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.

[1]  Parvin Kumar,et al.  CORAL: QSAR models of CB1 cannabinoid receptor inhibitors based on local and global SMILES attributes with the index of ideality of correlation and the correlation contradiction index , 2020 .

[2]  D. Sokolović,et al.  QSAR study of 2,4-dihydro-3H-1,2,4-triazol-3-ones derivatives as angiotensin II AT1 receptor antagonists based on the Monte Carlo method , 2018, Structural Chemistry.

[3]  Yong Pan,et al.  The cytotoxicity of nanomaterials: Modeling multiple human cells uptake of functionalized magneto-fluorescent nanoparticles via nano-QSAR. , 2020, Chemosphere.

[4]  E. Castro,et al.  Prediction of aqueous toxicity for heterogeneous phenol derivatives by QSAR , 2008 .

[5]  H. Budzinski,et al.  Toxicity of binary mixtures of pesticides to the marine microalgae Tisochrysis lutea and Skeletonema marinoi: Substance interactions and physiological impacts. , 2019, Aquatic toxicology.

[6]  S. Gayen,et al.  Good and bad molecular fingerprints for human rhinovirus 3C protease inhibition: identification, validation, and application in designing of new inhibitors through Monte Carlo-based QSAR study , 2020, Journal of biomolecular structure & dynamics.

[7]  Ashwani Kumar,et al.  QSAR Differential Model for Prediction of SIRT1 Modulation using Monte Carlo Method , 2016, Drug Research.

[8]  C. A. V. van Gestel,et al.  Toxicity of binary mixtures of Cu, Cr and As to the earthworm Eisenia andrei , 2020, Ecotoxicology.

[9]  S. Ahmadi,et al.  Structure-activity relationship of the radical scavenging activities of some natural antioxidants based on the graph of atomic orbitals , 2019, Journal of Molecular Structure.

[10]  S. Ahmadi.  Mathematical modeling of cytotoxicity of metal oxide nanoparticles using the index of ideality correlation criteria. , 2020, Chemosphere.

[11]  M. Scotti,et al.  Computer Aided Drug Design Studies in the Discovery of Secondary Metabolites Targeted Against Age-Related Neurodegenerative Diseases. , 2015, Current topics in medicinal chemistry.

[12]  D. Sokolović,et al.  Design and development of novel therapeutics for brucellosis treatment based on carbonic anhydrase inhibition , 2019, Journal of biomolecular structure & dynamics.

[13]  E. Benfenati,et al.  Pesticides, cosmetics, drugs: identical and opposite influences of various molecular features as measures of endpoints similarity and dissimilarity , 2020, Molecular Diversity.

[14]  Ord,et al.  Guidelines for the Health Risk Assessment of Chemical Mixtures , 2014 .

[15]  Hyung-Gi Byun,et al.  Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials. , 2019, Chemosphere.

[16]  A. Veselinović,et al.  QSAR modeling of dihydrofolate reductase inhibitors as a therapeutic target for multiresistant bacteria , 2018, Structural Chemistry.

[17]  Feng Luan,et al.  Prediction of the Toxicity of Binary Mixtures by QSAR Approach Using the Hypothetical Descriptors , 2018, International journal of molecular sciences.

[18]  Andrey A Toropov,et al.  The index of ideality of correlation: A criterion of predictive potential of QSPR/QSAR models? , 2017, Mutation research.

[19]  S. Gayen,et al.  Identification of structural fingerprints for in vivo toxicity by using Monte Carlo based QSTR modeling of nitroaromatics , 2019, Toxicology mechanisms and methods.

[20]  S. Gayen,et al.  Hydroxyethylamine derivatives as HIV-1 protease inhibitors: a predictive QSAR modelling study based on Monte Carlo optimization , 2017, SAR and QSAR in environmental research.

[21]  Andrey A Toropov,et al.  The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability? , 2017, The Science of the total environment.

[22]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

[23]  D. Yin,et al.  Using molecular docking-based binding energy to predict toxicity of binary mixture with different binding sites. , 2013, Chemosphere.

[24]  J. Leszczynski,et al.  Monte Carlo based QSAR models for toxicity of organic chemicals to Daphnia magna , 2016 .

[25]  Aleksandar M. Veselinovic,et al.  Development and design of novel cardiovascular therapeutics based on Rho kinase inhibition - In silico approach , 2019, Comput. Biol. Chem..

[26]  Fangyou Yan,et al.  Norm Index–Based QSAR Model for Acute Toxicity of Pesticides Toward Rainbow Trout , 2019, Environmental toxicology and chemistry.

[27]  E. Benfenati,et al.  Predicting acute contact toxicity of organic binary mixtures in honey bees (A. mellifera) through innovative QSAR models. , 2019, The Science of the total environment.

[28]  D. Sokolović,et al.  QSPR in forensic analysis - The prediction of retention time of pesticide residues based on the Monte Carlo method. , 2018, Talanta.

[29]  The index of ideality of correlation: models for flammability of binary liquid mixtures , 2019, Chemical Papers.

[30]  M. Fatemi,et al.  Application of nano-quantitative structure–property relationship paradigm to develop predictive models for thermal conductivity of metal oxide-based ethylene glycol nanofluids , 2020, Journal of Thermal Analysis and Calorimetry.

[31]  Emilio Benfenati,et al.  QSAR models for predicting acute toxicity of pesticides in rainbow trout using the CORAL software and EFSA's OpenFoodTox database. , 2017, Environmental toxicology and pharmacology.

[32]  Shilpi Chauhan,et al.  Use of Simplified Molecular Input Line Entry System and molecular graph based descriptors in prediction and design of pancreatic lipase inhibitors. , 2018, Future medicinal chemistry.

[33]  Hyung-Gi Byun,et al.  Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells. , 2018, Chemical research in toxicology.

[34]  Shovanlal Gayen,et al.  Identification of structural requirements and prediction of inhibitory activity of natural flavonoids against Zika virus through molecular docking and Monte Carlo based QSAR Simulation , 2019, Natural product research.

[35]  Mati Karelson,et al.  Quantitative Structure–Activity Relationship (QSAR) Modeling of EC50 of Aquatic Toxicities for Daphnia magna , 2009, Journal of toxicology and environmental health. Part A.

[36]  Manisha,et al.  Development of prediction model for fructose- 1,6- bisphosphatase inhibitors using the Monte Carlo method , 2019, SAR and QSAR in environmental research.

[37]  The index of ideality of correlation: models of the flash points of ternary mixtures , 2020 .

[38]  A. Akbari,et al.  Prediction of the adsorption coefficients of some aromatic compounds on multi-wall carbon nanotubes by the Monte Carlo method , 2018, SAR and QSAR in environmental research.

[39]  N. Cedergreen,et al.  Mixture toxicity of three toxicants with similar and dissimilar modes of action to Daphnia magna. , 2008, Ecotoxicology and environmental safety.

[40]  Francisco Torrens,et al.  Prediction of Aquatic Toxicity of Benzene Derivatives to Tetrahymena pyriformis According to OECD Principles. , 2016, Current pharmaceutical design.

[41]  A K Halder,et al.  Finding the structural requirements of diverse HIV-1 protease inhibitors using multiple QSAR modelling for lead identification , 2018, SAR and QSAR in environmental research.

[42]  C. Nwanyanwu,et al.  Toxicity of Binary Mixtures of Phenol, Zinc and Cadmium to Yeast Strains Isolated from Hydrocarbon Impacted Soil , 2020, OALib.

[43]  L. McCarty,et al.  Can mode of action predict mixture toxicity for risk assessment? , 2004, Toxicology and applied pharmacology.

[44]  Sanghun Kim,et al.  Reliable predictive computational toxicology methods for mixture toxicity: toward the development of innovative integrated models for environmental risk assessment , 2013, Reviews in Environmental Science and Bio/Technology.

[45]  Jerzy Leszczynski,et al.  Monte Carlo–based quantitative structure–activity relationship models for toxicity of organic chemicals to Daphnia magna , 2016, Environmental toxicology and chemistry.

[46]  K. Srinivasan,et al.  In Silico Drug-Designing Studies on Flavanoids as Anticolon Cancer Agents: Pharmacophore Mapping, Molecular Docking, and Monte Carlo Method-Based QSAR Modeling , 2017, Interdisciplinary Sciences: Computational Life Sciences.

[47]  J. Sindhu,et al.  QSAR Models for Nitrogen Containing Monophosphonate and Bisphosphonate Derivatives as Human Farnesyl Pyrophosphate Synthase Inhibitors Based on Monte Carlo Method , 2018, Drug Research.

[48]  D. Baird,et al.  Toxicity of binary mixtures of metals and pyrethroid insecticides to Daphnia magna Straus. Implications for multi-substance risks assessment. , 2006, Aquatic toxicology.

[49]  Alejandro Speck-Planche,et al.  Multitasking models for quantitative structure–biological effect relationships: current status and future perspectives to speed up drug discovery , 2015, Expert opinion on drug discovery.

[50]  S. Ahmadi,et al.  QSAR modeling of toxicities of ionic liquids toward Staphylococcus aureus using SMILES and graph invariants , 2020, Structural Chemistry.