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.
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Supplemental Figure 1
Loading plot using principal components 1 and 2 of MOE descriptors (JPEG 136 kb)
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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)
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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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DOI: https://doi.org/10.1007/s11095-013-1222-1