Clinical Pharmacokinetics最新文献

Background and objectives: Asciminib is indicated for the treatment of adult patients with newly diagnosed or previously treated Philadelphia chromosome-positive chronic myeloid leukemia in chronic phase (Ph+ CML-CP) at a total daily dose of 80 mg, as well as adult patients with Ph+ CML with the T315I mutation at 200 mg twice daily. In patients with the T315I mutation, responses have been observed at doses of ≥150 mg twice daily and dose reduction to 160 mg twice daily is permitted for management of adverse reactions. The objective of this study was to assess the effect of strong CYP3A4 induction, through phenytoin, on the pharmacokinetics (PK) of single dose asciminib 200 mg. Coproporphyrin-1 (CP-1) was also measured to evaluate the effect of asciminib 200 mg on organic anion transporting polypeptide 1B (OATP1B).

Methods: This Phase 1 open-label fixed-sequence study evaluated the PK of a single oral dose of asciminib in healthy participants when administered alone and in combination with phenytoin. A single dose of 200 mg asciminib was administered on Day 1, followed by the administration of phenytoin 100 mg three times daily from Day 6 to Day 23, taken 8 h apart to ensure full induction. On Day 20, the morning doses of phenytoin and asciminib were co-administered. Serial blood samples were collected for the assessment of asciminib PK and CP-1 plasma concentrations.

Results: All 15 participants who enrolled were male and 14 received study treatment per protocol. Following co-administration with phenytoin, asciminib-adjusted geometric mean maximum plasma concentration (C_max), area under the curve to the last plasma concentration (AUC_last) and AUC to infinity (AUC_0-inf) were reduced by 22%, 34%, and 34%, with test/reference ratios of 0.780 (90% CI: 0.718-0.847), 0.662 (90% CI: 0.624-0.703), and 0.664 (90% CI: 0.626-0.705), respectively. A single oral dose of asciminib 200 mg did not have a relevant effect on CP-1 plasma exposure.

Conclusions: These data support that, considering its large therapeutic window, asciminib 200 mg twice daily can be used without any dose adjustment when co-administered with a strong CYP3A4 inducer drug. Furthermore, asciminib is not an OATP1B inhibitor up to this dose.

Background and objective: Therapeutic drug monitoring (TDM) of linezolid for treating multidrug-resistant tuberculosis (MDR-TB) is recommended but is hindered by the invasive and logistical challenges of plasma sampling. Saliva is a promising alternative, but a saliva pharmacokinetic model to inform dosing is lacking. This study aimed to develop a saliva-based population pharmacokinetic (popPK) model and evaluate limited sampling strategies for linezolid in MDR-TB.

Methods: Plasma and saliva samples were collected at pre-dose, 2-, and 5-h post-dose from adults treated with linezolid for ≥ 7 days. A saliva-plasma popPK model was developed in NONMEM with covariate analysis. Bayesian estimation and Monte Carlo simulations were used to evaluate the predictive performance of saliva limited sampling strategies for predicting plasma AUC_0-24. A ±20% bias versus reference was considered acceptable.

Results: One-compartment plasma model incorporating a saliva hypothetical effect compartment with first-order absorption and elimination best described the data (102 paired saliva-plasma samples, 17 patients). Body weight influenced volume of distribution with an exponent of 1.1 (95% CI 1.01-1.18). The three-sample (pre-dose, 2-, and 5-h) saliva strategy adequately predicted plasma AUC_0-24 with < 5% median bias. A single saliva sample at pre-dose or 2 h post-dose provided accurate plasma AUC_0-24 predictions (< 5% median bias).

Conclusions: The validated model enables reliable estimation of plasma AUC_0-24 using limited saliva samples. A single saliva sample (pre-dose or 2 h post-dose) can accurately predict plasma AUC_0-24, supporting saliva-based TDM as a practical alternative to plasma-based monitoring of linezolid in MDR-TB.

Background and objective: Encorafenib is a potent and selective adenosine triphosphate-competitive BRAF V600-mutant kinase inhibitor. Encorafenib is approved for multiple indications in combination with binimetinib or cetuximab. This analysis aimed to develop a global population pharmacokinetics (popPK) model characterizing encorafenib disposition across tumor types.

Methods: The popPK analysis was based on nine phase 1 to 3 studies in participants with melanoma, colorectal cancer (CRC), non-small cell lung cancer, or other solid tumors, and healthy participants. A total of 1310 participants who received encorafenib as monotherapy or in combination were included. Nonlinear mixed effects modeling was performed using NONMEM v7.5.0. A semi-mechanistic enzyme turnover model was assessed to quantify the autoinduction effect. Stepwise covariate modeling using Perl-speaks-NONMEM (version 5.3.0) evaluated a broad range of covariates.

Results: A two-compartment model with first-order absorption and concentration-dependent autoinduction successfully characterized the concentration-time profile of encorafenib. Encorafenib apparent clearance (CL/F) was estimated to be 12.2 L/h after the first dose in a typical adult and increased by 186% to 35 L/h at steady state. This model indicates that maximum autoinduction is expected within 14 days of daily dosing (autoinduction half-life 64 h). Age and tumor type (melanoma, metastatic CRC, other [healthy, lung tumors, other solid tumors]) on CL/F and body weight on volume of distribution were significant covariates; these effects were not determined to be clinically significant.

Conclusions: This model successfully described the PK of encorafenib over time and across tumor types. No dose modifications are suggested on the basis of intrinsic or extrinsic factors evaluated.

Background and objectives: Therapeutic drug monitoring of protein kinase inhibitors (PKIs) usually relies on the measure of a single trough concentration at steady-state (C_min,ss). When the sampling time differs from the trough, it is theoretically possible to predict C_min,ss from maximum a posteriori (MAP) Bayesian estimates of PK parameters. However, several questions remain with regards to model-informed precision dosing (MIPD) of PKIs, such as choosing which model to use when several are available in the literature. Alternative techniques, such as flattened priors and model averaging may outperform standard analyses. The aim of this work is to report a comprehensive fit-for-purpose validation of MIPD for sunitinib and pazopanib.

Methods: Concentration data from 41 renal cancer patients included in the SUP-R trial (NCT02555748) measured 2 and 6 hours after an intake were analyzed (MAP-Bayesian estimation of PK parameters) in order to predict C_min,ss at the current cycle and at the next cycle. Different models from the literature were tested, as well as the model-averaging and flattened priors features available in the R package 'mapbayr'.

Results: The quality of C_min,ss predictions depended on the model used. Flattening priors rarely improved or worsened the predictions. Model averaging was robust across the different scenarios tested and should be preferred to using a single model. Overall, a precision of 20% to 25% was achieved, with a minimal bias (< 5%).

Conclusion: The benefit of the model-averaging method for the model-informed precision dosing of sunitinib and pazopanib is likely applicable to other protein kinase inhibitors. Thanks to 'mapbayr', this framework was implemented as a standalone shiny application to be used in clinical settings.

Gonadotropin-releasing hormone (GnRH) antagonists inhibit estrogen synthesis and secretion, making them promising treatment options for estrogen-dependent diseases, such as endometriosis. This study developed a population pharmacokinetic/pharmacodynamic (PK/PD) model for merigolix, a novel oral GnRH antagonist, to determine its optimal dosing in the treatment of endometriosis. Population PK/PD modeling was performed using NONMEM 7.4, incorporating data from phase I clinical studies involving single and multiple ascending dose (SAD and MAD) trials in healthy premenopausal volunteers. The PK profile was characterized using a two-compartment model incorporating first-order absorption and elimination processes. The temporal delay between merigolix concentration and subsequent estradiol (E2) suppression was described using an indirect response turnover model. The models were evaluated via visual predictive checks, goodness-of-fit plots, and bootstrap analysis. The PK model described merigolix concentrations across various doses (estimated clearance: 549 L/h, central volume of distribution: 1690 L). The PD model demonstrated dose-dependent E2 suppression (estimated maximum inhibitory effect [I_max]: 1, half-maximal inhibitory concentration [IC₅₀]: 0.209 ng/mL). Simulations suggested that, assuming a baseline E2 concentration of 100 pg/mL, daily doses of 120 and 160 mg achieved the clinically meaningful target E2 range of 20-40 pg/mL (partial suppression), while higher doses of 240 and 320 mg resulted in target E2 levels below 20 pg/mL (full suppression), effectively controlling symptoms and minimizing the risk of bone mineral density loss. This PK/PD model provides a quantitative framework for optimizing merigolix dosing and supports the selection of dosing regimens for future clinical trials, potentially offering a novel therapeutic option for endometriosis treatment.

Background and objective: There is limited information on the pharmacokinetics and optimal dosing of fluconazole in patients undergoing mechanical circulatory support (MCS) device implantation.

Aim: The aim of this study was to describe fluconazole pharmacokinetics and identify dosing regimens that achieve pharmacokinetic/pharmacodynamic targets in this patient cohort.

Methods: In this prospective, single-centre study, adults undergoing MCS device implantation received intravenous fluconazole 200 mg or 400 mg, continued once daily for 5 days. Fluconazole concentrations were measured at four peri-operative time points, on return to the intensive care unit, and on days 3 and 5 following implantation. The area under the concentration-time curve from time zero to 24 h (AUC_0-24) was estimated, with target exposures defined as AUC_0-24/minimum inhibitory concentration (MIC) ≥ 50 for prophylaxis and AUC_0-24/MIC ≥ 100 for therapy. Population pharmacokinetic modelling was performed using non-linear mixed-effects methods and for Monte Carlo dosing simulations.

Results: Sixty-five fluconazole concentrations from 10 male patients (median age 51.5 years; IQR 50.0-57.0) were included. A two-compartment model including an additional renal replacement therapy (RRT)-dependent clearance pathway best described the data. Parameter estimates from the final model included a central volume of distribution of 5.75 L, non-RRT clearance of 0.45 L/h, and RRT clearance of 2.22 L/h. Simulations showed that doses of 800 mg or 12 mg/kg would be required for the highest probability of target attainment for Candida fluconazole MICs up to 4 mg/L (AUC_0-24/MIC ≥ 50) and 2 mg/L (AUC_0-24/MIC ≥ 100).

Conclusion: In patients undergoing MCS device implantation, intravenous fluconazole 200 mg administered pre-operatively and continued daily for 5 days is insufficient for Candida spp. with MICs > 1 mg/L (prophylaxis, AUC_0-24/MIC ≥ 50) and > 0.5 mg/L (treatment, AUC_0-24/MIC ≥ 100).

Background: High-dose methotrexate (HDMTX) is a key treatment for lymphoma with central nervous system involvement. Whether incorporating cystatin C into glomerular filtration rate estimation improves methotrexate (MTX) clearance prediction remains unclear.

Objectives: We aimed to evaluate whether cystatin C-inclusive glomerular filtration rate equations improve MTX clearance prediction and to explore the relationship between MTX exposure and acute kidney injury (AKI) in adult patients with lymphoma receiving HDMTX.

Methods: This was a prospective single-center study performed on 80 adult patients with lymphoma receiving HDMTX (1.5-8 g/m²) over a 4-h infusion. A population pharmacokinetic model was constructed using data from 80 administrations of HDMTX and 427 serum MTX concentrations. The population pharmacokinetic model estimated MTX concentrations were included in a logistic regression to assess the relationship between MTX exposure and AKI.

Results: A two-compartment model best described the pharmacokinetic data, with baseline albumin and CKD-EPI creatinine-cystatin C (eGFRCr-CysC) as significant covariates on clearance. Seventeen patients (21%) developed any-stage AKI. Among those receiving ≤ 3.5 g/m², model-estimated 4-h MTX concentrations were associated with AKI (odds ratio: 1.02 per µmol/L; p = 0.0038), with an optimal threshold of 160 µmol/L (area under the concentration-time curve: 0.818). Patients above this threshold were 22 times more likely to experience AKI (p = 0.0005). This association was not observed in patients treated with 8 g/m². Despite a lower dose and exposure, patients receiving ≤ 3.5 g/m² demonstrated a stronger concentration-toxicity relationship.

Conclusions: Our results support the use of cystatin C-inclusive glomerular filtration rate estimates in MTX pharmacokinetic modeling and suggest early MTX concentration sampling may identify AKI risk, enabling proactive, AKI-mitigating clinical interventions during HDMTX therapy.

Background and objective: Prosthetic joint infections are serious infections that require perioperative antibiotic prophylaxis. Cefuroxime may be used as prophylaxis, and this study aims to evaluate the current dosing regimen to prevent infection during the perioperative period by performing population pharmacokinetic (PK) analysis of plasma and bone in patients with prosthetic joint infection (PJI).

Methods: Both plasma and bone samples were taken perioperatively and analyzed using NONMEM. Monte Carlo simulations were performed to determine the probability of target attainment (PTA) in both plasma and bone of various dosing regimens.

Results: A total of 44 plasma and 38 bone samples from 25 patients were included. The median bone concentration at 30-60 min after administration was 18.2 (9.9-25.3) mg/L and the bone-to-plasma ratio was 0.269 (interquartile range [IQR] 0.179-0.269), while the median bone concentration at 90-120 min after administration was 12.6 (5.9-18.6) mg/L and the bone-to-plasma ratio was 0.125 (IQR 0.073-0.160). A two-compartment model with allometric scaling and proportional errors best described plasma and bone concentrations. Only estimated glomerular filtration rate could partly explain the inter-individual variability (IIV) of clearance (CL). A single dose of 1500 mg cefuroxime failed to maintain bone concentrations above target concentrations for Staphylococcus aureus beyond 3.83 h post administration.

Conclusion: A population PK model was developed to characterize the disposition of cefuroxime in both plasma and bone compartments. Although adequate cefuroxime bone penetration within 1 h was observed in patients with PJI with rapid clearance, simulations predicted less optimal levels of cefuroxime after 3.5 h. The clinical implications need to be researched and confirmed.

Background: Mycophenolic acid (MPA) has complex pharmacokinetics in part due to enterohepatic recirculation (EHR). A deeper understanding of MPA pharmacokinetics and specifically how EHR and patterns of EHR affect exposure will improve immunosuppression outcomes. This study provides a contemporary and comprehensive assessment of MPA and metabolites in the blood and urine with a focus on EHR characteristics.

Methods: Kidney transplant recipients (n = 84) receiving mycophenolate mofetil (MMF) and tacrolimus underwent an intensive MPA pharmacokinetic assessment. Pharmacokinetics of MPA and its metabolites, EHR%, and number of secondary peaks were determined. The associations between EHR, the number of secondary peaks, and achievement of MPA therapeutic range were studied. MMF dose proportionality with MPA exposure was evaluated.

Results: MPA exhibited high pharmacokinetic variability, with 5.5-fold differences in AUC_0-12, 18.4-fold differences in trough concentrations, and a 3.4-fold difference in EHR%. Median MPA EHR% was 40.5%. There were no significant associations between EHR% and MPA AUC_0-12 or trough. MPA AUC_0-12 and trough were significantly associated with the number of MPA secondary peaks (0, 1, or ≥ 2 peaks). Participants without secondary peaks showed the highest percentage of subtherapeutic MPA AUC_0-12 and troughs, while participants with ≥ 2 peaks were more likely to be supratherapeutic.

Conclusions: There was no association between the EHR% and MPA AUC_0-12 or trough. We identified three distinct patterns of MPA secondary peaks (0, 1, or ≥ 2 peaks), which were significantly associated with MPA AUC_0-12 and trough. Studies to evaluate the relationship of MPA EHR measures and clinical outcomes are needed.

Trial registry: Clinical Trial Notation: clinicaltrials.gov, NCT04953715.