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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.

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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)]

Compounds: 238 | Models: 1 | Predictions: 3

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

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 134 0.666 0.588
Validation set external validation 44 0.636 0.604
External validation set external validation 60 0.634 0.630

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

Compounds: 238 | Models: 1 | Predictions: 3

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

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 134 0.626 0.612
Validation set external validation 44 0.611 0.615
External validation set external validation 60 0.559 0.650

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

Compounds: 238 | Models: 1 | Predictions: 3

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

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 134 0.731 0.598
Validation set external validation 44 0.632 0.706
External validation set external validation 60 0.704 0.565

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

Compounds: 238 | Models: 1 | Predictions: 3

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

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 134 0.762 0.594
Validation set external validation 44 0.719 0.646
External validation set external validation 60 0.712 0.582

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

Compounds: 238 | Models: 1 | Predictions: 3

Eq.8: QSAR model for highest membrane permeability

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 134 0.753 0.580
Validation set external validation 44 0.704 0.635
External validation set external validation 60 0.596 0.606

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

Compounds: 238 | Models: 1 | Predictions: 3

Eq.9: QSAR model for intrinsic membrane permeability

Regression model (regression)

Open in:QDB Explorer QDB Predictor

Name Type n

R2

σ

Training set training 134 0.745 0.957
Validation set external validation 44 0.757 0.911
External validation set external validation 60 0.645 0.827

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

Compounds: 238 | Models: 1 | Predictions: 3

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

Regression model (classification)

Open in:QDB Explorer QDB Predictor

Name Type n Accuracy
Training set training 134 0.903
Validation set external validation 44 0.909
External validation set external validation 60 0.900

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

Compounds: 238 | Models: 1 | Predictions: 3

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

Regression model (classification)

Open in:QDB Explorer QDB Predictor

Name Type n Accuracy
Training set training 134 0.866
Validation set external validation 44 0.750
External validation set external validation 60 0.783

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

Compounds: 238 | Models: 1 | Predictions: 3

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

Regression model (classification)

Open in:QDB Explorer QDB Predictor

Name Type n Accuracy
Training set training 134 0.918
Validation set external validation 44 0.909
External validation set external validation 60 0.817

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

Compounds: 238 | Models: 1 | Predictions: 3

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

Regression model (classification)

Open in:QDB Explorer QDB Predictor

Name Type n Accuracy
Training set training 134 0.933
Validation set external validation 44 0.932
External validation set external validation 60 0.750

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

Compounds: 238 | Models: 1 | Predictions: 3

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

Regression model (classification)

Open in:QDB Explorer QDB Predictor

Name Type n Accuracy
Training set training 134 0.933
Validation set external validation 44 0.909
External validation set external validation 60 0.817

Property logPo_class: Class of logarithmic intrinsic membrane permeability i

Compounds: 238 | Models: 1 | Predictions: 3

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

Regression model (classification)

Open in:QDB Explorer QDB Predictor

Name Type n Accuracy
Training set training 134 0.918
Validation set external validation 44 0.886
External validation set external validation 60 0.833

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dc.date.accessioned 2018-07-06T15:30:57Z
dc.date.available 2018-02-06T15:30:57Z
dc.date.issued 2018-07-06
dc.identifier.uri http://hdl.handle.net/10967/203
dc.identifier.uri http://dx.doi.org/10.15152/QDB.203
dc.description.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.
dc.publisher Mare Oja
dc.publisher Uko Maran
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.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.
qdb.property.endpoint 5. Toxicokinetics 5.3. Gastrointestinal absorption en_US
qdb.descriptor.application JChem software for Excel (version 16.9.500.1094) en_US
qdb.descriptor.application CODESSA PRO 1.0 en_US
qdb.descriptor.application XLOGP3 3.2.2 en_US
qdb.prediction.application CODESSA PRO 1.0 en_US
bibtex.entry article en_US
bibtex.entry.author Oja, M.
bibtex.entry.author Maran, U.
bibtex.entry.doi 10.1016/j.ejps.2018.07.014
bibtex.entry.journal Eur. J. Pharm. Sci. en_US
bibtex.entry.month Oct
bibtex.entry.pages 429-440
bibtex.entry.title pH-permeability profiles for drug substances: Experimental detection, comparison with human intestinal absorption and modelling en_US
bibtex.entry.volume 123
bibtex.entry.year 2018
qdb.model.type Regression model (regression) en_US
qdb.model.type Regression model (classification) en_US


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