2D QSAR STUDY ON SAPONINS OF PULSATILLA KOREANA AS AN ANTICANCER AGENT

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ABOUT AUTHORS:
Sagar Alone*, Sharda Deore, Bhushan Bawiskar
Department of Pharmacognosy & Phytochemistry
Govt. College of pharmacy, Kathora naka, Amravati-444604, India
*sagaralone123@gmail.com

ABSTRACT
Total seventeen saponins previously isolated from roots of Pulsatilla koreana having cytotoxic activity against 4 different cancer cell line (A-549, SK-OV-3, SK-MEL-2, HCT15) were used for 2D QSAR using V-life Molecular design suit. Using multiple linear regression method against 4 different cell lines develops QSAR model. QSAR model was generated by using training set of 11 and test set of 6 molecules having correlation coefficient (r²), significant cross validated correlation coefficient (q²) and F-test (For statistical significance) is as given below  (A-549: r²- 0.9281, q²- 0.8691, F-test- 51.6079),  (SK-OV-3: r²- 0.9554, q²- 0.9184, F-test- 85.7357),  (SK-MEL-2: r²- 0.9160, q²- 0.8285, F-test- 43.6084),  (HCT15: r²- 0.9203, q²- 0.8357, F-test- 46.1887). In this QSAR study Alignment independent descriptors such as T_2_C_7, T_O_O_5 and physicochemical descriptors like Chain path count such as 6 chain count and Chi chain such as Chi 6 chain were most responsible descriptors for anticancer activity.

REFERENCE ID: PHARMATUTOR-ART-2341

INTRODUCTION
Cancer is a disease of cell characterized by progressive, persistent, abnormal, purposeless, and uncontrolled proliferation of tissues. Currently, cancer is most dominating cause of death in world ^{[1, 2]}. Pulsatilla koreanabelongs to the family Ranunculaceae and is an endemic species in Korea containing 17 saponins in which eight lupane-type (1, 3, 5, 7, 9, 11, 13, 15) and nine oleanane-type (2, 4, 6, 8, 10, 12, 14, 16, 17), isolated from Pulsatilla koreana  roots.The roots of this plant have been widely used in traditional medicine for the treatment of several diseases, particular malaria and amoebic dysentery ^[3]. This plant was evaluated for their cytotoxic activity against four human solid tumor cell lines (A-549, SK-OV-3, SK-MEL-2, and HCT-15). ED50 values was observed that the saponins 5—17, possessed free carboxylic group at C-28, exhibited moderate to considerable cytotoxic activity (ED₅₀; 1.57—174.34m M) against tumor lines, whereas their sugar-bonded esters, that is, disdesmoside saponins 1, 2, 3, and 4were inactive ^[1] (ED₅₀ >300m M). Saponins molecules isolated from the Pulsatilla koreana as anticancer agent are taken from literature for QSAR analysis^[4].

ED₅₀ value was defined as the concentration of compound needed to reduce a 50% of absorbance relative to the Quantitative structure activity relationship (QSAR) gives information relating chemical to Biological activities by developing a QSAR model. Different molecular descriptors are used to determine the structural feature of lead molecule. The purpose of using QSAR descriptors is to calculate the properties of molecules that serve as numerical descriptions, using such an approach one could predict the activities of newly designed compounds before a decision is being made whether these compounds should be really synthesized and tested.

With respect to the above subjects and scope QSAR study is performed on Pulsatilla koreana saponins analogues in order to get a better understanding of their structural features and anticancer activity.

MATERIAL AND METHOD

2D QSAR Methodology

Data set:
17 saponins molecules isolated from the Pulsatilla koreana as anticancer agent are taken from literature for QSAR analysis. ^[4] 2D Structure of all above 17 saponins analogues molecules are sketched by using 2D structure drawing function of Vlife MDS system and all 2D structures are converted to 3D by using VLife MDS (Molecular Design Suite) ^TM 3.5 software supplied by VLife Sciences Technologies Pvt. Ltd., Pune, India. Batch optimization of 3D structure was done by using MMFF. To get more minimization we input 100000 cycles with the converse criteria (RMS gradient) of 0.01. The distance dependent function was kept at 1.0 with analytical gradient type. The 2D-QSAR models were generated for this series using multiple linear regression (MLR) against 4 different cancer cell lines. Selection of molecules in the training set and test is a key and important feature of any QSAR model. Therefore, the care was taken in such a way that biological activity of all compounds in test lie within the maximum and minimum value range of biological activities of training set of compounds. The Uni-Column Statistics of test and training sets further reflected the correct selection of test and training sets. A Uni-Column statistics for training set and test set were generated to check correctness of selection criteria for trainings and test set molecules those best models which come out with promising results are discussed here. QSAR models were generated by a training set of 11 molecules for each model. And a test set of 6 molecules with uniformly distributed biological activities. The structures of all the compounds along with their actual and predicted biological activities are presented in Table 1.

Table 1: Experimental and Predicted activity of Pulsatilla koreana saponins analogues

Expt. - Experimental activity

Pred. - Predicted activity

QSAR Study:
All types of 2D Physicochemical descriptors including Individual, Chi chain, Path count, Chiv, Element count and Kappa categories and Alignment independent including topological structure descriptors were calculated for QSAR analysis using Vlife MDS software. Multiple linear regression method is used to generate QSAR equation. For variable selection, stepwise forward-backward method was used. For validating the quality of the models. Selection of molecules in the training set and test is a key and important feature of any QSAR model. Therefore, the care was taken in such a way those biological activities of all compounds in test lie within the maximum and minimum value range of biological activities of training set of compounds. The Uni-Column Statistics of test and training sets further reflected the correct selection of test and training sets. A Uni-Column statistics for training set and test set were generated to check correctness of selection criteria for trainings and test set molecules.

Table 2: Uni-column statistics of the training and test sets for QSAR models:

Table 3: Statistics of the Models:

Here:
N - Number of molecules,

K - Number of descriptors in a model,

DOF - Degree of freedom (higher is better),

r2 - Coefficient of determination (> 0.7),

q2 - Cross-validated r (>0.5),

pred_r2 - r for external test set (>0.5),

F-test - F-test for statistical significance of the model (higher is better, for same set of descriptors and compounds),

r2_Se, q2_se, pred_r2_se = error for r2, q2, pred_r2 respectively.

Equations:

· For (A-549):
ED₅₀= + 55.3309 (± 6.5096) T_2_C_7 + 23.9224 (± 8.9012) 6 ChainCount - 616.7280

· For (SK-OV-3):
ED₅₀= + 53.2371(± 0.5713) T_2_C_7 + 553.1007(± 130.2520) chi6chain -612.2515

· For (SK-MEL-2):
ED₅₀ = + 53.9707(± 7.2274) T_2_C_7 + 7.3979(± 2.5587) T_O_O_5 - 493.8088

· For (HCT15):
ED₅₀ = + 53.2728(± 6.9414) T_2_C_7 + 7.3583(± 2.4574) T_O_O_5 - 484.2732

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Validation of QSAR Model-
Validation of QSAR study is important to test the internal stability and predictive ability of the QSAR models & was validated by the following procedure as given below. There are two types of validation
1) Internal validation
2) External validation

Internal validation:
It was carried out using leave-one-out (q2, LOO) method. For calculating q2, each molecule in the training set was eliminated once and the activity of the eliminated molecule was predicted by using the model developed by the remaining molecules. The q2 was calculated using the equation (Eq. 2), which describes the internal stability of a model.

Where yi (Act) and yi (Pred) arethe actual and predicted activity of the ith molecule in the training set, respectively, and y mean is the average activity of all molecules in the training set.

External validation:
The predictive ability of the selected model was also confirmed by external validation of test set compounds, which is also denoted with pred_r2. The pred_r2 value is calculated as follows

Where yi and yi are the actual and predicted activity of the I th molecule in the training set, respectively, and y mean is the average activity of all molecules in the training set. We have done both Internal & External validation with these formula and following values are obtained.

Table 4: Internal & External validation data:

RESULT & DISCUSSION
The aim of our study was to evaluate a series of analogs of Pulsatilla koreana saponins by doing 2D QSAR with the help of different descriptors, from the QSAR studies it was found that
- The Alignment Independent category in which T_2_C_7 descriptor is most powerful descriptor responsible for anticancer activity to all 4 types of cell lines at range of 70% - 80% (A-549, SK-OV-3, SK-MEL-2, and HCT15).
- T_O_O_5 descriptor is responsible for anticancer activity in SK-MEL-2 and HCT15 cancer cell line at range 20%- - 30%.
- 6 chain count and chi6chain responsible for anticancer activity for A-549, SK-OV-3 respectively.
- Existence of free carboxylic group is at C-28 position is most responsible for cytotoxic activity and hydroxyl group at C-23 had a negative effect on the cytotoxic activity.
- It is due to electron donating effect of two loan pair towards C-3 of aglycon. The results obtained from this 2D-QSAR study are in agreement with the observed SAR of Pulsatilla koreana saponins studied.
- There is difference in cytotoxic between oleanane and lupane saponins generally cytotoxicity of lupane type saponins were much weaker than those of oleanane type saponins that means oleanane type Pulsatilla koreana saponins analogues having more anticancer activity than that of lupane type.

Table 5: Descriptors responsible for anticancer activity