Repurposing of FDA-approved drugs as autophagy inhibitors in tumor cells

Abstract

Autophagy and apoptosis are the two crucial processes of programmed cell death found in all eukaryotic cells; however, the elevated physiological stress in the tumor microenvironment leads to uncontrolled up-regulation in the process of autophagy. Available literatures suggest that inhibiting up-regulated autophagy in the cancerous cells may lead to the apoptosis and thereby culminate to tumor clearance. Several studies have been performed to design autophagy-inhibitors using either Beclin-1 or Bcl-2 as a target in isolation. However, to overcome the constraints of the availability of small and potent autophagy inhibitors, we have attempted extensive computational approach of repurposing the FDA-approved drugs from the ZINC database in order to inhibit the interaction between the Beclin1 and Bcl-2. Out of 1565 FDA-approved drugs used in our computational work, we sorted the drugs Ponatinib, Simeprevir, and Nilotinib through various methods viz. molecular docking, Lipinski’s filter, MD simulation and MM/PBSA, and we found these aforementioned drugs to show good binding energy and favorable interaction with the BH3 domain of Beclin1. We anticipate from our computational results that these drugs may become potent candidates to inhibit autophagy and exhibit the apoptosis in the tumor microenvironment and combat the current limitation of potent autophagy inhibitors; however, to substantiate our in-silico results, further experimental validations of these drug molecules are currently in progress.

Communicated by Ramaswamy H. Sarma

Keywords:

• Molecular docking
• molecular dynamics simulation
• Beclin1
• Bcl-2
• autophagy
• apoptosis

Acknowledgements

The infrastructure facilities of IIT (BHU) Varanasi and DST funded I-DAPT Hub Foundation, IIT BHU [DST/NMICPS/TIH11/IIT(BHU)2020/02] are sincerely acknowledged. The work is supported by CSIR project [27(344)/19-EMR-II] to VKD. Further, the support and the resources provided by PARAM Shivay Facility under the National Supercomputing Mission, Government of India at the Indian Institute of Technology, Varanasi are gratefully acknowledged.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The infrastructure facilities of IIT (BHU) Varanasi and DST funded I-DAPT Hub Foundation, IIT BHU [DST/NMICPS/TIH11/IIT(BHU)2020/02] are sincerely acknowledged. The work is supported by CSIR project [27(344)/19-EMR-II] to VKD. Further, the support and the resources provided by PARAM Shivay Facility under the National Supercomputing Mission, Government of India at the Indian Institute of Technology, Varanasi are gratefully acknowledged.