Prediction of Human Intestinal Absorption of Drug Compounds from Molecular Structure

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

doi:10.1021/ci980029a

Cited by 326 publications

(162 citation statements)

References 128 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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“…As a result, the overall accuracy of Niwa et al's model as calculated using our accuracy measurement (SP × SE) yields a value of 0.667. This is a reoccurring problem with the other datasets in the literature that we considered 7,8,17 . Poorly-absorbed compounds are predicted better using our models due to the larger representation of this class in our TS1 training set and/or the use of varying misclassification costs.…”

Section: Discussion Of the Related Literaturementioning

confidence: 98%

“…This is due to the larger numbers of highly-absorbed compounds amongst the marketed drugs that constitute the datasets 7,8 . compounds with low absorption due to solubility issues 11 .…”

Section: Introductionmentioning

confidence: 99%

Coping with Unbalanced Class Data Sets in Oral Absorption Models

Newby

Freitas

Ghafourian

2013

J. Chem. Inf. Model.

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Class imbalance occurs frequently in drug discovery datasets. In oral absorption datasets, in the literature, there are considerably more of highly-absorbed compounds compared with poorly-absorbed compounds. This produces models that are biased towards highly-absorbed compounds which lack generalization to industry settings where more early stage drug candidates are poorly-absorbed. This paper presents two strategies to cope with unbalanced class datasets: Under-sampling the majority high absorption class and misclassification costs using classification decision trees. The published dataset by Hou et al (2007), which contained percentage human intestinal absorption of 645 drug and drug-like compounds, was used for the development and validation of classification trees using C&RT analysis. The results indicate that under-sampling the majority class, highly-absorbed compounds, leads to a balanced distribution (50:50) training set which can achieve better accuracies for poorlyabsorbed compounds, whereas the biased training set achieved higher accuracies for highlyabsorbed compounds. The use of misclassification costs resulted in improved class predictions, when applied to reduce false positives or false negatives. Moreover, it was shown that the classical overall accuracy measure used in many publications is particularly misleading in the case of unbalanced datasets and more appropriate measures presented here may be used for a more realistic assessment of the classification models' performance. Thus, these strategies offer improvements to cope with unbalanced class datasets to obtain classification models applicable in industry.

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“…In general, the absorption of class II is over predicted by absorption models because dissolution is the ratelimiting step of absorption. So, molecular features as solubility, permeability, and diffusion rates (36) can affect absorption and, consequently, bioavailability.…”

Section: Discussionmentioning

confidence: 99%

To Be Drug or Prodrug: Structure-Property Exploratory Approach Regarding Oral Bioavailability

et al. 2014

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-Purpose. Prodrug design is a strategy that can be used to adjust physicochemical properties of drugs in order to overcome pharmacokinetic problems, such as poor oral bioavailability. However, Lipinski´s and Veber´s rules predict whether compounds will have absorption problems even before the design of prodrugs. In this context, our goal was to evaluate the molecular properties which most influenced the absorption process of prodrugs compared to its precursor through exploratory data analysis approach. Methods: A variety of prodrugs and respective precursors were randomly selected and classified by its' percentage of human intestinal absorption. Subsequently, different molecular properties were calculated and hierarchical cluster analysis (HCA) and principal components analysis (PCA) were carried out. Results: According to the findings, antiviral, anti-hypertensive, and antibiotic prodrugs exhibited higher absorption levels than their respective precursors. Also, some relevant descriptors (molecular weight, MW, routable bonds, rot_bonds, hydrogen bond acceptors, HBA_count and polar surface area, PSA), which are included in Lipinski´s and Veber´s rules, influenced the separation process between prodrugs and drugs. Furthermore, other molecular properties, such as polarizability (α) and molar refractivity (MR), were pointed out. Conclusion: Lipinski´s and Veber´s rules proved to be important to design an orally administered drug but other descriptors should be considered by medicinal chemists in the prodrug designing process.

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Prediction of Human Intestinal Absorption of Drug Compounds from Molecular Structure

Cited by 326 publications

References 128 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?

Coping with Unbalanced Class Data Sets in Oral Absorption Models

To Be Drug or Prodrug: Structure-Property Exploratory Approach Regarding Oral Bioavailability

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