2D QSAR APPROACH TO DEVELOP NEWER GENERATION SMALL MOLECULES ACTIVE AGAINST SARS-COVIDHTML Full Text
2D QSAR APPROACH TO DEVELOP NEWER GENERATION SMALL MOLECULES ACTIVE AGAINST SARS-COVID
Supriyo Saha * and Dilip Kumar Pal
School of Pharmaceutical Sciences & Technology, Sardar Bhagwan Singh University, Dehradun, Uttarakhand, India.
ABSTRACT: We are in the half past of 2022, but still, we are facing the coronavirus pandemic situation. When a patient is hospitalized, only some FDA-approved drugs were administered to cure the patient. In treating coronavirus infection, nitazoxanide, granulocyte-macrophage colony-stimulating factor inhibitors, and various monoclonal antibodies are present. But all the molecules used in the treatment were not so effective in fully curing the patient. So, to break this jinx to develop of newer generation anti-SARS-CoV-2 drug molecules, computational approaches played an essential role. 2D QSAR studies related to anti-SARS-CoV-2 molecule development, some QSAR models observed with good statistical parameters such as R2: 0.748, cross-validated Q2 (LOO): 0.628, external predicted R2: 0.723 and another model suggested with R2: 0.764, Q2: 0.627 and Rm2: 0.610, Q2 (F1): 0.727, Q2 (F1): 0.652, MAE score: 0.127. We developed a new 2D QSAR model with a higher number of molecules and greater statistical parameters. A dataset of 84 anti-SARS-CoV2 molecules was obtained from literature followed by descriptor calculation PADEL software; the QSAR model was generated using the Modelability index, dataset pretreatment, division, MLR equation, validation, and Y randomization test. The model was pIC50 = -1.79268(+/-0.3652) +0.07995(+/-0.03551) naaaC -0.4051(+/-0.09672) nsssN -0.45945(+/-0.11025) SHsOH +1.23189(+/-0.28144) ETA_BetaP with R2 and Q2 values were 0.87028 and 0.70493 with MAE fitness score value: 0.14298. Atoms E-state and electronic features of the molecules directly related to anti-SARS-CoV-2 drug activity. It can be easily concluded that we want to develop a small molecule effective against SARS-CoV-2 disease in the near future.
Keywords: SARS-CoV-2, Modelability index, Kennard stone method, Multiple linear regression, Golbraikh and tropsha acceptable model criteria, Y Randomization test
INTRODUCTION: We are in the half past of 2021, but still, we are facing the coronavirus pandemic situation. Till date, globally 19, 2054, 106 cases were registered and among them, 41, 280, 58 deaths were reported (https://www.worldometers.info/coronavirus/ accessed on 25.07.2021).
Presently, FDA approved three vaccines were approved in US such as Pfizer-BioNTech, Moderna, Johnson & Johnson’s Janseen; whereas in other countries, oxford AstraZeneca, Sputnik V, Sinopharm, covaxin, etc. vaccines were approved 1, 2.
As per world data, 26.8% of the world population was vaccinated with the first dose, and 13.4% of the world population was vaccinated with both doses. As per WHO, we were already in the middle of the third wave of coronavirus pandemic situations 3, 4. WHO declared SARS-CoV-2 variants into three main categories: variant of interest, variants of interest and variants of high consequence 5. In US, variants like B.1.1.7 (α), B.1.351 (β), B.1.617.2 (δ) and P.1 (γ) were concerned as variants of concern without any variants of high consequence 6, 7. Variants of interest were certain genetic markers that hampered viral transmission, diagnosis, and response towards therapeutic dose; variants of concern created a high impact on the response towards diagnosis, treatment regime with a possible increment of viral transmission, disease condition, and response towards vaccination whereas variants with less response towards diagnosis, treatment and vaccination 8, 9.
B.1.427, B.1.429, B.1.525, B.1.526, B.1.617.1, B.1.617.3 were categorized under variants of interest and B.1.1.7, B.1.351, B.1.617.2, P.1 were categorized under variants variant of concern. In contrast, no variants were considered under variants of high consequence (https://www.cdc.gov/coronavirus/2019-ncov/variants/variant-info.html accessed on 29.07.2021) 10. When a patient is hospitalized, only some FDA-approved drugs were administered to cure the patient. Nowadays, in the treatment of coronavirus infection, nitazoxanide, granulocyte-macrophage colony-stimulating factor inhibitors (Gimsilumab, lenzilumab, namilumab, and otilimab), chloroquine, hydroxychloroquine, azithromycin, colchicine and various monoclonal antibodies (casirivimab, imdevimab) were used in the treatment (https://www.covid19treatmentguidelines.nih.gov/about-the-guidelines/whats-new/ accessed on 30.07.2021).
But all the molecules used in the treatment were not so effective in fully cure the patient. So, to break this jinx to develop of newer generation anti-SARS-CoV-2 drug molecules, computational approaches such as molecular docking studies, molecular dynamics simulation studies, and QSAR (2D or 3D) studies play an essential role 11, 12. In the category of 2D QSAR studies related to anti-SARS-CoV-2 drug development 13, some QSAR models observed with good statistical parameters such as R2: 0.748, cross-validated Q2 (LOO): 0.628, external predicted R2: 0.72314 and another model suggested with R2: 0.764, Q2: 0.627 (56 molecules in the training set) and Rm2: 0.610, Q2 (F1): 0.727, Q2 (F1): 0.652, MAE score: 0.127 (13 molecules in the test set)15. In this context, we developed a new 2D QSAR model with a higher number of molecules and validated statistical parameters.
MATERIALS AND METHODS:
Dataset and Descriptor Calculation: A dataset of 84 anti-SARS-CoV2 drug molecules obtained from different literatures, including the top twenty potential anti-SARS-CoV-2 drugs obtained from 1553 FDA approved drugs 16, top 20 Potential Anti-SARS-CoV-2 drugs separated from 7012 investigational or off-market drug molecules 17, existing protease inhibitors, some polyamines targeting cellular attachment and entry of coronavirus 18, SARS-Cov-2 Mpro inhibitors 19, recently developed novel coronavirus (2019-nCoV) inhibitors and inhibitors of coronavirus main protease 3CLpro20. All the molecules were drawn by ACD ChemSketch software followed by saved as MDL Mol format. Then two-dimensional descriptors were calculated of the molecules using PADEL descriptor 21. All the molecular descriptors along with their corresponding biological activities were tabulated in CSV format, where IC50values were changed into pIC50 values 22.
Modelability Index: Modelability index is an estimation feasibility tool defined by the ratio between activity class-weighted ratio of the number of nearest-neighbor pairs of compounds corresponding with same activity class and the total number of pairs 23. This concept correlated with the unnecessary efforts of a QSAR dataset associated with the development of QSAR model.
Descriptor Pretreatment: Then very closely related descriptors present within the dataset were removed by considering variance cut off and correlation coefficient values of 0.0001 and 0.99, respectively 24.
Dataset Division: Generally, the dataset was divided into training and test sets using Kennard Stone, Random Faster, and Euclidean Distance methods. Among them, here we considered the Kennard Stone method to divide the dataset of 84 molecules into training and test set. After the dataset division, 63 and 21 molecules were present in the training and test set, respectively 25-26.
Suitable Descriptor Selection: Suitable descriptor combination was selected using Stepwise MLR software with F values ranging from 3.9 to 4.0. The best subset combination was observed with 4 descriptors set with R2 cut-off value was 0.6 27-28.
Stepwise Regression: Stepwise multiple linear regression equation was built by multistep equation development involving three distinct steps: identification of an initial model, repetition of the previous step to achieve a better F and R2 value, and calibration of model 24.
The stepwise regression equation was developed using statistical SPSS software, and parameters were judged as explained variance (R2a), correlation coefficient (R), standard error of estimate (s), and variance ratio (F) with a specified DF. Finally, the LOO method validated the model with cross-validation R2 (Q2), SPRESS, and SDEP parameters 29.
QSAR Equation Development: The final QSAR model was developed by Multiple Linear Regression Plus valid software with all possible combinations of descriptors based on the quality of prediction and MAE-based fitness score 30-31.
QSAR Equation Validation: The developed QSAR model was validated by the Golbraikh and Tropsha acceptable model criteria. The acceptable model criteria were as follows 32:
- Threshold value Q2 greater than 0.5.
- Threshold value R2 greater than 0.6.
- Threshold value |r02-r'02| less than 0.3.
- Threshold value: [0.85<k<1.15 and ((r^2-r0^2)/r^2)<0.1 or [0.85<k'<1.15 and ((r^2-r'0^2)/r^2)<0.1].
QSAR Equation Validation: QSAR model was cross-validated using LOO process. Applicability domain of the model was checked by euclidean distance and mahalanobis distance methods. The distance of a test set to its nearest neighbor in the training set was compared with predefined applicability domain threshold value 33-34.
MLR Y Randomization Test: In the Y randomization test, a random multiple linear regression model was developed by a faster random technique by changing the dependent variable and making the independent variable static. The model with significantly higher R2 and Q2 values after several trials confirmed that the developed model was robust and reproducible 35-36. Another parameter, cRp2 was also calculated which should be more than 0.5 for passing this test 37.
RESULTS AND DISCUSSION: Initially, the model ability index value was checked. The data showed a 0.5926 value with 29 molecules in high total active/less and 55 molecules in less total active/toxic with a threshold value of model ability index 0.65. So model ability index value of the model was 0.5926, which reflected that the dataset was quite close to developing a good QSAR model 38-39. Then the dataset was divided into training and test sets using the Kennard-Stone method. Among them, 63 and 21 molecules were present in training and test sets, respectively 40. Stepwise multiple linear regression was used to identify the most probable set of descriptors to build a good QSAR model based on MAE value 41. After that, the data pretreatment process was also performed with variance cut-off and inter-correlation cut-off values 0.001 and 0.9, respectively, along with F value within (3.9-4.0) 42. Then, using the best possible set of descriptors best subset selection process was performed with R2 cut-off value 0.6 and inter-correlation between descriptors R2 cut-off value 0.5 43. Based on BAD, MODERATE, and GOOD MAE fitness scores, the final QSAR model was generated.
The Final QSAR Model was as follows:
pIC50 = -1.79268(+/-0.3652) +0.07995(+/-0.03551) naaaC -0.4051(+/-0.09672) nsssN -0.45945(+/-0.11025) SHsOH +1.23189(+/-0.28144) ETA_BetaP.
As per the model, naaaC, ETA_BetaP positively contributed to pIC50 value, whereas nsssN, SHsOH negatively contributed to pIC50. As per the internal validation parameter, SEE, R2, R2 adjusted, PRESS value, and F values were 0.38202, 0.75862, 0.74197, 8.46426, and 45.57092, respectively. As per LOO values Q2, average Rm2 and delta Rm2 were 0.70493, 0.59478, and 0.1822, respectively. As per external validation parameters (without scaling) and after scaling R2, R02, reverse R02, RMSEP, Q2f1/R2(Pred), Q2f2 were 0.87028, 0.86122, 0.87026, 0.22536, 0.86012, 0.85943 and average Rm2 (test), delta Rm2 (test) were 0.82128, 0.00268; respectively. As per the error-based judgment related to testing set prediction: MAE, standard deviation values for 95% of data were 0.14298 and 0.10498, which correspond with GOOD prediction criteria 44. Then the model was validated using Golbraikh and Tropsha acceptable model criteria. Outcomes showed that Q2, R2, |r02-r'02|, k, [(r2-r02)/r2], k' and [(r2-r'02)/r2] values were 0.70493, 0.87028, 0.00904, 0.96872, 0.01041, 0.95525 and 0.00002, respectively 45. The model successfully passed all the validation criteria per the validation parameter. In training set data, the residual values between actual and predicted pIC50 values were between 0.007 to 1.10 Table 1. In the case of the test set, residual values oscillated between 0.001 to 0.5 Table 2 46. The R2 values observed after plotting actual pIC50 and predicted pIC50 values in training Fig. 1 and test set were 0.7586 and 0.8703, respectively Fig 2. All the molecules in training and test sets were observed within the applicability domain. As per the Y randomization test data of the model, the average R, R2, Q2 (LOO), and cRp2 values were 0.233071617, 0.070303736, -0.100697065 and 0.733948537, respectively Table 3.
TABLE 1: ACTUAL PIC50, PREDICTED PIC50 AND RESIDUAL VALUES OF TRAINING SET MOLECULES
TABLE 2: ACTUAL PIC50, PREDICTED PIC50 AND RESIDUAL VALUES OF TEST SET MOLECULES
TABLE 3: Y RANDOMIZATION DATA OF THE QSAR MODEL
FIG. 1: GRAPH BETWEEN ACTUAL AND PREDICTED PIC50 VALUES IN THE TRAINING SET
FIG. 2: GRAPH BETWEEN ACTUAL AND PREDICTED PIC50 VALUES IN THE TEST SET
As per the QSAR model, four two-dimensional descriptors such as naaaC (count of atom-type E-State: C:), nsssN (count of atom-type E-State: >N), SHsOH (sum of atom-type H E-State: -OH) and ETA_BetaP (measurement of electronic features of the molecule relative to molecular size) were significantly contributed 47. As per the previous QSAR models related to anti-SARS-CoV-2 drug development, maximum R2 and Q2 values were 0.764 and 0.652 with MAE fitness score of 0.127, whereas our developed QSAR model was observed with higher statistical parameter values such as R2 and Q2 values were 0.87028 and 0.70493 with MAE fitness score value: 0.14298.
CONCLUSION: So, it was quite obvious that the newly developed QSAR model was statistically more validated than previous models with a large number of molecules present in the dataset. Also, atoms' E-state and electronic features of the molecules were directly related to anti-SARS-CoV-2 drug activity.
It can be easily concluded that if in the near future we want to develop a small molecule effective against SARS-CoV-2, the developed QSAR model will work as a good predictor of the activity profile with any chemical scaffold with possible descriptor combination.
ACKNOWLEDGEMENT: Declared none.
CONFLICTS OF INTEREST: The authors have no conflicts of interest, financial or otherwise.
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How to cite this article:
Saha S and Pal DK: 2D QSAR approach to develop newer generation small molecules active against SARS-Covid. Int J Pharm Sci & Res 2023; 14(3): 1372-91. doi: 10.13040/IJPSR.0975-8232.14(3).1372-91.
All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research. This Journal licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
Supriyo Saha * and Dilip Kumar Pal
School of Pharmaceutical Sciences & Technology, Sardar Bhagwan Singh University, Dehradun, Uttarakhand, India.
15 July 2022
19 August 2022
01 September 2022
01 March 2023