A mechanism-based pharmacokinetic model for the cytochrome P450 drug-drug interaction between cyclophosphamide and thioTEPA and the autoinduction of cyclophosphamide

Cyclophosphamide (CP) is widely used in high-dose chemotherapy regimens in combination with thioTEPA. CP is a prodrug and is activated by cytochrome P450 to 4-hydroxycyclophosphamide (HCP) which yields the final cyto toxic metabolite phosphoramide mustard (PM). The metabolism of CP into HCP exhibits autoinduction but is inhibited by thioTEPA. The aim of this study was to develop a population pharmacokinetic model for the bioactivation route of CP incorporating the phenomena of both autoinduction and the drug-drug interaction between CP and thioTEPA. Plasma samples were collected from 34 patients who received high-dose CP, thioTEPA and carboplatin in short infusions during 4 consecutive days. Elimination of CP was described by a noninducible route and an inducible route leading to HCP. The latter route was mediated by a hypothetical amount of enzyme. Autoinduction leads to a zero-order increase in amount of this enzyme during treatment. Inhibition by thioTEPA was modeled as a reversible, competitive, concentration-dependent inhibition. PM pharmacokinetics were described by first-order formation from HCP and first-order elimination. The final models for CP, HCP, and PM provided an adequate fit of the experimental data. The volume of distribution, noninducible and initial inducible clearances of CP were 31.0 L, 1.58 L/hr and 4.76 L/hr, respectively. The enzyme amount increased with a zero-order rate constant of 0.041 amount * hr ^-1. After each thioTEPA infusion, however, approximately 80% of the enzyme was inhibited. This inhibition was reversible with a half-life of 6.5 hr. The formation and elimination rate constants of PM were 1.58 and 0.338 hr ^-1, respectively. The developed model enabled the assessment of the complex pharmacokinetics of CP in combination with thioTEPA. This model provided an adequate description of enzyme induction and inhibition and can be used for treatment optimization in this combination.