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Introduction The Erythroblastosis Oncogene B homolog 2 (ERBB2) protein, also known as human epidermal growth factor receptor 2 (HER2), is a key player in cancer growth, especially in neuroblastoma and gastric cancers. Targeting ERBB2 has led to successful therapies, making it an important focus in cancer research with the potential to improve treatment for HER2-positive cancers. Objective The primary goal of this research is to employ a multifaceted computational approach to identify potential drug candidates targeting ERBB2. We aim to combine virtual screening, protein–protein docking, and functional partner prediction to provide insights into the molecular interactions and potential efficacy of the identified compounds. Additionally, we intend to assess the safety profiles of these compounds using advanced toxicity prediction tools. Materials and Methods Relevant protein sequence and structural data for ERBB2 and epidermal growth factor receptor (EGFR) were sourced from publicly available databases. Potential inhibitors from the Enamine and LifeChemicals databases were identified through virtual screening using AutoDock Vina. Functional partners of ERBB2 were explored using STRING, KEGG, and REACTOME servers. The identified compounds were subjected to toxicity prediction using the ProTox-II server. Results Virtual screening led to the selection of 10 compounds with favorable binding energies (–8.346 to –6.296 kcal/mol) and specific amino acid interactions (Thr5, Arg412, Leu414, and Ser441) with the receptor. On the other hand, EGFR was identified as the best functional partner for ERBB2. The EGFR residues Gln408, Lys463, Phe412, and Asp436 found key residues for the complex formation. The toxicity prediction analysis revealed that the majority of compounds exhibited acceptable safety profiles, although a subset of compounds showed lower prediction scores, suggesting the need for further consideration. Conclusion This comprehensive computational approach, integrating virtual screening, protein–protein docking, functional partner identification, and toxicity prediction, offers a systematic framework for efficient drug discovery. The identification of potential lead compounds targeting ERBB2, with emphasis on both binding affinity and safety, underscores the significance of such an approach in streamlining the drug development process. By prioritizing compounds with promising efficacy, functional relevance, and acceptable toxicity profiles, this study advances our understanding of potential therapeutic agents, enhancing the likelihood of successful translation from computational predictions to real-world drug candidates. [ABSTRACT FROM AUTHOR]

In the process of sterilization and disinfection, chemical reagents will react with natural organic matter and inorganic matter in water, and inevitably generate disinfection byproducts. These harmful substances are carcinogenic, teratogenic and mutagenic, and their content is low and difficult to detect. Therefore, in this study, we prepare Fe/CoFe-LDH electrodes by in-situ co-precipitation method, and their special structures and morphologies are used to achieve efficient removal of disinfection byproducts. On this basis, the prediction model of trichloroacetamide removal rate is constructed by using the backpropagation neural network, and the prediction model is used to predict the trichloroacetamide removal rate under different experimental parameters and the experiment is verified. The optimal process conditions are obtained, and the maximum trichloroacetamide removal rate is up to 81.62%. The feasibility of the data analysis and processing method based on machine learning in the prediction of disinfection by-products in drinking water is effectively proved. [ABSTRACT FROM AUTHOR]

Synthetic pollutants (SPs) are a significant environmental concern due to their extensive use and persistence in the environment. Various remediation strategies have been explored to address this issue, including photocatalysis, nano-remediation, and bioremediation. Among these, bioremediation stands out as a promising approach, particularly with the use of genetically engineered (GE) microorganisms. This review focuses on the role of GE microorganisms in reducing SPs from environmental systems. GE microorganisms have been modified to enhance their ability to degrade organic and inorganic SPs efficiently. Key genes responsible for contaminant degradation have been identified and modified to improve breakdown rates. Strategies to make engineered bacteria more acceptable are also discussed. Overall, GE microorganisms represent a viable and efficient alternative to native strains for pollutant degradation, highlighting their potential in addressing environmental pollution challenges. [ABSTRACT FROM AUTHOR]

Background: Rhubarb, containing raw rhubarb (RR) and two processed products (steamed rhubarb, SR; carbonized rhubarb, CR), is commonly used in high-doses for the treatment of peptic ulcer, especially gastric ulcer (GU). However, their active ingredients, therapeutic targets, and potential mechanism remain unclear. Meanwhile, the safety of these active ingredients is also worth studying. Methods: An offline two-dimensional low-pressure liquid chromatography/high-performance liquid chromatography coupled with high resolution mass spectrometry method was applied to identify the chemical constituents of RR, SR, and CR. Then, the plasma and urine samples of rats after oral administration of RR, SR, and CR were studied for metabolite profiling. Based on the analysis of ingredients in vivo , the key active constituents, core therapeutic targets and key signaling pathways of RR, SR, and CR against GU were screened via network pharmacology and molecular docking. Finally, the efficacy and safety of these key active ingredients were evaluated. Results: Totally, 183, 120 and 115 compounds were identified or tentatively characterized from RR, SR and CR, respectively. Meanwhile, 190, 182 and 180 components were identified after oral administration of RR, SR and CR. By network pharmacology and molecular docking, torachrysone, hydroxyemodin, 6-methylrhein, rhein and emodin anthrone might be the predominant effective constituents in RR, SR, and CR with AKT1 and EGFR being their key targets during the treatment of GU. Moreover, EGFR/PI3K/AKT signaling pathway might play a crucial role in the therapeutic mechanism of GU. In silio ADMET predictions categorized 5 compounds as drugs with good oral bioavailability, but these components may induce liver injury. Conclusion: Overall, our results not only clarified the active substances and molecular mechanism for enhancing our understanding about the traditional efficacy, but also pay attention to the clinical safety issues of raw and prepared rhubarbs. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. This study aims to uncover the genes and pathways involved in HCC through network pharmacology (NP) and to discover potential drugs via machine learning (ML)-based ligand screening. Additionally, toxicity prediction, molecular docking, and molecular dynamics (MD) simulations were conducted. NP study identified key genes related to HCC, particularly the enzymes AKT1 and GSK3β. Pathway analysis revealed that crucial pathways like PI3K-AKT and WNT signalling play pivotal roles in HCC progression. Using ML, potential inhibitors for AKT1 and GSK3β were identified, including CHEMBL2177361 and CHEMBL403354 for AKT1, and CHEMBL3652546 and CHEMBL4641631 for GSK3β. post-MD analyses, including RMSD, 2D-RMSD, RMSD cluster, RMSF, PCA, DCCM, residence time analysis, diffusion coefficient, autoencoder-based dimensionality reduction, FEL and MM/GBSA were performed to understand the protein–ligand interactions. The present study reveals the stable interactions of the inhibitors with AKT1 and GSK3β. The binding free energies of all the four complexes were −39.9, −46.8, −41.6, and −45.9 kcal/mol, respectively. This research provides novel insights into the genes and pathways involved in the progression and pathogenesis of HCC using bioinformatics tools. Furthermore, ML-based virtual screening identified potent inhibitors against the target proteins of HCC, such as AKT1 and GSK3β. [ABSTRACT FROM AUTHOR]

AbstractThe fast-increasing use of chemicals led to large numbers of chemical compounds entering the aquatic environment, raising concerns about their potential effects on ecosystems. Therefore, assessment of the ecotoxicological features of organic compounds on aquatic organisms is very important. Daphnia magna and Fathead minnow are two aquatic species that are commonly tested as standard test organisms for aquatic risk assessment and are typically chosen as the biological model for the ecotoxicology investigations of chemical pollutants. Herein, global quantitative structure–toxicity relationship (QSTR) models have been developed to predict the toxicity (pEC(LC)50) of a large dataset comprising 2106 chemicals toward Daphnia magna and Fathead minnow. The optimal descriptor of correlation weights (DCWs) is calculated using the notation of simplified molecular input line entry system (SMILES) and is used to construct QSTR models. Three target functions, TF1, TF2, and TF3 are utilized to generate 12 QSTR models from four splits, and their statistical characteristics are also compared. The designed QSTR models are validated using both internal and external validation criteria and are found to be reliable, robust, and excellently predictive. Among the models, those generated using the TF3 demonstrate the best statistical quality with R2 values ranging from 0.9467 to 0.9607, Q2 values ranging from 0.9462 to 0.9603 and RMSE values ranging from 0.3764 to 0.4413 for the validation set. The applicability domain and the mechanistic interpretations of generated models were also discussed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chinese Institute of Food Science & Technology / Zhongguo Shipin Xuebao is the property of Journal of Chinese Institute of Food Science & Technology Periodical Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research presents the development and validation of chromatographic method for analyzing sorafenib and its pharmacopeial impurities, with a focus on stability studies and degradation product (DP) characterization. Initial method optimization involved exploring various column and buffer combinations, ultimately achieving optimal separation and peak symmetry using an ODS‐AQ YMC (150 mm) column with 0.6 mL/min gradient flow of 10 mM ammonium formate buffer adjusted to pH 3.4 with formic acid as solvent A, and ethanol as solvent B as mobile phase and 246 nm wavelength. Method exhibits calibration curve linear in 50–300 µg/mL for sorafenib and 0.050–0.30 µg/mL for impurities with a detection limit of 0.015 µg/mL for impurities. A structural elucidation of DPs was performed using LC–MS/MS, providing valuable insights into their molecular compositions, and was characterized as 4‐[4‐(carboxyamino)phenoxy]pyridine‐2‐carboxylic acid (DP 1) and 4‐(4‐aminophenoxy)pyridine‐2‐carboxamide (DP 2). Using AGREE and GAPI metrics, evaluation highlighted method sustainability through ethanol–water solvents and shorter column to reduce energy consumption. Toxicity assessments revealed differences in environmental impact and toxicological profiles of DPs, emphasizing importance of managing safety considerations for sorafenib and its DPs. This research offers novel insights into sorafenib analysis by addressing pharmacopeial impurities, characterizing DPs, and evaluating method sustainability and safety. [ABSTRACT FROM AUTHOR]

This research delves into the exploration of the potential of tocopherol‐based nanoemulsion as a therapeutic agent for cardiovascular diseases (CVD) through an in‐depth molecular docking analysis. The study focuses on elucidating the molecular interactions between tocopherol and seven key proteins (1O8a, 4YAY, 4DLI, 1HW9, 2YCW, 1BO9 and 1CX2) that play pivotal roles in CVD development. Through rigorous in silico docking investigations, assessment was conducted on the binding affinities, inhibitory potentials and interaction patterns of tocopherol with these target proteins. The findings revealed significant interactions, particularly with 4YAY, displaying a robust binding energy of −6.39 kcal/mol and a promising Ki value of 20.84 μM. Notable interactions were also observed with 1HW9, 4DLI, 2YCW and 1CX2, further indicating tocopherol's potential therapeutic relevance. In contrast, no interaction was observed with 1BO9. Furthermore, an examination of the common residues of 4YAY bound to tocopherol was carried out, highlighting key intermolecular hydrophobic bonds that contribute to the interaction's stability. Tocopherol complies with pharmacokinetics (Lipinski's and Veber's) rules for oral bioavailability and proves safety non‐toxic and non‐carcinogenic. Thus, deep learning‐based protein language models ESM1‐b and ProtT5 were leveraged for input encodings to predict interaction sites between the 4YAY protein and tocopherol. Hence, highly accurate predictions of these critical protein–ligand interactions were achieved. This study not only advances the understanding of these interactions but also highlights deep learning's immense potential in molecular biology and drug discovery. It underscores tocopherol's promise as a cardiovascular disease management candidate, shedding light on its molecular interactions and compatibility with biomolecule‐like characteristics. [ABSTRACT FROM AUTHOR]

In this study, the dissipation and transformation of acetamiprid were investigated during the cowpea pickling process and the toxicity assessment software Ecological Structure Activity Relationship (ECOSAR) and the Toxicity Estimation Software Tool (T.E.S.T.) were used to predict the toxicity of acetamiprid and its transformation products (TPs). The results showed that acetamiprid was rapidly degraded during the pickling process with a degradation rate constant of 0.967 61 and a half-life of (34.95 ± 0.78) d. Four TPs from acetamiprid were found during cowpea pickling process. Among them, the oral lethal doses of TP1 and TP2 in rats were 358.17 and 385.7 mg/kg, respectively, which belonged to the “hazardous” category, and the developmental toxicity of TP1 and TP2 was significantly increased compared with that of acetamiprid. In addition, acetamiprid, TP1, TP2, and TP3 were detected in all real samples. The highest mass concentration of acetamiprid was 473.8 μg/L and the median concentration was 41.1 μg/L. These results reveal the degradation and biotransformation of acetamidine during pickling of cowpea and contribute to a better understanding of the possible health risks of pickling foods.

Quantitative structure-activity relationship modeling techniques are widely employed in diverse areas of chemistry, including environmental risk assessment of compounds and virtual screening of drugs. The modeling process entails optimizing the hyper parameters of the model and selecting appropriate molecular descriptors. It is a typical optimization problem with high-dimensional, nonlinear, continuous and discrete variables coexisting. To address this challenge, this paper proposes an enhanced snake optimizer with multiple strategies. First, new food emergence and temperature change mechanisms are introduced to balance the exploration and exploitation. Second, a death and regeneration mechanism is employed to enhance the algorithm's ability to escape from local optima. Third, new formulas for food searching, fighting, mating and eating are designed to simplify the complexity of the algorithm and improve its search efficiency and accuracy. Fourth, the performance of the proposed algorithm is evaluated using the CEC2017 benchmark functions, eight engineering cases with continuous variables, and six feature selection cases with discrete variables. Experimental results demonstrate that the algorithm significantly outperforms comparison algorithms. Finally, the algorithm was applied to quantitative structure-activity relationship modeling for environmental toxicity prediction. Compared with other algorithms, the algorithm can better configure hyper-parameters and select fewer and more effective features for environmental toxicity prediction model. It will be an effective tool to improve the performance of quantitative structure-activity relationship models. • New food emergence and temperature change mechanisms are presented to better balance exploration and exploitation. • Death and regeneration mechanism is adopted to avoid local optima. • The performance of the proposed algorithm is validated in CEC2017, engineering design and feature selection problems. • A joint optimization approach is proposed for quantitative structure-activity relationship modeling. • An environmental toxicity prediction model is obtained by jointly optimizing hyper-parameters and molecular descriptors. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

All areas of the modern society are affected by fluorine chemistry. In particular, fluorine plays an important role in medical, pharmaceutical and agrochemical sciences. Amongst various fluoro-organic compounds, trifluoromethyl (CF3) group is valuable in applications such as pharmaceuticals, agrochemicals and industrial chemicals. In the present study, following the strict OECD modelling principles, a quantitative structure–toxicity relationship (QSTR) modelling for the rat acute oral toxicity of trifluoromethyl compounds (TFMs) was established by genetic algorithm-multiple linear regression (GA-MLR) approach. All developed models were evaluated by various state-of-the-art validation metrics and the OECD principles. The best QSTR model included nine easily interpretable 2D molecular descriptors with clear physical and chemical significance. The mechanistic interpretation showed that the atom-type electro-topological state indices, molecular connectivity, ionization potential, lipophilicity and some autocorrelation coefficients are the main factors contributing to the acute oral toxicity of TFMs against rats. To validate that the selected 2D descriptors can effectively characterize the toxicity, we performed the chemical read-across analysis. We also compared the best QSTR model with public OPERA tool to demonstrate the reliability of the predictions. To further improve the prediction range of the QSTR model, we performed the consensus modelling. Finally, the optimum QSTR model was utilized to predict a true external set containing many untested/unknown TFMs for the first time. Overall, the developed model contributes to a more comprehensive safety assessment approach for novel CF3-containing pharmaceuticals or chemicals, reducing unnecessary chemical synthesis whilst saving the development cost of new drugs. [ABSTRACT FROM AUTHOR]

Background: Polycystic ovarian syndrome is a metabolic disorder majorly caused by the hormonal fluctuations in female and the current scenario explains that adolescents remain predominantly affected with this disorder. Bauhinia variegata assist in the treatment of various ailments and are used as an ingredient in targeting uterine disorders, yet the exact constituent that contributes on activity remains unknown. Aim: The main aim of our study is to determine the potent phytoconstituent of Bauhinia variegata to fight against the symptoms of PCOS through computational techniques. Materials and Methods: Five in silico techniques like Molecular docking analysis, Pharmacokinetics, toxicity prediction of the compounds, Biological activity and Molecular dynamics simulation studies were performed to identify the potent phytoconstituent. Results and Discussion: Molecular docking studies show that the major constituent lupeol had a good binding interaction and high docking score of -10.31 Kcal/mol and -11.52 Kcal/mol with both the proteins 3RUK and 1E3K. Pharmacokinetics, toxicity and biological activity studies reveal that it had ideal drug likeliness properties with proper biological activity values and were found to non-toxic in the analysed parameters. Lupeol complex was found to be potentially stable throughout the molecular dynamic’s simulation for 100 ns. Conclusion: Thus, through in silico analysis it is evident that from the list of phytoconstituents of Bauhinia variegata, lupeol possess potent activities in mitigating the symptoms of PCOS. Further in vitro and in vivo analysis on PCOS model is expected to yield favourable results. [ABSTRACT FROM AUTHOR]

Introduction: [177Lu]Lutetium (Lu)-oxodotreotide is a radiopharmaceutical drug used as peptide receptor radionuclide therapy (PRRT) for somatostatin receptor–expressing neuroendocrine neoplasms. It provides an additional effective alternative treatment for these rare cancers. Although well tolerated, its safety profile must continue to be characterized to support its use as a first-line treatment or for additional cycles. This study evaluated factors associated with the occurrence of [177Lu]Lu-oxodotreotide induced short-term toxicity. Materials and Methods: A retrospective observational monocentric study was carried out from July 2013 to October 2021. Inclusion criteria were defined as follows: patients who received at least four cycles of [177Lu]Lu-oxodotreotide and were followed up for 6 months after the last injection. Graduated toxicity was defined using the National Cancer Institute Common Terminology Criteria for Adverse Events 5.0. Cox regression was used in the analysis. Results: Forty patients were included. The most frequent toxicities occurred during the first cycle and were graded as G1 or G2. As expected, toxicities were predominantly hematological and hepatic, with incomplete reversibility after each cycle. The following factors were significantly related to the occurrence of hematological or hepatic toxicity during PRRT: gastrointestinal primary tumor diagnosis, bone metastases, peritoneal metastases, pancreatic metastases or pulmonary metastases, and high tumor grade. Conclusion: Knowledge and consideration of these factors in adjusting [177Lu]Lu-oxodotreotide treatment regimen could help prevent or reduce the severity of these toxicities. Further studies are still warranted to refine these results and improve treatment management. [ABSTRACT FROM AUTHOR]

• This study presents the development of several deep-learning models to evaluate different types of compound toxicity. • The models were tailored to specific types of toxicity, each with a unique GCN binary classification. • Evaluation metrics indicate that developed models were reliable in predicting compounds toxicity. • The models were integrated into a virtual screening pipeline to facilitate drug discovery. Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection. [ABSTRACT FROM AUTHOR]

BackgroundRhubarb, containing raw rhubarb (RR) and two processed products (steamed rhubarb, SR; carbonized rhubarb, CR), is commonly used in high-doses for the treatment of peptic ulcer, especially gastric ulcer (GU). However, their active ingredients, therapeutic targets, and potential mechanism remain unclear. Meanwhile, the safety of these active ingredients is also worth studying.MethodsAn offline two-dimensional low-pressure liquid chromatography/high-performance liquid chromatography coupled with high resolution mass spectrometry method was applied to identify the chemical constituents of RR, SR, and CR. Then, the plasma and urine samples of rats after oral administration of RR, SR, and CR were studied for metabolite profiling. Based on the analysis of ingredients in vivo, the key active constituents, core therapeutic targets and key signaling pathways of RR, SR, and CR against GU were screened via network pharmacology and molecular docking. Finally, the efficacy and safety of these key active ingredients were evaluated.ResultsTotally, 183, 120 and 115 compounds were identified or tentatively characterized from RR, SR and CR, respectively. Meanwhile, 190, 182 and 180 components were identified after oral administration of RR, SR and CR. By network pharmacology and molecular docking, torachrysone, hydroxyemodin, 6-methylrhein, rhein and emodin anthrone might be the predominant effective constituents in RR, SR, and CR with AKT1 and EGFR being their key targets during the treatment of GU. Moreover, EGFR/PI3K/AKT signaling pathway might play a crucial role in the therapeutic mechanism of GU. In silio ADMET predictions categorized 5 compounds as drugs with good oral bioavailability, but these components may induce liver injury.ConclusionOverall, our results not only clarified the active substances and molecular mechanism for enhancing our understanding about the traditional efficacy, but also pay attention to the clinical safety issues of raw and prepared rhubarbs.

Tetramethrin (TMT) is a commonly used insecticide and has a carcinogenic and neurodegenerative effect on humans. The binding mechanism and toxicological implications of TMT to human serum albumin (HSA) were examined in this study employing a combination of biophysical and computational methods indicating moderate binding affinity and potential hepato and renal toxicity. Fluorescence quenching experiments showed that TMT binds to HSA with a moderate affinity, and the binding process was spontaneous and predominantly enthalpy‐driven. Circular dichroism spectroscopy revealed that TMT binding did not induce any significant conformational changes in HSA, resulting in no changes in its alpha‐helix content. The binding site and modalities of TMT interactions with HSA as computed by molecular docking and molecular dynamics simulations revealed that it binds to Sudlow site II of HSA via hydrophobic interactions through its dimethylcyclopropane carboxylate methyl propanyl group. The structural dynamics of TMT induce proper fit into the binding site creating increased and stabilizing interactions. Additionally, molecular mechanics–Poisson Boltzmann surface area calculations also indicated that non‐polar and van der Waals were found to be the major contributors to the high binding free energy of the complex. Quantum mechanics (QM) revealed the conformational energies of the binding confirmation and the degree of deviation from the global minimum energy conformation of TMT. The results of this study provide a comprehensive understanding of the binding mechanism of TMT with HSA, which is important for evaluating the toxicity of this insecticide in humans. [ABSTRACT FROM AUTHOR]

Abstract Background The DNA in each cell in our body is constantly in danger of becoming damaged. Most DNA damage gets repaired straight away via many different proteins encoded by DNA—repair genes. MSH3 and MSH6 are pivotal DNA repair genes maintaining human genome integrity. Dysregulated expression of such genes has its implications resulting in developing of adverse reactions in cancer breast patients receiving taxanes. Cancer chemotherapy with some of taxane class of agents are associated with significant neurotoxicity, arthralgias and myalgias that may offset the therapeutic benefits of taxane use. Our aim is to identify gene expression pattern of MSH3 and MSH6 DNA mismatch repair genes in female breast cancer patients who develop adverse reactions to taxane-based therapy. One hundred and five patients with histologically proven breast cancer who received paclitaxel (PTX) as a single agent or combination therapy have been enrolled along with a group of 50 females with benign breast lesions serving as controls.Gene expression studies of mismatch repair genes (MMR) genes; MSH3 and MSH6; have been performed by real-time PCR. Patients were divided into groups according to the determined type/grade of PTX-based toxicity and fold changes of both genes were estimated. Results In the present work both MMR genes showed significantly lower expression in all the studied patients compared to benign cases as a control group. Toxicity findings were encountered in 75.2% of the studied patient cohort. The most common observed type of toxicity was peripheral neuropathy (PN), 58.1% of the studied patients. Both MSH3 and MSH6 genes were significantly down-regulated in the presence of high grade PN toxicity ≥ 2 (p = 0.034 and 0.01); diarrhea toxicity (p = 0.02 and 0.008); dyspnea (p = 0.01 and 0.016) respectively and bone pain (p = 0.024 for MSH6 only). Conclusion Dysregulated expression of MMR GENES [MSH3and MSH6] can be implicated in paclitaxel—induced toxicity experienced by some cancer breast patients.

A series of diverse organic compounds impose serious detrimental effects on the health of living organisms and the environment. Determination of the structural aspects of compounds that impart toxicity and evaluation of the same is crucial before public usage. The present study aims to determine the structural characteristics of compounds for Tetrahymena pyriformis toxicity using the q-RASTR (Quantitative Read Across Structure–Toxicity Relationship) model. It was developed using RASTR and 2-D descriptors for a dataset of 1792 compounds with defined endpoint (pIGC50) against a model organism, T. pyriformis. For the current study, the whole dataset was divided based on activity/property into the training and test sets, and the q-RASTR model was developed employing six descriptors (three latent variables) having r2, Q2F1 and Q2 values of 0.739, 0.767, and 0.735, respectively. The generated model was thoroughly validated using internationally recognized internal and external validation criteria to assess the model's dependability and predictability. It was highlighted that high molecular weight, aromatic hydroxyls, nitrogen, double bonds, and hydrophobicity increase the toxicity of organic compounds. The current study demonstrates the applicability of the RASTR algorithm in QSTR model development for the prediction of toxic chemicals (pIGC50) towards T. pyriformis. [ABSTRACT FROM AUTHOR]

To address the issue that dyes are recalcitrant to hydrolysis and conventional water treatment strategies, an ultraviolet light-emitting diode (UV-LED)/periodate (PI) advanced oxidation process was developed for dye removal. The results showed that this system was applicable to a wide pH range. The dyes were completely removed in 40 minutes optimally with 270 nm UV-LED and 1 mmol/L PI. NO3- and HCO3- showed inhibitory effects on dye decomposition, while Cl-, SO2-4 and humic acid had a negligible effect on the system. Quenching experiments and electron paramagnetic resonance experiments were conducted to explore the reactive species involved in the oxidation process. It was confirmed that hydroxyl radicals and singlet oxygen dominated the degradation of pollutants. In addition, the transformation products of methylene blue (MB) in the system were explored and the potential reaction pathway was proposed. Furthermore, the toxicity of MB and the transformation products were predicted via a series of in silico QSAR-based prediction approaches. It was found that the biodegradability of the transformed products was significantly improved. However, some products still exhibited acute/chronic toxicity.

Purpose: To establish the early prediction models of radiation-induced oral mucositis (RIOM) based on baseline CT-based radiomic features (RFs), dosimetric data, and clinical features by machine learning models for head and neck cancer (HNC) patients. Methods: In this single-center prospective study, 49 HNCs treated with curative intensity modulated radiotherapy (IMRT) were enrolled. Baseline CT images (i.e., CT simulation), dosimetric, and clinical features were collected. RIOM was assessed using CTCAE v.5.0. RFs were extracted from manually-contoured oral mucosa structures. Minimum-redundancy-maximum-relevance (mRMR) method was applied to select the most informative radiomics, dosimetric, and clinical features. Then, binary prediction models were constructed for predicting acute RIOM based on the top mRMR-ranked radiomics, dosimetric, and clinical features alone or in combination, using random forest classifier algorithm. The predictive performance of models was assessed using the area under the receiver operating curve (AUC), accuracy, weighted-average based sensitivity, precision, and F1-measure. Results: Among extracted features, the top 10 RFs, the top 5 dose-volume features, and the top 5 clinical features were selected using mRMR method. The model exploiting the integrated features (10-radiomics + 5-dosimetric + 5-clinical) achieved the best prediction with AUC, accuracy, sensitivity, precision, and F1-measure values of 91.7 %, 90.0 %, 83.0 % 100.0 %, and 91.0 %, respectively. The model developed using baseline CT RFs alone provided the best performance compared to dose-volume features or clinical features alone, with an AUC of 87.0 %. Conclusion: Our results suggest that the integration of baseline CT radiomic features with dosimetric and clinical features showed promising potential to improve the performance of machine learning models in early prediction of RIOM. The ultimate goal is to personalize radiotherapy for HNC patients.

Background: The DNA in each cell in our body is constantly in danger of becoming damaged. Most DNA damage gets repaired straight away via many different proteins encoded by DNA—repair genes. MSH3 and MSH6 are pivotal DNA repair genes maintaining human genome integrity. Dysregulated expression of such genes has its implications resulting in developing of adverse reactions in cancer breast patients receiving taxanes. Cancer chemotherapy with some of taxane class of agents are associated with significant neurotoxicity, arthralgias and myalgias that may offset the therapeutic benefits of taxane use. Our aim is to identify gene expression pattern of MSH3 and MSH6 DNA mismatch repair genes in female breast cancer patients who develop adverse reactions to taxane-based therapy. One hundred and five patients with histologically proven breast cancer who received paclitaxel (PTX) as a single agent or combination therapy have been enrolled along with a group of 50 females with benign breast lesions serving as controls.Gene expression studies of mismatch repair genes (MMR) genes; MSH3 and MSH6; have been performed by real-time PCR. Patients were divided into groups according to the determined type/grade of PTX-based toxicity and fold changes of both genes were estimated. Results: In the present work both MMR genes showed significantly lower expression in all the studied patients compared to benign cases as a control group. Toxicity findings were encountered in 75.2% of the studied patient cohort. The most common observed type of toxicity was peripheral neuropathy (PN), 58.1% of the studied patients. Both MSH3 and MSH6 genes were significantly down-regulated in the presence of high grade PN toxicity ≥ 2 (p = 0.034 and 0.01); diarrhea toxicity (p = 0.02 and 0.008); dyspnea (p = 0.01 and 0.016) respectively and bone pain (p = 0.024 for MSH6 only). Conclusion: Dysregulated expression of MMR GENES [MSH3and MSH6] can be implicated in paclitaxel—induced toxicity experienced by some cancer breast patients. [ABSTRACT FROM AUTHOR]

It is essential to develop an efficient technology for the elimination of refractory contaminants due to their high toxicity. In this study, a novel underwater bubbling pulsed discharge plasma (UBPDP) system was proposed for the degradation of Orange II (OII). The degradation performance experiments showed that by enhancing the peak voltage and pulse frequency, the degradation efficiency of OII increased gradually. The removal efficiencies under different air flow rates were close. Reducing OII concentration and solution conductivity could promote the elimination of OII. Compared with neutral and alkaline conditions, acidic condition was more beneficial to OII degradation. The active species including ·OH, ·O2−, 1O2, and hydrated electrons were all involved in OII degradation. The concentrations of O3 and H2O2 in OII solution were lower than those in deionized water. During discharge, the solution pH increased while conductivity decreased. The variation of UV–vis spectra with treatment time indicated the effective decomposition of OII. Possible degradation pathways were speculated based on LC-MS. The toxicity of intermediate products was predicted by the Toxicity Estimation Software Tool. Coexisting constituents including Cl−, SO42−, HCO3−, and humic acid had a negative effect on OII removal. Finally, the comparison with other technology depicted the advantage of the UBPDP system. [ABSTRACT FROM AUTHOR]

The ecotoxicological impact of pharmaceuticals has received considerable attention, primarily focusing on active pharmaceutical ingredients (APIs) while largely neglecting the potential hazards posed by pharmaceutical excipients. Therefore, we analyzed the ecotoxicity of 16 commonly used pharmaceutical excipients, as well as 26 API–excipient and excipient–excipient mixtures utilizing the Microtox® test. In this way, we assessed the potential risks that pharmaceutical excipients, generally considered safe, might pose to the aquatic environment. We investigated both their individual ecotoxicity and their interactions with tablet ingredients using concentration addition (CA) and independent action (IA) models to shed light on the often-overlooked ecotoxicological consequences of these substances. The CA model gave a more accurate prediction of toxicity and should be recommended for modeling the toxicity of combinations of drugs with different effects. A challenge when studying the ecotoxicological impact of some pharmaceutical excipients is their poor water solubility, which hinders the use of standard aquatic ecotoxicity testing techniques. Therefore, we used a modification of the Microtox® Basic Solid Phase protocol developed for poorly soluble substances. The results obtained suggest the high toxicity of some excipients, i.e., SLS and meglumine, and confirm the occurrence of interactions between APIs and excipients. Through this research, we hope to foster a better understanding of the ecological impact of pharmaceutical excipients, prompting the development of risk assessment strategies within the pharmaceutical industry. [ABSTRACT FROM AUTHOR]

In recent years, the paradigm of toxicity science is moving toward predictive science. New approach methodologies (NAMs), including in chemico, in silico, and in vitro approach, provides a new feasibility to minimize the whole animal toxicity testing. To support these efforts, the U.S. Environmental Protection Agency (EPA)’s web-based CompTox Chemical Dashboard was developed and providing relevant data through a curation of diverse substances and predictive tools. In this review, we presented a comprehensive analysis of EPA CompTox Chemical dashboard’s available data, predictive tools and recent case studies using them. We also suggested a perspective of utility of EPA CompTox Chemical Dashboard for implementation of next generation risk assessment and toxicity prediction of environmental chemicals.