European Journal of Drug Metabolism and Pharmacokinetics最新文献

Background and objectives: Hypertensive nephropathy (HN) has become one of the main causes of end-stage renal disease. Drug combination therapy is a common clinical treatment for HN. However, the impact of HN on drug-metabolizing enzymes and transporters, which may lead to drug-drug interactions (DDIs) and even trigger toxic side effects, remains unclear. The aim of this study was to investigate changes in major drug-metabolizing enzymes and transporters in the liver and kidney of HN rats to improve the scientific foundations for the clinical treatment of HN.

Methods: Spontaneously hypertensive rats (SHRs) were used as an animal HN model because their hypertension is similar to that of humans. Wistar-Kyoto rats (WKYs) were used as the control group. Body weight, blood pressure, hematoxylin-eosin (HE) staining and biochemical analysis were performed to evaluate whether the HN model was successfully constructed. Quantitative real-time polymerase chain reaction (PCR) and western blotting were used to evaluate the mRNA and protein expression of drug-metabolizing enzymes, transporters and related nuclear transcription factors.

Results: In HN rats, the mRNA expression of the drug-metabolizing enzymes cytochrome P450 (Cyp) 2b1, Cyp2c11, Cyp3a1 and Cyp7a1 was significantly upregulated. The protein level of CYP3A1 was consistent with its mRNA expression. Interestingly, the mRNA expression of the hepatic transporters organic cation transporter (Oct) 1, Oct2, organic anion transporter (Oat) 1, Oat2, multidrug resistant protein (Mrp) 2, multidrug resistance (Mdr) 1, organic anion transporting polypeptide (Oatp) 1b2 and na+/taurocholate cotransporting polypeptide (Ntcp) was also markedly upregulated. This may be directly influenced by the upregulation of the expression of the nuclear receptors farnesoid X receptor (Fxr), pregnane X receptor (Pxr), liver X-activated receptor (Lxr) and constitutive androstane receptor (Car). In the kidney of HN rats, the mRNA level of the drug-metabolizing enzyme Cyp2b1 significantly increased, while levels of Cyp1a1, Cyp2c11, Cyp3a1 and Cyp3a2 did not significantly change. The mRNA expression of the transporters multidrug and toxin extrusion (Mate) 1 and Mrp2 was obviously increased but was markedly depressed for peptide transporters (Pept) 1 and Pept2. These changes may be related to the cross effects of Pxr, Fxr and Car in kidney.

Conclusion: HN pathological status can alter the expression of drug-metabolizing enzymes and transporters in the liver and kidney to varying degrees, thus affecting the disposition of substrate drugs in vivo. This suggests that to avoid potential risks, caution should be exercised when administering combination therapy for HN treatment.

Background and objectives: Trofinetide, the first approved treatment for Rett syndrome (RTT), is primarily excreted unchanged in the urine; therefore, it is important to assess the extent to which the exposure is affected in patients with renal impairment. Pharmacokinetic modeling overcomes the challenge of dose finding in phase 1 studies that include special populations where there is the potential for increased exposure to study drug. The objectives of this phase 1 study were to evaluate trofinetide pharmacokinetics, safety, and tolerability in a population with moderate renal impairment and normal renal function. The observed pharmacokinetic profiles were used to validate the dosing adjustments in moderate renal impairment that were previously predicted using a physiologically-based pharmacokinetic (PBPK) model.

Methods: The PBPK model was first used to predict dose adjustments that are necessary to achieve similar exposure in the four stages of renal impairment (mild, moderate, severe, end stage renal disease) as in healthy controls. The predicted dose adjustment from 12 to 6 g for the moderate renal impairment category was then applied to the phase 1 clinical study. Subsequent validation of the PBPK model was achieved by comparing the model-predicted and clinically observed exposures in subjects with moderate renal impairment. In a phase 1, open-label study, trofinetide exposure was assessed in healthy (n = 10) and moderate renal impairment (n = 10) participants receiving single oral doses of 12 g or 6 g, respectively. Observed exposures [area under the blood concentration-time curve from time 0 to infinity (AUC_inf) and maximum concentration (C_max)] were compared with predicted exposures from simulations in virtual healthy and moderate renal impairment populations (n = 100) to validate a PBPK model of renal impairment that had previously predicted doses across renal impairment categories.

Results: Dose-normalized geometric mean ratios for C_max were comparable [1.02 (90% CI 0.69-1.50)] while AUC_inf was approximately two-fold higher [1.81 (90% CI 1.31-2.50)] in moderate renal impairment participants compared with healthy controls. These observed values closely aligned with predicted distributions. Treatment-emergent adverse events were reported in two (20.0%) participants with moderate renal impairment and four healthy participants (40.0%).

Conclusion: PBPK modeling of trofinetide in a virtual population with moderate renal impairment predicted a 50% dose reduction compared to individuals with normal renal function. Comparison of observed pharmacokinetic results from a phase 1 study in subjects with moderate renal impairment and matched healthy participants to the model-predicted exposures validated this dose reduction. No new safety concerns for trofinetide emerged.

BACKGROUND AND OBJECTIVE: Currently, there is no available method for the prediction of first-in-human (FIH) dose for chimeric antigen receptor-T (CAR-T) cells. The objective of this work was to predict the FIH dose of CAR-T cells from different doses given to mice.

Methods: In this study, six scaling methods were evaluated for the prediction of FIH dose for CAR-T cells. The methods were body weight-based fixed exponents such as 1.0 and 0.75, human equivalent dose (HED) using exponents 0.33, two modified HED methods such as using total animal dose (in place of per kg basis) and body surface area in place of body weight using total animal dose with exponent 0.33 and a physiological factor derived from physiological parameters. The FIH doses of six CAR-T cells were predicted in this study. The predicted human doses were compared with the recommended human dose by the US-FDA for four CAR-T cell products, and the literature data were used for the remaining two CAR-T cells.

Results: The results indicated that the two modified HED methods and physiological factor are the best and reliable methods for the prediction of FIH dose for CAR-T cells.

Conclusions: The proposed methods are simple and accurate in their predictive power and can be used on a spreadsheet.

Background and objective: For neonates and infants receiving intermittent vancomycin infusions, the area under the concentration-time curve during 24 h (AUC24) is often estimated with Bayesian forecasting using one or more measured vancomycin concentrations. When practical peak and trough concentrations are measured at steady state, AUC24 can also be calculated with first-order steady-state equations for a one-compartment model (Sawchuk-Zaske method), but previously this method has been applied only for adults. The objective of this study was to compare AUC24 values obtained with the Sawchuk-Zaske method and two Bayesian models.

Methods: AUC24 values were estimated retrospectively for 18 neonates and infants with steady-state peak and trough concentrations using traditional compartmental analysis with a one-compartment model (reference method), the Sawchuk-Zaske method, and Bayesian forecasting with two previously published models. In Bayesian forecasting, both original and modified residual error models were used. In the modified models, the residual error was reduced by setting the additive residual error to zero and the proportional error to 15%.

Results: AUC24 estimates obtained with the Sawchuk-Zaske method differed - 2.7 to 0.9% from the reference method. When both peak and trough concentrations were used in Bayesian forecasting, 61% and 33% of AUC24 estimates obtained with two original models differed less than 15% from the reference method, and these fractions increased to 83% and 72% with the modified models, respectively.

Conclusion: When practical peak and trough concentrations are measured at steady state, the simple Sawchuk-Zaske method is very useful for AUC24 estimation in neonates and infants. In Bayesian forecasting, the reduced residual error model can be used to improve the model fit.

Background and objectives: A pharmacokinetic model has been developed to quantify the drug-drug interactions of tacrolimus with concentration-dependent inhibition of cytochrome P450 (CYP) 3A4 from voriconazole and clarithromycin based on the CYP3A5 and CYP2C19 genotypes.

Methods: This retrospective study recruited unrelated bone marrow transplant recipients receiving oral tacrolimus concomitantly with voriconazole and clarithromycin. The published population pharmacokinetic model that implemented genotypes of CYP3A5 (tacrolimus) and CYP2C19 (voriconazole) was integrated. The tested CYP3A4 inhibition models (Sigmoid efficacy maximum [E_max], E_max, log-linear, and linear) were a function of competitive inhibition of voriconazole and mechanism-based inhibition of clarithromycin in a virtual enzyme compartment.

Results: The total tacrolimus trough concentrations were 119 points, with a median of 4.3 (range: 2.0-9.9) ng/mL (n = 3). The final model comprised the Sigmoid E_max model for voriconazole and clarithromycin, which depicted time-course alterations in tacrolimus concentration and clearance when given voriconazole and clarithromycin.

Conclusions: These findings could facilitate the model-informed precision dosing of tacrolimus after unrelated bone marrow transplant.

Background: The pharmacokinetics of immunosuppressant drugs may change with advancing age, potentially affecting patient outcomes.

Objective: To characterise the effects of age on the pharmacokinetic and exposure parameters of tacrolimus, mycophenolate, and prednisolone.

Methods: Pharmacokinetic profiling, involving whole blood tacrolimus, total and free plasma mycophenolic acid (MPA), total plasma mycophenolic acid glucuronide (MPAG), and total and free plasma prednisolone, was performed in an older and younger adult cohort. Thirteen samples were drawn on a single occasion, pre-oral dose and then at 0.25, 0.5, 0.75, 1, 1.25, 1.5, 2, 3, 4, 6, 9, and 12 h post-dose. Non-compartmental analysis was conducted using the PKNCA package, and pharmacokinetic and exposure parameters were compared between age groups using a Mann-Whitney test. A regression analysis was conducted for free MPA and MPAG using significant variables of interest.

Results: This exploratory study included 21 older and 18 younger adults. Dose-adjusted tacrolimus, total MPA and free prednisolone pharmacokinetic parameters were not different between age groups; however, for free MPA and MPAG, older recipients had significantly greater minimum and maximum concentrations, trough concentrations, and half-life. There was a two-fold increase in free MPA exposure in older adults (median dose-adjusted AUC_0-12: 1284 vs. 684 μg h/L, p < 0.0001); MPAG exposure similarly increased. Age was significantly associated with free MPA and MPAG exposure, and free MPA exposure was associated with haematocrit (p < 0.05).

Conclusion: Differences in MPA were found with advancing age and may be due to altered kidney function, haematocrit, plasma protein binding and/or drug absorption. Future research should explore specific covariate contributions to this further.

Background and objectives: Finerenone, a novel selective non-steroidal mineralocorticoid receptor antagonist, has been indicated in chronic kidney disease associated with type 2 diabetes mellitus. Considering the potential complications of diabetes, finerenone can be co-administered with various drugs, including fluconazole, diltiazem, and ritonavir. Given that finerenone is a substrate of cytochrome P450 (CYP) 3A4, the concurrent administration of finerenone with CYP3A4 inhibitors (diltiazem or fluconazole or ritonavir) could potentially lead to drug interactions, which may cause adverse events such as hyperkalemia. No studies have investigated interactions between finerenone and diltiazem or fluconazole or ritonavir. Therefore, this study aims to investigate the pharmacokinetic interaction of finerenone with diltiazem or fluconazole or ritonavir and to evaluate the impact of fluconazole on the pharmacodynamics of finerenone.

Methods: The pharmacokinetic study included four rat groups (n = 8 rats/group), including a control group (finerenone alone) and test groups (finerenone pretreated with diltiazem or fluconazole or ritonavir) using both non-compartment analysis (NCA) and population pharmacokinetic (pop-PK) modeling. The pop-PK model was developed using non-linear mixed-effects modeling in NONMEM^® (version 7.5.0). In the pharmacodynamic study, serum potassium (K⁺) levels were measured to assess the effects of fluconazole on finerenone-induced hyperkalemia.

Results: The NCA results indicated that the area under the plasma concentration-time curve (AUC) of finerenone increased by 1.86- and 1.95-fold when coadministered with fluconazole and ritonavir, respectively. In contrast, diltiazem did not affect the pharmacokinetics of finerenone. The pharmacokinetic profiles of finerenone were best described by a one-compartment disposition with first-order elimination and dual first-order absorption kinetics. The pop-PK modeling results demonstrated that the apparent clearance of finerenone decreased by 50.3% and 49.2% owing to the effects of fluconazole and ritonavir, respectively. Additionally, the slow absorption rate, which represents the absorption in the distal intestinal tract of finerenone, increased by 55.7% due to the effect of ritonavir. Simultaneously, a pharmacodynamic study revealed that finerenone in the presence of fluconazole caused a significant increase in K⁺ levels compared with finerenone alone.

Conclusions: Coadministration of finerenone with fluconazole or ritonavir increased finerenone exposure in rats. Additionally, the administration of finerenone in the presence of fluconazole resulted in elevated K⁺ levels in rats. Further clinical studies are required to validate these findings.

Background and objectives: The commonly used antiseizure medication lamotrigine is a substrate to ATP binding cassette subfamily G member 2 (ABCG2) transporter. The objective of this study was to evaluate the effect of the common loss-of-function polymorphism ABCG2 c.421C>A (rs2231142) on the lamotrigine trough concentrations at steady state in adults with epilepsy.

Methods: In two consecutive studies (Study 1, Study 2) in patients on lamotrigine monotherapy, carriers of the variant ABCG2 c.421C>A allele (CA/AA) were considered exposed, and wild-type homozygotes (CC) were considered controls. They were mutually balanced on covariates (age, sex, body weight, several polymorphisms in genes encoding other transporter proteins and lamotrigine-metabolizing enzymes that have been suggested to affect exposure to lamotrigine) to estimate the exposure effect (geometric means ratios, GMRs) in each study separately and overall (individual patient data meta-analysis). The overall estimate was evaluated for sensitivity to residual confounding.

Results: In both studies (exposed n = 28 vs. controls n = 103; exposed n = 44 vs. controls n = 153, in Study 1 and Study 2, respectively) and overall (exposed n = 72 vs. controls n = 256), dose-corrected lamotrigine trough concentrations were moderately lower in the exposed patients: frequentist GMR [95% CI] = 0.82 [0.63-1.08]; GMR = 0.69 [0.60-0.81] and GMR = 0.72 [0.63-0.83] in Study 1, Study 2 and overall, respectively; Bayes GMR [95% CrI] = 0.83 [0.68-1.00]; GMR = 0.69 [0.58-0.83] and GMR = 0.75 [0.65-0.86] in Study 1, Study 2 and overall, respectively. Estimates appeared resistant to unmeasured confounding-the E-values for the pooled point estimates were high, and estimates corrected for a strong hypothetical bias were GMR = 0.78 [0.68-0.90] frequentist and GMR = 0.81 [0.70-0.93] Bayes.

Conclusion: Polymorphism ABCG2 c.421C>A moderately reduces lamotrigine concentrations in adults with epilepsy.