Nitroaromatics as hypoxic cell radiosensitizers: A 2D-QSAR approach to explore structural features contributing to radiosensitization effectiveness

Abstract

Hypoxia is the prime component of tumor microenvironment that plays a pivotal role in cancer progression. Nitroaromatic compounds are known to enhance the sensitivity of hypoxic cells to ionizing radiation. The application of computational tools like Quantitative Structure-Activity Relationship (QSAR) can be used to predict newly developed nitroaromatics or compounds with missing data. In the present work, three datasets consisting of 18 nitrofurans, 11 nitrothiophenes and 84 nitroimidazoles were utilised for two-dimensional QSAR modeling to retrieve their structural features essential to elicit radiosensitivity. The work comprises two parts: (i) local modeling using individual datasets; and (ii) global modeling by clubbing the three datasets. The two-dimensional descriptors were calculated using Dragon (version 7.0) software. The developed models were obtained using various feature selection techniques applied in “Small Dataset Modeling” and “Double Cross Validation” tools available from https://dtclab.webs.com/software-tools. Finally, the models were validated using stringent metrics following the Organisation for Economic Co-operation and Development (OECD) guidelines. The developed models are robust, predictive, and are useful tools to predict the radiosensitization of newly developed nitroaromatics. Furthermore, the global model was used to predict two external sets comprising 10 and 47 compounds, and the prediction ability was validated using the “Prediction Reliability Indicator” tool.