Recent Advances on P-Glycoprotein (ABCB1) Transporter Modelling with In Silico Methods.

Area of Science:

• Pharmacology
• Computational Chemistry
• Biochemistry

Background:

• P-glycoprotein (P-gp) is a key ABC transporter influencing drug bioavailability and toxicity.
• Overexpression of P-gp is a major cause of multidrug resistance (MDR), preventing drugs from reaching cellular targets.
• Existing P-gp inhibitors face challenges due to toxicity, hindering clinical application.

Purpose of the Study:

• To review recent computational approaches in P-gp research.
• To highlight advances in machine learning and molecular modeling for P-gp inhibitor discovery.
• To discuss novel potential P-gp inhibitors and their role in modulating MDR.

Main Methods:

• Review of computational P-gp research from 2017-2022.
• Focus on machine learning (ML) applications in predicting P-gp activity.
• Analysis of molecular modeling techniques for drug-transporter interactions.

Main Results:

• Computational methods, including QSAR and ML, are valuable for early-stage drug development.
• Molecular modeling provides insights into drug-ABC transporter interactions.
• Recent research has identified potential P-gp inhibitors with improved profiles.

Conclusions:

• Computational studies significantly accelerate the development of effective P-gp inhibitors.
• Machine learning and molecular modeling are crucial tools for understanding and overcoming MDR.
• Further research into novel P-gp modulators holds promise for reversing multidrug resistance.