Abstract
A series of pyrazoline-based new heterocycles have recently been synthesized from our group where some of the compounds display potent anti-tubercular activity against Mycobacterium tuberculosis H37Rv. In order to further explore the potency of the compounds, quantitative structure activity relationship study is carried out using genetic function approximation. Statistically significant (r2 = 0.85) and predictive (r2pred=0.89 and r2m=0.74) QSAR models are developed. It is evident from the QSAR study that majority of the anti-tubercular activity is found to be driven by lipophilicity. Also, molecular solubility, Jurs and shadow descriptors influence the biological activity significantly. Also, positive contribution of molecular shadow descriptors suggests that molecules with bulkier substituents are more likely to enhance anti-tubercular activity. Since the developed QSAR models are found to be statistically significant and predictive, they potentially can be applied for predicting anti-tubercular activity of new molecules for prioritization of molecules for synthesis.
Highlights
World Health Organization (WHO) estimates that almost one-third of the world’s population, (~2 billion people) is infected with the tuberculosis [1]
Multidrug-resistant TB (MDRTB), a form of TB that does not respond to the first-line TB drugs and extensively drug-resistant TB (XDR-TB), an MDR-TB with resistance to aminoglycosides and fluoroquinolones has become a serious threat to control and treatment of tuberculosis
There are a few cases reported of totally drug resistant tuberculosis (TDR-TB); which has raised alarming concerns on the existing drug regimen [3]
Summary
World Health Organization (WHO) estimates that almost one-third of the world’s population, (~2 billion people) is infected with the tuberculosis [1]. There are a few cases reported of totally drug resistant tuberculosis (TDR-TB); which has raised alarming concerns on the existing drug regimen [3] This implies urgent need to discover newer anti-tubercular agents with newer molecular mechanisms. Quantitative structure activity relationship (QSAR) is one of the most widely used tools to design newer candidates for several therapeutic areas [4]-[6] It provides useful insights into the structural features which are responsible for the biological activity and help to generate a mathematical model which can predict activity of untested compounds quantitatively. We have successfully applied GFA to generate a variety of QSAR models [4] [5] Such models provide useful structure-activity insights, which can be used for prioritization of synthetic efforts to generate and lead optimization strategies
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