Clinical Pharmacokinetics最新文献

Background: Pharmacogenetic profiling and therapeutic drug monitoring (TDM) have both been proposed to manage inter-individual variability (IIV) in drug exposure. However, determining the most effective approach for estimating exposure for a particular drug remains a challenge. This study aimed to quantitatively assess the circumstances in which pharmacogenetic profiling may outperform TDM in estimating drug exposure, under three sources of variability (IIV, inter-occasion variability [IOV], and residual unexplained variability [RUV]).

Methods: Pharmacokinetic models were selected from the literature corresponding to drugs for which pharmacogenetic profiling and TDM are both clinically considered approaches for dose individualization. The models were used to simulate relevant drug exposures (trough concentration or area under the curve [AUC]) under varying degrees of IIV, IOV, and RUV.

Results: Six drug cases were selected from the literature. Model-based simulations demonstrated that the percentage of patients for whom pharmacogenetic exposure prediction is superior to TDM differs for each drug case: tacrolimus (11.0%), tamoxifen (12.7%), efavirenz (49.2%), vincristine (49.6%), risperidone (48.1%), and 5-fluorouracil (5-FU) (100%). Generally, in the presence of higher unexplained IIV in combination with lower RUV and IOV, exposure was best estimated by TDM, whereas, under lower unexplained IIV in combination with higher IOV or RUV, pharmacogenetic profiling was preferred.

Conclusions: For the drugs with relatively low RUV and IOV (e.g., tamoxifen and tacrolimus), TDM estimated true exposure the best. Conversely, for drugs with similar or lower unexplained IIV (e.g., efavirenz or 5-FU, respectively) combined with relatively high RUV, pharmacogenetic profiling provided the most accurate estimate for most patients. However, genotype prevalence and the relative influence of genotypes on the PK, as well as the ability of TDM to accurately estimate AUC with a limited number of samples, had an impact. The results could be used to support clinical decision making when considering other factors, such as the probability for severe side effects.

Background and objective: Icodec is a once-weekly insulin being developed to provide basal insulin coverage in diabetes mellitus. This study evaluated the effects of renal or hepatic impairment on icodec pharmacokinetics.

Methods: Two open-label, parallel-group, single-dose (1.5 U/kg subcutaneously) trials were conducted. In a renal impairment trial, 58 individuals were allocated to normal renal function (measured glomerular filtration rate ≥ 90 mL/min), mild (60 to < 90 mL/min), moderate (30 to < 60 mL/min) or severe (< 30 mL/min) renal impairment or end-stage renal disease. In a hepatic impairment trial, 25 individuals were allocated to normal hepatic function or mild (Child-Pugh Classification grade A), moderate (grade B) or severe (grade C) hepatic impairment. Blood was sampled frequently for a pharmacokinetic analysis until 35 days post-dose.

Results: The shape of the icodec pharmacokinetic profile was not affected by renal or hepatic impairment. Total icodec exposure was greater for mild (estimated ratio [95% confidence interval]: 1.12 [1.01; 1.24]), moderate (1.24 [1.12; 1.37]) and severe (1.28 [1.16; 1.42]) renal impairment, and for end-stage renal disease (1.14 [1.03; 1.28]), compared with normal renal function. It was also greater for mild (1.13 [1.00; 1.28]) and moderate (1.15 [1.02; 1.29]) hepatic impairment versus normal hepatic function. There was no statistically significant difference between severe hepatic impairment and normal hepatic function. Serum albumin levels (range 2.7-5.1 g/dL) did not statistically significantly influence icodec exposure.

Conclusions: The clinical relevance of the slightly higher icodec exposure with renal or hepatic impairment is limited as icodec should be dosed according to individual need. No specific icodec dose adjustment is required in renal or hepatic impairment.

Clinical trial registration: ClinicalTrials.gov identifiers: NCT03723785 and NCT04597697.

Background: Levetiracetam is an antiseizure medication used for several seizure types in adults and children aged 1 month and older; however, due to a lack of data, pharmacokinetic (PK) variability of levetiracetam is not adequately characterized in certain populations, particularly neonates, children younger than 2 years of age, and children older than 2 years of age with obesity.

Objective: This study aimed to address the gap by leveraging PK data from two prospective standard-of-care pediatric trials (n = 88) covering an age range from 1 month to 19 years, including those with obesity (64%), and applying a physiologically based PK (PBPK) modeling framework.

Methods: A published PBPK model of levetiracetam for children aged 2 years and older was extended to pediatric patients younger than 2 years of age and patients older than 2 years of age with obesity by accounting for the obesity and age-related changes in PK using PK-Sim^® software. The prospective pediatric data, along with the literature data for neonates and children younger than 2 years of age, were used to evaluate the extended PBPK models.

Results: Overall, 82.4% of data fell within the 90% interval of model-predicted concentrations, with an average fold error within twofold of the accepted criteria. PBPK modeling revealed that children with obesity had lower weight-normalized clearances (0.053 L/h/kg) on average than children without obesity (0.063 L/h/kg). The effect of maturation was well-characterized, resulting in comparable PBPK-simulated, weight-normalized clearances for neonates and children younger than 2 years of age reported from the literature.

Conclusions: PBPK modeling simulations revealed that the current US FDA-labeled pediatric dosing regimen listed in the prescribing information can produce the required exposure of levetiracetam in these target populations with dose adjustments for children with obesity aged 4 years to younger than 16 years.

The renal secretion of many drugs is facilitated by membrane transporters, including organic cation transporter 2, multidrug and toxin extrusion protein 1/2-K and organic anion transporters 1 and 3. Inhibition of these transporters can reduce renal excretion of drugs and thereby pose a safety risk. Assessing the risk of inhibition of these membrane transporters by investigational drugs remains a key focus in the evaluation of drug-drug interactions (DDIs). Current methods to predict DDI risk are based on generating in vitro data followed by a clinical assessment using a recommended exogenous probe substrate for the individual drug transporter. More recently, monitoring plasma-based and urine-based endogenous biomarkers to predict transporter-mediated DDIs in early phase I studies represents a promising approach to facilitate, improve and potentially avoid conventional clinical DDI studies. This perspective reviews the evidence for use of these endogenous biomarkers in the assessment of renal transporter-mediated DDI, evaluates how endogenous biomarkers may help to expand the DDI assessment toolkit and offers some potential knowledge gaps. A conceptual framework for assessment that may complement the current paradigm of predicting the potential for renal transporter-mediated DDIs is outlined.

Background: The establishment of optimal dosing regimens for intravenous (IV) lidocaine in the perioperative setting, aiming to balance effective pain relief with minimisation of potential side effects, is a topic of ongoing debate. This discussion stems from the significant variability in lidocaine's pharmacokinetic (PK) parameters and its relatively narrow safety margin. Population pharmacokinetic (popPK) modelling has emerged as a valuable tool for understanding the factors contributing to this observed variability in drug kinetics.

Objectives: This systematic review compiles the existing knowledge on lidocaine's PK properties and published popPK models, with a focus on significant covariates.

Methods: A systematic search on Cochrane CENTRAL, Medline, and EMBASE was performed from inception to June 2023. Original clinical studies that administered IV lidocaine to adults and performed PK analyses using a nonlinear mixed effects modelling approach were included. The quality of the included studies was assessed by compliance with the Clinical Pharmacokinetics (ClinPK) statement checklist.

Results: Seven studies were included, which involved a diverse adult population, including both volunteers and patients with various comorbidities. Lidocaine PK was primarily characterised by a two- or three-compartment model. The volume of distribution at steady state ranged from 66 to 194 L, and the total clearance ranged from 22 to 49 L/h. Despite adjusting for significant covariates like heart failure status, alpha-1-acid glycoprotein, duration of lidocaine infusion, and body weight, each study revealed substantial variability in PK parameters. The potential impact of hepatic or renal function biomarkers on these PK parameters calls for further investigation. Incomplete reporting of key aspects of developed models may hinder the models' reliability and clinical application.

Conclusion: The findings emphasise the importance of tailoring drug dosage to ensure the safe and effective use of intravenous lidocaine. Optimal design methodologies may be incorporated for a more efficient identification of important covariates. Utilising contemporary model evaluation methods like visual predictive checks and bootstrapping would enhance the robustness of popPK models and the reliability of their predictions. This comprehensive review advances our understanding of lidocaine's pharmacokinetics and lays the groundwork for further research in this critical area of perioperative pain management. Review protocol registered on 25 August 2023 in PROSPERO (CRD42023441113). This work was supported by the Fundamental Research Grant Scheme, the Ministry of Higher Education, Malaysia (FRGS/1/2020/SKK01/UM/02/2).

Human milk is a remarkable biofluid that provides essential nutrients and immune protection to newborns. Breastfeeding women consuming medications could pass the drug through their milk to neonates. Drugs can be transferred to human milk by passive diffusion or active transport. The physicochemical properties of the drug largely impact the extent of drug transfer into human milk. A comprehensive understanding of the physiology of human milk formation, composition of milk, mechanisms of drug transfer, and factors influencing drug transfer into human milk is critical for appropriate selection and use of medications in lactating women. Quantification of drugs in the milk is essential for assessing the safety of pharmacotherapy during lactation. This can be achieved by developing specific, sensitive, and reproducible analytical methods using techniques such as liquid chromatography coupled with mass spectrometry. The present review briefly discusses the physiology of human milk formation, composition of human milk, mechanisms of drug transfer into human milk, and factors influencing transfer of drugs from blood to milk. We further expand upon and critically evaluate the existing analytical approaches/assays used for the quantification of drugs in human milk.

Background and objective: Posaconazole is a pharmacotherapeutic pillar for prophylaxis and treatment of invasive fungal diseases. Dose individualization is of utmost importance as achieving adequate antifungal exposure is associated with improved outcome. This study aimed to select and evaluate a model-informed precision dosing strategy for posaconazole.

Methods: Available population pharmacokinetic models for posaconazole administered as a solid oral tablet were extracted from the literature and evaluated using data from a previously published prospective study combined with data collected during routine clinical practice. External evaluation and selection of the most accurate and precise model was based on graphical goodness-of-fit and predictive performance. Measures for bias and imprecision included mean percentage error (MPE) and normalized relative root mean squared error (NRMSE), respectively. Subsequently, the best-performing model was evaluated for its a posteriori fit-for-purpose and its suitability in a limited sampling strategy.

Results: Seven posaconazole models were evaluated using 764 posaconazole plasma concentrations from 143 patients. Multiple models showed adequate predictive performance illustrated by acceptable goodness-of-fit and MPE and NRMSE below ± 10% and ± 25%, respectively. In the fit-for-purpose analysis, the selected model showed adequate a posteriori predictive performance. Bias and imprecision were lowest in the presence of two prior measurements. Additionally, this model showed to be useful in a limited sampling strategy as it adequately predicted total posaconazole exposure from one (non-)trough concentration.

Conclusion: We validated an MIPD strategy for posaconazole for its fit-for-purpose. Thereby, this study is an important first step towards MIPD-supported posaconazole dosage optimization with the goal to improve antifungal treatment in clinical practice.

Background and objective: The use of bedaquiline as a treatment option for drug-resistant tuberculosis meningitis (TBM) is of interest to address the increased prevalence of resistance to first-line antibiotics. To this end, we describe a whole-body physiologically based pharmacokinetic (PBPK) model for bedaquiline to predict central nervous system (CNS) exposure.

Methods: A whole-body PBPK model was developed for bedaquiline and its metabolite, M2. The model included compartments for brain and cerebrospinal fluid (CSF). Model predictions were evaluated by comparison to plasma PK time profiles following different dosing regimens and sparse CSF concentrations data from patients. Simulations were then conducted to compare CNS and lung exposures to plasma exposure at clinically relevant dosing schedules.

Results: The model appropriately described the observed plasma and CSF bedaquiline and M2 concentrations from patients with pulmonary tuberculosis (TB). The model predicted a high impact of tissue binding on target site drug concentrations in CNS. Predicted unbound exposures within brain interstitial exposures were comparable with unbound vascular plasma and unbound lung exposures. However, unbound brain intracellular exposures were predicted to be 7% of unbound vascular plasma and unbound lung intracellular exposures.

Conclusions: The whole-body PBPK model for bedaquiline and M2 predicted unbound concentrations in brain to be significantly lower than the unbound concentrations in the lung at clinically relevant doses. Our findings suggest that bedaquiline may result in relatively inferior efficacy against drug-resistant TBM when compared with efficacy against drug-resistant pulmonary TB.