Chinese Chemical Letters  2014, Vol.25 Issue (07):937-977   PDF    
QAAR exploration on pesticides with high solubility:An investigation on sulfonylurea herbicide dimers formed through π-π stacking interactions
Shuang Xiaa, Yue Fenga, Jia-Gao Chenga , Hai-Bin Luoc, Zhong Lia , Zheng-Ming Lib    
aShanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China;
bState Key Laboratory of Elemento-Organic Chemistry, Institute of Elemento-Organic Chemistry, Nankai University, Tianjin 300071, China;
cSchool of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou 510006, China
Abstract: Bioactive compounds could form aggregates that influence the bio-interactive processes. In this letter, based on π-π stacking models, quantitative aggregation-activity relationship (QAAR) studies were carried out on a series of sulfonylurea herbicides with good solubility. Four QAAR/QSAR models were constructed, which indicated that the bioactivity may strongly depend on both the characters of the dimeric aggregates and the monomer. The QAAR approach based on dimer-aggregates was also applicable for the highly water-soluble sulfonylurea herbicides that can form π-π stacking interactions. It was expected that the QAAR studies based on molecular aggregation state would be applied to other pesticide systems.
Key words: Aggregate     QAAR/QSAR     π-π Interaction     Sulfonylurea    
1. Introduction

Compounds could aggregate through intermolecular noncovalent bonding interactions such as hydrogen bonds,electrostatic interactions,hydrophobic interactions,π-π interactions and so on,which contribute to different biological or chemical properties [1]. In drug discovery,some small molecules could aggregate into submicrometer particles,nonspecifically inhibit different enzymes [2, 3],or promote enzyme unfolding [4]. The aggregate size of compounds was also associated with their oral bioavailability for some hydrophobic molecules [5]. Thus,not only the molecular structure but also their aggregation states could affect the bioactivity of compounds.

For pesticide,the final outcome depends on the spraying, distribution,transportation,interaction with target and other processes,rendering the interpretation of thein vivo efficacy difficult. Many pesticides showed distinct bioactivityin vivoandin vitro,which can be explained by hydrophilicity [6, 7] but might also be influenced by the molecular aggregation states. In order to explore the relationship between aggregation state and bioactivity, the quantitative aggregation-activity relationship (QAAR) method was proposed in our earlier work,in which dimer was used as the simplest aggregation state,due to the complexity and difficulty in modeling the multimolecular aggregation states. QAAR modeling was performed on two series of benzoylphenylureas insect growth regulators [8, 9]. Better regression of the QAAR models indicated that the bioactivity might strongly correlate with the molecular aggregation states. However,the benzoylphenylureas compounds used in our previous research are of poor water solubility and their dimer-aggregate models are generated based on intermolecular hydrogen-bonds. Thus,the following two questions are still remained to be clarified: (1) Whether molecular aggregation properties relate to the bioactivity of pesticides with good solubility,and (2) whether other non-covalent interactions such asπ-π interactions could be used in building the aggregate models in a QAAR study.

To answer the two questions above,QAAR studies were performed on sulfonylurea herbicides,inhibitors of acetohydroxyacid synthase (AHAS),with high efficiency,low toxicity and good environment compatibility [10, 11]. Some series of the highly soluble sulfonylurea compounds had been reported, such as N-[2-(4-methyl)pyrimidinyl]-N'-2-methoxycarbonylbenzene sulfonylurea [12, 13, 14, 15, 16]. QSAR studies based on monomeric structures had also been performed to analyze the effects of substituents on bioactivity [17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]. Interestingly,a stable intermolecular π-π stacking dimeric structural motif was observed in the crystal structure ofN-[2-(4-methyl)pyrimidinyl]-N'-2-methoxycarbonyl-benzene sulfonylurea [14]. In this study,π-π stacking dimer models of 24 sulfonylurea compounds were constructed,followed by QAAR and QSAR investigations based on dimer-aggregate and monomer descriptors,respectively. To a certain degree,the QAAR and QSAR models elucidated the possible roles of aggregation state on the bioactivity of highly soluble pesticide systems. 2. Materials and methods 2.1. Biological data sets

All the 24 sulfonylurea compounds reported in our previous study [15] were used to develop the QAAR/QSAR models,20 of which were selected randomly as training set and the rest 4 were used as the test set. All the structures and herbicidal activity were listed in Table 1.

Table 1
Structures and bioactivity of sulfonylurea compounds.
2.2. Generating structures

Based on the crystal structure ofN-[2-(4-methyl)pyrimidinyl]-N'-2-methoxycarbonyl-benzene sulfonylurea,the homo-dimeraggregate structures were established through π-π stacking interactions (Fig. 1). The B97D/TZVP method,a density functional theory (DFT) method that could produce more reliable results for π-π interaction systems [28],was applied to optimize all monomeric and homo-dimeric structures. All calculations were performed using the GAUSSIAN 09 software (Gaussian,Inc., Wallingford,CT,2009).

Fig. 1. Homo-dimer-aggregate structure of compound 4 based onπ-π stacking interaction.
2.3. Descriptors calculation

The optimized structures of monomeric and dimeric systems were imported into databases of the Molecular Operating Environment (MOE 2008.10). Then,more than 300 physicochemical descriptors were generated. The definitions of these descriptors included in the models are listed and described below. The calculated descriptors were initially screened for invariant nature, insignificance using QuaSAR Contingency module of MOE,which is a statistical application designed to assist in the selection of descriptors for QSAR. Further,the interrelation research was performed to limit the number of descriptors for the study. 2.4. QAAR/QSAR modeling

Both monomer and aggregate based QSAR models were built using the reduced descriptor pool as an independent variable and pIC50 as a dependent variable by forward stepwise regression analyses. QSAR equations were acquired according to different combinations of various descriptors. The data matrix was analyzed using the partial least squares (PLS) method [29]. The quality of each regression model was evaluated,using a squared correlation coefficient (r2),cross validation squared correlation coefficient (q2), and root mean square error (RMSE). The correlation coefficientr2indicated how well the equation fits the data. The ‘leave-one-out’ (LOO) cross-validation coefficient q2was considered as an indicator of the predictive performance and stability of a QSAR model [30, 31]. 3. Results and discussion

Theπ-π stacking aggregate models were generated by the B97D/TZVP method. The optimized geometry of dimeric compound 4 was depicted in Fig. 1. The distance between the centroids of two benzene rings was 3.75 Å ,which showed a good π-π interaction. Then,QAAR/QSAR investigations were performed on the descriptors calculated from the optimized aggregate/monomer structures. The best established QSAR equations including two or three monomeric descriptors were summarized in Eqs. (1) and (3) respectively along with their statistical parameters,as well as their corresponding QAAR Eqs. (2) and (4) based on aggregate descriptors. The descriptors used in QSAR building were defined as follows: radius of gyration (rgyr),log of the aqueous solubility (logS),log of the octanol/water partition coefficient (log P(o/w)), surface globularity (vsurf_G).

Rgyr is a common QSAR descriptor,which is a measure of molecular compactness for long chain molecules such as polymers. It is calculated as the root mean square distance of the objects’ parts from either its center of gravity or a given axis. Small values are obtained when most of the atoms are close to the center of mass [32]. Rgyr is often used to describe the structural changes in the protein. The change of the Rgyr correlates to the degree that the structure spreads out from its center [33]. Here,it was used to characterize the aggregation state changing from dimer to monomer.

Log S is calculated from an atom contribution linear atom type model [34]. The aqueous solubility of a compound significantly affects its absorption and distribution characteristics. Typically, low solubility results in poor absorption. Aqueous solubility is among the most important characteristics in ADME studies and the relevant physicochemical descriptors in QSAR studies [32].

The log P(o/w) value is known as a measure of lipophilicity. In drug research,partition coefficients are useful,for example,in estimating distribution of drugs in the body [35]. LogPhas been widely used to study biological processes relevant to drug action, cellular uptake,metabolism,bio-availability,and toxicity [36]. Hydrophobic drugs with high octanol/water partition coefficients are preferentially distributed to hydrophobic compartments such as lipid bilayers of cells while hydrophilic drugs (low octanol/water partition coefficients) are often found in hydrophilic compartments such as blood serum.

Vsurf_G is defined as the ratio of the molecular surface to the surface area of a sphere of the same volume. Globularity is 1.0 for perfect spherical molecules. It assumes values greater than 1.0 for real spheroidal molecules [32]. It is specifically designed for the prediction of pharmacokinetic properties [37] and is also related to molecular flexibility.

In the QAAR/QSAR equations,nis the number of compounds in the training set,r2is the correlation coefficient,q2is the LOO crossvalidated coefficient,RMSE is the root mean square error. A good QAAR/QSAR model is indicated byr2andq2values close to 1.0,as well as small RMSE. As a rule of thumb,the equations with regression coefficients r2>0.80 and q2>0.50 are considered reasonable. It can be found from Fig. 2 that all the equations exhibited good regressions between the experimental and predicted activity.

The equations implied that the physical-chemical properties logSand log P(o/w),which were independent of conformation, rgyr and vsurf_G,which depended on the structure connectivity and conformation,played important roles in bioactivity. All QAAR/ QSAR equations showed a negative correlation between rgyr and activity. It indicated that smaller rgyr values may be associated with higher activity. As shown in Table 2,the aggregate rgyr values were smaller than the sum of those of two monomers,which indicated the closer proximity between two monomers in the aggregation state and more structural changes during the deaggregation,which would increase the bioactivity. Similarly, vsurf_G had a negative correlation with activity. Forming aggregates reduced the values of vsurf_G,thereby,improved activity. LogSand log P(o/w) showed opposite and negative sign in the equations respectively,which meant that high solubility and low octanol/water partition coefficients were favorable for activity.Taking together,the compounds that had high activity should have small values of rgyr,vsurf_G,log P(o/w) and a large value of logS. For example,compound 4 had the largest logSvalue,small rgyr and vsurf_G values,and nearly the smallest log P(o/w) value,and also had the highest activity. In contrast,compound 16 had the lowest logSvalue,the biggest log P(o/w) value,large rgyr and vsurf_G values,and compound 24 had the largest rgyr and vsurf_G values,a small logSvalue,big log P(o/w) value. As a result,both compounds 16 and 24 had weak activity.

Table 2
Descriptors of monomer QSAR and aggregate QAAR models.

Fig. 2. Correlation plot of experimental activityversuspredicted activity,respectively for the monomer QSAR and aggregate QAAR models. (&) Values for compounds in the training set and (*) values for compounds in the test set.

All equations established here displayed a good predictive ability and small errors between the predicted and experimental activity, which indicated that the bioactivity of compounds deeply depended on the properties of molecular aggregation state,as well as the monomer. Furthermore,all aggregate-based QAAR equations showed slightly better correlations and predictability than the monomeric QSAR ones. Especially for the Eq. (4),which had three aggregate descriptors,achieved the highestr2andq2values. It revealed that,in herbicide system with high solubility,the bioactivity was strongly correlated with the properties of molecular aggregation state,which is consistent with the findings in our previous study on poorly water-soluble insect growth regulators. In the current study,π-π interactions instead of hydrogen bonds were successfully used in the building of dimer-aggregate models,which revealed thatπ-π interaction is also a satisfactory non-covalent interaction that can be used in QAAR studies. 4. Conclusion

The QAAR/QSAR studies were carried out on a series of highly water-soluble sulfonylurea herbicidal compounds using dimeric and monomeric descriptors. Four QAAR and QSAR equations were successfully established. The QAAR/QSAR models revealed that low values of rgyr and vsurf_G for the formation and dissociation of dimers,as well as high values of logSand low values of log P(o/w) would lead to high bioactivity. Accordingly,the QAAR approach was not only appropriate for poorly water-soluble insect growth regulators,but also for highly water-soluble sulfonylurea herbicide,and not only hydrogen bonds but alsoπ-π interactions could be successfully introduced in QAAR investigations. It is believed that the QAAR studies based on aggregate models could be applicable in other pesticide systems,which might facilitate the discovery of improved compounds. Acknowledgments

We thanks for the financial supports from National Key Technology R&D Program of China (No. 2011BAE06B05),National Natural Science Foundation of China (No. 21172070),National High Technology Research Development Program of China (No. 2011AA10A207),National Basic Research Program of China (No. 2010CB126100) and the Fundamental Research Funds for the Central Universities.

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