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In silico local QSAR modeling of bioconcentration factor of organophosphate pesticides

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Abstract

The persistent and accumulative nature of the pesticide of indiscriminate use emerged as ecotoxicological hazards. The bioconcentration factor (BCF) is one of the key elements for environmental assessments of the aquatic compartment. Limitations of prediction accuracy of global model facilitate the use of local predictive models in toxicity modeling of emerging compounds. The BCF data of diverse organophosphate (n = 55) was collected from the Pesticide Properties Database and used as a model data set in the present study to explore physicochemical properties and structural alert concerning BCF. The structures were downloaded from Pubchem, ChemSpider database. Two splitting techniques (biological sorting and structure-based) were used to divide the whole dataset into training and test set compounds. The QSAR study was carried out with two-dimensional descriptors (2D) calculated from PaDEL by applying genetic algorithm (GA) as chemometric tools using QSARINS software. The models were statistically robust enough both internally as well as externally (Q2: 0.709–0.722, Q2Ext: 0.717–0.903, CCC: 0.857–0.880). Overall molecular mass, presence of fused, and heterocyclic ring with electron-withdrawing groups affect the BCF value. The developed models reflected extended applicability domain (AD) and reliable predictions than the reported models for the studied chemical class. Finally, predictions of unknown organophosphate pesticides and the toxic nature of unknown organophosphate pesticides were commented on. These findings may be useful for the scientific community in prioritizing high potential pesticides of organophosphate class.

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References

  • Aranda JF, Bacelo DE, Leguizamón Aparicio MS, Ocsachoque MA, Castro EA, Duchowicz PR (2017) Predicting the bioconcentration factor through a conformation-independent QSPR study. SAR QSAR Environ Res 28:749–763

    Article  CAS  PubMed  Google Scholar 

  • Arnot JA, Gobas FA (2006) A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms. Environ Rev 14:257–297

    Article  CAS  Google Scholar 

  • Banjare P, Singh J, Roy PP (2017) Design and combinatorial library generation of 1H 1,4 benzodiazepines 2,5 diones as photosystem-II inhibitors: a public QSAR approach. Beni-SuefUni J Bas App Sci 6:219–231

    Google Scholar 

  • Bermúdez-Saldaña J, Escuder-Gilabert ML, Medina-Hernández MJ, Villanueva-Camañas RM, Sagrado S (2005) Modelling bioconcentration of pesticides in fish using biopartitioning micellar chromatography. J Chromatogr A 1063:153–160

    Article  PubMed  CAS  Google Scholar 

  • Bintein S, Devillers J, Karcher W (1993) Nonlinear dependence of fish bioconcentration on n-octanol/water partition coefficient. SAR QSAR Environ Res 1:29–39

    Article  CAS  PubMed  Google Scholar 

  • Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?—Arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250

    Article  Google Scholar 

  • Chirico N, Gramatica P (2011) Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J ChemInf Model 51(9):2320–2335

    Article  CAS  Google Scholar 

  • Chirico N, Gramatica P (2012) Real external predictivity of QSAR Models. Part2. New inter-comparable thresholds for different validation criteria and the need for scatter plot inspection. J Chem inf Model 52(8):2044–2058

  • Cochran WG, Snedecor GW (2021) Statistical Methods. Oxford & IBH, New Delhi

  • Consonni V, Ballabio D, Todeschini R (2009) Comments on the definition of the Q2 parameter for QSAR validation. J ChemInf Model 49:1669–1678

    Article  CAS  Google Scholar 

  • Debnath AK, Ghose AK, Viswanadhan VN (2001) Combinatorial library design and evaluation: principles, software, tools and application in drug discovery. Marcel Dekker Inc, New York, pp 73–129

    Google Scholar 

  • Devillers J, BinteinS DD (1996) Comparison of BCF models based on log P. Chemosphere 33:1047–1065

    Article  CAS  Google Scholar 

  • Eriksson L, Wold S (1995) In: Waterbeemd, HVD (Eds) Chemometric methods in molecular design. Willy VCH: Weinheim, 312–317

  • Freitas MR, Barigye SJ, Daré JK, Freitas MP (2016) Quantitative modeling of bioconcentration factors of carbonyl herbicides using multivariate image analysis. Chemosphere 152:190–195

    Article  CAS  PubMed  Google Scholar 

  • Fujikawa M, Nakao K, Shimizu R, Akamatsu M (2009) The usefulness of an artificial membrane accumulation index for estimation of the bioconcentration factor of Organophosphorus pesticide. Chemosphere 74:751–757

    Article  CAS  PubMed  Google Scholar 

  • Garg R, Smith CJ (2014) Predicting the bioconcentration factor of highly hydrophobic organic chemicals. Food ChemToxicol 69:252–259

    Article  CAS  Google Scholar 

  • Gavrilescu M (2005) Fate of pesticides in the environment and its bioremediation. Eng Life Sci 30:497–526

    Article  CAS  Google Scholar 

  • Gerwick BC, Sparks TC (2014) Natural products for pest control: an analysis of their role, value and future. Pest Manag Sci 70:1169–1185

    Article  CAS  PubMed  Google Scholar 

  • Golbraikh A, Harten P, Martin TM, Muratov EN, Young DM, Tropsha A, Zhu H (2012) Does rational selection of training and test sets improve the outcome of QSAR modeling? J Chem Inf Mod 52:2570–2578

    Article  CAS  Google Scholar 

  • Gramatica P (2007) Principles of QSAR models validation: internal and external. Qsar Comb Sci 26:694–770

    Article  CAS  Google Scholar 

  • Gramatica P (2020) Principles of QSAR modeling: comments and suggestions from personal experience. Int J Quant Struc Prop Relat 5(3):1–37

    Google Scholar 

  • Gramatica P, Papa E (2003) QSAR modeling of bioconcentration factor by theoretical molecular descriptors. QSAR Comb Sci 22:374–385

    Article  CAS  Google Scholar 

  • Gramatica P, Papa E (2005) An update of the BCF QSAR model based on theoretical molecular descriptors. QSAR Comb Sci 24:953–960

    Article  CAS  Google Scholar 

  • Gramatica P, Cassani S, Roy PP, Kovarich S, Yap CW, Papa E (2012) QSAR modeling is not “push a button and find a correlation”: a case study of toxicity of (benzo-)triazoles on algae. Mol Inf 31:817–835

    Article  CAS  Google Scholar 

  • Gramatica P, Chirico N, Papa E, Kovarich S, Cassani S (2013) QSARINS: a new software for the development, analysis, and validation of QSAR MLR models. J ComputChemSoftw News Updates 34:2121–2132

    CAS  Google Scholar 

  • Gramatica P, Cassani S, Chirico N (2014) QSARINS-Chem: insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS. J ComputChem 35:1036–1044

    CAS  Google Scholar 

  • Grisoni F, Consonni V, Villa S, Vighi M, Todeschini R (2015) QSAR models for bioconcentration: is the increase in the complexity justified by more accurate predictions? Chemosphere 127:171–179

    Article  CAS  PubMed  Google Scholar 

  • Grisoni F, Consonni V, Vighi M, Villa S, Todeschini R (2016) Expert QSAR system for predicting the bioconcentration factor under the REACH regulation. Env Res 148:507–512

    Article  CAS  Google Scholar 

  • Hao GF, Jiang W, Ye YN, Wu FX, Zhu XL, Guo FB, Yang GF (2016) ACFIS: a web server for fragment-based drug discovery. Nucl Acid Res 44(W1):W550–W556

    Article  CAS  Google Scholar 

  • Igor TV, Uko M, Tropsha A (2017) Public (Q)SAR services, integrated modeling environments, and model repositories on the web: state of the art and perspectives for future development. MolInf 36:1–14

    Google Scholar 

  • Ivanciuc T, Ivanciuc O, Klein DJ (2006) Modelling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quantitative super-structure/activity relationships (QSSAR). Mol Divers 10(2):133–145

    Article  CAS  PubMed  Google Scholar 

  • Köhler HR, Triebskorn R (2013) Wildlife ecotoxicology of pesticides: can we track effects to the population level and beyond? Science 341:759–765

    Article  PubMed  CAS  Google Scholar 

  • Lema E, Machunda R, Njau KN (2014) Agrochemicals use in horticulture industry in Tanzania and their potential impact to water resources. Int J Biol Chem Sci 8:831–842

    Article  Google Scholar 

  • Lin L (1992) Assay validation using the concordance correlation coefficient. Biometrics 48:599–660

    Article  Google Scholar 

  • Mackay D (1982) Correlation of bioconcentration factors. Environ Sci Tech 16:274–278

    Article  CAS  Google Scholar 

  • Mackay D, Fraser A (2000) Bioaccumulation of persistent organic chemicals: mechanisms and models. Environ Pollut 110:375–391

    Article  CAS  PubMed  Google Scholar 

  • Nendza M, Herbst T (2011) Screening for low aquatic bioaccumulation (2): physico-chemical constraints. SAR QSAR Environ Res 22:351–364

    Article  CAS  PubMed  Google Scholar 

  • Neve P, Vila-Aiub M, Roux F (2009) Evolutionary-thinking in agricultural weed management. The New Phyto 184:783–793

  • Oerke EC (2006) Crop losses to pest. J Agric Sci 144:31–43

    Article  Google Scholar 

  • Papa E, Dearden J, Gramatica P (2007) Linear QSAR regression models for the prediction of bioconcentrationfactors by physicochemical properties and structural theoretical molecular descriptors. Chemosphere 67:351–358

    Article  CAS  PubMed  Google Scholar 

  • Pliška V, Testa B, Waterbeemd H (2008) Lipophilicity in drug action and toxicology. In: Methods and principles in medicinal chemistry

  • Ragno R (2019) www.3d-qsar.com: a web portal that brings 3-D QSAR to all electronic devices—the Py-CoMFA web application as tool to build models from pre-aligned datasets. J Comp Aid Mole Des 33:855–864

    Article  CAS  Google Scholar 

  • Reach in Brief, European Commission, Environment Directorate General (2007)

  • Roy K (2007) On some aspects of validation of predictive quantitative structure-activity relationship models. Exp Opin Drug Discov 2:1567–1577

    Article  CAS  Google Scholar 

  • Roy PP, Roy K (2008) On some aspects of variable selection for partial least squares regression models. QSAR Comb Sci 27:302–313

    Article  CAS  Google Scholar 

  • Roy PP, Leonard JT, Roy K (2008) Exploring the impact of the size of training sets for the development of predictive QSAR models. ChemomIntell Lab Syst 90:31–42

    Article  CAS  Google Scholar 

  • Roy PP, Kovarich S, Gramatica P (2011) QSAR model reproducibility and applicability: a case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo-)triazoles. J Comput Chem 32(11):2386–2396

    Article  CAS  PubMed  Google Scholar 

  • Roy PP, Banjare P, Verma S, Singh J (2019) acute rat and mouse oral toxicity determination of anticholinesterase inhibitor carbamate pesticides: a QSTR approach. MolInf 38:1–17

    Google Scholar 

  • Schüurmann G, Ebert RU, Wang B, Kuehne R (2008) External validation and prediction employing the predictive squared correlation coefficient—test set activity mean vs training set activity mean. J ChemInf Model 48:2140–2145

    Article  CAS  Google Scholar 

  • Shi LM, Fang H, Tong WD, Wu J, Perkins R, Blair RM, Branham WS, Dial SL, Moland CI, Sheehan DM (2001) QSAR models using a large diverse set of estrogens. J ChemInf Comput Sci 41:186–195

    Article  CAS  Google Scholar 

  • Voutsas E, Magoulas K, Tassios D (2002) Prediction of the bioaccumulation of persistent organic pollutants in aquatic food webs. Chemosphere 48:645–651

    Article  CAS  PubMed  Google Scholar 

  • Wang Y, Wen Y, Li JJ, He J, Qin WC, Su LM, Zhao YH (2014) Investigation on the relationship between bioconcentration factor and distribution coefficient based on class-based compounds: The factors that affect bioconcentration. Environ Toxicol Pharmacol 38:388–396

    Article  CAS  PubMed  Google Scholar 

  • Wang F, Yang JF, Wang MY, Jia CY, Shi XX, Hao GF, Yang GF (2020) Graph attention convolutional neural network model for chemical poisoning of honey bees’ prediction. Sci Bull 65(14):1–8

    Article  Google Scholar 

  • Wang YL, Wang F, Shi XX, Jia CY, Wu FX, Hao GF, Yang GF (2020) Cloud 3D-QSAR: a web tool for the development of quantitative structure–activity relationship models in drug discovery. Brief Bioinfo: 1–8

  • Yang JF, Wang F, Chen YZ, Hao GF, Yang GF (2020) LARMD: integration of bioinformatic resources to profile ligand-driven protein dynamics with a case on the activation of estrogen receptor. Brief Bioinf 21(6):2206–2218

    Article  Google Scholar 

  • Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474

    Article  CAS  PubMed  Google Scholar 

  • Yuan J, Xie C, Zhang T, Sun J, Yuan X, Yu S, Zhang Y, Cao Y, Yu X, Yang X, Yao W (2016) Linear and nonlinear models for predicting fish bioconcentration factors for pesticides. Chemosphere 156:334–340

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

Financial assistance from the SCIENCE& ENGINEERING RESEARCHBOARD (SERB) DST, Govt.of India, New Delhi (File No. EMR/2017/004497) is gratefully acknowledged by Dr. Partha Pratim Roy. The authors acknowledge Prof. Paola Gramatica for the free license of QSARINS

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  1. Department of Pharmacy, Guru GhasidasVishwavidyalaya (A Central University), Bilaspur, 495009, India

    Purusottam Banjare, Balaji Matore, Jagadish Singh & Partha Pratim Roy

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  1. Purusottam Banjare
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  2. Balaji Matore
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Correspondence to Partha Pratim Roy.

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Banjare, P., Matore, B., Singh, J. et al. In silico local QSAR modeling of bioconcentration factor of organophosphate pesticides. In Silico Pharmacol. 9, 28 (2021). https://doi.org/10.1007/s40203-021-00087-w

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