risk assessment considering the applicability domain (AD). These insights are pivotal for designing safer chemicals and emphasize the need for sustainable management of salmon populations., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The escalating introduction of pesticides/veterinary drugs into the environment has necessitated a rapid evaluation of their potential risks to ecosystems and human health. The developmental toxicity of pesticides/veterinary drugs was less explored, and much less the large-scale predictions for untested pesticides, veterinary drugs and bio-pesticides. Alternative methods like quantitative structure-activity relationship (QSAR) are promising because their potential to ensure the sustainable and safe use of these chemicals. We collected 133 pesticides and veterinary drugs with half-maximal active concentration (AC 50 ) as the zebrafish embryo developmental toxicity endpoint. The QSAR model development adhered to rigorous OECD principles, ensuring that the model possessed good internal robustness (R 2 > 0.6 and Q LOO 2 > 0.6) and external predictivity (R test 2 > 0.7, Q Fn 2 >0.7, and CCC test > 0.85). To further enhance the predictive performance of the model, a quantitative read-across structure-activity relationship (q-RASAR) model was established using the combined set of RASAR and 2D descriptors. Mechanistic interpretation revealed that dipole moment, the presence of C-O fragment at 10 topological distance, molecular size, lipophilicity, and Euclidean distance (ED)-based RA function were main factors influencing toxicity. For the first time, the established QSAR and q-RASAR models were combined to prioritize the developmental toxicity of a vast array of true external compounds (pesticides/veterinary drugs/bio-pesticides) lacking experimental values. The prediction reliability of each query molecule was evaluated by leverage approach and prediction reliability indicator. Overall, the dual computational toxicology models can inform decision-making and guide the design of new pesticides/veterinary drugs with improved safety profiles., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Fluoroquinolone antibiotics (FQs) have been used worldwide due to their extended antimicrobial spectrum. However, the overuse of FQs leads to frequent detection in the environment and cannot be efficiently removed. Microalgae-based constructed wetland systems have been proven to be a relatively proper method to treat FQs, mainly by microalgae, plants, microorganisms, and sediments. To improve the removal efficiency of microalgae-constructed wetland, a systematic molecular design, screening, functional, and risk evaluation method was developed using three-dimensional quantitative structure-activity relationship models, molecular dynamics simulation, molecular docking, and TOPKAT approaches. Five designed ciprofloxacin alternatives with improved bactericidal effects and lower human health risks were found to be more easily degraded by microalgae (16.11-167.88 %), plants (6.72-58.86 %), microorganisms (9.10-15.02 %), and sediments (435.83 %-1763.51 %) compared with ciprofloxacin. According to the mechanism analysis, the removal effect of the FQs can be affected via changes in the number, bond energy, and molecular descriptors of favorable and unfavorable amino acids. To the best of our knowledge, this is the first comprehensive study of improving the microalgae, plants, microorganisms, and sediment removal efficiency of FQs in constructed wetlands, which provides theoretical support for the treatment of FQ pollution., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Computer-aided drug discovery plays a vital role in developing novel medications for various diseases. The COVID-19 pandemic has heightened the need for innovative approaches to design lead compounds with the potential to become effective drugs. Specifically, designing promising inhibitors of the SARS-CoV-2 main protease (Mpro) is crucial, as it plays a key role in viral replication. Phytochemicals, primarily flavonoids and flavonols from medicinal plants, were screened. Fifty small molecules were selected for molecular docking analysis against SARS-CoV-2 Mpro (PDB ID: 6LU7). Binding energies and interactions were analyzed and compared to those of the anti-SARS-CoV-2 inhibitor Nirmatrelvir. Using these 50 structures as a training set, a QSAR model was built employing simple, reversible topological descriptors. An inverse-QSAR analysis was then performed on 2⁹ = 512 hydroxyl combinations at nine possible positions on the flavone and flavonol scaffold. The model predicted three novel, promising compounds exhibiting the most favorable binding energies (-8.5 kcal/mol) among the 512 possible hydroxyl combinations: 3,6,7,2',4'-pentahydroxyflavone (PF9), 6,7,2',4'-tetrahydroxyflavone (PF11), and 3,6,7,4'-tetrahydroxyflavone (PF15). Molecular dynamics (MD) simulations demonstrated the stability of the PF9/Mpro complex over 300 ns of simulation. These predicted structures, reported here for the first time, warrant synthesis and further evaluation of their biological activity through in vitro and in vivo studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Non-alcoholic fatty liver disease (NAFLD) is a growing global health concern due to its potential to progress into severe liver diseases. Targeting the bile acid receptor FXR has emerged as a promising strategy for managing NAFLD. Building upon our previous research on FXR partial agonism, the present study investigates a series of 1,3,4-trisubstituted-pyrazol amide derivatives as FXR antagonists, aiming to delineate the structural features for antagonism. By means of 2D-QSAR (quantitative structure-activity relationships) modelling techniques, we elucidated the key structural elements responsible for the antagonistic properties of these derivatives. We then employed QPhAR, an open-access software, to identify key molecular features within the compounds that enhance their antagonistic activity. Additionally, 3D-QSAR modelling allowed us to analyse the steric and electrostatic fields of aligned 3D structures, further refining our understanding of structure-activity relationships. Subsequent molecular dynamics simulations provided insights into the binding mode interactions between the compounds and FXR, with varying potencies, confirming and complementing the findings from 2D-QSAR, pharmacophore, and 3D-QSAR modelling. Particularly, our study highlighted the significance of hydrophobic interactions in conferring potent antagonism by the 1,3,4-trisubstituted-pyrazol amide derivatives against FXR. Overall, this work underscores the potential of 1,3,4-trisubstituted-pyrazol amides as FXR antagonists for NAFLD treatment. Notably, our reliance on open-access software fosters reproducibility and broadens the accessibility of our findings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

A completely green protocol was developed for the synthesis of a series of arylaminonaphthol derivatives in the presence of N-ethylethanolamine (NEEA) as a catalyst under ultrasonic irradiation and solventless conditions. The major assets of this methodology were the use of non-toxic organic medium, available catalyst, mild reaction condition, and good to excellent yield of desired products. All of the synthesized products were screened for their in vitro antioxidant activity using DPPH, ABTS, and Ferric-phenanthroline assays and it was found that most of them are potent antioxidant agents. Also, their butyrylcholinesterase inhibitory activity has been investigated in vitro. All tested compounds exhibited potential inhibitory activity toward BuChE when compared to standard reference drug galantamine, however, compounds 4r, 4u, 4 g and 4x gave higher butyrylcholinesterase inhibitory with IC 50 values of 14.78 ± 0.65 µM, 16.18 ± 0.50 µM, 20.00 ± 0.50 µM, and 20.28 ± 0.08 µM respectively. On the other hand, we employed density functional theory (DFT), calculations to analyze molecular geometry and global reactivity descriptors, and MESP analysis to predict electrophilic and nucleophilic attacks. A quantitative structure-activity relationship (QSAR) investigation was conducted on the antioxidant and butyrylcholinesterase properties of 25 arylaminonaphthol derivatives, resulting in robust and satisfactory models. To evaluate their anti-Alzheimer's activity, compounds 4 g, 4q, 4r, 4u, and 4x underwent docking simulations at the active site of the acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), revealing why these compounds displayed superior activity, consistent with the biological findings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

To develop a method for predicting chronic toxicity of pharmaceuticals in Daphnia, we investigated the feasibility of combining the presence of drug-target orthologs in Daphnia magna, classification based on pharmacological effects, and ecotoxicity quantitative structure-activity relationship (QSAR) prediction. We established datasets on the chronic toxicity of pharmaceuticals in Daphnia, including information on therapeutic categories, target proteins, and the presence or absence of drug-target orthologs in D. magna, using literature and databases. Chronic toxicity was predicted using ecotoxicity prediction QSAR (Ecological Structure Activity Relationship and Kashinhou Tool for Ecotoxicity), and the differences between the predicted and measured values and the presence or absence of drug-target orthologs were examined. For pharmaceuticals without drug-target orthologs in D. magna or without expected specific actions, the ecotoxicity prediction QSAR analysis yielded acceptable predictions of the chronic toxicity of pharmaceuticals. In addition, a workflow model to assess the chronic toxicity of pharmaceuticals in Daphnia was proposed based on these evaluations and verified using an additional dataset. The addition of biological aspects such as drug-target orthologs and pharmacological effects would support the use of QSARs for predicting the chronic toxicity of pharmaceuticals in Daphnia., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Hiroshi Yamamoto reports financial support was provided by Japan Agency of Medical Research and Development. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

5-Substituted benzylidene 3-acylthiotetronic acids are antifungal. A series of 3-acylthiotetronic acid derivatives with varying substitutions at the 5-position were designed, synthesized, and characterized, based on the binding pose of 3-acyl thiolactone with the protein C171Q KasA. Fungicidal activities of these compounds were screened against Valsa Mali, Curvularia lunata, Fusarium graminearum, and Fusarium oxysporum f. sp. lycopersici. Most target compounds exhibited excellent fungicidal activities against target fungi at the concentration of 50 μg·mL−1. Compounds 11c and 11i displayed the highest activity with a broad spectrum. The median effective concentration (EC50) values of 11c and 11i were 1.9–10.7 and 3.1–7.8 μg·mL−1, respectively, against the tested fungi, while the EC50 values of the fungicides azoxystrobin, carbendazim, and fluopyram were respectively 0.30, 4.22, and > 50 μg·mL−1 against V. Mali; 6.7, 41.7, and 0.18 μg·mL−1 against C. lunata; 22.4, 0.42, and 0.43 μg·mL−1 against F. graminearum; and 4.3, 0.12, and > 50 μg·mL−1 against F. oxysporum f. sp. lycopersici. The structures and activities of the target compounds against C. lunata were analyzed to obtain a statistically significant comparative molecular field analysis (CoMFA) model with high prediction abilities (q2 = 0.9816, r2 = 0.8060), and its reliability was verified. The different substituents on the benzylidene at the 5-position had significant effects on the activity, while the introduction of a halogen atom at the benzene ring of benzylidene was able to improve the activity against the tested fungi.

Cyclooxygenase-2 (COX-2) overexpression in many humans plays a key role in carcinogenic and inflammation-associated diseases. Several therapeutic and pharmaceutic drugs have been developed but some of them either lack the potency to control the COX-2 related diseases or promote unwanted side effects. The prospect and possibility for the development of drugs with novel therapeutic and pharmacological properties using the quantitative structure–activity relationship (QSAR) model were investigated. The model involves the application of chemical descriptors and supervised machine learning to predict the bioactivity classes of molecules for COX-2 inhibition using real multidimensional COX-2 inhibitors obtained from a curated database. The PubChem Fingerprints is the class of descriptor used in developing the model. A model performance check is carried out on 22 SciKit-Learn models and a comparative analysis of their predictive performance in classifying bioactivity of compounds is given in terms of validation accuracy. Unsupervised learning using principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithms are applied as techniques to explore the effect of dimensionality in the training data. The PCA algorithm is a tool used to reduce the number of variables contained in the dataset, though preserving as much information as possible. The t-SNE algorithm is mainly used for data exploration and visualization of the multi-dimensional data set. Despite their high predictive performances, the eXtreme Gradient Boosting Classifier (XGB Classifier) algorithm is the ultimate performer. More so, hyperparameter tuning, and regularization account for excellent model statistics with higher predictive power at 10-fold cross-validation. Model metrics including log-loss probability (0.1208), accuracy score (0.9484), Matthew’s correlation coefficient (0.8741), among others proved adequately significant. Furthermore, the developed model is validated using recommended OECD metrics, such as Precision, Recall, and Balanced Accuracy for classification. The results in this study offer important pharmacological insight, and this insight can lead to designing novel bioactive drugs with undesired side effects. The proposed QSAR approach in this study achieves a futuristic performance when applied in drug development schemes different from conventional methods.

Ionic liquids, i.e., organic salts with a low melting point, can be used as gas chromatographic liquid stationary phases. These stationary phases have some advantages such as peculiar selectivity, high polarity, and thermostability. Many previous works are devoted to such stationary phases. However, there are still no large enough retention data sets of structurally diverse compounds for them. Consequently, there are very few works devoted to quantitative structure-retention relationships (QSRR) for ionic liquid-based stationary phases. This work is aimed at closing this gap. Three ionic liquids with substituted pyridinium cations are considered. We provide large enough data sets (123-158 compounds) that can be used in further works devoted to QSRR and related methods. We provide a QSRR study using this data set and demonstrate the following. The retention index for a polyethylene glycol stationary phase (denoted as RI_PEG), predicted using another model, can be used as a molecular descriptor. This descriptor significantly improves the accuracy of the QSRR model. Both deep learning-based and linear models were considered for RI_PEG prediction. The ability to predict the retention indices for ionic liquid-based stationary phases with high accuracy is demonstrated. Particular attention is paid to the reproducibility and reliability of the QSRR study. It was demonstrated that adding/removing several compounds, small perturbations of the data set can considerably affect the results such as descriptor importance and model accuracy. These facts have to be considered in order to avoid misleading conclusions. For the QSRR research, we developed a software tool with a graphical user interface, which we called CHERESHNYA. It is intended to select molecular descriptors and construct linear equations connecting molecular descriptors with gas chromatographic retention indices for any stationary phase. The software allows the user to generate several hundred molecular descriptors (one-dimensional and two-dimensional). Among them, predicted retention indices for popular stationary phases such as polydimethylsiloxane and polyethylene glycol are used as molecular descriptors. Various methods for selecting (and assessing the importance of) molecular descriptors have been implemented, in particular the Boruta algorithm, partial least squares, genetic algorithms, L1-regularized regression (LASSO) and others. The software is free, open-source and available online: https://github.com/mtshn/chereshnya., Competing Interests: Declaration of competing interest The authors have declared they have no conflict of interest with regard to this content, (Copyright © 2024 Elsevier B.V. All rights reserved.)

Quantitative Structure-Retention Relationship models were developed to identify phenolic compounds using a typical LC- system, with both UV and MS detection. A new chromatographic method was developed for the separation of fifty-two standard phenolic compounds. Over 5000 descriptors for each standard were calculated using AlvaDesc software and then selected through Genetic Algorithm. The selected descriptors were used as variables for models construction and to obtain a better understanding of the retention behaviour of phenols during reverse-phase separation. Three distinct molecule sets, including fifty-two phenolic compounds (Set 1), 32 flavonoids (Set 2) and 15 mono-substituted flavonoids were divided into training and validation sets to build Partial Least Square, Multiple Linear Regression and Partial Least Square-Artificial Neural Network models. To assess the predictivity of the models, these were tested on a bergamot juice sample. Partial Least Square and Partial Least Square-Artificial Neural Network exhibit the lowest prediction error, and the latter showed the best predictive power in real sample recognition. The building and implementation of such predictive models showed to be a powerful tool to identify phenolic compounds based on retention data and avoiding the use of expensive and sophisticated detectors such as tandem MS., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

As the United States and the European Union continue their steady march towards the acceptance of new approach methodologies (NAMs), we need to ensure that the available tools are fit for purpose. Critics will be well-positioned to caution against NAMs acceptance and adoption if the tools turn out to be inadequate. In this paper, we focus on Quantitative Structure Activity-Relationship (QSAR) models and highlight how the training database affects quality and performance of these models. Our analysis goes to the point of asking, "are the endpoints extracted from the experimental studies in the database trustworthy, or are they false negatives/positives themselves?" We also discuss the impacts of chemistry on QSAR models, including issues with 2-D structure analyses when dealing with isomers, metabolism, and toxicokinetics. We close our analysis with a discussion of challenges associated with translational toxicology, specifically the lack of adverse outcome pathways/adverse outcome pathway networks (AOPs/AOPNs) for many higher tier endpoints. We recognize that it takes a collaborate effort to build better and higher quality QSAR models especially for higher tier toxicological endpoints. Hence, it is critical to bring toxicologists, statisticians, and machine learning specialists together to discuss and solve these challenges to get relevant predictions., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: FMK, AH, and MF work for companies that develop crop protection products, including pesticides. LDB provides toxicology, computational toxicology, and biostatistical consulting services to companies that develop crop protection products., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The study aims at assessing the potential of graphene-based adsorbents to reduce environmental impacts of Iodinated Contrast Media Agents (ICMs). We analyze an extensive collection of ICMs. A modeling approach resting on molecular docking and Density Functional Theory simulations is employed to examine the adsorption process at the molecular level. The study also relies on a Quantitative Structure-Activity Relationship (QSAR) modeling framework to correlate molecular properties with the adsorption energy (E ad ) of ICMs, thus enabling identification of the key mechanisms underpinning adsorption and of the key factors contributing to it. A collection of distinct QSAR-based models is developed upon relying on Multiple Linear Regression and a standard genetic algorithm method. Having at our disposal multiple models enables us to take into account the uncertainty associated with model formulation. Maximum Likelihood and formal model identification/discrimination criteria (such as Bayesian and/or information theoretic criteria) are then employed to complement the traditional QSAR modeling phase. This has the advantage of (a) providing a rigorous ranking of the alternative models included in the selected set and (b) quantifying the relative degree of likelihood of each of these models through a weight or posterior probability. The resulting workflow of analysis enables one to seamlessly embed DFT and QSAR studies within a theoretical framework of analysis that explicitly takes into account model and parameter uncertainty. Our results suggest that graphene-based surfaces constitute a promising adsorbent for ICMs removal, π-π stacking being the primary mechanism behind ICM adsorption. Furthermore, our findings offer valuable insights into the potential of graphene-based adsorbent materials for effectively removing ICMs from water systems. They contribute to ascertain the significance of various factors (such as, e.g., the distribution of atomic van der Waals volumes, overall molecular complexity, the presence and arrangement of Iodine atoms, and the presence of polar functional groups) on the adsorption process., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Adenylate kinase (AK) plays a crucial role in the metabolic monitoring of cellular adenine nucleotide homeostasis by catalyzing the reversible transfer of a phosphate group between ATP and AMP, yielding two ADP molecules. By regulating the nucleotide levels and energy metabolism, the enzyme is considered a disease modifier and potential therapeutic target for various human diseases, including malignancies and inflammatory and neurodegenerative disorders. However, lacking approved drugs targeting AK hinders broad studies on this enzyme's pathological importance and therapeutic potential. In this work, we determined the effect of a series of dinucleoside polyphosphate derivatives, commercially available (11 compounds) and newly synthesized (8 compounds), on the catalytic activity of human adenylate kinase isoenzyme 1 (hAK1). The tested compounds belonged to the following groups: (1) diadenosine polyphosphates with different phosphate chain lengths, (2) base-modified derivatives, and (3) phosphate-modified derivatives. We found that all the investigated compounds inhibited the catalytic activity of hAK1, yet with different efficiencies. Three dinucleoside polyphosphates showed IC 50 values below 1 µM, and the most significant inhibitory effect was observed for P 1 -(5'-adenosyl) P 5 -(5'-adenosyl) pentaphosphate (Ap 5 A). To understand the observed differences in the inhibition efficiency of the tested dinucleoside polyphosphates, the molecular docking of these compounds to hAK1 was performed. Finally, we conducted a quantitative structure-activity relationship (QSAR) analysis to establish a computational prediction model for hAK1 modulators. Two PLS-regression-based models were built using kinetic data obtained from the AK1 activity analysis performed in both directions of the enzymatic reaction. Model 1 (AMP and ATP synthesis) had a good prediction power (R 2  = 0.931, Q 2  = 0.854, and MAE = 0.286), while Model 2 (ADP synthesis) exhibited a moderate quality (R 2  = 0.913, Q 2  = 0.848, and MAE = 0.370). These studies can help better understand the interactions between dinucleoside polyphosphates and adenylate kinase to attain more effective and selective inhibitors in the future., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Anna Kozakiewicz-Piekarz reports financial support was provided by National Science Centre Poland. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Polybrominated biphenyls (PBBs) are associated with an increased risk of thyroid cancer; however, relevant mechanistic studies are lacking. In this study, we investigated the mechanisms underlying PBB-induced human thyroid cancer. Molecular docking and molecular dynamics methods were employed to investigate the metabolism of PBBs by the cytochrome P450 enzyme under aryl hydrocarbon receptor mediation into mono- and di-hydroxylated metabolites. This was taken as the molecular initiation event. Subsequently, considering the interactions of PBBs and their metabolites with the thyroxine-binding globulin protein as key events, an adverse outcome pathway for thyroid cancer caused by PBBs exposure was constructed. Based on 2D quantitative structure activity relationship (2D-QSAR) models, the contribution of amino acid residues and binding energy were analyzed to understand the mechanism underlying human carcinogenicity (adverse effect) of PBBs. Hydrogen bond and van der Waals interactions were identified as key factors influencing the carcinogenic adverse outcome pathway of PBBs. Analysis of non-bonding forces revealed that PBBs and their hydroxylation products were predominantly bound to the thyroxine-binding globulin protein through hydrophobic and hydrogen bond interactions. The key amino acids involved in hydrophobic interactions were alanine 330, arginine 381 and lysine 270, and the key amino acids involved in hydrogen bond interactions were arginine 381 and lysine 270. This study provides valuable insights into the mechanisms underlying human health risk associated with PBBs exposure., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The joint toxicity effects of mixtures, particularly reproductive toxicity, one of the main causes of aquatic ecosystem degradation, are often overlooked as it is impractical to test all mixtures. This study developed and evaluated the following models to predict the concentration response curve concerning the joint reproductive toxicity of mixtures of three bisphenol analogues (BPA, BPF, BPAF) on the rotifer Brachionus calyciflorus: concentration addition (CA), independent action (IA), and two deep neural network (DNN) models. One applied mixture molecular descriptors as input variables (DNN-QSAR), while the other applied the ratios of chemicals in the mixtures (DNN-Ratio). Descriptors related to molecular mass were found to be of greater importance and exhibited a proportional relationship with toxic effects. The results indicate that the range of correlation coefficients (R 2 ) between predicted and measured values for various mixture rays by CA and IA models is 0.372 to 0.974 and - 0.970 to 0.586, respectively. The R 2 values for DNN-Ratio and DNN-QSAR were 0.841 to 0.984 and 0.834 to 0.991, respectively, demonstrating that models developed by DNN significantly outperform traditional models in predicting the joint toxicity of mixtures. Furthermore, DNN-QSAR not only predicts mixture toxicity but also provides accurate toxicity predictions for BPA, BPF, and BPAF, with R 2 values of 0.990, 0.616, and 0.887, respectively, while DNN-Ratio yields values of 0.920, 0.355, and - 0.495. The study also found that the joint effects of mixtures are primarily influenced by the total concentration of the mixtures, and an increase in total concentration shifts the joint effects towards addition. This study introduces a novel approach to predict joint toxicity and analyze the influencing factors of joint effects, providing a more comprehensive assessment of the ecological risk posed by mixtures., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Developing mechanistic non-animal testing methods based on the adverse outcome pathway (AOP) framework must incorporate molecular and cellular key events associated with target toxicity. Using data from an in vitro assay and chemical structures, we aimed to create a hybrid model to predict hepatotoxicants. We first curated a reference dataset of 869 compounds for hepatotoxicity modeling. Then, we profiled them against PubChem for existing in vitro toxicity data. Of the 2560 resulting assays, we selected the mitochondrial membrane potential (MMP) assay, a high-throughput screening (HTS) tool that can test chemical disruptors for mitochondrial function. Machine learning was applied to develop quantitative structure-activity relationship (QSAR) models with 2536 compounds tested in the MMP assay for screening new compounds. The MMP assay results, including QSAR model outputs, yielded hepatotoxicity predictions for reference set compounds with a Correct Classification Ratio (CCR) of 0.59. The predictivity improved by including 37 structural alerts (CCR = 0.8). We validated our model by testing 37 reference set compounds in human HepG2 hepatoma cells, and reliably predicting them for hepatotoxicity (CCR = 0.79). This study introduces a novel AOP modeling strategy that combines public HTS data, computational modeling, and experimental testing to predict chemical hepatotoxicity., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The extensive use of various pesticides in the agriculture field badly affects both chickens and humans, primarily through residues in food products and environmental exposure. This study offers the first quantitative structure-toxicity relationship (QSTR) and quantitative read-across-structure toxicity relationship (q-RASTR) models encompassing the LOEL and NOEL endpoints for acute toxicity in chicken, a widely consumed protein. The study's significance lies in the direct link between chemical toxicity in chicken, human intake, and environmental damage. Both the QSTR and the similarity-based read-across algorithms are applied concurrently to improve the predictability of the models. The q-RASTR models were generated by combining read-across derived similarity and error-based parameters, alongside structural and physicochemical descriptors. Machine Learning approaches (SVM and RR) were also employed with the optimization of relevant hyperparameters based on the cross-validation approach, and the final test set prediction results were compared. The PLS-based q-RASTR models for NOEL and LOEL endpoints showed good statistical performance, as traced from the external validation metrics Q 2 F1 : 0.762-0.844; Q 2 F2 : 0.759-0.831 and MAE test : 0.195-0.214. The developed models were further used to screen the Pesticide Properties DataBase (PPDB) for potential toxicants in chickens. Thus, established models can address eco-toxicological data gaps and development of novel and safe eco-friendly pesticides., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

GRK5 holds a pivotal role in cellular signaling pathways, with its overexpression in cardiomyocytes, neuronal cells, and tumor cells strongly associated with various chronic degenerative diseases, which highlights the urgent need for potential inhibitors. In this study, multiclass classification-based QSAR models were developed using diverse machine learning algorithms. These models were built from curated compounds with experimentally derived GRK5 inhibitory activity. Additionally, a pharmacophore model was constructed using active compounds from the dataset. Among the models, the SVM-based approach proved most effective and was initially used to screen DrugBank compounds within the applicability domain. Compounds showing significant GRK5 inhibitory potential underwent evaluation for key pharmacophoric features. Prospective compounds were subjected to molecular docking to assess binding affinity towards GRK5's key active site amino acid residues. Stability at the binding site was analyzed through 200 ns molecular dynamics simulations. MM-GBSA analysis quantified individual free energy components contributing to the total binding energy with respect to binding site residues. Metadynamics analysis, including PCA, FEL, and PDF, provided crucial insights into conformational changes of both apo and holo forms of GRK5 at defined energy states. The study identifies DB02844 (S-Adenosyl-1,8-Diamino-3-Thiooctane) and DB13155 (Esculin) as promising GRK5 inhibitors, warranting further in vitro and in vivo validation studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

DNA gyrase and topoisomerase IV play significant role in maintaining the correct structure of DNA during replication and they have been identified as validated targets in antibacterial drug discovery. Inadequate pharmacokinetic properties are responsible for many failures during drug discovery and their estimation in the early phase of this process maximizes the chance of getting useful drug candidates. Passive gastrointestinal absorption of a selected group of thirteen dual DNA gyrase and topoisomerase IV inhibitors was estimated using two in vitro tests - parallel artificial membrane permeability assay (PAMPA) and biopartitioning micellar chromatography (BMC). Due to good correlation between obtained results, passive gastrointestinal absorption of remaining ten compounds was estimated using only BMC. With this experimental setup, it was possible to identify compounds with high values of retention factors (k) and highest expected passive gastrointestinal absorption, and compounds with low values of k for which low passive gastrointestinal absorption is predicted. Quantitative structure-retention relationship (QSRR) modelling was performed by creating multiple linear regression (MLR), partial least squares (PLS) and support vector machines (SVM) models. Descriptors with the highest influence on retention factor were identified and their interpretation can be used for the design of new compounds with improved passive gastrointestinal absorption., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Environmental exposures are the main cause of cancer, and their carcinogenicity has not been fully evaluated, identifying potential carcinogens that have not been evaluated is critical for safety. This study is the first to propose a weight of evidence (WoE) approach based on computational methods to prioritize potential carcinogens. Computational methods such as read across, structural alert, (Quantitative) structure-activity relationship and chemical-disease association were evaluated and integrated. Four different WoE approach was evaluated, compared to the best single method, the WoE-1 approach gained 0.21 and 0.39 improvement in the area under the receiver operating characteristic curve (AUC) and Matthew's correlation coefficient (MCC) value, respectively. The evaluation of 681 environmental exposures beyond IARC list 1-2B prioritized 52 chemicals of high carcinogenic concern, of which 21 compounds were known carcinogens or suspected carcinogens, and eight compounds were identified as potential carcinogens for the first time. This study illustrated that the WoE approach can effectively complement different computational methods, and can be used to prioritize chemicals of carcinogenic concern., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Global warming and climate change have made dengue disease a global health issue. More than 50 % of the world's population is at danger of dengue virus (DENV) infection, according to the World Health Organization (WHO). Therefore, a clinically approved dengue fever vaccination and effective treatment are needed. Peptide medication development is new pharmaceutical research. Here we intend to recognize the structural features inhibiting the DENV NS2B/NS3 serine protease for a series of peptide-hybrid inhibitors (R 1 -R 2 -Lys-R 3 -NH 2 ) by the 3D-QSAR technique. Comparative molecular field analysis (q 2  = 0.613, r 2  = 0.938, r 2 pred  = 0.820) and comparative molecular similarity indices analysis (q 2  = 0.640, r 2  = 0.928, r 2 pred  = 0.693) were established, revealing minor, electropositive, H-bond acceptor groups at the R 1 position, minor, electropositive, H-bond donor groups at the R 2 position, and bulky, hydrophobic groups at the R 3 position for higher inhibitory activity. Docking studies revealed extensive H-bond and hydrophobic interactions in the binding of tripeptide analogues to the NS2B/NS3 protease. This study provides an insight into the key structural features for the design of peptide-based inhibitors of DENV NS2B/NS3 protease., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Density Functional Theory (DFT) is a quantum chemical computational method used to predict and analyze the electronic properties of atoms, molecules, and solids based on the density of electrons rather than wavefunctions. It provides insights into the structure, bonding, and behavior of different molecules, including those involved in the development of chemotherapeutic agents, such as histone deacetylase inhibitors (HDACis). HDACs are a wide group of metalloenzymes that facilitate the removal of acetyl groups from acetyl-lysine residues situated in the N-terminal tail of histones. Abnormal HDAC recruitment has been linked to several human diseases, especially cancer. Therefore, it has been recognized as a prospective target for accelerating the development of anticancer therapies. Researchers have studied HDACs and its inhibitors extensively using a combination of experimental methods and diverse in-silico approaches such as machine learning and quantitative structure-activity relationship (QSAR) methods, molecular docking, molecular dynamics, pharmacophore mapping, and more. In this context, DFT studies can make significant contribution by shedding light on the molecular properties, interactions, reaction pathways, transition states, reactivity and mechanisms involved in the development of HDACis. This review attempted to elucidate the scope in which DFT methodologies may be used to enhance our comprehension of the molecular aspects of HDAC inhibitors, aiding in the rational design and optimization of these compounds for therapeutic applications in cancer and other ailments. The insights gained can guide experimental efforts toward developing more potent and selective HDAC inhibitors., Competing Interests: Declaration of competing interest The authors have no conflict of interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

We have developed a quantitative safety prediction model for subchronic repeated doses of diverse organic chemicals on rats using the novel quantitative read-across structure-activity relationship (q-RASAR) approach, which uses similarity-based descriptors for predictive model generation. The experimental -Log (NOAEL) values have been used here as a potential indicator of oral subchronic safety on rats as it determines the maximum dose level for which no observed adverse effects of chemicals are found. A total of 186 data points of diverse organic chemicals have been used for the model generation using structural and physicochemical ( 0 D- 2 D) descriptors. The read-across-derived similarity, error, and concordance measures (RASAR descriptors) have been extracted from the preliminary 0 D- 2 D descriptors. Then, the combined pool of RASAR and the identified 0 D- 2 D descriptors of the training set were employed to develop the final models by using the partial least squares (PLS) algorithm. The developed PLS model was rigorously validated by various internal and external validation metrics as suggested by the Organization for Economic Co-operation and Development (OECD). The final q-RASAR model is proven to be statistically sound, robust and externally predictive (R 2 = 0.85, Q 2 LOO = 0.94), superseding the internal as well as external predictivity of the corresponding quantitative structure-activity relationship (QSAR) model as well as previously reported subchronic repeated dose toxicity model found in the literature. In a nutshell, the q-RASAR is an effective approach that has the potential to be used as a good alternative way to improve external predictivity, interpretability, and transferability for subchronic oral safety prediction as well as ecotoxicity risk identification.2 F1 = 0.94), superseding the internal as well as external predictivity of the corresponding quantitative structure-activity relationship (QSAR) model as well as previously reported subchronic repeated dose toxicity model found in the literature. In a nutshell, the q-RASAR is an effective approach that has the potential to be used as a good alternative way to improve external predictivity, interpretability, and transferability for subchronic oral safety prediction as well as ecotoxicity risk identification., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Sophoridine, which is derived from the Leguminous plant Sophora alopecuroides L., has certain pharmacological activity as a new anticancer drug. Herein, a series of novel N-substituted sophoridine derivatives was designed, synthesized and evaluated with anticancer activity. Through QSAR prediction models, it was discovered that the introduction of a benzene ring as a main pharmacophore and reintroduced into a benzene in para position on the phenyl ring in the novel sophoridine derivatives improved the anticancer activity effectively. In vitro, 28 novel compounds were evaluated for anticancer activity against four human tumor cell lines (A549, CNE-2, HepG-2, and HEC-1-B). In particular, Compound 26 exhibited remarkable inhibitory effects, with an IC 50 value of 15.6 μM against HepG-2 cells, surpassing cis-Dichlorodiamineplatinum (II). Molecular docking studies verified that the derivatives exhibit stronger binding affinity with DNA topoisomerase I compared to sophoridine. In addition, 26 demonstrated significant inhibition of DNA Topoisomerase I and could arrest cells in G 0 /G 1 phase. This study provides valuable insights into the design and synthesis of N-substituted sophoridine derivatives with anticancer activity., Competing Interests: Declaration of competing interest The authors report there are no competing interests to declare., (Copyright © 2024. Published by Elsevier B.V.)

Amidst the Zn 2+ -dependant isoforms of the HDAC family, HDAC6 has emerged as a potential target associated with an array of diseases, especially cancer and neuronal disorders like Rett's Syndrome, Alzheimer's disease, Huntington's disease, etc. Also, despite the availability of a handful of HDAC inhibitors in the market, their non-selective nature has restricted their use in different disease conditions. In this situation, the development of selective and potent HDAC6 inhibitors will provide efficacious therapeutic agents to treat different diseases. In this context, this study has been carried out to evaluate the potential structural contributors of quinazoline-cap-containing HDAC6 inhibitors via machine learning (ML), conventional classification-dependant QSAR, and MD simulation-based binding mode of interaction analysis toward HDAC6 binding. This combined conventional and modern molecular modeling study has revealed the significance of the quinazoline moiety, substitutions present at the quinazoline cap group, as well as the importance of molecular property, number of hydrogen bond donor-acceptor functions, carbon-chlorine distance that significantly affects the HDAC6 binding of these inhibitors, subsequently affecting their potency . Interestingly, the study also revealed that the substitutions such as the chloroethyl group, and bulky quinazolinyl cap group can affect the binding of the cap function with the amino acid residues present in the loops proximal to the catalytic site of HDAC6. Such contributions of cap groups can lead to both stabilization and destabilization of the cap function after occupying the hydrophobic catalytic site by the aryl hydroxamate linker-ZBG functions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that can influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Sodium-glucose co-transporter 2 (SGLT2) is one of the important targets against type II diabetes mellitus. A typical SGLT2 inhibitor acts by inhibiting glucose reabsorption, thus lowering the blood glucose level. Unlike SGLT1, SGLT2 is responsible for almost 90% glucose reabsorption from glomerular filtrate. The current SGLT2 inhibitors include gliflozins, often prescribed as second or third-line agents in diabetes mellitus. The SGLT2 inhibitors also benefit patients with heart and kidney disease. Due to instability issues with the natural O-aryl glycoside analogues C-glycoside analogues were developed and showed improved stability. Despite enhanced bioavailability and selectivity of newer derivatives, some serious side effects are associated with gliflozin analogues. At the present study, we applied in-silico approaches to find new glycomimetic compounds as potent SGLT2 inhibitors that could show improvement in side effects associated with current analogues. This work applied both ligand-based and structure-based drug approaches to find potential compounds. We developed a 3D-QSAR method to screen potential inhibitors from a library of ten thousand compounds and performed docking studies. The compounds were ranked based on predicted pIC50 and docking score. An initial screening of five thousand compounds was conducted, and the subsequently selected top 12 compounds were based on binding free energy calculations. These selected compounds were subjected to molecular dynamics (MD) simulations. Remarkably, our simulations identified nine compounds that exhibited significant and sustained binding affinity compared to the co-crystallized Empagliflozin. Collectively, considering the anticipated pharmacokinetic profiles and toxicity assessments, several of these compounds emerged as promising candidates for further in-depth evaluation., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Prerna Ganwir reports financial support was provided by Department of Science and Technology, India. There is no fanatical or other interest to declare amongst the co-authors If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Despite the theoretical risk of forming halogenated methylparabens (halo-MePs) during water chlorination in the absence or presence of bromide ions, there remains a lack of in vivo toxicological assessments on vertebrate organisms for halo-MePs. This research addresses these gaps by investigating the lethal (assessed by embryo coagulation) or sub-lethal (assessed by hatching success/heartbeat rate) toxicity and teratogenicity (assessed by deformity rate) of MeP and its mono- and di-halogen derivatives (Cl - or Br - ) using Japanese medaka embryos. In assessing selected apical endpoints to discern patterns in physiological or biochemical alterations, heightened toxic impacts were observed for halo-MePs compared to MeP. These include a higher incidence of embryo coagulation (4-36 fold), heartbeat rate decrement (11-36 fold), deformity rate increment (32-223 fold), hatching success decrement (11-59 fold), and an increase in Reactive Oxygen Species (ROS) level (1.2-7.4 fold)/Catalase (CAT) activity (1.7-2.8 fold). Experimentally determined LC 50 values are correlated and predicted using a Quantitative Structure Activity Relationship (QSAR) based on the speciation-corrected liposome-water distribution ratio (D lipw , pH 7.5). The QSAR baseline toxicity aligns well with (sub)lethal toxicity and teratogenicity, as evidenced by toxic ratio (TR) analysis showing TR < 10 for MeP exposure in all cases, while significant specific or reactive toxicity was found for halo-MeP exposure, with TR > 10 observed (excepting three values). Our extensive findings contribute novel insights into the intricate interplay of embryonic toxicity during the early-life-stage of Japanese medaka, with a specific focus on highlighting the potential hazards associated with halo-MePs compared to the parent compound MeP., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)