Oja, M.; Sild, S.; Maran, U. Logistic Classification Models for pH-Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical Classification System. J. Chem. Inf. Model. 2019, 59, 2442-2455.

QsarDB Repository

Oja, M.; Sild, S.; Maran, U. Logistic Classification Models for pH-Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical Classification System. J. Chem. Inf. Model. 2019, 59, 2442-2455.

QDB archive DOI: 10.15152/QDB.206   DOWNLOAD

QsarDB content

Property Class_pH3: Class of membrane permeability at pH 3 i

M1: Model at pH 3 with hydrophobicity descriptor

Logistic regression (classification)

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NameTypenAccuracy
Training settraining670.910
Validation setexternal validation1110.820
External validation setexternal validation600.900
FDA reference drugs for BCS permeability class itesting32N/A
M7: Model at pH 3 with theoretical molecular descriptors

Logistic regression (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining670.881
Validation setexternal validation1110.793
External validation setexternal validation600.833
FDA reference drugs for BCS permeability class itesting32N/A

Property Class_pH5: Class of membrane permeability at pH 5 i

M2: Model at pH 5 with hydrophobicity descriptor

Logistic regression (classification)

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NameTypenAccuracy
Training settraining890.843
Validation setexternal validation890.730
External validation setexternal validation600.800
FDA reference drugs for BCS permeability class itesting32N/A
M8: Model at pH 5 with theoretical molecular descriptors

Logistic regression (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining890.764
Validation setexternal validation890.753
External validation setexternal validation600.733
FDA reference drugs for BCS permeability class itesting32N/A

Property Class_pH7.4: Class of membrane permeability at pH 7.4 i

M3: Model at pH 7.4 with hydrophobicity descriptor

Logistic regression (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining1340.858
Validation setexternal validation440.795
External validation setexternal validation600.883
FDA reference drugs for BCS permeability class itesting32N/A
M9: Model at pH 7.4 with theoretical molecular descriptors

Logistic regression (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining1340.828
Validation setexternal validation440.886
External validation setexternal validation600.717
FDA reference drugs for BCS permeability class itesting32N/A

Property Class_pH9: Class of membrane permeability at pH 9 i

M4: Model at pH 9 with hydrophobicity descriptor

Logistic regression (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining1340.858
Validation setexternal validation440.864
External validation setexternal validation600.717
FDA reference drugs for BCS permeability class itesting32N/A
M10: Model at pH 9 with theoretical molecular descriptors

Logistic regression (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining1340.813
Validation setexternal validation440.909
External validation setexternal validation600.767
FDA reference drugs for BCS permeability class itesting32N/A

Property Class_highest: Class of highest membrane permeability for pH range 3 to 9 i

M5: Model for highest membrane permeability with hydrophobicity descriptor

Logistic regression (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining1070.850
Validation setexternal validation710.887
External validation setexternal validation600.733
FDA reference drugs for BCS permeability class itesting32N/A
M11: Model for highest membrane permeability with theoretical molecular descriptors

Logistic regression (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining1070.850
Validation setexternal validation710.817
External validation setexternal validation600.800
FDA reference drugs for BCS permeability class itesting32N/A

Property Class_logPo: Class of intrinsic membrane permeability i

M6: Model for intrinsic membrane permeability (logPo) with hydrophobicity descriptor

Logistic regression (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining910.857
Validation setexternal validation870.897
External validation setexternal validation600.767
FDA reference drugs for BCS permeability class itesting32N/A
M12: Model for intrinsic membrane permeability (logPo) with theoretical molecular descriptors

Logistic regression (classification)

Open in:QDB ExplorerQDB Predictor

NameTypenAccuracy
Training settraining910.813
Validation setexternal validation870.851
External validation setexternal validation600.783
FDA reference drugs for BCS permeability class itesting32N/A

Property Class_FDA: BCS permeability class for FDA reference drugs i

Fig5_1: Decision tree based on models with hydrophobicity descriptors

Ensemble model (classification)

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NameTypenAccuracy
FDA reference drugs for BCS permeability classtraining320.813
Fig5_2: Decision tree based on the models with theoretical molecular descriptors

Ensemble model (classification)

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NameTypenAccuracy
FDA reference drugs for BCS permeability classtraining320.906

Citing

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

  • Oja, M.; Sild, S.; Maran, U. Data for: Logistic Classification Models for pH-Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical Classification System. QsarDB repository, QDB.206. 2019. http://dx.doi.org/10.15152/QDB.206

  • Oja, M.; Sild, S.; Maran, U. Logistic Classification Models for pH-Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical Classification System. J. Chem. Inf. Model. 2019, 59, 2442-2455. http://dx.doi.org/10.1021/acs.jcim.8b00833

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Title: Oja, M.; Sild, S.; Maran, U. Logistic Classification Models for pH-Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical Classification System. J. Chem. Inf. Model. 2019, 59, 2442-2455.
Abstract:Permeability is used to describe and evaluate the absorption of drug substances in the human gastrointestinal tract (GIT). Permeability is largely dependent on fluctuating pH that causes the ionization of drug substances and also influences regional absorption in the GIT. Therefore, classification models that characterize permeability at wide range of pH-s were derived in the current study. For this, drug substances were described with six data series that were measured with a parallel artificial membrane permeability assay (PAMPA), including a permeability profile at four pH-s (3, 5, 7.4 and 9), and the highest and intrinsic membrane permeability. Logistic regression classification models were developed and compared by using two distinct sets of descriptors: 1) hydrophobicity descriptor, the logarithm of the octanol-water partition (logPow) or distribution (logD) coefficient, and 2) theoretical molecular descriptors. In both cases, models have good classification and descriptive capabilities for training set (accuracy: 0.76 to 0.91). Triple-validation with three sets of drug substances shows good prediction capability for all models: validation set (accuracy: 0.73 to 0.91), external validation set (accuracy: 0.72 to 0.9) and the permeability classes of FDA reference drugs for the biopharmaceutical classification system (BCS) (accuracy: 0.72 to 0.88). The identification of BCS permeability classes was further improved with decision trees that consolidated predictions from models with each descriptor type. These decision trees have higher confidence and accuracy (0.91 for theoretical molecular descriptors and 0.81 for hydrophobicity descriptors) than the individual models in assigning drug substances into BCS permeability classes. A detailed analysis of classification models and related decision trees suggests that they are suitable for predicting classes of permeability for passively transported drug substances, including specifically within the BCS framework.
URI:http://hdl.handle.net/10967/206
http://dx.doi.org/10.15152/QDB.206
Date:2019-02-21


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