The Journal of Clinical Pharmacology最新文献

Dosing vancomycin for critically ill neonates is challenging owing to substantial alterations in pharmacokinetics (PKs) caused by variability in physiology, disease, and clinical interventions. Therefore, an adequate PK model is needed to characterize these pathophysiological changes. The intent of this study was to develop a physiologically based pharmacokinetic (PBPK) model that reflects vancomycin PK and pathophysiological changes in neonates under intensive care. PK-sim software was used for PBPK modeling. An adult model (model 0) was established and verified using PK profiles from previous studies. A neonatal model (model 1) was then extrapolated from model 0 by scaling age-dependent parameters. Another neonatal model (model 2) was developed based not only on scaled age-dependent parameters but also on quantitative information on pathophysiological changes obtained via a comprehensive literature search. The predictive performances of models 1 and 2 were evaluated using a retrospectively collected dataset from neonates under intensive care (chictr.org.cn, ChiCTR1900027919), comprising 65 neonates and 92 vancomycin serum concentrations. Integrating literature-based parameter changes related to hypoalbuminemia, small-for-gestational-age, and co-medication, model 2 offered more optimized precision than model 1, as shown by a decrease in the overall mean absolute percentage error (50.6% for model 1; 37.8% for model 2). In conclusion, incorporating literature-based pathophysiological changes effectively improved PBPK modeling for critically ill neonates. Furthermore, this model allows for dosing optimization before serum concentration measurements can be obtained in clinical practice.

This study aimed to analyze the incidence, clinical characteristics, and risk factors of moxifloxacin-related arrhythmias and electrocardiographic alterations in hospitalized patients using real-world data. Concurrently, a nomogram was established and validated to provide a practical tool for prediction. Retrospective automatic monitoring of inpatients using moxifloxacin was performed in a Chinese hospital from January 1, 2017, to December 31, 2021, to obtain the incidence of drug-induced arrhythmias and electrocardiographic alterations. Propensity score matching was conducted to balance confounders and analyze clinical characteristics. Based on the risk and protective factors identified through logistic regression analysis, a prediction nomogram was developed and internally validated using the Bootstrap method. Arrhythmias and electrocardiographic alterations occurred in 265 of 21,711 cases taking moxifloxacin, with an incidence of 1.2%. Independent risk factors included medication duration (odds ratio [OR] 1.211, 95% confidence interval [CI] 1.156-1.270), concomitant use of meropenem (OR 4.977, 95% CI 2.568-9.644), aspartate aminotransferase >40 U/L (OR 3.728, 95% CI 1.800-7.721), glucose >6.1 mmol/L (OR 2.377, 95% CI 1.531-3.690), and abnormally elevated level of amino-terminal brain natriuretic peptide precursor (OR 2.908, 95% CI 1.640-5.156). Concomitant use of cardioprotective drugs (OR 0.430, 95% CI 0.220-0.841) was a protective factor. The nomogram showed good differentiation and calibration, with enhanced clinical benefit. The incidence of moxifloxacin-related arrhythmias and electrocardiographic alterations is in the range of common. The nomogram proves valuable in predicting the risk in the moxifloxacin-administered population, offering significant clinical applications.

We conducted this three-part study in healthy subjects to investigate the pharmacokinetics of tasurgratinib (orally available selective inhibitor of fibroblast growth factor receptor 1-3) and M2 (its major metabolite) under different conditions. In Part A, subjects received tasurgratinib 35 mg either fed with a high-fat meal or fasted. In Parts B and C, subjects received tasurgratinib 35 mg alone or with either rabeprazole (acid-reducing agent) 20 mg (Part B) or rifampin (strong CYP3A inducer) 600 mg (Part C). Primary endpoints were maximum concentration (C_max), and areas under the plasma concentration-time curve to time of last quantifiable concentration (AUC_(0-t)) and extrapolated to infinite time (AUC_(0-inf)). Forty-two subjects were enrolled, 14 each into Parts A, B, and C. In Part A, administration of tasurgratinib with a high-fat meal resulted in 33% reduction in C_max and ∼23% reduction in AUC_(0-t) and AUC_(0-inf) of tasurgratinib, and 47% reduction in C_max with ∼30% reduction in AUC_(0-t) and AUC_(0-inf) of M2. In Part B, co-administration of rabeprazole at steady state resulted in no/weak interaction with tasurgratinib (∼8% increase in AUC_(0-t) and AUC_(0-inf) without an effect on C_max) and M2 (∼18% increase in AUC_(0-t) and AUC_(0-inf) without an effect on C_max). In Part C, co-administration of rifampin at steady state resulted in a weak interaction with tasurgratinib (∼16% reduction in AUC_(0-t) and AUC_(0-inf)) and M2 (∼12% reduction in AUC_(0-t) and AUC_(0-inf)). Administration of tasurgratinib with a high-fat meal appeared to reduce systemic exposure of tasurgratinib, however co-administration with an acid-reducing agent or a CYP3A inducer had a minimal impact on pharmacokinetics.

A systematic literature review and meta-analysis was performed to evaluate the effects of dapagliflozin on low-density lipoprotein (LDL) cholesterol in type 2 diabetes mellitus. Data on changes in LDL cholesterol, adverse cardiac events (ACEs), glycated hemoglobin (HbA1c), and fasting blood glucose (FBG) were pooled in a meta-analysis. Data from dose comparison trials were separately pooled, and meta-analysis was conducted by using RevMan (5.4.1) and R (4.1.2). Dapagliflozin increased LDL cholesterol by 2.33 mg/dL (95% CI, 1.46 to 3.19; I² = 0%; P < .00001), increased risk of ACEs by 1.56 (95% CI, 1.02 to 2.39; I² = 0%; P < .04), decreased HbA1c by −0.41% (95% CI, −0.44 to −0.39; I² = 85%; P < .00001), and decreased FBG by −13.51 mg/dL (95% CI, −14.43 to −12.59; I² = 92%; P < .00001) versus any placebo or active comparator. Dapagliflozin 10 mg monotherapy increased LDL cholesterol by 1.71 mg/dL (95% CI, −1.20 to 4.62; I² = 53%; P = .25) versus a 5 mg dose and by 1.04 mg/dL (95% CI, −1.17 to 3.26; I² = 62%; P = .36) versus a 2.5 mg dose. Dapagliflozin 10 mg monotherapy increased LDL cholesterol by 3.13 mg/dL (95% CI, 1.31 to 4.95; I² = 0%; P = .0008), increased the risk of ACEs by 1.26 (95% CI, 0.56 to 2.87; I² = 0%; P = .58), decreased HbA1c by −0.4% (95% CI, −0.45 to −0.35; I² = 89%; P < .00001), and decreased FBG by −8.39 mg/dL (95% CI, −10 to −6.77; I² = 96%; P < .00001) versus a placebo or active comparator. Dapagliflozin monotherapy resulted in a minimal but statistically significantly (P = .0002) increase in LDL cholesterol. However, this minor change does not increase the risk of ACEs (P = .17) when compared with placebo or active comparator.

Our goal is to create a population pharmacokinetic (PK) model and identify the best loading dose (LD) of intravenous valproic acid for hospitalized Thai patients. Data from patients who received intravenous valproic acid and underwent measurement of serum valproic acid concentrations during hospitalization were collected retrospectively. A nonlinear mixed-effects modeling approach was conducted to estimate the PK parameters of valproic acid. Covariates affecting the PK parameters of valproic acid were examined and ranked based on their impact on the model's performance. Monte Carlo simulations of 1000 patients were conducted to estimate the optimal LD of valproic acid. A total of 120 hospitalized patients (51.7% male) with 167 valproic acid concentrations were included in the study. A linear one-compartment model with constant residual error was the best base model. An age-covariate model was the best predictor of valproic acid clearance (CL). The typical values of CL and volume of distribution for valproic acid were 0.77 L/h and 14.56 L, respectively. The LD of 1000-1200 mg intravenous was identified as the pragmatic option as an empirical regimen for hospitalized Thai patients. The recommended time to initiate maintenance dose (MD) is 4-8 h following the LD. The population PK model and optimal LD of valproic acid in hospitalized Thai patients has been established, and it may be advisable to initiate the MD at a later time for the elderly.

Valbenazine is a highly potent and selective inhibitor of synaptic vesicular monoamine transporter 2. The current therapeutic doses of valbenazine for tardive dyskinesia (TD) are 40, 60, or 80 mg capsules, given orally, once daily (QD). While 40 and 80 mg were investigated in phase 3 KINECT^® 3 trial and initially approved, the approval of valbenazine 60 mg was based on the analysis utilizing the Model-informed drug development (MIDD) approach, facilitated through the US Food and Drug Administration's MIDD Pilot Program. This study aimed to demonstrate the efficacy of 60 mg QD dose through model simulations using an established exposure-response (E-R) relationship between valbenazine active metabolite [+]-α-dihydrotetrabenazine exposure and the change from baseline in Abnormal Involuntary Movement Scale total score (AIMS-CFB). A longitudinal E–R model was constructed based on the 40 and 80 mg data from the KINECT 3 trial. The final E_max model adequately predicted dose-dependent improvement in the primary endpoint and was used to interpolate AIMS-CFB for 60 mg at week 6. The efficacy of the unstudied 60 mg dose regimen is expected to be within the range of doses studied clinically with predicted mean AIMS-CFB (95% confidence interval) of −2.69 (−3.30, −2.13) between observed mean AIMS-CFB for 40 mg of −1.92 and 80 mg of −3.39. Results from this analysis provided the key evidence to establish efficacy of 60 mg QD without the need for an additional clinical trial. The availability of valbenazine 60 mg dose fills an existing medical need for patients with TD who could benefit from this third effective dose.

Adavosertib (AZD1775) is a potent small-molecule inhibitor of Wee1 kinase. This analysis utilized pharmacokinetic data from 8 Phase I/II studies of adavosertib to characterize the population pharmacokinetics of adavosertib in patients (n = 538) with solid tumors and evaluate the impact of covariates on exposure. A nonlinear mixed-effects modeling approach was employed to estimate population and individual parameters from the clinical trial data. The model for time dependency of apparent clearance (CL) was developed in a stepwise manner and the final model validated by visual predictive checks (VPCs). Using an adavosertib dose of 300 mg once daily on a 5 days on/2 days off dosing schedule given 2 weeks out of a 3-week cycle, simulation analyses evaluated the impact of covariates on the following exposure metrics at steady state: maximum concentration during a 21-day cycle, area under the curve (AUC) during a 21-day cycle, AUC during the second week of a treatment cycle, and AUC on day 12 of a treatment cycle. The final model was a linear 2-compartment model with lag time into the dosing compartment and first-order absorption into the central compartment, time-varying CL, and random effects on all model parameters. VPCs and steady-state observations confirmed that the final model satisfied all the requirements for reliable simulation of randomly sampled Phase I and II populations with different covariate characteristics. Simulation-based analyses revealed that body weight, renal impairment status, and race were key factors determining the variability of drug-exposure metrics.

The World Health Organization has recommended the use of dolutegravir (DTG) for both first and second-line antiretroviral treatment in both adults and children down to 4 weeks of age. We developed a population pharmacokinetic(PopPK) model following oral administration of DTG 50 mg QD and 50 mg BID in HIV-infected treatment-experienced adults (607) based on pooled data from four phase 2/3 trials. DTG population pharmacokinetics are described by a one-compartment model with first-order absorption, absorption lag-time, and first-order elimination. The PopPK parameter estimates were apparent oral clearance (CL/F) = 1.00 L/h, apparent volume of distribution (V/F) = 18.9 L, absorption rate constant (Ka) = 1.99 per hour, and absorption lag time = 0.333 h, respectively. The final model included inter-individual and inter-occasion variability on apparent clearance (CL/F). Weight, smoking status, use of metabolic inducers as part of background antiretroviral therapy (ART) classified by their level of induction, use of atazanavir or atazanavir-ritonavir as part of background ART, and albumin level were predictors of CL/F; weight and albumin level were predictors of V/F; and sex and concomitant use of metal cation-containing vitamin/mineral supplements were predictors of relative bioavailability (F). The current model-based analysis suggests that the DTG dose adjustment is not required based on the demographics, laboratory values, smoking status, concomitant use of mild metabolic inducers or inhibitors in the background therapy, or use of metal cation-containing vitamin/mineral supplements because these covariate effects are not predicted to have a clinically relevant impact on safety and efficacy.

In classic galactosemia (CG) patients, aldose reductase (AR) converts galactose to galactitol. In a phase 1/2, placebo-controlled study (NCT04117711), safety, pharmacokinetics (PK), and pharmacodynamics (PD) of govorestat were evaluated after single and multiple ascending doses (0.5-40 mg/kg) in healthy adults (n = 81) and CG patients (n = 14). Levels of govorestat in plasma and cerebrospinal fluid (CSF) and blood levels of galactitol, galactose, and galactose-1-phosphate (Gal-1p) were measured for population PK and PK/PD analyses. Govorestat was well tolerated. Adverse event frequency was comparable between placebo and govorestat. Govorestat PK displayed a 2-compartment model with sequential zero- and first-order absorption, and no effect of demographic factors. Multiple-dose PK of govorestat was linear in the 0.5-40 mg/kg range, and CSF levels increased dose dependently. Elimination half-life was ∼10 h. PK/PD modeling supported once-daily dosing. Change from baseline in galactitol was −15% ± 9% with placebo and −19% ± 10%, −46% ± 4%, and −51% ± 5% with govorestat 5, 20, and 40 mg/kg, respectively, thus was similar for 20 and 40 mg/kg. Govorestat did not affect galactose or Gal-1p levels. In conclusion, govorestat displayed a favorable safety, PK, and PD profile in humans, and reduced galactitol levels in the same magnitude (∼50%) as in a rat model of CG that demonstrated an efficacy benefit on neurological, behavioral, and ocular outcomes.

As detailed information on the pharmacokinetics (PK) of labetalol in pregnant people are lacking, the aims of this study were: (1) to build a physiologically based PK (PBPK) model of labetalol in non-pregnant individuals that incorporates different CYP2C19 genotypes (specifically, *1/*1, *1/*2 or *3, *2/*2, and *17/*17); (2) to translate this model to the second and third trimester of pregnancy; and (3) to combine the model with a previously published direct pharmacodynamic (PD) model to predict the blood pressure lowering effect of labetalol in the third trimester. Clinical data for model evaluation was obtained from the scientific literature. In non-pregnant populations, the mean ratios of simulated versus observed peak concentration (C_max), time to reach C_max (T_max), and exposure (area under the plasma concentration–time curve, AUC) were 0.94, 0.82, and 1.16, respectively. The pregnancy PBPK model captured the observed PK adequately, but clearance was slightly underestimated with mean ratios of simulated versus observed C_max, T_max, and AUC of 1.28, 1.30, and 1.39, respectively. The results suggested that pregnant people with CYP2C19 *2/*2 alleles have similar labetalol exposure and trough levels compared to non-pregnant controls, whereas those with other alleles were found to have increased exposure and trough concentrations. Importantly, the pregnancy PBPK/PD model predicted that, despite increased exposure in some genotypes, the blood pressure lowering effect was broadly comparable across all genotypes. In view of the large inter-individual variability and the potentially increasing blood pressure during pregnancy, patients may need to be closely monitored for achieving optimal therapeutic effects and avoiding adverse events.