s2fieldsofstudy: Chemistry Biology

text:
Structural Investigation for Optimization of Anthranilic Acid Derivatives as Partial FXR Agonists by in Silico Approaches

In this paper, a three level in silico approach was applied to investigate some important structural and physicochemical aspects of a series of anthranilic acid derivatives (AAD) newly identified as potent partial farnesoid X receptor (FXR) agonists. Initially, both two and three-dimensional quantitative structure activity relationship (2D- and 3D-QSAR) studies were performed based on such AAD by a stepwise technology combined with multiple linear regression and comparative molecular field analysis. The obtained 2D-QSAR model gave a high predictive ability (R2train = 0.935, R2test = 0.902, Q2LOO = 0.899). It also uncovered that number of rotatable single bonds (b_rotN), relative negative partial charges (RPC−), oprea's lead-like (opr_leadlike), subdivided van der Waal’s surface area (SlogP_VSA2) and accessible surface area (ASA) were important features in defining activity. Additionally, the derived3D-QSAR model presented a higher predictive ability (R2train = 0.944, R2test = 0.892, Q2LOO = 0.802). Meanwhile, the derived contour maps from the 3D-QSAR model revealed the significant structural features (steric and electronic effects) required for improving FXR agonist activity. Finally, nine newly designed AAD with higher predicted EC50 values than the known template compound were docked into the FXR active site. The excellent molecular binding patterns of these molecules also suggested that they can be robust and potent partial FXR agonists in agreement with the QSAR results. Overall, these derived models may help to identify and design novel AAD with better FXR agonist activity.

Introduction
Farnesoid X receptor (FXR) is a nuclear receptor expressed in liver, gall bladder, intestine, kidney, and adrenal glands. It regulates important physiological roles in various metabolic pathways involved in bile acid, triglyceride, and glucose homeostasis. Now, FXR has become an attractive target for treating a wide range of metabolic diseases, including diabetes, cholestasis, liver fibrosis, and inflammatory bowel diseases [1][2][3]. Therefore, a number of synthetic steroidal and nonsteroidal FXR agonists have been developed so far. 6-ethyl-chenodeoxycholic acid20 (6-ECDCA) and GW4064 were the most important and widely used steroidal and nonsteroidal FXR agonists [4]. They both constitute full FXR agonists with low nanomolar EC 50 values of 85 nM and 0.9 M in a reporter gene assay [5], respectively. In addition, a recent study indicated that GW4064 was active on several off-targets [6]. Considering that metabolic diseases require a stable long-term therapy, well tolerated and low toxicity FXR agonists are predominantly required that can be applied over long time. Moreover, full activation of a ligand activated transcription factor may cause many side effects in long-term treatment [7]. Therefore, new potent partial FXR agonists aimed at providing a stable long-term therapy for metabolic diseases have attracted more and more attention nowadays.
Recently, a novel series of partial FXR agonists based on anthranilic acid skeleton have been reported by Merk et al. [8,9]. The continued interest in the development of more potent partial FXR agonists prompted us to explore the relationship between structures of AAD and FXR agonist activity. Here, quantitative structure activity relationship (QSAR) methods were introduced to guide lead optimization and study the action mechanism for partial FXR agonists. In this QSAR method, the bioactivity of compounds can be predicted by a mathematical model between physicochemical properties and bioactivity. The mathematical model can be achieved by many general algorithms such as linear and nonlinear algorithms or other new methods such as the spectral-structure activity relationship algorithm [10,11]. This QSAR method has become very useful and is widely applied in many fields for predicting compound properties [12,13], including physical property prediction, biological activity prediction, and toxicity prediction.
To the best of our knowledge, no QSAR study has yet been reported in AAD as FXR agonists so far. In this paper, we attempted to investigate the significant structural and physicochemical features required for improving biological activity and to obtain highly predictive 2D-and 3D-QSAR models so as to assist in the design of new potent partial FXR agonists. Firstly, a stepwise technology combined with multiple linear regression (MLR) was applied to develop predictive 2D-QSAR models for uncovering physicochemical features on FXR activity of AAD. Subsequently, a 3D-QSAR study was also performed to obtain more understanding with respect to chemical structures and biological activity using comparative molecular field analysis (CoMFA). The CoMFA model can provide identification of regions in space where the interactive fields may influence the biological activities in the form of contour maps, which would generate graphical visualization of crucial steric and electrostatic features in 3D Cartesian space [14]. Finally, some important observations were also made during the study concerning nine newly designed AAD with high predicted bioactivity and their interactions with the FXR active site by molecular docking. Molecular docking aims to predict the binding-conformation of ligands to the appropriate target binding site. The success of a docking program depends on two components: the search algorithm and the scoring function. A variety of conformational search strategies have been reported such as systematic or stochastic search or genetic algorithms or simplified molecular input line entry system conformation [15]. Most scoring functions are physics-based molecular mechanics force fields that estimate the energy of the pose within the binding site. where, N train and N test are the number of compounds in the training set and the test set, respectively. R 2 train and R 2 test are the squared correlation coefficient of training set and test set, respectively; Q 2 LOO is the leave-one-out (LOO) cross-validation squared correlation coefficient; F is the F-test value; RMSE is root mean standard error. The selected variables and their chemical meanings, standard coefficients are shown in Table 1. A variable inflation factor (VIF) (VIF = 1/(1´R j 2 ), R j 2 represents the multiple correlation coefficient of one descriptor's effect on the remaining molecular descriptors) was calculated to determine if multicollinearity existed among the descriptors in models. If VIF arrays from 1.0 to 5.0, the linked equation is suitable [16]. As shown in Table 1, the VIF of all descriptors were smaller than 4, indicating that the generated model possessed statistic significance and good stability. Table 2 shows the correlation matrix of the selected descriptors. From this table, it can be seen that the linear correlation coefficient value for each pair of descriptors was smaller than 0.85, suggesting that the selected descriptors were independent, meeting the important criterion for the model selections [17]. The predicted results of the MLR model are given in Table 3 and shown in Figure 1A. As described in Table 4, obviously, the MLR model was very successfully built with statistical significance and good prediction ability. The R 2 train value of this model reveals that it can explain 93.5% of the variance in activity. The Q 2 LOO value of 0.899 was much larger than 0.5, indicating that the developed model had very good stability and predictive ability. In addition, the value of R 2 test for the external prediction was 0.902, showing the good prediction and generalization ability. calculated to determine if multicollinearity existed among the descriptors in models. If VIF arrays from 1.0 to 5.0, the linked equation is suitable [16]. As shown in Table 1, the VIF of all descriptors were smaller than 4, indicating that the generated model possessed statistic significance and good stability. Table 2 shows the correlation matrix of the selected descriptors. From this table, it can be seen that the linear correlation coefficient value for each pair of descriptors was smaller than 0.85, suggesting that the selected descriptors were independent, meeting the important criterion for the model selections [17]. The predicted results of the MLR model are given in Table 3 and shown in Figure 1A. As described in Table 4, obviously, the MLR model was very successfully built with statistical significance and good prediction ability. The R 2 train value of this model reveals that it can explain 93.5% of the variance in activity. The Q 2 LOO value of 0.899 was much larger than 0.5, indicating that the developed model had very good stability and predictive ability. In addition, the value of R 2 test for the external prediction was 0.902, showing the good prediction and generalization ability.        Finally, to confirm the robustness of the model, the Y-randomization test was performed in this study. The dependent variable vector is randomly shuffled and a new model is constructed. If the new model gives significantly lower values for both R 2 train and Q 2 LOO statistics compared to the original model, the original generated model is not considered as resulting from a chance correlation [18]. The results of ten Y-randomization tests are summarized in Table 5. As can be seen, all new R 2 train and Q 2 LOO values were much lower than those of the original model. Thereby, the good results for the MLR model were not due to a chance correlation or structural dependency of the training set.

Model Applicability Domain Analysis for the MLR Model
Finally, to evaluate the generalization degree of the generated model, the applicability domain (AD) was defined by a Williams plot. In the Williams plot, leverage values versus standardized residuals were plotted to detect both the structurally influential chemicals (X outliers) and the response outliers (Y outliers) [19]. The leverage value h is defined as: where x i is the descriptor row vector of compound, X is the matrix of the descriptor values of the training set and n is the number of training sets. The superscript "T" refers to the transposed value of the matrix/vector [19,20]. When a leverage value h is higher than the threshold value h* (calculated as 3(m + 1)/n, where m is the number of model parameters and n is the number of the training set), it is considered as X outliers. In addition, a value of˘3.0 standard deviation units is widely used as a cut off value for defining Y outliers. In this study, the Williams plot for the MLR model is shown in Figure 2. From this Figure 2, it can be found that no Y outliers existed in the investigated data set. Nevertheless, there were five molecules (compound 18, 20, 32, 37 and 26) with a leverage value higher than the warning leverage limit (0.581), but their predicted values were very satisfactory, with standard residuals lower than 1.0 standard deviation units. Hence, these molecules were not influential in the fitting performance of the model. Conversely, it further showed the reliability of the predictions of the generated model as well as confirmed its good generalization ability [19]. Therefore, compounds with high value of leverage and good fitting in the developed model can stabilize the model, and not be considered as X outliers.
As can be seen from the above results, the MLR model was significantly highly predictive, reliable and robust. It can be used to predict the FXR agonist activity of new AAD.

Interpretation of the Descriptors
The MLR model encompassed five descriptors: b_rotN, RPC − , opr_leadlike, SlogP_VSA2 and ASA, indicating some vital physicochemical features of AAD to govern the FXR agonist activity. The relative importance of the descriptors in the model was varied in view of their standardized regression coefficients shown in Table 1 [19]. Therefore, the relative importance order is ASA > SlogP_VSA2 > b_rotN > RPC − > opr_leadlike. ASA is the water accessible surface area calculated using a radius of 1.4 A for the water molecule. This showed that the water accessible surface area of FXR agonists might influence their agonist activity. Its positive coefficient value indicated that high polar groups tend to increase the agonist activity. For instance, it can be observed from the agonist activity of compounds 9 (having 3-cyanophenyl with pEC50 = 6.638) and 10 (having 3-methoxyphenyl with pEC50 = 6.420) or 1 (having 3-carboxyphenyl with pEC50 = 6.553) and 8 (having acetylphenyl with pEC50 = 6.319) in Table 3. Slogp_VSA2 is the subdivided surface area descriptor, which is based on the sum of the approximate accessible van der Waal's surface area, calculated for each atom with contribution to the log of the partition coefficient (octanol/water) in the range of (−0.2,0). Its negative coefficient value indicated that high hydrophobicity tended to decrease the agonist activity. Obviously, the bioactivity of molecules with aliphatic chains at region A in Table 3, such as 34 (with pEC50 of 5.602) or 35, 36, 37 and 38 (with pEC50 of 5.066-5.357), was lower than those without aliphatic chains such as 1 (with pEC50 of 6.553) or 39 and 40 (with pEC50 of 5.824-6.000). B_rotN is the number of rotatable single bonds. Its positive coefficient illustrated that more b_rotN was favorable to the FXR agonist activity. For instance, the bioactivity of compounds 15 and 16 or 35, 36 and 38 are varied in order: 16 (having b_rotN of 10) > 15 (having b_rotN of 9) or 38 (having b_rotN of 11) > 36 (having b_rotN of 10) > 35 (having b_rotN of 9). RPC − is a relative negative partial charge descriptor that depends on the partial charge of each atom of a chemical structure. The positive sign of this descriptor illustrated that the relative negative partial charge of the molecule was favorable for the agonist activity. It can be quickly understood by comparing molecules 13 (having -CONH2 headgroup with pEC50 = 7.131) and 1 (having -COOH headgroup with pEC50 = 6.553). Opr_leadlike belongs to atom count and bond count descriptors that refer to the number of violations of the Oprea's lead-like test. The positive contribution of this descriptor indicated that the high value of opr_leadlike was beneficial to the bioactivity.
Therefore, high polar groups such as the acidic headgroup together with high values of RPC − and b_rotN are favorable for FXR agonist activity. Further, the aliphatic chain has a negative effect on it.

Interpretation of the Descriptors
The MLR model encompassed five descriptors: b_rotN, RPC´, opr_leadlike, SlogP_VSA2 and ASA, indicating some vital physicochemical features of AAD to govern the FXR agonist activity. The relative importance of the descriptors in the model was varied in view of their standardized regression coefficients shown in Table 1 [19]. Therefore, the relative importance order is ASA > SlogP_VSA2 > b_rotN > RPC´> opr_leadlike. ASA is the water accessible surface area calculated using a radius of 1.4 A for the water molecule. This showed that the water accessible surface area of FXR agonists might influence their agonist activity. Its positive coefficient value indicated that high polar groups tend to increase the agonist activity. For instance, it can be observed from the agonist activity of compounds 9 (having 3-cyanophenyl with pEC 50 = 6.638) and 10 (having 3-methoxyphenyl with pEC 50 = 6.420) or 1 (having 3-carboxyphenyl with pEC 50 = 6.553) and 8 (having acetylphenyl with pEC 50 = 6.319) in Table 3. Slogp_VSA2 is the subdivided surface area descriptor, which is based on the sum of the approximate accessible van der Waal's surface area, calculated for each atom with contribution to the log of the partition coefficient (octanol/water) in the range of (´0.2,0). Its negative coefficient value indicated that high hydrophobicity tended to decrease the agonist activity. Obviously, the bioactivity of molecules with aliphatic chains at region A in Table 3, such as 34 (with pEC 50 of 5.602) or 35, 36, 37 and 38 (with pEC 50 of 5.066-5.357), was lower than those without aliphatic chains such as 1 (with pEC 50 of 6.553) or 39 and 40 (with pEC 50 of 5.824-6.000). B_rotN is the number of rotatable single bonds. Its positive coefficient illustrated that more b_rotN was favorable to the FXR agonist activity. For instance, the bioactivity of compounds 15 and 16 or 35, 36 and 38 are varied in order: 16 (having b_rotN of 10) > 15 (having b_rotN of 9) or 38 (having b_rotN of 11) > 36 (having b_rotN of 10) > 35 (having b_rotN of 9). RPC´is a relative negative partial charge descriptor that depends on the partial charge of each atom of a chemical structure. The positive sign of this descriptor illustrated that the relative negative partial charge of the molecule was favorable for the agonist activity. It can be quickly understood by comparing molecules 13 (having -CONH 2 headgroup with pEC 50 = 7.131) and 1 (having -COOH headgroup with pEC 50 = 6.553). Opr_leadlike belongs to atom count and bond count descriptors that refer to the number of violations of the Oprea's lead-like test. The positive contribution of this descriptor indicated that the high value of opr_leadlike was beneficial to the bioactivity.
Therefore, high polar groups such as the acidic headgroup together with high values of RPC´and b_rotN are favorable for FXR agonist activity. Further, the aliphatic chain has a negative effect on it.

CoMFA Analysis
To graphically visualize the key chemical structural features that attributed to enhance the FXR agonist activity, CoMFA models were derived. The results of the CoMFA studies are listed in Table 4. The optimum number of components and filtering value for the CoMFA models were four and six, which were calculated by selecting the highest Q 2 LOO value. The generated CoMFA model illustrated a  Table 4). The pEC 50 values predicted by the CoMFA model are listed in Table 3. Figure 1B demonstrates the correlation between experimental and predicted pEC 50 values by the CoMFA model.

CoMFA Contour Maps
The steric and electrostatic contour maps derived by the CoMFA model based on the reference molecule (compound 30) are shown in Figure 3. The steric interactions are represented by green and yellow contours, while electrostatic interactions are represented by red and blue contours. In the green region of the steric contour plot, bulky substitutes enhance biological activity, while in the yellow regions, these are likely to decrease the activity. Blue contours represent regions where positive charge increases activity, whereas red-colored regions represent areas where negative charge enhances activity [17]. The three regions A, B, and C of compound 30 are depicted in Figure 4.

CoMFA Analysis
To graphically visualize the key chemical structural features that attributed to enhance the FXR agonist activity, CoMFA models were derived. The results of the CoMFA studies are listed in Table 4. The optimum number of components and filtering value for the CoMFA models were four and six, which were calculated by selecting the highest Q 2 LOO value. The generated CoMFA model illustrated a  Table 4). The pEC50 values predicted by the CoMFA model are listed in Table 3. Figure 1B demonstrates the correlation between experimental and predicted pEC50 values by the CoMFA model.

CoMFA Contour Maps
The steric and electrostatic contour maps derived by the CoMFA model based on the reference molecule (compound 30) are shown in Figure 3. The steric interactions are represented by green and yellow contours, while electrostatic interactions are represented by red and blue contours. In the green region of the steric contour plot, bulky substitutes enhance biological activity, while in the yellow regions, these are likely to decrease the activity. Blue contours represent regions where positive charge increases activity, whereas red-colored regions represent areas where negative charge enhances activity [17]. The three regions A, B, and C of compound 30 are depicted in Figure 4.

CoMFA Analysis
To graphically visualize the key chemical structural features that attributed to enhance the FXR agonist activity, CoMFA models were derived. The results of the CoMFA studies are listed in Table 4. The optimum number of components and filtering value for the CoMFA models were four and six, which were calculated by selecting the highest Q 2 LOO value. The generated CoMFA model illustrated a Q 2 LOO value of 0.802 (>0.5) by four components (RMSELOO = 0.383). The non-cross-validated PLS analysis with the four components resulted in R 2 train of 0.944, F value of 109.711 and RMSEtrain of 0.203 and R 2 test of 0.892. The contributions of steric and electrostatic fields calculated by the CoMFA model were 49.7% and 50.3% of the variance, respectively. The obtained high R 2 train, Q 2 train and F values along with the lower RMSEtrain indicated the satisfactory predictive ability of the derived model ( Table 4). The pEC50 values predicted by the CoMFA model are listed in Table 3. Figure 1B demonstrates the correlation between experimental and predicted pEC50 values by the CoMFA model.

CoMFA Contour Maps
The steric and electrostatic contour maps derived by the CoMFA model based on the reference molecule (compound 30) are shown in Figure 3. The steric interactions are represented by green and yellow contours, while electrostatic interactions are represented by red and blue contours. In the green region of the steric contour plot, bulky substitutes enhance biological activity, while in the yellow regions, these are likely to decrease the activity. Blue contours represent regions where positive charge increases activity, whereas red-colored regions represent areas where negative charge enhances activity [17]. The three regions A, B, and C of compound 30 are depicted in Figure 4.   As shown in Figure 3A, there are two large yellow contours near regions A and C, one medium yellow contour near region B and one small yellow contour near region A, indicating that the bioactivity of molecules would be influenced by the introduction of bulky groups near these regions. According to Table 3, this can be explained by a comparison between molecules 21 (having -CH 3 group at position 4 of region A with pEC 50 = 7.347) and 32 (having -OCH 3 at position 4 of region A with pEC 50 = 7.328). The small yellow contour near position 6 of region A also suggested that the agonist activity would be decreased by the introduction of bulky groups here, such as compounds (18,20,19,1) where the use of bulky groups (-OCH 3 > -Cl > -F > -H) resulted in lower pEC 50 values (5.328 < 5.959 < 6.319 < 6.553). A medium yellow contour near R 2 at region B indicated that bulky groups here would cause lower activity. This can be observed by the comparison of molecules 27 (substituted by -CH 3 with pEC 50 value of 7.367) and 32 (substituted by -OCH 3 with pEC 50 value of 7.060), where the volume of -CH 3 was smaller than -OCH 3 . This can also be observed by a comparison of compounds 25 (substituted by -Cl with pEC 50 value of 7.328) and 29 (substituted by -Br with pEC 50 value of 7.319). Another large yellow contour near region C showed that bulky groups at positions 3 and 5 of region C would lead to lower activity. For instance, the agonist activity of compounds 35 or 40 (substituted by naphthalen-2-yl) and 34 or 1 (substituted by 4-tert-butylphenyl) was varied in the order: 35 < 34 or 40 < 1. One small green contour near positions 2 and 3 of region A indicated that FXR agonist activity would be enhanced by introduction of bulky groups here. This can be observed by comparing molecules 17 (having -CH 3 group at position 2 of region A) and 1 (having -H group at position 2 of region A), where using a bulky group influenced the outcome of pEC 50 values (7.377 > 6.553). This can also be observed by comparing the bioactivity of molecules 16 and 15, where using bulky groups (-CH 2 CH 2 COOH > -CH 2 COOH) at position 3 of region A lead to higher pEC 50 values (7.194 > 6.377). Another two small green contours near R 3 substituent groups at region C showed the favorable effect of bulky groups here in increasing the biological activity of compounds. For instance, this can be explained by comparing the activity of compounds 1 (substituted by -C(CH 3 ) 3 with pEC 50 value of 6.553) and 2 (substituted by -CF 3 with pEC 50 value of 5.161) or compounds 34 (substituted by -C(CH 3 ) 3 with pEC 50 value of 5.602) and 33 (substituted by -CH 2 CH 3 group with pEC 50 value of 5.237).
As can be seen from Figure 3B, there was one medium blue contour near positions 2 and R 1 of region A, which showed the favorable effect of electro-donating groups in increasing the biological activity of compounds. For instance, it can be observed by molecules 17 (having -CH 3 group at position 2 of region A with pEC 50 value of 7.377) and 1 (having -H group at position 2 of region A with pEC 50 value of 6.553). This also can be explained by comparing the activity of compounds 13, 1 and 8, where using electro-donating substituents at R 1 (-NH 2 > -OH > -CH 3 ) would result in higher pEC 50 values (7.131 > 6.553 > 6.319).

Chemical Structure Design
According to the information derived from the contour maps generated by the 3D-QSAR models, some important information about the chemical structures requirement was presented to investigate the effect of each kind of group as the substituent for regions A, B and C on FXR agonist activity. The bulky groups with lower electronegativity at positions 2 and R 1 substituent of region A together with bulky groups at R 3 of region C were considered to enhance the FXR agonist activity. However, the presence of bulky groups at positions 4 and 6 of region A, R 2 of region B and 3 and 5 of region C would decrease the agonist activity. Therefore, some new compounds as potent FXR agonists were designed and are listed in Table 6. To investigate the results of each substituent on the activity results, CoMFA was the best modeling tool for use. The newly designed compounds showed that the bulky groups with lower electronegativity at R 1 of region A had positive effects. This can be observed by comparing compound N3 (having -N(CH 3 ) 2 at R 1 of region A with predicted pEC 50 value of 8.322) with template compound T30 (having -OH at R 1 of region A with predicted and actual pEC 50 values of 8.175 and 8.097, respectively). The next attempt was to improve the effects of functional groups at R 2 of region B where bulky effects were presented. It was observed that the addition of less bulky groups (such as -CH 3 ) at R 2 of region B (Table 6) can lead to better agonist activity (compound N2 with predicted pEC 50 value of 8.323). Then, to investigate the bulky effects at R 3 of region C, different bulky groups were tried. This can be observed by comparing compounds N1, N2, N4 (having -C(CF 3 ) 3 , -C(CH 3 ) 3 and -CI 3 at R 3 of region C with predicted pEC 50 values of 8.350, 8.323 and 8.304, respectively). Finally, to observe the effects of the addition of lower electronegativity groups at positions 2 of region A, the donor groups were investigated. It can be seen that using donor group substituent (-H < -CH 3 < -OH) at R 2 of region A would lead to an increase in the predicted pEC 50 values in the compounds: N7 (having -CH 3 with pEC 50 = 8.374), N9 (having -OH with pEC 50 = 8.388) and N1 (having -H with pEC 50 = 8.350). Among the designed compounds, N9 presented the highest activity with a pEC 50 value of 8.388. To understand the origin of this increase in activity, compounds T30 and N3 can be compared. groups at R 2 of region B where bulky effects were presented. It was observed that the addition of less bulky groups (such as -CH3) at R 2 of region B (Table 6) can lead to better agonist activity (compound N2 with predicted pEC50 value of 8.323). Then, to investigate the bulky effects at R 3 of region C, different bulky groups were tried. This can be observed by comparing compounds N1, N2, N4 (having -C(CF3)3, -C(CH3)3 and -CI3 at R 3 of region C with predicted pEC50 values of 8.350, 8.323 and 8.304, respectively). Finally, to observe the effects of the addition of lower electronegativity groups at positions 2 of region A, the donor groups were investigated. It can be seen that using donor group substituent (-H < -CH3 < -OH) at R 2 of region A would lead to an increase in the predicted pEC50 values in the compounds: N7 (having -CH3 with pEC50 = 8.374), N9 (having -OH with pEC50 = 8.388) and N1 (having -H with pEC50 = 8.350). Among the designed compounds, N9 presented the highest activity with a pEC50 value of 8.388. To understand the origin of this increase in activity, compounds T30 and N3 can be compared.

Molecular Docking Study
These molecules were ideally best based on their chemical structures, physicochemical properties and biological activity. Hence, molecular docking embedded in Molecular Operating Environment (MOE2008.10, Chemical Computing Group, Montreal, QC, Canada) was applied to study the binding modes and important interactions.
Prior to the docking, the crystal structure of FXR complexed with benzimidazole-based partial agonistic ligand was first downloaded from a protein data bank (PDB: 3OLF). The protein was protonated using the AMBER99 force field. A set of possible conformations of nine newly designed molecules was prepared by the conformational generation function of MOE. Then, molecular docking was carried out by following parameters: the binding site was defined by the ligand atom mode; triangle matcher was used as a placement method; two rescoring methods were computed, rescoring 1 was selected as London dG; rescoring 2 was selected as affinity; force field was used as a refinement [21].
A critical factor that determines the effectiveness of a docking program is its ability to reproduce ligand poses in the receptor as close to those found in X-ray deduced structures as possible [22]. In this docking study, the root-mean-square distance (RMSD) parameter measured between the complexed ligand and the redocked ligand was 0.5749 Å, suggesting that the docking results were very suitable and reliable. Docking results are listed in Table 6. Obviously, these newly designed

Molecular Docking Study
These molecules were ideally best based on their chemical structures, physicochemical properties and biological activity. Hence, molecular docking embedded in Molecular Operating Environment (MOE2008.10, Chemical Computing Group, Montreal, QC, Canada) was applied to study the binding modes and important interactions.
Prior to the docking, the crystal structure of FXR complexed with benzimidazole-based partial agonistic ligand was first downloaded from a protein data bank (PDB: 3OLF). The protein was protonated using the AMBER99 force field. A set of possible conformations of nine newly designed molecules was prepared by the conformational generation function of MOE. Then, molecular docking was carried out by following parameters: the binding site was defined by the ligand atom mode; triangle matcher was used as a placement method; two rescoring methods were computed, rescoring 1 was selected as London dG; rescoring 2 was selected as affinity; force field was used as a refinement [21].
A critical factor that determines the effectiveness of a docking program is its ability to reproduce ligand poses in the receptor as close to those found in X-ray deduced structures as possible [22]. In this docking study, the root-mean-square distance (RMSD) parameter measured between the complexed ligand and the redocked ligand was 0.5749 Å, suggesting that the docking results were very suitable and reliable. Docking results are listed in Table 6. Obviously, these newly designed compounds had higher docking scores for FXR than the known template compound T30, which was in agreement with the 2D-and 3D-QSAR results. The best docked orientation of compounds is shown in Figure 5, showing that newly designed compounds can be well docked into the ligand binding site of FXR. The best docked conformation of the most active compound N9, as shown in Figure 6A, revealed that the presence of perfluoroalkyl chain substituted groups at region C allowed for potentiation of strong hydrophobic interactions with Met369, Leu291, Trp458, Met454, Ile361, Leu455 and Phe333 in the active site of FXR and formed two H-bonds with Arg335 and Tyr373. Comparative molecular docking between compound N9 and the complexed ligand, shown in Figure 6, indicated that the former had a better binding score than the latter, suggesting that hydrophobic interactions between groups at region C with these amino acids played a dominant role in the FXR agonist activity. Thereby, the hydrophobic interaction of groups at region C seems to stabilize the compound within the binding site, thus contributing greater activity. compounds had higher docking scores for FXR than the known template compound T30, which was in agreement with the 2D-and 3D-QSAR results. The best docked orientation of compounds is shown in Figure 5, showing that newly designed compounds can be well docked into the ligand binding site of FXR. The best docked conformation of the most active compound N9, as shown in Figure 6A, revealed that the presence of perfluoroalkyl chain substituted groups at region C allowed for potentiation of strong hydrophobic interactions with Met369, Leu291, Trp458, Met454, Ile361, Leu455 and Phe333 in the active site of FXR and formed two H-bonds with Arg335 and Tyr373. Comparative molecular docking between compound N9 and the complexed ligand, shown in Figure 6, indicated that the former had a better binding score than the latter, suggesting that hydrophobic interactions between groups at region C with these amino acids played a dominant role in the FXR agonist activity. Thereby, the hydrophobic interaction of groups at region C seems to stabilize the compound within the binding site, thus contributing greater activity.

Data Set
The structures and biological activities of 41 AAD as FXR agonists used for the QSAR analyses were taken from Merk et al. [8,9] and are listed in Table 3. The agonist activity (EC50) value was converted to a logarithmic-scale pEC50 value, which was taken as the dependent parameter for the QSAR study. In order to establish a reliable model, the data set was randomly divided into two subsets, a training set of 31 compounds (approximately 75% of the data) that represented a wide compounds had higher docking scores for FXR than the known template compound T30, which was in agreement with the 2D-and 3D-QSAR results. The best docked orientation of compounds is shown in Figure 5, showing that newly designed compounds can be well docked into the ligand binding site of FXR. The best docked conformation of the most active compound N9, as shown in Figure 6A, revealed that the presence of perfluoroalkyl chain substituted groups at region C allowed for potentiation of strong hydrophobic interactions with Met369, Leu291, Trp458, Met454, Ile361, Leu455 and Phe333 in the active site of FXR and formed two H-bonds with Arg335 and Tyr373. Comparative molecular docking between compound N9 and the complexed ligand, shown in Figure 6, indicated that the former had a better binding score than the latter, suggesting that hydrophobic interactions between groups at region C with these amino acids played a dominant role in the FXR agonist activity. Thereby, the hydrophobic interaction of groups at region C seems to stabilize the compound within the binding site, thus contributing greater activity.

Data Set
The structures and biological activities of 41 AAD as FXR agonists used for the QSAR analyses were taken from Merk et al. [8,9] and are listed in Table 3. The agonist activity (EC50) value was converted to a logarithmic-scale pEC50 value, which was taken as the dependent parameter for the QSAR study. In order to establish a reliable model, the data set was randomly divided into two subsets, a training set of 31 compounds (approximately 75% of the data) that represented a wide

Data Set
The structures and biological activities of 41 AAD as FXR agonists used for the QSAR analyses were taken from Merk et al. [8,9] and are listed in Table 3. The agonist activity (EC 50 ) value was converted to a logarithmic-scale pEC 50 value, which was taken as the dependent parameter for the QSAR study. In order to establish a reliable model, the data set was randomly divided into two subsets, a training set of 31 compounds (approximately 75% of the data) that represented a wide range of varied structures and a test set of 10 compounds (approximately 25%) that followed the distribution of the activity values for the training set [14]. The training set is to build models, while the test set marked by the asterisk in Table 3 will be used to evaluate the prediction ability of the final training set model.

Descriptors Calculation
All 2D structures of the molecules in Table 3 were sketched and their 3D structures were subjected to energy minimization using the molecular mechanics force field (MMFF) method with a convergence criterion of 0.01 kcal/mol and partial atomic charges. The final geometry optimization of each energy-minimized structure was carried out by stochastic conformational search. Then, only the lowest energy conformer of each structure was used to calculate 327 descriptors by employing the QuaSAR module of MOE [23]. These calculated descriptors include three classes: 2D descriptors, which use the atoms and connection information of the molecules, internal 3D (i3D), which uses 3D coordinate information about each molecule and external 3D (x3D), which uses 3D coordinate information with an absolute frame of reference. All the above processes were performed using MOE2008.10 package.

Stepwise Multiple Linear Regression (SW-MLR)
In this study, a stepwise technology combined with MLR (SW-MLR) was employed to select a set of the most relevant descriptors for model constructions. The stepwise regression combines forward and backward selections. It selects statistically meaningful variables that can appreciably increase the residual sum of squares checked by the Fisher test [24]. Therefore, different MLR models will be derived in this procedure. The selection of a good MLR equation is made by statistical parameters such as the squared correlation coefficient (R 2 ), root mean standard error (RMSE), and Fisher statistic [25]. The best MLR model should have high R 2 and Fisher statistic, and low RMSE.

Molecular Alignment
The 3D-QSAR model was constructed by CoMFA embedded in SYBYL 6.9. Because the prediction accuracy of CoMFA is highly dependent on the structural alignment of the molecules with a reference compound, the selection of the template molecule plays an important role in performing CoMFA. Generally, the lowest energy conformer of the most active compound is selected as a template molecule for superimposition of all other compounds [26]. Therefore, compound 30 (as shown in Figure 4) was identified as a reference molecule in view of its highest activity, and all of the remaining compounds were aligned on it to derive CoMFA models. The structures of the aligned molecules are demonstrated in Figure 7. range of varied structures and a test set of 10 compounds (approximately 25%) that followed the distribution of the activity values for the training set [14]. The training set is to build models, while the test set marked by the asterisk in Table 3 will be used to evaluate the prediction ability of the final training set model.

Descriptors Calculation
All 2D structures of the molecules in Table 3 were sketched and their 3D structures were subjected to energy minimization using the molecular mechanics force field (MMFF) method with a convergence criterion of 0.01 kcal/mol and partial atomic charges. The final geometry optimization of each energy-minimized structure was carried out by stochastic conformational search. Then, only the lowest energy conformer of each structure was used to calculate 327 descriptors by employing the QuaSAR module of MOE [23]. These calculated descriptors include three classes: 2D descriptors, which use the atoms and connection information of the molecules, internal 3D (i3D), which uses 3D coordinate information about each molecule and external 3D (x3D), which uses 3D coordinate information with an absolute frame of reference. All the above processes were performed using MOE2008.10 package.

Stepwise Multiple Linear Regression (SW-MLR)
In this study, a stepwise technology combined with MLR (SW-MLR) was employed to select a set of the most relevant descriptors for model constructions. The stepwise regression combines forward and backward selections. It selects statistically meaningful variables that can appreciably increase the residual sum of squares checked by the Fisher test [24]. Therefore, different MLR models will be derived in this procedure. The selection of a good MLR equation is made by statistical parameters such as the squared correlation coefficient (R 2 ), root mean standard error (RMSE), and Fisher statistic [25]. The best MLR model should have high R 2 and Fisher statistic, and low RMSE.

Molecular Alignment
The 3D-QSAR model was constructed by CoMFA embedded in SYBYL 6.9. Because the prediction accuracy of CoMFA is highly dependent on the structural alignment of the molecules with a reference compound, the selection of the template molecule plays an important role in performing CoMFA. Generally, the lowest energy conformer of the most active compound is selected as a template molecule for superimposition of all other compounds [26]. Therefore, compound 30 (as shown in Figure 4) was identified as a reference molecule in view of its highest activity, and all of the remaining compounds were aligned on it to derive CoMFA models. The structures of the aligned molecules are demonstrated in Figure 7.

CoMFA Modeling
After aligning the molecules within the lattice that extended 4 Å units beyond the align molecules in all directions, an sp 3 hybridized carbon was utilized as a probe atom to generate the steric and electrostatic fields with a charge of +1.0 and a van der Waals radius of 1.52 Å. The steric and electrostatic contributions were set as a default cut-off energy value of 30 kcal/mol. A partial least-squares (PLS) method, an extension of multiple regression analysis, was applied to calculate the minimal set of grid points and then linearly correlate the CoMFA fields to the pEC 50 values in order to generate the CoMFA model [27].

Model Validation
The predictive ability and reliability of 2D-and 3D-QSAR models were evaluated by internal and external validations. The leave-one-out (LOO) cross-validation technology is often considered as the best way to internally validate the quality of derived models [28]. The LOO produces a number of models by deleting one object from the training set, which employs all the information available. Generally, when the value of LOO crossed validated correlation coefficient (Q 2 LOO ) goes over a threshold value of 0.5, the model is acceptable [29]. In addition, external validation is also essential and significant to evaluate the generalization performance of the proposed model [25,30]. The statistical parameters, such as the root mean square errors (RMSE test ) and the squared correlation coefficient (R 2 test ) of the external test set were calculated to assess the performance of the model [31].
All algorithms were written in MATLAB 8.0 and run on a computer [Intel(R) Pentium(R), 2.00-GB RA].

Conclusions
In this paper, a three level in silico approach was applied to investigate some important structural and physicochemical aspects of highly potent partial FXR agonists. Initially, 2D-QSAR using methods of both SW-MLR and 3D-QSAR CoMFA studies was performed based on forty-one AAD. Satisfactory results were obtained with the proposed methods. The best derived 2D-QSAR model by SW-MLR can explain 93.5% of the variance in activity with a low RMSE of 0.219. In addition, the 2D-QSAR study demonstrated that b_rotN, RPC´, opr_leadlike, SlogP_VSA2, ASA of molecules had high correlation with the FXR agonist activity. Meanwhile, the best 3D-QSAR model presented higher predictive ability (R 2 train = 0.944, RMSE train = 0.203, Q 2 LOO = 0.802, R 2 test = 0.892) compared with the 2D-QSAR models. The derived contour maps from the 3D-QSAR model suggested the significant structural features (steric and electronic effects) required for improving biological activity. Consequently, the bulky groups with lower electronegativity at positions 2 and R 1 substituent of region A together with bulky groups at R 3 of region C were considered to enhance the FXR agonist activity. However, the presence of bulky groups at positions 4 and 6 of region A, R 2 of region B and 3 and 5 of region C would decrease the agonist activity. Therefore, the obtained 2D-and 3D-QSAR models could provide valuable guidance for future design of new potent partial FXR agonists with an anthranilic acid skeleton in the drug discovery process. Finally, nine newly designed AAD with predicted pEC 50 values higher than the known template compound were docked to the ligand binding domain of FXR. The molecular binding patterns and docking scores of these nine molecules also suggested that they can be robust and potent partial FXR agonists in agreement with the QSAR results. This also revealed that the hydrophobic interaction of groups at region C with Met369, Leu291, Trp458, Met454, Ile361, Leu455 and Phe333 seemed to stabilize the compound within the binding site, thus contributing greater activity. To the best of our knowledge, this work constituted the first in silico study for AAD as partial FXR agonists.


extfieldsofstudy: Chemistry Medicine

text:
A New Methodology for Assessing Macromolecular Click Reactions and Its Application to Amine–Tertiary Isocyanate Coupling for Polymer Ligation

Click reactions have provided access to an array of remarkably complex polymer architectures. However, the term “click” is often applied inaccurately to polymer ligation reactions that fail to respect the criteria that typify a true “click” reaction. With the purpose of providing a universal way to benchmark polymer–polymer coupling efficiency at equimolarity and thus evaluate the fulfilment of click criteria, we report a simple one-pot methodology involving the homodicoupling of α-end-functionalized polymers using a small-molecule bifunctional linker. A combination of SEC analysis and chromatogram deconvolution enables straightforward quantification of the coupling efficiency. We subsequently employ this methodology to evaluate an overlooked candidate for the click reaction family: the addition of primary amines to α-tertiary isocyanates (α-tNCO). Using our bifunctional linker coupling strategy, we show that the amine–tNCO reaction fulfills the criteria for a polymer–polymer click reaction, achieving rapid, chemoselective, and quantitative coupling at room temperature without generating any byproducts. We demonstrate that amine–tNCO coupling is faster and more efficient than the more common amine–tertiary active ester coupling under equivalent conditions. Additionally, we show that the α-tNCO end group is unprecedentedly stable in aqueous media. Thus, we propose that the amine–tNCO ligation is a powerful new click reaction for efficient macromolecular coupling.

■ INTRODUCTION
Polymer science has benefited immensely from the development of highly efficient coupling reactions for post-polymerization modification. 1 Ideally, these reactions should proceed rapidly (<1 h) under mild conditions, require no catalyst, produce no byproducts, and, most importantly, not require an excess of either coupling partner (i.e., operate at equimolarity), since the purification of a mixture of macromolecules can be laborious and time consuming. 2 Click reactions, which satisfy these criteria by definition, 3 have been instrumental for the coupling of small molecules to each other 4 and to (bio)macromolecular substrates. 5 However, relatively few smallmolecule click reactions perform optimally when used for coupling one macromolecule to another, and often an excess of one coupling partner is required to drive the reaction to completion. 6 There are only a handful of reactions that enable highly efficient macromolecular coupling at equimolarity: the Heck coupling, 7 the nitrile imine-mediated tetrazole-ene cycloaddition reaction, 8 the photoinduced Diels−Alder reaction, 9 the RAFT hetero Diels−Alder reaction, 10 and CuAAC. 11 The development of new click reactions for post-polymerization modification therefore remains an important ongoing challenge in polymer chemistry. 12 The nucleophile−isocyanate addition reaction has received little attention as a macromolecular ligation strategy despite its significance to the polymer and fine chemical industries. 13 Organic isocyanates are useful substrates due to their high reactivity toward amines, 14 thiols, 15 and alcohols, 16 often reacting rapidly and quantitatively to produce ureas, thioureas, and urethanes, respectively. Compared to functional groups encountered in other common coupling methodologies (e.g., thiol−ene/yne, CuAAC, active ester ligation), isocyanates are particularly attractive because they are unreactive toward radicals (unlike e.g., alkenes, alkynes, thiols, azides), their addition reactions with nucleophiles do not release any byproducts, and, in the case of amines, no catalysts or smallmolecule promoters are required. The low prevalence of isocyanate-based ligation strategies in polymer chemistry can be attributed to a few perceived drawbacks of organic isocyanates: (i) synthetic routes involving highly toxic reagents (e.g., phosgene); (ii) a lack of chemoselectivity toward various nucleophiles; (iii) the need for a catalyst for coupling with alcohols and thiols; and (iv) high sensitivity to moisture, making them difficult to handle and store. 17 Recently, we developed a strategy for preparing α-tertiary isocyanate (α-t NCO) end-functional polymers using a novel carbonyl-azide functionalized CTA, 18 which avoids the use of blocked isocyanates. 19 In this early work, we demonstrated how the carbonyl azide R group of a RAFT agent would rearrange into a tertiary isocyanate group during polymerization to form α-t NCO polymers. Preliminary experiments suggested that the tertiary isocyanate group reacted efficiently with low molar mass alcohols and amines. However, a more demanding challenge in polymer chemistry is the coupling of endfunctional polymers to other macromolecules at equimolarity. Indeed, this is a critical feature of a click reaction in the context of polymer chemistry. Presently, the performance of α-t NCO polymers in macromolecule-to-macromolecule couplings remains an open question. We thus sought to determine the efficiency of this reaction for macromolecular coupling and to assess whether it meets the criteria of a click reaction.
Surprisingly, the existing literature lacks a universal methodology to evaluate whether a given macromolecular coupling reaction fulfills the criteria of a click reaction (e.g., efficiency at equimolarity, reaction time scale, modularity, chemoselectivity). 2b Consequently, we have developed a simple one-pot strategy for quantifying the coupling efficiency at equimolarity of a polymer−polymer ligation reaction using a combination of SEC analysis and chromatogram deconvolution. We apply this methodology to assess whether the amine−α-t NCO reaction is sufficiently chemoselective, efficient, and robust to be considered a click reaction in the context of macromolecular coupling. The efficiency of the amine− t NCO reaction is also benchmarked against the amine−active-ester reaction (Nhydroxysuccin-imidyl ester, NHS), which is the most widely used reaction for attaching polymers to amine-bearing substrates.
■ RESULTS AND DISCUSSION One-Pot Bifunctional Linker Strategy for Quantifying Polymer−Polymer Click Reaction Efficiency. In order to perform a polymer−polymer ligation reaction at equimolarity, one must know precisely the number of moles of reactive endgroup in a sample. The molecular weight distribution of the polymer, however, obfuscates this information, as the average molecular weight does not represent the number of moles of end-group. To circumvent this limitation and to accurately assess the real efficiency of a polymer−polymer coupling reaction, we have designed a simple one-pot methodology that relies on the coupling of two α-end-functional polymers with a single bifunctional linker (Scheme 1). This approach hinges upon one-pot polymerization, as it avoids any mass loss due to purification. We are thus able to work at equimolarity since the number of moles of α-end functional group is equal to the number of moles of CTA initially introduced (or moles of initiator in other types of controlled polymerizations). In this way, it becomes straightforward to perform post-polymerization modification with an equimolar amount of a bifunctional linker (e.g., ethylenediamine), which simulates an ideal polymer− polymer dicoupling reaction. 11 To quantify the coupling efficiency (i.e., coupled vs noncoupled polymer chains), we employ a simple SEC-based workflow whereby the SEC traces are plotted in terms of number distribution (P(M) vs M) (SI Figure S1), then the underlying component distributions are extracted by multiple peak deconvolution of the overall SEC envelope. Conversion to number distribution is necessary because visual inspection of SEC distributions (w(log M) vs log M) of partially successful coupling reactions can be misleading. 20,21 It is important to note that, in degenerative transfer RDRP techniques, polymerizations must be performed using a high [CTA] 0 /[initiator] consumed ratio to ensure that the number of initiator-derived chains remains low, 22 as the initiator-derived chains will not possess an α-end functional group and thus cannot react with the diamine linker. Consequently, any initiator-derived chains will appear as apparently unconsumed α-end functional polymer after attempts at performing dimerization.
This general methodology can be applied to any ligation strategy involving α-end-functional polymers, enabling straightforward assessment of key click criteria such as coupling efficiency at equimolarity, reaction time, and chemoselectivity (by varying the functional groups of the linker). Consequently, it is possible to make quantitative comparisons between different coupling reactions using different polymer types.
Assessment of Amine− t NCO Coupling As a Click Reaction for Polymer Ligation. Having established a methodology to assess some of the key click criteria in macromolecular coupling, our aim was to synthesize, using RAFT polyermzation, a range of α-t NCO end-functional polymers and subsequently: (i) test the coupling efficiency of the amine− t NCO reaction using different polymer types and chain lengths; (ii) examine the chemoselectivity of the α-t NCO for primary amines over primary thiols and primary alcohols; (iii) ascertain the hydrolytic stability of the α-t NCO end-group; and (iv) compare the efficiency of the amine− t NCO reaction with the amine− t NHS ester reaction under the same conditions. Scheme 1. One-Pot Bifunctional-Linker Coupling Strategy Used to Quantify Polymer−Polymer Coupling at Equimolarity a a Workflow steps: (a) Moles of CTA initially introduced is equal to the final number of moles of α-end group after polymerization (R group of the CTA); (b) direct addition of 0.5 equiv bifunctional linker to the polymerization mixture produces (c) the polymer-linker-polymer conjugate; (d) analysis of the reaction mixture by SEC. Coupling efficiency is calculated by Gaussian multiple peak deconvolution of the number distribution chromatogram (SI Figure S1).

Journal of the American Chemical Society
Article Preliminary Considerations for Amine−Isocyanate Reaction. We employ a RAFT polymerization strategy in which a carbonyl-azide functional CTA undergoes an in situ Curtius rearrangement to a tertiary isocyanate group during polymerization (SI Figure S2). 18 In order to accurately assess the efficiency of the amine−isocyanate reaction, we must first ensure that the carbonyl-azide CTA is converted quantitatively into α-t NCO polymer end-groups and that the rate of amine− t NCO addition is faster than aminolysis of the trithiocarbonate (TTC) group. To verify that these criteria are satisfied in our system, a very short α-NCO-polymer (DP = 10) was prepared, and then, in a second step (i.e., directly after polymerization, without purification), 2 equiv (with respect to CTA) of benzylamine were added in successive 1 equiv aliquots separated by a 1 h interval ( Figure 1). Addition of the first equivalent establishes a competition experiment between amine− t NCO addition and aminolysis of the TTC group; the second equivalent consumes any isocyanate or TTC still remaining after the first addition. An interval of 1 h was chosen according to the kinetics of benzylamine addition to tert-butyl isocyanate, which was used as a model reaction for these experiments (SI Figure S4). Results of this model reaction were highly encouraging: ESI-TOF-MS analysis of the mixture after 4 h of polymerization (99% monomer conversion) showed quantitative rearrangement of the acyl-azide CTA into an α-t NCO-polymer ( Figure  1a) with a single polymer population observed (NCO-PNAM-TTC, Na + adduct). After addition of the first equivalent of benzylamine (Figure 1b), only a single population (Bz-PNAM-TTC, Na + adduct) was again observed, corresponding to the coupling product between benzylamine and the α-NCOpolymer. Interestingly, no starting material (NCO-PNAM-TTC) and no aminolyzed polymer chains (NCO-PNAM-SH) were observed, indicating complete consumption of the α-t NCO end-groups without aminolysis of the TTC. The high selectivity of amine− t NCO coupling was further demonstrated by the addition of a second equivalent of benzylamine: After 2 h (SI Figure S5a

Journal of the American Chemical Society
Article mass spectrum (Bz-PNAM-SH). It is therefore clear that the amine− t NCO coupling is faster than aminolysis of the TTC, conveniently preserving the latent thiol functionality of the polymer.
Amine− t NCO Coupling for Polymer−Polymer Ligation. The effects of various macromolecular parameters on the efficiency of amine− t NCO coupling were assessed in order to ascertain the utility of this reaction for polymer−polymer conjugation (Table 1): (i) the chemical nature of the polymer (comparison among poly(acrylamide), poly(methacrylate), and poly(acrylate)); (ii) the chemical nature of the linker (primary or secondary amine); (iii) the polymer chain length (comparison between DP 40, 100, and 200).
We observed highly efficient diamine-mediated dicoupling between α-t NCO-polymers of 3−6 kDa bearing different repeating units (runs 1−4, Table 1). Remarkably, quantitative homodicoupling was achieved using α-NCO-PNAM 40 and ethylenediamine (run 1, Table 1, Figure 2a,b), clearly exemplifying the click nature of the amine− t NCO reaction on a polymeric substrate. Chromatograms after 1 h coupling showed a clean shift to higher molar mass while maintaining a narrow and monomodal molar mass distribution (Đ = 1.11 before and after dimerization, Figure 2a). The isocyanate− amine reaction thus proceeds without any measurable sidereactions (e.g., isocyanate hydrolysis by adventitious water or aminolysis of the TTC) that would alter the theoretical α-endgroup stoichiometry. We observed similarly high polymer− polymer coupling efficiencies of 93% and 96% using α-t NCO-PMMA 30 (SI Figure S9) and α-t NCO-PtBA 33 (SI Figure S10a), indicating that the coupling reaction is not sensitive to the chemical nature of the polymer. The lower apparent yield of coupling with the α-t NCO-PMMA 30 (i.e., 93%) can be attributed to the relatively lower fraction of α-t NCO chainends obtained at the end of the polymerization (96.3%, run 2, Table 1).
The type of diamine linker was observed to have little influence on the efficiency of polymer−polymer coupling: comparison of runs 3 and 4 (SI Figure S10a,b) both performed by homodicoupling of α-t NCO-PtBA using either a primary amine linker (e.g., ethylenediamine) or a secondary amine linker (e.g., piperazine) showed high coupling yields of ca. 96% and 95%.
As is typically the case with macromolecular ligation reactions, the length of the polymer chain was observed to have a strong influence on coupling efficiency. Comparison of    Table 1) and (b) its number distribution representation.

Journal of the American Chemical Society
Article the runs 1, 5, and 6 (Table 1) clearly shows that the coupling efficiency decreases drastically as the polymer chain length increasespresumably due to steric blocking of the tertiary isocyanate end-groupdecreasing from 100% for α-t NCO-PNAM 40 (Figure 2a,b) to 91% for α-t NCO-PNAM 100 (SI Figure S11a) and to 78% for α-t NCO-PNAM 200 (SI Figure  S11b).
In conclusion, results from the first set of experiments (runs 1−6, Table 1) with a small diamine linker (ethylene diamine or piperazine) confirm that the amine− t NCO reaction is rapid, reaching completion in <1 h at ambient temperature under equimolar coupling conditions and is compatible with a wide range of polymer types. Interestingly, polymer−polymer conjugation is as fast as the model reaction between benzylamine and tert-butyl isocyanate.
Assessing the Chemoselectivity of Amine− t NCO Coupling. To be considered a true click reaction, the amine− t NCO reaction must be orthogonal to the closely related thiol− t NCO and alcohol− t NCO addition reactions. In this section, we examine the reactivity of α-t NCO functional polymers by employing the bifunctional linker strategy using ethylene glycol and 1,2-ethanedithiol (runs 11−13, Table 2).
The rate of thiol-t NCO addition was initially modeled by observing the reaction between benzylmercaptan (1 equiv) and tert-butyl isocyanate in the presence of triethylamine (Et 3 N, 1 equiv). In situ 1 H NMR reaction analysis (SI Figure S12) showed that thiol− t NCO coupling was complete after 2 days at room temperature. This is in striking contrast to the analogous amine− t NCO model reaction (SI Figure S4), which reached completion within 1 h at room temperature at the same concentration and without the use of a catalyst. Surprisingly, the reaction of NCO-PNAM 40 with 1,2-ethanedithiol as a reactive linker in the presence of either Et 3 N or 1,8diazabicyclo [5.4.0]undec-7-ene (DBU) (runs 11 and 12, respectively) did not proceed at room temperature (SI Figure  S13a,b, respectively) or upon addition of dibutyltin dilaurate (DBTDL) and heating at 50°C. By contrast, isocyanate polymer−polymer conjugation using ethylene glycol as the reactive linker (run 13) in the presence of DBTDL produced a better result. After 48 h, the reaction between NCO-PtBA 39 and ethylene glycol at room temperature afforded the coupled product with a coupling efficiency of ca. 51% (SI Figure S14). Although the alcohol−isocyanate coupling reaction does proceed, the rate is very slow and the reaction requires a catalyst. It is therefore clear that the amine− t NCO reaction is fully orthogonal to alcohol− t NCO and thiol− t NCO reactions. 14b, 23 Hydrolytic Stability of the Tertiary Isocyanate End-Group in Aqueous Medium. Isocyanates are powerful electrophiles and, consequently, are generally sensitive to moisture and often difficult to handle under ambient conditions. 17 It is therefore surprising that the α-NCO endfunctional polymers obtained from a carbonyl-azide CTA can be prepared and functionalized using nonanhydrous solvents. 18 We attribute the unprecedented water tolerance of the αisocyanate polymer to the steric hindrance of the tertiary isocyanate group, which makes it less reactive toward nucleophiles, as explored in the previous section mainly with alcohols. Performing post-polymerization functionalization reactions in water is an area of great interest for the lifescience applications of polymer chemistry. In this section, we aim to assess the scope and limitations of amine−isocyanate coupling for post-polymerization functionalization (e.g., bioconjugation) in the presence of water. Water-soluble t NCOpoly(4-acryloylmorpholine) (α-t NCO-PNAM 40 ) was prepared in dioxane at 65°C (run 14, Table 3). After polymerization and precipitation by dropwise addition into diethyl ether, α-t NCO-PNAM 40 was dissolved in pure water (1230 equiv of H 2 O with respect to t NCO end-group) at room temperature. Aliquots were extracted after 1 and 24 h, and the viability of the isocyanate group was measured both by SEC and FT-IR. With such a large excess of water, we expected that the isocyanate

Journal of the American Chemical Society
Article group would rapidly decompose, via a transient carbamic acid, into a primary amine (which itself might react with another α-t NCO end-group and form a urea linkage) and gaseous carbon dioxide. Surprisingly, after 1 h in water the characteristic NCO stretch at 2275 cm −1 was still observed by ATR-FTIR, suggesting that hydrolysis of the isocyanate into an amine was much slower than expected (Figure 4a). After 24 h, the isocyanate signal had disappeared completely. SEC-THF analysis was highly informative (Figure 4a), showing a bimodal distribution after only 1 h in water with a characteristic population at twice the molar mass of the starting material (Figure 4b). This peak was attributed to the coupling of two polymer chains via urea-bond formation.
To confirm that the amine− t NCO addition occurs preferentially over isocyanate hydrolysis, t NCO-PNAM 40 (1 equiv) was added to a solution of ethylenediamine (0.5 equiv) in pure water (1230 equiv, run 15, Table 3). After 1 h at room temperature, the SEC chromatogram revealed the presence of an almost pure polymer at the double of the molar mass (efficiency >99%) (Figure 4c). Comparison with the experiment without the diamine linker (run 14) confirmed that amine addition to the α-t NCO group is very efficient and much faster than hydrolysis. It is remarkable that the amine− t NCO coupling reaction proceeds perfectly in water, making this a very promising strategy for polymer bioconjugation reactions.
To further assess the tolerance of the α-t NCO toward water, RAFT polymerization of NAM was carried out in dioxane with 10% (v/v) water (46 equiv with respect to NCO group) at 65°C (run 16, Table 3). After 4 h, near complete monomer conversion was achieved. SEC analysis of the polymer revealed a small shoulder at twice the molar mass of the main distribution, which we attribute to the urea-linked dimer arising from an α-t NCO-PNAM chain reacting with an α-aminofunctional PNAM, derived from isocyanate hydrolysis (SI Figure S15a). However, the extent of degradation is minor, especially considering the large molar excess of water present (46 equiv per isocyanate) and the moderate temperature (i.e., 65°C), thus emphasizing the good hydrolytic tolerance of the α-t NCO end group. Ethylenediamine (0.5 equiv with respect to the theoretical amount of NCO group) was subsequently added to the polymerization mixture to confirm that the isocyanate end-group had indeed survived polymerization in the presence of water. The final chromatogram shows a coupling conversion of 87% (SI Figure S15b), clearly confirming that a large proportion of the isocyanate end-group is retained during RAFT polymerization in the presence of water.
The isocyanate end-group is also sufficiently robust to survive precipitation of the polymer into water-rich solvents. For example, after precipitation of α-t NCO-PBA 35 in MeOH/H 2 O 50/50 (v/v) (run 17, Table 3), characterization of the purified product by SEC-THF (SI Figure S16a) and FT-IR (SI Figure  S16b) confirmed that no significant degradation of the NCO group has occurred. The addition of ethylenediamine (0.5 equiv) to the purified polymer afforded the coupled product in 89% yield after 1 h at room temperature. Nonquantitative coupling, which is obvious from the bimodal chromatographic profile (SI Figure S16c), most likely arises due to the incorrect coupling stoichiometry, since the number of moles of endgroup in the purified polymer was determined from average molar mass measurements, which have inherent uncertainties (vide supra). This result confirms the excellent retention of the t NCO group after purification by precipitation into MeOH/ H 2 O, which is an unprecedented result that challenges the perception that isocyanate-bearing compounds are difficult to handle in the presence of adventitious water.
Comparing Efficiency of Amine−NHS and Amine−NCO Reactions for Polymer Ligation. The amine−active ester coupling reaction is a common strategy for attaching endfunctional polymers to amine-bearing substrates. Despite their prevalence in polymer chemistry, active esters suffer from a key drawback in that their performance represents a trade-off between reactivity, chemoselectivity, and hydrolytic stability. To demonstrate that the amine− t NCO reaction is superior to the more conventional amine−active ester reaction for polymer−polymer coupling, we compared the reactivity of ethylenediamine with α-t NHS-PNAM 40 and α-t NCO-PNAM 40 (runs 7 vs 8, Table 1). Unlike other previous coupling experiments, the ethylene diamine linker was added portionwise instead of in a single aliquot in order to follow the coupling progress by SEC. Homodicoupling of α-t NCO-PNAM 40 proceeds much faster than α-t NHS-PNAM 40 (Figure  3a vs 3b) and achieves a higher coupling efficiency of 93% (Run showing the degradation of the α-t NCO end-functional PNAM 40 in the presence of a large excess of water (1230 equiv). The peak at double molar mass is assigned to two polymer chains coupled by a urea bond and confirmed by the disappearance of the NCO peak in the FTIR spectrum (run 14, inset). (b) The large proportion of coupled product 3 observed when 1 is reacted in water suggests that amine−isocyanate coupling is much faster than isocyanate hydrolysis (i.e., k hydrolysis < k amine−NCO ). Thus, 2 reacts with 1 as soon as it forms.  Table 1) compared with 75% for the active ester (Run 8, Table 1) even after prolonged reaction times (up to 7 days). It should be noted that withdrawing an aliquot from the reaction mixture for analysis before each new diamine linker addition slightly alters the overall reaction stoichiometry, which accounts for the lower coupling efficiency of run 7 compared to run 1 (100% via single aliquot addition). This result demonstrates that, under the identical conditions tested herein, the amine− t NCO reaction outperforms the amine− t NHS ester reaction.
Finally, we investigated the coupling reactions of α-t NCO and α-t NHS polymers to a more sophisticated diamine linker: a cyclic peptide synthesized from 8 alternating D-and L-amino acids, containing two nucleophilic lysine residues (runs 9 vs 10, Table 1). Irrespective of the coupling strategy (i.e., isocyanate or active ester), the one-pot diamine-linker approach that we have described herein allows us to precisely control the stoichiometry of the coupling partners (i.e., polymer and cyclic peptide). After polymerization of NAM to full conversion in dioxane, a solution of protonated cyclic peptide (0.5 equiv) in DMSO containing N-methylmorpholine (6 equiv) was added directly to the polymerization reaction mixture (SI Scheme S1 with the acyl azide CTA and SI Scheme S2 with the t NHS-CTA). Comparison of the SEC-chromatograms at different time intervals (Figure 3c,d) shows that the α-t NCO-polymer reaches the same degree of coupling after 1 h as the α-t NHSpolymer achieves in 24 h, again highlighting the superior rate of the amine−NCO reaction; neither reaction was observed to proceed beyond 4 days, at which point the coupling efficiencies were 93 ± 7% and 61 ± 13% for the α-NCO and α-NHS polymers, respectively. The lower coupling efficiencies observed with the cyclic peptide, compared to the small diamines, might be attributed to incipient self-assembly of the cyclic peptide into hydrogen-bonded supramolecular nanotubes. 24

■ CONCLUSION
We have described an innovative methodology to quantify the efficiency at equimolarity of macromolecular conjugation reactions. This simple approach relies on the one-pot polymerization and subsequent homodicoupling of α-endfunctional polymers in the presence of a bifunctional linker. This strategy is simple, effective, and can be adopted as a universal route to assess macromolecular coupling. Subsequently, we employed this protocol to demonstrate that the amine− t NCO reaction is a highly efficient macromolecular ligation strategy for a wide range of vinyl-derived polymers. Indeed, the excellent performance of the amine− t NCO reaction under the range of conditions described herein demonstrates that this coupling strategy fulfills several of the criteria of a click reaction for macromolecular coupling: It proceeds to completion rapidly (<1 h) at equimolarity under ambient conditions (e.g., room temperature in the presence of atmospheric moisture and oxygen) without generating any products or requiring any catalysts. Furthermore, the amine− t NCO outperforms the ubiquitous amine− t NHS coupling on comparable substrates. Although we have focused this study on the use of RAFT polymerization to enable a onepot reaction strategy that facilitates the use of equimolar reactants, we anticipate the use of tertiary isocyanate can be extended to other types of polymers as a powerful and versatile click-type reaction.  Table 1) and (b) α-NHS-PNAM 40 (run 8, Table 1) with a portion-wise addition of ethylenediamine (0.1 equiv per hour) performed at 25°C in dioxane directly after polymerization; (c) α-NCO-PNAM 40 (run 9, Table 1) and (d) NHS-PNAM 40 (run 10, Table 1) with a single-aliquot addition diamino-cyclic peptide (0.5 equiv) at 25°C in nonanhydrous DMSO/dioxane.


extfieldsofstudy:
Physics
Materials Science

s2fieldsofstudy:
Chemistry
Materials Science

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The shape conversion of silver nanoparticles through heating and its application as homogeneous catalyst in reduction of 4- nitrophenol

The shape conversion of silver nanoparticles (AgNPs) through heating and its application as a homogeneous catalyst in the reduction of 4-nitrophenol is reported here. Synthesis of AgNPs by reduction of AgNO3 using NaBH4 and sodium citrate as reducing agent were successfully conducted. The addition of PVP was used as stabilizing agent. The synthesized AgNPs were heated at 95 °C and observed using UV-Vis spectrophotometer, transmission electron microscopy (TEM), Fourier-transformed infrared (FTIR) spectroscopy and particle size analyzer (PSA). Characteristics of AgNPs before heated were blue with UV- Vis absorbance spectrum at λmax = 786 nm and the shape was pseudo nano prism sized ± 28 nm. During the heating process, the color changed gradually from blue (λmax = 786 nm) to orange (λmax = 486 nm) and also its shape from nano prism to nanodisk. Silver nano prism has a lattice constant, 4.160 Å, larger than the silver nanodisk, 4.081 Å, which was possibly achieved through rearrangement of silver atoms on the surface of AgNPs. Both silver nanodisk and nano prism were tested as a homogeneous catalyst for the reduction of 4-nitrophenol (4- NP) with NaBH4.

Introduction
Silver nanoparticles (AgNPs) received much attention from researchers in the world due to its unique. Besides having a good antimicrobial ability [1,2], AgNPs also have unique optical properties, namely LSPR (Localized Surface Plasmon Resonance). The LSPR properties lead AgNPs to have an attractive color and luminescence [3,4]. Therefore, AgNPs have many benefits such as chemical sensors, biological sensors, catalysts, as well as the diagnosis and cure of disease.
Differences synthesis conditions will produce different shapes and sizes of AgNPs. Generally, the synthesis of AgNPs with reduction method produces spherical particles at a maximum wavelength ( max ) 395 nm [7]. However with certain techniques such as photochemical, thermochemical and biochemical [8][9][10], the resulting nanoparticles can be deformed. Tang et al. [11] reported shape conversion mechanism of AgNPs from triangular to spherical shapes with heating (thermochemical). Shape conversion of AgNPs can be monitored via color change from blue (triangular) to orange (spherical) at  max 655 nm to 472 nm.
Metal nanoparticles are often used in redox catalysis process because they have a large surface area. Even though silver is not the best redox catalyst, silver can be used as an active component of the catalyst for the partial oxidation [12]. AgNPs are effectively used as a homogeneous catalyst and can be immobilized on a suitable support material such as silica, alumina, carbon, zeolites and metal oxides as heterogeneous catalysts [13]. In this research, we studied the effect of the shape conversion of AgNPs through heating to their catalytic activity for the first time. Reaction model used to test the catalytic activity of AgNPs was the reduction reaction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP). Previous studies have been made out in order to optimize the 4-NP reduction using AgNPs catalyst with a wide variety of reactant conditions [5]. But until this day, there has never been a report about the effect of the shape conversion of AgNPs to their catalytic activity for 4-NP reduction.

Heat assisted shape conversion of AgNPs
The initial triangular AgNPs were synthesized according to the Mirkin's method [14]. An aqueous solution of 100 mL AgNO 3 0.1 mM, 1.8 mL trisodium citrate 100 mM, 0.06 g PVP, and 0.24 mL hydrogen peroxide 30 wt% were mixed and vigorously stirred under ambient condition. Then, 1,5 mL NaBH 4 solution 100 mM was rapidly injected into the mixtures. After about 30 min, colloidal blue silver nano prism was obtained. The synthesized silver nano prism was placed in a water bath at 95 C. The color of colloidal AgNPs changed from blue to purple, red and then orange during the heating process. The AgNPs colloid were monitored using ultraviolet-visible spectrophotometer (Shimadzu 2600), Fourier transmits infrared (FTIR) (Shimadzu Prestige 21), particle size analyzer (PSA) Malvern ZEN 1600 and TEM (JEM 1400).

Catalytic activity test of 4-NP reduction
The catalytic activity of AgNPs as a homogeneous catalyst was determined by adding 1 mL of NaBH 4 0,1 M into 5 mL of 4-NP 8,6x10 -5 M in a centrifuge tube. The solution showed a change in color of 4-NP which are colorless into yellow. Blue (AgNPs-blue) and orange (AgNPs-orange) homogeneous catalysts were added to the mixture, vortexed at the variation of time and characterized using UV-Vis spectrophotometer.

Shape conversion of AgNPs
The AgNPs-Blue colloid gradually changed into purple, maroon and orange during heated at 95 C as shown in figure 1. The color changes related to the SPR (surface plasmon resonance) properties of AgNPs. Color changes which are accompanied by changes in UV-Vis absorption spectra indicate that heating could lead to the shape conversion of AgNPs [9].  TEM characterization has been used to clarify the shape conversion of AgNPs through the differences of heating time. Figure 3 and 4 shows the differences in morphology between AgNPs-blue and AgNPs-orange. AgNPs-blue was the initial color after synthesis while AgNPs-orange was the last color change of AgNPs colloidal after heating ± 30 minutes.   The Morphology of AgNPs-blue showed that most AgNPs have pseudo triangular shape with side length ± 25 nm while AgNPs-orange is almost spherical with diameters ± 12 nm. Based on the results, (1) AgNPs morphology can be converted from triangular to spherical by heating at 95 C, (2) AgNPs size get smaller with increasing heating time. Shape and size conversion process of AgNPs by heating are illustrated in figure 1.
Shape conversion of AgNPs likely to occur because of the migration of silver atoms from the less stable sides to the more stable, thereby causing surface energy difference and selective adsorption of citrate. According to Tang et al. [11], the sharp edges on the triangular AgNPs have a larger surface energy and less stable, so it can lead to changes the shape of triangular become spherical with heating. This raises the idea that spherical AgNPs can be formed because of the displacement of the silver atoms on the less stable side with a high surface energy and the rearrangement of the atoms that have been "dissolved" in the spherical AgNPs colloidal formed by heat-assisted. Displacement of the silver atom from sharp edges on triangular particles to colloids causes the formation of very small and unstable AgNPs to allow the rearrangement of these particles on spherical AgNPs surfaces. Rearrangement of the silver atoms can be proved through the analysis of SAED data from TEM characterization. Based on the diffraction pattern ( Figure 5 and 6), the crystal structure of AgNPs can be determined.  Table 1 are consistent with the plane families of pure face-centered cubic (fcc) silver structure (JCPDS, File No. 4-0787). The Fcc structure of AgNPs-Blue and AgNPs-Orange in this study have lattice parameter values 4,160 Å and 4.081 Å, respectively. Decrease in the value of the lattice parameter of triangular AgNPs-Blue to spherical AgNPs-Orange indicates rearrangement of the AgNPs atoms which was initially shaped triangular then atoms on the edges "detached" and undergo rearrangement into spherical nanoparticles.

Catalytic activity of AgNPs for 4-NP reduction
The catalytic activity of AgNPs as homogeneous catalysts was tested using reaction reduction model of 4-NP into 4-AP. The reaction model was used because in addition to the interest of reducing the harmful compounds into the medicinal raw materials compounds, also due to the two compounds can be easily detected using UV-Vis spectrophotometer on a laboratory scale. Reduction reaction of 4-NP into 4-AP in the presence of NaBH 4 can occur thermodynamically when the value of potential standard (E 0 ) for 4-NP / 4-AP = -0.76 V and for H 3 BO 3 / BH 4 = -1.33 V. But in kinetics, the reaction without the presence of catalyst restricted (no changes absorbance for approximately 2 days) [15]. Therefore, the presence of AgNPs as catalyst expected the reaction runs more effectively. 4-NP (λ max 317 nm) was reduced to nitrophenolate ions (λ max 400 nm) in the presence of NaBH 4 . However, with the presence of AgNPs catalyst, absorbance peak decreased, and a new peak appeared at λ maks 300 nm which is the absorbance of 4-AP (Figure 7). The reaction was assumed to be a pseudo-first order reaction because of the excessive amount of NaBH 4 in comparison to 4-NP [6].
(3) Figure 8 shows the decrease of 4-NP's concentration against reaction time using AgNPs-Blue and AgNPs-Orange catalyst, in which the rate constant k are 0.0458 min -1 and 0.0356 min -1 , respectively. Based on TEM characterization, the AgNPs-Blue catalyst that showed higher catalytic activity in this study has pseudo triangular particles shape. While AgNPs-Orange catalyst has a spherical shape.

Figure 8. Plots of ln [A] versus reaction time for AgNPs-Blue and AgNPs-Orange Catalyst
Although AgNPs-Blue catalyst has a larger particle size than AgNPs-Orange catalyst, based on its shape, AgNPs-Blue has more active side than AgNPs-Orange catalyst so that the catalytic activity in the electron-transfer process becomes larger.
Mahmood et al. [16] state that the catalytic activity of metal nanoparticles that have sharper edges and corners are more active than the smooth and blunt. That is because particles with sharper edges and corners have more valence-unsatisfied surface atoms, atoms that do not have the complete number of bonds that they can chemically accommodate, which will act as active side than fine and blunt particles Accordingly, the catalytic activity of AgNPs with triangular shape was better than AgNPs with a spherical shape for 4-NP reduction despite larger particle size. Triangular shape has sharp edges that act as an active site on the catalyst as electron transfer media due to more valency-unsatisfied surface atoms than spherical AgNPs catalysts. The amount of the active side of triangular AgNPs accelerates the 4-NP reduction in the presence of NaBH 4 .

Conclusions
Shape conversion of AgNPs have been done through the heating at 95C and applied for reduction catalyst of 4NP to 4-AP. TEM characterization results show the occurrence of morphological changes of triangular to spherical AgNPs. The catalytic activity of triangular AgNPs was better than a spherical one.


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Antidiabetic activity of the chemical constituents of Combretum dolichopetalum root in mice

The root of Combretum dolichopetalum (Combreatacea) is used in ethnomedicine for the management of diabetes mellitus. Though some compounds have been isolated from it, the antidiabetic principles have not been identified. The present study was designed to evaluate the chemical constituents from the root of C. dolichopetalum with a view to identifying the antidiabetic principles. The constituents include the alkaloids, echinulin (1) and arestrictin B (2), the terpenoids, arjunolic acid (3) and 4'-dihydrophaseic acid (4) as well as the phenolic acids, ellagic acid (5) and 3, 4, 3'-tri-O-methylellagic acid (6). Twenty eight mice (in seven groups, n = 4) were made diabetic using alloxan monohydrate (i.p., 120 mg/kg) and treated orally with either the vehicle (control group), any of the constituents or glibenclamide (standard drug). The fasting blood glucose of the diabetic animals was monitored for nine hours. Results showed that all the chemical constituents (1-6) exhibited significant (p < 0.05) antidiabetic activity comparable to glibenclamide. The alkaloids exhibited the most profound antidiabetic activity. The present study has thus identified the antidiabetic principles of C. dolichopetalum root as echinulin, arestrictin B, arjunolic acid, 4'-dihydrophaseic acid, ellagic acid and 3, 4, 3'-tri-O-methylellagic acid. The study has further validated the ethnomedicinal use of the root of C. dolichopetalum in diabetes.

INTRODUCTION
Diabetes mellitus is a chronic metabolic disorder which is characterized by hyperglycemia and long term complications such as retinopathy, nephropathy, neuropathy, and angiopathy. Diabetes remains one of the leading causes of death worldwide. The 2015 estimate by the International Diabetes Federation (IDF) shows that 1 in 11 adults have diabetes (415 million) and the figure is projected to rise to1 adult in 10 (642 million) in 2040. The estimate also shows that diabetes caused 5.0 million deaths implying that every six seconds a person dies from diabetes. This metabolic disorder caused at least USD 673 billion, representing 12 % of global health expenditure (IDF, 2015). The increasing worldwide incidence of diabetes constitutes a significant impact on the health, quality of life, and life expectancy of patients, as well as on the global public health care system.
In conventional practice, management of type 1 diabetes is by exogenous insulin while type 2 is by oral agents including the sulfonylureas, biguanides, α-glucosidase inhibitors, glitazones, gliptins, and the SGLT 2 inhibitors, which are used as monotherapy or in combination to achieve better glycemic regulation since they have different modes of action. Despite the existing pharmacotherapy, it is still difficult to attain adequate glycemic control amongst many diabetic patients due to the progressive decline in β-cell function (Wallace and Matthews, 2000). Moreover, many of these oral antidiabetic agents have a number of serious adverse effects (Pepato et al., 2005). The low efficacy and the adverse effects of some of the conventional antidiabetic agents together with the increasing global incidence of diabetes necessitate the urgent search for more potent and safer alternatives from natural sources such as medicinal plants.
As part of our on-going search for antidiabetic agents from natural sources Uzor et al., 2014), we have furthered our investigation on the root of Combretum dolichopetalum (Combretaceae) Engl. and Diels. The plant is a scandent shrub or forest liana. The root extract is used in traditional medicine for dysentery, indigestion and in the management of diabetes . The antisecretory (Asuzu and Onu, 1990), anti-hepatotoxic (Udem et al., 1997) and trypanocidal (Udem et al., 1996) activities of this plant have been re-ported. We have recently investigated the antidiabetic activity of the root extract and fractions of this plant on diabetic animals and established that the antidiabetic fractions contain steroids, terpenes, alkaloids and flavonoids . Additionally, our more recent chemical investigation led to the isolation of six known metabolites (Figure 1) from the root of the plant, including the alkaloids echinulin (1) and arestrictin B (2), terpenoids, arjunolic acid (3) and 4'-dihydrophaseic acid (4), as well as phenolic acids ellagic acid (5) and 3, 4, 3'-tri-O-methylellagic acid (6) (Uzor et al., 2015). These compounds were investigated for their cytotoxic potentials using the L5178Ymouse lymphoma cell lines but their antidiabetic potentials have not been assessed. Thus the antidiabetic constituents of the root of this plant have not been identified. The current study was therefore designed to evaluate the antidiabetic activity of the isolated constituents from the root of this plant. To the best of our knowledge, this is the first report of antidiabetic compounds from C. dolichopetalum root.

Chemicals
Alloxan monohydrate was purchased from Sigma-Aldrich while glibenclamide was purchased from NGC. All other chemicals were of analytical grade.

Plant material
Roots of Combretum dolichopetalum were collected in Nsukka, Nigeria, in April 2014 and identified by Mr. A. Ozioko, a taxonomist with the International Center for Ethnomedicine and Drug Development (In-terCEDD), Nsukka. A voucher specimen (In-terCEDD/853) is deposited at the Herbarium of the Department of Pharmaceutical and Medicinal Chemistry, University of Nigeria, Nsukka.

Extraction and isolation
The procedures for the isolation of the constituents of C. dolichopetalum root to obtain 1-6 have been recently described (Uzor et al., 2015). Compounds 1 and 2 (or 1/2), two isomeric indole-containing diketopiperazine alkaloids, were obtained as a mixture (56:54). They were thus evaluated as one sample.

Animals
Swiss albino mice (18.0-24.2 g) of either sex were used for the studies. The animals were kept in standard polypropylene cages at room temperature and 60-65 % relative humidity during the experimental work with 12 h day: 12 h night cycle. They were fed with normal laboratory diet and allowed to drink water ad libitum. The experimental protocols followed the guidelines of the Ethics Committee of the University of Nigeria and the European Community guidelines (EEC Directive of 1986; 86/609/EEC) (EEC, 1986).

Antidiabetic study
Twenty eight (28) albino mice were fasted for 12 h prior to induction of hyperglycemia. Diabetes was induced using freshly prepared solution of alloxan mono-hydrate (i.p. at 120 mg/kg body weight) as previously reported . After the induction, the diabetic animals were randomly divided into seven groups (n = 4) and treated orally according to the following protocols: Group A: alloxan + 2 mL/kg of vehicle Group B: alloxan +20 mg/kg of 1/2 Group C: alloxan + 20 mg/kg of 3 Group D: alloxan + 10 mg/kg of 4 Group E: alloxan + 20 mg/kg of 5 Group F: alloxan + 20 mg/kg of 6 Group G: alloxan + 0.2 mg/kg of glibenclamide Blood sample was withdrawn from the tail vein and fasting blood glucose (FBG) was measured with a glucometer (Accu-chek®, Roche) at 0, 1, 3, 6 and 9 h. Mice model was used since the compounds were limited in quantity.

Statistical analysis
The data obtained were analyzed using a statistical software (SPSS version 21) and results expressed as mean ± SEM. The means were subjected to one way analysis of variance (ANOVA) for determining the significant difference. The significance between the various groups and the control was analyzed further by post hoc Dunnet's test (2sided). Group mean was considered to be significantly different from that of the control group at p < 0.05.

RESULTS
The results of the antidiabetic activities of the isolated chemical constituents are shown in Table 1. All the constituents (1-6) exhibited significant (p < 0.05) reduction of the FBG of the diabetic mice after 3 h but the effect of the alkaloids, (1/2), was most pronounced and sustained. The antidiabetic effect of the alkaloids was maximal at 3 h after administration (53.40 % reduction of FBG). Among the constituents, 1/2 was followed in antidiabetic potency by 5 (23.7 % maximal reduction in 9 h), 6 (22.7 % reduction in 3 h), 4 (17.91 % maximal reduction of FBG in 6 h), and then by 3 (17.27 % reduction in Results are expressed as mean ± SEM (n = 4). *p < 0.05, ** p < 0.01, *** p < 0.001, as compared with Control group (one way ANOVA followed by Dunnet's t-test). Values in parenthesis denote percentage reduction of blood glucose from 0 h. 3 h). Even at 10 mg/kg, 4 exhibited significant antidiabetic activity by lowering the FBG (17.91 %) of the diabetic mice. Overall, the effect of the compounds was comparable to that of glibenclamide (reduction of 48.72 % in 9 h).

DISCUSSION
The present study has shown that the compounds from the root of C. dolichopetalum possess antidiabetic activity. The present observation validates the traditional use of the plant in diabetes. Other plant species from Combretaceae have been known to contain diverse chemical constituents such as ellagic acid, gallic acid, ellagitannins and gallotannins; they have been also known for a long time to show pharmacological effects like antioxidant, antibacterial and antidiabetic activities (Sabu and Kuttan, 2002). Previous report shows that echinuline (1) exhibits moderate protein tyrosine phosphatase 1B (PTP1B) inhibitory activity (Sohn et al., 2013) which suggests the possible antidiabetic activity of 1. The biological activity of arestrictin B (2) was not evaluated in a study where it was first isolated in a pure form from xerophilic fungi (Itabashi et al., 2006). Thus it is possible that one or both of the two isomeric alkaloids possess antidiabetic activity or they could be acting synergistically. Further studies on the separation of the alkaloids and evaluation of their individual antidiabetic activities are envisaged. These two compounds have been isolated singly espe-cially from fungal sources but they were reported, for the first time, as having occurred together in C. dolichopetalum root (Uzor et al., 2015).
Similar to the alkaloids above, our findings showed that arjunolic acid (3) was one of the antidiabetic constituents of C. dolichopetalum. The triterpenoid, 3, is also possibly acting synergistically with other bioactive constituents. Previous reports support our present observation. Compound 3 was isolated from Lagerstroemia speciosa leaves and tested for its alpha glucosidase activity using yeast alpha glucosidase enzyme. It was shown that 3 inhibited the enzyme with IC 50 of 18.63 ± 0.32 µg/mL while the standard drugs, acarbose and voglibose, showed no inhibition (Hou et al., 2009). Other studies have also supported the beneficial role of 3 in diabetes (Ghosh and Sil, 2013;Manna et al., 2009a, b;Manna and Sil, 2012).
The results of the present study have shown that dihydrophaseic acid (4), a derivative of S(+)-abscisic acid (ABA) (Hirai et al., 2003) exhibits significant (p < 0.05) antidiabetic property. ABA, a plant hormone, has been shown to exhibit antidiabetic effect through peroxisome proliferator-activated receptor gamma (PPAR γ) activation ( Guri et al., 2010). It is therefore possible that 4 exhibits its antidiabetic activity through PPAR γ activation similar to its precursor, ABA. The present study is probably the first report of the antidiabetic activity of this ABA metabolite. However, one study shows that 4 exhibited some biological activities such as cytotoxicity, lipid peroxidation inhibition effect, mild hydroxyl radical scavenging activity, and DNA protective effect (Lin et al., 2014).
We have also observed that ellagic acid (5) was one of the bioactive antidiabetic principles of C. dolichopetalum root. Previous study has demonstrated the antidiabetic activity of 5 in vivo (Malini et al., 2011). Moreover, 5 has been widely reported as an inhibitor of aldose reductase enzyme and inhibitor of the formation of advanced glycation end products (Kim et al., 2008). Al-dose reductase inhibitors are an attractive pharmacological target for the treatment of diabetic complications. Compound 6, which is a methylated derivative of 5 also showed in vivo antidiabetic activity in the present study. Earlier studies showed the αglucosidase inhibitory effect of the methylated derivatives of 5 (Tabopda et al., 2008). Furthermore, methylated derivatives of 5 are known to play some roles in antidiabetic activities of plants. While 6 showed glucose transport stimulatory activity, 5 did not show such activity (Bai et al., 2008). This suggests that methylation of the OH group of 5 is necessary for antidiabetic activity.

CONCLUSION
The present study has shown that all the isolated constituents of C. dolichopetalum root including the alkaloids (echinulin and arestrictin B), arjunolic acid, 4'-dihydrophaseic acid, ellagic acid and as 3, 4, 3'-tri-O-methylellagic acid were identified as the major antidiabetic active principles of the plant. It is possible that these metabolites exert synergistic effect in their antidiabetic activities. The study has further validated the ethnomedicinal use of the root of C. dolichopetalum in the management of diabetes.