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

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: 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.

Keywords: Indenoisoquinoline, lung cancer, QSAR, multiple linear regression, partial least-square, artificial neural network.