Hydrophobic property of (R)-3 amidinophenylalanine inhibitors contributes to their inhibition constants with thrombin enzyme

Science & Technology Development Journal, 22(3):348- 352 Open Access Full Text Article Research Article 1Department of Chemistry, Faculty of Sciences, Nong Lam University, Vietnam 2Department of Chemical Technology, Faculty of Chemical and Food Technology, Ho Chi Minh City University of Technology and Education, Vietnam Correspondence HoangMinh Hao, Department of Chemical Technology, Faculty of Chemical and Food Technology, Ho Chi Minh City University of Technology and Education, Vietnam Email: haohm@hcmute.edu.vn History  Received: 2019-05-31  Accepted: 2019-09-18  Published: 2019-09-30 DOI : https://doi.org/10.32508/stdj.v22i3.1684 Copyright © VNU-HCM Press. This is an open- access article distributed under the terms of the Creative Commons Attribution 4.0 International license. Hydrophobic Property of (R)-3 Amidinophenylalanine Inhibitors Contributes to their Inhibition Constants with Thrombin Enzyme Nguyen Van Hien1, Pham Thi Bich Van1, HoangMinh Hao2,* Use your smartphone to scan this QR code and download this article ABSTRACT Introduction: Thrombin is the key enzyme of fibrin formation in the blood coagulation cas- cade. Thrombin is released by the hydrolysis of prothrombinase which is generated from factor Xa and factor Va in the presence of calcium ion and phospholipid. The inhibition of thrombin is of therapeutic interest in blood clot treatment. Currently, potent thrombin inhibitors of (R)-3- amidinophenylalanine, derived from benzamidine-containing amino acid, have been developed so far. In order to quantitatively express a relationship between chemical structures and inhibition constants (Ki with thrombin enzyme in a data set of (R)-3-amidinophenylalanine inhibitors), we developed a quantitative structure-activity relationship (QSAR) modeling from a group of 60 (R)-3- amidinophenylalanine inhibitors. Methods: A database containing chemical structures of 60 in- hibitors and their Ki values was put intomolecular operating environment (MOE) 2008.10 software, and the two-dimensional (2D) physicochemical descriptors were numerically calculated. After re- moving the irrelevant descriptors, a QSAR modeling was developed from the 2D-descriptors and Ki values by using the partial least squares (PLS) regression method. Results: The results showed that the hydrophobic property, reflected through n-octanol/water partition coefficient (P) of a drug molecule, contributes mainly to Ki values with thrombin. The statistic parameters that give the in- formation about the goodness of fit of a 2D-QSARmodel (such as squared correlation coefficient of R2 = 0.791, rootmean square error (RMSE) = 0.443, cross-validated Q2cv = 0.762, and cross-validated RMSEcv = 0.473) were statistically obtained for a training set (60 inhibitors). The R2 and RMSE values were obtained by using a developed model for the testing set (9 inhibitors) ; the total set has sta- tistically significant parameters. Furthermore, the 2D-QSAR modeling was also applied to predict the Ki values of the 69 inhibitors. A linear relationship was found between the experimental and predicted pKi values of the inhibitors. Conclusion: The results support the promising application of established 2D-QSARmodeling in the prediction and design of new (R)-3-amidinophenylalanine candidates in the pharmaceutical industry. Key words: (R)-3-Amidinophenylalanine inhibitors, blood clot, thrombin, 2D-QSAR INTRODUCTION Fibrin clot formation is an important process that heals a wound and stops any unwanted bleeding. However, an abnormal clot in the bloodstream leads to pain and swelling because the blood gathers be- hind the clot. As a result, a heart attack can occur. There are pathways (mechanisms) which lead to fib- rin formation. The intrinsic pathway was proposed in which fibrin formation resulted from a series of step- wise reactions involving only proteins circulating in blood as precursors or inactive forms1–3. Proteins were activated by proteolytic reactions and converted to thrombin. The intrinsic mechanism can be trig- gered when thrombin is generated, leading to the ac- tivation of factor XI2. The extrinsic pathway requires tissue factor VII in blood 2–5. Initially, a complex in- cluding factor VII was formed via calcium ion depen- dent reaction and then converted factor VII to factor VIIa (a: activated). The activation of many factors, including factor V, VIII, IX and X, in sequence re- sults in the generation and release of thrombin. When thrombin is formed, it converts fibrinogen to fibrin by proteolysis. Finally, the cross-linking reactions were catalyzed by an activated factor XIIIa to form a very strong fibrin clot2. As discussed above, thrombin is a key enzyme in fib- rin formation. Therefore, inhibitors selective toward thrombin have been developed; these include pep- tide aldehydes6 and boronic acid derivatives7. The anticoagulants derived from 3-amidinophenylalanine that are associated with their inhibition constants (Ki values) toward thrombin enzyme have been re- ported8,9. The inhibition constant is an equilibrium constant of the reversible combination of the en- zyme with a competitive inhibitor, I + E IE (Ki = [IE]/[I][E] ([I], [E] and [IE] are the equilibrium con- Cite this article : Van Hien N, Bich Van P T, Hao H M. Hydrophobic Property of (R)-3 Amidinopheny- lalanine Inhibitors Contributes to their Inhibition Constants with Thrombin Enzyme. Sci. Tech. Dev. J.; 22(3):348-352. 348 Science & Technology Development Journal, 22(3):348-352 centrations of inhibitor (I), enzyme (E), and enzyme- inhibitor complex (IE)) 10. The Ki value reflects the binding affinity of drug to target. Thegreater the bind- ing affinity, the larger the Ki value is, i.e., the less amount of medication needed to inhibit the enzyme. The design and synthesis of thrombin inhibitors could be improved in several ways. The two dimensional-quantitative structure-activity relation- ship (2D-QSAR) is one of the in silico drug discov- ery approaches due to its reliability and interpretabil- ity. In principle, the 2D-QSAR can be used to ex- tract physicochemical properties (descriptors) which mainly contribute to the bioactivity of drug candi- dates11. In the present work, in order to express the 2D-descriptors playing a crucial role on Ki of a se- ries of (R)-3-amidinophenylalanine inhibitors, we ap- plied 2D-QSAR method to develop a mathematical QSAR equation from 60 inhibitors as a training set. The modeling was then used to predict Ki values of 69 inhibitors toward thrombin enzyme. METHODS A data set of 69 inhibitors derived from (R)- 3- amidinophenylalanine and their logarithm of in- hibition constants, pKi = - logKi; toward throm- bin enzyme was selected for the 2D-QSAR study8 (Figure 1). Chemical structures of inhibitors were drawn in molecular operating environment (MOE) 2008.10 software and then optimized energetically prior to doing calculations. In order to develop a mathematical 2D-QSAR model, a training set con- taining 60 inhibitors was randomly selected in MOE. The selection of a training set was done when all pa- rameters such as squared correlation coefficient (R2), cross-validated correlation coefficient of Q2cv; and root-mean-square error (RMSE) of internal and ex- ternal validations were statistically significant. In our study, this was repeated 8 times to obtain a satisfied training set. The remaining 9 inhibitors were used as a testing set to evaluate the reliability of the model. The input data were chemical structures and pKi values of inhibitors. The 2D-molecular physicochemical prop- erties (descriptors) are numerical values and calcu- lated by using MOE.The inhibition constants, Ki, de- pended on 184 2D-molecular descriptors. However, the irrelevant descriptors which showed a zero value, a low correlation (< 0.07) with Ki,and high intercor- relation (> 0.7) between themselves were discarded. These descriptors were screened out using the Rapid- miner 5 software. In addition, QuaSAR-Contigency and Principle Components inMOE 2008.10 were also used to screen the most relevant descriptors. The par- tial least squares (PLS) regression method was used to develop a 2D-QSAR model. This model was used to predict the Ki values of 69 inhibitors and were pre- dicted via the QuaSAR Fit validation panel in MOE. RESULTS 2D-QSARmodeling The first goal of this work is to develop a 2D- QSAR modeling which presents molecular descrip- tors of (R)-3-amidinophenylalanine inhibitors which predominantly contribute to the inhibition constant, Ki. The selected 2D-QSAR equation is given below: pKi = 5:7742:458SlogP_VSA0+1:318 SlogP_VSA1+1:559SlogPVSA3 (1) Here, SlogP_VSA0, SlogP_VSA1, SlogP_VSA3 are molecular descriptors associated with coefficients. The training set was randomly selected, we have ana- lyzed to develop significant models by using different training set with additional descriptors. The goal was to explain and search for other descriptors that relate to the inhibition constant. Unfortunately, other de- veloped models possessed poor R2, Q2cv and RMSE parameters. Therefore, those models could not be used for further analysis and discussion. Statistical parameters The statistical parameters (such as R2, Q2cv and RMSE) give information about the goodness of fit of a model. The best model is selected when it pos- sesses highest R2 values, Q2cv (> 0.5) values, and low- est RMSE (< 0.5)11. Table 1 shows the significantly statistical parameters of the internal, external (testing set), and total validations. Predicted pKi values using a developed 2D- QSARmodel Lastly, the pKi values of 69 inhibitors were predicted using the established 2D-QSAR modeling. The pKi values of all molecules are listed in Figure 1. A plot of experimental vs. predicted pKi is shown in Figure 2. Table 1: Statistically significant parameters of the established 2D-QSARmodel Training set Cross- validation Testing set No 60 60 9 69 R2 0.791 0.962 0.771 Q2cv 0.762 RMSE 0.443 0.473 0.161 0.460 349 Science & Technology Development Journal, 22(3):348-352 Figure 1: Chemical structures, experimental (Exp) pKi 8,9 and predicted (Pred) pKi values toward thrombin of (R)-3-amidinophenylalanine inhibitors. R1 and R2 are the substituted groups in (R)-3- amidinophenylalanine skeleton 350 Science & Technology Development Journal, 22(3):348-352 Figure 2: The plot of correlations representing the experimental vs. predicted pKi values for 69 (R)-3-amidinophenylalanine inhibitors. DISCUSSION Molecular descriptors By using the partial least squares regression method, a 2D-QSAR modeling was established from a data composing of numerically relevant descriptors and pKi values of 60 inhibitors with thrombin. The de- veloped modeling expressed the dependence of pKi on the hydrophobic descriptor. The logP refers to logarithm of the n-octanol/water partition coefficient (P). This property is an atomic contribution model that calculates logP from the given structure12. This descriptor was used as a measure of cell permeabil- ity of the drug molecule. The partition coefficient is a ratio between the concentrations of a solute in lipid phase (n-octanol) and in aqueous phase (P = Cnoctanol/Caqueous). Compounds possessing P > 1 are lipophilic or hydrophobic while compounds for which P < 1 are hydrophilic. LogP of a molecule was calculated from fragmental or atomic contribu- tions (surface area, molecular properties, and solva- tochromic parameters) and various correction factors (electronic, steric, or hydrogen-bonding effects)11,13. Each atom has an accessible van der Waals surface area (VSA), ai, along with an atomic property, pi. This property is in a specified range (a, b) and contributes to the descriptor. Slog P_VSA is the sum of ai of all atoms, such that pi value of each atom i is in a range of (a, b) (Table 2) ; pi contributes to descriptor logP13. The sign andmagnitude of the descriptors co- efficients re present the contribution of each descrip- tor to pKi. Positive coefficients imply that pKi values of molecules increase with increasing SlogP_VSA val- ues, while negative values demonstrate an increase in pKi (i.e., Ki -binding affinity decreases) with decreas- ing values of the descriptors. The higher the abso- lute coefficient value is, the more crucial the contri- bution of the descriptor on the binding affinity. The modeling indicates that inhibitors possessing higher SlogP_VSA1 and SlogP_VSA3propertieswill result in a decrease in Ki values, i.e., binding affinities decrease while an increase in SlogP_VSA0 property would in- duce a better binding affinity. Table 2: Molecular descriptors in 2D-QSARmodeling Descriptor Code Description SlogP_VSA0 Sum of ai such that pi <= -0.4 SlogP_VSA1 Sum of ai such that pi is in (-0.4, -0.2] SlogP_VSA3 Sum of ai such that pi is in (0.0, 0.1] 2D-QSARmodeling and its validation The selected 2D-QSAR modeling is a model pos- sessing statistically significant parameters of inter- nal and external validations. The developed model (Equation (1)) from the training set has showed R2 value of 0.791 and RMSE value of 0.443. These values confirmed the reliability of the model. As mentioned, the reliability and statistical relevance of the 2D-QSAR modeling was examined by internal and external validation procedures. Internal valida- tion was applied by Leave One Out (LOO) cross- validation (CV)11,14. The values of Q2cv > 0.5 and RMSE< 0.5 (Table 1) further supported the reliabil- ity and interpretability of the modeling. The pKi val- ues of inhibitors were predicted by applying an estab- lished 2D-QSAR modeling on a total set. By plotting the predicted pKi values vs. the experimental ones (Figure 2), there is a linear relationship between the predicted and experimental pKi values of inhibitors, i.e., both pKi values are high (a low inhibitory activ- ity) or low (a good inhibitory activity). These results show that the modeling is reliable to predict the pKi values of the inhibitors. CONCLUSIONS The 2D-QSAR modeling has been successfully developed from 2D-descriptors of 60 (R)-3- amidinophenylalanine inhibitors associated with their inhibition constants, Ki. The established QSAR modeling was internally, externally, and totally validated, demonstrating satisfactory statistical pa- rameters. Hydrophobicity is an important descriptor 351 Science & Technology Development Journal, 22(3):348-352 in the modelling of binding affinity. The 2D-QSAR equation was applied to predict Ki values of all inhibitors. The results revealed a good predictability of the modeling. Based on the developed 2D-QSAR modeling, the design of the new inhibitors derived from (R)-3-amidinophenylalanine should focus on the hydrophobicity of derivatives by theoretical calculations to obtain the numerical values of hy- drophobic descriptors. The chemical structures of inhibitors possessing lower values of SlogP_VSA1, SlogP_VSA3 descriptors and higher SlogP_VSA0 descriptor should be further studied in synthetic experiments. 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