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Comprehensive Physiologically Based Pharmacokinetic Model to Assess Drug-Drug Interactions of Phenytoin

Overview
Journal Pharmaceutics
Publisher MDPI
Date 2023 Oct 28
PMID 37896246
Authors
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Abstract

Regulatory agencies worldwide expect that clinical pharmacokinetic drug-drug interactions (DDIs) between an investigational new drug and other drugs should be conducted during drug development as part of an adequate assessment of the drug's safety and efficacy. However, it is neither time nor cost efficient to test all possible DDI scenarios clinically. Phenytoin is classified by the Food and Drug Administration as a strong clinical index inducer of CYP3A4, and a moderate sensitive substrate of CYP2C9. A physiologically based pharmacokinetic (PBPK) platform model was developed using GastroPlus to assess DDIs with phenytoin acting as the victim (CYP2C9, CYP2C19) or perpetrator (CYP3A4). Pharmacokinetic data were obtained from 15 different studies in healthy subjects. The PBPK model of phenytoin explains the contribution of CYP2C9 and CYP2C19 to the formation of 5-(4'-hydroxyphenyl)-5-phenylhydantoin. Furthermore, it accurately recapitulated phenytoin exposure after single and multiple intravenous and oral doses/formulations ranging from 248 to 900 mg, the dose-dependent nonlinearity and the magnitude of the effect of food on phenytoin pharmacokinetics. Once developed and verified, the model was used to characterize and predict phenytoin DDIs with fluconazole, omeprazole and itraconazole, i.e., simulated/observed DDI AUC ratio ranging from 0.89 to 1.25. This study supports the utility of the PBPK approach in informing drug development.

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References
1.
Chiang P, Wong H . Incorporation of physiologically based pharmacokinetic modeling in the evaluation of solubility requirements for the salt selection process: a case study using phenytoin. AAPS J. 2013; 15(4):1109-18. PMC: 3787220. DOI: 10.1208/s12248-013-9519-x. View

2.
Mauro L, Mauro V, Brown D, Somani P . Enhancement of phenytoin elimination by multiple-dose activated charcoal. Ann Emerg Med. 1987; 16(10):1132-5. DOI: 10.1016/s0196-0644(87)80471-7. View

3.
Shin J, Choi K, Kim Y, Lee M . Dose-dependent pharmacokinetics of itraconazole after intravenous or oral administration to rats: intestinal first-pass effect. Antimicrob Agents Chemother. 2004; 48(5):1756-62. PMC: 400537. DOI: 10.1128/AAC.48.5.1756-1762.2004. View

4.
Tsamandouras N, Rostami-Hodjegan A, Aarons L . Combining the 'bottom up' and 'top down' approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data. Br J Clin Pharmacol. 2013; 79(1):48-55. PMC: 4294076. DOI: 10.1111/bcp.12234. View

5.
Boucher B, Rodman J, Jaresko G, Rasmussen S, Watridge C, Fabian T . Phenytoin pharmacokinetics in critically ill trauma patients. Clin Pharmacol Ther. 1988; 44(6):675-83. DOI: 10.1038/clpt.1988.211. View