Classification analysis of P-glycoprotein substrate specificity.
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 beta (that can be crudely approximated by the sum of O and N atoms), and (iii) ionization given by the acid and base pKa values. Very roughly, S(PGP) can be estimated by the "rule of fours". Compounds with (N + O) > or = 8, MW > 400 and acid pKa > 4 are likely to be Pgp substrates, whereas compounds with (N + O) < or = 4, MW < 400 and base pKa < 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.
Didziapetris
Remigijus
R
Pharma Algorithms, Inc., Mickeviciaus 29, Vilnius 2001, Lithuania.
Japertas
Pranas
P
Avdeef
Alex
A
Petrauskas
Alanas
A
D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1 / Q000235:genetics*; D000251:Adenosine Triphosphatases / Q000378:metabolism; D000465:Algorithms; D000818:Animals; D001693:Biological Transport, Active; D002478:Cells, Cultured; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D004285:Dogs; D019008:Drug Resistance, Neoplasm; D004789:Enzyme Activation; D017143:Fuzzy Logic; D019450:Genes, MDR / Q000235:genetics; D006801:Humans; D006859:Hydrogen / Q000378:metabolism; D018374:LLC-PK1 Cells; D008954:Models, Biological; D015233:Models, Statistical; D008970:Molecular Weight; D013379:Substrate Specificity / Q000235:genetics*; D013552:Swine; D014162:Transfection; D019076:Transgenes / Q000235:genetics
D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1; D006859:Hydrogen; D000251:Adenosine Triphosphatases