Clinical Pharmacokinetics最新文献

Letermovir and maribavir have demonstrated efficacy in the prevention and treatment, respectively, of immunosuppressed patients with cytomegalovirus (CMV) infection and disease. These patients often have polypharmacy making them at risk for drug-drug interactions. Both letermovir and maribavir can be perpetrators and victims of drug-drug interactions. Letermovir is a moderate inhibitor of CYP3A, CYP2C8 and OATP1B1/3, and a moderate inducer of CYP2C19. It is a substrate of UGT1A1/3, BCRP, P-gp and OATP1B1/3. Maribavir is a moderate CYP2C9 inhibitor and a substrate of CYP3A. Drug-drug interactions between these anti-CMV agents and a number of therapeutic classes, such as immunosuppressants, antifungal agents, and hemato-oncological agents, can have clinical consequences and deserve dose modification or close monitoring. In a number of examples, three-way drug interactions need to be assessed. The objective of this review is to provide clinicians with guidance for drug-drug interaction management, based on existing data from drug-drug interaction studies, and extrapolation to other relevant co-medications that have not (yet) been studied but that are frequently used in these patient populations.

Asciminib is a first-in-class allosteric inhibitor of the kinase activity of BCR::ABL1, specifically targeting the ABL myristoyl pocket (STAMP). This review focuses on the pharmacokinetic (PK) and pharmacodynamic data of asciminib, which is approved at a total daily dose of 80 mg for the treatment of adult patients with chronic myeloid leukemia in chronic phase who are either resistant or intolerant to ≥ 2 tyrosine kinase inhibitors or those harboring the T315I mutation (at a dose of 200 mg twice daily). Asciminib is predicted to be almost completely absorbed from the gut, with an absolute bioavailability (F) of approximately 73%. It should be administered in a fasted state, as food (particularly high-fat meals) reduces exposure. Asciminib displays a slightly greater than dose-proportional increase in exposure, with no time-dependent changes in PK observed following repeated dosing. This drug shows low clearance (6.31 L/h), with a moderate volume of distribution (111 L) and high human plasma protein binding (97.3%). The apparent terminal elimination half-life (t_1/2) across studies was estimated to be between 7 and 15 h. The PK of asciminib is not substantially affected by body weight, age, gender, race, or renal or hepatic impairment. Asciminib is primarily metabolized via CYP3A4-mediated oxidation (36.0%) and UGT2B7- and UGT2B17-mediated glucuronidation (13.3% and 7.8%, respectively); biliary secretion via breast cancer resistance protein contributes to about 31.1% to total systemic clearance, which is mainly through hepatic metabolism and biliary secretion through the fecal pathway, with renal excretion playing a minor role. The potential for PK drug interaction for asciminib both as a victim and a perpetrator has been summarized here based on clinical and predicted drug-drug interaction studies. Robust exposure-response models characterized asciminib exposure-efficacy and exposure-safety relationships. In patients without the T315I mutation, the exposure-efficacy analysis of the time course of BCR::ABL1^IS percentages highlighted the existence of a slightly positive, albeit not clinically significant, relationship. Higher exposure was required for efficacy in patients harboring the T315I mutation compared with those who did not. The exposure-safety relationship analysis showed no apparent association between exposure and adverse events of interest over the broad range of exposure or dose levels investigated. Asciminib has also been shown to have no clinically relevant effect on cardiac repolarization. Here, we review the clinical pharmacology data available to date for asciminib that supported its clinical development program and regulatory applications.

Background and objective: CYP450 (CYP) phenotyping involves quantifying an individual's plasma clearance of CYP-specific probe drugs, as a proxy for in vivo CYP enzyme activity. It is increasingly applied to study alterations in CYP enzyme activity under various (patho)physiological conditions, such as inflammation, obesity, or pregnancy. The phenotyping approach assumes that changes in plasma clearance of probe drugs are driven by changes in CYP enzyme activity. However, plasma clearance is also influenced by protein binding, blood-to-plasma ratio, and hepatic blood flow, all of which may change under (patho)physiological conditions.

Methods: Using a physiologically based pharmacokinetic (PBPK) workflow, we aimed to evaluate whether the plasma clearance of commonly used CYP probe drugs is indeed directly proportional to alterations in CYP enzyme activity (sensitivity), and to what extent alterations in protein binding, blood-to-plasma ratio, and hepatic blood flow observed under (patho)physiological conditions impact plasma clearance (specificity).

Results: Plasma clearance of CYP probe drugs is sensitive to alterations in CYP enzyme activity, since alterations in intrinsic clearance between - 90% and + 150% resulted in near-proportional changes in plasma clearance, except for midazolam in the case of > 50% CYP3A4 induction. However, plasma clearance also changed near-proportionally with alterations in the unbound drug fraction, diminishing probe specificity. This was particularly relevant for high protein-bound probe drugs, as alterations in plasma protein binding resulted in larger relative changes in the unbound drug fraction. Alterations in the blood-to-plasma ratio and hepatic blood flow of ± 50% resulted in plasma clearance changes of less than ± 16%, meaning they limitedly impacted plasma clearance of CYP probe drugs, except for midazolam. In order to correct for the impact of non-metabolic determinants on probe drug plasma clearance, an R script was developed to calculate how much the CYP enzyme activity is actually altered under (patho)physiological conditions, when alterations in the unbound drug fraction, blood-to-plasma ratio, and/or hepatic blood flow also impact probe drug plasma clearance.

Conclusions: As plasma protein binding can change under (patho)physiological conditions, alterations in unbound drug fraction should be accounted for when using CYP probe drug plasma clearance as a proxy for CYP enzyme activity in patient populations. The tool developed in this study can support researchers in determining alterations in CYP enzyme activity in patients with (patho)physiological conditions.

Background and objective: Population pharmacokinetic/pharmacodynamic (PK/PD) modelling of antibiotics including C-reactive protein (C-RP) dynamics could be helpful in predicting the efficacy of antimicrobials. We developed a PK/PD model for assessing the impact of continuous infusion (CI) meropenem PK/PD target attainment on C-RP dynamics in critically ill patients with documented Gram-negative hospital- (HAP) or ventilator-acquired pneumonia (VAP).

Methods: Patients were grouped according to the type of antibiotic treatment received [meropenem monotherapy; meropenem plus empirical anti-MRSA (methicillin-resistant Staphylococcus aureus) therapy; meropenem in combination with another anti-Gram-negative active agent; meropenem plus a targeted anti-MRSA therapy]. A one-compartment population PK model of CI meropenem was developed by including all patients. A full C-RP production inhibition model was developed for fitting the PD data by including only patients receiving meropenem monotherapy or meropenem plus empirical anti-MRSA therapy. Monte Carlo simulations explored the relationship between the type of PK/PD target attainment of CI meropenem, defined as optimal (steady-state plasma concentration [C_ss] to minimum inhibitory concentration [MIC] ratio = 4-8), quasi-optimal (C_ss/MIC = 1-4) and sub-optimal (C_ss/MIC < 1) and the magnitude of C-RP production inhibition over time.

Results: A total of 64 patients providing 211 meropenem concentrations were included in the PK analysis, whereas 47 patients providing 328 C-RP data were included in the PD model. Simulations showed that optimal PK/PD target attainment was associated with the highest and most rapid C-RP production inhibition (44% and 56% at days 2 and 4, respectively). Conversely, sub-optimal PK/PD target attainment was shown to be almost ineffective (< 5% at day 4 and < 10% at day 10).

Conclusion: Our PK/PD model predicted that attaining optimal PK/PD target with CI meropenem may grant prompt and intense C-RP decrease among critically ill patients receiving targeted monotherapy for Gram-negative HAP/VAP, thus anticipating efficacy.

Background and objective: Different empirical lactation models have been published to predict the milk-to-plasma (M/P) ratio of drugs to gain knowledge on the extent of drug distribution to the breastmilk. M/P ratios will likely vary across the lactation period due to differences in physiological milk pH and fat content, which are not routinely reported in clinical lactation pharmacokinetic studies. This work aims to evaluate the sensitivity of two (a theory-based phase distribution and a log-transformed regression) lactation models for M/P prediction at different physiological milk pH and fat content.

Methods: A literature search was conducted to collate reported M/P ratios for different drugs and their physicochemical parameters required for the prediction of the M/P ratio. Two distribution models were used for M/P ratio predictions. The M/P ratio of drugs was predicted under the physiological milk pHs of 6.8, 7.0, 7.2, and 7.4 and at of 1%, 3%, and 6% fat content. Calculated M/P ratios were compared with the observed M/P ratios.

Results: A total of 200 M/P ratios for 130 compounds (40 acids and 90 bases) were collected from clinical studies and included in the analysis. For both model, precision decreases and bias increases outside the milk pH range 7.0-7.2 and fat contents more than 3%. Significant variability exists in the observed M/P ratios. Both milk pH and fat content are important parameters for model prediction.

Conclusion: Calculated M/P ratios are influenced by multiple covariates, including milk pH and fat content. The phase distribution model is less sensitive to these covariates than the log-transformed model, especially for acidic compounds. For complex matrices such as breastmilk, the actual physiological parameters of the sampled milk, at least milk fat and pH, and their distributions are required covariates to improve the prediction outcomes, design lactation pharmacokinetic studies, and inform the potential breastfed infant dose.

Background and objective: Valproic acid (VPA) demonstrates nonlinear pharmacokinetics (PK) due to a capacity-limited protein binding, which has potential implications on its total and unbound plasma concentrations, especially during hypoalbuminemia. A physiologically based pharmacokinetic (PBPK) model was developed to assess the nonlinear dose-exposure relationship of VPA with special emphasis on pediatric patients with hypoalbuminemia.

Methods: A PBPK model was first developed and evaluated in adults using PK-Sim^® and MoBi^® (v.11) and the scaled to children 1 year and older. The capacity-limited protein binding was characterized by second-order kinetics between VPA and albumin with a 2:1 molar ratio. All drug-specific parameters were informed by literature and optimized using published PK data of VPA. PK simulations were performed in virtual populations with normal and low albumin levels.

Results: The reported concentration-time profiles of total and unbound VPA were adequately predicted by the PBPK model across the age and dose range (3-120 mg/kg). The model was able to characterize the nonlinear PK, as the concentration-dependent fraction unbound (f_u) and the related dose-dependent clearance values were well predicted. Simulated steady-state trough concentrations of total VPA were less than dose-proportional and were within the therapeutic drug monitoring range of 50-100 mg/L for doses between 30 and 45 mg/kg per day in children with normal albumin concentrations. However, virtual children with hypoalbuminemia largely failed to achieve the target exposure.

Conclusion: The PBPK model helped assess the nonlinear dose-exposure relationship of VPA and the impact of albumin concentrations on the achievement of target exposure.

Background and objective: Tucatinib is a highly selective, oral, reversible, human epidermal growth factor receptor 2 (HER2)-specific tyrosine kinase inhibitor. Tucatinib is approved at a 300-mg twice-daily dose in adults in combination with trastuzumab and capecitabine for advanced HER2-postitive (HER2+) unresectable or metastatic breast cancer and in combination with trastuzumab for RAS wild-type HER2+ unresectable or metastatic colorectal cancer. This study sought to characterize the pharmacokinetics (PK) and assess sources of PK variability of tucatinib in healthy volunteers and in patients with HER2+ metastatic breast or colorectal cancers.

Methods: A population pharmacokinetic model was developed based on data from four healthy participant studies and three studies in patients with either HER2+ metastatic breast cancer or metastatic colorectal cancer using a nonlinear mixed-effects modeling approach. Clinically relevant covariates were evaluated to assess their impact on exposure, and overall model performance was evaluated by prediction-corrected visual predictive checks.

Results: A two-compartment pharmacokinetic model with linear elimination and first-order absorption preceded by a lag time adequately described tucatinib pharmacokinetic profiles in 151 healthy participants and 132 patients. Tumor type was identified as a significant covariate affecting tucatinib bioavailability and clearance, resulting in a 1.2-fold and 2.1-fold increase in tucatinib steady-state exposure (area under the concentration-time curve) in HER2+ metastatic colorectal cancer and HER2+ metastatic breast cancer, respectively, compared with healthy participants. No other covariates, including mild renal or hepatic impairment, had an impact on tucatinib pharmacokinetics.

Conclusions: The impact of statistically significant covariates identified was not considered clinically meaningful. No tucatinib dose adjustments are required based on the covariates tested in the final population pharmacokinetic model.

Clinical trial registration: NCT03723395, NCT03914755, NCT03826602, NCT03043313, NCT01983501, NCT02025192.