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

Abstract

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. This study aimed to identify crucial molecular features and structural requirements for potent topoisomerase- 1 inhibition. 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. 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). 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.