Clinical Pharmacokinetics最新文献

Background and objectives: Nedosiran (Rivfloza^®) is an RNA interference (RNAi) therapy approved for individuals aged ≥ 2 years with primary hyperoxaluria type 1 (PH1), a rare autosomal-recessive disorder causing renal failure and systemic oxalosis. Nedosiran silences lactate dehydrogenase (LDH) mRNA in hepatocytes, reducing oxalate levels. This study evaluated the model-informed clinical development of nedosiran to support proposed doses in children aged 2 to < 12 years with PH1.

Methods: A population pharmacokinetic/pharmacodynamic (Pop-PK/PD) model characterizing the plasma concentration-time profile of nedosiran and its effect on the spot urine oxalate-to-creatinine ratio (Uox/Cr) was developed using data from six trials. Simulations assessed spot Uox/Cr reduction in children aged 2 to < 12 years for the proposed dosing regimen versus those aged ≥ 12 years weighing ≥ 50 kg with similar renal function.

Results: The datasets included 2087 PK (N = 148) and 668 spot Uox/Cr (N = 41, with PH1) observations. Body weight, estimated glomerular filtration rate (eGFR), and PH type were covariates in the PK model, with body weight in low and high percentiles affecting nedosiran exposures. Moderate renal impairment (eGFR 30-59 mL/min/1.73 m²) increased exposure, while only age was significant for baseline Uox/Cr in the PD model. Simulations showed similar Uox/Cr reduction and times to maximum effect in children aged 2 to < 12 years, treated once-monthly (Q1M) with 3.5 mg/kg, compared to those aged ≥ 12 years treated Q1M with 170 mg.

Conclusions: Simulations based on the final Pop-PK/PD model support the 3.5 mg/kg Q1M dosing regimen in children aged 2 to < 12 years with PH1 and relatively intact kidney function (eGFR ≥30 mL/min/1.73 m²).

Trial registration: Trials are registered at ClinicalTrials.gov with study numbers NCT03392896 (PHYOX1), NCT03847909 (PHYOX2), NCT04042402 (PHYOX3), and NCT05001269 (PHYOX8) and at EudraCT with study numbers 2018-003098-91 (PHYOX2) and 2018-003099-10 (PHYOX3).

Malnutrition significantly alters the pharmacokinetics of medications, particularly in vulnerable populations such as children, pregnant women, elderly individuals, and individuals in low- and middle-income countries. These populations are often more vulnerable to the effects of malnutrition because of physiological, metabolic and socioeconomic factors. Changes in body composition, organ function and plasma protein levels associated with malnutrition can impact drug absorption, distribution, metabolism and excretion. In malnourished individuals, decreased serum albumin levels may increase the free (unbound) fraction of highly protein-bound acidic drugs, potentially elevating the risk of toxicity. However, this relationship is not universally straightforward, as it depends on the drug's protein-binding characteristics, hepatic and renal function, volume of distribution and compensatory changes in drug clearance. In addition, malnutrition's effects on liver enzymes, such as cytochrome P450 isoforms, and kidney function can result in unpredictable drug clearance, particularly for narrow-therapeutic-index medications. Emerging evidence also highlights the interplay between malnutrition and pharmacogenomics, with genetic variations further modulating drug metabolism and response. Addressing these complexities requires the development of tailored dosing regimens and adaptive therapeutic strategies to optimise treatment outcomes in these at-risk groups. This review accentuates the critical need for more robust research to inform clinical guidelines and improve health equity in managing malnourished populations globally.

Background and objective: Individualized drug dosing is a highly effective strategy for optimizing therapeutic outcomes, especially for drugs with high inter-individual variability. Population pharmacokinetic modeling is a widely used approach to characterize inter-individual variability in therapeutic drug monitoring. However, the development of population pharmacokinetic models is labor intensive and requires significant technical expertise. Machine learning (ML) represents a promising alternative for personalized drug dosing strategies. Despite numerous studies applying ML in this context, no previous work has comprehensively reviewed and compared their methodologies and predictive performance. This scoping review addresses this gap in the existing literature with the aim to examine the methodological approaches used in ML-based pharmacokinetic modeling for dose optimization.

Methods: Five databases were systematically searched from their inception to May 2025. Studies comparing predictions of drug concentrations or pharmacokinetic parameters between ML and population pharmacokinetic models were included. Studies published in non-English language, reviews, protocols, or studies that did not employ ML models for individualized dose regimens or treatment plans were excluded.

Results: Fifty-eight studies were included. We found that boosting-based models, tree-based models, instance-based, and regression-based models were the most commonly used ML approaches. Approximately 31% of the studies integrated ML with population pharmacokinetic models, while the remainder developed stand-alone ML models. Inconsistencies in reporting were evident, as only 60% of the studies detailed their feature selection methods. Model evaluation approaches also varied: 47% of ML models used internal test sets, while the remainder employed external datasets or mixed approaches. In terms of predictive accuracy, ML models performed comparably to or better than population pharmacokinetic models, especially for drugs with significant pharmacokinetic variability.

Conclusions: This review identifies substantial heterogeneity in ML modeling approaches, feature selection, and model evaluation. To enhance the reproducibility and clinical applicability of ML models in individualized drug dosing, standardization in reporting and methodological practices is essential.

Background and objectives: Emerging evidence suggests that comorbidities like human immunodeficiency virus (HIV) infection, diabetes mellitus (DM), and malnutrition in tuberculosis (TB) patients can alter drug concentrations, thereby affecting the treatment outcomes. For these populations, personalised strategies such as therapeutic drug monitoring (TDM) may be essential. We investigated the variations of drug levels within comorbid populations and analysed the differences in patterns observed between sub-Saharan Africa (SSA) and non-SSA regions.

Methods: We performed a systematic review and meta-analysis of rifampicin drug pharmacokinetics (PK) through searches of major databases from 1980 to December 2023. A random-effects meta-analysis model using R-studio version 4.3.2 was conducted to estimate pooled serum rifampicin exposure (area under the concentration-time curve [AUC], and peak maximum concentration [C_max]) between patients with TB-HIV infection, and TB-DM.

Results: From 3300 articles screened, 24 studies met inclusion criteria, contributing 33 comorbidity subgroups for meta-analysis. In SSA, 14 subgroups assessed rifampicin PK in TB-HIV, 1 in TB-DM, and none in TB-malnutrition. The pooled mean C_max was below the recommended range (8-24 mg/L) for all subgroups. For TB-HIV, the pooled C_max was 5.59 mg/L, 95% CI (4.59-6.59), I² = 97% for SSA populations and 5.59 mg/L, 95% CI (3.65; 6.59) for non-SSA populations. The C_max for TB-DM in SSA (9.60 ± 4.4 mg/L) exceeded non-SSA (4.27 mg/L, 95% CI [2.77-5.76]). The lowest AUC was in TB-HIV (SSA, 29.09 mg/L h, 95% CI [21.06; 37.13, I² = 91%]). High variability and heterogeneity (I² >90%) were observed, with most studies (20/23) showing low bias.

Conclusion: Our results emphasise the need for individualised dosing and targeted TDM implementation among TB-HIV and TB-DM populations on rifampicin in SSA. Although all populations exhibited low C_max levels, TB-HIV populations may be prioritised as AUC levels were lowest. In clinical settings in SSA, C_max-based TDM is more practical, but AUC can be used in treatment where feasible.

Introduction: Benralizumab is approved as add-on subcutaneous therapy in patients aged ≥ 12 years with severe eosinophilic asthma in > 80 countries, including mainland China.

Objective: The study objective was to update benralizumab population pharmacokinetic (popPK) and exposure-response (ER) models in Chinese, Asian (including Chinese), and non-Asian participants.

Methods: Benralizumab popPK/ER models for asthma exacerbation rate and pre-bronchodilator forced expiratory volume in 1 second (FEV₁) were updated for three benralizumab trials involving Chinese, Asian (including Chinese), and non-Asian participants. The ER analysis examined correlations between pharmacokinetic quartiles and annual asthma exacerbation rate (AAER) ratios with simulations comparing predicted clinical outcomes.

Results: Updated data included 17,465 benralizumab concentrations (n = 2855). The updated model predicted a slight, and not clinically relevant, increase (< 14%) in benralizumab exposure for Chinese versus non-Asian adults. Median exposure increased in Chinese adolescents versus adults owing to body weight differences, but no dose adjustment was needed. Chinese children weighing < 35 kg receiving a 10 mg dose had similar exposure to those weighing ≥ 35 kg receiving a 30 mg dose. In Chinese versus non-Chinese participants, there was no trend concerning AAER ratios across different trough concentration quartiles; the maximal treatment effect significantly increased (+127%; p < 0.001), and there was no statistically significant effect on pre-bronchodilator FEV₁. Steady-state simulations showed lower predicted AAER ratios in Chinese (0.38; 95% confidence interval [CI] 0.32-0.45) than in non-Chinese adults (0.64; 95% CI 0.60-0.71), and no relevant differences between Chinese adults (0.46; 95% CI 0.38-0.54) and adolescents (0.46; 95% CI 0.37-0.55).

Conclusion: The benralizumab popPK/ER models showed good predictive performance across Chinese demographics.

Trial registration number: NCT03186209.

Trial registration date: 6 July 2017.

Background and objective: Binimetinib is approved for multiple indications at a therapeutic dose of 45 mg twice a day (BID), in combination with encorafenib. A clinical hepatic impairment (HI) study was designed to evaluate the pharmacokinetics (PK), safety, and tolerability of a single oral dose of binimetinib in participants with mild, moderate, and severe HI compared with demographically matched healthy participants with respect to age, gender, and body weight.

Methods: Participants were enrolled according to National Cancer Institute (NCI) classification criteria for hepatic function based on their total bilirubin and aspartate aminotransferase levels at screening. Participants enrolled into Group 1 (normal hepatic function) were matched to participants enrolled into Groups 2, 3, and 4 (mild, moderate, and severe HI, respectively) with respect to age, gender, and body weight. Dose-normalized PK parameters were evaluated because of a difference in doses for the severe HI group compared to the other groups, with the dose reduction due to the increased exposures observed in the moderate HI group.

Results: Among 27 PK evaluable participants, changes in binimetinib dose-normalized PK parameters C_max/D and AUC_inf/D were minimal in participants with mild HI compared to the normal hepatic function group. Both the moderate and severe HI groups had significant changes as AUC_inf/D increased by 81% and 111%, respectively, compared to the normal hepatic function group. Unbound AUC_last/D for the moderate and severe HI groups increased by 280% and 248% compared to the normal hepatic function group, respectively.

Conclusion: Based on these findings on total and unbound exposures, dose reductions are recommended for binimetinib in cancer patients with moderate and severe HI.

Clinical trial registration: ClinicalTrials.gov NCT02050815, registered 29 January 2014.

Background and objective: Obesity can alter the physiological profile of individuals, potentially impacting the pharmacokinetics of anesthetic agents. This study compared the pharmacokinetic profiles of lidocaine and its metabolites between obese patients and normal-weight patients following a single intravenous bolus during surgical operation, to inform dosing strategies for the obese Chinese population.

Methods: Twenty-nine obese patients scheduled for laparoscopic sleeve gastrectomy and 29 normal-weight patients for laparoscopic cholecystectomy were enrolled. Lidocaine (2%, 1.5 mg/kg) was administered intravenously to obese patients and normal-weight patients on the basis of adjusted body weight (ABW) and total body weight, respectively. Plasma samples were collected to analyze the pharmacokinetic profiles of lidocaine and its metabolites. Adverse events (AEs) were recorded throughout the study.

Results: Obese patients had a significantly longer half-life for lidocaine (2.27 ± 0.69 h vs 0.94 ± 0.16 h, p < 0.0001), a higher volume of distribution (105 ± 27.3 L vs 54.9 ± 14.0 L, p < 0.0001), and a lower clearance (33.6 ± 9.08 L/h vs 40.5 ± 8.67 L/h, p = 0.008) compared to normal-weight patients. Although exposure to lidocaine was similar between groups within 2 hours, obese patients had lower metabolite concentrations due to decreased metabolic capacity. The plasma concentrations in all patients remained below the toxic concentration of 5 μg/mL, and no serious lidocaine-related AEs were reported.

Conclusions: Obesity significantly affects the pharmacokinetics of lidocaine and its active metabolites, and administering lidocaine intravenously via ABW is safe and reasonable for obese patients.

Clinical trial registration: ChiCTR2200064980, 25 October 2022.

Background and objective: End-stage kidney disease (ESKD) patients undergoing haemodialysis (HD) require a dosing regimen that balances the low endogenous clearance with the additional dialyser clearance. This study aimed to expand a previously proposed general-purpose pharmacokinetic model for piperacillin/tazobactam with a new population of ESKD patients undergoing intermittent high-flux haemodialysis.

Methods: Inter- and intradialytic blood samples were collected in ESKD patients undergoing intermittent high-flux haemodialysis, in HD or haemodiafiltration (HDF) mode, who received piperacillin/tazobactam during routine care. The previous general-purpose model was expanded to reflect changes in the pharmacokinetics in the new patient population. A covariate search was performed focussing on factors that explained variability between patients in endogenous and dialysis clearance. Simulations were performed to determine the probability of target attainment with current dosing recommendations in this specific population.

Results: In 20 ESKD patients, 195 piperacillin/tazobactam concentrations were determined. The general-purpose model was successfully expanded, wherein endogenous piperacillin/tazobactam clearance in patients with/without residual diuresis was 63% (95% confidence interval [CI] 49.5-73.0%) and 78.6% (95% CI 66.3-86.4%) lower compared with the general population, respectively. Extraction ratios of piperacillin and tazobactam ranged from 64 to 80%. Differences in probability of target attainment (PTA) for piperacillin were observed between patients with normal kidney function and ESKD patients undergoing haemodialysis with current dosing recommendations.

Conclusion: We successfully expanded a general-purpose model to reflect the piperacillin/tazobactam pharmacokinetics in ESKD patients undergoing intermittent haemodialysis using high-flux dialysers. The current dosing recommendations provide inconsistent probability of target attainment in ESKD patients compared with the general population.

Aim: To investigate the effect of amiodarone on apixaban pharmacokinetics in cardiac surgery patients with postoperative atrial fibrillation.

Methods: Apixaban concentrations of postoperative cardiac surgery patients with or without amiodarone therapy were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in clinical routine. A population pharmacokinetic model was built using nonlinear mixed effects modeling in NONMEM^® 7.5 using first-order conditional estimation with interaction. The impact of amiodarone and creatinine clearance (CrCL) on apixaban exposure under various dosing regimens was analyzed using Simulx^® (Lixoft).

Results: A total of 33 patients with 76 apixaban concentrations were included. A one-compartment model best described the pharmacokinetics of apixaban with a clearance (CL/F) of 3.05 L/h, apparent volume of distribution (V_d/F) of 23.7 L, and an absorption rate constant (k_a) of 0.652/h. Interindividual variability (IIV) was observed in CL/F but not in V_d/F and k_a. The covariates amiodarone and CrCL were independently associated with apixaban CL/F. Under concomitant amiodarone therapy, simulations predicted an increase of 44-49% in apixaban area under the concentration-time curve (AUC), and AUC nearly doubled at CrCL 35 mL/min. A dose of 2.5 mg apixaban twice daily (b.i.d.) was identified as a potential dosing option in the CrCL range of 15-50 mL/min under amiodarone comedication.

Conclusions: Concomitant amiodarone therapy reduced apixaban CL/F and increased the risk of high exposure in patients with impaired renal function. A dose of 2.5 mg apixaban b.i.d. for a CrCL range of 30-50 mL/min under concomitant amiodarone therapy was identified as a new dosing option.

Background: Thioguanine (TG) has recently been rediscovered as an immunosuppressive agent in the treatment of inflammatory bowel disease (IBD). This prodrug is directly converted into its active metabolites, 6-thioguanine nucleotides (6-TGNs), targeting the inhibition of RAC1 GTPase in inflammatory conditions, disrupting key cellular signaling pathways necessary for T-cell activation and survival, thereby contributing to its immunosuppressive action. In IBD, TG is used fixed dose and may benefit from model-informed precision dosing (MIPD) to optimize treatment efficacy and minimize toxicity. However, a population pharmacokinetic (PopPK) model to do so is lacking.

Objective: To develop a PopPK model for TG in IBD patients, enhancing the understanding of TG's pharmacokinetics and supporting the implementation of model-informed precision dosing (MIPD).

Methods: We employed a dataset comprising 131 6-TGN trough concentrations from 28 IBD patients treated with TG. The data were analyzed using nonlinear mixed-effects modeling (NONMEM) to estimate pharmacokinetic parameters and explore the influence of covariates such as weight and 5-ASA use on drug disposition. Model fit-for-purpose was evaluated through computation of the model's forecasting performance.

Results: The developed PopPK model was a one-compartment model with first-order absorption. A one-compartment TG model was stable, and able to estimate pharmacokinetic parameters with good precision (relative standard error [RSE] 15%) with weight and aminosalicylic acid (5-ASA) use significantly affected TG clearance. Forecasting performance was also adequate with a relative root mean squared error (rRMSE) of 24.1% and practically no systematic bias (mean percentage error [MPE] 0.2%).

Conclusion: This study presents the first PopPK model of thioguanine for IBD, offering a novel tool for MIPD in clinical settings. Future studies should explore additional covariates such as TPMT genotype and drug interactions to further refine dosing recommendations for diverse patient populations.