Abstract
Development of a drug involves several aspects, one of which is an adequate DMPK profile that is related to its absorption, distribution, metabolism and excretion. The distribution of the drug to its site of action is partly regulated by several biological membrane barriers. One such barrier is created by the brain capillaries of the endothelial cells, also known as the Blood-Brain-Barrier (BBB). Depending on the therapeutic action, one may need higher permeation of the drug through BBB if the site of action is in the CNS, or minimize the entry through the BBB if this biological target is located in the periphery. The physicochemical properties of the drug usually regulate its permeability through the BBB and constitute passive permeability. However, “non-passive permeation” may also exist and is affected by other transporter mechanisms present in the BBB, and may involve both efflux as well as influx systems. Amongst these, the PGlycoprotein (Pgp) has been the most extensively characterized efflux transporter. The “passive BBB” has been well studied and characterized by various theoretical groups, but the “non-passive BBB” (often caused by Pgp, for example) has gained more attention from computational methodologies in recent years. This review will provide a brief summary of the computational strategies that have addressed Pgp efflux inhibition, especially in the context of optimizing CNS penetration during rational drug design. The advances in the computational methods that have modeled the Pgp recognition while addressing non-passive permeation will be a chief focus, but coverage is also given to recent and impactful Pgp modeling approaches. These include computational approaches that analyze data from assays targeting Pgp in particular or multidrug resistance reversal assays where Pgp is a chief implicating factor.
Keywords: CNS penetration, P-glycoprotein, non-passive permeation, blood brain barrier, in silico modeling, transporters
21