QSAR Model for Drug Human Oral Bioavailability.

Yoshida, Fumitaka; Topliss, John G.

doi:10.1021/jm000397h

Cited by 70 publications

(92 citation statements)

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“…3 In recent years, several prediction models of oral bioavailability based on quantitative-structure activity relationship (QSAR) analysis have been reported. [4][5][6][7][8] In 2000, Andrews and co-workers reported a regression model to predict bioavailability. 5 Compared to Lipinski's Rule-of-Five, the false negative predictions were reduced from 5% to 3%, while the false positive predictions decreased from 78% to 53%.…”

Section: Introductionmentioning

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.

159

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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: Introductionmentioning

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.

159

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“…Quantitative structure-activity relationships (QSAR) have been successfully established to predict different important biopharmaceutical properties, such as metabolism, 6 toxicity, 7 oral bioavailability, 8 etc. Given the importance of drug binding to HSA, it should be extremely useful to develop QSAR models to predict the binding affinity to HSA.…”

Section: Introductionmentioning

confidence: 99%

QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine

Xue

Zhang

Liu

et al. 2004

J. Chem. Inf. Comput. Sci.

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The binding affinities to human serum albumin for 94 diverse drugs and drug-like compounds were modeled with the descriptors calculated from the molecular structure alone using a quantitative structure-activity relationship (QSAR) technique. The heuristic method (HM) and support vector machine (SVM) were utilized to construct the linear and nonlinear prediction models, leading to a good correlation coefficient (R 2 ) of 0.86 and 0.94 and root-mean-square errors (rms) of 0.212 and 0.134 albumin drug binding affinity units, respectively. Furthermore, the models were evaluated by a 10 compound external test set, yielding R 2 of 0.71 and 0.89 and rms error of 0.430 and 0.222. The specific information described by the heuristic linear model could give some insights into the factors that are likely to govern the binding affinity of the compounds and be used as an aid to the drug design process; however, the prediction results of the nonlinear SVM model seem to be better than that of the HM.

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“…The relationships between oral bioavailability and molecular structures have been investigated recently [151][152][153][154] The prediction of oral bioavailability is relatively difficult, because it depends on a superposition of two processes: absorption and liver first-pass metabolism. Yoshida and Topliss constructed a QSAR model with a set of physiochemical parameters [151].…”

Section: In Silico Prediction Of Adme Propertiesmentioning

confidence: 99%

“…Yoshida and Topliss constructed a QSAR model with a set of physiochemical parameters [151]. The oral bioavailability determined in human adults was assigned one of four ratings and analyzed in relation to physicochemical and structural factors by the ORMUCS (ordered multicategorical classification method using the simplex technique) method.…”

Section: In Silico Prediction Of Adme Propertiesmentioning

confidence: 99%

Recent Development and Application of Virtual Screening in Drug Discovery: An Overview

Hou¹,

Xu²

2012

Frontiers in Medicinal Chemistry - (Volume 3)

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Virtual screening, especially the structure-based virtual screening, has emerged as a reliable, cost-effective and time-saving technique for the discovery of lead compounds. Here, the basic ideas and computational tools for virtual screening have been briefly introduced, and emphasis is placed on aspects of recent development of docking-based virtual screening, scoring functions in molecular docking and ADME/Tox-based virtual screening in the past three years (2000 to 2003). Moreover, successful examples are provided to further demonstrate the effectiveness of virtual screening in drug discovery.

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QSAR Model for Drug Human Oral Bioavailability.

Cited by 70 publications

References 0 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?

QSAR Models for the Prediction of Binding Affinities to Human Serum Albumin Using the Heuristic Method and a Support Vector Machine

Recent Development and Application of Virtual Screening in Drug Discovery: An Overview

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