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Multivariate Statistical 2D QSAR Analysis of Indenoisoquinoline-based Topoisomerase- I Inhibitors as Anti-lung Cancer Agents
Author(s):
Supriya Singh, Bharti Mangla*, Shamama Javed, Pankaj Kumar and Waquar Ahsan* Pages 2237 - 2247 ( 11 )
Abstract:
<P>Background: Indenoisoquinoline-based compounds have shown promise as topoisomerase-I inhibitors, presenting an attractive avenue for rational anticancer drug design. However, a detailed QSAR study on these derivatives has not been performed till date. <p> Objective: This study aimed to identify crucial molecular features and structural requirements for potent topoisomerase- 1 inhibition. <P> Methods: A comprehensive two-dimensional (2D) QSAR analysis was performed on a series of 49 indenoisoquinoline derivatives using TSAR3.3 software. A robust QSAR model based on a training set of 33 compounds was developed achieving favorable statistical values: r<sup>2</sup> = 0.790, r<sup>2</sup>CV = 0.722, f = 36.461, and s = 0.461. Validation was conducted using a test set of nine compounds, confirming the predictive capability of the model (r<sup>2</sup> = 0.624). Additionally, artificial neural network (ANN) analysis was employed to further validate the significance of the derived descriptors. <P> Results: The optimized QSAR model revealed the importance of specific descriptors, including molecular volume, Verloop B2, and Weiner topological index, providing essential insights into effective topoisomerase-1 inhibition. We also obtained a robust partial least-square (PLS) analysis model with high predictive ability (r<sup>2</sup> = 0.788, r<sup>2</sup>CV = 0.743). The ANN results further reinforced the significance of the derived descriptors, with strong r<sup>2</sup> values for both the training set (r<sup>2</sup> = 0.798) and the test set (r<sup>2</sup> = 0.669). <P> Conclusion: The present 2D QSAR analysis offered valuable molecular insights into indenoisoquinoline-based topoisomerase- I inhibitors, supporting their potential as anti-lung cancer agents. These findings contribute to the rational design of more effective derivatives, advancing the development of targeted therapies for lung cancer treatment.</P>
Keywords:
Indenoisoquinoline, lung cancer, QSAR, multiple linear regression, partial least-square, artificial neural network.
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