Recursive Partitioning Analysis and Anti-Tubercular Screening of 3- Aminopyrazine-2-Carbohydrazide Derivatives

Author:

Miniyar Pankaj1,Mahajan Anand2,Anuse Dattatray1,Kumar Ashish1,Barmade Mahesh3,Sarkar Dhiman4,Arkile Manisha4,Khedkar Vijay4

Affiliation:

1. Department of Pharmaceutical Chemistry, Sinhgad Institute of Pharmacy, Narhe, Pune- 411041, Affiliated to Savitribai Phule Pune University, Ganeshkhind, Pune 411007, India

2. Goa College of Pharmacy, 18th June Road, Panaji, Goa 403 001, India

3. The M.S. University of Baroda, Vadodara 390001, India

4. CombiChem Bio Resource Center, National Chemical Laboratory, Pune 411008, India

Abstract

Background: Treating tuberculosis is a challenge due to the development of drug resistance. Hence, it is imperative to develop novel leads having high potency and efficacy to curb drug resistance. Methods: The present research work is focused on microwave-assisted synthesis of novel twenty-six 3-amino-N’-benzylidenepyrazine-2-carbohydrazide derivatives (3a-z), where, lyophilization technique was used for isolation of the major intermediate, 3-aminopyrazin-2-carbohydrazide. All synthesized compounds were subjected for anti-tubercular screening against Mycobacterium tuberculosis H37Ra by using XTT Reduction Menadione Assay (XRMA) protocol. Results: Out of 26 synthesized compounds, four N’-substitutedbenzaldehyde-3-amino-pyrazine-2- carbohydrazide derivatives viz. 3i, 3j 3v and 3z showed significant activity against M. tuberculosis H37Ra. The compounds 3i, 3j, 3v and 3z showed 99, 98, 92 and 87 % inhibition respectively as compared to 94% inhibition shown by the standard drug rifampicin. The MIC and IC50 values were in the range of 24.3-110 and 5.9-20.8 µg/ml respectively. Conclusion: A classification model called Recursive Partitioning (RP) based on binary Quantitative Structure-Activity Relationship (QSAR) was derived for the establishment of structure-activity relationship (SAR). The predictions derived on the basis of RP model were found to be in agreement with anti-tubercular screening data.

Funder

BCUD, University of Pune

Publisher

Bentham Science Publishers Ltd.

Subject

Drug Discovery,Pharmaceutical Science,Molecular Medicine

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1. Classification and Design of HIV-1 Integrase Inhibitors Based on Machine Learning;Computational and Mathematical Methods in Medicine;2021-04-01

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