Computationally accelerated identification of P-glycoprotein inhibitors

Area of Science:

• Biochemistry
• Pharmacology
• Computational Chemistry

Background:

• P-glycoprotein (P-gp) overexpression drives multidrug resistance (MDR) in cancer, limiting chemotherapy efficacy.
• Existing P-gp inhibitors often fail due to substrate properties and targeting transmembrane domains.
• The cytoplasmic nucleotide-binding domains of P-gp represent an underexplored therapeutic target.

Purpose of the Study:

• To develop and apply an enhanced computational pipeline for identifying novel P-gp inhibitors.
• To target the cytoplasmic nucleotide-binding domains of P-gp for reversing MDR.
• To discover non-toxic P-gp inhibitors with reduced substrate potential.

Main Methods:

• Utilized computationally accelerated drug discovery with iterative molecular docking against multiple P-gp conformations.
• Employed targeted molecular dynamics simulations to generate diverse docking receptor conformations.
• Screened custom Tanimoto chemical datasets to maximize ligand diversity.

Main Results:

• Identified nine novel P-gp inhibitors with a 13.4% hit rate.
• Demonstrated reversal of MDR in P-gp-overexpressing human cancer cell lines.
• Found all identified inhibitors to be non-toxic to normal human cells, with six not being likely P-gp substrates.

Conclusions:

• The cytoplasmic nucleotide-binding domains of P-gp are a viable and promising target for overcoming MDR.
• The developed computational pipeline effectively identifies novel MDR-reversing agents.
• The novel P-gp inhibitors are chemically diverse and suitable for lead optimization in cancer therapy.