Alanas Petrauskas | PubMed Health

PubMed2003

Classification structure-activity relations (C-SAR) in prediction of human intestinal absorption.

AB/HIA is a "soft" filter for identifying compounds with poor intestinal membrane permeability. The analyzed data set included over 1000 drug-like compounds with experimental human intestinal absorption (HIA) values. A sequence of recursive partitioning analyses based on multiple physicochemical and structural descriptors led to the derivation of the rule-based algorithm (filter). The obtained rules reveal a simple physicochemical model of intestinal permeability; they also account for the specific effects caused by quarternary nitrogens and biphosphonate groups. Comparison of the observed and predicted values revealed very low percent of disagreement (15% false-positives and 3% false-negatives). The unusual absorption of compounds that deviated from the predicted values was explained in terms of active transport, efflux, chemical stability, chelating ability, and solubility. Most of these effects can be accounted for by new, substructure-specific rules that can be added into the existing filter. This can lead to the development of a reliable theoretical model for predicting human intestinal absorption. If combined with other models for predicting first pass metabolism, the updated AB/HIA filter can be very useful in predicting oral bioavailability.

Humans↗

PubMed2003

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.

ATP Binding Cassette Transporter, Subfamily B, Mem↗

PubMed2003

Progress in toxinformatics: the challenge of predicting acute toxicity.

Historically, acute toxicity based on LC(50) and LD(50) tests has been analyzed using various quantitative structure-activity relationships (QSARs). The obtained QSAR equations cannot be related to the multiple health effects reflected in the experimental data of analyzed compounds. Therefore little predictive power for novel compounds can be achieved. New methods based on classification SAR (C-SAR) analysis offer new mechanistic knowledge that can be related to new health effects, resulting in better predictive power. To this end, a very careful interpretation of the obtained results is required, implying the use of the existing mechanistic information to the maximum possible extent. The current mini-review aims at determining ways of automated extraction of new mechanistic knowledge from existing data, as well as ways of relating this knowledge to various health effects. A comparison of "statistical induction" and "knowledge-based" approaches is provided. The existing and future developments in predictive acute toxicity are discussed.

Algorithms↗