Multivariate Statistical 2D QSAR Analysis of Indenoisoquinoline-based Topoisomerase- I Inhibitors as Anti-lung Cancer Agents

Abstract

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. Objective:: To study aimed to identify crucial molecular features and structural requirements for potent topoisomerase- 1 inhibition. objective: In order to characterize the molecular features and structural requirements, a complete 2D QSAR study was performed on a series of 49 indenoisoquinoline derivatives using TSAR3.3 software. 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: r2=0.790, r2CV=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 (r2=0.624). Additionally, artificial neural network (ANN) analysis was employed to further validate the significance of the derived descriptors. 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 (r2=0.788, r2CV=0.743). The ANN results further reinforced the significance of the derived descriptors, with strong r2 values for both the training set (r2=0.798) and the test set (r2=0.669). Conclusion:: 2D QSAR analysis offers 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. other: No any