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Journal of Environmental Science and Health, Part B: Pesticides, Food Contaminants, and Agricultural Wastes Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/lesb20
QSPR prediction of chromatographic retention times of pesticides: Partition and fractal indices a
b
Francisco Torrens & Gloria Castellano a
Institut Universitari de Ciència Molecular, Universitat de València, Edifici d’Instituts de Paterna, València, Spain b
Facultad de Veterinaria y Ciencias Experimentales, Universidad Católica de Valencia San Vicente Mártir, València, Spain Published online: 24 Apr 2014.
To cite this article: Francisco Torrens & Gloria Castellano (2014) QSPR prediction of chromatographic retention times of pesticides: Partition and fractal indices, Journal of Environmental Science and Health, Part B: Pesticides, Food Contaminants, and Agricultural Wastes, 49:6, 400-407, DOI: 10.1080/03601234.2014.894773 To link to this article: http://dx.doi.org/10.1080/03601234.2014.894773
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Journal of Environmental Science and Health, Part B (2014) 49, 400–407 Copyright Ó Taylor & Francis Group, LLC ISSN: 0360-1234 (Print); 1532-4109 (Online) DOI: 10.1080/03601234.2014.894773
QSPR prediction of chromatographic retention times of pesticides: Partition and fractal indices FRANCISCO TORRENS1 and GLORIA CASTELLANO2 1
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2
Institut Universitari de Ciencia Molecular, Universitat de Valencia, Edifici d’Instituts de Paterna, Valencia, Spain Facultad de Veterinaria y Ciencias Experimentales, Universidad Cat olica de Valencia San Vicente M artir, Valencia, Spain
The high-performance liquid-chromatographic retentions of red-wine pesticide residues are modeled by structure–property relationships. The effect of different types of features is analyzed: geometric, lipophilic, etc. The properties are fractal dimensions, partition coefficient, etc., in linear and nonlinear correlation models. Biological plastic evolution is an evolutionary perspective conjugating the effect of acquired characters and relations that emerge among the principles of evolutionary indeterminacy, morphological determination and natural selection. It is applied to design the co-ordination index that is used to characterize pesticide retentions. The parameters used to calculate the co-ordination index are the molar formation enthalpy, molecular weight and surface area. The morphological and co-ordination indices barely improve the correlations. The fractal dimension averaged for non‑buried atoms, partition coefficient, etc. distinguishes the pesticide molecular structures. The structural and constituent classification is based on nonplanarity, and the number of cycles, and O, S, N and Cl atoms. Different behavior depends on the number of cycles. Keywords: Biological plastic evolution, co-ordination index, fractal dimension, 1-octanol–water partition coefficient, morphological index, solvation parameter model.
Introduction Twenty-six billion liters of wine were produced worldwide and 24 billion liters were consumed in 2010 according to the International Organization of Vine and Wine. Wine, especially red one, is rich in polyphenols (e.g., resveratrol, catechin and epicatechin), which are antioxidants that protect cells from oxidative damage caused by free radicals. Red-wine antioxidants inhibit cancers development, for example, prostate one. Red-wines consumption presents heart-healthy benefits. Application of pesticides (e.g., fungicides and insecticides) to improve grape yields is common; however, pesticides permeate via plant tissues and remain in harvested grapes/processed products (e.g., grape juice and wine). Because pesticides are potential source of toxicants that are harmful to human beings, it is important to test for levels in grapes, juice and wine. Although the European Unit set maximum residue levels (MRLs)
Address correspondence to Francisco Torrens, Institut Universitari de Ciencia Molecular, Universitat de Valencia, Edifici d’ Instituts de Paterna, P. O. Box 22085, E-46071 Valencia, Spain; E-mail: [email protected] or [email protected] Received November 13, 2013. Color versions of one or more of the figures in this article can be found online at www.tandfonline.com/lesb.
for pesticides in wine grapes of 0.01–10 mg kg1, it did not set MRLs for wine.[1] A study of wine bought within EU revealed that 34 of 40 bottles contained at least one pesticide. Average number was >4 pesticides per bottle, whereas highest number was 10. Pesticides analysis in red wine is challenging because of matrix complexity that contains alcohol, organic acids, sugars, phenols and pigments, for example, anthocyanins. Traditional red-wine sample preparation methods include liquid–liquid extraction (LLE) with organic solvents [2,3] and solid-phase extraction (SPE) with reversed-phase C18/polymeric sorbents;[4–6] however, LLE is labor-intensive, consumes large amounts of organic solvents and forms emulsions making difficult to separate organic/aqueous phases. In contrast SPE demands more method development. Solid-phase microextraction (SPME),[7,8] hollow-fiber liquid-phase microextraction [9] and stir-bar sorptive extraction (SBSE)[10] are lessly reproducible. Typical detections include gas chromatography (GC), GC coupled to mass spectrometry (MS) (GC–MS) and liquid chromatography coupled to tandem MS (LC–MS–MS). Quick, easy, cheap, effective, rugged and safe (QuEChERS) is a sample preparation method that was reported for pesticide-residues determination in vegetables/fruits;[11] it was used for pesticides/compounds analysis in various food, oil and beverage matrices;[12–14] it involves pesticides extraction from a sample with high water content into
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QSPR prediction of chromatographic retention times of pesticides acetonitrile, with addition of salts to separate phases and partition pesticides into organic layer that is followed by dispersive SPE (dSPE) to clean up various matrix co‑extractives and achieved mixing an aliquot of sample extract with sorbents prepacked in a centrifuge tube. Wang and Telepchak reported pesticides determination in red wine by QuEChERS extraction, rapid mini‑cartridge cleanup and LC–MS–MS detection; they developed the method using QuEChERS but easier/faster cleanup compared to dSPE to clean up red-wine co‑extractives.[1] Sample cleanup way is based on filter-and-clean concept: red-wine extract is pushed via mini‑cartridge with anhydrous MgSO4 and primary-secondary-amine (PSA) sorbent. Eight pesticides belonging to insecticide (methamid ophos, diazinone, pyrazophos and chlorpyrifos), fungicide (carbendazim, thiabendazole, pyrimethanil, cyprodinil and pyrazophos) and parasiticide (thiabendazole) classes were selected. Pesticides polarities are different. Four pesticides are planar (carbendazim, thiabendazole, pyrimethanil and cyprodinil). Cyprodinil was most usually detected on grapes, with chlorpyrifos, diazinone and methamidoph os, frequent. Carbendazim was detected in three of six red-wine samples. The model is an extension of solvent-dependent conformational analysis program (SCAP) octanol–water model to organic solvents.[15] In earlier publications, SCAP was applied for partitions of porphyrins, phthalocyanines, benzobisthiazoles, fullerenes, acetanilides, local anesthetics,[16] lysozyme,[17] barbiturates, hydrocarbons,[18] polystyrene,[19] Fe/S proteins,[20] C-nanotubes [21] and D‑glucopyr anoses.[22] Biological plastic evolution was applied to pheny lalcohols, 4‑alkylanilines,[23] valence-isolectronic se ries of aromatics [24] and phenylurea herbicides.[25] The pre sent report describes quantitative structure–property relationship (QSPR) analysis and prediction of pesticides retention. The aim of this report is to find properties that distinguish pesticides according with retentions. This study applies chemical index to pesticides. The goal is index usefulness validation via capability to differentiate pesticides and interest as predictive index for retention compared with partition and fractal dimension. The following section describes the method. The next two sections present and discuss the results. Finally, the last section summarizes our conclusions.
Computational method Biological plastic evolution is a perspective of the evolutionary process conjugating the effect of (1) the acquired characters and (2) relations among the principles of evolutionary indeterminacy, morphological determination and natural selection. Morphology–functionality relation in organisms lies in that the former is material support of the latter that is the dynamic effect of morphology in the context of physical environment–living matter interaction. Morphology, functionality, energy cost and vital viability are mutually influenced: when a morphology is functional,
it accomplishes its work with minimal energy cost and the vital viability of organ/organism is maximal. Quantifying concepts involves defining functional co-ordination index Ic that is formulated as the ratio between the work accomplished by morphology T and representative morphological index Im, according to: Ic ¼ T =Im :
ð1Þ
The greater the work T achieved by a concrete morphology Im, the greater is Ic. For an organism, Ruiz-Bustos proposed Im as the ratio between morphological surface area S and body weight W:[26] Im ¼ S=W :
ð2Þ
The substitution of Eq. (2) in Eq. (1) results: Ic ¼ T =ðS=W Þ ¼ W T =S:
ð3Þ
At the same time the expression of T by its equivalence in classical mechanics gives: T ¼ W xd 2 x=dt2 :
ð4Þ
Substituting Eq. (4) in Eq. (3) results: Ic ¼ W 2 xd 2 x=ðSdt2 Þ:
ð5Þ
The Ic is greater after conditions because (1) the greater the body weight at equal travelled time/space, the greater is Ic; (2) the Ic is proportional to the space travelled in shortest possible time; and (3) the smaller the body surface, the greater is the Ic, and function–morphology co‑ordination requires smaller energy cost. Program SCAP is based on Hopfinger model parameterized for 1‑octanol/water solvents. The hypothesis is that one can center a solvation sphere on every group of the molecule.[27,28] Intersecting volume V between solvation sphere and van der Waals spheres of resting atoms is calculated. SCAP manages parameters for a solvent: (1) n: maximum number of solvent molecules filling the solvation sphere; (2) Dgo: change of Gibbs free energy associated with the extraction of one solvent molecule out of the solvation sphere;[29,30] (3) Rv: radius of the solvation sphere; (4) Vf: free volume available for a solvent molecule in the solvation sphere.[31] In the solvation sphere, part of the volume excludes solvent molecules. The volume consists of the van der Waals volume of the group at which sphere is centered and a volume representing the groups is bonded to the central one. The latter is represented by a set of cylinders. The difference between total volume of solvation sphere and that excluded to solvent molecules represents volume V0 that is available for n solvent molecules. The Vf is calculated as: Vf ¼ V0 /n Vs. Change of free energy
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associated with extraction of all solvent molecules out of solvation sphere of a group R is: DGR ¼ nDg (1 V / V0 ), and solvation-free energy of a molecule results: DGsolv ¼ SR ¼ 1NDGR . 1‑Octanol/water partition coefficients P is: o o RT ln P ¼ DGsolv ðwaterÞ DGsolv ð1-octanolÞ
ð6Þ
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at given temperature T taken as 298 K, where R is gas constant, and DGsolv (1‑octanol) and DGsolv (water), in kJmol1, is standard-state Gibbs free energies of solvation. Generalizing SCAP for different solvents, the parameters of 1‑octanol were modified taking into account the effect of permittivity and molecular volume on 1-octanol parameters. For a general solvent, maximum number of solvent molecules which allowed filling solvation sphere is related to the molecular volume of solvent as: log nnow =log V o
ns ¼ no ðVs =Vo Þ
Vw
;
ð7Þ
where Vo, Vw and Vs are the molecular volumes of 1-octanol, water and general solvent, respectively. The no, nw and ns are the maximum numbers of molecules of 1-octanol, water and general solvent, respectively, which permitted filling the solvation sphere. The variation in standard Gibbs free energy associated with extraction of one solvent molecule out of solvation sphere, Dgs , is calculated using the generalized Born equation: Dgso ¼ Dgoo ð1 1=es Þ=ð1 1=eo Þ ¼ Dgoo eo ðes 1Þ=½es ðeo 1Þ;
ð8Þ
where Dgs is Dg for 1-octanol, and eo and es are the relative permittivities of 1-octanol and general solvent, respectively. The radius of solvation sphere is related to the molecular volume of solvent molecule as: Rv;s ¼ Rv;o ðVs =Vo Þ1=3 ;
ð9Þ
(Rt Rt )/Rt were calculated. The fractal dimensions were computed with our program TOPO.[32] The co-ordination index application to molecules chemical characterization requires adapting variables T, S and W in Eq. (3): T is redefined as a minus standard formation enthalpy in kJ mol1, S is a molecular surface area in A2 and W is a molecular weight in g mol1. The formation enthalpy and the molecular surface are computed with codes MOPAC–AM1 [33] and TOPO, respectively. Chemical indices for pesticide characterization (cf. Table 2) show that Im remains constant; however, Ic decays with W. Indices variation for pesticides versus molecular weight W (cf. Fig. 2) shows that some points and trend lines Ic and T collapse. The only descriptor that remains almost constant is Im. A linear correlation was observed between S and W. Descriptors more sensitive to W decay: S > T Ic > Im. Variations of (Rt Rt )/Rt versus morphological index Im show fit; the regression turns out to be: ðRt Rot Þ=Rot ¼ 8:12 5:59Im ;
ð11Þ
where n ¼ 9, r ¼ 0.375, s ¼ 1.255, F ¼ 1.1, MAPE ¼ 39.68% and AEV ¼ 0.8596, and where mean absolute percentage error (MAPE) is 39.68% and approximation error variance (AEV) is 0.8596. Best quadratic model, inclusion of Ic or exclusion of TPP does not improve correlation. The behavior is caused by Ic alternating signs because of signs inconstancy in T. Use of pKa betters fit: ðRt Rot Þ=Rot ¼ 2:58 0:120 pKa ;
ð12Þ
where n ¼ 9, r ¼ 0.384, s ¼ 1.250, F ¼ 1.2, MAPE ¼ 34.89% and AEV ¼ 0.8523, and AEV decays by 1%. If partition coefficient log P is utilized in the fit, the results are improved: ðRt Rot Þ=Rot ¼ 0:639 þ 0:668 logP;
ð13Þ
where Rv,o is Rv in 1-octanol. Free volume available for a solvent molecule in the solvation sphere is:
where n ¼ 9, r ¼ 0.910, s ¼ 0.563, F ¼ 33.5, MAPE ¼ 16.85% and AEV ¼ 0.1727, and AEV drops by 80%. If IS TPP is excluded, better correlation is obtained:
Vf ;s ¼ Vf ;o Vs =Vo ;
ðRt Rot Þ=Rot ¼ 0:569 þ 0:719 logP;
ð10Þ
where Vf,o is Vf in 1-octanol.
Calculation results For pesticides (cf. Fig. 1), LC–MS–MS retention time Rt data were taken from Wang and Telepchak.[14] Methamidophos was used as a reference retention time Rt because of least Rt (cf. Table 1). Internal standard (IS) triphenyl pho-sphate (TPP) was included in analyses. The quotients
ð14Þ
where n ¼ 8, r ¼ 0.919, s ¼ 0.561, F ¼ 32.8, MAPE ¼ 16.07% and AEV ¼ 0.1547, and AEV decreases by 82%. If molecular fractal dimension D is employed in fit, results improve: ðRt Rot Þ=Rot ¼ 23:3 þ 19:2 D;
ð15Þ
where n ¼ 9, r ¼ 0.941, s ¼ 0.458, F ¼ 54.0, MAPE ¼ 13.91% and AEV ¼ 0.1147, and AEV decays by 87%. If IS
403
QSPR prediction of chromatographic retention times of pesticides O H3C
O CH3
P
H N
O
S
OCH3 NH
NH2
a
N
b
H N S N
H3C
N
N
c
d
H N
N
N
N H
N
S
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N N
f
P
S
H2 C N
O
N
H3C
O P
O
g
O
CH3
O
H3C
O
O
e
O
C H2 H2 C
Cl CH3
N
Cl
O
S
N
P O
O
Cl
h
Fig. 1. Structures of eight pesticides selected in study: (a) methamidophos, (b) carbendazim, (c) thiabendazole, (d) pyrimethanil, (e) cyprodinil, (f) diazinone, (g) pyrazophos and (h) chlorpyrifos.
TPP is excluded, the correlation is bettered: ðRt Rot Þ=Rot ¼ 24:1 þ 19:9D;
ð16Þ
and AEV ¼ 0.1108, and AEV drops by 87.1%. If fractal dimension averaged for external atoms D0 is used, fit expands: ðRt Rot Þ=Rot ¼ 16:2 þ 13:1D0 ;
where n ¼ 8, r ¼ 0.943, s ¼ 0.475, F ¼ 48.2, MAPE¼14.67%,
ð17Þ
Table 1. Retention time, log P, pKa, fractal dimensions molecular/for nonburied atoms for pesticides. Compound Methamidophos Carbendazim Thiabendazole Pyrimethanil Cyprodinil TPP (IS) Diazinone Pyrazophos Chlorpyrifos
Rt (min)
RtRt (min)
(RtRt )/Rt
log P
pKa
D
D0
2.78 6.48 6.91 10.43 11.44 11.78 11.92 12.24 13.42
0.00 3.70 4.13 7.65 8.66 9.00 9.14 9.46 10.64
0.00000 1.33094 1.48561 2.75180 3.11511 3.23741 3.28777 3.40288 3.82734
0.779 1.52 2.47 2.558 3.012 4.63 3.766 2.810 5.004
0.58 5.66 3.40 4.41 4.22 5 1.21 1.37 5.28
1.235 1.284 1.288 1.314 1.344 1.394 1.398 1.403 1.394
1.266 1.332 1.331 1.407 1.470 1.504 1.509 1.505 1.494
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Table 2. Vector property (cyc123, O0345, NP, S ¼ N13, Cl3) and indices for characterization of pesticides. Compound 1. Methamidophos 2. Carbendazim 3. Thiabendazole 4. Pyrimethanil 5. Cyprodinil 6. TPP (IS) 7. Diazinone 8. Pyrazophos 9. Chlorpyrifos All series
Wa
Tb
Sc
Imd
Ice
141 191 201 199 225 326 304 373 351 141–373
601.3 38.0 452.4 297.3 418.9 588.8 57.5 222.8 86.7 452.4–601.3
151.2 195.6 193.9 221.1 247.8 305.4 317.8 357.4 299.5 151.2–357.4
1.071 1.023 0.963 1.110 1.100 0.936 1.044 0.957 0.854 0.854–1.110
561.5 37.1 469.7 267.9 380.8 629.0 55.1 232.7 101.4 469.7–629.0
W: molecular weight (g mol1). T: minus standard formation enthalpy (kJ mol1). c S: molecular surface area (A2). d Im: morphological index (mol A2 g1 ). e Ic: co-ordination index (kJ g mol2 A2). a
where n ¼ 9, r ¼ 0.966, s ¼ 0.348, F ¼ 98.9, MAPE ¼ 10.59% and AEV ¼ 0.0661, and AEV decreases by 92%. If IS TPP is excluded, the results are improved: ðRt Rot Þ=Rot ¼ 16:8 þ 13:6D0 ;
ð18Þ
where n ¼ 8, r ¼ 0.969, s ¼ 0.350, F ¼ 93.8, MAPE ¼ 11.00% and AEV ¼ 0.0601, and AEV decays by 93%. If more fundamental-free energy of solvation in 1‑octanol DGsolv,oct is included, better correlation is attained: ðRt Rot Þ=Rot ¼ 0:619 þ 0:512 log P o 0:0388DGsolv;oct ;
ð19Þ
500
Chemical index
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b
S Ic 0
T Im
where n ¼ 8, r ¼ 0.972, s ¼ 0.365, F ¼ 43.2, MAPE ¼ 10.32% and AEV ¼ 0.0547, and AEV drops by 93.6%. If the fractal dimension averaged for nonburied atoms minus molecular fractal dimension D0 D is included in the fit, the results are improved: ðRt Rot Þ=Rot ¼ 0:188 þ 0:367 log P þ19:6ðD0 DÞ;
ð20Þ
where n ¼ 9, r ¼ 0.973, s ¼ 0.337, F ¼ 53.3, MAPE ¼ 8.77% and AEV ¼ 0.0533, and AEV decreases by 93.8%. If IS TPP is excluded, the correlation is bettered: ðRt Rot Þ=Rot ¼ 0:229 þ 0:420 log P þ19:1ðD0 DÞ;
ð21Þ
where n ¼ 8, r ¼ 0.982, s ¼ 0.295, F ¼ 67.5, MAPE ¼ 7.17% and AEV ¼ 0.0357, and AEV decays by 96%. If external dimension D0 is included in the fit, the results are improved: ðRt Rot Þ=Rot ¼ 11:6 þ 0:272 log P þ 9:44D0 ;
ð22Þ
where n ¼ 8, r ¼ 0.987, s ¼ 0.253, F ¼ 93.2, MAPE ¼ 5.81% and AEV ¼ 0.0261, and AEV drops by 97%. The best quadratic model versus D0 acquires better correlation: ðRt Rot Þ=Rot ¼ 112 þ 151D0 49:3D02 ;
ð23Þ
-500 150
200 250 300 Molecular weight (g/mol)
350
Fig. 2. Variation of chemical indices for the pesticides versus molecular weight: y ¼ 46.7 þ 0.808x, r ¼ 0.966; y ¼ 13.1 0.172x; y ¼ 24.6 0.229x; y ¼ 1.19–0.000699x.
where n ¼ 9, r ¼ 0.988, s ¼ 0.224, F ¼ 125.2, MAPE ¼ 5.39% and AEV ¼ 0.0234, and AEV decreases by 97.3%. If IS TPP is excluded, the results are improved: ðRt Rot Þ=Rot ¼ 107 þ 144D0 46:7D02 ;
ð24Þ
405
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QSPR prediction of chromatographic retention times of pesticides where n ¼ 8, r ¼ 0.989, s ¼ 0.228, F ¼ 114.4, MAPE ¼ 5.84% and AEV ¼ 0.0214, and AEV decays by 97.5%. Model (22) is linear and expected to perform better than Eqs. (23) and (24) for extrapolation; however, the latter are nonlinear and could function better than Eq. (22) for intrapolation. Additional fitting parameters were tested: absolute/differential formation enthalpies, molecular dipole mo ment, organic solvent–water partition coefficients, free energies of solvation and transfer water!organic solvents, mole cular volume, surface area, globularity, rugosity, hydrophobic, hydrophilic and total solvent accessible surfaces and numbers of P and total atoms. Notwithstanding, re sults do not improve Eqs. (22)–(24). The first step in quantifying similarity for pesticides is to list their most important molecular characteristics. A vector of properties i ¼ is associated with every pesticide i, whose components correspond to different molecular features in a hierarchical order according to their expected importance in retention. If characteristic mth is more significant for retention than kth then m < k. Components ik are either “1” or “0”, according to whether a similar characteristic of rank k is either present or absent in molecule i compared to a reference. Analysis includes six structural and constitutional characteristics: presence of cycle (cyc123), occurrence of either none or 3– 5 O atoms (O0345), nonplanarity (NP), double-bonded S atom (S ¼ ), either one or three N atoms (N13) and three Cl atoms (Cl3, Fig. 1). It is assumed that the chemical characteristics can be ranked according to their contribution to retention decaying: cyc123 > O0345 > NP > S ¼ > N13 > Cl3. Index i1 ¼ 1 denotes cyc123 (i1 ¼ 0 for cyc0), i2 ¼ 1 means O0345, i3 ¼ 1 signifies NP, i4 ¼ 1 indicates S, i5 ¼ 1 stands for N13 and i6 ¼ 1 represents Cl3. Chlorpyrifos shows cyc1, O3, NP, S ¼ N1 and Cl3; its associated vector is (Table 2): it was selected as a reference because of greatest retention. A principal component analysis (PCA) was performed. Factor F1 explains 39% variance (61% error), F1/2, 66% variance (34% error), F1–3, 87% variance (13% error), etc. Scores plot of PCA F2–F1 clearly distinguished three clusters: class 1 (two compounds, F1 < F2, cf. Fig. 3, left), grouping 2 (three substances, F1 > F2, right).
Discussion Molecular studies were used to predict several parameters related to the bioactivities of drugs. It was concluded that direct correlation of molecular descriptors with bioactivity is possible. Thus, the chromatographic behavior of drugs in phases of different polarity might contain information of use in describing their pharmacological performance, for example, for barbiturates and neuroleptics. Later studies established that chromatographic parameters in a polar stationary phase system correlate better with some
1
3,4,5
6 0
7
2
Class 1 8 F2
9 -1
Class 2 Class 3
-2 1 -1
0 F1
1
Fig. 3. Principal component analysis F2 versus F1 scores plot for the pesticides.
molecular indices, whereas Kovats parameters, obtained from the apolar phase interaction, correlate the best with some other terms. Molecular descriptors satisfactorily predict chromatographic parameters, for example , retention times in GC/LC and RF in thin-layer chromatography (TLC). Topological descriptors were used in chromatographic chiral separations. The chromatographic properties for a group of natural phenolic derivatives were predicted by molecular topology. The properties of chiral compounds were forecasted by molecular topology. A study was made of the relationship between the retention times obtained by LC–MS–MS chromatography for a group of pesticides and molecular descriptors. By using multivariate regression, the corresponding molecular functions were obtained, which were selected on the basis of their respective statistical parameters. Regression analysis of the molecular functions showed a correct prediction of the experimental elution sequence for this set of molecules. In order to predict correctly the experimental elution sequence in the group of molecules, a two-variable model was necessary, in which the simultaneous appearance of indices log P and D0 reveals the importance of lipophilicity and the fractal effect in the studied property, allowing the use of such equations in predicting the value of the property; molecular structures may be differentiated even in other pesticide derivatives not included in the series.
Conclusion The objective of this study was to develop a structure– property relationship for the qualitative and quantitative
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406 prediction of the high-performance liquid-chromatographic retention time of pesticides. It is hoped that the results of the present work contribute to an increased scientific knowledge in the field of relationship prediction of pesticide residues, in food and environmental samples. The SCAP program allows the hydration and solvation-free energies and partition coe fficients, which show that for a given atom energies and partition coefficients are sensitive to the presence in the molecule of other atoms and functional groups. The parameters needed to calculate the co‑ordination index are the molar formation enthalpy, molecular weight and surface area. Linear and nonlinear correlation models were obtained for the chromatographic retention time. The morphological and co‑ordination indices hardly imp roved the multivariable regression equations for the chromatographic retention. The correlation between the molecular surface area and weight points not only to a homogeneous molecular structure of the pesticides, but also to the ability to predict and tailor their properties. The latter is nontrivial in environmental toxicology. The effect of different types of features was analyzed: geometric, lipophilic, etc. The fractal dimensions, partition coefficient, etc. distinguished pesticides in linear and nonlinear fits. Several criteria, selected to reduce the analysis to manageable quantity of pesticides, referred to structural and constitutional characteristics related to nonplanarity, and the number of cycles, and O, double-bonded S, N and Cl atoms. The molecules were classified in agreement with the principal component analyses. An extension of this study to other pesticides would give an insight into the possible generality of the conclusions above.
Funding One of the authors, F. T., acknowledges support from the Spanish Ministerio de Ciencia e Innovaci on (Project No. BFU2010-19118).
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grapes, must and wine by on-line microextraction and gas chromatography. J. Chromatogr., A 1999, 833, 43–51. Jimenez, J.J.; Bernal, J.L.; del Nozal, M.J.; Toribio, L.; Arias, E. Analysis of pesticide residues in wine by solid-phase extraction and gas chromatography with electron capture and nitrogen-phosphorus detection. J. Chromatogr., A 2001, 919, 147–156. Wang, J.-F.; Luan, L.; Wang, Z.-Q.; Jiang, S.-R.; Pan, C.-P. Determination of 19 multi-residue pesticides in grape wine by gas chromatography-electron capture detector. Chin. J. Anal. Chem. 2007, 35, 1430–1434. Economou, A.; Botitsi, H.; Antoniou, S.; Tsipi, D. Determination of multi-class pesticides in wines by solid-phase extraction and liquid chromatography-tandem mass spectrometry. J. Chromatogr., A 2009, 1216, 5856–5867. Hu, Y.; Liu, W.M.; Zhou, Y.M.; Guan, Y.F. Determination of organophosphorous pesticide residues in red wine by solid phase microextraction-gas chromatography. Se Pu 2006, 24, 290–293. Wu, J.; Tragas, C.; Lord, H.; Pawliszyn, J. Analysis of polar pesticides in water and wine samples by automated in‑tube solidphase microextraction coupled with high-performance liquid chromatography–mass spectrometry. J. Chromatogr., A 2002, 976, 357–367. Bola~ nos, P.P.; Romero-Gonz alez, R.; Frenich, A.G.; Vidal, J.L. Application of hollow fibre liquid phase microextraction for the multiresidue determination of pesticides in alcoholic beverages by ultra-high pressure liquid chromatography coupled to tandem mass spectrometry. J. Chromatogr., A 2008, 1208, 16–24. Vi~ nas, P.; Aguinaga, N.; Campillo, N.; Hern andez-C ordoba, M. Comparison of stir bar sorptive extraction and membrane-assisted solvent extraction for the ultra-performance liquid chromatographic determination of oxazole fungicide residues in wines and juices. J. Chromatogr., A 2008, 1194, 178–183. Anastassiades, M.; Lehotay, S.J.; Stajnbaher D.; Schenck, F.J. Fast and easy multiresidue method employing acetonitrile extraction/partitioning and “dispersive-solid phase extraction” for the determination of pesticide residues in produce. J. AOAC Int. 2003, 86, 412–431. Lehotay, S.J. QuEChERS sample preparation approach for mass spectrometric analysis of pesticide residues in foods. In Mass Spectrometry in Food Safety; Zweigenbaum, J., Ed.; Humana: Totowa, NJ, 2011; 65–91. Cunha, S.C.; Lehotay, S.J.; Mastovska, K.; Fernandes, J.O.; Beatriz, M.; Oliveira, P.P. Evaluation of the QuEChERS sample preparation approach for the analysis of pesticide residues in olives. J. Sep. Sci. 2007, 30, 620–632. Whelan, M.; Kinsella, B.; Furey, A.; Moloney, M.; Cantwell, H.; Lehotay, S.J.; Danaher, M. Determination of anthelmintic drug residues in milk using ultra high performance liquid chromatography–tandem mass spectrometry with rapid polarity switching. J. Chromatogr. A 2010, 1217, 4612–4622. Torrens, F.; S anchez-Marın, J.; Nebot-Gil, I. Universal model for the calculation of all organic solvent–water partition coefficients. J. Chromatogr. A 1998, 827, 345–358. Torrens, F. Universal organic solvent-water partition coefficient model. J. Chem. Inf. Comput. Sci. 2000, 40, 236–240. Torrens, F. Calculation of partition coefficient and hydrophobic moment of the secondary structure of lysozyme. J. Chromatogr. A 2001, 908, 215–221. Torrens, F. Free energy of solvation and partition coefficients in methanol–water binary mixtures. Chromatographia 2001, 53, S199–S203. Torrens, F.; Soria, V. Stationary-mobile phase distribution coefficient for polystyrene standards. Sep. Sci. Technol. 2002, 37, 1653–1665.
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[20] Torrens, F. Calculation of organic solvent–water partition coefficients of iron–sulfur protein models. Polyhedron 2002, 21, 1357–1361. [21] Torrens, F. Calculation of solvents and co‑solvents of single-wall carbon nanotubes: Cyclopyranoses. Nanotechnology 2005, 16, S181–S189. [22] Torrens, F.; Castellano, G. (Co-)solvent selection for single-wall carbon nanotubes: Best solvents, acids, superacids and guest–host inclusion complexes. Nanoscale 2011, 3, 2494–2510. [23] Torrens, F. A new chemical index inspired by biological plastic evolution. Indian J. Chem., Sect. A 2003, 42, 1258–1263. [24] Torrens, F. A chemical index inspired by biological plastic evolution: Valence-isoelectronic series of aromatics. J. Chem. Inf. Comput. Sci. 2004, 44, 575–581. [25] Torrens, F.; Castellano, G. QSPR prediction of retention times of phenylurea herbicides by biological plastic evolution. Curr. Drug Saf. 2012, 7, 262–268. [26] Ruız-Bustos, A. La Evoluci on Pl astica. Andalucıa: Granada, Spain, 1994.
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[27] Hopfinger, A.J. Polymer-solvent interactions for homopolypeptides in aqueous solution. Macromolecules 1971, 4, 731–737. [28] Hopfinger, A.J.; Battershell, R.D. Application of SCAP to drug design: 1. Prediction of octanol–water partition coefficients using solvent-dependent conformational analyses. J. Med. Chem. 1976, 19, 569–573. [29] Gibson, K.D.; Scheraga, H.A. Minimization of polypeptide energy, I. Preliminary structures of bovine pancreatic ribonuclease S-peptide. Proc. Natl. Acad. Sci. U.S.A. 1967, 58, 420–427. [30] Rekker, R.F. The Hydrophobic Fragmental Constant. Elsevier: Amsterdam, 1976. [31] Pascal, P. Program SCAP. Universite Henry Poincare-Nancy I: Nancy, France, 1991. [32] Torrens, F. Characterizing cavity-like spaces in active-site models of zeolites. Comput. Mater. Sci. 2003, 27, 96–101. [33] Dewar, M.J.S.; Zoebisch, E.G.; Healy, E.F.; Stewart, J.J.P. AM1: A new general purpose quantum mechanical model. J. Am. Chem. Soc. 1985, 107, 3902–3909.
Journal of Environmental Science and Health, Part B: Pesticides, Food Contaminants, and Agricultural Wastes Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/lesb20
QSPR prediction of chromatographic retention times of pesticides: Partition and fractal indices a
b
Francisco Torrens & Gloria Castellano a
Institut Universitari de Ciència Molecular, Universitat de València, Edifici d’Instituts de Paterna, València, Spain b
Facultad de Veterinaria y Ciencias Experimentales, Universidad Católica de Valencia San Vicente Mártir, València, Spain Published online: 24 Apr 2014.
To cite this article: Francisco Torrens & Gloria Castellano (2014) QSPR prediction of chromatographic retention times of pesticides: Partition and fractal indices, Journal of Environmental Science and Health, Part B: Pesticides, Food Contaminants, and Agricultural Wastes, 49:6, 400-407, DOI: 10.1080/03601234.2014.894773 To link to this article: http://dx.doi.org/10.1080/03601234.2014.894773
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Journal of Environmental Science and Health, Part B (2014) 49, 400–407 Copyright Ó Taylor & Francis Group, LLC ISSN: 0360-1234 (Print); 1532-4109 (Online) DOI: 10.1080/03601234.2014.894773
QSPR prediction of chromatographic retention times of pesticides: Partition and fractal indices FRANCISCO TORRENS1 and GLORIA CASTELLANO2 1
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2
Institut Universitari de Ciencia Molecular, Universitat de Valencia, Edifici d’Instituts de Paterna, Valencia, Spain Facultad de Veterinaria y Ciencias Experimentales, Universidad Cat olica de Valencia San Vicente M artir, Valencia, Spain
The high-performance liquid-chromatographic retentions of red-wine pesticide residues are modeled by structure–property relationships. The effect of different types of features is analyzed: geometric, lipophilic, etc. The properties are fractal dimensions, partition coefficient, etc., in linear and nonlinear correlation models. Biological plastic evolution is an evolutionary perspective conjugating the effect of acquired characters and relations that emerge among the principles of evolutionary indeterminacy, morphological determination and natural selection. It is applied to design the co-ordination index that is used to characterize pesticide retentions. The parameters used to calculate the co-ordination index are the molar formation enthalpy, molecular weight and surface area. The morphological and co-ordination indices barely improve the correlations. The fractal dimension averaged for non‑buried atoms, partition coefficient, etc. distinguishes the pesticide molecular structures. The structural and constituent classification is based on nonplanarity, and the number of cycles, and O, S, N and Cl atoms. Different behavior depends on the number of cycles. Keywords: Biological plastic evolution, co-ordination index, fractal dimension, 1-octanol–water partition coefficient, morphological index, solvation parameter model.
Introduction Twenty-six billion liters of wine were produced worldwide and 24 billion liters were consumed in 2010 according to the International Organization of Vine and Wine. Wine, especially red one, is rich in polyphenols (e.g., resveratrol, catechin and epicatechin), which are antioxidants that protect cells from oxidative damage caused by free radicals. Red-wine antioxidants inhibit cancers development, for example, prostate one. Red-wines consumption presents heart-healthy benefits. Application of pesticides (e.g., fungicides and insecticides) to improve grape yields is common; however, pesticides permeate via plant tissues and remain in harvested grapes/processed products (e.g., grape juice and wine). Because pesticides are potential source of toxicants that are harmful to human beings, it is important to test for levels in grapes, juice and wine. Although the European Unit set maximum residue levels (MRLs)
Address correspondence to Francisco Torrens, Institut Universitari de Ciencia Molecular, Universitat de Valencia, Edifici d’ Instituts de Paterna, P. O. Box 22085, E-46071 Valencia, Spain; E-mail: [email protected] or [email protected] Received November 13, 2013. Color versions of one or more of the figures in this article can be found online at www.tandfonline.com/lesb.
for pesticides in wine grapes of 0.01–10 mg kg1, it did not set MRLs for wine.[1] A study of wine bought within EU revealed that 34 of 40 bottles contained at least one pesticide. Average number was >4 pesticides per bottle, whereas highest number was 10. Pesticides analysis in red wine is challenging because of matrix complexity that contains alcohol, organic acids, sugars, phenols and pigments, for example, anthocyanins. Traditional red-wine sample preparation methods include liquid–liquid extraction (LLE) with organic solvents [2,3] and solid-phase extraction (SPE) with reversed-phase C18/polymeric sorbents;[4–6] however, LLE is labor-intensive, consumes large amounts of organic solvents and forms emulsions making difficult to separate organic/aqueous phases. In contrast SPE demands more method development. Solid-phase microextraction (SPME),[7,8] hollow-fiber liquid-phase microextraction [9] and stir-bar sorptive extraction (SBSE)[10] are lessly reproducible. Typical detections include gas chromatography (GC), GC coupled to mass spectrometry (MS) (GC–MS) and liquid chromatography coupled to tandem MS (LC–MS–MS). Quick, easy, cheap, effective, rugged and safe (QuEChERS) is a sample preparation method that was reported for pesticide-residues determination in vegetables/fruits;[11] it was used for pesticides/compounds analysis in various food, oil and beverage matrices;[12–14] it involves pesticides extraction from a sample with high water content into
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QSPR prediction of chromatographic retention times of pesticides acetonitrile, with addition of salts to separate phases and partition pesticides into organic layer that is followed by dispersive SPE (dSPE) to clean up various matrix co‑extractives and achieved mixing an aliquot of sample extract with sorbents prepacked in a centrifuge tube. Wang and Telepchak reported pesticides determination in red wine by QuEChERS extraction, rapid mini‑cartridge cleanup and LC–MS–MS detection; they developed the method using QuEChERS but easier/faster cleanup compared to dSPE to clean up red-wine co‑extractives.[1] Sample cleanup way is based on filter-and-clean concept: red-wine extract is pushed via mini‑cartridge with anhydrous MgSO4 and primary-secondary-amine (PSA) sorbent. Eight pesticides belonging to insecticide (methamid ophos, diazinone, pyrazophos and chlorpyrifos), fungicide (carbendazim, thiabendazole, pyrimethanil, cyprodinil and pyrazophos) and parasiticide (thiabendazole) classes were selected. Pesticides polarities are different. Four pesticides are planar (carbendazim, thiabendazole, pyrimethanil and cyprodinil). Cyprodinil was most usually detected on grapes, with chlorpyrifos, diazinone and methamidoph os, frequent. Carbendazim was detected in three of six red-wine samples. The model is an extension of solvent-dependent conformational analysis program (SCAP) octanol–water model to organic solvents.[15] In earlier publications, SCAP was applied for partitions of porphyrins, phthalocyanines, benzobisthiazoles, fullerenes, acetanilides, local anesthetics,[16] lysozyme,[17] barbiturates, hydrocarbons,[18] polystyrene,[19] Fe/S proteins,[20] C-nanotubes [21] and D‑glucopyr anoses.[22] Biological plastic evolution was applied to pheny lalcohols, 4‑alkylanilines,[23] valence-isolectronic se ries of aromatics [24] and phenylurea herbicides.[25] The pre sent report describes quantitative structure–property relationship (QSPR) analysis and prediction of pesticides retention. The aim of this report is to find properties that distinguish pesticides according with retentions. This study applies chemical index to pesticides. The goal is index usefulness validation via capability to differentiate pesticides and interest as predictive index for retention compared with partition and fractal dimension. The following section describes the method. The next two sections present and discuss the results. Finally, the last section summarizes our conclusions.
Computational method Biological plastic evolution is a perspective of the evolutionary process conjugating the effect of (1) the acquired characters and (2) relations among the principles of evolutionary indeterminacy, morphological determination and natural selection. Morphology–functionality relation in organisms lies in that the former is material support of the latter that is the dynamic effect of morphology in the context of physical environment–living matter interaction. Morphology, functionality, energy cost and vital viability are mutually influenced: when a morphology is functional,
it accomplishes its work with minimal energy cost and the vital viability of organ/organism is maximal. Quantifying concepts involves defining functional co-ordination index Ic that is formulated as the ratio between the work accomplished by morphology T and representative morphological index Im, according to: Ic ¼ T =Im :
ð1Þ
The greater the work T achieved by a concrete morphology Im, the greater is Ic. For an organism, Ruiz-Bustos proposed Im as the ratio between morphological surface area S and body weight W:[26] Im ¼ S=W :
ð2Þ
The substitution of Eq. (2) in Eq. (1) results: Ic ¼ T =ðS=W Þ ¼ W T =S:
ð3Þ
At the same time the expression of T by its equivalence in classical mechanics gives: T ¼ W xd 2 x=dt2 :
ð4Þ
Substituting Eq. (4) in Eq. (3) results: Ic ¼ W 2 xd 2 x=ðSdt2 Þ:
ð5Þ
The Ic is greater after conditions because (1) the greater the body weight at equal travelled time/space, the greater is Ic; (2) the Ic is proportional to the space travelled in shortest possible time; and (3) the smaller the body surface, the greater is the Ic, and function–morphology co‑ordination requires smaller energy cost. Program SCAP is based on Hopfinger model parameterized for 1‑octanol/water solvents. The hypothesis is that one can center a solvation sphere on every group of the molecule.[27,28] Intersecting volume V between solvation sphere and van der Waals spheres of resting atoms is calculated. SCAP manages parameters for a solvent: (1) n: maximum number of solvent molecules filling the solvation sphere; (2) Dgo: change of Gibbs free energy associated with the extraction of one solvent molecule out of the solvation sphere;[29,30] (3) Rv: radius of the solvation sphere; (4) Vf: free volume available for a solvent molecule in the solvation sphere.[31] In the solvation sphere, part of the volume excludes solvent molecules. The volume consists of the van der Waals volume of the group at which sphere is centered and a volume representing the groups is bonded to the central one. The latter is represented by a set of cylinders. The difference between total volume of solvation sphere and that excluded to solvent molecules represents volume V0 that is available for n solvent molecules. The Vf is calculated as: Vf ¼ V0 /n Vs. Change of free energy
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associated with extraction of all solvent molecules out of solvation sphere of a group R is: DGR ¼ nDg (1 V / V0 ), and solvation-free energy of a molecule results: DGsolv ¼ SR ¼ 1NDGR . 1‑Octanol/water partition coefficients P is: o o RT ln P ¼ DGsolv ðwaterÞ DGsolv ð1-octanolÞ
ð6Þ
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at given temperature T taken as 298 K, where R is gas constant, and DGsolv (1‑octanol) and DGsolv (water), in kJmol1, is standard-state Gibbs free energies of solvation. Generalizing SCAP for different solvents, the parameters of 1‑octanol were modified taking into account the effect of permittivity and molecular volume on 1-octanol parameters. For a general solvent, maximum number of solvent molecules which allowed filling solvation sphere is related to the molecular volume of solvent as: log nnow =log V o
ns ¼ no ðVs =Vo Þ
Vw
;
ð7Þ
where Vo, Vw and Vs are the molecular volumes of 1-octanol, water and general solvent, respectively. The no, nw and ns are the maximum numbers of molecules of 1-octanol, water and general solvent, respectively, which permitted filling the solvation sphere. The variation in standard Gibbs free energy associated with extraction of one solvent molecule out of solvation sphere, Dgs , is calculated using the generalized Born equation: Dgso ¼ Dgoo ð1 1=es Þ=ð1 1=eo Þ ¼ Dgoo eo ðes 1Þ=½es ðeo 1Þ;
ð8Þ
where Dgs is Dg for 1-octanol, and eo and es are the relative permittivities of 1-octanol and general solvent, respectively. The radius of solvation sphere is related to the molecular volume of solvent molecule as: Rv;s ¼ Rv;o ðVs =Vo Þ1=3 ;
ð9Þ
(Rt Rt )/Rt were calculated. The fractal dimensions were computed with our program TOPO.[32] The co-ordination index application to molecules chemical characterization requires adapting variables T, S and W in Eq. (3): T is redefined as a minus standard formation enthalpy in kJ mol1, S is a molecular surface area in A2 and W is a molecular weight in g mol1. The formation enthalpy and the molecular surface are computed with codes MOPAC–AM1 [33] and TOPO, respectively. Chemical indices for pesticide characterization (cf. Table 2) show that Im remains constant; however, Ic decays with W. Indices variation for pesticides versus molecular weight W (cf. Fig. 2) shows that some points and trend lines Ic and T collapse. The only descriptor that remains almost constant is Im. A linear correlation was observed between S and W. Descriptors more sensitive to W decay: S > T Ic > Im. Variations of (Rt Rt )/Rt versus morphological index Im show fit; the regression turns out to be: ðRt Rot Þ=Rot ¼ 8:12 5:59Im ;
ð11Þ
where n ¼ 9, r ¼ 0.375, s ¼ 1.255, F ¼ 1.1, MAPE ¼ 39.68% and AEV ¼ 0.8596, and where mean absolute percentage error (MAPE) is 39.68% and approximation error variance (AEV) is 0.8596. Best quadratic model, inclusion of Ic or exclusion of TPP does not improve correlation. The behavior is caused by Ic alternating signs because of signs inconstancy in T. Use of pKa betters fit: ðRt Rot Þ=Rot ¼ 2:58 0:120 pKa ;
ð12Þ
where n ¼ 9, r ¼ 0.384, s ¼ 1.250, F ¼ 1.2, MAPE ¼ 34.89% and AEV ¼ 0.8523, and AEV decays by 1%. If partition coefficient log P is utilized in the fit, the results are improved: ðRt Rot Þ=Rot ¼ 0:639 þ 0:668 logP;
ð13Þ
where Rv,o is Rv in 1-octanol. Free volume available for a solvent molecule in the solvation sphere is:
where n ¼ 9, r ¼ 0.910, s ¼ 0.563, F ¼ 33.5, MAPE ¼ 16.85% and AEV ¼ 0.1727, and AEV drops by 80%. If IS TPP is excluded, better correlation is obtained:
Vf ;s ¼ Vf ;o Vs =Vo ;
ðRt Rot Þ=Rot ¼ 0:569 þ 0:719 logP;
ð10Þ
where Vf,o is Vf in 1-octanol.
Calculation results For pesticides (cf. Fig. 1), LC–MS–MS retention time Rt data were taken from Wang and Telepchak.[14] Methamidophos was used as a reference retention time Rt because of least Rt (cf. Table 1). Internal standard (IS) triphenyl pho-sphate (TPP) was included in analyses. The quotients
ð14Þ
where n ¼ 8, r ¼ 0.919, s ¼ 0.561, F ¼ 32.8, MAPE ¼ 16.07% and AEV ¼ 0.1547, and AEV decreases by 82%. If molecular fractal dimension D is employed in fit, results improve: ðRt Rot Þ=Rot ¼ 23:3 þ 19:2 D;
ð15Þ
where n ¼ 9, r ¼ 0.941, s ¼ 0.458, F ¼ 54.0, MAPE ¼ 13.91% and AEV ¼ 0.1147, and AEV decays by 87%. If IS
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QSPR prediction of chromatographic retention times of pesticides O H3C
O CH3
P
H N
O
S
OCH3 NH
NH2
a
N
b
H N S N
H3C
N
N
c
d
H N
N
N
N H
N
S
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N N
f
P
S
H2 C N
O
N
H3C
O P
O
g
O
CH3
O
H3C
O
O
e
O
C H2 H2 C
Cl CH3
N
Cl
O
S
N
P O
O
Cl
h
Fig. 1. Structures of eight pesticides selected in study: (a) methamidophos, (b) carbendazim, (c) thiabendazole, (d) pyrimethanil, (e) cyprodinil, (f) diazinone, (g) pyrazophos and (h) chlorpyrifos.
TPP is excluded, the correlation is bettered: ðRt Rot Þ=Rot ¼ 24:1 þ 19:9D;
ð16Þ
and AEV ¼ 0.1108, and AEV drops by 87.1%. If fractal dimension averaged for external atoms D0 is used, fit expands: ðRt Rot Þ=Rot ¼ 16:2 þ 13:1D0 ;
where n ¼ 8, r ¼ 0.943, s ¼ 0.475, F ¼ 48.2, MAPE¼14.67%,
ð17Þ
Table 1. Retention time, log P, pKa, fractal dimensions molecular/for nonburied atoms for pesticides. Compound Methamidophos Carbendazim Thiabendazole Pyrimethanil Cyprodinil TPP (IS) Diazinone Pyrazophos Chlorpyrifos
Rt (min)
RtRt (min)
(RtRt )/Rt
log P
pKa
D
D0
2.78 6.48 6.91 10.43 11.44 11.78 11.92 12.24 13.42
0.00 3.70 4.13 7.65 8.66 9.00 9.14 9.46 10.64
0.00000 1.33094 1.48561 2.75180 3.11511 3.23741 3.28777 3.40288 3.82734
0.779 1.52 2.47 2.558 3.012 4.63 3.766 2.810 5.004
0.58 5.66 3.40 4.41 4.22 5 1.21 1.37 5.28
1.235 1.284 1.288 1.314 1.344 1.394 1.398 1.403 1.394
1.266 1.332 1.331 1.407 1.470 1.504 1.509 1.505 1.494
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Table 2. Vector property (cyc123, O0345, NP, S ¼ N13, Cl3) and indices for characterization of pesticides. Compound 1. Methamidophos 2. Carbendazim 3. Thiabendazole 4. Pyrimethanil 5. Cyprodinil 6. TPP (IS) 7. Diazinone 8. Pyrazophos 9. Chlorpyrifos All series
Wa
Tb
Sc
Imd
Ice
141 191 201 199 225 326 304 373 351 141–373
601.3 38.0 452.4 297.3 418.9 588.8 57.5 222.8 86.7 452.4–601.3
151.2 195.6 193.9 221.1 247.8 305.4 317.8 357.4 299.5 151.2–357.4
1.071 1.023 0.963 1.110 1.100 0.936 1.044 0.957 0.854 0.854–1.110
561.5 37.1 469.7 267.9 380.8 629.0 55.1 232.7 101.4 469.7–629.0
W: molecular weight (g mol1). T: minus standard formation enthalpy (kJ mol1). c S: molecular surface area (A2). d Im: morphological index (mol A2 g1 ). e Ic: co-ordination index (kJ g mol2 A2). a
where n ¼ 9, r ¼ 0.966, s ¼ 0.348, F ¼ 98.9, MAPE ¼ 10.59% and AEV ¼ 0.0661, and AEV decreases by 92%. If IS TPP is excluded, the results are improved: ðRt Rot Þ=Rot ¼ 16:8 þ 13:6D0 ;
ð18Þ
where n ¼ 8, r ¼ 0.969, s ¼ 0.350, F ¼ 93.8, MAPE ¼ 11.00% and AEV ¼ 0.0601, and AEV decays by 93%. If more fundamental-free energy of solvation in 1‑octanol DGsolv,oct is included, better correlation is attained: ðRt Rot Þ=Rot ¼ 0:619 þ 0:512 log P o 0:0388DGsolv;oct ;
ð19Þ
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b
S Ic 0
T Im
where n ¼ 8, r ¼ 0.972, s ¼ 0.365, F ¼ 43.2, MAPE ¼ 10.32% and AEV ¼ 0.0547, and AEV drops by 93.6%. If the fractal dimension averaged for nonburied atoms minus molecular fractal dimension D0 D is included in the fit, the results are improved: ðRt Rot Þ=Rot ¼ 0:188 þ 0:367 log P þ19:6ðD0 DÞ;
ð20Þ
where n ¼ 9, r ¼ 0.973, s ¼ 0.337, F ¼ 53.3, MAPE ¼ 8.77% and AEV ¼ 0.0533, and AEV decreases by 93.8%. If IS TPP is excluded, the correlation is bettered: ðRt Rot Þ=Rot ¼ 0:229 þ 0:420 log P þ19:1ðD0 DÞ;
ð21Þ
where n ¼ 8, r ¼ 0.982, s ¼ 0.295, F ¼ 67.5, MAPE ¼ 7.17% and AEV ¼ 0.0357, and AEV decays by 96%. If external dimension D0 is included in the fit, the results are improved: ðRt Rot Þ=Rot ¼ 11:6 þ 0:272 log P þ 9:44D0 ;
ð22Þ
where n ¼ 8, r ¼ 0.987, s ¼ 0.253, F ¼ 93.2, MAPE ¼ 5.81% and AEV ¼ 0.0261, and AEV drops by 97%. The best quadratic model versus D0 acquires better correlation: ðRt Rot Þ=Rot ¼ 112 þ 151D0 49:3D02 ;
ð23Þ
-500 150
200 250 300 Molecular weight (g/mol)
350
Fig. 2. Variation of chemical indices for the pesticides versus molecular weight: y ¼ 46.7 þ 0.808x, r ¼ 0.966; y ¼ 13.1 0.172x; y ¼ 24.6 0.229x; y ¼ 1.19–0.000699x.
where n ¼ 9, r ¼ 0.988, s ¼ 0.224, F ¼ 125.2, MAPE ¼ 5.39% and AEV ¼ 0.0234, and AEV decreases by 97.3%. If IS TPP is excluded, the results are improved: ðRt Rot Þ=Rot ¼ 107 þ 144D0 46:7D02 ;
ð24Þ
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QSPR prediction of chromatographic retention times of pesticides where n ¼ 8, r ¼ 0.989, s ¼ 0.228, F ¼ 114.4, MAPE ¼ 5.84% and AEV ¼ 0.0214, and AEV decays by 97.5%. Model (22) is linear and expected to perform better than Eqs. (23) and (24) for extrapolation; however, the latter are nonlinear and could function better than Eq. (22) for intrapolation. Additional fitting parameters were tested: absolute/differential formation enthalpies, molecular dipole mo ment, organic solvent–water partition coefficients, free energies of solvation and transfer water!organic solvents, mole cular volume, surface area, globularity, rugosity, hydrophobic, hydrophilic and total solvent accessible surfaces and numbers of P and total atoms. Notwithstanding, re sults do not improve Eqs. (22)–(24). The first step in quantifying similarity for pesticides is to list their most important molecular characteristics. A vector of properties i ¼ is associated with every pesticide i, whose components correspond to different molecular features in a hierarchical order according to their expected importance in retention. If characteristic mth is more significant for retention than kth then m < k. Components ik are either “1” or “0”, according to whether a similar characteristic of rank k is either present or absent in molecule i compared to a reference. Analysis includes six structural and constitutional characteristics: presence of cycle (cyc123), occurrence of either none or 3– 5 O atoms (O0345), nonplanarity (NP), double-bonded S atom (S ¼ ), either one or three N atoms (N13) and three Cl atoms (Cl3, Fig. 1). It is assumed that the chemical characteristics can be ranked according to their contribution to retention decaying: cyc123 > O0345 > NP > S ¼ > N13 > Cl3. Index i1 ¼ 1 denotes cyc123 (i1 ¼ 0 for cyc0), i2 ¼ 1 means O0345, i3 ¼ 1 signifies NP, i4 ¼ 1 indicates S, i5 ¼ 1 stands for N13 and i6 ¼ 1 represents Cl3. Chlorpyrifos shows cyc1, O3, NP, S ¼ N1 and Cl3; its associated vector is (Table 2): it was selected as a reference because of greatest retention. A principal component analysis (PCA) was performed. Factor F1 explains 39% variance (61% error), F1/2, 66% variance (34% error), F1–3, 87% variance (13% error), etc. Scores plot of PCA F2–F1 clearly distinguished three clusters: class 1 (two compounds, F1 < F2, cf. Fig. 3, left), grouping 2 (three substances, F1 > F2, right).
Discussion Molecular studies were used to predict several parameters related to the bioactivities of drugs. It was concluded that direct correlation of molecular descriptors with bioactivity is possible. Thus, the chromatographic behavior of drugs in phases of different polarity might contain information of use in describing their pharmacological performance, for example, for barbiturates and neuroleptics. Later studies established that chromatographic parameters in a polar stationary phase system correlate better with some
1
3,4,5
6 0
7
2
Class 1 8 F2
9 -1
Class 2 Class 3
-2 1 -1
0 F1
1
Fig. 3. Principal component analysis F2 versus F1 scores plot for the pesticides.
molecular indices, whereas Kovats parameters, obtained from the apolar phase interaction, correlate the best with some other terms. Molecular descriptors satisfactorily predict chromatographic parameters, for example , retention times in GC/LC and RF in thin-layer chromatography (TLC). Topological descriptors were used in chromatographic chiral separations. The chromatographic properties for a group of natural phenolic derivatives were predicted by molecular topology. The properties of chiral compounds were forecasted by molecular topology. A study was made of the relationship between the retention times obtained by LC–MS–MS chromatography for a group of pesticides and molecular descriptors. By using multivariate regression, the corresponding molecular functions were obtained, which were selected on the basis of their respective statistical parameters. Regression analysis of the molecular functions showed a correct prediction of the experimental elution sequence for this set of molecules. In order to predict correctly the experimental elution sequence in the group of molecules, a two-variable model was necessary, in which the simultaneous appearance of indices log P and D0 reveals the importance of lipophilicity and the fractal effect in the studied property, allowing the use of such equations in predicting the value of the property; molecular structures may be differentiated even in other pesticide derivatives not included in the series.
Conclusion The objective of this study was to develop a structure– property relationship for the qualitative and quantitative
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406 prediction of the high-performance liquid-chromatographic retention time of pesticides. It is hoped that the results of the present work contribute to an increased scientific knowledge in the field of relationship prediction of pesticide residues, in food and environmental samples. The SCAP program allows the hydration and solvation-free energies and partition coe fficients, which show that for a given atom energies and partition coefficients are sensitive to the presence in the molecule of other atoms and functional groups. The parameters needed to calculate the co‑ordination index are the molar formation enthalpy, molecular weight and surface area. Linear and nonlinear correlation models were obtained for the chromatographic retention time. The morphological and co‑ordination indices hardly imp roved the multivariable regression equations for the chromatographic retention. The correlation between the molecular surface area and weight points not only to a homogeneous molecular structure of the pesticides, but also to the ability to predict and tailor their properties. The latter is nontrivial in environmental toxicology. The effect of different types of features was analyzed: geometric, lipophilic, etc. The fractal dimensions, partition coefficient, etc. distinguished pesticides in linear and nonlinear fits. Several criteria, selected to reduce the analysis to manageable quantity of pesticides, referred to structural and constitutional characteristics related to nonplanarity, and the number of cycles, and O, double-bonded S, N and Cl atoms. The molecules were classified in agreement with the principal component analyses. An extension of this study to other pesticides would give an insight into the possible generality of the conclusions above.
Funding One of the authors, F. T., acknowledges support from the Spanish Ministerio de Ciencia e Innovaci on (Project No. BFU2010-19118).
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