Rapid Prediction of Human Intestinal Absorption

Deretey, Eugen; Fehér, Miklós; Schmidt, J.

doi:10.1002/1521-3838(200211)21:5<493::aid-qsar493>3.0.co;2-5

Cited by 27 publications

(13 citation statements)

References 16 publications

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“…According to the above analysis, we found that no rules based on molecular properties can effectively identify these unfavorably bioavailable compounds. Figure S3 in the Supporting Information shows the scatter plot of bioavailability (%F) vs intestinal absorption (%HIA) [12][13][14] for 214 compounds. The %HIA values for these 214 compounds are listed in Table S3 in the Supporting Information.…”

Section: Resultsmentioning

confidence: 99%

ADME Evaluation in Drug Discovery. 6. Can Oral Bioavailability in Humans Be Effectively Predicted by Simple Molecular Property-Based Rules?

Hou

Wang

Zhang

et al. 2007

J. Chem. Inf. Model.

157

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A critically evaluated database of human oral bioavailability for 768 chemical compounds is described in this study (http://modem.ucsd.edu/adme), which provides the scientific community a publicly available and reliable source for developing predictive models of human oral bioavailability. The correlations between several important molecular properties and human oral bioavailability were investigated and compared with an earlier report by analyzing the rat oral bioavailability data (J. Med. Chem. 2002Chem. , 45, 2615. We showed that the percentages of compounds meeting the criteria based on molecular properties does not distinguish compounds with poor oral bioavailability from those with acceptable values, which may suggest that no simple rule based on molecular properties can be used as general filters to predict oral bioavailability with high confidence. A data set of intestinal absorption was also examined and compared with that of oral bioavailability. The performance of these rules based on molecular properties in the prediction of intestinal absorption is obviously much better than that of oral bioavailability in term of false positive rate, and, therefore, the applications of the "rule-based" approaches on the prediction of human bioavailability should be very cautious.

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Section: Resultsmentioning

confidence: 99%

ADME Evaluation in Drug Discovery. 6. Can Oral Bioavailability in Humans Be Effectively Predicted by Simple Molecular Property-Based Rules?

Hou

Wang

Zhang

et al. 2007

J. Chem. Inf. Model.

157

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“…(1) Hydrophobicity Descriptor. The experimental log D values at pH 7.4 for all compounds were obtained from the literature. − ,− ,, It should be noted that the log D values for the same compound might exist with some differences in a different reference. For example, the log D values for atenolol from refs and are −1.29 and −2.14, respectively.…”

Section: Methodsmentioning

confidence: 99%

ADME Evaluation in Drug Discovery. 5. Correlation of Caco-2 Permeation with Simple Molecular Properties

Hou

Zhang

Xia

et al. 2004

J. Chem. Inf. Comput. Sci.

173

153

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The correlations between Caco-2 permeability (logP app ) and molecular properties have been investigated. A training set of 77 structurally diverse organic molecules was used to construct significant QSAR models for Caco-2 cell permeation. Cellular permeation was found to depend primarily upon experimental distribution coefficient (logD) at pH ) 7.4, high charged polar surface area (HCPSA), and radius of gyration (rgyr). Among these three descriptors, logD may have the largest impact on diffusion through Caco-2 cell because logD shows obvious linear correlation with logP app (r)0.703) when logD is smaller than 2.0. High polar surface area will be unfavorable to achieve good Caco-2 permeability because higher polar surface area will introduce stronger H-bonding interactions between Caco-2 cells and drugs. The comparison among HCPSA, PSA (polar surface area), and TPSA (topological polar surface area) implies that high-charged atoms may be more important to the interactions between Caco-2 cell and drugs. Besides logD and HCPSA, rgyr is also closely connected with Caco-2 permeabilities. The molecules with larger rgyr are more difficult to cross Caco-2 monolayers than those with smaller rgyr. The descriptors included in the prediction models permit the interpretation in structural terms of the passive permeability process, evidencing the main role of lipholiphicity, H-bonding, and bulk properties. Besides these three molecular descriptors, the influence of other molecular descriptors was also investigated. From the calculated results, it can be found that introducing descriptors concerned with molecular flexibility can improve the linear correlation. The resulting model with four descriptors bears good statistical significance, n ) 77, r ) 0.82, q ) 0.79, s ) 0.45, F ) 35.7. The actual predictive abilities of the QSAR model were validated through an external validation test set of 23 diverse compounds. The predictions for the tested compounds are as the same accuracy as the compounds of the training set and significantly better than those predicted by using the model reported. The good predictive ability suggests that the proposed model may be a good tool for fast screening of logP app for compound libraries or large sets of new chemical entities via combinatorial chemistry synthesis.

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“…(2296) The predictions were improved if the chemicals were classified according to similarity and the sigmoidal dependence was applied to the subsets. (2297)…”

Section: Comparison With Other Approachesmentioning

confidence: 99%

Modeling Kinetics of Subcellular Disposition of Chemicals

Baláž

2009

Chem. Rev.

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Rapid Prediction of Human Intestinal Absorption

Cited by 27 publications

References 16 publications

ADME Evaluation in Drug Discovery. 6. Can Oral Bioavailability in Humans Be Effectively Predicted by Simple Molecular Property-Based Rules?

ADME Evaluation in Drug Discovery. 6. Can Oral Bioavailability in Humans Be Effectively Predicted by Simple Molecular Property-Based Rules?

ADME Evaluation in Drug Discovery. 5. Correlation of Caco-2 Permeation with Simple Molecular Properties

Modeling Kinetics of Subcellular Disposition of Chemicals

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