Using molecular docking-based binding energy to predict toxicity of binary mixture with different binding sites

A new parameter for quantitatively characterizing antibiotic hormesis: QSAR construction and joint toxic action judgment

Antibiotics usually induce the hormetic effects on bacteria, featured by low-dose stimulation and high-dose inhibition, which challenges the central belief in toxicity assessment and environmental risk assessment of antibiotics. However, there are currently no ideal parameters to quantitatively characterize hormesis. In this study, an effective area in hormesis (AH) was developed to quantify the biphasic dose-responses of single antibiotics (sulfonamides (SAs), sulfonamides potentiators (SAPs), and tetracyclines (TCs)) and binary mixtures (SAs-SAPs, SAs-TCs, and SAs-SAs) to the bioluminescence of Aliivibrio fischeri. Using Ebind (the lowest interaction energy between antibiotic and target protein) and Kow (octanol-water partition coefficient) as the structural descriptors, the reliable quantitative structure-activity relationship (QSAR) models were constructed for the AH values of test antibiotics and mixtures. Furthermore, a novel method based on AH was established to judge the joint toxic actions of binary antibiotics, which mainly exhibited synergism. The results also indicated that SAPs (or TCs) contributed more than SAs in the hormetic effects of antibiotic mixtures. This study proposes a new quantitative parameter for characterizing and predicting antibiotic hormesis, and considers hormesis as an integrated whole to reveal the combined effects of antibiotics, which will promote the development of risk evaluation for antibiotics and their mixtures.

Structure-based modeling to assess binding and endocrine disrupting potential of polycyclic aromatic hydrocarbons in Daniorerio

Environmental contaminants, such as polycyclic aromatic hydrocarbons (PAHs), have raised concerns regarding their potential endocrine-disrupting effects on aquatic organisms, including fish. In this study, molecular docking and molecular dynamics techniques were employed to evaluate the endocrine-disrupting potential of PAHs in zebrafish, as a model organism. A virtual screening with 72 PAHs revealed a correlation between the number of PAH aromatic rings and their binding affinity to proteins involved in endocrine regulation. Furthermore, PAHs with the highest binding affinities for each protein were identified: cyclopenta[cd]pyrene for AR (-9.7 kcal/mol), benzo(g)chrysene for ERα (-11.5 kcal/mol), dibenzo(a,e)pyrene for SHBG (-8.7 kcal/mol), dibenz(a,h)anthracene for StAR (-11.2 kcal/mol), and 2,3-benzofluorene for TRα (-9.8 kcal/mol). Molecular dynamics simulations confirmed the stability of the protein-ligand complexes formed by the PAHs with the highest binding affinities throughout the simulations. Additionally, the effectiveness of the protocol used in this study was demonstrated by the receiver operating characteristic curve (ROC) analysis, which effectively distinguished decoys from true ligands. Therefore, this research provides valuable insights into the endocrine-disrupting potential of PAHs in fish, highlighting the importance of assessing their impact on aquatic ecosystems.

A comprehensive analysis of royal jelly protection against cypermethrin-induced toxicity in the model organism Allium cepa L., employing spectral shift and molecular docking approaches

In this study, the toxicity of the pesticide cypermethrin and the protective properties of royal jelly against this toxicity were investigated using Allium cepa L., a model organism. Toxicity was evaluated using 6 mg/L cypermethrin, while royal jelly (250 mg/L and 500 mg/L) was used in combination with cypermethrin to test the protective effect. To comprehend toxicity and protective impact, growth, genotoxicity, biochemical, comet assay and anatomical parameters were employed. Royal jelly had no harmful effects when applied alone. On the other hand, following exposure to cypermethrin, there was a reduction in weight increase, root elongation, rooting percentage, mitotic index (MI), and chlorophyll a and b. Cypermethrin elevated the frequencies of micronucleus (MN) and chromosomal aberrations (CAs), levels of proline and malondialdehyde (MDA), and the activity rates of the enzymes catalase (CAT) and superoxide dismutase (SOD). A spectral change in the DNA spectrum indicated that the interaction of cypermethrin with DNA was one of the reasons for its genotoxicity, and molecular docking investigations suggested that tubulins, histones, and topoisomerases might also interact with this pesticide. Cypermethrin also triggered some critical meristematic cell damage in the root tissue. At the same time, DNA tail results obtained from the comet assay revealed that cypermethrin caused DNA fragmentation. When royal jelly was applied together with cypermethrin, all negatively affected parameters due to the toxicity of cypermethrin were substantially restored. However, even at the maximum studied dose of 500 mg/L of royal jelly, this restoration did not reach the levels of the control group. Thus, the toxicity of cypermethrin and the protective function of royal jelly against this toxicity in A. cepa, the model organism studied, were determined by using many different approaches. Royal jelly is a reliable, well-known and easily accessible protective functional food candidate against the harmful effects of hazardous substances such as pesticides.

A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations

Environmental chemicals, such as PFAS, exist as mixtures and are frequently encountered at varying concentrations, which can lead to serious health effects, such as cancer. Therefore, understanding the dose-dependent toxicity of chemical mixtures is essential for health risk assessment. However, comprehensive methods to assess toxicity and identify the mechanisms of these harmful mixtures are currently absent. In this study, the dose-dependent toxicity assessments of chemical mixtures are performed in three methodologically distinct phases. In the first phase, we evaluated our machine-learning method (AI-HNN) and pathophysiology method (CPTM) for predicting toxicity. In the second phase, we integrated AI-HNN and CPTM to establish a comprehensive new approach method (NAM) framework called AI-CPTM that is targeted at refining prediction accuracy and providing a comprehensive understanding of toxicity mechanisms. The third phase involved experimental validations of the AI-CPTM predictions. Initially, we developed binary, multiclass classification, and regression models to predict binary, categorical toxicity, and toxic potencies using nearly a thousand experimental mixtures. This empirical dataset was expanded with assumption-based virtual mixtures, compensating for the lack of experimental data and broadening the scope of the dataset. For comparison, we also developed machine-learning models based on RF, Bagging, AdaBoost, SVR, GB, KR, DT, KN, and Consensus methods. The AI-HNN achieved overall accuracies of over 80%, with the AUC exceeding 90%. In the final phase, we demonstrated the superior performance and predictive capability of AI-CPTM, including for PFAS mixtures and their interaction effects, through rigorous literature and statistical validations, along with experimental dose-response zebrafish-embryo toxicity assays. Overall, the AI-CPTM approach significantly improves upon the limitations of standalone AI models, showing extensive enhancements in identifying toxic chemicals and mixtures and their mechanisms. This study is the first to develop a hybrid NAM that integrates AI with a pathophysiology method to comprehensively predict chemical-mixture toxicity, carcinogenicity, and mechanisms.

Toxicity of individual and mixture of organic compounds to P. Phosphoreum and S. Capricornutum using interpretable simple structural parameters

Human and environmental ecosystem beings are exposed to multicomponent compound mixtures but the toxicity nature of compound mixtures is not alike to the individual chemicals. This work introduces four models for the prediction of the negative logarithm of median effective concentration (pEC50) of individual chemicals to marine bacteria Photobacterium Phosphoreum (P. Phosphoreum) and algal test species Selenastrum Capricornutum (S. Capricornutum) as well as their mixtures to P. Phosphoreum, and S. Capricornutum. These models provide the simplest approaches for the forecast of pEC50 of some classes of organic compounds from their interpretable structural parameters. Due to the lack of adequate toxicity data for chemical mixtures, the largest available experimental data of individual chemicals (55 data) and their mixtures (99 data) are used to derive the new correlations. The models of individual chemicals are based on two simple structural parameters but chemical mixture models require further interaction terms. The new model's results are compared with the outputs of the best accessible quantitative structure-activity relationships (QSARs) models. Various statistical parameters are done on the new and comparative complex QSAR models, which confirm the higher reliability and simplicity of the new correlations.

Molecular docking study of frequently used food additives for selected targets depending on the chromosomal abnormalities they cause

Food additives (FAs) (flavor enhancers, sweeteners, etc.) protect foods during storage and transportation, making them attractive to consumers. Today, while the desire to access natural foods is increasing, the chemicals added to foods have started to be questioned. In this respect, genotoxicity tests have gained importance. Studies show that some food additives may have genotoxic risks. Previous studies carried out in our laboratory also revealed genotoxic effects of Monopotassium glutamate (MPG), Monosodium glutamate (MSG), Magnesium diglutamate (MDG) as flavor enhancers; Potassium benzoate (PB), Potassium sorbate (PS), Sodium benzoate (SB), Sodium sorbate (SS) as preservatives; Acesulfame potassium (ACE-K), Xylitol (XYL) as sweeteners. In this study, we determined the interactions of these food additives with ATM and p53 proteins, which are activated in the cell due to genotoxic effects, and with DNA by employing the molecular docking method for the first time. Among the food additives, SB (-4.307) for ATM, XYL (-4.629) for p53, and XYL (-4.927) for DNA showed the highest affinity. Therefore, flexible docking (IFD) scores were determined for SB, XYL, and MDG from flavor enhancers. The potential binding modes of the food additives to target molecules' possible inhibition mechanisms were determined by molecular docking. Thus, new information was obtained to show how these additives cause chromosomal abnormalities.

Ameliorative effects of bilirubin on cell culture model of non-alcoholic fatty liver disease

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is defined as the most prevalent hepatic disorder that affects a significant population worldwide. There are several genes/proteins, involving in the modulation of NAFLD pathogenesis; sirtuin1 (SIRT1), TP53-inducible regulator gene (TIGAR), and autophagy-related gene 5 (Atg5) are considered a chief group of these modulators that principally act by regulating the hepatic lipid metabolism, as well as preventing the lipid accumulation. Surprisingly, bilirubin, especially in its unconjugated form, might be able to alleviate NAFLD progression by decreasing lipid accumulation and regulating the expression levels of the above-stated genes.

METHODS AND RESULTS

Herein, the interactions between bilirubin and the corresponding genes' products were first analyzed by docking assessments. Afterwards, HepG2 cells were cultured under the optimum conditions, and then were incubated with high concentrations of glucose to induce NAFLD. After treating normal and fatty liver cells with particular bilirubin concentrations for 24- and 48-hour periods, 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl-tetrazolium bromide (MTT) assay, colorimetric method, and quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) were employed to assess cell viability status, intracellular triglycerides content, and mRNA expression levels of the genes, respectively. Intracellular lipid accumulation of HepG2 cells was significantly decreased after treating with bilirubin. Bilirubin also increased SIRT1 and Atg5 gene expression levels in fatty liver cells. TIGAR gene expression levels were variable upon the conditions and the cell type, suggesting a dual role for TIGAR during the NAFLD pathogenesis.

CONCLUSION

Our findings indicate the potential of bilirubin in the prevention from or amelioration of NAFLD through influencing SIRT1-related deacetylation and the process of lipophagy, as well as decreasing the intrahepatic lipid content. In vitro model of NAFLD was treated with unconjugated bilirubin under the optimal conditions.Desirably, bilirubin moderated the accumulation of triglycerides within the cells possibly through modulation of the expression of SIRT1, Atg5, and TIGAR genes. In the context, bilirubin was shown to increase the expression levels of SIRT1 and Atg5, while the expression of TIGAR was demonstrated to be either increased or decreased, depending on the treatment conditions. Created with BioRender.com.

Toxicity Assessment of the Binary Mixtures of Aquatic Organisms Based on Different Hypothetical Descriptors

Modern industrialization has led to the creation of a wide range of organic chemicals, especially in the form of multicomponent mixtures, thus making the evaluation of environmental pollution more difficult by normal methods. In this paper, we attempt to use forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNN) to establish quantitative structure–activity relationship models (QSARs) to predict the toxicity of 79 binary mixtures of aquatic organisms using different hypothetical descriptors. To search for the proper mixture descriptors, 11 mixture rules were performed and tested based on preliminary modeling results. The statistical parameters of the best derived MLR model were Ntrain = 62, R2 = 0.727, RMS = 0.494, F = 159.537, Q2LOO = 0.727, and Q2pred = 0.725 for the training set; and Ntest = 17, R2 = 0.721, RMS = 0.508, F = 38.773, and q2ext = 0.720 for the external test set. The RBFNN model gave the following statistical results: Ntrain = 62, R2 = 0.956, RMS = 0.199, F = 1279.919, Q2LOO = 0.955, and Q2pred = 0.855 for the training set; and Ntest = 17, R2 = 0.880, RMS = 0.367, F = 110.980, and q2ext = 0.853 for the external test set. The quality of the models was assessed by validating the relevant parameters, and the final results showed that the developed models are predictive and can be used for the toxicity prediction of binary mixtures within their applicability domain.

Insights into toxicity of polychlorinated naphthalenes to multiple human endocrine receptors: Mechanism and health risk analysis

This study explored the combined disruption mechanism of polychlorinated naphthalenes (PCNs) on the three key receptors (estrogen receptor, thyroid receptor, and adrenoceptor) of the human endocrine system. The intensity of PCN endocrine disruption on these receptors was first determined using a molecular docking method. A comprehensive index of PCN endocrine disruption to human was quantified by analytic hierarchy process and fuzzy analysis. The mode of action between PCNs and the receptors was further identified to screen the molecular characteristics influencing PCN endocrine disruption through molecular docking and fractional factorial design. Quantitative structure-activity relationship (QSAR) models were established to investigate the toxic mechanism due to PCN endocrine disruption. The results showed that the lowest occupied orbital energy (E_LUMO) was the most important factor contributing to the toxicity of PCNs on the endocrine receptors, followed by the orbital energy difference (ΔE) and positive Millikan charge (q⁺). Furthermore, the strategies were formulated through adjusting the nutritious diet to reduce health risk for the workers in PCN contaminated sites and the effectiveness and feasibility were assessed by molecular dynamic simulation. The simulation results indicated that the human health risk caused by PCN endocrine disruption could be effectively decreased by nutritional supplementation. The binding ability between PCNs and endocrine receptors significantly declined (up to -16.45%) with the supplementation of vitamins (A, B₂, B₁₂, C, and E) and carotene. This study provided the new insights to reveal the toxic mechanism of PCNs on human endocrine systems and the recommendations on nutritional supplements for health risk reduction. The methodology and findings could serve as valuable references for screening of potential endocrine disruptors and developing appropriate strategies for PCN or other persistent organic pollution control and health risk management.

Can the Monte Carlo method predict the toxicity of binary mixtures?

Risk assessment of toxicants mainly is a result of experiments with single substances. However, toxicity in natural ecosystems typically does not result from single toxicant exposure but is rather a result of exposure to mixtures of toxicants. It is not surprising a mixture of toxicity is a subject of eco-toxicological interest for several decades. A quantitative structure-activity relationships (QSAR)-based approach is an attractive approach to assessing the joint effects in the binary mixtures. The validity of the proposed approach was demonstrated by comparing the predicted values against the experimentally determined values. Simplified molecular input-line entry system (SMILES) is used for the representation of the molecular structures of components of two-component mixtures to build up QSAR. The SMILES-based models are improving if the Monte Carlo optimization aimed to define 2D-optimal descriptors apply the so-called index of ideality of correlation (IIC), which is a mathematical function of both the correlation coefficient and mean absolute error calculated for the positive and negative difference between observed and calculated values of toxicity. The average statistical quality of these models (for the validation set) is n=25, R²=0.95, and RMSE=0.375.

Inhibitory Effects of Bilirubin on Colonization and Migration of A431 and SK-MEL-3 Skin Cancer Cells Compared with Human Dermal Fibroblasts (HDF)

This study evaluated the inhibitory effects of bilirubin on colony formation and cell migration of melanoma and non-melanoma skin cancer cell lines SK-MEL-3 and A431, compared with normal human dermal fibroblasts (HDF). The IC₅₀ obtained from the MTT assay was 125, 100, and 75 μM bilirubin for HDF, A431, and SK-MEL-3 cells, respectively. The colony formation and cell migration of cancer cells, treated with 100 μM bilirubin, were reduced significantly (p < 0.05). Bilirubin decreased cell adhesion and inhibited cell colonization via inducing apoptosis and cell death. Also by interaction with migration main factors, bilirubin caused inhibition the cell migration.

Prediction of the joint action of binary mixtures based on characteristic parameter k∙ECx from concentration-response curves

The evaluation of joint toxicity of mixtures is an important topic in toxicology. Previous studies have found that the parameter k∙EC_x of concentration response curves (CRCs) can be used to assess the applicability of concentration addition model (CA). This study further assesses the predictability of k∙EC_x on the joint toxicity evaluation. The toxicities of the twelve environmental pollutants, as well as those of binary mixtures with an equivalent-effect concentration ratio, to Vibrio fischeri were determined by using the microplate toxicity analysis. The toxicity evaluation of mixtures was conducted by CA and independent action model (IA). The relationship between the joint toxicity (measured by the relative model deviation ratio (rMDR)) and the k∙EC_x was studied. The results shows that the k∙EC_x could reflect the shape of CRCs in the whole concentration range. According to the IA and CA, 65% of the mixtures produce strong antagonistic or synergistic effect due to the significant difference of k∙EC_x. The percentage of the relative difference of k∙EC_x of components and the rMDR_x can be fitted by an exponential function. Different types of interactions could be described using this function. It is suggested that the joint toxicity of binary mixtures can be assessed with the parameter k∙EC_x, which can quickly get very important data when planning experiments, but also reduce the number of experiments.

Prediction of aquatic toxicity of chemical mixtures by the QSAR approach using 2D structural descriptors

The rapid industrialization has led to the generation of various organic chemicals and multi-component mixtures which affect the environment adversely. Although organic chemicals are often exposed to the environment as a form of chemical mixtures rather than individual compounds, there is insufficient toxicity data available for the chemical mixtures due to the associated complexities. Most importantly, the nature of toxicity of mixtures is completely different from the individual chemicals, which makes the evaluation more difficult and challenging. In this paper, we have developed QSAR models for various individual and mixture data sets for the prediction of the aquatic toxicity. We have used Partial Least Squares (PLS) regression as a statistical tool to build the models. The various structural features of the individual chemicals and the mixture components have been modeled against the toxicity end point pEC₅₀ (negative logarithm of median effective concentration in molar scale) of the aquatic organisms Photobacterium phosphoreum (marine bacterium) and Selenastrum capricornutum (freshwater algae). The mixture descriptors have been calculated by the weighted descriptor generation approach. The models were developed in accordance with OECD guidelines, and the quality of each model has been adjudged by strict validation parameters. The final models are robust, extremely predictive and interpretable mechanistically which can be used for the prediction of toxicity of untested chemical mixtures under the domain of applicability of the developed models.

BNNmix: A new approach for predicting the mixture toxicity of multiple components based on the back-propagation neural network

The chemical mixtures in various environmental media not only have concentration diversity but also mixture-ratio diversity. It is impossible to experimentally determine the toxicities of all mixtures; therefore, it is necessary to develop effective methods based on models to predict mixture toxicity. In this study, a new approach (BNNmix) based on the back-propagation neural network (BPNN) was developed and used to predict the toxicities of seven-component mixtures (consisting of two substituted phenols, two pesticides, two ionic liquids, and one heavy metal) on Caenorhabditis elegans. We found that the combined toxicities of various mixtures used in the experiments were neither global concentration-additive nor global response-additive, which implied that it was impossible to accurately predict the toxicities of such mixtures by using common models such as concentration addition (CA) and response addition (independent action, IA). Using the BNNmix approach to estimate or predict the toxicities of the mixtures under test, it was found that the predictive toxicities of various mixtures with different mixture ratios and concentrations were almost in accordance with those observed experimentally. Unlike the CA and IA models, the BNNmix approach can predict not only the toxicities of mixtures having toxicological interactions but also those with global concentration or response additivities.

Mathematical Modeling Approaches for Assessing the Joint Toxicity of Chemical Mixtures Based on Luminescent Bacteria: A Systematic Review

Developments in industrial applications inevitably accelerate the discharge of enormous substances into the environment, whereas multi-component mixtures commonly cause joint toxicity which is distinct from the simple sum of independent effect. Thus, ecotoxicological assessment, by luminescent bioassays has recently brought increasing attention to overcome the environmental risks. Based on the above viewpoint, this review included a brief introduction to the occurrence and characteristics of toxic bioassay based on the luminescent bacteria. In order to assess the environmental risk of mixtures, a series of models for the prediction of the joint effect of multi-component mixtures have been summarized and discussed in-depth. Among them, Quantitative Structure-Activity Relationship (QSAR) method which was widely applied in silico has been described in detail. Furthermore, the reported potential mechanisms of joint toxicity on the luminescent bacteria were also overviewed, including the Trojan-horse type mechanism, funnel hypothesis, and fishing hypothesis. The future perspectives toward the development and application of toxicity assessment based on luminescent bacteria were proposed.

Toward a Computational Ecotoxicity Assay

Thousands of anthropogenic chemicals are released into the environment each year, posing potential hazards to human and environmental health. Toxic chemicals may cause a variety of adverse health effects, triggering immediate symptoms or delayed effects over longer periods of time. It is thus crucial to develop methods that can rapidly screen and predict the toxicity of chemicals to limit the potential harmful impacts of chemical pollutants. Computational methods are being increasingly used in toxicity predictions. Here, the method of molecular docking is assessed for screening potential toxicity of a variety of xenobiotic compounds, including pesticides, pharmaceuticals, pollutants, and toxins derived from the chemical industry. The method predicts the binding energy of pollutants to a set of carefully selected receptors under the assumption that toxicity in many cases is related to interference with biochemical pathways. The strength of the applied method lies in its rapid generation of interaction maps between potential toxins and the targeted enzymes, which could quickly yield molecular-level information and insight into potential perturbation pathways, aiding in the prioritization of chemicals for further tests. Two scoring functions are compared: Autodock Vina and the machine-learning scoring function RF-Score-VS. The results are promising, although hampered by the accuracy of the scoring functions. The strengths and weaknesses of the docking protocol are discussed, as well as future directions for improving the accuracy for the purpose of toxicity predictions.

Determination and reduced life expectancy model and molecular docking analyses of estrogenic potentials of 17β-estradiol, bisphenol A and nonylphenol on expression of vitellogenin gene (vtg1) in zebrafish

This study determined and evaluated the estrogenic potentials on hepatic vitellogenin gene (vtg1) of adult male zebrafish which were exposed to low level concentrations (ng/L-μg/L levels) of individual and binary mixtures of 17β-estradiol (E₂), bisphenol A (BPA) and nonylphenol (NP) through the use of reduced life expectancy (RLE) model and molecular docking analysis. The order of in vivo estrogenic potentials of individual and binary exposure of E₂, BPA and NP was as follows: E₂+BPA＞E₂＞E₂+NP＞BPA＞BPA + NP >>>NP. Binary mixtures of E₂, BPA and NP had weak synergistic effects under the exposure concentration ranges. With the expression of hepatic vtg1 gene, the hepatic toxicity was analyzed by using the RLE model. All plots of the linear RLE model had negative slopes indicating that EC₅₀ was negatively correlated with the natural logarithm of exposure time (lnET₅₀). The RLE model analyses can be useful to evaluate the exposure time effects of zebrafish by using EC₅₀ as toxicity endpoint. Molecular docking analysis revealed that the interaction potential of E₂ (Binding energy: -10.1 kcal/mol) for the zebrafish ERα-LBD was the most potent (stable), followed by BPA (-8.0 kcal/mol) and NP (-6.8 kcal/mol). Molecular docking analysis can be useful to understand interactive effects of E₂, BPA and NP.

Exploration of Computational Approaches to Predict the Toxicity of Chemical Mixtures

Industrial advances have led to generation of multi-component chemicals, materials and pharmaceuticals which are directly or indirectly affecting the environment. Although toxicity data are available for individual chemicals, generally there is no toxicity data of chemical mixtures. Most importantly, the nature of toxicity of these studied mixtures is completely different to the single components, which makes the toxicity evaluation of mixtures more critical and challenging. Interactions of individual chemicals in a mixture can result in multifaceted and considerable deviations in the apparent properties of its ingredients. It results in synergistic or antagonistic effects as opposed to the ideal case of additive behavior i.e., concentration addition (CA) and independent action (IA). The CA and IA are leading models for the assessment of joint activity supported by pharmacology literature. Animal models for toxicity testing are time- and money-consuming as well as unethical. Thus, computational approaches are already proven efficient alternatives for assessing the toxicity of chemicals by regulatory authorities followed by industries. In silico methods are capable of predicting toxicity, prioritizing chemicals, identifying risk and assessing, followed by managing, the risk. In many cases, the mechanism behind the toxicity from species to species can be understood by in silico methods. Until today most of the computational approaches have been employed for single chemical's toxicity. Thus, only a handful of works in the literature and methods are available for a mixture's toxicity prediction employing computational or in silico approaches. Therefore, the present review explains the importance of evaluation of a mixture's toxicity, the role of computational approaches to assess the toxicity, followed by types of in silico methods. Additionally, successful application of in silico tools in a mixture's toxicity predictions is explained in detail. Finally, future avenues towards the role and application of computational approaches in a mixture's toxicity are discussed.

In silico approaches and tools for the prediction of drug metabolism and fate: A review

The fate of administered drugs is largely influenced by their metabolism. For example, endogenous enzyme-catalyzed conversion of drugs may result in therapeutic inactivation or activation or may transform the drugs into toxic chemical compounds. This highlights the importance of drug metabolism in drug discovery and development, and accounts for the wide variety of experimental technologies that provide insights into the fate of drugs. In view of the high cost of traditional drug development, a number of computational approaches have been developed for predicting the metabolic fate of drug candidates, allowing for screening of large numbers of chemical compounds and then identifying a small number of promising candidates. In this review, we introduce in silico approaches and tools that have been developed to predict drug metabolism and fate, and assess their potential to facilitate the virtual discovery of promising drug candidates. We also provide a brief description of various recent models for predicting different aspects of enzyme-drug reactions and provide a list of recent in silico tools used for drug metabolism prediction.

Prediction of the Toxicity of Binary Mixtures by QSAR Approach Using the Hypothetical Descriptors

Organic compounds are often exposed to the environment, and have an adverse effect on the environment and human health in the form of mixtures, rather than as single chemicals. In this paper, we try to establish reliable and developed classical quantitative structure⁻activity relationship (QSAR) models to evaluate the toxicity of 99 binary mixtures. The derived QSAR models were built by forward stepwise multiple linear regression (MLR) and nonlinear radial basis function neural networks (RBFNNs) using the hypothetical descriptors, respectively. The statistical parameters of the MLR model provided were N (number of compounds in training set) = 79, R² (the correlation coefficient between the predicted and observed activities)= 0.869, LOOq² (leave-one-out correlation coefficient) = 0.864, F (Fisher's test) = 165.494, and RMS (root mean square) = 0.599 for the training set, and N_ext (number of compounds in external test set) = 20, R² = 0.853, qext2 (leave-one-out correlation coefficient for test set)= 0.825, F = 30.861, and RMS = 0.691 for the external test set. The RBFNN model gave the statistical results, namely N = 79, R² = 0.925, LOOq² = 0.924, F = 950.686, RMS = 0.447 for the training set, and N_ext = 20, R² = 0.896, qext2 = 0.890, F = 155.424, RMS = 0.547 for the external test set. Both of the MLR and RBFNN models were evaluated by some statistical parameters and methods. The results confirm that the built models are acceptable, and can be used to predict the toxicity of the binary mixtures.

QSAR prediction of additive and non-additive mixture toxicities of antibiotics and pesticide

Antibiotics and pesticides may exist as a mixture in real environment. The combined effect of mixture can either be additive or non-additive (synergism and antagonism). However, no effective predictive approach exists on predicting the synergistic and antagonistic toxicities of mixtures. In this study, we developed a quantitative structure-activity relationship (QSAR) model for the toxicities (half effect concentration, EC₅₀) of 45 binary and multi-component mixtures composed of two antibiotics and four pesticides. The acute toxicities of single compound and mixtures toward Aliivibrio fischeri were tested. A genetic algorithm was used to obtain the optimized model with three theoretical descriptors. Various internal and external validation techniques indicated that the coefficient of determination of 0.9366 and root mean square error of 0.1345 for the QSAR model predicted that 45 mixture toxicities presented additive, synergistic, and antagonistic effects. Compared with the traditional concentration additive and independent action models, the QSAR model exhibited an advantage in predicting mixture toxicity. Thus, the presented approach may be able to fill the gaps in predicting non-additive toxicities of binary and multi-component mixtures.

A QSAR-based mechanistic study on the combined toxicity of antibiotics and quorum sensing inhibitors against Escherichia coli

Quorum sensing inhibitors (QSIs) have attracted increasing attention due to their potential roles as the antibiotic alternatives. The combination of QSIs and antibiotics in clinical use and their subsequent release into the environment may result in joint effects on the ecology and environment, which has not received enough concerns yet. In this study, eight potential QSIs and three types of commonly used antibiotics, i.e., sulfonamides (SAs), β-lactams and tetracyclines (TCs), were investigated for their combined toxicity on Escherichia coli (E. coli). The QSAR models for the combined toxicity were constructed using the interaction energies between the chemicals and their target proteins as calculated by molecular docking. It was revealed that the SAs and QSIs presented either additive or antagonistic joint effects in the mixture toxicity test, while β-lactams and TCs showed only antagonistic effects with the QSIs. The analysis on the coefficients in the QSAR models suggested that the QSIs in the mixtures were more involved in the interaction with the proteins than the antibiotics. This study will help better understand the risks of joint exposure to the antibiotics and QSIs, and provide a new perspective for the study of the combined toxicity mechanism.

Similarities and differences in combined toxicity of sulfonamides and other antibiotics towards bacteria for environmental risk assessment

Antibiotics as a type of environmental contaminants are typically exposed to chemical mixtures over long periods of time, so chronic combined toxicity is the best way to perform an environmental risk assessment. In this paper, the individual and combined toxicity of sulfonamides (SAs), sulfonamide potentiators (SAPs), and doxycycline hyclate (DH) were tested on gram-positive (Bacillus subtilis, B. subtilis) and gram-negative (Escherichia coli, E. coli) bacteria. The individual toxicity of antibiotics on the two bacteria could be ranked in the same order: SAs < SAPs < DH. But E. coli was more sensitive than B. subtilis to the antibiotics, which was likely due to both the different abilities of antibiotics to pass through the cell membrane and the varied capacities to bind target proteins between the two bacteria. In addition, the binary mixtures of SAs-SAPs, SAs-DH, and SAs-SAs exhibited synergistic, antagonistic, and additive effects on both of the bacteria but in different magnitudes as represented by the toxicity units (TU). And we found the different TU values were result from the different effective concentrations of antibiotic mixtures based on the approach of molecular docking and quantitative structure-activity relationships (QSARs). Moreover, from the results of risk assessment, it should be noted that the mixture of SAs and other antibiotics may pose a potential environmental risk assessment due to their combined action with the current environmentally realistic concentrations.

Prediction of mixture toxicity from the hormesis of a single chemical: A case study of combinations of antibiotics and quorum-sensing inhibitors with gram-negative bacteria

The 50% effect level of a single chemical in the real environment is almost impossible to determine at the low exposure concentration, and the prediction of the concentration of a mixture at the 50% effect level from the concentration of a single chemical at the low effect level is even more difficult. The current literature does not address this problem. Thus, to determine solutions for this question, single/mixture chronic toxicities of sulfonamides (SAs) and quorum-sensing inhibitors (QSIs) were determined using Gram-negative bacteria (Vibrio fischeri and E. coli.) and Gram-positive bacteria (B. subtilis) as the target organisms. The results showed that the joint effects of SAs and QSIs were primarily antagonistic responses. In addition, the toxicity mechanisms of mixtures of SAs and QSIs were investigated further, and the results revealed that the chronic joint effects were primarily an antagonistic response due to the QSI competing against acyl-homoserine lactones (AHL) for luxR in V. fischeri and SdiA in E. coli generated by the SAs, leading to negative effects exerted by the QSI-luxR or QSI-SdiA complexes on luxI in V. fischeri or FtsZ in E. coli. This phenomenon eventually weakened the stimulatory effect caused by the SAs. Based on the mixture toxicity mechanism, the relationship between the mixture toxicity and the simulation effect was formulated.

Acute toxicity of benzophenone-type UV filters for Photobacterium phosphoreum and Daphnia magna: QSAR analysis, interspecies relationship and integrated assessment

The hazardous potential of benzophenone (BP)-type UV filters is becoming an issue of great concern due to the wide application of these compounds in many personal care products. In the present study, the toxicities of BPs to Photobacterium phosphoreum and Daphnia magna were determined. Next, density functional theory (DFT) and comparative molecular field analysis (CoMFA) descriptors were used to obtain more detailed insight into the structure - activity relationships and to preliminarily discuss the toxicity mechanism. Additionally, the sensitivities of the two organisms to BPs and the interspecies toxicity relationship were compared. Moreover, an approach for providing a global index of the environmental risk of BPs to aquatic organisms is proposed. The results demonstrated that the mechanism underlying the toxicity of BPs to P. phosphoreum is primarily related to their electronic properties, and the mechanism of toxicity to D. magna is hydrophobicity. Additionally, D. magna was more sensitive than P. phosphoreum to most of the BPs, with the exceptions of the polyhydric BPs. Moreover, comparisons with published data revealed a high interspecies correlation coefficient among the experimental toxicity values for D. magna and Dugesia japonica. Furthermore, hydrophobicity was also found to be the most important descriptor of integrated toxicity. This investigation will provide insight into the toxicity mechanisms and useful information for assessing the potential ecological risk of BP-type UV filters.

A computational approach to evaluate the androgenic affinity of iprodione, procymidone, vinclozolin and their metabolites

Our research is aimed at devising and assessing a computational approach to evaluate the affinity of endocrine active substances (EASs) and their metabolites towards the ligand binding domain (LBD) of the androgen receptor (AR) in three distantly related species: human, rat, and zebrafish. We computed the affinity for all the selected molecules following a computational approach based on molecular modelling and docking. Three different classes of molecules with well-known endocrine activity (iprodione, procymidone, vinclozolin, and a selection of their metabolites) were evaluated. Our approach was demonstrated useful as the first step of chemical safety evaluation since ligand-target interaction is a necessary condition for exerting any biological effect. Moreover, a different sensitivity concerning AR LBD was computed for the tested species (rat being the least sensitive of the three). This evidence suggests that, in order not to over-/under-estimate the risks connected with the use of a chemical entity, further in vitro and/or in vivo tests should be carried out only after an accurate evaluation of the most suitable cellular system or animal species. The introduction of in silico approaches to evaluate hazard can accelerate discovery and innovation with a lower economic effort than with a fully wet strategy.