Document Type : Research Articles
Authors
1
Laboratory of Spectroscopy, Molecular Modeling, Materials, Nanomaterials, Water and Environment, (CERNE2D), Mohammed V University in Rabat, Faculty of Science, AV Ibn Battouta, BP1014, Agdal, Rabat, Morocco.
2
Mohammed V University in Rabat, Scientific Institute of Rabat, AV Ibn Battouta, BP1014, Agdal, Rabat, Morocco.
3
Improvement and Valuation of Plant Resources, Faculty of Sciences, Ibn Tofaïl University KENITRA-University Campus, Kenitra 14000, Morocco.
4
Science and Technical Research Lab, ESEF, University of Ibn Zohr, Morocco.
5
Department of Chemical and Materials Engineering, College of Engineering, Northern Border University, Arar, Saudi Arabia.
Abstract
Objective: Breast cancer is the most common cancer in women; its mortality ranking varies by region, despite recent improvements in early detection and treatment. To account for these factors and modify them during the design of novel drugs, our research aimed to identify potential descriptors influencing Steroid Sulfatase (STS) inhibitory activity by analyzing a series of molecules, specifically 4-(thio)chromenones, to establish a quantitative relationship between the inhibitory activity of Steroid Sulfatase (STS) and the structure of these molecules. Methods: Using 24 derivatives of 2-substituted 4-(thio)chromenone 6-O-sulfamate compounds, obtained from PubChem and whose biological activity was assessed experimentally, we created a QSAR model. Multiple linear regression (MLR) was used as the statistical technique to create the 2D QSAR model after the 2D descriptors were computed using PaDEL-Descriptor. We developed the QSAR model and used Lipinski’s Rule of Five and Veber’s rule to validate the pharmacokinetic profile of the 24 compounds. Consequently, we examined combinations of two and three descriptors. Multiple linear regression (MLR), which is helpful for producing highly predictive and comprehensible QSAR models, was utilized to create the QSAR model equation using the selected combinations. Results: At the molecular level, Steroid Sulfatase (STS) is expressed in 87% of breast cancers. Inhibiting STS could decrease estrogen levels. A 2D QSAR analysis using the multiple linear regression (MLR) method was conducted on a dataset of 24 derivatives of 2-substituted 4-(thio)chromenone 6-O-sulfamate acting as Steroid Sulfatase inhibitors to establish a quantitative relationship between biological activity and molecular descriptors and to identify potential descriptors affecting STS inhibition. A robust and predictive model was developed with a good R² value of 0.89 and a leave-one-out cross-validation coefficient (LOO Q²) of 0.83, indicating that the model adequately explains the quantitative relationship. In addition to the QSAR model, the pharmacokinetic profiles of the 24 compounds were validated according to Lipinski’s Rule of Five and Veber’s rule to assess their potential as drug candidates. Conclusion: Provided that untested compounds fall within the model’s applicability domain, the validation results indicate that this model may be used to predict their biological activity. Furthermore, based on a comparison of the computational results with the criteria of Lipinski’s Rule of Five and Veber’s rule, the 24 compounds can be considered potential drug candidates, as they satisfy the requirements of both rules.
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