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

Hou, Tingjun; Wang, Junmei; Zhang, Wei; Xu, Xiaojie

doi:10.1021/ci6003515

Cited by 157 publications

(106 citation statements)

References 16 publications

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“…The bioavailability data were obtained from our previous work. 20 For compounds with %FA values reported as being complete, the value considered was 100%. For compounds with %FA values reported as being poor, the value considered was 5%.…”

Section: Methodsmentioning

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.

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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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Section: Methodsmentioning

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.

Self Cite

180

163

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“…Area under ROC curve was found to be 0.967, whereas Youden's index was calculated as 0.84. Quite a few researchers have been tried to generate absorption models using different machine learning approaches and reported good results [6,24]. This is the first time we are presenting a comparative study between three potential machine learning approaches viz.…”

Section: Resultsmentioning

confidence: 95%

“…The constructed classifier is applied on the independent drug data set in order to predict the class label for it. We have defined two classes for the dataset 'low orally bioavailable (LO, less than 30 percent)' and 'high orally bioavailable (HO, greater than or equal to 30 percent)' [6]. Class labels were defined as 0 and 1 for LO and HO respectively.…”

Section: Implementation Of Classification Approachesmentioning

confidence: 99%

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Classification of oral bioavailability of drugs by machine learning approaches: a comparative study.

Kumar¹,

Sharma²,

Varadwaj³

et al. 2012

JCIS

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Oral Bioavailability is the rate and extent to which an active drug substance is absorbed and becomes available to the general circulation. A computational model for the prediction of oral bioavailability is a vital initial step in the drug discovery. It is decisive for selecting the promising compounds for the next level optimizations and recognition for the clinical trials. In the present investigation we aimed to perform the oral bioavailability prediction by comparing three machine learning methods i.e. Support Vector Machine (SVM) based kernel learning, Artificial Neural Network (ANN) and Bayesian classification approach. The overall prediction efficiency of SVM based model for the test set was 96.85%, whereas according to the Bayesian classifier and ANN methods prediction efficiency was found to be 92.19% and 94.53% respectively. Thus the present results clearly suggested that the SVM based prediction of oral bioavailability of drugs is more efficient binary classification approach for the data under consideration.

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“…In particular, we calculated the compliance of compounds to the Lipinski's rule of five 39,40 . Predictions of ADME properties for the studied compounds are shown in Table 3.…”

Section: Biological Evaluationmentioning

confidence: 99%

Synthesis and biological evaluation of novel series of aminopyrimidine derivatives as urease inhibitors and antimicrobial agents

Adsul

Bandgar

Chavan

et al. 2012

Journal of Enzyme Inhibition and Medicinal Chemistry

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

Cited by 157 publications

References 16 publications

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

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

Classification of oral bioavailability of drugs by machine learning approaches: a comparative study.

Synthesis and biological evaluation of novel series of aminopyrimidine derivatives as urease inhibitors and antimicrobial agents

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