Neonicotinoid insecticide design: molecular docking, multiple chemometric approaches, and toxicity relationship with Cowpea aphids

Abstract

Neonicotinoids are the fastest-growing class of insecticides successfully applied in plant protection, human and animal health care. The significant resistance increases led to the urgent need for alternative new neonicotinoids, with improved insecticidal activity. We performed molecular docking to describe a common binding mode of neonicotinoids into the nicotinic acetylcholine receptor, and to select the appropriate conformations to derive models. These were further used in a QSAR study employing both linear and nonlinear approaches to model the inhibitory activity against the Cowpea aphids. Linear modeling was performed by multiple linear regression and partial least squares and nonlinear modeling by artificial neural networks and support vector machine methods. The OECD principles were considered for QSAR models validation. Robust models with predictive power were found for neonicotinoid diverse structures. Based on our QSAR and docking outcomes, five new insecticides were predicted, according to the model applicability domain, the ligand efficiencies, and the binding mode. Therefore, the developed models can be confidently used for the prediction of the insecticidal activity of new chemicals, saving a substantial amount of time and money and, also, contributing to the chemical risk assessment.

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Abbreviations

QSAR:

Quantitative structure–activity relationship

LC50 :

Inhibitory activity

OECD:

Organization for Economic Cooperation and Development

IMI:

Imidacloprid

nAChR:

Nicotinic acetylcholine receptor

C. Aphids :

Cowpea aphids or Aphis craccivora

MLR:

Multiple linear regression

PLS:

Partial least squares

ANNs:

Artificial neural networks

SVM:

Support vector machine

PCA:

Principal component analysis

Ls-AChBP:

Lymnaea stagnalis Acetylcholine Binding Protein

CG4:

Chemgauss 4

RMSE:

Root-mean-square error

VIP:

Variable Importance in the Projection

SD:

Standard deviation

RMSD:

Root-mean-square deviation

AD:

Applicability domain

q 2

Cross-validation correlation coefficient

LOO:

Leave-one-out

L7O:

Leave-seven-out

LMO:

Leave-more-out

MAE:

Mean absolute error

CCC:

Concordance correlation coefficient

tr:

Training

ext:

External

cv:

Cross-validation

scr:

Scrambled

h :

Leverage value

VIF:

Variance inflation factor

MCDM:

Multi-Criteria Decision Making

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Acknowledgments

Access to the Chemaxon Ltd., OpenEye Ltd., and QSARINS (from Prof. Paola Gramatica from the University of Insubria, Varese, Italy) software is greatly acknowledged by the authors.

Funding

This work was financially supported by the Project No. 1.1/2017 of the Institute of Chemistry Timişoara of Romanian Academy.

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Correspondence to Simona Funar-Timofei.

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Bora, A., Suzuki, T. & Funar-Timofei, S. Neonicotinoid insecticide design: molecular docking, multiple chemometric approaches, and toxicity relationship with Cowpea aphids. Environ Sci Pollut Res 26, 14547–14561 (2019). https://doi.org/10.1007/s11356-019-04662-9

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Keywords

  • Neonicotinoids
  • Cowpea aphids
  • QSAR
  • MLR
  • PLS
  • ANN
  • SVM
  • Docking