No abstract

The increasing knowledge of both structure and activity of compounds provides a good basis for enhancing the pharmacological characterization of chemical libraries. In addition, pharmacology can be seen as incorporating both advances from molecular biology as well as chemical sciences, with innovative insight provided from studying target-ligand data from a ligand molecular point of view. Predictions and profiling of libraries of drug candidates have previously focused mainly on certain cases of oral bioavailability. Inclusion of other administration routes and disease-specificity would improve the precision of drug profiling. In this work, recent data are extended, and a probability-based approach is introduced for quantitative and gradual classification of compounds into categories of drugs/nondrugs, as well as for disease- or organ-specificity. Using experimental data of over 1067 compounds and multivariate logistic regressions, the classification shows good performance in training and independent test cases. The regressions have high statistical significance in terms of the robustness of coefficients and 95% confidence intervals provided by a 1000-fold bootstrapping resampling. Besides their good predictive power, the classification functions remain chemically interpretable, containing only one to five variables in total, and the physicochemical terms involved can be easily calculated. The present approach is useful for an improved description and filtering of compound libraries. It can also be applied sequentially or in combinations of filters, as well as adapted to particular use cases. The scores and equations may be able to suggest possible routes for compound or library modification. The data is made available for reuse by others, and the equations are freely accessible at http://hermes.chem.ut.ee/~alfx/druglogit.html.

In silico models for membrane permeability have been based on values measured for single pH. Depending on the diet (fasted/fed state) and part of human intestine the range of pH varies approximately from 2.4 to 8.0. This motivated to study and model the membrane permeability of chemicals considering the whole range of pH in the human intestine. For this, effective membrane permeability values were measured for 65 drugs and drug-like compounds using PAMPA method at four pHs (3, 5, 7.4, 9) over 48 h, introducing technological innovations for the time-dependence measurement. The highest permeability value of a compound from four pHs was used to derive QSAR analyzing a large pool of molecular descriptors and introducing new descriptor. Using stepwise forward selection approach a significant QSAR model was derived that included only two mechanistically relevant descriptors, the logarithmic octanol-water partition coefficient and hydrogen bonding surface area. Prediction confidence of the model was blind tested with a true external validation set of 15 compounds. The resulting QSAR model shows potential to combine permeability values from various pH-s into one descriptive and predictive model for estimating maximum permeability in human gastrointestinal tract. The QSAR model and data are available through the QsarDB repository (http://dx.doi.org/10.15152/QDB.137).

Absorption in gastrointestinal tract compartments varies and is largely influenced by pH. Therefore, considering pH in studies and analyses of membrane permeability provides an opportunity to gain a better understanding of the behaviour of compounds and to obtain good permeability estimates for prediction purposes. This study concentrates on relationships between the chemical structure and membrane permeability of acidic and basic drugs and drug-like compounds. The membrane permeability of 36 acidic and 61 basic compounds was measured using the parallel artificial membrane permeability assay (PAMPA) at pH 3, 5, 7.4 and 9. Descriptive and/or predictive single-parameter quantitative structure-permeability relationships were derived for all pH values. For acidic compounds, membrane permeability is mainly influenced by hydrogen bond donor properties, as revealed by models with r(2) > 0.8 for pH 3 and pH 5. For basic compounds, the best (r(2) > 0.7) structure-permeability relationships are obtained with the octanol-water distribution coefficient for pH 7.4 and pH 9, indicating the importance of partition properties. In addition to the validation set, the prediction quality of the developed models was tested with folic acid and astemizole, showing good matches between experimental and calculated membrane permeabilities at key pHs. Selected QSAR models are available at the QsarDB repository ( http://dx.doi.org/10.15152/QDB.166 ).

Human intestinal absorption is a key property for orally administered drugs and is dependent on pH. This study focuses on neutral and amphoteric compounds and their membrane permeabilities across the range of pH values found in the human intestine. The membrane permeability values for 15 neutral and 60 amphoteric compounds at pH 3, 5, 7.4 and 9 were measured using the parallel artificial membrane permeability assay (PAMPA). For each data series the quantitative structure-permeability relationships were developed and analysed. The results show that the membrane permeability of neutral compounds is attributed to a single structural characteristic, the hydrogen bond donor ability. Amphoteric compounds are more complex because of their chemical constitution, and therefore require three-parameter models to describe and predict membrane permeability. Analysis of the models for amphoteric compounds reveals that membrane permeability depends on multiple structural characteristics: the partition coefficient, hydrogen bond properties and the shape of the molecules. In addition to conventional validation strategies, two external compounds (isradipine and omeprazole) were tested and revealed very good agreement of pH profiles between experimental and predicted membrane permeability for all of the developed models. Selected QSAR models are available at the QsarDB repository ( http://dx.doi.org/10.15152/QDB.184 ).