This study aimed to establish a population pharmacokinetic (PopPK) model using data from 2 clinical trials of zimberelimab, evaluate the pharmacokinetics (PKs) of zimberelimab, explore the feasibility of 360 mg once every 3 weeks (Q3W) and 480 mg once every 4 weeks (Q4W) as alternative dosage regimens, and analyze the exposure-response relationship of the efficacy and safety of zimberelimab for advanced tumors. The PKs of zimberelimab were described using the 2-compartment model with time-dependent nonlinear elimination. The prediction-corrected visual predictive check was used to evaluate the model\'s predictive value on blood drug concentrations. In total, 2165 PK observations from 321 participants were included. The PopPK model demonstrated a high level of concordance between the observed data and the predicted values, indicative of a robust fit to the PK data of zimberelimab. The PK variables were similar for the 240 mg once every 2 weeks, 360 mg Q3W, and 480 mg Q4W regimens. No covariates significantly affecting the PK variables in the final model were found. The exposure variables of zimberelimab have no obvious correlations with efficacy and safety, and 360 mg Q3W and 480 mg Q4W are worthy of further study. This study establishes a PopPK model and analyzes the exposure-response relationship of zimberelimab, which helps to explore the potential for alternative dosing regimens and offers a foundation for optimizing therapeutic strategies for advanced cancer patients through simulation-based methods.

The dosing of fluconazole for young infants remains empirical because of the limited pharmacokinetic (PK) data. We aimed to establish a population PK model and assess the systematic exposure-response of commonly used regimens of fluconazole in Chinese infants. We included infants with a postnatal age of less than 120 days and received intravenous fluconazole. Both scheduled and scavenged plasma samples were collected, and fluconzaole concentration was determined by a validated ultra-performance liquid chromatography-tandem mass spectrometry assay. Population PK analysis was conducted using Phoenix NLME, and then Monte Carlo simulation was conducted to predict the probability of target attainment (PTA) of empirically used regimens of both prophylactic and therapeutic purposes. Based on 304 plasma samples from 183 young infants, fluconazole concentration data was best described by a one-compartment model with first-order elimination. Gestational Age (GA), postnatal age (PNA), and body weight (BW) were included in the final model as CL = 0.02*(GA/214)2.77*(PNA/13)0.24*exp(nCL); V = 1.56*(BW/1435)0.90*exp(nV). Model validation revealed the final model had qualified stability and acceptable predictive properties. Monte Carlo simulation indicated that under the same minimum inhibitory concentration (MIC) value and administration regimen, PTA decreased with GA and PNA. The commonly used prophylactic regimens can meet the clinical need, while higher doses might be needed for treatment of invasive candidiasis. This population PK model of fluconazole discriminated the impact of GA and PNA on CL and BW on V. Dosing adjustment was needed according to the GA and PNA of infants to achieve targeted exposures.

UNASSIGNED: The population pharmacokinetic (PPK) model-based machine learning (ML) approach offers a novel perspective on individual concentration prediction. This study aimed to establish a PPK-based ML model for predicting tacrolimus (TAC) concentrations in Chinese renal transplant recipients.
UNASSIGNED: Conventional TAC monitoring data from 127 Chinese renal transplant patients were divided into training (80%) and testing (20%) datasets. A PPK model was developed using the training group data. ML models were then established based on individual pharmacokinetic data derived from the PPK basic model. The prediction performances of the PPK-based ML model and Bayesian forecasting approach were compared using data from the test group.
UNASSIGNED: The final PPK model, incorporating hematocrit and CYP3A5 genotypes as covariates, was successfully established. Individual predictions of TAC using the PPK basic model, postoperative date, CYP3A5 genotype, and hematocrit showed improved rankings in ML model construction. XGBoost, based on the TAC PPK, exhibited the best prediction performance.
UNASSIGNED: The PPK-based machine learning approach emerges as a superior option for predicting TAC concentrations in Chinese renal transplant recipients.

OBJECTIVE: Several population pharmacokinetic models of tacrolimus in liver transplant patients were built, and their predictability was evaluated in their settings. However, the extrapolation in the prediction was unclear. This study aimed to evaluate the predictive performance of published tacrolimus models in adult liver transplant recipients using data from the Thai population as an external dataset.
METHODS: The selected published models were systematically searched and evaluated for their quality. The external dataset of patients who underwent the first liver transplant and received immediate-release tacrolimus was used to assess the predictive performance of each selected model. Trough concentrations between 3 and 6 months were retrospectively collected to evaluate the predictability of each model using prediction-based diagnostics, simulation-based diagnostics, and Bayesian forecasting.
RESULTS: Sixty-seven patients with 360 trough concentrations and eight selected published models were included in this study. None of the models met the predictive precision criteria in prediction-based diagnostics. Meanwhile, four published population pharmacokinetic models showed a normal distribution in NPDE testing. Regarding Bayesian forecasting, all models improved their forecasts with at least one prior information data point.
CONCLUSIONS: Bayesian forecasting is more accurate and precise than other testing methods for predicting drug concentrations. However, none of the evaluated models provides satisfactory predictive performance for generalization to Thai liver transplant patients. This underscores the need for future research to develop population PK models tailored to the Thai population. Such efforts should consider the inclusion of nonlinear pharmacokinetics and region-specific factors, including genetic variability, to improve model accuracy and applicability.

Clozapine is characterized by a large within- and between-patient variability in its pharmacokinetics, attributed to non-genetic and genetic factors. A cross-sectional analysis of clozapine trough concentration (Clz C0) issued from Tunisian schizophrenic patients was collected and analysed using a nonparametric modelling approach. We assessed the impact of demographic covariates (age, weight and sex), patient\'s habits (smoking status, alcohol and caffeine intake) and the genetic factors (CYP1A2*1C, CYP1A2*1F and CYP2C19*2 polymorphisms) on each pharmacokinetic parameter. An external validation of this pharmacokinetic model using an independent data set was performed. Fit goodness between observed- and individual-predicted data was evaluated using the mean prediction error (% MPE), the mean absolute prediction error (% MAPE) as a measure of bias, and the root mean squared error (% RMSE) as a measure of precision. Sixty-three CLz C0 values issued from 51 schizophrenic patients were assessed in this study and divided into building and validation groups. CYP1A2*1F polymorphism and smoking status were the only covariates significantly associated with clozapine clearance. Precision parameters were as follows: 1.02%, 0.95% and 22.4%, respectively, for % MPE, % MAPE and % RMSE. We developed and validated an accurate pharmacokinetic model able to predict Clz C0 in Tunisian schizophrenic patients using the two parameters CYP1A2*1F polymorphism and smoking.

A fixed-dose combination (FDC) of 50 mg dolutegravir and 300 mg lamivudine is indicated for the treatment of HIV-1 infection. This analysis aimed to characterize the population pharmacokinetics (PK) of dolutegravir and lamivudine based on data from a phase 3 study (TANGO) in virologically suppressed adults living with HIV-1 switching to dolutegravir/lamivudine FDC. These analyses included 362 participants who contributed 2,629 dolutegravir and 2,611 lamivudine samples collected over 48 weeks. A one-compartment model with first-order absorption and elimination parameterized by apparent oral clearance (CL/F), apparent volume of distribution (V/F), and absorption rate constant (Ka) described dolutegravir PK. Covariate search yielded body weight, bilirubin, and ethnicity as predictors of CL/F, and weight was predictive for V/F. The estimates of CL/F, V/F, and Ka were 0.858 L/h, 16.7 L, and 2.15 h-1, respectively. A two-compartment model with first-order absorption and elimination parameterized by CL/F, apparent intercompartmental clearance (Q/F), apparent central volume of distribution (V2/F), apparent peripheral volume of distribution (V3/F), and Ka described lamivudine PK. Covariate search yielded eGFR and race as predictors of CL/F, and weight was predictive for V2/F. The estimated parameter values were CL/F = 19.6 L/h, Q/F = 2.97 L/h, V2/F = V3/F = 105 L, and Ka = 2.30 h-1. The steady-state prediction suggested that the effect of covariates dolutegravir and lamivudine exposures was small (<20%) and not clinically relevant. Therefore, no dose adjustments are recommended based on these analyses. The results support the use of dolutegravir/lamivudine FDC in the treatment of HIV-1 infection in adults.CLINICAL TRIALSThis study is registered with ClinicalTrials.gov as NCT03446573.

The pharmacological activity of oxcarbazepine (OXC) is primarily exerted through its active 10-monohydroxy metabolite (MHD). Nonetheless, there is limited pharmacokinetic information available regarding paediatric patients with epilepsy treated with OXC, especially in infants and toddlers. Concurrently, this drug exhibits substantial variability in pharmacokinetics and therapeutic response across different individuals. We aimed to develop a model to quantitatively investigate factors that affect MHD pharmacokinetics to formulate a dosage guideline for OXC in Chinese paediatric patients. A total of 297 MHD trough concentrations were obtained from 287 epileptic children. Six body weight (BW)-based allometric models were used for population pharmacokinetic modelling, while investigating the impact of other covariates on the apparent clearance. The one-compartment model and age cut-off model for the apparent clearance (CL/F) were established to describe the pharmacokinetics of MHD. The probability to obtain target trough concentration ranges (TTCRs) of MHD between 3 and 35 mg/L was determined by Monte Carlo simulations for doses ranging from 8 to 90 mg/kg/day. A new dose optimization strategy combining the dosage guidelines and Bayesian method provides a tailored approach for Chinese paediatric epileptic patients based on their individual BW and desired TTCRs of MHD, and also supports current dose recommendations, with the exception of children weighing ≤5 kg.

OBJECTIVE: Methotrexate is widely utilized in the chemotherapy of malignant tumors and autoimmune diseases in the pediatric population, but dosing can be challenging. Several population pharmacokinetic models were developed to characterize factors influencing variability and improve individualization of dosing regimens. However, significant covariates included varied across studies. The primary objective of this review was to summarize and discuss population pharmacokinetic models of methotrexate and covariates that influence pharmacokinetic variability in pediatric patients.
METHODS: Systematic searches were conducted in the PubMed and EMBASE databases from inception to 7 July 2023. Reporting Quality was evaluated based on a checklist with 31 items. The characteristics of studies and information for model construction and validation were extracted, summarized, and discussed.
RESULTS: Eighteen studies (four prospective studies and fourteen retrospective studies with sample sizes of 14 to 772 patients and 2.7 to 93.1 samples per patient) were included in this study. Two-compartment models were the commonly used structural models for methotrexate, and the clearance range of methotrexate ranged from 2.32 to 19.03 L/h (median: 6.86 L/h). Body size and renal function were found to significantly affect the clearance of methotrexate for pediatric patients. There were limited reports on the role of other covariates, such as gene polymorphisms and co-medications, in the pharmacokinetic parameters of methotrexate pediatric patients. Internal and external evaluations were used to assess the performance of the population pharmacokinetic models.
CONCLUSIONS: A more rigorous external evaluation needs to be performed before routine clinical use to select the appropriate PopPK model. Further research is necessary to incorporate larger cohorts or pool analyses in specific susceptible pediatric populations to improve the understanding of predicted exposure profiles and covariate identification.

The objective of this project is to compare the results of the same study carried out on NONMEM and nlmixr2. This analysis consists of evaluating previously published population pharmacokinetic models of gentamicin and tobramycin in our population of interest with sparse concentrations. A literature review was performed to determine the gentamicin and tobramycin models in critically ill adult patients. In parallel, gentamicin and tobramycin dosing data, information on the treatment, the patient, and the bacteria were collected retrospectively in 2 Quebec establishments. The external evaluations were previously performed using NONMEM Version 7.5. Model equations were rewritten with R, and external evaluations were performed using nlmixr2. Predictive performance was assessed based on the estimation of bias and imprecision of the prediction error for maximum a posteriori (MAP) Bayesian PK parameter and observed concentrations. Comparison between nlmixr2 and NONMEM was performed on 4 gentamicin and 3 tobramycin population pharmacokinetic models. Compared to NONMEM, for gentamicin and tobramycin clearance and central volume of distribution, nlmixr2 produced individual pharmacokinetic parameters with bias values ranging from -32.5% to 5.67% and imprecision values ranging from 6.33% to 32.5%. Despite these differences, population bias and imprecision for sparse concentrations were low and ranged from 0% to 5.3% and 0.2% to 6.5%, respectively. The external evaluations performed with both software packages resulted in the same interpretation in terms of population predictive performance for all 7 models. Nlmxir2 showed comparable predictive performance with NONMEM with sparse concentrations that are, at most, sampled twice within a single dose administration (peak and trough).

Imipenem is a broad-spectrum antibiotic that has been used in treating severe infections and exhibits a time-dependent PK/PD profile. Its dose should be adjusted based on renal function. However, there is little experience with imipenem dosing in obese adolescent patients with augmented renal clearance (ARC) and history of schizophrenia. This case reported successful dosing of imipenem in an obese adolescent patient with ARC based on therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD). A 15-year-old male adolescent patient with history of schizophrenia was diagnosed with ventilator-associated pneumonia due to carbapenem-susceptible Klebsiella pneumoniae and received imipenem treatment (0.5 g every 8 hours with a 1-hour infusion). However, the exposure of imipenem was suboptimal due to ARC, and there is no available model for MIPD in this patient. Thus, we utilized prediction error to find a population pharmacokinetic model that fit this patient and ran Maximum a posteriori Bayesian estimation and Monte Carlo simulation based on screened models to predict changes in drug concentrations. The dose of imipenem was adjusted to 0.5 g every 6 hours with a 2-hour infusion, and subsequent TDM revealed that dosing adjustment was accurate and successful. Finally, the patient\'s status of infection improved. This study will be beneficial to imipenem dosing in similar cases in the future, thereby improving the safety and effectiveness of imipenem or other antibiotics.