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Dexamethasone (DEX) is the substrate of CYP3A. However, the activity of CYP3A could be induced by DEX when DEX was persistently administered, resulting in auto-induction and time-dependent pharmacokinetics (pharmacokinetics with time-dependent clearance) of DEX. In this study we investigated the pharmacokinetic profiles of DEX after single or multiple doses in human breast cancer xenograft nude mice and established a semi-mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model for characterizing the time-dependent PK of DEX as well as its anti-cancer effect. The mice were orally given a single or multiple doses (8 mg/kg) of DEX, and the plasma concentrations of DEX were assessed using LC-MS/MS. Tumor volumes were recorded daily. Based on the experimental data, a two-compartment model with first order absorption and time-dependent clearance was established, and the time-dependence of clearance was modeled by a sigmoid Emax equation. Moreover, a semi-mechanism-based PK/PD model was developed, in which the auto-induction effect of DEX on its metabolizing enzyme CYP3A was integrated and drug potency was described using an Emax equation. The PK/PD model was further used to predict the drug efficacy when the auto-induction effect was or was not considered, which further revealed the necessity of adding the auto-induction effect into the final PK/PD model. This study established a semi-mechanism-based PK/PD model for characterizing the time-dependent pharmacokinetics of DEX and its anti-cancer effect in breast cancer xenograft mice. The model may serve as a reference for DEX dose adjustments or optimization in future preclinical or clinical studies.
Time Dependent Pharmacokinetics Pdf 13
As widely reported, DEX is the substrate of cytochrome P450 3A (CYP3A)11,12. However, the activity of CYP3A could be induced by DEX when DEX was persistently administered, resulting in auto-induction and time-dependent pharmacokinetics (pharmacokinetics with time-dependent clearance) of DEX12,13. It is well-known that the auto-induction effect of DEX may increase its clearance and decrease its bioavailability and drug efficacy after long-term used12. Therefore, it is of great importance to study the time-course of pharmacokinetics and anti-cancer effect of DEX for long-term use.
Pharmacokinetic-pharmacodynamic (PK/PD) modeling and simulation has been widely applied in drug research and development. The model could appropriately quantify the process of drug behaviors and actions in vivo, and it could help people better understand the time-course of drug efficacy and safety. In addition, a mechanism- or semi-mechanism-based PK/PD model has more reliability and predictability than a traditional model14. Regarding to the PK/PD relationship of DEX in a breast tumor xenograft model, Yuan et al established the PK/PD model of DEX, but the time-dependence of the pharmacokinetics was not considered. Additionally, the drug effect was modeled as a linear relationship with the drug concentration in their study, which was somewhat defective and did not sufficiently account for the drug mechanisms10. To the best of our knowledge, there is no report referring to the PK/PD model for characterizing the time-dependent pharmacokinetics of DEX and its anti-cancer effect. Therefore, the aim of this study was to investigate the pharmacokinetic profiles of DEX after single or multiple doses as well as to establish a semi-mechanism-based PK/PD model for characterizing the time-dependent clearance of DEX and its anti-cancer effect in a breast cancer xenograft model.
During the study, 33 nude mice were given DEX by gavage at 8 mg/kg for a single dose, and the plasma samples were collected via extracting eyeballs at 20 min, 30 min, 40 min, 1 h, 1.5 h, 2 h, 3 h, 5 h, 8 h, 12 h and 16 h after administration. In addition, DEX was given daily at 8 mg/kg in another 27 mice for multiple doses; in this group, plasma samples of 18 mice were collected after the 9th dose at 20 min, 30 min, 40 min, 2 h, 5 h and 8 h and those from the other 9 mice were collected at 5 h after the 3rd, 6th and 13th doses. Three nude mice were euthanized per time point, and the two batches of PK studies after single or multiple doses were performed separately.
A two-compartment model with first order absorption and time-dependent clearance was used to characterize the PK of DEX (Figure 1). Time-dependent clearance was modelled with a sigmoid Emax equation. The differential equations of the PK model are as follows:
Schematic representation of the established PK/PD model of DEX in nude mice. Xa, Xc and Xp represent the level of DEX in the absorption, central and peripheral compartments of the PK model, respectively; X represents the tumor size with proliferating tumor cells in the tumor compartment; Ka represents the first order absorption rate; CL represents the systemic clearance, which was time-dependent; Q represents the clearance between compartments; Vc and Vp are the apparent volumes of distribution in the central and peripheral compartments, respectively; and F is the bioavailability.
where Xa, Xc and Xp represent the amount of DEX in the absorption, central and peripheral compartments, respectively; Vc and Vp are the apparent volumes of distribution in the central and peripheral compartments, respectively; Cc and Cp represent the drug concentrations in the central and peripheral compartments, respectively; CL represents the systemic clearance; Q represents the clearance between compartments; F is the bioavailability; Time indicates the time after the first dose of DEX; Emax and ET50 represent the maximal effect of auto-induction and the time when half of the maximal induction effect was achieved, respectively; and γ is the shape factor.
All modeling and simulations in this study were performed using NONMEM 7.2 (ICON Development Solutions, Ellicott City, MD, USA) and PsN 4.2.0 (Uppsala University, Uppsala, Sweden) with a first order conditional estimation with interaction (FOCEI) method. It was assumed that the IIV and IOV of parameters, as well as the residual error variabilities, followed a logarithmic normal distribution. The 95% confidence intervals (95% CI) or relative standard errors (RSE) were provided to assess the precision and reliability of the estimated parameters. A mixed error model was selected as the residual error model for both the PK model and integrated PK/PD model. Model evaluation and selection depended on the rationality of the estimated parameters, decline of the objective function value (OFV), diagnostic plots and visual predictive check (VPC) based on 1000 times of simulation. Tumor growth curves with or without the auto-induction effect were simulated by fixing the parameters estimated by the final integrated model (Eq 2 was changed to Eq 9 for the model without an auto-induction effect of DEX on metabolizing enzymes in the process of simulation), revealing the necessity of considering the auto-induction effect while modeling the drug effect of DEX after multiple doses. In addition, tumor growth rates under different DEX dose regimens were further simulated by the final PK/PD model with auto-induction effect.
The concentration-time curves of DEX after single or multiple doses at 8 mg/kg are shown in Figure 2A. Both DEX concentrations and the area under the curve (AUC) after the 9th dose were obviously lower than that of a single dose, and the concentrations at 5 h after the 3rd, 6th, 9th and 13th dose gradually trended to decline, which is consistent with previously reported PK results for DEX in rats12. Multiple studies have demonstrated that DEX is the substrate and inducer of CYP3A11,12,13. Therefore, the time-dependent changes of DEX concentration in nude mice might be considered to result from auto-induction of CYP3A by DEX after multiple doses.
The pharmacokinetic and tumor growth data used for developing the PK/PD model. (A) Concentration-time curves of DEX after single or multiple doses at 8 mg/kg in nude mice (n=3). (B) Anti-cancer effects after different doses of DEX in MCF-7/Adr xenograft nude mice (n=5).
The detailed inhibitory effects of DEX on a MCF-7/Adr xenograft tumor was published elsewhere4. Tumor growth after different dose regimens is shown in Figure 2B. It can be clearly identified that DEX significantly suppressed the tumor growth in a dose-dependent manner.
The PK profile of DEX in nude mice was characterized by a two-compartment model with first order absorption and time-dependent clearance. The estimated PK parameters are summarized in Table 1. The precisions of all parameters were acceptable, suggesting the reliability of the established model. The typical value of Emax was 1.09, indicating that the systemic clearance might be induced to more than twofold the initial value along with persistent dosing, which somewhat suggests the importance of considering the auto-induction effect with persistent DEX use.
The VPCs of the concentration-time profiles after single and multiple doses were evaluated by the final PK model, and the results are presented in Figure 3. Most of the observed concentration data were within the 90% confidence interval of the predictions, implying that the established PK model could fit the current data very well and had good predictability. Moreover, Supplementary Figure S1 presents the goodness-of-fit (GOF) plots of the model, which also demonstrated the fitness of the established PK model.
Visual predictive check (VPC) of the concentration-time profiles after a single dose (A); the 9th dose (B); and at 5 h after the 3rd, 6th, 9th and 13th doses (C), which was evaluated by the final time-dependent PK model. 2ff7e9595c
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