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.

The validity of log P calculations is checked for the substructure methods CLOGP, KOWWIN, and AB/logP and the whole-molecule method SciLogP via experimental log P for 174 molecules, comprising 90 simple organics and 84 more complex drugs. Averaged absolute residual sums (AARS) give the following ranking for the entire set:Separate analysis of simple organics yields: CLOGP > KOWWIN > AB/logP > SciLogP. For the drugs we find: CLOGP % KOWWIN % AB/LogP > SciLogP. In a second step, we compared the validity of the calculation programs focussing on structural factors with a critical impact on log P such as resonance and H-bonding interactions. AARS values show that CLOGP and KOWWIN scored slightly better than AB/LogP and SciLogP; this agrees with the good performance of CLOGP and KOWWIN when dealing with simple compounds. AB/LogP averaged correction factors obtained from both simple and complex compounds, so it produced a slightly lower accuracy. aEffects, representing strong interactions between conjugated p-electrons within polar functional groups, were identified from compounds lacking ™isolating carbons∫, which break a-effects. All compounds in this data set are difficult to deal with for the substructure methods, but should be easy to deal with for the whole-molecule approach. In practice, however, SciLogP performed worse than the substructure methods. The best performance was shown by CLOGP, followed by KOWWIN and AB/LogP. Taken together, all substructure methods produced better results than the whole-molecule method. The possible explanation may be that substructure methods automatically account for unknown effects by splitting compounds into fragments and/or conducting class-specific analyses. Whole-molecule approaches cannot account for unknown effects, as long as they neglect class-specific analyses. Among the substructure approaches, our results correlate with the methodology of algorithm development. CLOGP and KOWWIN were developed in a long iterative process, using simple organics for increment derivation and complex drugs for algorithm refinement. AB/LogP was developed in a fast two-step procedure that did not discriminate between simple and complex compounds. So it produced slightly lower accuracy for simple organics, but not lower accuracy for the complex drugs.

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Fragmental methods (FMs) have great potential in many practical areas related to the design of new lead compounds. Advanced Algorithm Builder TM (AAB) is a new software system which employs FMs in (i) building QSPR, QSAR and SAR models, (ii) converting them to custom (in-house) algorithms and screening filters, and (iii) predicting physical properties and biological activities for new compounds. This review demonstrates how FMs and AAB can be used to substantiate our intuition, interpret observations, validate hypotheses and obtain new algorithms for predicting physical properties and biological activities. Applications for practical and theoretical chemists in the design of new lead compounds are discussed.