QSAR Analysis on Some Novel 4-Quinolylhydrazone Derivatives as Anti- Tubercular agent

About Authors:
Dr. A. K. Pathak, Parul Sengar, Kamlesh Kumar*
Department of Pharmacy, Barkatullah University
Bhopal, M. P.

A Quantitative Structure Activity Relationship study on a Series of 40 molecules of (4-Quinolylhydrazone compounds) with anti-tubercular activity analogues was made using combination of various physicochemical descriptors (Thermodynamic, electronic and spatial). Several statistical expressions for 2D QSAR & 3D QSAR were developed using stepwise partial least square (PLS) regression analysis and K-Nearest neighboring molecular field analysis (K-NNMFA) respectively. The studies on 2D-QSAR, suggested the four descriptors T_C_C_2, T_C_N_4, Surface area excluding P & S, SsCH3E-index, and H donor count were common and highly contributed the activity. 2DQSAR model developed using partial least square regression approach. Negative logarithmic value of (MIC) was taken as dependent variable and selected discriptors were taken as independent varable. The analysis resulted in the following 2D-equation suggest that, MIC50 = [+0.1587 T_C_C_2- 0.0593 SsCH3E-index- 0.1395 T_C_N_4+ 0.3888 H-Donor Count -3.1556], n =26; Degree of freedom = 22; r2 = 0.75; q2 = 0.67; F test = 22.63;  r2 se = 0.20 ; q2 se = 0.23; pred_r2 = 0.46 ; pred_r2 se = 0.24, a H-donor group at Ar, is important for guiding the design of a new molecule. 3DQSAR model developed using K-nearest neighbour method (training set =33 and test set = 7). The best model derived by the method have q2 = 0.51, q2_se = 0.25, Predr2 = 0.71, pred_r2se = 0.16, n=33, k Nearest Neighbor is 2, Degree of freedom =28. The steric and electrostatic descriptors at the grid points, E_916 (0.1865, 0.3006); S_591 (-0.2851, -0.2580); S_1027 (-0.3273, -0.3090); S926 (30.000, 30.000) plays important role for design of new molecule. 3DQSAR analysis of series of 4-Quinolylhydrazone compounds informed that electropositive and less bulky group increases the biological activity.

Reference Id: PHARMATUTOR-ART-1170

Tuberculosis (TB) is one of the most prevalent infectious Diseases, about 2 billion people, equal to one-third of the world’s total population, are infected with Mycobacterium tuberculosis (MTB), the microbes that cause tuberculosis.[1] Tuberculosis is a leading killer among HIV-infected people with weakened immune systems; about 200,000 people living with HIV/AIDS die from TB every year. Multidrug-resistant TB (MDR-TB) is a form of TB that does not respond to the standard treatments using first-line drugs. [2]
The quinoline skeleton is often used for the design of many synthetic compounds with diverse pharmacological profile like antifungal, antitumor, antimycobacterial, antimalarial, Antihistaminic, antiacetylcholine, antioxidant, antihypertensive (angiotensin II receptor antagonists), antileishmanial, antidyslipidemic, antioxidative, analgesic, anti-inflammatory, and anti-HIV activity [3-12]. The present work focuses on the QSAR analysis on some quinolyl hydrazones derivatives for the development of new quinolyl compounds for the potent antimycobacterial activity (TB). In the present study, 2D-QSAR, 3D-QSAR analysis of some novel 4-Quinolylhydrazone compounds with anti-tubercular activity was performed by using Partial least square regression (PLS) and k-nearest neighbour method (KNN) approach. A data set of 40 molecules was taken from Sandra Gemma et al. in Bioorganic & Medicinal Chemistry Journal 2009 available online at www.sciencedirect.com.[13] and MIC value of molecules were converted to negative logarithmic values (MIC) by using software VLIFE MDS 3.5 [14].

Materials and Methods
The dataset consist of structurally diverse compounds reported for MTB H37RV inhibitory activities. The selected series comprises of forty (40) 4-Quinolylhydrazone analogues reported by Sandra Gemma et al. [13] (Table 1).The Anti-tubercular activity of compounds in the series is reported as MIC values where MIC refers to minimum concentration required to inhibit 50% of Antimicrobial activity. The compounds in the selected series were randomly divided into two sets with one set as a training set in developing regression models and the remaining as validation set (Test set) in the prediction of biological activity.


Table 1: Biological activity data and structures of the compounds in the series


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