Classification Analysis of P-Glycoprotein Substrate Specificity

Abstract

Prediction of P-glycoprotein substrate specificity (S_PGP) can be viewed as a constituent part of a compound's “pharmaceutical profiling” in drug design. This task is difficult to achieve due to several factors that raised many contradictory opinions: (i) the disparity between the S_PGP values obtained in different assays, (ii) the confusion between Pgp substrates and inhibitors, (iii) the confusion between lipophilicity and amphiphilicity of Pgp substrates, and (iv) the dilemma of describing class-specific relationships when Pgp has no binding sites of high ligand specificity. In this work, we compiled S_PGP data for 1000 compounds. All data were represented in a binary format, assigning S_PGP=1 for substrates and S_PGP=0 for non-substrates. Each value was ranked according to the reliability of experimental assay. Two data sets were considered. Set 1 included 220 compounds with S_PGP from polarized transport across MDR1 transfected cell monolayers. Set 2 included the entire list of 1000 compounds, with S_PGP values of generally lower reliability. Both sets were analysed using a stepwise classification structure–activity relationship (C-SAR) method, leading to derivation of simple rules for crude estimation of S_PGP values. The obtained rules are based on the following factors: (i) compound's size expressed through molar weight or volume, (ii) H-accepting given by the Abraham's β (that can be crudely approximated by the sum of O and N atoms), and (iii) ionization given by the acid and base pK_a values. Very roughly, S_PGP can be estimated by the “rule of fours”. Compounds with (N+O)≥8, MW>400 and acid pK_a > 4 are likely to be Pgp substrates, whereas compounds with (N+O)≤4, MW<400 and base pK_a<8 are likely to be non-substrates. The obtained results support the view that Pgp functioning can be compared to a complex “mini-pharmacokinetic” system with fuzzy specificity. This system can be described by a probabilistic version of Abraham's solvation equation, suggesting a certain similarity between Pgp transport and chromatographic retention. The chromatographic model does not work in the case of “marginal” compounds with properties close to the “global” physicochemical cut-offs. In the latter case various class-specific rules must be considered. These can be associated with the “amphiphilicity” and “biological similarity” of compounds. The definition of class-specific effects entails construction of the knowledge base that can be very useful in ADME profiling of new drugs.

Keywords::

• P-glycoprotein
• Blood–brain barrier
• CNS
• ADME predictions
• Fuzzy specificity
• Property-based design
• α, Abraham's H-bond acidity (H-donating)
• β, Abraham's H-bond basicity (H-accepting)
• BBB, Blood–brain barrier
• C-SAR, Classification structure–activity relationship
• HA, Hydrogen acceptor
• HB, Hydrogen bond
• HD, Hydrogen donor
• Indicator variable for acid
• Indicator variable for base
• Log, Ionization-dependent octanol/water partition coefficient
• Log, Octanol/water partition coefficient
• MR, Molar refraction
• MV, Molar volume
• MW, Molar weight
• Lipinski's number of H-donors (OH+NH)
• Lipinski's number of H-acceptors (O+N)
• Pgp, P-glycoprotein
• K, Ionization constant
• pKaA: Ionization constant for the strongest acid group
• pKaB: Ionization constant for the strongest base group
• SA, Surface area
• SAR, Structure–activity relationship
• Pgp substrate specificity
• T, Ertl's polar surface area
• McGowan's characteristic volume