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

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.

Keywords: cytochrome P450 2C19 (CYP2C19); cytochrome P450 2C9 (CYP2C9); drug–drug interactions (DDIs); phenytoin; physiologically based pharmacokinetic modelling (PBPK).

Figure 1

Workflow of the phenytoin PBPK modeling strategy: “model development and verification” on the top of the graph, and “model application to DDI scenarios” on the bottom of the graph. PBPK: physiologically based pharmacokinetics; MW: molecular weight; logP: partition coefficient; pKa: acid dissociation constant; Kp: tissue plasma partition coefficients; fup: fraction unbound in plasma; fub: fraction unbound in blood; fu_inc: the free fraction of the compound in the microsomal incubation; Rbp: Blood: plasma concentration ratio; CYP: cytochrome P450; Km: Michaelis–Menten constant; Vmax: maximum reaction velocity; Emax: maximum effect; EC50: half-maximal effective concentration; Peff: effective permeability; GMFE: geometric mean fold error; GOF: goodness-of-fit plot.

Figure 2

Overview of the modeled DDIs for phenytoin. Black arrow means sensitive victim in the specific metabolic pathway for phenytoin (CYP2C9 and CYP2C19) and itraconazole (CYP3A4). Red curve line means inhibition, e.g., fluconazole is a competitive inhibitor of phenytoin’s CYP2C9- and CYP2C19-mediated metabolism. Omeprazole inhibits the CYP2C19-mediated enzymatic conversion of phenytoin via competitive inhibition and mechanism-based inactivation (MBI). Green curve means the induction, e.g., phenytoin induces the CYP3A4-mediated enzymatic biotransformation of itraconazole.

Figure 3

Model simulations of phenytoin concentration–time profiles from four studies in the training dataset (a–c) after a single dose (250 and 300 mg), for establishing distribution, metabolism and absorption phases, respectively. (d) Simulated profiles of phenytoin in comparison to the observed data after administering 300 mg in multiple doses for exploring phenytoin autoinduction mechanism [8,36,37]. Observed data are shown as red dots ± SD, and simulations are shown as blue or green solid lines. sd: single dose; iv: intravenous; IR: immediate release; qd: once daily.

Figure 4

Nonlinear PK exploration of phenytoin after single and multiple i.v and oral doses of 200, 300, 400, 600 and 900 mg from Gugler et al., 1975 [37]. (a) i.v single dose, (b) i.v multiple dose, (c) oral single dose and (d) oral multiple dose. Orange line represents the area under curve/dose (AUC/Dose) overdose, blue line represents the clearance overdose, and purple line depicts the fraction dissolved (FD) and fraction absorbed (Fa) overdose. The percentage values describe the reduction changes of the absorption and elimination from dose of 200 mg.

Figure 5

Phenytoin PBPK model. Comparison of predicted to observed (a) AUC0-inf values and (b) Cmax values of all analyzed studies. The line of identity is shown as a solid line; 1.25-fold deviation is shown as a dotted line; 2-fold deviation is shown as a dashed lines. Cmax: maximum concentration; AUC: area under the curve; IV: intravenous; MD: multiple dose; PO: oral administration; SD: single dose.

Figure 6

Simulated and observed phenytoin DDIs. Concentration–time profiles of phenytoin in comparison to observed data after a (a) multiple and (b) single dose with and without fluconazole (200 and 400 mg), respectively, (c) phenytoin with and without omeprazole 40 mg in normal CYP2C19 metabolizers (NM) and (d) phenytoin with and without omeprazole 40 mg in intermediate CYP2C19 metabolizers (IM). (e) itraconazole concentration–times profiles before and during phenytoin co-administration [15,25,26,28]. Observed data are shown as red and yellow dots, and simulations are shown as blue and green solid lines. q.d.: once daily; sd: single dose; PHT: phenytoin; IR: immediate release.

Figure 7

DDI PBPK model performance of phenytoin. Simulated vs. observed DDI AUC ratio is shown in (a) and simulated vs. observed DDI Cmax ratio is shown in (b). The line of identity is shown as a straight solid line; 1.25-fold deviation is shown as a dotted line; 2-fold deviation is shown as a dashed line. The curve solid lines show the prediction success limits proposed by Guest et al. allowing for 1.25-fold variability of the DDI ratios [64]. AUC: area under the curve; Cmax: maximum concentration.