ChemDIS-Mixture: an online tool for analyzing potential interaction effects of chemical mixtures

Evaluating the MDCK cell permeability of greenly synthesize bimetallic Ag/Zn Nanoparticles using leaf extract of Vallaris solanacea as a potential antipesticide-resistant agent

Bimetallic nanoparticles, particularly Ag/Zn bimetallic nanoparticles, have gained increasing attention due to their unique properties, making them suitable for a variety of applications such as catalysis, water treatment, and environmental remediation. This study aimed to elucidate the use of bimetallic nanoparticles of Ag/Zn as an alternative to resistant pesticides for pest control. Furthermore, this research demonstrates that BNPs can target specific pollutants and degrade them through various mechanisms. BNP docking with the Nilaparvata lugens cytochrome P450 (CYP6ER1) protein exhibited the lowest binding energy of -7.5 kcal/mol. The cell permeability analysis of BNP in plant cells reveals that the BNP has 0 % permeability towards any cell at -10 kcal/mol energy, which is the lowest free energy translocation pathway. The harmful leftover residues of the pesticides have a higher chance of degradability in case of interaction with BNP validated by chemical-chemical interaction analysis. Additionally, MDCK permeability coefficient of small molecules based on the regression model was calculated for BNP which authenticated the efficiency of BNP. Moreover, Swiss ADMET simulated absorption using a boiled egg model with no blood-brain barrier and gastrointestinal crossing for the expected BNP molecule has been observed. Significantly, the findings indicate that employing bimetallic nanoparticles like Ag/Zn is a crucial strategy for bioremediation because they proficiently decompose pesticides while posing no risk to humans. Our results will facilitate the design of novel BNPs materials for environmental remediation and pest control ensuring human health safety that are predicated on bimetallic nanoparticles.

Computational modeling approaches for developing a synergistic effect prediction model of estrogen agonistic activity

The concerns regarding the potential health threats caused by estrogenic endocrine-disrupting chemicals (EDCs) and their mixtures manufactured by the chemical industry are increasing worldwide. Conventional experimental tests for understanding the estrogenic activity of mixtures are expensive and time-consuming. Although non-testing methods using computational modeling approaches have been developed to reduce the number of traditional tests, they are unsuitable for predicting synergistic effects because current prediction models consider only a single chemical. Thus, the development of predictive models is essential for predicting the mixture toxicity, including chemical interactions. However, selecting suitable computational modeling approaches to develop a high-performance prediction model requires considerable time and effort. In this study, we provide a suitable computational approach to develop a predictive model for the synergistic effects of estrogenic activity. We collected datasets on mixture toxicity based on the synergistic effect of estrogen agonistic activity in binary mixtures. Using the model deviation ratio approach, we classified the labels of the binary mixtures as synergistic or non-synergistic effects. We assessed five molecular descriptors, four machine learning-based algorithms, and a deep learning-based algorithm to provide a suitable computational modeling approach. Compared with other modeling approaches, the prediction model using the deep learning-based algorithm and chemical-protein network descriptors exhibited the best performance in predicting the synergistic effects. In conclusion, we developed a new high-performance binary classification model using a deep neural network and chemical-protein network-based descriptors. The developed model will be helpful for the preliminary screening of the synergistic effects of binary mixtures during the development process of chemical products.

Network pharmacology integrated with molecular docking reveals the anticancer mechanism of Jasminum sambac Linn. essential oil against human breast cancer and experimental validation by in vitro and in vivo studies

Jasminum sambac L. (J. sambac) belongs to the family Oleaceae and it is an ornamental subtropical evergreen shrub used in traditional treatments of certain ailments and diseases. This study aimed at devising an integrated strategy attempts to evaluate the bioactive components in the J. sambac essential oil (JEO) against human breast cancer. JEO extracted by distillation process and analyzed by GC-MS was subjected to screening of therapeutic components in their allegiance to the drug-likeness index. The utility and efficacy of its molecular mechanism relating to anticancer potential were probed with network pharmacology analysis. Gene ontology, pathway enrichment, and compound-target-pathway network by Cytoscape helped to harp on hub targets and pathways involved in curative action. Drawing from the network data, molecular docking analysis of selected compounds on breast cancer targets was approached. The anti-proliferative study was carried out in MCF-7 and MDA-MB-231 to evaluate the cytotoxicity of JEO. Finally, in vivo anticancer activity was verified using rat models. The results showed MDA-MB-231 cell growth was highly inhibited than the MCF-7 cell line. Alongside this in vitro trial, in situ effectiveness of JEO was evaluated using female Sprague-Dawley rat animal models. In vivo experiments and histopathological analysis showed convincing results in DMBA tumor-induced rats. The larger aim of this study is to identify the potential ingredients of the JEO in cancer apoptosis by integrating network pharmacology and experimental validation achieved to certain extent confers credence to the concept of hiring J. sambac as floral therapy in dealing with the disastrous disease.

In vitro and in silico study on the effect of carvedilol and sorafenib alone and in combination on the growth and inflammatory response of melanoma cells

Melanoma is an aggressive skin cancer. Increasing evidence has shown the role of β-adrenergic receptors in the pathogenesis of melanoma. Carvedilol is a widely used non-selective β-AR antagonist with potential anticancer activity. The purpose of the study was to estimate the influence of carvedilol and sorafenib alone and in combination on the growth and inflammatory response of C32 and A2058 melanoma cells. Furthermore, this study also aimed to predict the probable interaction of carvedilol and sorafenib when administered together. Predictive study of the interaction of carvedilol and sorafenib was performed using the ChemDIS-Mixture system. Carvedilol and sorafenib alone and in combination showed a growth inhibitory effect on cells. The greatest synergistic antiproliferative effect on both cell lines was observed at Car 5 μM combined with Sor 5 μM. Analysis in silico identified diseases, proteins, and metabolic pathways that can be affected by the interaction of carvedilol and sorafenib. The results obtained demonstrated that carvedilol and sorafenib modulated the secretion of IL-8 by IL-1β-stimulated by melanoma cell lines but the use of a combination of both drugs did not intensify the effect. In summary, the results presented indicate that the combination of carvedilol and sorafenib may have a promising anticancer effect on melanoma cells.

Ecogenotoxicity of environmentally relevant atrazine concentrations: A threat to aquatic bioindicators

Atrazine (ATZ) is a herbicide that is frequently present in surface waters and may result in damage to the health of various organisms, including humans. However, most scientific literature reports injuries caused by ATZ at high concentrations, which are not found in the environment. Therefore, the scope of this study was to investigate the impacts of realistic concentrations of ATZ found in surface waters (1, 2, 5, 10, 15 and 20 μg/L) using the bioindicators Allium cepa, Daphnia magna and zebrafish (Danio rerio). ATZ elicited a genotoxic effect in A. cepa, manifested by the induction of chromosomal aberrations, and a mutagenic effect with increased incidence of micronuclei formation, promotion of cell death and reduction in nuclear size revealed by flow cytometry analysis. D. magna exposed to 10, 15 and 20 μg/L of ATZ showed significant reduction in body size after 21 days, delayed first-brood release, decreased egg production and total offspring, as well as swimming behavioral changes. ATZ exposure promoted physiological and developmental alterations in zebrafish embryos, including an increased spontaneous movement rate, which led to premature hatching at all concentrations investigated. Increase in total body length, decrease of the yolk sac area, pericardial edema and higher heart rate were also detected in ATZ-treated zebrafish. In summary, environmentally relevant concentrations of ATZ can induce substantial alterations in the three bioindicators investigated. This study evidences the deleterious effects of ATZ on three aquatic bioindicators employing established and current techniques, and may contribute to elucidate the risks caused by this widely used herbicide even at low concentrations and short-to-medium-term exposure.

A machine learning-driven approach for prioritizing food contact chemicals of carcinogenic concern based on complementary in silico methods

Carcinogenicity is one of the most critical endpoints for the risk assessment of food contact chemicals (FCCs). However, the carcinogenicity of FCCs remains insufficiently investigated. To fill the data gap, the application of standard experimental methods for identifying chemicals of carcinogenic concerns from a large set of FCCs is impractical due to their resource-intensive nature. In contrast, computational methods provide an efficient way to quickly screen chemicals with carcinogenic potential for subsequent experimental validation. Since every model was developed based on a limited number of training samples, the use of single models for carcinogenicity assessment may not cover the complex mechanisms of carcinogenesis. This study proposed a novel machine learning-based weight-of-evidence (WoE) model for prioritizing chemical carcinogenesis. The WoE model can nonlinearly integrate complementary computational methods of structural alerts, quantitative structure-activity relationship models and in silico toxicogenomics models into a WoE-score. Compared to the best single method, the WoE model gained 8% and 19.7% improvement in the area under the receiver operating characteristic curve (AUC) value and chemical coverage, respectively. The prioritization of 1623 FCCs concludes 44 chemicals of high carcinogenic concern. The machine learning-based WoE approach provides a fast and comprehensive way for prioritizing chemicals of carcinogenic concern.

Environmental impacts of COVID-19 treatment: Toxicological evaluation of azithromycin and hydroxychloroquine in adult zebrafish

One of the most impact issues in recent years refers to the COVID-19 pandemic, the consequences of which thousands of deaths recorded worldwide, are still inferior understood. Its impacts on the environment and aquatic biota constitute a fertile field of investigation. Thus, to predict the impact of the indiscriminate use of azithromycin (AZT) and hydroxychloroquine (HCQ) in this pandemic context, we aim to assess their toxicological risks when isolated or in combination, using zebrafish (Danio rerio) as a model system. In summary, we observed that 72 h of exposure to AZT and HCQ (alone or in binary combination, both at 2.5 μg/L) induced the reduction of total protein levels, accompanied by increased levels of thiobarbituric acid reactive substances, hydrogen peroxide, reactive oxygen species and nitrite, suggesting a REDOX imbalance and possible oxidative stress. Molecular docking analysis further supported this data by demonstrating a strong affinity of AZT and HCQ with their potential antioxidant targets (catalase and superoxide dismutase). In the protein-protein interaction network analysis, AZT showed a putative interaction with different cytochrome P450 molecules, while HCQ demonstrated interaction with caspase-3. The functional enrichment analysis also demonstrated diverse biological processes and molecular mechanisms related to the maintenance of REDOX homeostasis. Moreover, we also demonstrated an increase in the AChE activity followed by a reduction in the neuromasts of the head when zebrafish were exposed to the mixture AZT + HCQ. These data suggest a neurotoxic effect of the drugs. Altogether, our study demonstrated that short exposure to AZT, HCQ or their mixture induced physiological alterations in adult zebrafish. These effects can compromise the health of these animals, suggesting that the increase of AZT and HCQ due to COVID-19 pandemic can negatively impact freshwater ecosystems.

Curation of cancer hallmark-based genes and pathways for in silico characterization of chemical carcinogenesis

Exposure to toxic substances in the environment is one of the most important causes of cancer. However, the time-consuming process for the identification and characterization of carcinogens is not applicable to a huge amount of testing chemicals. The data gaps make the carcinogenic risk uncontrollable. An efficient and effective way of prioritizing chemicals of carcinogenic concern with interpretable mechanism information is highly desirable. This study presents a curation work for genes and pathways associated with 11 hallmarks of cancer (HOCs) reported by the Halifax Project. To demonstrate the usefulness of the curated HOC data, the interacting HOC genes and affected HOC pathways of chemicals of the three carcinogen lists from IARC, NTP and EPA were analyzed using the in silico toxicogenomics ChemDIS system. Results showed that a higher number of affected HOCs were observed for known carcinogens than the other chemicals. The curated HOC data is expected to be useful for prioritizing chemicals of carcinogenic concern.

Database URL: The HOC database is available at https://github.com/hocdb-KMU-TMU/hocdb and the website of Database journal as Supplementary Data.

Interaction of flavonoids with serum albumin: A review

Flavonoids are plant products abundant in every day diet and claimed to be beneficial for human health. After absorption, flavonoids are transported by the serum albumin (SA), the most abundant carrier blood protein, through formation of flavonoids-SA complex. This review deals with the current state of knowledge on flavonoids-SA complex over the past 10 years, mainly involved multi-spectroscopic techniques and molecular dynamics simulation studies to explore the binding mechanism, thermodynamics and structural aspects of flavonoids binding to SA. Especially, the novel method, capillary electrophoresis, high performance affinity chromatography approach, native mass spectrometry and microscale thermophoresis used in characterization of the interaction between flavonoids and SA as well as flavonoid-based fluorescent probe for SA measurement are also included in this review.

Leveraging complementary computational models for prioritizing chemicals of developmental and reproductive toxicity concern: an example of food contact materials

The evaluation of developmental and reproductive toxicity of food contact materials (FCMs) is an important task for food safety. Since traditional experiments are both time-consuming and labor-intensive, only a small number of FCMs have sufficient toxicological data for evaluating their effects on human health. While computational methods such as structural alerts and quantitative structure-activity relationships can serve as first-line tools for the identification of chemicals of high toxicity concern, models with binary outputs and unsatisfied accuracy and coverage prevent the use of computational methods for prioritizing chemicals of high concern. This study proposed a genetic algorithm-based method to develop a weight-of-evidence (WoE) model leveraging complementary methods of structural alerts, quantitative structure-activity relationships and in silico toxicogenomics models for chemical prioritization. The WoE model was applied to evaluate 623 food contact chemicals and identify 26 chemicals of high toxicity concern, where 13 chemicals have been reported to be developmental or reproductive toxic and further experiments are suggested for the remaining 13 chemicals without toxicity data related to developmental and reproductive effects. The proposed WoE model is potentially useful for prioritizing chemicals of high toxicity concern and the methodology may be applied to toxicities other than developmental and reproductive toxicity.

Carcinogenicity assessment: Addressing the challenges of cancer and chemicals in the environment

Cancer is a key public health concern, being the second leading cause of worldwide morbidity and mortality after cardiovascular diseases. At the global level, cancer prevalence, incidence and mortality rates are increasing. These trends are not fully explained by a growing and ageing population: with marked regional and socioeconomic disparities, lifestyle factors, the resources dedicated to preventive medicine, and the occupational and environmental control of hazardous chemicals all playing a role. While it is difficult to establish the contribution of chemical exposure to the societal burden of cancer, a number of measures can be taken to better assess the carcinogenic properties of chemicals and manage their risks. This paper discusses how these measures can be informed not only by the traditional data streams of regulatory toxicology, but also by using new toxicological assessment methods, along with indicators of public health status based on biomonitoring. These diverse evidence streams have the potential to form the basis of an integrated and more effective approach to cancer prevention.

Drug Combinations: Mathematical Modeling and Networking Methods

Treatments consisting of mixtures of pharmacological agents have been shown to have superior effects to treatments involving single compounds. Given the vast amount of possible combinations involving multiple drugs and the restrictions in time and resources required to test all such combinations in vitro, mathematical methods are essential to model the interactive behavior of the drug mixture and the target, ultimately allowing one to better predict the outcome of the combination. In this review, we investigate various mathematical methods that model combination therapies. This survey includes the methods that focus on predicting the outcome of drug combinations with respect to synergism and antagonism, as well as the methods that explore the dynamics of combination therapy and its role in combating drug resistance. This comprehensive investigation of the mathematical methods includes models that employ pharmacodynamics equations, those that rely on signaling and how the underlying chemical networks are affected by the topological structure of the target proteins, and models that are based on stochastic models for evolutionary dynamics. Additionally, this article reviews computational methods including mathematical algorithms, machine learning, and search algorithms that can identify promising combinations of drug compounds. A description of existing data and software resources is provided that can support investigations in drug combination therapies. Finally, the article concludes with a summary of future directions for investigation by the research community.

ChemDIS 2: an update of chemical-disease inference system

Computational inference of affected functions, pathways and diseases for chemicals could largely accelerate the evaluation of potential effects of chemical exposure on human beings. Previously, we have developed a ChemDIS system utilizing information of interacting targets for chemical-disease inference. With the target information, testable hypotheses can be generated for experimental validation. In this work, we present an update of ChemDIS 2 system featured with more updated datasets and several new functions, including (i) custom enrichment analysis function for single omics data; (ii) multi-omics analysis function for joint analysis of multi-omics data; (iii) mixture analysis function for the identification of interaction and overall effects; (iv) web application programming interface (API) for programmed access to ChemDIS 2. The updated ChemDIS 2 system capable of analyzing more than 430 000 chemicals is expected to be useful for both drug development and risk assessment of environmental chemicals.

Database URL: ChemDIS 2 is freely accessible via https://cwtung.kmu.edu.tw/chemdis