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Critical Evaluation of Human Oral Bioavailability for Pharmaceutical Drugs by Using Various Cheminformatics Approaches

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Abstract

Purpose

Oral bioavailability (%F) is a key factor that determines the fate of a new drug in clinical trials. Traditionally, %F is measured using costly and time-consuming experimental tests. Developing computational models to evaluate the %F of new drugs before they are synthesized would be beneficial in the drug discovery process.

Methods

We employed Combinatorial Quantitative Structure-Activity Relationship approach to develop several computational %F models. We compiled a %F dataset of 995 drugs from public sources. After generating chemical descriptors for each compound, we used random forest, support vector machine, k nearest neighbor, and CASE Ultra to develop the relevant QSAR models. The resulting models were validated using five-fold cross-validation.

Results

The external predictivity of %F values was poor (R2 = 0.28, n = 995, MAE = 24), but was improved (R2 = 0.40, n = 362, MAE = 21) by filtering unreliable predictions that had a high probability of interacting with MDR1 and MRP2 transporters. Furthermore, classifying the compounds according to the %F values (%F < 50% as “low”, %F ≥ 50% as ‘high”) and developing category QSAR models resulted in an external accuracy of 76%.

Conclusions

In this study, we developed predictive %F QSAR models that could be used to evaluate new drug compounds, and integrating drug-transporter interactions data greatly benefits the resulting models.

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Abbreviations

%F:

Oral bioavailability

AD:

Applicability domain

ANOVA:

Analysis of variance

CCR:

Correct classification rate (balanced accuracy)

CNT:

Continuous activity scale

Combi-QSAR:

Combinatorial quantitative structure-activity relationship

CPT:

Consensus prediction threshold

CTG:

Category activity scale

CYP:

Cytochrome P450

D:

Dragon descriptors

HIT:

Human intestinal transporter

kNN:

k nearest neighbor

MAE:

Mean absolute error

MDR1:

Multidrug resistance protein 1 (P-gp, ABCB1)

MOE:

Molecular Operating Environment

MPOI:

Mean probability of interaction

MRP2:

Multidrug resistance-associated protein 2 (ABCC2)

POI:

Probability of interaction

QSAR:

Quantitative structure-activity relationship

R2 :

Coefficient of determination

RF:

Random forest

SVM:

Support vector machine

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Acknowledgments and Disclosures

We thank Kimberlee Moran of Rutgers-Camden for her help with the manuscript preparation for the entire project.

Research reported in this publication was supported, in part, by the National Institute of Environmental Health Sciences of the National Institutes of Health under Award Number R15ES023148 and the Colgate-Palmolive Grant for Alternative Research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Authors and Affiliations

  1. Department of Chemistry, Rutgers University, Camden, New Jersey, 08102, USA

    Marlene T. Kim & Hao Zhu

  2. The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey, 08102, USA

    Marlene T. Kim & Hao Zhu

  3. Division of Chemical Biology and Medicinal Chemistry Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, USA

    Alexander Sedykh

  4. Multicase Inc, 23811 Chagrin Boulevard, Suite 305, Beachwood, Ohio, 44122, USA

    Suman K. Chakravarti & Roustem D. Saiakhov

Authors
  1. Marlene T. Kim
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  3. Suman K. Chakravarti
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  4. Roustem D. Saiakhov
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  5. Hao Zhu
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Corresponding author

Correspondence to Hao Zhu.

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Supplemental Figure 1

Loading plot using principal components 1 and 2 of MOE descriptors (JPEG 136 kb)

Supplemental Figure 2

Loading plot using principal components 1 and 3 of MOE descriptors (JPEG 129 kb)

Supplemental Figure 3

Loading plot using principal components 2 and 3 of MOE descriptors (JPEG 128 kb)

Supplemental Table I

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Supplemental Table II

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Supplemental Table III

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Kim, M.T., Sedykh, A., Chakravarti, S.K. et al. Critical Evaluation of Human Oral Bioavailability for Pharmaceutical Drugs by Using Various Cheminformatics Approaches. Pharm Res 31, 1002–1014 (2014). https://doi.org/10.1007/s11095-013-1222-1

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