Prediction of Human Intestinal Absorption of Drug Compounds from Molecular Structure

Wessel, Matthew D.; Jurs, Peter C.; Tolan, John W.; Muskal, Steven M.

doi:10.1007/978-1-4615-4141-7_30

Cited by 86 publications

(150 citation statements)

References 9 publications

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“…Not very successfully, Ghuloum et al [7] used a novel numerical molecular representation, called the``molecular hashkey'' for the description of intestinal absorption on the same 20 drugs employed by Palm et al [6]. For the intestinal absorption of 86 drugs Jurs et al [8] proposed a nonlinear six-descriptor neural network model:…”

Section: Introductionmentioning

confidence: 99%

Quantitative Estimation of Drug Absorption in Humans for Passively Transported Compounds on the Basis of Their Physico-chemical Parameters

Raevsky¹,

Fetisov

Trepalina

et al. 2000

Quant. Struct.-Act. Relat.

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Good sigmoid relationships are found between human intestinal absorption for 32 passively transported drugs and their hydrogen bond descriptors. When only one-parameter correlations are considered, the sum of absolute H-bond acceptor and donor factor values (SC ad ), characterizing the total ability of a compound to form H-bonds, is the best descriptor. Such models show that H-bonding has a signi®cant negative effect on human intestinal absorption. Drugs with SC ad ! 22 are poorly absorbed, while drugs with SC ad 8 are completely absorbed. Such behavior may be rationalized by the bene®cial in¯uence that just a few H-bonding interactions might have on a drug's ability to penetrate biological membranes. In the case of many H-bonds, however, the penetration of a drug through a membrane may be completely forbidden. A two-parameter sigmoid model, where the sum of H-bond acceptor factors and the sum of H-bond donor factors are independent variables, demonstrates that both acceptor and donor groups on drugs have important interactions with surrounding water molecules or membrane surfaces. These models have improved statistical criteria and ensure good quantitative predictions of human intestinal absorption for passively transported drugs. Polarizability makes a minor negative contribution while partial atomic charge makes a minor positive contribution to absorption in humans. Lipophilicity expressed as log D did not give a satisfactory correlation with absorption.

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Section: Introductionmentioning

confidence: 99%

Quantitative Estimation of Drug Absorption in Humans for Passively Transported Compounds on the Basis of Their Physico-chemical Parameters

Raevsky¹,

Fetisov

Trepalina

et al. 2000

Quant. Struct.-Act. Relat.

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show abstract

“…However such knowledge could reduce the solution space by selecting the appropriate descriptors as input variables. This can be done by the machine learning process itself in a feature selection step, for example, with genetic algorithms [6][7][8]. However, feature selection requires additional training data.…”

Section: Machine Learningmentioning

confidence: 99%

Prediction of Drug‐Likeness and its Integration into the Drug Discovery Process

Schuffenhauer

Glick²

2009

Hit and Lead Profiling

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“…In addition, 566 2D molecular fragments, derived from over 7000 compounds in the CMC database, were added to the ADAPT descriptors for the analysis of intestinal absorption [23]. The authors used a variable selection routine, discarding variables having similar information content and keeping one variable from each such family for further analysis.…”

Section: Adapt Descriptorsmentioning

confidence: 99%

Calculated Molecular Properties and Multivariate Statistical Analysis in Absorption Prediction

Norinder

Haeberlein

2003

Methods and Principles in Medicinal Chemistry

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Prediction of Human Intestinal Absorption of Drug Compounds from Molecular Structure

Cited by 86 publications

References 9 publications

Quantitative Estimation of Drug Absorption in Humans for Passively Transported Compounds on the Basis of Their Physico-chemical Parameters

Quantitative Estimation of Drug Absorption in Humans for Passively Transported Compounds on the Basis of Their Physico-chemical Parameters

Prediction of Drug‐Likeness and its Integration into the Drug Discovery Process

Calculated Molecular Properties and Multivariate Statistical Analysis in Absorption Prediction

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