qsar modeling of neonicotinoid insecticides for their selective affinity towards drosophila...

QSAR modeling of neonicotinoid insecticides fortheir selective affinity towards Drosophilanicotinic receptors over mammalian �4�2

receptors

Anindya Basu, Shovanlal Gayen, Soma Samanta, Parthasarathi Panda,Kolloru Srikanth, and Tarun Jha

Abstract: Neonicotinoids are emerging as a major class of insecticides with promising insecticidal activity having aspecific affinity towards the nicotinic acetylcholine receptors (nAChR). A quantitative structure–activity relationship(QSAR) study was performed on some azidopyridinyl neonicotinoids for their selective insecticidal activity over mam-malian toxicity. The result showed that increased surface area of the molecules may help to increase the binding affin-ity of the compounds towards the Drosophila receptor and the presence of the azido group on the other hand may bedetrimental towards the affinity. Compounds having low polarity, increased probability of nucleophilic attack at the par-ticular position (N-1), and a higher positive charge at the C-12 position can reduce the binding affinity of these com-pounds towards the mammalian receptor.

Key words: QSAR, neonicotinoids, Drosophila nicotinic receptor, mammalian α 4β2 receptor, AM1.

Résumé : Les néonicotinoïdes émergent comme une classe majeure de composés possédant une activité insecticide in-téressante et une activité spécifique pour les récepteurs de l’acétylcholine nicotinique (« nAChR »). Une étude de rela-tion structure–activité quantitative (RSAQ) a été réalisée sur quelques néonicotinoïdes de l’azidopyridinyle pour évaluerleur activité insecticide sélective sur la toxicité mammifère. Le résultat montre que la superficie accrue de la surfacedes molécules pourrait éventuellement leur permettre d’augmenter les affinités de fixation des composés sur le récep-teur Drosophila alors que, par ailleurs, la présence du groupe azido pourrait nuisible à cette affinité. Les composés defaible polarité augmentent la probabilité d’une attaque nucléophile à la position particulière (N-1) et d’une charge pluspositive en position C-12 et peuvent réduire l’affinité de fixation de ces composés vis-à-vis des récepteurs mammifères.

Mots clés : RSTQ, néonicotinoïdes, récepteur nicotinique Drosophila, récepteur mammifère α 4β2, AM1.

[Traduit par la Rédaction] Basu et al. 463

Introduction

The neonicotinoids (1) are one of the major classes of in-secticides, having very promising potency (2). They are nic-otinic agonists that interact with nicotinic acetylcholinereceptors (nAChR) in a different way than the conventionalnicotinoids. Unlike the protonated nicotinoids, which requirea cation–π interaction for binding to the vertebrate receptor,the neonicotinoids do not get protonated at physiologicalpH. This difference provides the neonictinoids with favour-able toxicological profiles (3). The neonicotinoids act asagonists at the nAChR of insects and mammals (particularlythe α4β2 subtype) (3), which are mainly responsible for thetoxicity of the compounds.

Nicotine, together with other nicotinoids, are also consid-ered as candidate analgesics and for the treatment of neuro-

degenerative diseases (4–7). The potential activity of neoni-cotinoids is therefore of particular interest. However, thephysical, chemical, or biological properties of a compounddepend on the 3D arrangement of the atoms in the molecule.As a part of our composite program of rational drug design(8–22), an attempt was made to explore the quantitativestructure–activity relationship (QSAR) study on a series of20 azidopyridinyl neonicotinoids reported by Zhang et al.(1). Previously, we have reported the QSAR studies of thesederivatives using topological descriptors, i.e., electrotopo-logical state atom (ETSA) (8) and refractotopological stateatom (RTSA) (9) indices. In the present QSAR study, weused semiempirical quantum descriptors, along withphysicochemical and indicator parameters, to understand thestructural and physicochemical properties requisite for insec-ticidal activity at the molecular level. The general structure

Can. J. Chem. 84: 458–463 (2006) doi:10.1139/V06-029 © 2006 NRC Canada

458

Received 23 November 2005. Published on the NRC Research Press Web site at http://canjchem.nrc.ca on 7 April 2006.

A. Basu, S. Gayen, S. Samanta, P. Panda, and T. Jha.1 Division of Medicinal and Pharmaceutical Chemistry, Department ofPharmaceutical Technology, P.O. Box 17020, Jadavpur University, Kolkata 700 032, India.K. Srikanth. Department of Medicinal Chemistry, College of Pharmacy, 8-125 Weaver Densford Hall, 308, Harvard St. SE,University of Minnesota, Minneapolis, MN 55455, USA.

1Corresponding author (e-mail: [email protected]; [email protected]).

of the neonicotinoids used in the present study is shown inFig. 1.

Materials and methods

For the QSAR study, the binding affinities of azido-pyridinyl neonicotinoids for mammalian α 4β2 [Ki(α 4β2)]and Drosophila [Ki Drosophila)] nicotinic receptors weretaken as dependent parameters. Zhang et. al. (1) measuredthe binding affinities of these neonicotinoids. These weredetermined with mammalian α 4β2 and Drosophila nAChRsusing radiopharmaceuticals. Semiempirical calculations ofthe neonicotinoids, whose activity data are shown in Table 1,were done according to the AM1 method (23) using theHyperchem Release 7.0 Pro. Package. AM1 is a semi-empirical, self-consistent implementation of the Hartree–Fock–Roothaan equations based on the neglect of differen-

tial diatomic overlap (NDDO) approximation. The AM1method produces reliable total energies as a function ofatomic positions and is able to probe the energy surface tofind the minimum energy configurations (24). The structuresof the compounds were drawn and model-built by theHyperChem software (25) to convert the 2D sketch into 3Dstructures. To shorten the total time required for energyminimization by the semiempirical method, the structureswere first energy minimized using the molecular mechanics(MM+) method based on the classical force field withoutany cutoffs for nonbonded interactions, solvation, and con-straints. These energy-minimized structures were then usedas the starting structures for geometry optimization by theAM1 method using the Polak–Ribiere (conjugate gradient)algorithm with a RMS gradient of 0.1 kcal/Å mol (1 cal =4.184 J). During calculations we used a Pentium IV2.40 GHz computer with 512 MB RAM.

Quantum chemical descriptors such as atomic charges,molecular orbital energies (HOMO, LUMO), frontier elec-tron density, approximate surface area (SA), polarizability,dipole moment, energies (total energy, binding energy, heatof formation), etc., were calculated by the method describedin the previous paragraph.

These were subjected to a correlation study. Multiple lin-ear regressions (MLR) were performed with different combi-nations of the parameters and the biological activity(mammalian α 4β2 and Drosophila nAChR agonistic activity)of the neonicotinoids. The correlation and regression analy-

© 2006 NRC Canada

Basu et al. 459

Cpd.a Y X R1 R2 R3

Kib

(nmol/L)pKi

c

(α4β2)Ki

d

(nmol/L)pKi

e

(Drosophila)

1* NH NH H H Cl 2.2 –0.342 800 –2.9032* NH NH H N3 Cl 2.6 –0.415 5700 –3.756

3 NH S H N3 H 8.3 –0.919 7200 –3.857

4 NH S N3 H H 820 –2.914 30000 –4.477

5 NH S H H Cl 0.39 0.409 100 –2.0006 NH S H N3 Cl 0.47 0.328 750 –2.875

7 NH S N3 H Cl 110 –2.041 3200 –3.505

8* NNO2 NH H H Cl 720 –2.857 2.2 –0.342

9* NNO2 NH H N3 Cl 340 –2.531 24 –1.380

10 NNO2 NH H N3 H 5400 –3.732 540 –2.732

11 NNO2 NH N3 H H 95800 –4.981 3600 –3.556

12 NNO2 NH H H Cl 470 –2.672 0.85 0.071

13 NNO2 NH H N3 Cl 300 –2.477 13 –1.114

14 NNO2 NH N3 H Cl 16900 –4.228 460 –2.663

15 NNO2 S H H Cl 170 –2.230 0.35 0.456

16 NNO2 S H N3 Cl 170 –2.230 3.9 –0.591

17* CHNO2 NH H H Cl 60 –1.778 0.12 0.921

18* CHNO2 NH H N3 Cl 40 –1.602 0.72 0.143

19 NCN S H H Cl 120 –2.079 1.4 –0.14620 NCN S H N3 Cl 120 –2.079 28 –1.447

Note: For the compound numbers marked with an asterisk, CH2-CH2 at atom numbers 9 and 10.aTaken from eq. [2].bKi value for α 4β2.cpKi for the Ki value of α 4β2 in molar units.dKi value for Drosophila.epKi for the Ki value of Drosophila in molar units.

Table 1. Binding affinity data of neonicotinoids for mammalian (α 4β2, [Ki(α 4β2)] and Drosophila [Ki(Drosophila)] nicotinic receptors.

X

N

N

Y

R

R

R

1

2

3

1

2

3

45

6

7

8

11

13

12

9 10

Fig. 1. General structure of the azidopyridinyl neonicotinoidswith arbitrarily numbered common skeletal atoms.

ses were carried out by a computer program called Multi Re-gress, developed in our laboratory (26). The frontier electrondensities, charge on the different atoms, approximate surfacearea (SA), and log P values were used to develop the QSARequations and are recorded in Table 2.

The statistical quality of the regression equations werejustified by parameters such as the correlation coefficient(R), the percentage of explained variance (%EV), the vari-ance ratio (F), the probability factor related to the F ratio(p), and the standard error of estimate (s). The predictivepowers of the equations were validated by the leave-one-out(LOO) cross validation method (20–23). Predicted residualsum of squares (PRESS), total sum of squares (SSY), cross-validated R2 (R cv

2 ), standard deviation error of prediction(SDEP), and standard error of PRESS (SPRESS) were consid-ered for the validation of the QSAR models.

Results and discussion

Attempts were made to develop QSAR models for the in-secticidal activity (pKi(Drosophila)) of azidopyridinylneonicotinoids. The best univariate relation (eq. [1]) was ob-tained by taking the approximate surface area (SA) of thewhole molecule as an independent parameter.

[1] pKi(Drosophila) = –12.675 (±2.776)

+ 3.059 (±0.776)SA

where n = 20; R = 0.681; %EV = 46.34; F(1,18) = 15.544;p < 0.001; s = 1.246; PRESS = 32.395; SSY = 52.101; R cv

2 =0.378; SDEP = 1.273; and SPRESS = 1.342.

Values within the parenthesis are the standard error of cor-responding parameters. Equation [1] explains up to 46.34%of the variances in the activity data and suggests the impor-tance of the approximate surface area of the whole mole-cules in biological activity. The positive coefficient of theapproximate surface area implies that higher values of thewhole molecular surface area correspond to higher bindingaffinity towards the Drosophila receptor.

An indicator parameter (IN3) was used to understand theimportance of the azido moiety towards insecticidal activity.The indicator parameter was found to have a good correla-tion to the biological activity under consideration, and wasthus used for the QSAR study. The model obtained is shownin eq. [2].

[2] pKi(Drosophila) = –12.775 (±1.208)

– 2.271 (±0.257)IN3 + 3.502 (±0.341)SA


2 =0.859; SDEP = 0.606; and SPRESS = 0.657.

Compared with eq. [1], eq. [2] has a better statistical qual-ity. It explains up to 90.44% of the variances of the activitydata. The negative coefficient of IN3 strongly suggested thedetrimental effect of the azido group towards insecticidal ac-tivity.

Attempts were also made to obtain QSAR models usingthe mammalian activity data. The different possible combi-nations of semiempirical and physicochemical parameters,together with the indicator parameter, were used for thestudy. Combinations of N1L, qC12, and log P (frontier elec-tron density for nucleophilic attack at the N1 atom, charge atthe C12 position, and the logarithm of the octanol–waterpartition coefficient of the compounds under consideration,respectively) values produced a model for mammalian α4β2agonistic activity as shown in eq. [3].

[3] pKi(α4β2) = 0.708 (±0.472) – 9.909 (±2.315)N1L

– 2.842 (±1.480)qC12 – 0.486 (±0.115) log P


2 =0.624; SDEP = 0.837; and SPRESS = 0.936.

Equation [3] explains up to 74.87% of the variances of theactivity data. However, the deletion of compounds 19 and20, which have different substituents than compounds 1–18and may act through a different mechanism of action, pro-duced eq. [4].

[4] pKi(α4β2) = 1.089 (±0.443) – 10.878

(±2.092)N1L – 3.830 (±1.364)qC12

– 0.507 (±0.102) log P

where the deleted compounds (DC) = 19, 20; n = 18; R =0.910; %EV = 82.84; F(3,14) = 22.529; p < 0.000; s =0.676; PRESS = 10.443; SSY = 37.267; R cv

2 = 0.720;SDEP = 0.762; and SPRESS = 0.864.

© 2006 NRC Canada

460 Can. J. Chem. Vol. 84, 2006

Cpd. SAa IN3b log Pc N1Ld qC12e

1 3.117 0.000 2.03 0.043 0.1252 3.423 1.000 2.88 0.003 0.1373 3.206 1.000 2.44 0.009 –0.0104 2.811 1.000 2.44 0.183 –0.0285 3.147 0.000 2.02 0.045 –0.0096 3.487 1.000 2.87 0.001 –0.0387 3.217 1.000 2.87 0.229 –0.0388 3.535 0.000 4.93 0.073 0.2629 4.056 1.000 6.31 0.002 0.255

10 3.696 1.000 5.36 0.006 0.26211 3.154 1.000 5.36 0.215 0.26612 3.541 0.000 4.93 0.073 0.26213 3.828 1.000 5.78 0.000 0.26114 3.689 1.000 5.78 0.156 0.05615 3.748 0.000 5.45 0.007 0.00416 3.990 1.000 6.3 0.003 0.00117 3.716 0.000 1.6 0.046 0.23918 4.200 1.000 2.44 0.000 0.22319 3.610 0.000 2.56 0.054 –0.00520 4.013 1.000 3.41 0.001 –0.005

aSA is the approximate surface area scaled by 0.01 (Å2).bIN3 is an indicator parameter indicating the presence of the azido

group in the compounds.clog P is the logarithm of the partition coefficient of the whole

molecule.dN1L is the frontier electron density for nucleophilic attack at the N1

position.eqC12 is the charge at atom C-12.

Table 2. Values of the properties of azidopyridinylneonicotinoids and indicator parameters.

© 2006 NRC Canada

Basu et al. 461

SA log P NIL q13 qC12 IN3 α4β2a DROb

SA 1.00 0.44 –0.58 0.04 0.30 0.15 –0.14 0.68log P 1.00 –0.03 0.16 0.37 0.24 –0.65 0.18N1L 1.00 0.22 –0.10 0.09 –0.50 –0.42q13 1.00 –0.46 0.18 –0.21 –0.21qC12 1.00 –0.09 –0.41 0.31IN3 1.00 –0.23 –0.56α4β2

a 1.00 –0.03DROb 1.00

aα4β2 stands for pKi(α4β2).bDRO stands for pKi(Drosophila).

Table 3. Correlation matrices for the biological activity and independent parameters.

Equation Intercepts and parameters t-value p-value

1 Intercept –4.567 0.000SA 3.943 0.001

2 Intercept –10.575 0.000IN3 –8.833 0.000SA 10.257 0.000

3 Intercept 1.502 0.153N1L –4.281 0.001qC12 –1.919 0.073log P –4.239 0.001

4 Intercept 2.458 0.028N1L –5.201 0.000qC12 –2.807 0.014log P –4.959 0.000

Table 4. t-value and p-value for the QSAR equations.

Equation [2] Equation [4]

Cpd.Observed[pKi(Drosophila)] Calcd. Res. LOO Pred. Pres

Observed[pKiα 4β2)] Calcd. Res. LOO Pred. Pres

1 –2.903 –1.874 –1.029 –1.620 –1.283 –0.342 –0.880 0.538 –0.973 0.6312 –3.756 –3.060 –0.696 –2.9929 –0.764 –0.415 –0.921 0.506 –0.988 0.5733 –3.857 –3.819 –0.038 –3.813 –0.044 –0.919 –0.211 –0.708 –0.056 –0.8634 –4.477 –5.202 0.726 –5.547 1.070 –2.914 –2.035 –0.879 –1.713 –1.2015 –2.000 –1.756 –0.244 –1.699 –0.301 0.409 –0.393 0.802 –0.556 0.9656 –2.875 –2.834 –0.041 –2.830 –0.045 0.328 –0.232 0.560 –0.377 0.7057 –3.505 –3.779 0.274 –3.821 0.316 –2.041 –2.710 0.669 –3.113 1.0728 –0.342 –0.397 0.055 –0.407 0.065 –2.857 –3.205 0.348 –3.258 0.4019 –1.380 –0.842 –0.538 –0.739 –0.641 –2.531 –3.104 0.573 –3.254 0.723

10 –2.732 –2.103 –0.629 –2.048 –0.684 –3.732 –2.692 –1.040 –2.503 –1.22911 –3.556 –3.999 0.444 –4.081 0.525 –4.981 –4.989 0.008 –4.994 0.01312 0.071 –0.375 0.446 –0.450 0.521 –2.672 –3.209 0.537 –3.290 0.61813 –1.114 –1.641 0.527 –1.698 0.584 –2.477 –2.836 0.359 –2.914 0.43714 –2.663 –2.127 –0.536 –2.080 –0.583 –4.228 –3.751 –0.477 –3.595 –0.63215 0.456 0.352 0.104 0.330 0.126 –2.23 –1.763 –0.467 –1.617 –0.61316 –0.591 –1.074 0.483 –1.151 0.560 –2.23 –2.143 –0.087 –2.098 –0.13217 0.921 0.239 0.682 0.105 0.816 –1.778 –1.138 –0.640 –0.872 –0.90718 0.143 –0.338 0.481 –0.474 0.617 –1.602 –1.001 –0.601 –0.848 –0.75419 –0.146 –0.131 –0.014 –0.129 –0.017 –2.079 — — — —20 –1.447 –0.992 –0.455 –0.915 –0.532 –2.079 — — — —

Table 5. Observed, calculated (Calcd.), residual (Res.), LOO-predicted (LOO Pred.), and predicted residual (Pres) activities of eqs. [2]and [4].

Equation [4] is of much better statistical quality and ex-plains up to 82.84% of the variances. The equation suggeststhe importance of atoms N1 and C12 with respect to the tox-icity of the compounds towards mammals. The negative co-efficient of N1L in eq. [4] suggests that a higher probabilityof a nucleophillic attack at N1 reduces the binding affinityof the compound with the mammalian receptor, thus reduc-ing the mammalian toxicity of the compounds under consid-eration. Further, the negative coefficient of qC12 suggeststhat increased positive charge at C12 diminishes the bindingaffinity of the compounds towards the mammalian receptorand thus helps in reducing the toxicity of the compounds.Another important parameter of eq. [4] is log P. A negativecoefficient of this parameter in this equation indicates thatcompounds having low polarity will have reduced toxic ef-fects on the mammals.

The correlation matrix of the dependent and independentvariables used in this study are shown in Table 3. Table 4represents the t and p values of the coefficients obtained inthe different equations. The final equations obtained in boththe cases, i.e., eqs. [2] and [4], were further validated by theLOO method. The LOO predicted values as obtained utiliz-ing eqs. [2] and [4] and are shown in Table 5.

Conclusion

The QSAR study suggests that increased surface area ofthe molecules helps to increase the insecticidal properties ofthe compounds by increasing the binding affinity of thecompound with the receptor nAChR (insect). The presenceof the azido group on the other hand is detrimental to thisactivity. The study also reveals the importance of atoms N1and C12 with respect to the binding affinity of the com-pounds towards the receptor nAChR (mammals). The modelshowed that a higher probability of a nucleophillic attack atthe N1 position may reduce the binding affinity of the com-pound towards the mammalian receptor. It also explainedthat increased positive charge at C12 may diminish the bind-

ing affinity of the compounds towards the mammalian re-ceptor and thus helps in reducing the toxicity of the com-pounds. The model also demonstrates that compoundshaving low polarity may inhibit the binding affinity of thecompound to the mammalian nAChR receptor and may re-duce the toxic effects to the mammals. Thus, designing com-pounds having low polarity, increased probability ofnucleophilic attack at the N-1 position, and a higher positivecharge at the C-12 position can reduce toxicity of these com-pounds towards the mammalian receptor.

Our prediction is supported by the 3D isosurface electro-static potential maps of compounds 6 and 12 obtained byAM1 calculations and shown in Figs. 2 and 3, respectively.The N1 atom of compound 6 possesses a low probability ofnucleophilic attack as is evident from the value of the fron-tier electron density for nucleophilic attack at that particularposition, recorded in Table 2. Compound 12, on the otherhand, possesses low toxicity towards mammals and a highinsecticidal action. Fig. 3 clearly depicts the absence of theazido moiety in the compound, as well as the presence of ahigh positive charge at position C12, which according to ourstudy should be one of the criteria for an ideal neonicotinoidinsecticide. We have already shown that the binding affinityof these azido-pyridinyl neonicotinoids were quantitativelyrelated with topological indices (8, 9). In this article, we alsodemonstrate that the pharmacological activity may also beexplained with quantum chemical parameters.

Acknowledgements

The authors are thankful to the University Grants Com-mission (UGC), New Delhi and the All India Council forTechnical Education (AICTE), New Delhi for providing fi-nancial support. Two of the authors (SG and SS) are gratefulto the Indian Council of Medical Research (ICMR), NewDelhi and the UGC for awarding a Senior Research Fellow-ship (SRF) and a Junior Research Fellowship (JRF), respec-

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462 Can. J. Chem. Vol. 84, 2006

Fig. 2. Three-dimensional isosurface electrostatic potential map of compound 6.

tively. Two of the authors (AB and PP) are grateful to theUGC for awarding Post Graduate Fellowships.

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Basu et al. 463

Fig. 3. Three-dimensional isosurface electrostatic potential map of compound 12.

qsar modeling of neonicotinoid insecticides for their selective affinity towards drosophila...

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