A novel approach for predicting P-glycoprotein (ABCB1) inhibition using molecular interaction fields

Area of Science:

• Pharmacology
• Computational Chemistry
• Drug Discovery

Background:

• P-glycoprotein (Pgp or ABCB1) is a key ABC transporter influencing drug absorption, metabolism, and brain penetration.
• Pgp inhibition affects drug bioavailability and safety, and can overcome multidrug resistance.
• Reliable in silico methods for predicting Pgp inhibition are essential for drug development.

Purpose of the Study:

• To develop and validate a robust in silico model for predicting P-glycoprotein inhibition.
• To aid medicinal chemists in identifying potential Pgp inhibitors during drug discovery.

Main Methods:

• A large literature collection of over 1200 structures was curated.
• A predictive model was built using molecular interaction field-based technologies.
• The model incorporated pharmacophoric features and physicochemical properties related to membrane partitioning.

Main Results:

• The model demonstrated high accuracy through internal validation with two sets.
• In-house evaluation of molecules with previously unavailable Pgp inhibition data confirmed model robustness.
• The derived information was rationalized into a pharmacophore for competitive Pgp inhibition.

Conclusions:

• The developed in silico model is robust and suitable for assisting medicinal chemists.
• The model can effectively predict P-glycoprotein inhibition, aiding drug discovery efforts.
• The identified pharmacophore provides insights into competitive Pgp inhibition mechanisms.