On the mechanism of human intestinal absorption

Abraham, Michael H.; Zhao, Yuan H.; Le, Joelle; Hersey, Anne; Luscombe, Christopher N.; Reynolds, Derek P.; Beck, Gordon; Sherborne, Brad; Cooper, Ian

doi:10.1016/s0223-5234(02)01384-3

Cited by 69 publications

(72 citation statements)

References 32 publications

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“…33,34 A total of 131 HIA+ and 65 HIA-agents are collected. In general, a relatively smaller number of agents with low intestinal absorption is specifically reported in the literature.…”

Section: Methodsmentioning

confidence: 99%

Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents

Yang

Yap

et al. 2004

J. Chem. Inf. Comput. Sci.

154

182

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Statistical-learning methods have been developed for facilitating the prediction of pharmacokinetic and toxicological properties of chemical agents. These methods employ a variety of molecular descriptors to characterize structural and physicochemical properties of molecules. Some of these descriptors are specifically designed for the study of a particular type of properties or agents, and their use for other properties or agents might generate noise and affect the prediction accuracy of a statistical learning system. This work examines to what extent the reduction of this noise can improve the prediction accuracy of a statistical learning system. A feature selection method, recursive feature elimination (RFE), is used to automatically select molecular descriptors for support vector machines (SVM) prediction of P-glycoprotein substrates (P-gp), human intestinal absorption of molecules (HIA), and agents that cause torsades de pointes (TdP), a rare but serious side effect. RFE significantly reduces the number of descriptors for each of these properties thereby increasing the computational speed for their classification. The SVM prediction accuracies of P-gp and HIA are substantially increased and that of TdP remains unchanged by RFE. These prediction accuracies are comparable to those of earlier studies derived from a selective set of descriptors. Our study suggests that molecular feature selection is useful for improving the speed and, in some cases, the accuracy of statistical learning methods for the prediction of pharmacokinetic and toxicological properties of chemical agents.

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“…33,34 A total of 131 HIA+ and 65 HIA-agents are collected. In general, a relatively smaller number of agents with low intestinal absorption is specifically reported in the literature.…”

Section: Methodsmentioning

confidence: 99%

Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents

Yang

Yap

et al. 2004

J. Chem. Inf. Comput. Sci.

154

182

View full text Add to dashboard Cite

show abstract

“…However, this simplified approach may not be as appropriate for describing chemical exchange in the GIT. For example, an analysis of empirical human GIT absorption efficiencies for acids and bases [54] concluded that, as a first approximation, gut absorption efficiency can be estimated using the properties of the neutral form alone. In accordance with the empirical analysis of human GIT absorption efficiencies [54], uptake efficiencies calculated for stronger bases (pK a !…”

Section: Eliminationmentioning

confidence: 99%

“…For example, an analysis of empirical human GIT absorption efficiencies for acids and bases [54] concluded that, as a first approximation, gut absorption efficiency can be estimated using the properties of the neutral form alone. In accordance with the empirical analysis of human GIT absorption efficiencies [54], uptake efficiencies calculated for stronger bases (pK a ! 10) using a theoretical passive absorption model of the human GIT [47] are not substantially reduced from what is calculated for neutral compounds with similar hydrophobicity.…”

Section: Eliminationmentioning

confidence: 99%

Development and evaluation of a mechanistic bioconcentration model for ionogenic organic chemicals in fish

Armitage

Arnot

Wania

et al. 2012

Enviro Toxic and Chemistry

155

241

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Abstract-A mechanistic mass balance bioconcentration model is developed and parameterized for ionogenic organic chemicals (IOCs) in fish and evaluated against a compilation of empirical bioconcentration factors (BCFs). The model is subsequently applied to a set of perfluoroalkyl acids. Key aspects of model development include revised methods to estimate the chemical absorption efficiency of IOCs at the respiratory surface (E W ) and the use of distribution ratios to characterize the overall sorption capacity of the organism. Membranewater distribution ratios (D MW ) are used to characterize sorption to phospholipids instead of only considering the octanol-water distribution ratio (D OW ). Modeled BCFs are well correlated with the observations (e.g., r 2 ¼ 0.68 and 0.75 for organic acids and bases, respectively) and accurate to within a factor of three on average. Model prediction errors appear to be largely the result of uncertainties in the biotransformation rate constant (k M ) estimates and the generic approaches for estimating sorption capacity (e.g., D MW ). Model performance for the set of perfluoroalkyl acids considered is highly dependent on the input parameters describing hydrophobicity (i.e., log K OW of the neutral form). The model applications broadly support the hypothesis that phospholipids contribute substantially to the sorption capacity of fish, particularly for compounds that exhibit a high degree of ionization at biologically relevant pH. Additional empirical data on biotransformation and sorption to phospholipids and subsequent incorporation into property estimation approaches (e.g., k M , D MW ) are priorities with respect to improving model performance. Environ. Toxicol. Chem. 2013;32:115-128. # 2012 SETAC

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“…So the isolated positive charge may have a great negative effect on the diffusion of drugs. 47 3.b. Hydrophobicity.…”

Section: Relationship Between Oral Bioavailability Andmentioning

confidence: 99%

ADME Evaluation in Drug Discovery. 7. Prediction of Oral Absorption by Correlation and Classification

Hou

Wang

Zhang

et al. 2006

J. Chem. Inf. Model.

180

163

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A critically evaluated database of human intestinal absorption for 648 chemical compounds is reported in this study, among which 579 are believed to be transported by passive diffusion. The correlation analysis between the intestinal absorption and several important molecular properties demonstrated that no single molecular property could be used as a good discriminator to efficiently distinguish the poorly absorbed compounds from those that are well absorbed. The theoretical correlation models for a training set of 455 compounds were proposed by using the genetic function approximation technique. The best prediction model contains four molecular descriptors: topological polar surface area, the predicted distribution coefficient at pH ) 6.5, the number of violations of the Lipinski's rule-of-five, and the square of the number of hydrogenbond donors. The model was able to predict the fractional absorption with an r ) 0.84 and a prediction error (absolute mean error) of 11.2% for the training set. Moreover, it achieves an r ) 0.90 and a prediction error of 7.8% for a 98-compound test set. The recursive partitioning technique was applied to find the simple hierarchical rules to classify the compounds into poor (%FA e 30%) and good (%FA > 30%) intestinal absorption classes. The high quality of the classification model was validated by the satisfactory predictions on the training set (correctly identifying 95.9% of the compounds in the poor-absorption class and 96.1% of the compounds in the good-absorption class) and on the test set (correctly identifying 100% of the compounds in the poor-absorption class and 96.8% of the compounds in the good-absorption class). We expect that, in the future, the rules for the prediction of carrier-mediated transporting and first pass metabolism can be integrated into the current hierarchical rules, and the classification model may become more powerful in the prediction of intestinal absorption or even human bioavailability. The databases of human intestinal absorption reported here are available for download from the supporting Web site: http:// modem.ucsd.edu/adme.

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On the mechanism of human intestinal absorption

Cited by 69 publications

References 32 publications

Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents

Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents

Development and evaluation of a mechanistic bioconcentration model for ionogenic organic chemicals in fish

ADME Evaluation in Drug Discovery. 7. Prediction of Oral Absorption by Correlation and Classification

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