Identifying Structural Features of Sulforaphane Derivatives Based on QM Force Field for Predicting the Anti-Cancer Activity
Neena Elsa Eapen1, Md Afroz Alam2

1Neena Elsa Eapen, Pursuing M. Tech, Department of Biotechnology, Karunya Institute of Technology and Sciences, Coimbatore – Tamil Nadu, India.
2Md Afroz Alam*, Assistant Professor, Department of Biotechnology, School of Agriculture and Biosciences, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 1631-1636 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7894038620/2020©BEIESP | DOI: 10.35940/ijrte.F7894.038620

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Abstract: Sulforaphane (SFN) is a biologically active compound-based drug obtained from cruciferous vegetables, which has been investigated for its anti-tumor and chemopreventive effects. SFN shows a potential mechanism of its anti-cancer activity by binding to Macrophage Migration Inhibitory Factor (MIF) which is a pleiotropic cytokine that overexpresses in cancer cells increasing the aggressiveness of the disease. SFN can significantly inhibit the action of MIF on angiogenesis and the prevention of apoptosis in cancer cells. Preclinical studies on the anti-cancer activity of SFN showed promising results but in clinical studies, it is not yet convincing. Screening of a set of compounds chemically related to SFN can have a chance of showing promising anticancer activity. The quantitative structure activity relationship (QSAR) based on quantum mechanics has been done to derive the best mathematical model of these selected derivatives of sulforaphane for the calculation of its biological activity. These sulforaphane derivatives have been evaluated with respect to their ADMET and physicochemical properties. Validation was done to indicate the predictiveness of the model. The significant R2 value of 0.5676 between experimental and predicted biological activity and R2cv value of 0.554 depicts a decent statistical fit of the model. A best QSAR model has been selected which has a future scope of helping in designing anti-cancerous drugs.
Keywords: Sulforaphane, MIF, Molecular docking, Binding affinity, QSAR, and ADMET properties.
Scope of the Article: Structural Engineering.