Are population pharmacokinetic and/or pharmacodynamic models adequately evaluated? A survey of the literature from 2002 to 2004

Predictive Performance of a Gentamicin Pharmacokinetic Model in Term Neonates with Perinatal Asphyxia Undergoing Controlled Therapeutic Hypothermia

BACKGROUND

Model validation procedures are crucial when population pharmacokinetic (PK) models are used to develop dosing algorithms and to perform model-informed precision dosing. We have previously published a population PK model describing the PK of gentamicin in term neonates with perinatal asphyxia during controlled therapeutic hypothermia (TH), which showed altered gentamicin clearance during the hypothermic phase dependent on gestational age and weight. In this study, the predictive performance and generalizability of this model were assessed using an independent data set of neonates with perinatal asphyxia undergoing controlled TH.

METHODS

The external data set contained a subset of neonates included in the prospective observational multicenter PharmaCool Study. Predictive performance was assessed by visually inspecting observed-versus-predicted concentration plots and calculating bias and precision. In addition, simulation-based diagnostics, model refitting, and bootstrap analyses were performed.

RESULTS

The external data set included 323 gentamicin concentrations of 39 neonates. Both the model-building and external data set included neonates from multiple centers. The original gentamicin PK model predicted the observed gentamicin concentrations with adequate accuracy and precision during all phases of controlled TH. Model appropriateness was confirmed with prediction-corrected visual predictive checks and normalized prediction distribution error analyses. Model refitting to the merged data set (n = 86 neonates with 935 samples) showed accurate estimation of PK parameters.

CONCLUSIONS

The results of this external validation study justify the generalizability of the gentamicin dosing recommendations made in the original study for neonates with perinatal asphyxia undergoing controlled TH (5 mg/kg every 36 or 24 h with gestational age 36-41 and 42 wk, respectively) and its applicability in model-informed precision dosing.

Population Pharmacokinetics and Model-Based Dose Optimization of Vancomycin in Sudanese Adult Patients with Renal Impairment

Purpose

The study aimed to perform a population pharmacokinetic (PK) analysis to obtain vancomycin PK parameter estimates in Sudanese adult patients. The population PK model is then applied to perform model-based dose optimization.

Patients and Methods

Data were collected through a retrospective, single-center, observational cohort study performed in Aliaa Specialist Hospital, Khartoum, Sudan. A population PK model was developed using the MonolixSuite 2020R1 to explore the potential effects of demographics and laboratory covariates on vancomycin PK. Monte Carlo simulations were performed to optimize dosage regimens as a function of creatinine clearance (CLcr) and virtual patients were partitioned into five CLcr groups.

Results

We retrospectively collected 194 vancomycin plasma concentrations from 99 adults. The median (interquartile range) for age (years) and CLcr (mL/min) were 65 (50-75) and 12.7 (5.52-25.78), respectively. Vancomycin PK data were best fitted using a one-compartment model with linear elimination. The estimates of clearance and volume of distribution were 2.02 L/h and 65 L, respectively. CLcr was identified as the main covariate explaining the PK variability in vancomycin CL. CL significantly decreased with decreasing CLcr. For the five CLcr groups evaluated, a tailored vancomycin daily maintenance dose (using patients' CLcr) ranged from 200 to 1650 mg. Overall, simulations showed that 45% (CI; 41.11-47.36%) of patients would achieve a target AUC with the suggested dosages.

Conclusion

A population PK model of vancomycin was developed using data obtained from adult Sudanese patients. Model-based dose optimization can aid clinicians in selecting initial vancomycin doses that will maximize the likelihood of a favorable treatment response.

Limited sampling strategies for therapeutic drug monitoring of anti-tuberculosis medications: A systematic review of their feasibility and clinical utility

Therapeutic drug monitoring (TDM) is recommended for medications with high inter-individual variability, narrow therapeutic index drugs, possible drug-drug interactions, drug toxicity, and subtherapeutic concentrations, as well as to assess noncompliance. The area under the plasma concentration-time curve (AUC) is a significant pharmacokinetic parameter since it calculates the drug's total systematic exposure in the body. However, multiple blood samples from the patient are required to calculate the area under the curve, which is inconvenient for both the patient and the healthcare professional. To alleviate the issue, the limited sampling strategy (LSS) was devised, in which sampling is minimized while obtaining complete and precise findings to anticipate the area under the curve. One can reduce costs, labor, and discomfort for patients and healthcare workers by applying this limited sampling strategy. This article examines a systematic evaluation of all the limited sampling done in anti-tuberculosis (anti-TB) medications resulting from the literature search of several research papers. This article also briefly describes the two methodologies: Multiple regression analysis (MRA) and the Bayesian approach used to develop a limited sampling strategy model. Anti-TB medications have been found to have considerable inter-individual variability, and isoniazid has a narrow therapeutic index, both of which are criteria for therapeutic drug monitoring. To avoid multi-drug resistance and therapy failure, it is proposed that limited sampling strategy-based therapeutic drug monitoring of anti-TB medications be undertaken to generate an individualized dose regimen, particularly for individuals at high risk of treatment failure or delayed response.

Safety of medicines during breastfeeding - from case report to modeling : A contribution from the ConcePTION project

INTRODUCTION

Despite many research efforts, current data on the safety of medicines during breastfeeding are either fragmented or lacking, resulting in restrictive labeling of most medicines. In the absence of pharmacoepidemiologic safety studies, risk estimation for breastfed infants is mainly derived from pharmacokinetic (PK) information on the medicine. This manuscript provides a description and a comparison of the different methodological approaches that can yield reliable information on medicine transfer into human milk and the resulting infant exposure.

AREA COVERED

Currently, most information on medicine transfer in human milk relies on case reports or traditional PK studies, which generate data that can hardly be generalized to the population. Some methodological approaches, such as population PK (popPK) and physiologically-based PK (PBPK) modeling, can be used to provide a more complete characterization of infant medicine exposure through human milk and simulate the most extreme situations, while decreasing the burden of sampling in breastfeeding women.

EXPERT OPINION

PBPK and popPK modeling are promising approaches to fill the gap of knowledge in medicine safety in breastfeeding, as illustrated with our escitalopram example.

External Evaluation of Population Pharmacokinetic Models for Precision Dosing: Current State and Knowledge Gaps

Predicting drug exposures using population pharmacokinetic models through Bayesian forecasting software can improve individual pharmacokinetic/pharmacodynamic target attainment. However, selecting the most adapted model to be used is challenging due to the lack of guidance on how to design and interpret external evaluation studies. The confusion around the choice of statistical metrics and acceptability criteria emphasises the need for further research to fill this methodological gap as there is an urgent need for the development of standards and guidelines for external evaluation studies. Herein we discuss the scientific challenges faced by pharmacometric researchers and opportunities for future research with a focus on antibiotics.

Population pharmacokinetics of doxorubicin: A systematic review

Because of the high interindividual pharmacokinetic variability, several population pharmacokinetic (PopPK) models of doxorubicin (DOX) were developed to characterize factors influencing such variability. However, significant predictors for DOX pharmacokinetics identified using PopPK models varied across studies. Thus, this review aims to summarize PopPK models of DOX and its metabolites (if any) as well as significant covariates influencing DOX (and its metabolites) pharmacokinetic variability. A systematic search from PubMed, CINAHL Complete, Science Direct, and SCOPUS databases identified 503 studies. Of these, 16 studies met the inclusion criteria and were included in this review. DOX pharmacokinetics was described with two- or three-compartment models. Most studies found a significant increase in DOX clearance with an increase in body surface area from the median value of 1.8 m² . Moreover, this review identified that while a 10-year increase in patient age resulted in a decrease in DOX clearance in adults and the elderly, younger children had lower DOX clearance compared to older children. Further, low DOX exposure was observed in pregnant women, and thus dosage adjustment is required. Concerning model applicability, predictive performance assessment of these published models should be performed before implementing such models in clinical practice.

Integration of Pharmacokinetics, Pharmacodynamics, Safety, and Efficacy into Model-Informed Dose Selection in Oncology First-in-Human Study: A Case of Roblitinib (FGF401)

Model-informed dose selection has been drawing increasing interest in oncology early clinical development. The current paper describes the example of FGF401, a selective fibroblast growth factor receptor 4 (FGFR4) inhibitor, in which a comprehensive modeling and simulation (M&S) framework, using both pharmacometrics and statistical methods, was established during its first-in-human clinical development using the totality of pharmacokinetics (PK), pharmacodynamic (PD) biomarkers, and safety and efficacy data in patients with cancer. These M&S results were used to inform FGF401 dose selection for future development. A two-compartment population PK (PopPK) model with a delayed 0-order absorption and linear elimination adequately described FGF401 PK. Indirect PopPK/PD models including a precursor compartment were independently established for two biomarkers: circulating FGF19 and 7α-hydroxy-4-cholesten-3-one (C4). Model simulations indicated a close-to-maximal PD effect achieved at the clinical exposure range. Time-to-progression was analyzed by Kaplan-Meier method which favored a trough concentration (C_trough )-driven efficacy requiring C_trough above a threshold close to the drug concentration producing 90% inhibition of phospho-FGFR4. Clinical tumor growth inhibition was described by a PopPK/PD model that reproduced the dose-dependent effect on tumor growth. Exposure-safety analyses on the expected on-target adverse events, including elevation of aspartate aminotransferase and diarrhea, indicated a lack of clinically relevant relationship with FGF401 exposure. Simulations from an indirect PopPK/PD model established for alanine aminotransferase, including a chain of three precursor compartments, further supported that maximal target inhibition was achieved and there was a lack of safety-exposure relationship. This M&S framework supported a dose selection of 120 mg once daily fasted or with a low-fat meal and provides a practical example that might be applied broadly in oncology early clinical development.

Ulcerative Colitis and Acute Severe Ulcerative Colitis Patients Are Overlooked in Infliximab Population Pharmacokinetic Models: Results from a Comprehensive Review

Ulcerative colitis (UC) is part of the inflammatory bowels diseases, and moderate to severe UC patients can be treated with anti-tumour necrosis α monoclonal antibodies, including infliximab (IFX). Even though treatment of UC patients by IFX has been in place for over a decade, many gaps in modelling of IFX PK in this population remain. This is even more true for acute severe UC (ASUC) patients for which early prediction of IFX pharmacokinetic (PK) could highly improve treatment outcome. Thus, this review aims to compile and analyse published population PK models of IFX in UC and ASUC patients, and to assess the current knowledge on disease activity impact on IFX PK. For this, a semi-systematic literature search was conducted, from which 26 publications including a population PK model analysis of UC patients receiving IFX therapy were selected. Amongst those, only four developed a model specifically for UC patients, and only three populations included severe UC patients. Investigations of disease activity impact on PK were reported in only 4 of the 14 models selected. In addition, the lack of reported model codes and assessment of predictive performance make the use of published models in a clinical setting challenging. Thus, more comprehensive investigation of PK in UC and ASUC is needed as well as more adequate reports on developed models and their evaluation in order to apply them in a clinical setting.

Systematic Evaluation of Voriconazole Pharmacokinetic Models without Pharmacogenetic Information for Bayesian Forecasting in Critically Ill Patients

Voriconazole (VRC) is used as first line antifungal agent against invasive aspergillosis. Model-based approaches might optimize VRC therapy. This study aimed to investigate the predictive performance of pharmacokinetic models of VRC without pharmacogenetic information for their suitability for model-informed precision dosing. Seven PopPK models were selected from a systematic literature review. A total of 66 measured VRC plasma concentrations from 33 critically ill patients was employed for analysis. The second measurement per patient was used to calculate relative Bias (rBias), mean error (ME), relative root mean squared error (rRMSE) and mean absolute error (MAE) (i) only based on patient characteristics and dosing history (a priori) and (ii) integrating the first measured concentration to predict the second concentration (Bayesian forecasting). The a priori rBias/ME and rRMSE/MAE varied substantially between the models, ranging from −15.4 to 124.6%/−0.70 to 8.01 mg/L and from 89.3 to 139.1%/1.45 to 8.11 mg/L, respectively. The integration of the first TDM sample improved the predictive performance of all models, with the model by Chen (85.0%) showing the best predictive performance (rRMSE: 85.0%; rBias: 4.0%). Our study revealed a certain degree of imprecision for all investigated models, so their sole use is not recommendable. Models with a higher performance would be necessary for clinical use.

External Validation of a Vancomycin Population Pharmacokinetic Model and Developing a New Dosage Regimen in Neonates

BACKGROUND AND OBJECTIVE

Vancomycin is the drug of choice in the treatment of MRSA infections. In a published vancomycin population pharmacokinetic study on neonates in Singapore healthcare institutions, it was found that vancomycin clearance was predicted by weight, postmenstrual age, and serum creatinine. The aim of this study was to externally validate the vancomycin population pharmacokinetic model to develop a new dosage regimen in neonates, and to compare this regimen with the existing institutional and NeoFax^® dosage regimens.

METHODS

A retrospective chart review of neonates who received vancomycin therapy and therapeutic drug monitoring was conducted. The median prediction error percentage was calculated to assess bias, while the median absolute prediction error percentage and the root mean squared error percentage were calculated to assess precision. The new dosage regimen was developed using Monte Carlo simulation.

RESULTS

A total of 20 neonates were included in the external validation dataset. Eighteen of them were premature, with a median gestational age of 27.7 (25.9-31.5) weeks and postmenstrual age of 30.5 (27.3-34.3) weeks at the point of vancomycin initiation. No apparent systematic bias was found in the predictions of the model. The external validation performed in the current study found the model to be generally unbiased. Our new vancomycin dosage regimen was able to achieve target trough concentrations and area under the curve (AUC₂₄) at a greater proportion as compared to existing institutional and NeoFax^® dosage regimens.

CONCLUSION

The pharmacokinetic model built in the previous study can be used to conduct reliable population simulations of our Asian neonatal population in Singapore. The new dosage regimen was able to achieve target trough concentrations and AUC₂₄ better than existing institutional and NeoFax^® dosage regimens.

Using a Validated Population Pharmacokinetic Model for Dosing Recommendations of Continuous Infusion Piperacillin for Critically Ill Adult Patients

BACKGROUND AND OBJECTIVE: Piperacillin is a broad-spectrum β-lactam antibiotic commonly prescribed in intensive care units. Many piperacillin population pharmacokinetic models have been published, but few underwent an external evaluation. External evaluation is an important process to determine a model's capability of being generalized to other hospitals. We aimed to assess the predictive performance of these models with an external validation dataset.

METHODS

Six models were evaluated with a dataset consisting of 30 critically ill patients (35 samples) receiving piperacillin by continuous infusion. Models were subject to prediction-based (bias and imprecision) and simulation-based evaluations. When a model had an acceptable evaluation, it was used for dosing simulations to evaluate the probability of target attainment.

RESULTS

Bias and imprecision ranged from - 35.7 to 295% and from 22.7 to 295%, respectively. The models of Klastrup et al. and of Udy et al. were acceptable according to our criteria and were used for dosing simulations. Simulations showed that a loading dose of 4 g followed by a maintenance dose of 16 g/24 h of piperacillin infused continuously was necessary to remain above a pharmacokinetic-pharmacodynamic target set as a minimal inhibitory concentration of 16 mg/L in 90% of patients, for a median patient with a creatinine clearance of 76 mL/min.

CONCLUSIONS

Despite the considerable variation in the predictive performance of the models with the external validation dataset, this study was able to validate two of these models and led to the elaboration of a dosing nomogram for piperacillin by continuous infusion that can be used by clinicians in intensive care units.

Parametric and Nonparametric Population Pharmacokinetic Models to Assess Probability of Target Attainment of Imipenem Concentrations in Critically Ill Patients

Population pharmacokinetic modeling and simulation (M&S) are used to improve antibiotic dosing. Little is known about the differences in parametric and nonparametric M&S. Our objectives were to compare (1) the external validation of parametric and nonparametric models of imipenem in critically ill patients and (2) the probability of target attainment (PTA) calculations using simulations of both models. The M&S software used was NONMEM 7.2 (parametric) and Pmetrics 1.5.2 (nonparametric). The external predictive performance of both models was adequate for eGFRs ≥ 78 mL/min but insufficient for lower eGFRs, indicating that the models (developed using a population with eGFR ≥ 60 mL/min) could not be extrapolated to lower eGFRs. Simulations were performed for three dosing regimens and three eGFRs (90, 120, 150 mL/min). Fifty percent of the PTA results were similar for both models, while for the other 50% the nonparametric model resulted in lower MICs. This was explained by a higher estimated between-subject variability of the nonparametric model. Simulations indicated that 1000 mg q6h is suitable to reach MICs of 2 mg/L for eGFRs of 90-120 mL/min. For MICs of 4 mg/L and for higher eGFRs, dosing recommendations are missing due to largely different PTA values per model. The consequences of the different modeling approaches in clinical practice should be further investigated.

Validation of population pharmacokinetic models: a comparison of internal and external validation approaches for hydrochlorothiazide

1. Model evaluation is an important issue in population analyses. Our aim was to perform and illustrate metrics and techniques for internal and external evaluation with an application to population pharmacokinetics of hydrochlorothiazide (HCTZ).2. A nonlinear mixed effects model was used to study the pharmacokinetics of HCTZ. In addition, different types of internal assessment tools and external metrics were used for model evaluation. External evaluation was performed using an alternative dataset that included data from an independent group of subjects. For comparison, a previously published population pharmacokinetic model for HCTZ was applied to the same data.3. A two-compartment model with first-order oral absorption using a constant time delay between administration and absorption and first-order elimination best described HCTZ pharmacokinetics. Age had a statistically significant effect on HCTZ clearance. The final model performed adequately in the internal and external assessment tests. The final model showed better predictive performance than the other previously published HCTZ model.4. Finally, a robust population pharmacokinetic model for HCTZ in adults was constructed and validated internally and externally. Incorporating analytical assessment of nonlinear pharmacokinetics into the modelling may be a promising approach to improve the predictive power of the model.

The PHARMACOM-EPI Framework for Integrating Pharmacometric Modelling Into Pharmacoepidemiological Research Using Real-World Data: Application to Assess Death Associated With Valproate

In pharmacoepidemiology, it is usually expected that the observed association should be directly or indirectly related to the pharmacological effects of the drug/s under investigation. Pharmacological effects are, in turn, strongly connected to the pharmacokinetic and pharmacodynamic properties of a drug, which can be characterized and investigated using pharmacometric models. Recently, the use of pharmacometrics has been proposed to provide pharmacological substantiation of pharmacoepidemiological findings derived from real-world data. However, validated frameworks suggesting how to combine these two disciplines for the aforementioned purpose are missing. Therefore, we propose PHARMACOM-EPI, a framework that provides a structured approach on how to identify, characterize, and apply pharmacometric models with practical details on how to choose software, format dataset, handle missing covariates/dosing data, how to perform the external evaluation of pharmacometric models in real-world data, and how to provide pharmacological substantiation of pharmacoepidemiological findings. PHARMACOM-EPI was tested in a proof-of-concept study to pharmacologically substantiate death associated with valproate use in the Danish population aged ≥ 65 years. Pharmacological substantiation of death during a follow-up period of 1 year showed that in all individuals who died (n = 169) individual predictions were within the subtherapeutic range compared with 52.8% of those who did not die (n = 1,084). Of individuals who died, 66.3% (n = 112) had a cause of death possibly related to valproate and 33.7% (n = 57) with well-defined cause of death unlikely related to valproate. This proof-of-concept study showed that PHARMACOM-EPI was able to provide pharmacological substantiation for death associated with valproate use in the study population.

External Evaluation of Population Pharmacokinetic Models and Bayes-Based Dosing of Infliximab

Despite the well-demonstrated efficacy of infliximab in inflammatory diseases, treatment failure remains frequent. Dose adjustment using Bayesian methods has shown in silico its interest in achieving target plasma concentrations. However, most of the published models have not been fully validated in accordance with the recommendations. This study aimed to submit these models to an external evaluation and verify their predictive capabilities. Eight models were selected for external evaluation, carried out on an independent database (409 concentrations from 157 patients). Each model was evaluated based on the following parameters: goodness-of-fit (comparison of predictions to observations), residual error model (population weighted residuals (PWRES), individual weighted residuals (IWRES), and normalized prediction distribution errors (NPDE)), and predictive performances (prediction-corrected visual predictive checks (pcVPC) and Bayesian simulations). The performances observed during this external evaluation varied greatly from one model to another. The eight evaluated models showed a significant bias in population predictions (from -7.19 to 7.38 mg/L). Individual predictions showed acceptable bias and precision for six of the eight models (mean error of -0.74 to -0.29 mg/L and mean percent error of -16.6 to -0.4%). Analysis of NPDE and pcVPC confirmed these results and revealed a problem with the inclusion of several covariates (weight, concomitant immunomodulatory treatment, presence of anti-drug antibodies). This external evaluation showed satisfactory results for some models, notably models A and B, and highlighted several prospects for improving the pharmacokinetic models of infliximab for clinical-biological application.

Predictive Performance of Population Pharmacokinetic Models of Levetiracetam in Children and Evaluation of Dosing Regimen

Levetiracetam is a broad-spectrum antiepileptic drug that exhibits high interindividual variability in serum concentrations in children. A population pharmacokinetic approach can be used to explain this variability and optimize dosing schemes. The objectives are to identify the best predictive population pharmacokinetic model for children and to evaluate recommended doses using simulations and Bayesian forecasting. A validation cohort included children treated with levetiracetam who had a serum drug concentration assayed during therapeutic drug monitoring. We assessed the predictive performance of all the population pharmacokinetic models published in the literature using mean prediction errors, root mean squared errors, and visual predictive checks. A population model was finally constructed on the data, and dose simulations were performed to evaluate doses. We included 267 levetiracetam concentrations ranging from 2 to 69 mg/L from 194 children in the validation cohort. Six published models were externally evaluated. Most of the models underestimated the variability of our population. A 1-compartment model with first-order absorption and elimination with allometric scaling was finally fitted on our data. In our cohort, 57% of patients had a trough concentration <12 mg/L and 12% <5 mg/L. To reach a trough concentration >5 mg/L, doses ≥30 mg/kg/d for patients ≤50 kg and ≥2000 mg/d for patients >50 kg are required. In our population, a high percentage of children had low trough concentrations. Our population pharmacokinetic model could be used for therapeutic drug monitoring of levetiracetam in children.

Establishment of a population pharmacokinetics model of vancomycin in 94 infants with septicemia and its application in individualized therapy

Background

We aim to develop a population pharmacokinetics (PopPK) model of vancomycin for the treatment of septicemia in infants younger than one year. Factors influence of the PK was investigated to optimize vancomycin dosing regimen.

Methods

The nonlinear mixed effects modelling software (NONMEM) was used to develop the PopPK model of vancomycin. The stability and predictive ability of the final model were assessed by using normalized prediction distribution errors (NPDE) and bootstrap methods. The final model was subjected to Monte Carlo simulation in order to determine the optimal dose.

Results

A total of 205 trough and peak concentrations in 94 infants (0–1 year of age) with septicemia were analyzed. The interindividual variability of the PK parameter was described by the exponential model. Residual error was better described by the proportional model than the mixed proportional and addition models. Serum creatinine concentration and body weight are the major factors that affect the PK parameters of vancomycin. The clearance was shown to be higher when ceftriaxone was co-treated. More than two model evaluation methods showed better stability than the base model, with superior predictive performance, which can develop individualized dosing regimens for clinical reference. Through prediction of final model, the trough concentration was more likely < 5 mg/L when a routine dose of 10 mg/kg is administered every 6 h to 3–9-month-old infants. Therefore, the dose should be increased in the treatment of infant septicemia.

Conclusions

The stable and effective PopPK model of vancomycin in Chinese infants with septicemia was established. This model has satisfactory predictive ability for clinically individualized dosing regimens in this vulnerable population.

A systematic review of population pharmacokinetic analyses of polyclonal immunoglobulin G therapy

BACKGROUND

Population pharmacokinetics (popPK) using the nonlinear mixed-effect (NLME) modeling approach is an essential tool for guiding dose individualization. Several popPK analyses using the NLME have been conducted to characterize the pharmacokinetics of immunoglobulin G (IgG).

OBJECTIVE

To summarize the current information on popPK of polyclonal IgG therapy.

METHOD

A systematic search was conducted in the PubMed and Web of Science databases from inception to December 2020. Additional relevant studies were also included by reviewing the reference list of the reviewed articles. All popPK studies that employed the NLME modeling approach were included and data were synthesized descriptively.

RESULTS

This review included seven studies. Most of the popPK models were developed in patients with primary immunodeficiency (PID). IgG pharmacokinetics was described as a two-compartment model in five studies, while it was described as a one-compartment model in two other studies. Among all tested covariates, weight was consistently identified as a significant predictor for clearance (CL) of IgG. Whereas, weight and disease type were found to be significant predictors for the volume of distribution in central compartment (Vc). In a typical 70 kg adult, the median estimated values of Vc and CL were 4.04 L and 0.144 L/day, respectively. The between subject variability of Vc was considered large. Only two studies evaluated their models using external data.

CONCLUSIONS

Seven popPK studies of IgG were found and discussed, with only weight being a significant covariate across all studies. Future studies linking pharmacokinetics with pharmacodynamics in PID and other patient populations are required.

External evaluation of the predictive performance of seven population pharmacokinetic models for phenobarbital in neonates

AIM

Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external evaluation of the predictive performance in published pharmacokinetic models.

METHODS

Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control was used for external evaluation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external evaluation. The incorporation of allometric scaling for body size and maturation factors into the published models was also tested for prediction improvement.

RESULTS

A total of 79 serum concentrations from 28 subjects were included in the external dataset. Seven population pharmacokinetic studies of PB were identified as relevant in the literature search and included for our evaluation. The model by Voller et al showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al and Marsot et al) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, with improvement as observed in the model of Vucicevic et al. CONCLUSIONS: The predictive performance of published pharmacokinetic models of PB was diverse. Bayesian forecasting and incorporation of both size and maturation factors could improve the predictability of the models for neonates.

Population Pharmacokinetics of Levetiracetam: a Systematic Review

BACKGROUND

The use of levetiracetam (LEV) has been increasing given its favorable pharmacokinetic profile. Numerous population pharmacokinetic studies for LEV have been conducted. However, there are some discrepancies regarding factors affecting its pharmacokinetic variability. Therefore, this systematic review aimed to summarize significant predictors for LEV pharmacokinetics as well as the need for dosage adjustments.

METHODS

We performed a systematic search for population pharmacokinetic studies of LEV conducted using a nonlinear-mixed effect approach from PubMed, Scopus, CINAHL Complete, and Science Direct databases from their inception to March 2020. Information on study design, model methodologies, significant covariate-parameter relationships, and model evaluation was extracted. The quality of the reported studies was also assessed.

RESULTS

A total of 16 studies were included in this review. Only two studies were conducted with a two-compartment model, while the rest were performed with a one-compartment structure. Bodyweight and creatinine clearance were the two most frequently identified covariates on LEV clearance (CLLEV). Additionally, postmenstrual age (PMA) or postnatal age (PNA) were significant predictors for CLLEV in neonates. Only three studies externally validated the models. Two studies conducted pharmacodynamic models for LEV with relatively small sample size.

CONCLUSION

Significant predictors for LEV pharmacokinetics are highlighted in this review. For future research, a population pharmacokinetic-pharmacodynamic model using a larger sample size should be conducted. From a clinical perspective, the published models should be externally evaluated before clinical implementation.

Population Pharmacokinetic Studies of Digoxin in Adult Patients: A Systematic Review

BACKGROUND

Digoxin is a cardiac glycoside that was introduced to cardiovascular medicine more than 200 years ago. Its use is associated with large variability, which complicates achieving the desired therapeutic outcomes.

OBJECTIVES

To present a synthesis of the available literature on the population pharmacokinetics of digoxin in adults and to identify the sources of variability in its pharmacokinetics.

METHODS

This is a PROSPERO registered systematic review (CRD42018105300). A literature search was conducted using the ISI Web of Science, Science Direct, PubMed, and SCOPUS databases to identify digoxin population pharmacokinetic studies of adults that utilized the nonlinear mixed-effect modeling approach.

RESULTS

Sixteen articles were included in the present analysis. Only two studies were conducted in elderly subjects as a separate population. Both the pharmacokinetics and pharmacodynamics of digoxin were investigated in one study. Furthermore, the reviewed studies were mostly conducted in East Asian populations (68.8%). Digoxin's pharmacokinetics were usually described by a one-compartment model because of the nature of the collected data. Weight, age, kidney function, presence of heart failure, and co-administered medications such as calcium channel blockers were the most commonly identified predictors of digoxin clearance. The value of apparent clearance in a typical study individual ranged from 0.005 to 0.2 l/h/kg, while the value of the apparent volume of distribution ranged from 3.14 to 15.2 l/kg. The quality of model evaluation was deemed excellent only in 31.3% of the studies.

CONCLUSION

This review provides information about variables that need to be considered when prescribing digoxin. The results highlight the need for prospective studies that allow two-compartment pharmacokinetic/pharmacodynamic models to be established, with a special focus on the elderly subpopulation.

Predictive performance of parent-metabolite population pharmacokinetic models of (S)-ketamine in healthy volunteers

Purpose

The recent repurposing of ketamine as treatment for pain and depression has increased the need for accurate population pharmacokinetic (PK) models to inform the design of new clinical trials. Therefore, the objectives of this study were to externally validate available PK models on (S)-(nor)ketamine concentrations with in-house data and to improve the best performing model when necessary.

Methods

Based on predefined criteria, five models were selected from literature. Data of two previously performed clinical trials on (S)-ketamine administration in healthy volunteers were available for validation. The predictive performances of the selected models were compared through visual predictive checks (VPCs) and calculation of the (root) mean (square) prediction errors (ME and RMSE). The available data was used to adapt the best performing model through alterations to the model structure and re-estimation of inter-individual variability (IIV).

Results

The model developed by Fanta et al. (Eur J Clin Pharmacol 71:441–447, 2015) performed best at predicting the (S)-ketamine concentration over time, but failed to capture the (S)-norketamine C_max correctly. Other models with similar population demographics and study designs had estimated relatively small distribution volumes of (S)-ketamine and thus overpredicted concentrations after start of infusion, most likely due to the influence of circulatory dynamics and sampling methodology. Model predictions were improved through a reduction in complexity of the (S)-(nor)ketamine model and re-estimation of IIV.

Conclusion

The modified model resulted in accurate predictions of both (S)-ketamine and (S)-norketamine and thereby provides a solid foundation for future simulation studies of (S)-(nor)ketamine PK in healthy volunteers after (S)-ketamine infusion.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00228-021-03104-1.

Population pharmacokinetics of vancomycin in patients with external ventricular drain-associated ventriculitis

BACKGROUND

To determine the distribution of vancomycin into the cerebrospinal fluid (CSF) in patients with external ventricular drain (EVD)-associated ventriculitis, the pharmacokinetics of vancomycin were evaluated and covariate relationships explored.

METHODS

For the population pharmacokinetic model patients were recruited in a neurocritical care unit at the University Hospital of Muenster in the period between January 2014 and June 2015. All patients had a clinical evidence of EVD-associated ventriculitis. Population pharmacokinetic analysis of vancomycin was performed using NONMEM.

RESULTS

A total of 184 blood and 133 CSF samples were collected from 29 patients. The final population pharmacokinetic model is a three-compartment model with linear elimination. Creatinine clearance (Cl_Cr ) and CSF-lactate were detected as significant covariates, showing that the total vancomycin plasma clearance (Cl) depends on Cl_Cr and furthermore the clearance (Cl_dif ) between the central and CSF compartment correlates with CSF lactate concentration. Based on the final model, the following values were estimated by NONMEM: Cl = 5.15 L/h, Q (intercompartmental clearance) = 3.31 L/h, Cl_dif  = 0.0031 L/h, V_central  = 42.1 L, V_CSF  = 0.32 L and the value of V_peripheral was fixed to 86.2 L. With the developed pharmacokinetic model, area under the curve (AUC) values as well as CSF trough levels were simulated.

CONCLUSION

Based on our analysis, the dosing of vancomycin should be referred to the degree of inflammation (derived from the CSF lactate concentration) and renal function (derived from Cl_Cr ).

Pharmacokinetic variability of phenobarbital: a systematic review of population pharmacokinetic analysis

AIMS AND BACKGROUND

Population pharmacokinetics with Bayesian forecasting provides for an effective approach when individualized drug dosing, while phenobarbital is a narrow therapeutic index drug that requires therapeutic drug monitoring. To date, several population pharmacokinetic models have been developed for phenobarbital, these showing a number of significant predictors of phenobarbital clearance and volume of distribution. We have therefore conducted a systematic review to summarize how these predictors affect phenobarbital pharmacokinetics as well as their relationships with pharmacokinetic parameters.

METHOD

A systematic search for studies of phenobarbital population pharmacokinetics that were carried out in humans and that employed a nonlinear mixed-effect approaches was made using the PubMed, Scopus, CINAHL Complete, and ScienceDirect databases. The search covered the period from these databases' inception to March 2020.

RESULTS

Eighteen studies were included in this review, all of which used a one-compartment structure. The estimated phenobarbital clearance and volume of distribution ranged from 0.0034 to 0.0104 L/h/kg and 0.37 to 1.21 L/kg, respectively, with body weight, age, and concomitant antiepileptic drugs being the three most frequently identified predictors of clearance. Most models were validated through the use of an advanced internal approach.

CONCLUSION

Phenobarbital clearance may be predicted from previously developed population pharmacokinetic models and their significant covariate-parameter relationships along with Bayesian forecasting. However, when applying these models in a target population, an external evaluation of these models using the target population is warranted, and it is recommended that future research be conducted to investigate the link between population pharmacokinetics and pharmacodynamics.

Can Population Pharmacokinetics of Antibiotics be Extrapolated? Implications of External Evaluations

BACKGROUND AND OBJECTIVE

External evaluation is an important issue in the population pharmacokinetic analysis of antibiotics. The purpose of this review was to summarize the current approaches and status of external evaluations and discuss the implications of external evaluation results for the future individualization of dosing regimens.

METHODS

We systematically searched the PubMed and EMBASE databases for external evaluation studies of population analysis and extracted the relevant information from these articles. A total of 32 studies were included in this review.

RESULTS

Vancomycin was investigated in 17 (53.1%) articles and was the most studied drug. Other studied drugs included gentamicin, tobramycin, amikacin, amoxicillin, ceftaroline, meropenem, fluconazole, voriconazole, and rifampicin. Nine (28.1%) studies were prospective, and the sample size varied widely between studies. Thirteen (40.6%) studies evaluated the population pharmacokinetic models by systematically searching for previous studies. Seven (21.9%) studies were multicenter studies, and 27 (84.4%) adopted the sparse sampling strategy. Almost all external evaluation studies of antibiotics (93.8%) used metrics for prediction-based diagnostics, while relatively fewer studies were based on simulations (46.9%) and Bayesian forecasting (25.0%).

CONCLUSION

The results of external evaluations in previous studies revealed the poor extrapolation performance of existing models of prediction- and simulation-based diagnostics, whereas the posterior Bayesian method could improve predictive performance. There is an urgent need for the development of standards and guidelines for external evaluation studies.

External evaluation of population pharmacokinetic models for continuous administration of meropenem in critically ill adult patients

PURPOSE

Beta-lactams (BL), the most commonly prescribed class of antibiotics, are recommended as the first-line therapy for multiple indications in infectious disease guidelines. Meropenem (MERO) is frequently used in intensive care units (ICU) to treat bacterial infections with or without sepsis. The pharmacokinetics of MERO display a large variability in patients admitted to ICUs due to altered pathophysiology. The aim of this study was to perform an external evaluation of published population pharmacokinetic models of MERO in order to test their predictive performance in a cohort of ICU adult patients.

METHODS

A literature search in PubMed/Medline database was made following the PRISMA statement. External evaluation was performed using NONMEM software, and the bias and inaccuracy values were calculated.

RESULTS

An external validation dataset from the Timone Hospital in Marseille, France, included 84 concentration samples from 27 patients. Four models of MERO were identified according to the inclusion criteria of the study. None of the models presented acceptable values of bias and inaccuracy.

CONCLUSION

While performing external evaluations on some populations may confirm a model's suitability to diverse groups of patients, there is still some variability that cannot be explained nor solved by the procedure. This brings to light the difficulty to develop only one model for ICU patients and the need to develop one specific model to each population of critically ill patients.

Assessing parameter uncertainty in small-n pharmacometric analyses: value of the log-likelihood profiling-based sampling importance resampling (LLP-SIR) technique

Assessing parameter uncertainty is a crucial step in pharmacometric workflows. Small datasets with ten or fewer subjects appear regularly in drug development and therapeutic use, but it is unclear which method to assess parameter uncertainty is preferable in such situations. The aim of this study was to (i) systematically evaluate the performance of standard error (SE), bootstrap (BS), log-likelihood profiling (LLP), Bayesian approaches (BAY) and sampling importance resampling (SIR) to assess parameter uncertainty in small datasets and (ii) to evaluate methods to provide proposal distributions for the SIR. A simulation study was conducted and the 0-95% confidence interval (CI) and coverage for each parameter was evaluated and compared to reference CIs derived by stochastic simulation and estimation (SSE). A newly proposed LLP-SIR, combining the proposal distribution provided by LLP with SIR, was included in addition to conventional SE-SIR and BS-SIR. Additionally, the methods were applied to a clinical dataset. The determined CIs differed substantially across the methods. The CIs of SE, BS, LLP and BAY were not in line with the reference in datasets with ≤ 10 subjects. The best alignment was found for the LLP-SIR, which also provided the best coverage results among the SIR methods. The best overall results regarding the coverage were provided by LLP and BAY across all parameters and dataset sizes. To conclude, the popular SE and BS methods are not suitable to derive parameter uncertainty in small datasets containing ≤ 10 subjects, while best performances were observed with LLP, BAY and LLP-SIR.

A systematic review of population pharmacokinetic analyses of digoxin in the paediatric population

This is a PROSPERO registered systematic review (CRD42018105207), conducted to summarize the available knowledge regarding the population pharmacokinetics of digoxin in paediatrics and to identify the sources of variability in its disposition. PubMed, ISI Web of Science, SCOPUS and Science Direct databases were searched from inception to January 2019. All paediatric population pharmacokinetic studies of digoxin that utilized the nonlinear mixed-effect modelling approach were incorporated in this review, and data were synthesized descriptively. After application of the inclusion-exclusion criteria 8 studies were included. Most studies described digoxin pharmacokinetics as a 1-compartment model with only 1 study describing its pharmacokinetics as 2-compartments. Age was an important predictor of clearance in studies involving neonates or infants, other predictors of clearance were weight, height, serum creatinine, coadministration of spironolactone and presence of congestive heart failure. Congestive heart failure was also associated with an increased volume of distribution in 1 study. The estimated value of apparent clearance in a typical individual standardized by mean weight ranged between 0.24 and 0.56 L/h/kg, the interindividual variability in clearance ranged between 7.0 and 35.1%. Half of the studies evaluated the performance of their developed models via external evaluation. In conclusion, substantial predictors of digoxin pharmacokinetics in the paediatric population in addition to model characteristics and evaluation techniques are presented. For clinicians, clearance could be predicted using age especially in neonates or infants, weight, height, serum creatinine, coadministration of medications and disease status. For future researchers, designing pharmacokinetic studies that allow 2-compartment modelling and linking pharmacokinetics with pharmacodynamics is recommended.

Results of a phase 1 multicentre investigation of dexmedetomidine bolus and infusion in corrective infant cardiac surgery

BACKGROUND

Dexmedetomidine (DEX) is increasingly used intraoperatively in infants undergoing cardiac surgery. This phase 1 multicentre study sought to: (i) determine the safety of DEX for cardiac surgery with cardiopulmonary bypass; (ii) determine the pharmacokinetics (PK) of DEX; (iii) create a PK model and dosing for steady-state DEX plasma levels; and (iv) validate the PK model and dosing.

METHODS

We included 122 neonates and infants (0-180 days) with D-transposition of the great arteries, ventricular septal defect, or tetralogy of Fallot. Dose escalation was used to generate NONMEM® PK modelling, and then validation was performed to achieve low (200-300 pg ml-1), medium (400-500 pg ml-1), and high (600-700 pg ml-1) DEX plasma concentrations.

RESULTS

Five of 122 subjects had adverse safety outcomes (4.1%; 95% confidence interval [CI], 1.8-9.2%). Two had junctional rhythm, two had second-/third-degree atrioventricular block, and one had hypotension. Clearance (CL) immediately postoperative and CL on CPB were reduced by approximately 50% and 95%, respectively, compared with pre-CPB CL. DEX clearance after CPB was 1240 ml min-1 70 kg-1. Age at 50% maximum clearance was approximately 2 days, and that at 90% maximum clearance was 18 days. Overall, 96.1% of measured DEX concentrations fell within the 5th-95th percentile prediction intervals in the PK model validation. Dosing strategies are recommended for steady-state DEX plasma levels ranging from 200 to 1000 pg ml-1.

CONCLUSIONS

When used with a careful dosing strategy, DEX results in low incidence and severity of adverse safety events in infants undergoing cardiac surgery with cardiopulmonary bypass. This validated PK model should assist clinicians in selecting appropriate dosing. The results of this phase 1 trial provide preliminary data for a phase 3 trial of DEX neuroprotection.

CLINICAL TRIALS REGISTRATION

NCT01915277.

Effect of renal clearance and continuous renal replacement therapy on appropriateness of recommended meropenem dosing regimens in critically ill patients with susceptible life-threatening infections

Background

Meropenem plasma concentration above a pathogen's MIC over the whole dosing interval (100% ƒT>MIC) is a determinant of outcome in severe infections. Significant variability of meropenem pharmacokinetics is reported in ICU patients.

Objectives

To characterize meropenem pharmacokinetics in variable CLCR or renal replacement therapy and assess the appropriateness of recommended regimens for MIC coverage.

Methods

A pharmacokinetic analysis (NONMEM) was conducted with external model validation. Patient characteristics were tested on meropenem clearance estimates, differentiated according to the presence/absence of continuous renal replacement therapy (CRRT, CLCRRT or CLno-CRRT). Simulations evaluated the appropriateness of recommended dosing for achieving 100% fT>MIC in 90% of patients.

Results

A total of 101 patients were studied: median 63 years (range 49-70), 56% male, SAPS II 38 (27-48). 32% had a CLCR >60 mL/min, 49% underwent CRRT and 32% presented severe sepsis or septic shock. A total of 127 pathogens were documented: 76% Gram-negatives, 24% Gram-positives (meropenem MIC90 2 mg/L, corresponding to EUCAST susceptibility breakpoint). Three hundred and eighty plasma and 129 filtrate-dialysate meropenem concentrations were analysed: two-compartment modelling best described the data. Predicted meropenem CLno-CRRT was 59% lower in impaired (CLCR 30 mL/min) compared to normal (CLCR 100 mL/min) renal function. Simulations showed that recommended regimens appropriately cover MIC90 in patients with CLCR <60 mL/min. Patients with CLCR of 60 to <90 mL/min need 6 g/day to achieve appropriate coverage. In patients with CLCR ≥90 mL/min, appropriate exposure is achieved with increased dose, frequency of administration and infusion duration, or continuous infusion.

Conclusions

Recommended meropenem regimens are suboptimal in ICU patients with normal or augmented renal clearance. Modified dosing or infusion modalities achieve appropriate MIC coverage for optimized antibacterial efficacy in meropenem-susceptible life-threatening infections.

Population pharmacokinetics and dosing regimen optimization of tacrolimus in Chinese pediatric hematopoietic stem cell transplantation patients

1. Several tacrolimus population pharmacokinetic (PPK) models in hematopoietic stem cell transplantation (HSCT) patients have been set up to recommend an optimal dosage schedule. However, the PPK model of Chinese pediatric HSCT patients has not been reported. The study is to investigate whether published PPK models of HSCT patients can be used to simulate Chinese pediatric HSCT patients and establish the tacrolimus PPK model of Chinese pediatric HSCT patients.2. Published PPK models were collected from the literature and assessed using Chinese pediatric HSCT patients via the individual prediction error method. The establishment of tacrolimus PPK model in Chinese pediatric HSCT patients was characterized with nonlinear mixed-effects modeling (NONMEM).3. Three published HSCT PPK models were identified, two of which could be applied to our external dataset. However, these models were dissatisfactory in terms of individual prediction error and, hence, inadequate for extrapolation. Finally, a new tacrolimus PPK model in Chinese pediatric HSCT patients was established. Based on the simulation results of our model, new initial dosage suggestions were recommended. In conclusion, the tacrolimus PPK model in Chinese pediatric HSCT patients was presented and the model could be used to predict individualized dosing regimens in children with HSCT.

Population pharmacokinetics of tacrolimus in pediatric refractory nephrotic syndrome and a summary of other pediatric disease models

Different tacrolimus (TAC) population pharmacokinetic (PPK) models have been established in various pediatric disease populations. However, a TAC PPK model for pediatric refractory nephrotic syndrome (PRNS) has not been well characterized. The current study aimed to establish a TAC PPK model in Chinese PRNS and provide a summary of previous literature concerning TAC PPK models in different pediatric diseases. A total of 147 TAC conventional therapeutic drug monitoring (TDM) data from multiple blood samples obtained from 65 Chinese patients with PRNS were characterized using nonlinear mixed-effects modeling. The impacts of demographic features, biological characteristics and drug combination were evaluated. Model validation was assessed using the bootstrap method. A one-compartment model with first-order absorption and elimination was determined to be the most suitable model for TDM data in PRNS. The absorption rate constant (Ka) was set at 4.48 h⁻¹. The typical values of apparent oral clearance (CL/F) and apparent volume of distribution (V/F) in the final model were 5.46 l/h and 57.1 l, respectively. The inter-individual variability of CL/F and V/F were 22.2 and 0.2%, respectively. The PPK equation for TAC was: CL/F = 5.46 × exponential function (EXP)(0.0323 × age) × EXP(−0.359 × cystatin-C) × EXP(0.148 × daily dose of TAC). No significant effects of covariates on V/F were observed. In conclusion, the current study developed and validated the first TAC PPK model for patients with PRNS. The study also provided a summary of previous literature concerning other TAC PPK models in different pediatric diseases.

Reproducible pharmacokinetics

Reproducibility is a highly desired feature of scientific investigation in general, and it has special connotations for research in pharmacokinetics, a vibrant field with over 500,000 publications to-date. It is important to be able to differentiate between genuine heterogeneity in pharmacokinetic parameters from heterogeneity that is due to errors and biases. This overview discusses efforts and opportunities to diminish the latter type of undesirable heterogeneity. Several reporting and research guidance documents and standards have been proposed for pharmacokinetic studies, but their adoption is still rather limited. Quality problems in the methods used and model evaluations have been examined in some empirical studies of the literature. Standardization of statistical and laboratory tools and procedures can be improved in the field. Only a small fraction of pharmacokinetic studies become pre-registered and only 9995 such studies have been registered in ClinicalTrials.gov as of August 2018. It is likely that most pharmacokinetic studies remain unpublished. Publication bias affecting the results and inferences has been documented in case studies, but its exact extent is unknown for the field at-large. The use of meta-analyses in the field is still limited. Availability of raw data, detailed protocols, software and codes is hopefully improving with multiple ongoing initiatives. Several research practices can contribute to greater transparency and reproducibility for pharmacokinetic investigations.

Systematic external evaluation of published population pharmacokinetic models of mycophenolate mofetil in adult kidney transplant recipients co-administered with tacrolimus

AIMS

Various mycophenolate mofetil (MMF) population pharmacokinetic (popPK) models have been developed to describe its PK characteristics and facilitate its optimal dosing in adult kidney transplant recipients co-administered with tacrolimus. However, the external predictive performance has been unclear. Thus, this study aimed to comprehensively evaluate the external predictability of published MMF popPK models in such populations and investigate the potential influencing factors.

METHODS

The external predictability of qualified popPK models was evaluated using an independent dataset. The evaluation included prediction- and simulation-based diagnostics, and Bayesian forecasting. In addition, factors influencing model predictability, especially the impact of structural models, were investigated.

RESULTS

Fifty full PK profiles from 45 patients were included in the evaluation dataset and 11 published popPK models were identified and evaluated. In prediction-based diagnostics, the prediction error within ±30% was less than 50% in most published models. The prediction- and variability-corrected visual predictive check and posterior predictive check showed large discrepancies between the observations and simulations in most models. Moreover, the normalized prediction distribution errors of all models did not follow a normal distribution. Bayesian forecasting demonstrated an improvement in the model predictability. Furthermore, the predictive performance of two-compartment (2CMT) models incorporating the enterohepatic circulation (EHC) process was not superior to that of conventional 2CMT models.

CONCLUSIONS

The published models showed large variability and unsatisfactory predictive performance, which indicated that therapeutic drug monitoring was necessary for MMF clinical application. Further studies incorporating potential covariates need to be conducted to investigate the key factors influencing model predictability of MMF.

Population Pharmacokinetics of Tacrolimus in Transplant Recipients: What Did We Learn About Sources of Interindividual Variabilities?

Tacrolimus, a calcineurin inhibitor, is a common immunosuppressant prescribed after organ transplantation and has notable inter- and intrapatient pharmacokinetic variability. The sources of variability have been investigated using population pharmacokinetic modeling over the last 2 decades. This article provides an updated synopsis on published nonlinear mixed-effects analyses developed for tacrolimus in transplant recipients. The objectives were to establish a detailed overview of the current data and to investigate covariate relationships determined by the models. Sixty-three published analyses were reviewed, and data regarding the study design, modeling approach, and resulting findings were extracted and summarized. Most of the studies investigated tacrolimus pharmacokinetics in adult and pediatric renal and liver transplants after administration of the immediate-release formulation. Model structures largely depended on the study sampling strategy, with ∼50% of studies developing a 1-compartment model using trough concentrations and a 2-compartment model with delayed absorption from intensive sampling. The CYP3A5 genotype, as a covariate, consistently impacted tacrolimus clearance, and dosing adjustments were required to achieve similar drug exposure among patients. Numerous covariates were identified as sources of interindividual variability on tacrolimus pharmacokinetics with limited consistency across these studies, which may be the result of the study designs. Additional analyses are required to further evaluate the potential impact of these covariates and the clinical implementation of these models to guide tacrolimus dosing recommendations. This article may be useful for guiding the design of future population pharmacokinetic studies and provides recommendations for the selection of an existing optimal model to individualize tacrolimus therapy.

Parameters of vancomycin pharmacokinetics in postoperative patients with renal dysfunction: comparing the results of a pharmacokinetic study and mathematical modeling

Mathematical modeling of pharmacokinetic (PK) and pharmacodynamic (PD) parameters essential for establishing correct dosing regimens is an alternative to pharmacokinetic studies (PKS) adopted in the clinical setting. The aim of this work was to compare the values of PK parameters for vancomycin obtained in an actual PKS and through MM in postoperative patients with kidney injury. Our prospective study included 61 patients (47 males and 14 females aged 60.59 ± 12.23 years). During PKS, drug concentrations at steady state Сtrough and Cpeak were measured by high-performance liquid chromatography followed by the calculation of the area under the plasma concentration-time curve AUC24. For mathematical modeling, a single-compartment model was employed; PK parameters were estimated using R 3.4.0. The values of Ctrough measured 48 h after the onset of antibiotic therapy during PKS were significantly lower than those predicted by MM (р = 0.004). In a group of patients with acute kidney injury (AKI), AUC24 measured at the end of treatment was significantly higher than its value predicted by MM (р = 0.011). The probability of achieving the target AUC24 to MIC ratio of over 400 μg•h /ml is higher in the group of patients with Ctrough = 10–15 μg /ml. Our findings confirm that the use of MM in postoperative patients with renal dysfunction is limited and therapeutic drug monitoring should be used instead.

Personalized weight change prediction in the first week of life

BACKGROUND & AIMS

Almost all neonates show physiological weight loss and consecutive weight gain after birth. The resulting weight change profiles are highly variable as they depend on multiple neonatal and maternal factors. This limits the value of weight nomograms for the early identification of neonates at risk for excessive weight loss and related morbidities. The objective of this study was to characterize weight changes and the effect of supplemental feeding in late preterm and term neonates during the first week of life, to identify and quantify neonatal and maternal influencing factors, and to provide an educational online prediction tool.

METHODS

Longitudinal weight data from 3638 healthy term and late preterm neonates were prospectively recorded up to 7 days of life. Two-thirds (n = 2425) were randomized to develop a semi-mechanistic model characterizing weight change as a balance between time-dependent rates of weight gain and weight loss. The dose-dependent effect of supplemental feeding on weight gain was characterized. A population analysis applying nonlinear mixed-effects modeling was performed using NONMEM 7.3. The model was evaluated on the remaining third of neonates (n = 1213).

RESULTS

Key population characteristics (median [range]) of the whole sample were gestational age 39.9 [34.4-42.4] weeks, birth weight 3400 [1980-5580] g, maternal age 32 [15-51] years, cesarean section 26%, and girls 50%. The model demonstrated good predictive performance (bias 0.01%, precision 0.56%), and is able to accurately predict individual weight change (bias 0.15%, precision 1.43%) and the dose-dependent effects of supplemental feeding up to 1 week after birth based on weight measurements during the first 3 days of life, including birth weight, and the following characteristics: gestational age, gender, delivery mode, type of feeding, maternal age, and parity.

CONCLUSIONS

We present the first mathematical model not only to describe weight change in term and late preterm neonates but also to provide an educational online tool for personalized weight prediction in the first week of life.

Semi-mechanistic pharmacokinetic-pharmacodynamic modelling of antibiotic drug combinations

BACKGROUND

Deriving suitable dosing regimens for antibiotic combination therapy poses several challenges as the drug interaction can be highly complex, the traditional pharmacokinetic-pharmacodynamic (PKPD) index methodology cannot be applied straightforwardly, and exploring all possible dose combinations is unfeasible. Therefore, semi-mechanistic PKPD models developed based on in vitro single and combination experiments can be valuable to suggest suitable combination dosing regimens.

AIMS

To outline how the interaction between two antibiotics has been characterized in semi-mechanistic PKPD models. We also explain how such models can be applied to support dosing regimens and design future studies.

SOURCES

PubMed search for published semi-mechanistic PKPD models of antibiotic drug combinations.

CONTENT

Thirteen publications were identified where ten had applied subpopulation synergy to characterize the combined effect, i.e. independent killing rates for each drug and bacterial subpopulation. We report the various types of interaction functions that have been used to describe the combined drug effects and that characterized potential deviations from additivity under the PKPD model. Simulations from the models had commonly been performed to compare single versus combined dosing regimens and/or to propose improved dosing regimens.

IMPLICATIONS

Semi-mechanistic PKPD models allow for integration of knowledge on the interaction between antibiotics for various PK and PD profiles, and can account for associated variability within the population as well as parameter uncertainty. Decisions on suitable combination regimens can thereby be facilitated. We find the application of semi-mechanistic PKPD models to be essential for efficient development of antibiotic combination regimens that optimize bacterial killing and/or suppress resistance development.

External Evaluation of Two Fluconazole Infant Population Pharmacokinetic Models

Fluconazole is an antifungal agent used for the treatment of invasive candidiasis, a leading cause of morbidity and mortality in premature infants. Population pharmacokinetic (PK) models of fluconazole in infants have been previously published by Wade et al. (Antimicrob Agents Chemother 52:4043-4049, 2008, https://doi.org/10.1128/AAC.00569-08) and Momper et al. (Antimicrob Agents Chemother 60:5539-5545, 2016, https://doi.org/10.1128/AAC.00963-16). Here we report the results of the first external evaluation of the predictive performance of both models. We used patient-level data from both studies to externally evaluate both PK models. The predictive performance of each model was evaluated using the model prediction error (PE), mean prediction error (MPE), mean absolute prediction error (MAPE), prediction-corrected visual predictive check (pcVPC), and normalized prediction distribution errors (NPDE). The values of the parameters of each model were reestimated using both the external and merged data sets. When evaluated with the external data set, the model proposed by Wade et al. showed lower median PE, MPE, and MAPE (0.429 μg/ml, 41.9%, and 57.6%, respectively) than the model proposed by Momper et al. (2.45 μg/ml, 188%, and 195%, respectively). The values of the majority of reestimated parameters were within 20% of their respective original parameter values for all model evaluations. Our analysis determined that though both models are robust, the model proposed by Wade et al. had greater accuracy and precision than the model proposed by Momper et al., likely because it was derived from a patient population with a wider age range. This study highlights the importance of the external evaluation of infant population PK models.

Predicting CYP3A-mediated midazolam metabolism in critically ill neonates, infants, children and adults with inflammation and organ failure

AIMS

Inflammation and organ failure have been reported to have an impact on cytochrome P450 (CYP) 3A-mediated clearance of midazolam in critically ill children. Our aim was to evaluate a previously developed population pharmacokinetic model both in critically ill children and other populations, in order to allow the model to be used to guide dosing in clinical practice.

METHODS

The model was evaluated externally in 136 individuals, including (pre)term neonates, infants, children and adults (body weight 0.77-90 kg, C-reactive protein level 0.1-341 mg l^-1 and 0-4 failing organs) using graphical and numerical diagnostics.

RESULTS

The pharmacokinetic model predicted midazolam clearance and plasma concentrations without bias in postoperative or critically ill paediatric patients and term neonates [median prediction error (MPE) <30%]. Using the model for extrapolation resulted in well-predicted clearance values in critically ill and healthy adults (MPE <30%), while clearance in preterm neonates was over predicted (MPE >180%).

CONCLUSION

The recently published pharmacokinetic model for midazolam, quantifying the influence of maturation, inflammation and organ failure in children, yields unbiased clearance predictions and can therefore be used for dosing instructions in term neonates, children and adults with varying levels of critical illness, including healthy adults, but not for extrapolation to preterm neonates.

External evaluation of population pharmacokinetic models for ciclosporin in adult renal transplant recipients

AIMS

Several population pharmacokinetic (popPK) models for ciclosporin (CsA) in adult renal transplant recipients have been constructed to optimize the therapeutic regimen of CsA. However, little is known about their predictabilities when extrapolated to different clinical centres. Therefore, this study aimed to externally evaluate the predictive ability of CsA popPK models and determine the potential influencing factors.

METHODS

A literature search was conducted and the predictive performance was determined for each selected model using an independent data set of 62 patients (471 predose and 500 2-h postdose concentrations) from our hospital. Prediction-based diagnostics and simulation-based normalized prediction distribution error were used to evaluate model predictability. The influence of prior information was assessed using Bayesian forecasting. Additionally, potential factors influencing model predictability were investigated.

RESULTS

Seventeen models extracted from 17 published popPK studies were assessed. Prediction-based diagnostics showed that ethnicity potentially influenced model transferability. Simulation-based normalized prediction distribution error analyses indicated misspecification in most of the models, especially regarding variance. Bayesian forecasting demonstrated that the predictive performance of the models substantially improved with 2-3 prior observations. The predictability of nonlinear Michaelis-Menten models was superior to that of linear compartmental models when evaluating the impact of structural models, indicating the underlying nonlinear kinetics of CsA. Structural model, ethnicity, covariates and prior observations potentially affected model predictability.

CONCLUSIONS

Structural model is the predominant factor influencing model predictability. Incorporation of nonlinear kinetics in CsA popPK modelling should be considered. Moreover, Bayesian forecasting substantially improved model predictability.

Predictive Performance of a Recently Developed Population Pharmacokinetic Model for Morphine and its Metabolites in New Datasets of (Preterm) Neonates, Infants and Children

BACKGROUND AND OBJECTIVE

Model validation procedures are crucial when models are to be used to develop new dosing algorithms. In this study, the predictive performance of a previously published paediatric population pharmacokinetic model for morphine and its metabolites in children younger than 3 years (original model) is studied in new datasets that were not used to develop the original model.

METHODS

Six external datasets including neonates and infants up to 1 year were obtained from four different research centres. These datasets contained postoperative patients, ventilated patients and patients on extracorporeal membrane oxygenation (ECMO) treatment. Basic observed versus predicted plots, normalized prediction distribution error analysis, model refitting, bootstrap analysis, subpopulation analysis and a literature comparison of clearance predictions were performed with the new datasets to evaluate the predictive performance of the original morphine pharmacokinetic model.

RESULTS

The original model was found to be stable and the parameter estimates were found to be precise. The concentrations predicted by the original model were in good agreement with the observed concentrations in the four datasets from postoperative and ventilated patients, and the model-predicted clearances in these datasets were in agreement with literature values. In the datasets from patients on ECMO treatment with continuous venovenous haemofiltration (CVVH) the predictive performance of the model was good as well, whereas underprediction occurred, particularly for the metabolites, in patients on ECMO treatment without CVVH.

CONCLUSION

The predictive value of the original morphine pharmacokinetic model is demonstrated in new datasets by the use of six different validation and evaluation tools. It is herewith justified to undertake a proof-of-principle approach in the development of rational dosing recommendations - namely, performing a prospective clinical trial in which the model-based dosing algorithm is clinically evaluated.

Phenobarbital in intensive care unit pediatric population: predictive performances of population pharmacokinetic model

An external evaluation of phenobarbital population pharmacokinetic model described by Marsot et al. was performed in pediatric intensive care unit. Model evaluation is an important issue for dose adjustment. This external evaluation should allow confirming the proposed dosage adaptation and extending these recommendations to the entire intensive care pediatric population. External evaluation of phenobarbital published population pharmacokinetic model of Marsot et al. was realized in a new retrospective dataset of 35 patients hospitalized in a pediatric intensive care unit. The published population pharmacokinetic model was implemented in nonmem 7.3. Predictive performance was assessed by quantifying bias and inaccuracy of model prediction. Normalized prediction distribution errors (NPDE) and visual predictive check (VPC) were also evaluated. A total of 35 infants were studied with a mean age of 33.5 weeks (range: 12 days-16 years) and a mean weight of 12.6 kg (range: 2.7-70.0 kg). The model predicted the observed phenobarbital concentrations with a reasonable bias and inaccuracy. The median prediction error was 3.03% (95% CI: -8.52 to 58.12%), and the median absolute prediction error was 26.20% (95% CI: 13.07-75.59%). No trends in NPDE and VPC were observed. The model previously proposed by Marsot et al. in neonates hospitalized in intensive care unit was externally validated for IV infusion administration. The model-based dosing regimen was extended in all pediatric intensive care unit to optimize treatment. Due to inter- and intravariability in pharmacokinetic model, this dosing regimen should be combined with therapeutic drug monitoring.

Evidence-based drug treatment for special patient populations through model-based approaches

The majority of marketed drugs remain understudied in some patient populations such as pregnant women, paediatrics, the obese, the critically-ill, and the elderly. As a consequence, currently used dosing regimens may not assure optimal efficacy or minimal toxicity in these patients. Given the vulnerability of some subpopulations and the challenges and costs of performing clinical studies in these populations, cutting-edge approaches are needed to effectively develop evidence-based and individualized drug dosing regimens. Five key issues are presented that are essential to support and expedite the development of drug dosing regimens in these populations using model-based approaches: 1) model development combined with proper validation procedures to extract as much valid information from available study data as possible, with limited burden to patients and costs; 2) integration of existing data and the use of prior pharmacological and physiological knowledge in study design and data analysis, to further develop knowledge and avoid unnecessary or unrealistic (large) studies in vulnerable populations; 3) clinical proof-of-principle in a prospective evaluation of a developed drug dosing regimen, to confirm that a newly proposed regimen indeed results in the desired outcomes in terms of drug concentrations, efficacy, and/or safety; 4) pharmacodynamics studies in addition to pharmacokinetics studies for drugs for which a difference in disease progression and/or in exposure-response relation is anticipated compared to the reference population; 5) additional efforts to implement developed dosing regimens in clinical practice once drug pharmacokinetics and pharmacodynamics have been characterized in special patient populations. The latter remains an important bottleneck, but this is essential to truly realize evidence-based and individualized drug dosing for special patient populations. As all tools required for this purpose are available, we have the moral and societal obligation to make safe and effective pharmacotherapy available for these patients too.