Prediction Of Toxicity Research Articles

Prediction of response to promising first-line chemotherapy in ovarian cancer patients with residual peritoneal tumors: practical biomarkers and robust multiplex models.

Platinum/taxane (TC) chemotherapy with debulking surgery stays the mainstay of the treatment in ovarian cancer patients with peritoneal metastasis, and recently its novel modality, intraperitoneal carboplatin with dose-dense paclitaxel (ddTCip), was shown to have greater therapeutic impact. Nevertheless, the response varies among patients and consequent recurrence, or relapse often occurs. Discovery of therapeutic response predictor to ddTCip and/or TC therapy is eagerly awaited to improve the treatment outcome. Using datasets in 76 participants in our ddTCip study and published databases on patients received TC therapy, we first validated a total of 75 previously suggested markers, sought out more active biomarkers through the association analyses of genome-wide transcriptome and genotyping data with progression-free survival (PFS) and adverse events, and then developed multiplex statistical prediction models for PFS and toxicity by mainly using multiple regression analysis and the classification and regression tree (CART) algorithm. The association analyses revealed that SPINK1 could be a possible biomarker of ddTCip efficacy, while ABCB1 rs1045642 and ERCC1 rs11615 would be a predictor of hematologic toxicity and peripheral neuropathy, respectively. Multiple regression analyses and CART algorithm finally provided a potent efficacy prediction model using 5 gene expression data and robust multiplex toxicity prediction models-CART models using a total of 4 genotype combinations and multiple regression models using 15 polymorphisms on 12 genes. Biomarkers and multiplex models composed here could work well in the response prediction of ddTCip and/or TC therapy, which might contribute to realize optimal selection of the key therapy.

Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction.

The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.

Evaluation of antioxidant activity in different Egyptian barley cultivars: an in vitro and in silico study

BackgroundCereals have historically played a crucial role in the human diet, serving as a significant natural source of energy and offering various health benefits. Barley (Hordeum vulgare L.) has been given significant attention in recent years due to its exceptional nutritional value, surpassing that of other cereals. The objective of this research is to evaluate the antioxidant activity of various solvent extracts obtained from three different barley cultivars.ResultsThe G.136 variety's acetone extract exhibited the highest level of antioxidant activity in both the DPPH assay, with an IC50 of 55.62 µg/ml, and the FRAP assay, with 447 μM trolox/mg extract. The dominant compounds identified before in the acetone fraction were subjected to an evaluation of their docking scores, along with an assessment of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) and TOPKAT (Toxicity Prediction by Komputer Assisted Technology) studies. Notably, hordatine A1, prodelphinidin B3, hordatine B1, procyanidin B2, and isovitexin 7-O-glucoside were the major compounds with the highest LipDock scores compared to trolox the reference drug with polyphenol oxidase.ConclusionsThe findings indicate that the acetone extract from all three cultivars demonstrates noteworthy results, surpassing the efficacy of other solvent extracts against the antioxidant activity.

FAIR assessment of nanosafety data reusability with community standards.

Nanomaterials hold great promise for improving our society, and it is crucial to understand their effects on biological systems in order to enhance their properties and ensure their safety. However, the lack of consistency in experimental reporting, the absence of universally accepted machine-readable metadata standards, and the challenge of combining such standards hamper the reusability of previously produced data for risk assessment. Fortunately, the research community has responded to these challenges by developing minimum reporting standards that address several of these issues. By converting twelve published minimum reporting standards into a machine-readable representation using FAIR maturity indicators, we have created a machine-friendly approach to annotate and assess datasets' reusability according to those standards. Furthermore, our NanoSafety Data Reusability Assessment (NSDRA) framework includes a metadata generator web application that can be integrated into experimental data management, and a new web application that can summarize the reusability of nanosafety datasets for one or more subsets of maturity indicators, tailored to specific computational risk assessment use cases. This approach enhances the transparency, communication, and reusability of experimental data and metadata. With this improved FAIR approach, we can facilitate the reuse of nanosafety research for exploration, toxicity prediction, and regulation, thereby advancing the field and benefiting society as a whole.

Isolation, Virtual Screening, and Evaluation of Hazelnut-Derived Immunoactive Peptides for the Inhibition of SARS-CoV-2 Main Protease.

The aim of this study is to validate the activity of hazelnut (Corylus avellana L.)-derived immunoactive peptides inhibiting the main protease (Mpro) of SARS-CoV-2 and further unveil their interaction mechanism using in vitro assays, molecular dynamics (MD) simulations, and binding free energy calculations. In general, the enzymatic hydrolysis components, especially molecular weight < 3 kDa, possess good immune activity as measured by the proliferation ability of mouse splenic lymphocytes and phagocytic activity of mouse peritoneal macrophages. Over 866 unique peptide sequences were isolated, purified, and then identified by nanohigh-performance liquid chromatography/tandem mass spectrometry (NANO-HPLC-MS/MS) from hazelnut protein hydrolysates, but Trp-Trp-Asn-Leu-Asn (WWNLN) and Trp-Ala-Val-Leu-Lys (WAVLK) in particular are found to increase the cell viability and phagocytic capacity of RAW264.7 macrophages as well as promote the secretion of the cytokines nitric oxide (NO), tumor necrosis factor-α (TNF-α), and interleukin-1β (IL-1β). Fluorescence resonance energy transfer assay elucidated that WWNLN and WAVLK exhibit excellent inhibitory potency against Mpro, with IC50 values of 6.695 and 16.750 μM, respectively. Classical all-atom MD simulations show that hydrogen bonds play a pivotal role in stabilizing the complex conformation and protein-peptide interaction. Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) calculation indicates that WWNLN has a lower binding free energy with Mpro than WAVLK. Furthermore, adsorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions illustrate favorable drug-likeness and pharmacokinetic properties of WWNLN compared to WAVLK. This study provides a new understanding of the immunomodulatory activity of hazelnut hydrolysates and sheds light on peptide inhibitors targeting Mpro.

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac

BackgroundRadiation induced acute skin toxicity (AST) is considered as a common side effect of breast radiation therapy. The goal of this study was to design dosiomics-based machine learning (ML) models for prediction of AST, to enable creating optimized treatment plans for high-risk individuals.MethodsDosiomics features extracted using Pyradiomics tool (v3.0.1), along with treatment plan-derived dose volume histograms (DVHs), and patient-specific treatment-related (PTR) data of breast cancer patients were used for modeling. Clinical scoring was done using the Common Terminology Criteria for Adverse Events (CTCAE) V4.0 criteria for skin-specific symptoms. The 52 breast cancer patients were grouped into AST 2 + (CTCAE ≥ 2) and AST 2 − (CTCAE < 2) toxicity grades to facilitate AST modeling. They were randomly divided into training (70%) and testing (30%) cohorts. Multiple prediction models were assessed through multivariate analysis, incorporating different combinations of feature groups (dosiomics, DVH, and PTR) individually and collectively. In total, seven unique combinations, along with seven classification algorithms, were considered after feature selection. The performance of each model was evaluated on the test group using the area under the receiver operating characteristic curve (AUC) and f1-score. Accuracy, precision, and recall of each model were also studied. Statistical analysis involved features differences between AST 2 − and AST 2 + groups and cutoff value calculations.ResultsResults showed that 44% of the patients developed AST 2 + after Tomotherapy. The dosiomics (DOS) model, developed using dosiomics features, exhibited a noteworthy improvement in AUC (up to 0.78), when spatial information is preserved in the dose distribution, compared to DVH features (up to 0.71). Furthermore, a baseline ML model created using only PTR features for comparison with DOS models showed the significance of dosiomics in early AST prediction. By employing the Extra Tree (ET) classifiers, the DOS + DVH + PTR model achieved a statistically significant improved performance in terms of AUC (0.83; 95% CI 0.71–0.90), accuracy (0.70), precision (0.74) and sensitivity (0.72) compared to other models.ConclusionsThis study confirmed the benefit of dosiomics-based ML in the prediction of AST. However, the combination of dosiomics, DVH, and PTR yields significant improvement in AST prediction. The results of this study provide the opportunity for timely interventions to prevent the occurrence of radiation induced AST.

Ultrasound-assisted synthesis of novel Schiff bases from 3-(2-oxo-2H-chromen-3-yl)-1-(4-phenylthiazol-2-yl)-1H-pyrazole-4-carboxaldehyde and their cytotoxicity, apoptosis, cell cycle, molecular docking, and ADMET profiling

With the ultimate goal of discovering new anticancer agents, this study involved the design and synthesis of fifteen novel Schiff bases 4a,b, 5, 6a–d, 7a–e, and 8–10 which contain 3-(2-oxo-2H-chromen-3-yl)-1-(4-phenylthiazol-2-yl)-1H-pyrazole moiety. The synthetic method depended on reaction of 3-(2-oxo-2H-chromen-3-yl)-1-(4-phenylthiazol-2-yl)-1H-pyrazole-4-carboxaldehyde (3) with a series of aromatic and heteroaryl amines under ultrasound irradiation to explore the influence of aromatic and heteroaryl rings on biological activity. The chemical structures of these Schiff bases were fully elucidated using various spectral and elemental analyses. The antiproliferative activities of the Schiff bases were studied by the standard SRB method. Among the new 15 Schiff bases, derivatives 4a,b, 5, and 7b have significant cytotoxic effects against PC3, HepG2, and HCT116 cancer cell lines. These four bioactive Schiff bases significantly increased the late apoptosis of all studied tumor cells. Also, both products 4a and 4b arrested the cell cycle at the G1 phase, while both compounds 5 and 7b arrested the S and G2 phases against PC3 cells. In addition, the products 4a, 4b, 5, and 7b have promising high abilities to arrest the cell cycle at the G2 phase against HepG2 and HCT116 cells. The different substitutions on the aryl ring were the basis for the structure–activity relationship study. The molecular docking study confirmed good binding interactions of these compounds with Cyclin-dependent kinase 8 (CDK-8) receptor, while the absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction supported that these bioactive products can be promising anticancer agents.

Multi-strategy enhanced snake optimizer for quantitative structure-activity relationship modeling

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.

Mobility inhibition of arsenic in the soil: the role of green synthesized silica nanoparticles

The studies showed the effectiveness of green-synthesized SiO2NPs in mitigating the toxicity of Arsenic. Density Functional Theory (DFT) is a computational method used to determine electronic structure, energy gap, and toxicity prediction. Experimentally, silicon nanoparticles of 0 (S0) and 100% v/v (S100) were applied to the surface of the soil. 150 mL of Arsenic trioxide was applied twice at a rate of 0 (As0) and 3.2 g/mL (As3.2) at an interval of three weeks. Green synthesized SiO2NPs possessed a higher chemical potential (µ) and electrophilicity index; consequently, charges could be transferred and easily polarized. The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) levels of the green synthesized SiO2NPs enable them to donate electrons and complex with arsenic, reducing their bioavailability and toxicity. Evidence from the studies further showed that SiO2NPs had buffered the soil acidity and electric conductivity, posing a high binding site and reactivity with exchangeable cations and micronutrients due to their smaller energy gap. Furthermore, the catalytic activities of the soil enzymes dehydrogenase (DHA) and peroxidase (POD) were greatly increased, which enhanced the electrostatic interaction between the SiO2NPs and As.

New prospective insecticidal agents based on thiazolo[4,5-b]quinoxaline derivatives against cotton leafworm Spodoptera litura (Fabricius): Design, synthesis, toxicological, morphology, histological, and biomedical studies

In this study, a new series of thiazolo[4,5-b]quinoxaline derivatives 3–8 were synthesized by treating 2,3-dichloroquinoxaline with thiosemicarbazone and thiourea derivatives under reflux conditions. The chemical structure of the newly designed derivatives was conducted using spectroscopic techniques. The insecticidal bioassay of the designed derivatives was evaluated against the 2nd and 4th larvae of S. litura after five days as toxicity agents via median lethal concentration (LC50) and the lethal time values (LT50). The results indicated that all the tested compounds had insecticidal effects against both instar larvae of S. litura with variable values. Among them, thiazolo[4,5-b]quinoxaline derivative 3 was the most toxic, with LC50 = 261.88 and 433.68 ppm against 2nd and 4th instar larvae, respectively. Moreover, the thiazolo[4,5-b]quinoxaline derivative 3 required the least time to kill the 50% population (LT50) of 2nd larvae were 20.88, 13.2, and 15.84 hs with 625, 1250, and 2500 ppm, respectively, while for the 4th larval instar were 2.75, 2.08, and 1.76 days with concentrations of 625, 1250, and 2500 ppm, respectively. Larvae's morphological and histological studies for the most active derivative 3 were investigated. According to SEM analysis, the exterior morphology of the cuticle and head capsule was affected. In addition, there were some histological alterations in the cuticle layers and the midgut tissues. Columnar cells began breaking down, and vacuolization occurred in the peritrophic membrane. Moreover, treating 4th S litura larvae hemolymph with compound 3 showed significant changes in biochemical analysis, such as total proteins, GPT, GOT, acetylcholinesterase (AChE), and alkaline phosphatase (AlP). Finally, the toxicity prediction of the most active derivative revealed non-corrosive, non-irritant to the eye, non-respiratory toxicity, non-sensitivity to the skin, non-hepatotoxic, and don't have toxicity on minnow toxicity and T. pyriformis indicating a good toxicity profile for human.

Antimicrobial activity of Cinnamomum zeylanicum essential oil against colistin-resistant gram-negative bacteria

ABSTRACT In the current study, we evaluated the antimicrobial activity of Cinnamomum zeylanicum Blume essential oil (Cinn-EO) against a group of thirteen clinical colistin-resistant Gram-negative bacteria, including Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The GCMS analysis showed that cinnamaldehyde was the major compound (94.29%) of the Cinn-EO. The diameter of the inhibition zone by Cinn-EO varied from 24 to 37 mm. The minimum inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) values ranged between 0.625 and 5 mg/mL. Interestingly, the MBC/MIC was equal to 1 for most tested bacterial strains, indicating an advanced bactericidal effect of Cinn-EO against colistin-resistant Gram-negative bacteria. The absorption, distribution, metabolism, elimination, and toxicity (ADMET) prediction showed good pharmacokinetic properties of the tested cinnamaldehyde. The results suggest that cinnamaldehyde could be a potential alternative to treat infection caused by colistin-resistant Gram-negative bacteria.

Abstract PO4-11-01: Validation of a CARG-BC-based score for the prediction of therapy-induced toxicities in patients with metastatic breast cancer

Abstract Introduction Prediction of treatment-induced toxicities is important in order to tailor and adjust oncologic therapies. As limited tools exist to predict the risk of therapy-induced toxicities in patients with metastatic cancer, we aimed to develop and to validate a score for the prediction of therapy-induced toxicities in patients with metastatic breast cancer treated with chemotherapy and immunotherapy. The score was based on the Cancer and Aging Research Group-Breast Cancer early-stage breast cancer score for prediction of chemotherapy-induced toxicities. Methods All patients treated for metastatic breast cancer with chemotherapy and immunotherapy at the Department of Gynecology, Obstetrics and Reproductive Medicine at Saarland University Hospital between 01/2014 and 05/2022 were included in this retrospective study. Patient characteristics and therapy-relevant oncological parameters were recorded, as well as therapy-induced toxicities (recorded according to CTC), hospitalization rates, toxicity-related dose reductions and therapy discontinuations. In the first cohort (development cohort), a new score for the prediction of therapy-induced toxicities was developed using ROC analysis and multivariate analysis based on the CARG-BC score. In the second cohort (validation cohort), this was validated using Pearson’s correlation coefficient. Results Data from 32 patients were analyzed for the development cohort, and inclusion of 102 patients contributed to the validation cohort. The newly developed score consists of seven independent factors for the prediction of toxicities ≥ CTC III° and includes the parameters “presence of visceral metastases” (yes/no), “received previous lines of therapy” (yes/no), “limited mobility/able to walk less than one kilometer” (yes/no), “a fall in the last six months” (yes/no), “social interactions” (yes/no), “pretherapeutic change of transaminases” (yes/no) and “pretherapeutic change of hemoglobin” (hemoglobin &amp;lt;/&amp;gt; 10 g/dl). Based on the results, three risk groups were defined (low (0-2), medium (3-5) and high (6-15) risk). 28% of patients were in the low-risk group, 37% in the intermediate-risk group and 34% in the high-risk group. Overall, 49% of the patients had toxicities ≥ CTC III°. Distributed across the subgroups, these occurred in 14% in the low-risk group, 34% in the intermediate-risk group and in 52% in the high-risk group (p ≤ 0.01). Risk score results showed a correlation with treatment discontinuation and hospitalization rate (p ≤ 0.01), but not with dose reduction (p = 0.77). Conclusion The available data show a correlation between the risk score results and the occurrence of systemic therapy-induced toxicities in patients with metastatic breast cancer. The results can be used to further identify risk populations and to systematically apply preventive and supportive measures in order to approve quality of life in patients with metastatic breast cancer. Citation Format: Julia SM Zimmermann, Aylin Aygün, Askin Kaya, Marc P Radosa, Christoph G Radosa, Erich F Solomayer, Julia C Radosa. Validation of a CARG-BC-based score for the prediction of therapy-induced toxicities in patients with metastatic breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-11-01.

Integrated convolution and self-attention for improving peptide toxicity prediction.

Peptides are promising agents for the treatment of a variety of diseases due to their specificity and efficacy. However, the development of peptide-based drugs is often hindered by the potential toxicity of peptides, which poses a significant barrier to their clinical application. Traditional experimental methods for evaluating peptide toxicity are time-consuming and costly, making the development process inefficient. Therefore, there is an urgent need for computational tools specifically designed to predict peptide toxicity accurately and rapidly, facilitating the identification of safe peptide candidates for drug development. We provide here a novel computational approach, CAPTP, which leverages the power of convolutional and self-attention to enhance the prediction of peptide toxicity from amino acid sequences. CAPTP demonstrates outstanding performance, achieving a Matthews correlation coefficient of approximately 0.82 in both cross-validation settings and on independent test dataset. This performance surpasses that of existing state-of-the-art peptide toxicity predictors. Importantly, CAPTP maintains its robustness and generalizability even when dealing with data imbalances. Further analysis by CAPTP reveals that certain sequential patterns, particularly in the head and central regions of peptides, are crucial in determining their toxicity. This insight can significantly inform and guide the design of safer peptide drugs. The source code for CAPTP is freely available at https://github.com/jiaoshihu/CAPTP. Supplementary data are available at Bioinformatics online.

A developed and validated centrifugal ultrafiltration coupled with high performance liquid chromatography-tandem mass spectrometry method for rapid quantification of unbound lenvatinib in human plasma

In clinical practice, the determination of unbound drug concentration is very important for dose adjustment and toxicity prediction because only the unbound fraction can achieve a pharmacological effect. A fast, sensitive and accurate analytical method of centrifugal ultrafiltration coupled with high performance liquid chromatography-tandem mass spectrometry method was developed and applied to allow the quantification of unbound lenvatinib concentration. The application of linear regression analysis was used to examine the effects of centrifugal force, centrifugal time, and protein content on ultrafiltrate volume (Vu). The results indicated that the centrifugal force and centrifugal time have an influence on Vu that is significantly positive (P < 0.05). This developed method with good linearity (r2 = 0.9996), good accuracy (bias % ≤ 2.24 %), good precision (CV % ≤ 7.10 %), and good recovery (95.46 %−106.46 %) was suitable for routine clinical practice and studies. Particularly, the ultrafiltration membrane had no non-specific binding to lenvatinib. The unbound fractions can be separated in just 15 min. This method was applied to quantify clinical samples and to determine the plasma protein binding and unbound fraction of lenvatinib. This study provides a more effective and promising method for determination of unbound lenvatinib. It could be beneficial to measure the unbound concentration of lenvatinib in personalized medicine and therapeutic drug monitoring in routine clinical practice.

Anticorrosive evaluation of an environmentally benign chromone-thiazole hybrid with excellent anticancer activity

The research article comprehensively presents salient applications of an environment friendly chromone-thiazole hybrid (CTH) as a potential anticancer molecule and as an organic inhibitor against mild steel (MS) corrosion. The SRB assay used for antiproliferative screening gives excellent IC50 (10.8 μM) against MDA-MB-231 (breast cancer) cell line. Further, CTH has excellent inhibition efficacy (∼97 % at 400 ppm) against MS corrosion in 1 M HCl. The in vitro results of anticancer and anticorrosive evaluation are in good agreement with the computational studies, which include docking, ADME, DFT, MD simulation, and toxicity prediction.

Synthesis and computational study of metal complex of α-mangostin as an anticancer candidate

Context: The number of breast cancer patients from year to year is increasing, accompanied by an increase in global mortality; the position in Indonesia has reached 60-70% with end-stage conditions. ERα antiproliferative activity exists in α-mangostin compounds, but this compound still has several physicochemical problems, including its solubility. Therefore, efforts are needed to increase the activity of α-mangostin and its affinity as an alpha estrogen receptor antagonist through a structure-based design method. Aims: To obtain new compounds derived from α-mangostin in the form of a metal complex with cobalt (AM-Co), platinum (AM-Pt), and iron (AM-Fe) as medicinal ingredients that can be used as breast cancer therapeutic agents. Methods: The research methods include in silico studies (pharmacokinetic and toxicity prediction, molecular docking, and molecular dynamics), semi-synthesis to form a complex compound, and testing cytotoxic activity against MCF-7 and T-47D human breast cancer cells. Results: From the in vitro test results, it can be seen that the AM-Pt compound showed the best anticancer activity on MCF-7 cells compared to others from IC50 and SI values; this was supported by the results of in silico studies either through molecular docking or molecular dynamics, where this compound was more effective. Conclusions: Furthermore, it can be used as an anticancer candidate; particularly, its activity is predicted to be more potent as an ER antagonist than -mangostin and cisplatin.

A deep learning based multi-model approach for predicting drug-like chemical compound’s toxicity

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.

Hydrogeochemical arsenic simulation of V-Ti-Fe tailings pond ecosystem: A case study from Panxi region, China

The V-Ti-Fe mine tailings waste releases high concentrations of aquatic arsenic (As) ions to downstream ecosystems. The hydrogeochemical As simulation facilitates the prediction of toxicity (or redox state) and devising control strategies to mitigate the negative impacts. Therefore, this study utilizes the Wanniangou tailings pond (a V-Ti-Fe mine tailings reservoir) in the Panxi region, Sichuan province, China, as a case study to elucidate the As hydrogeochemical behaviour under varying pH and redox potential (Eh) conditions. Our study reveals that (1) the water-oxygen exchange and Mn are important to regulating the redox conditions in the filtrates. The Eh determines the valence states of As, which vary from reduced states to oxidized states during the runoff from the tailings pond to downstream (Rehe River). The flow of wastewater in Rehe River minimizes both the As toxicity and concentration, eventually improving water safety. (2) In a reducing environment, hematite exhibits a limited capacity for As(III) adsorption. In the case of oxidation conditions, goethite mainly adsorbed As(V), with an elevated adsorption capacity. Meanwhile, Fe-minerals remain relatively stable under varying redox and weakly acidic conditions. Consequently, targeted engineering interventions can achieve two crucial outcomes: (1) Minimize the acidity of the mine environment to retard the release of heavy metals from the tailings waste. (2) Prolonging the residence time of wastewater in Rehe River to promote the transition from a reducing state to an oxidizing state, thereby synergistically mitigating As toxicity.

Fabrication of black phosphorus/CdS heterostructure with enhancement photocatalytic degradation activity for tetrabromobisphenol A and toxicity prediction of intermediates

Black phosphorus nanosheets (BPNs)/CdS heterostructure was successfully synthesized via hydrothermal method. The experimental results indicated that BPNs modified the surface of CdS nanoparticles uniformly. Meanwhile, the BPNs/CdS heterostructure exhibited a distinguished high rate of photocatalytic activity for Tetrabromobisphenol A (TBBPA) degradation under visible light irradiation ( > 420 nm), the kinetic constant of TBBPA degradation reached 0.0261 min−1 was approximately 5.68 and 9.67 times higher than that of CdS and P25, respectively. Moreover, superoxide radical (•O2−) is the main active component in the degradation process of TBBPA (the relative contribution is 91.57%). The photocatalytic mechanism and intermediates of the TBBPA was clarified, and a suitable model and pathway for the degradation of TBBPA were proposed. The results indicated that the toxicities of some intermediates were higher than the parent pollutant. This research provided an efficient approach by a novel photocatalyst for the removal of TBBPA from wastewater, and the appraisal methods for the latent risks from the intermediates were reported in this paper.

Toxicity toward freshwater and marine water organisms of the cytostatic drugs bleomycin and vincristine and their binary mixture

Antineoplastic drugs are not effectively removed by wastewater treatment plants, ending up in surface waters. Since these drugs can interfere with the structure and functions of DNA, they pose a potential threat to aquatic biota. Unfortunately, many chemotherapeutic agents have not been studied in an environmental context. Additionally, there is a significant lack of information about the impact of anticancer drugs on marine organisms compared to freshwater species, and most studies only focus on the toxicity of single compounds rather than considering their occurrence as complex mixtures in the environment. Therefore, the aim of this study was to evaluate the ecotoxicity of two commonly used cytostatics, bleomycin and vincristine, toward six biomodels: Pseudokirchneriella subcapitata, Phaeodactylum tricornutum, Brachionus plicatilis, Brachionus calyciflorus, Thamnocephalus platyurus, and Artemia franciscana. These selected aquatic organisms are representatives of both freshwater and marine environments and belong to different trophic levels. The pharmaceuticals were investigated both individually and in combination. Binary mixture toxicity predictions were performed according to the Response Additivity and Independent Action models. Additionally, the toxicity data obtained from these experiments were utilized for risk assessment in the context of the drugs' environmental occurrence. The results indicated that freshwater species were generally more sensitive to both tested compounds than marine organisms, with T. platyurus being the most sensitive. Based on the tests performed on this biomodel, bleomycin was categorized as extremely toxic, while vincristine was considered moderately toxic. Neither of the applied models suitably predicted binary mixture toxicity, as the combination of drugs showed additive, synergistic, and antagonistic effects, suggesting that single compound toxicity data are insufficient for predicting the aquatic toxicities of cytostatics mixtures. The environmental risk of vincristine ranged from low to high, and for bleomycin varied from moderate to high, depending on the matrices examined. Therefore, further research on drug removal is recommended.