Future directions for drug transporter modelling

Abstract

Since the late 1980s computational methods such as quantitative structure–activity relationship (QSAR) and pharmacophore approaches have become more widely applied to assess interactions between drug-like molecules and transporters, starting with P-glycoprotein (P-gp). Identifying molecules that interact with P-gp and other transporters is important for drug discovery, but it is normally ascertained using laborious in vitro and in vivo studies. Computational QSAR and pharmacophore models can be used to screen commercial databases of molecules rapidly and suggest those likely to bind as substrates or inhibitors for transporters. These predictions can then be readily verified in vitro, thus representing a more efficient route to screening. Recently, the application of this approach has seen the identification of new substrates and inhibitors for several transporters. The successful application of computational models and pharmacophore models in particular to predict transporter binding accurately represents a way to anticipate drug–drug interactions of novel molecules from molecular structure. These models may also see incorporation in future pharmacokinetic–pharmacodynamic models to improve predictions of in vivo drug effects in patients. The implications of early assessment of transporter activity, current advances in QSAR, and other computational methods for future development of these and systems-based approaches will be discussed.

Key Words::

• Transporter
• pharmacophore
• quantitative structure–activity relationship (QSAR)
• P-glycoprotein
• database
• pharmacokinetic–pharmacodynamic (PKPD)
• in vitro–in vivo correlation (IVIVC)