Discovering new mTOR inhibitors for cancer treatment through virtual screening methods and in vitro assays

Area of Science:

• Biochemistry
• Pharmacology
• Computational Chemistry

Background:

• The mammalian target of rapamycin (mTOR) pathway is a crucial regulator of cell growth and proliferation, making it a significant target for anticancer drug development.
• Previous in silico models were developed to differentiate between mTOR inhibitors and non-inhibitors.

Purpose of the Study:

• To develop an integrated strategy for identifying novel mTOR inhibitors using cascaded in silico screening models.
• To evaluate the anticancer potential and inhibitory mechanisms of newly discovered compounds targeting mTOR.

Main Methods:

• Utilized cascaded in silico screening models to identify potential mTOR inhibitors.
• Conducted in vitro assays to determine IC50 values and assess anticancer activity against various tumor cell lines.
• Performed cellular studies and western blot analyses to investigate the mechanism of action, including apoptosis induction and cell cycle arrest.
• Employed molecular dynamics simulations and MM/GBSA analyses to elucidate the binding and inhibitory mechanisms of lead compounds with mTOR.

Main Results:

• Discovered fifteen new mTOR kinase inhibitors, with four compounds exhibiting IC50 values below 10 μM.
• Compound 17 showed significant anticancer activity against MCF-7, HeLa, MGC-803, and C6 cell lines, with IC50 values ranging from 1.90 to 11.05 μM.
• Compound 17 was found to induce apoptosis by targeting both mTORC1 and mTORC2 and arrest the cell cycle of HeLa cells at the G1/G0 phase.
• Molecular simulations provided insights into the inhibitory mechanisms of compounds 13, 17, and 40 against mTOR.

Conclusions:

• The integrated in silico strategy successfully identified novel mTOR kinase inhibitors with potent anticancer activities.
• Compound 17 represents a promising lead candidate for further preclinical development due to its efficacy and mechanism of action.
• The study highlights the potential of computational approaches in accelerating the discovery of targeted cancer therapeutics.