Population Pharmacokinetic Parameters Research Articles

Pharmacokinetics of Vancomycin in Healthy Korean Volunteers and Monte Carlo Simulations to Explore Optimal Dosage Regimens in Patients with Normal Renal Function.

Background/Objectives: To date, population pharmacokinetic (PK) studies of vancomycin on healthy Korean adults have not been conducted. This study aimed to investigate the PK properties of vancomycin in healthy volunteers and to identify optimal dosing regimens based on the area under the concentration-time curve (AUC) in adult patients with normal renal function. Methods: We conducted a prospective clinical study, analysing PK samples from 12 healthy participants using noncompartmental analysis and non-linear mixed-effects modelling. The population PK parameters derived were employed in Monte Carlo simulations to evaluate the adequacy of the current dosing regimen and to formulate dosing recommendations. Results: The PK profiles were optimally described by a two-compartment model, with body weight and age as significant covariates affecting total clearance. The simulations indicated that to achieve a therapeutic target-defined as an AUC at steady-state over 24 h of 400-600 mg·h/L-daily doses ranging from 60 to 70 mg/kg are necessary in adults with normal renal function. Conclusions: This study underscores the need to actively adjust dosage and administration based on a vancomycin PK model that adequately reflects the demographic characteristics of patients to meet both safety and efficacy standards.

Are We Dosing Correctly? Population Pharmacokinetics of Cefepime in Critically Ill Pediatric Patients

Abstract Background Dosing guidelines of cefepime are well defined in healthy pediatric and adult populations but have not been extensively studied in critically ill pediatric patients. Patients who are critically ill may have significant alterations in antibiotic pharmacokinetics (PK) and pharmacodynamics (PD) due to many factors. Receipt of ECMO or CRRT may further alter PK/PD due to changes in clearance and volume of distribution and the depletion of plasma proteins. Since critically ill pediatric patients are likely to receive multiple courses of antimicrobials, optimization of drug dosing and delivery is critically important for individual patient outcomes. This study will test the hypothesis that the PK of cefepime is substantially altered in critically ill pediatric patients. Methods We prospectively enrolled patients in VUMC pediatric ICUs receiving cefepime, including those receiving ECMO or CRRT. Plasma samples were collected at opportune timepoints enriched for specific PK/PD metrics. Cefepime plasma concentrations were measured by LC-MS/MS. Weight, dose, interval, and administration time were extracted from the medical record. Monolix was used to fit population PK models using one and two-compartment models with a proportional error model. Covariate modeling was performed with a priori selected covariates: ECMO, CRRT, sex, race, ethnicity, gestational age, and creatinine clearance (CrCl). The final covariate model was used to perform probability of target attainment (PTA) analysis using Monte Carlo simulation. 6,000 subjects were simulated for each dosing regimen, with 3 weight categories (≤9 kg, 9-25 kg, and >25 kg) and 2 CrCl categories (≤90 mL/min and >90 mL/min). PTA was calculated over a grid of possible MIC values based on three targets, 65% fT>MIC, 100% fT>MIC, and 100% fT>4×MIC. Results Preliminary data from 84 pediatric participants receiving cefepime were analyzed. Of these, 9 received CRRT and 10 received ECMO. An average of 3 samples were collected per participant. A two compartment with proportional error model was selected as the base model. Population PK parameter estimates for the base model, base model with weight, and final covariate model were calculated. Inclusion of weight and CrCl significantly improved model fit. CRRT, ethnicity, and gestational age did significantly improve model fit, but their inclusion in the model did not provide evidence of being significantly better than the model with only weight and CrCl. The results from the PTA analysis are seen in Figure 1. We observed an overall ordering of PTA by dosing regimen across all three targets, with q8h 3-hour infusion having the highest PTA and q12h 30-minute infusion having the lowest PTA. Increased CrCl led to reduced PTA and as weight increased, PTA also increased. Conclusion This study will be among the first and largest to characterize cefepime PK in the critically ill pediatric population. Clinicians should incorporate local antibiograms with these population PK models to determine optimal dosing in critically ill pediatric patients. The use of extended infusions may be beneficial for PK/PD target achievement, particularly for resistant pathogens. Since clinical covariates affect PK parameters, this population may also benefit from therapeutic drug monitoring. Figure 1. Comparison of PTA among four dosing regimens of cefepime at targets of 65% fT>MIC, 100% fT>MIC, and 100% fT>4×MIC.

Population Pharmacokinetic Model of Intravenous Immunoglobulin in Patients Treated for Various Immune System Disorders

Intravenous immunoglobulin (IVIG) is used to treat various immune system disorders, but the factors influencing its disposition are not well understood. This study aimed to estimate the population pharmacokinetic parameters of IVIG and to investigate the effect of genetic polymorphism of the FCGRT gene encoding the neonatal Fc receptor (FcRn) and clinical variability on the pharmacokinetic properties of IVIG in patients with immune system disorders. Patients were recruited from 4 hospitals in Malaysia. Clinical data were recorded, and blood samples were taken for pharmacokinetic and genetic studies. Population pharmacokinetic parameters were estimated by nonlinear mixed-effects modeling in Monolix. Age, weight, baseline immunoglobulin G concentration, ethnicity, sex, genotype, disease type, and comorbidity were investigated as potential covariates. Models were evaluated using the difference in objective function value, goodness-of-fit plots, visual predictive checks, and bootstrap analysis. A total of 292 blood samples were analyzed from 79 patients. The IVIG concentrations were best described by a 2-compartment model with linear elimination. Weight was found to be an important covariate for volume of distribution in the central compartment (Vc), volume of distribution in the peripheral compartment (Vp), and clearance in the central compartment, whereas disease type was found to be an important covariate for Vp. Goodness-of-fit plots indicated that the model fit the data adequately. Genetic polymorphism of the FCGRT gene encoding the neonatal Fc receptor did not affect the pharmacokinetic properties of IVIG. This study supports the use of dosage based on weight as per current practice. The study findings highlight that Vp is significantly influenced by the type of disease being treated with IVIG. This relationship suggests that different disease types, particularly inflammatory and autoimmune conditions, may alter tissue permeability and fluid distribution due to varying degrees of inflammation. Increased inflammation can lead to enhanced permeability and retention of IVIG in peripheral tissues, reflecting higher Vp values.

Hierarchical deep compartment modeling: A workflow to leverage machine learning and Bayesian inference for hierarchical pharmacometric modeling.

Population pharmacokinetic (PK) modeling serves as the cornerstone for understanding drug behavior within a specific population. It integrates subject covariates to elucidate the variability in PK parameters, thus enhancing predictive accuracy. However, covariate modeling within this framework can be intricate and time-consuming due to the often obscure structural relationship between covariates and PK parameters. Previous attempts, such as deep compartment modeling (DCM), aimed to streamline this process using machine learning techniques. Nonetheless, DCM fell short in assessing residual errors and interindividual variability (IIV), potentially leading to model misspecification and overfitting. Furthermore, DCM lacked the ability to quantify model uncertainty. To address these limitations, we introduce hierarchical deep compartment modeling (HDCM) as an advancement of DCM. HDCM harnesses machine learning to discern the interplay between covariates and PK parameters while simultaneously evaluating diverse levels of random effects and quantifying uncertainty through Bayesian inference. This tutorial provides a comprehensive application of the HDCM workflow using open-source Julia tools.

Handling delayed or missed direct oral anticoagulant doses: model-informed individual remedial dosing

AbstractNonadherence to direct oral anticoagulant (DOAC) pharmacotherapy may increase the risk of thromboembolism or bleeding, and delayed or missed doses are the most common types of nonadherence. Current recommendations from regulatory agencies or guidelines regarding this issue lack evidence and fail to consider individual differences. This study aimed to develop individual remedial dosing strategies when the dose was delayed or missed for DOACs, including rivaroxaban, apixaban, edoxaban, and dabigatran etexilate. Remedial dosing regimens based on population pharmacokinetic (PK)-pharmacodynamic (PD) modeling and simulation strategies were developed to expeditiously restore drug concentration or PD biomarkers within the therapeutic range. Population PK-PD characteristics of DOACs were retrieved from previously published literature. The effects of factors that influence PK and PD parameters were assessed for their impact on remedial dosing regimens. A web-based dashboard was established with R-shiny to recommend remedial dosing regimens based on patient traits, dosing schedules, and delay duration. Addressing delayed or missed doses relies on the delay time and specific DOACs involved. Additionally, age, body weight, renal function, and polypharmacy may marginally affect remedial strategies. The proposed remedial dosing strategies surpass current recommendations, with less deviation time beyond the therapeutic range. The online dashboard offers quick and convenient solutions for addressing missed or delayed DOACs, enabling individualized remedial dosing strategies based on patient characteristics to mitigate the risks of bleeding and thrombosis.

Prediction of teicoplanin plasma concentration in critically ill patients: a combination of machine learning and population pharmacokinetics.

Teicoplanin has been widely used in patients with infections caused by Staphylococcus aureus, especially for critically ill patients. The pharmacokinetics (PK) of teicoplanin vary between individuals and within the same individual. We aim to establish a prediction model via a combination of machine learning and population PK (PPK) to support personalized medication decisions for critically ill patients. A retrospective study was performed incorporating 33 variables, including PPK parameters (clearance and volume of distribution). Multiple algorithms and Shapley additive explanations were employed for feature selection of variables to determine the strongest driving factors. The performance of each algorithm with PPK parameters was superior to that without PPK parameters. The composition of support vector regression, categorical boosting and a backpropagation neural network (7:2:1) with the highest R2 (0.809) was determined as the final ensemble model. The model included 15 variables after feature selection, of which the predictive performance was superior to that of models considering all variables or using only PPK. The R2, mean absolute error, mean squared error, absolute accuracy (±5 mg/L) and relative accuracy (±30%) of external validation were 0.649, 3.913, 28.347, 76.12% and 76.12%, respectively. Our study offers a non-invasive, fast and cost-effective prediction model of teicoplanin plasma concentration in critically ill patients. The model serves as a fundamental tool for clinicians to determine the effective plasma concentration range of teicoplanin and formulate individualized dosing regimens accordingly.

A Meta-Analysis Methodology in Stan to Estimate Population Pharmacokinetic Parameters from Multiple Aggregate Concentration-Time Datasets: Application to Gevokizumab mPBPK Model.

The aim of the present study was to develop and evaluate the performance of a methodology to estimate the population pharmacokinetic (PK) parameters along with the inter-individual variabilities (IIVs) from patients' reported aggregate concentration-time data, in particular, mean plasma concentrations and their standard deviations (SDs) versus time, such as those often found in published graphs. This method was applied to the published data of gevokizumab, a novel monoclonal anti-interleukin-1β antibody, in order to estimate the drug's population pharmacokinetic (PopPK) parameters of a second-generation minimal physiologically based pharmacokinetic (mPBPK) model. Assuming this mPBPK model, a mixed effects approach was utilized to allow accounting for the random inter-group variability (IGV) that was assumed among different dosage groups. The entire analysis was performed using R software (Rstudio) and the Bayesian software tool RStan was used for the application of Bayesian priors on the parameters. Conclusively, the proposed method could be applied to monoclonal antibodies for which the second-generation mPBPK model has been proposed as well as to other drugs with different PK models when only a published graph with aggregate concentration-time data is available. In addition, the method could be used when multiple aggregate datasets from different sources need to be combined in a meta-analysis approach in order to estimate the PopPK parameters of a drug.

Pharmacokinetic Models of Tafenoquine: Insights for Optimal Malaria Treatment Strategies.

Tafenoquine (TQ) is a new 8-aminoquinoline antimalarial drug developed by the US Army for Plasmodium vivax malaria treatment. Modeling and simulation are essential tools for drug development and improving rationality in pharmacotherapy, and different modeling approaches are used. This study aims to summarize and explore the pharmacokinetic (PK) models available for tafenoquine in the literature. An integrative methodology was used to collect and review published data. Fifteen articles were identified using three modeling approaches: non-compartmental analysis (NCA), population pharmacokinetic analysis (popPK), and pharmacokinetic/pharmacodynamic analysis (PK/PD). An NCA was mainly used to describe the PK profile of TQ and to compare its PK profile alone to those obtained in association with other drugs. PopPK was used to assess TQ population PK parameters, covariates' impact, and dose selection. PK/PD helped understand the relationship between TQ concentrations, some adverse events common for 8-aminoquilones, and the efficacy assessment for Plasmodium falciparum. In summary, pharmacokinetic models were widely used during TQ development. However, there is still a need for different modeling approaches to support further therapeutic questions, such as treatment for special populations and potential drug-drug interactions.

Evaluation and optimization of sample size of neonates and infants for pediatric clinical studies on cefiderocol using a model-based approach.

When planning pediatric clinical trials, optimizing the sample size of neonates/infants is essential because it is difficult to enroll these subjects. In this simulation study, we evaluated the sample size of neonates/infants using a model-based optimal approach for identifying their pharmacokinetics for cefiderocol. We assessed the usefulness of data for estimation performance (accuracy and variance of parameter estimation) from adults and the impact of data from very young subjects, including preterm neonates. Stochastic simulation and estimation were utilized to assess the impact of sample size allocation for age categories in estimation performance for population pharmacokinetic parameters in pediatrics. The inclusion of adult pharmacokinetic information improved the estimation performance of population pharmacokinetic parameters as the coefficient of variation (CV) range of parameter estimation decreased from 4.9%-593.7% to 2.3%-17.3%. When sample size allocation was based on the age groups of gestational age and postnatal age, the data showed 15 neonates/infants would be necessary to appropriately estimate pediatric pharmacokinetic parameters (<20%CV). By using the postmenstrual age (PMA), which is theoretically considered to be associated with the maturation of organs, the number of neonates/infants required for appropriate parameter estimation could be reduced to seven (one and six with <32 and >32 weeks PMA, respectively) to nine (three and six with <37 and >37 weeks PMA, respectively) subjects. The model-based optimal design approach allowed efficient evaluation of the sample size of neonates/infants for estimation of pediatric pharmacokinetic parameters. This approach to assessment should be useful when designing pediatric clinical trials, especially those including young children.

Simulation-based evaluation of the Pharmpy Automatic Model Development tool for population pharmacokinetic modeling in early clinical drug development.

The Pharmpy Automatic Model Development (AMD) tool automates the building of population pharmacokinetic (popPK) models by utilizing a systematic stepwise process. In this study, the performance of the AMD tool was assessed using simulated datasets. Ten true models mimicking classical popPK models were created. From each true model, dataset replicates were simulated assuming a typical phase I study design-single and multiple ascending doses with/without dichotomous food effect, with rich PK sampling. For every dataset replicate, the AMD tool automatically built an AMD model utilizing NONMEM for parameter estimation. The AMD models were compared to the true and reference models (true model fitted to simulated datasets) based on their model components, predicted population and individual secondary PK parameters (SP) (AUC0-24, cmax, ctrough), and model quality metrics (e.g., model convergence, parameter relative standard errors (RSEs), Bayesian Information Criterion (BIC)). The models selected by the AMD tool closely resembled the true models, particularly in terms of distribution and elimination, although differences were observed in absorption and inter-individual variability components. Bias associated with the derived SP was low. In general, discrepancies between AMD and true SP were also observed for reference models and therefore were attributed to the inherent stochasticity in simulations. In summary, the AMD tool was found to be a valuable asset in automating repetitive modeling tasks, yielding reliable PK models in the scenarios assessed. This tool has the potential to save time during early clinical drug development that can be invested in more complex modeling activities within model-informed drug development.

Model-informed dose optimization of mycophenolic acid in pediatric kidney transplant patients

PurposeWe aimed to develop and evaluate a population PK model of mycophenolic acid (MPA) in pediatric kidney transplant patients to aid MPA dose optimization.MethodsData were collected from pediatric kidney transplant recipients from a Dutch academic hospital (Radboudumc, the Netherlands). Pharmacokinetic model-building and model-validation analyses were performed using NONMEM. Subsequently, we externally evaluated the final model using data from another academic hospital. The final model was used to develop an optimized dosing regimen.ResultsThirty pediatric patients were included of whom 266 measured MPA plasma concentrations, including 20 full pharmacokinetic (PK) curves and 24 limited sampling curves, were available. A two-compartment model with a transition compartment for Erlang-type absorption best described the data. The final population PK parameter estimates were Ktr (1.48 h−1; 95% CI, 1.15–1.84), CL/F (16.0 L h−1; 95% CI, 10.3–20.4), Vc/F (24.9 L; 95% CI, 93.0–6.71E25), Vp/F (1590 L; 95% CI, 651–2994), and Q/F (36.2 L h−1; 95% CI, 9.63–74.7). The performance of the PK model in the external population was adequate. An optimized initial dose scheme based on bodyweight was developed. With the licensed initial dose, 35% of patients were predicted to achieve the target AUC, compared to 42% using the optimized scheme.ConclusionWe have successfully developed a pharmacokinetic model for MPA in pediatric renal transplant patients. The optimized dosing regimen is expected to result in better target attainment early in treatment. It can be used in combination with model-informed follow-up dosing to further individualize the dose when PK samples become available.

Retrospective Study of Factors Affecting the Accuracy of Predicting Vancomycin Concentrations in Patients Aged 75 Years and Above.

Background and Objectives: The predicted serum concentrations of vancomycin are determined using population pharmacokinetic parameters. However, the accuracy of predicting vancomycin serum concentrations in the older population remains unclear. Therefore, this study aimed to investigate the accuracy of predicting vancomycin serum concentrations and identifying elements that diminish the prediction accuracy in older people. Materials and Methods: A total of 144 patients aged 75 years or older were included. The serum vancomycin concentrations in the patients were predicted based on population pharmacokinetic parameters common in Japan. We examined the accuracy of serum vancomycin concentration prediction in elderly individuals by comparing the predicted and measured serum vancomycin concentrations in each patient. The prediction accuracy was evaluated using the mean prediction error (ME) and mean absolute error of prediction (MAE) calculated from the measured and predicted serum vancomycin concentrations in each patient. Results: The ME for all patients was 0.27, and the 95% CI included 0, indicating that the predicted values were not significantly biased compared to the measured values. However, the predicted serum concentrations in the <50 kg body weight and serum creatinine (Scr) < 0.6 mg/dL groups were significantly biased compared to the measured values. The group with a history of intensive care unit (ICU) admission showed the largest values for the ME and MAE. Conclusions: Our prediction accuracy was satisfactory but tended to be lower in underweight patients, those with low creatinine levels, and patients admitted to the ICU. Patients with multiple of these factors may experience a greater degree of decreased predictive accuracy.

Joint use of population pharmacokinetics and machine learning for prediction of valproic acid plasma concentration in elderly epileptic patients

BackgroundValproic acid (VPA) is a commonly used broad-spectrum antiepileptic drug. For elderly epileptic patients, VPA plasma concentrations have a considerable variation. We aim to establish a prediction model via a combination of machine learning and population pharmacokinetics (PPK) for VPA plasma concentration. MethodsA retrospective study was performed incorporating 43 variables, including PPK parameters. Recursive Feature Elimination with Cross-Validation was used for feature selection. Multiple algorithms were employed for ensemble model, and the model was interpreted by Shapley Additive exPlanations. ResultsThe inclusion of PPK parameters significantly enhances the performance of individual algorithm model. The composition of categorical boosting, light gradient boosting machine, and random forest (7:2:1) with the highest R2 (0.74) was determined as the ensemble model. The model included 11 variables after feature selection, of which the predictive performance was comparable to the model that incorporated all variables. ConclusionsOur model was specifically tailored for elderly epileptic patients, providing an efficient and cost-effective approach to predict VPA plasma concentration. The model combined classical PPK with machine learning, and underwent optimization through feature selection and algorithm integration. Our model can serve as a fundamental tool for clinicians in determining VPA plasma concentration and individualized dosing regimens accordingly.

Population Pharmacokinetics and Loading Dose Optimization of Intravenous Valproic Acid in Hospitalized Thai Patients.

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.

Population pharmacokinetics of tamsulosine in patients with benign prostatic hyperplasia.

The study aimed to determine the typical clearance and volume of distribution values of tamsulosin in patients with benign prostatic hyperplasia (BPH), and to identify factors with a measurable impact on the drug's elimination. This open-label, single-arm population pharmacokinetic study involved 65 adult men with BPH who had been on tamsulosin therapy for at least seven days. The steady-state serum concentrations of tamsulosin were measured using liquid chromatography-tandem quadrupole mass spectrometry. Population pharmacokinetic parameters, their variability, and influencing factors were estimated based on a two-compartment pharmacokinetic model using NONMEM software. The estimated tamsulosin clearance in BPH patients was 0.719L/h, and the steady-state volume of distribution was 32L. Neither renal nor liver function parameters had a statistically significant effect on tamsulosin clearance. However, a positive correlation was observed between hemoglobin levels and tamsulosin clearance in the BPH patient cohort. Our investigation reveals significant associations between tamsulosin pharmacokinetics and specific characteristics of patients with lower urinary tract symptoms (LUTS) due to BPH. The study highlights that tamsulosin clearance is associated with hemoglobin levels in patients with LUTS/BPH. This study underscores the importance of considering patient-specific factors when managing BPH treatment with tamsulosin, emphasizing associations rather than causative relationships.

Population Pharmacokinetic Analysis of Dolutegravir in Treatment-Experienced Adults Living with HIV-1.

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.

External Evaluation of Population Pharmacokinetic Models of Piperacillin in Preterm and Term Patients from Neonatal Intensive Care.

Piperacillin/tazobactam is extensively used off-label to treat late-onset neonatal sepsis, but safety and pharmacokinetic data in this population are limited. Additionally, the organic immaturity of the newborns contributes to a high piperacillin pharmacokinetic variability. This affects the clinical efficacy of the antibiotic treatment and increases the probability of developing drug resistance. This study aimed to evaluate the predictive performance of reported piperacillin population pharmacokinetic models for their application in a model-informed precision dosing strategy in preterm and term Mexican neonatal intensive care patients. Published population pharmacokinetic models for piperacillin which included neonates in their study population were identified. From the reference models, structured models, population pharmacokinetic parameters, and interindividual and residual variability data were extracted to be replicated in pharmacokinetic software (NONMEM® version 7.4). For the clinical study, a sampling schedule was designed, and 2-3 blood samples of 250 µL were taken from neonates who met the inclusion criteria. Piperacillin plasma concentrations were determined by liquid chromatography/tandem mass spectrometry. The clinical treatment data were collected, and piperacillin plasma concentrations were estimated using reference pharmacokinetic models for an a priori or Bayesian approach. Statistical methods were used in terms of bias and precision to evaluate the differences between observed and estimated neonatal piperacillin plasma concentrations with the different approaches and to identify the pharmacokinetic model that best fits the neonatal data. A total of 70 plasma samples were collected from 25 neonatal patients, of which 15 were preterm neonates. The overall median value (range) postnatal age, gestational age, body weight, and serum creatinine at the sampling collecting day were 12 (3-26) days, 34.2 (26-41.1) weeks, 1.78 (0.08-3.90) Kg, 0.47 (0.20-0.90) mg/dL, respectively. Three population pharmacokinetic models for piperacillin in infants up to 2months were identified, and their predictive performance in neonatal data was evaluated. No pharmacokinetic model was suitable for our population using an a priori approach. The model published by Cohen-Wolkowiez et al. in 2014 with a Bayesian approach showed the best performance of the pharmacokinetic models evaluated in our neonatal data. The procedure requires two blood samples (predose and postdose), and, when applied, it predicted 66.6% of the observations with a relative median absolute predicted error of less than 30%. The population pharmacokinetic model developed by Cohen-Wolkowiez et al. in 2014 demonstrated superior performance in predicting the plasma concentration of piperacillin in preterm and term Mexican neonatal intensive care patients. The Bayesian approach, including two different piperacillin plasma concentrations, was clinically acceptable regarding bias and precision. Its application for model-informed precision dosing can be an option to optimize the piperacillin dosage in our population.

Population pharmacokinetics of Ainuovirine and exposure-response analysis in human immunodeficiency virus-infected individuals.

Ainuovirine (ANV) is a new generation of non-nucleoside reverse transcriptase inhibitor for the treatment of human immunodeficiency virus (HIV) type 1 infection. This study aimed to evaluate the population pharmacokinetic (PopPK) profile and exposure-response relationship of ANV among people living with HIV. Plasma concentration-time data from phase 1 and phase 3 clinical trials of ANV were pooled for developing the PopPK model. Exposure estimates obtained from the final model were used in exposure-response analysis for virologic responses and safety responses. ANV exhibited a nonlinear pharmacokinetic profile, which was best described by a two-compartment model with first-order elimination. There were no significant covariates correlated to the pharmacokinetic parameters of ANV. The PopPK parameter estimate (relative standard error [%]) for clearance adjusted for bioavailability (CL/F) was 6.46 (15.00) L/h, and the clearance of ANV increased after multiple doses. The exposure-response model revealed no significant correlation between the virologic response (HIV-RNA <50 copies/mL) at 48 weeks and the exposure, but the incidence of adverse events increased with the increasing exposure ( P value of steady-state trough concentration and area under the steady-state curve were 0.0177 and 0.0141, respectively). Our PopPK model supported ANV 150 mg once daily as the recommended dose for people living with HIV, requiring no dose adjustment for the studied factors. Optimization of ANV dose may be warranted in clinical practice due to an increasing trend in adverse reactions with increasing exposure. Chinese Clinical Trial Registry https://www.chictr.org.cn (Nos. ChiCTR1800018022 and ChiCTR1800019041).

Population pharmacokinetics and dose optimization of magnesium sulfate in Chinese preeclampsia population

ObjectiveTo establish the population pharmacokinetics (PPK) of magnesium sulfate (MgSO4)in women with preeclampsia (PE), and to determine the key covariates having an effect in magnesium pharmacokinetics in Chinese PE.MethodsPregnant women with PE prescribed MgSO4 were enrolled in this prospective study from April 2021 to April 2023. On the initial day of administration, the patients were administered a loading dose of 5 g in conjunction with 10 g of magnesium sulfate as a maintenance dose. On the second day, only the maintenance dose was administration, and maternal blood samples were taken at 0, 4, 5, and 12 h after the second day’s 10 g maintenance dose. The software Phoenix was used to estimate PPK parameters of MgSO4, such as clearance (CL) and volume of distribution (V), and to model PPK models with patient demographic, clinical, and laboratory covariates.ResultsA total of 199 blood samples were collected from 51 women with PE and PPK profiles were analyzed. The PPK of MgSO4 is consistent with to a one-compartment model. The base model adequately described the maternal serum magnesium concentrations after magnesium administration. The population parameter estimates were as follows: CL was 2.98 L/h, V was 25.07 L. The model predictions changed significantly with covariates (BMI, creatinine clearance, and furosemide). Furosemide statistically influences V. The creatinine clearance, BMI and furosemide jointly affects CL. Monte Carlo simulation results showed that a loading dose combined with a maintenance dose would need to be administered daily to achieve the therapeutic blood magnesium concentrations. For the non-furosemide group, the optimal dosing regimen was a 5 g loading dose combined with a 10 g maintenance dose of MgSO4. For the furosemide group, the optimal dosing regimen was a 2.5 g loading dose combined with a 10 g maintenance dose of MgSO4.ConclusionsThe magnesium PPK model was successfully developed and evaluated in Chinese preeclampsia population, and the dose optimization of MgSO4 was completed through Monte Carlo simulation.

Population Pharmacokinetics and Absolute Oral Bioavailability of Lasalocid after Single Intravenous and Intracrop Administration in Laying Hens.

Lasalocid sodium is a polyether carboxylic ionophore agent authorized by the EU for use as a coccidiostat in broilers, turkeys, and pullets up to 16 weeks of age, except for laying hens. However, laying hens are the most common nontarget species exposed to lasalocid sodium, mainly due to cross-contamination from feed mills. This exposure may result in potential drug residue deposition in eggs, which could potentially expose consumers to the drug. The breeds commonly used for commercial egg production in Poland are Isa Brown and Green-legged Partridge hens, which have been found to significantly differ in egg-laying performance. This variability may also affect the pharmacokinetics of lasalocid. Data on lasalocid plasma pharmacokinetics in laying hens are lacking. In this study, we aimed to determine typical population pharmacokinetic parameters, absolute oral bioavailability, and how breed may influence the pharmacokinetics of lasalocid. Twenty-layer hens of the two breeds were used in this study. Lasalocid was administered orally at a single dose of either 1 mg or 5 mg/kg body weight or intravenously at a dose of 1 mg/kg body weight, in a crossover design with a three-week washout period between study periods. Blood samples were collected for 72 h, and lasalocid concentrations were measured using high-performance liquid chromatography with fluorescence detection. A population pharmacokinetic analysis was conducted using nonlinear mixed effects modeling. Standard numerical and graphical criteria were used to select the best model, and a stepwise covariate modeling approach was used to determine any influencing factors. The best model was a three-compartment mammillary model with first-order absorption, transit compartments, and linear elimination. The estimated absolute oral bioavailability was low (36%). It was found that breed significantly influenced not only absorption but also the elimination of lasalocid. This study revealed that lasalocid absorption and elimination varied between the two breeds. This variability in pharmacokinetics may result in breed-related differences in drug residue accumulation in eggs, and ultimately, the risk associated with consumer exposure to drug residues may also vary.