Oja, M.; Maran, U. pH-permeability profiles for drug substances: Experimental detection, comparison with human intestinal absorption and modelling. Eur. J. Pharm. Sci. 2018, 123, 429-440.

QsarDB Repository

Oja, M.; Maran, U. pH-permeability profiles for drug substances: Experimental detection, comparison with human intestinal absorption and modelling. Eur. J. Pharm. Sci. 2018, 123, 429-440.

QDB archive DOI: 10.15152/QDB.203   DOWNLOAD

QsarDB content

Property logPe_pH3: Logarithmic effective membrane permeability at pH 3 [log(cm/s)]

Eq.4: QSAR model for membrane permeability at pH 3

Regression model (regression)

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining1340.6660.588
Validation setexternal validation440.6360.604
External validation setexternal validation600.6340.630

Property logPe_pH5: Logarithmic effective membrane permeability at pH 5 [log(cm/s)]

Eq.5: QSAR model for membrane permeability at pH 5

Regression model (regression)

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining1340.6260.612
Validation setexternal validation440.6110.615
External validation setexternal validation600.5590.650

Property logPe_pH7.4: Logarithmic effective membrane permeability at pH 7.4 [log(cm/s)]

Eq.6: QSAR model for membrane permeability at pH 7.4

Regression model (regression)

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining1340.7310.598
Validation setexternal validation440.6320.706
External validation setexternal validation600.7040.565

Property logPe_pH9: Logarithmic effective membrane permeability at pH 9 [log(cm/s)]

Eq.7: QSAR model for membrane permeability at pH 9

Regression model (regression)

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining1340.7620.594
Validation setexternal validation440.7190.646
External validation setexternal validation600.7120.582

Property logPe_highest: Highest logarithmic effective membrane permeability for pH range 3 to 9 [log(cm/s)]

Eq.8: QSAR model for highest membrane permeability

Regression model (regression)

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining1340.7530.580
Validation setexternal validation440.7040.635
External validation setexternal validation600.5960.606

Property logPo: Logarithmic intrinsic membrane permeability [log(cm/s)]

Eq.9: QSAR model for intrinsic membrane permeability

Regression model (regression)

Open in:QDB ExplorerQDB Predictor

NameTypen

R2

σ

Training settraining1340.7450.957
Validation setexternal validation440.7570.911
External validation setexternal validation600.6450.827

Property logPe_pH3_class: Class of logarithmic effective membrane permeability at pH 3 i

Eq.4_class: Classification model for membrane permeability at pH 3 i

Regression model (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining1340.903
Validation setexternal validation440.909
External validation setexternal validation600.900

Property logPe_pH5_class: Class of logarithmic effective membrane permeability at pH 5 i

Eq.5_class: Classification model for membrane permeability at pH 5 i

Regression model (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining1340.866
Validation setexternal validation440.750
External validation setexternal validation600.783

Property logPe_pH7.4_class: Class of logarithmic effective membrane permeability at pH 7.4 i

Eq.6_class: Classification model for membrane permeability at pH 7.4 i

Regression model (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining1340.918
Validation setexternal validation440.909
External validation setexternal validation600.817

Property logPe_pH9_class: Class of logarithmic effective membrane permeability at pH 9 i

Eq.7_class: Classification model for membrane permeability at pH 9 i

Regression model (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining1340.933
Validation setexternal validation440.932
External validation setexternal validation600.750

Property logPe_highest_class: Class of highest logarithmic effective membrane permeability for pH range 3 to 9 i

Eq.8_class: Classification model for highest membrane permeability i

Regression model (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining1340.933
Validation setexternal validation440.909
External validation setexternal validation600.817

Property logPo_class: Class of logarithmic intrinsic membrane permeability i

Eq.9_class: Classification model for intrinsic membrane permeability i

Regression model (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining1340.918
Validation setexternal validation440.886
External validation setexternal validation600.833

Citing

When using this QDB archive, please cite (see details) it together with the original article:

  • Oja, M.; Maran, U. Data for: pH-permeability profiles for drug substances: Experimental detection, comparison with human intestinal absorption and modelling. QsarDB repository, QDB.203. 2018. http://dx.doi.org/10.15152/QDB.203

  • Oja, M.; Maran, U. pH-permeability profiles for drug substances: Experimental detection, comparison with human intestinal absorption and modelling. Eur. J. Pharm. Sci. 2018, 123, 429-440. http://dx.doi.org/10.1016/j.ejps.2018.07.014

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Title: Oja, M.; Maran, U. pH-permeability profiles for drug substances: Experimental detection, comparison with human intestinal absorption and modelling. Eur. J. Pharm. Sci. 2018, 123, 429-440.
Abstract:The influence of pH on human intestinal absorption is frequently not considered in early drug discovery studies in the modelling and subsequent prediction of intestinal absorption for drug candidates. To bridge this gap, in this study, experimental membrane permeability data were measured for current and former drug substances with a parallel artificial membrane permeability assay (PAMPA) at different pH values (3, 5, 7.4 and 9). The presented data are in good agreement with human intestinal absorption, showing a clear influence of pH on the efficiency of intestinal absorption. For the measured data, simple and general quantitative structure-activity relationships (QSARs) were developed for each pH that makes it possible to predict the pH profiles for passive membrane permeability (i.e., a pH-permeability profile), and these predictions coincide well with the experimental data. QSARs are also proposed for the data series of highest and intrinsic membrane permeability. The molecular descriptors in the models were analysed and mechanistically related to the interaction pattern of permeability in membranes. In addition to the regression models, classification models are also proposed. All models were successfully validated and blind tested with external data.
URI:http://hdl.handle.net/10967/203
http://dx.doi.org/10.15152/QDB.203
Date:2018-07-06
Funding:This work was supported by the Ministry of Education and Research, Republic of Estonia [grant number IUT34-14] and the European Union European Regional Development Fund [grant number TK143, Centre of Excellence in Molecular Cell Engineering].


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