Kim M, Sedykh A, Chakravarti SK, Saiakhov RD, Zhu H. Critical evaluation of human oral bioavailability for pharmaceutical drugs by using various cheminformatics approaches.
Pharm Res 2014;
31:1002-14. [PMID:
24306326 PMCID:
PMC3955412 DOI:
10.1007/s11095-013-1222-1]
[Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2013] [Accepted: 09/30/2013] [Indexed: 01/07/2023]
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 (R(2) = 0.28, n = 995, MAE = 24), but was improved (R(2) = 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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