Assessing Predictive Performance of Published Population Pharmacokinetic Models of Intravenous Tobramycin in Pediatric Patients

Is there a multinational consensus of tobramycin prescribing and monitoring for cystic fibrosis? Survey of current therapeutic drug monitoring practices in USA/Canada, UK/Ireland, and Australia/New Zealand

OBJECTIVES

Sophisticated scientific methods have facilitated dose individualisation with substantial advancements in therapeutic drug monitoring (TDM) practice. It is unclear whether these methods have translated to the clinical setting. This study aimed to determine current TDM practice for tobramycin monitoring in cystic fibrosis (CF) centres in the USA and Canada, UK and Ireland, and Australia and New Zealand due to a high prevalence of CF.

METHODS

A web-based survey was developed and circulated via CF specialist groups within the targeted geographical regions. Themes included centre demographics, tobramycin usage, dosing and infusion practices, TDM practices, and blood sampling methods.

RESULTS

In total 77 responses were received from 75 different CF centres over the 3-month evaluation period (October 2019-January 2020). Respondents were from the USA and Canada (60%), Australia and New Zealand (25%), and the UK and Ireland (15%). Tobramycin was used in 97% of sites, with an international variation in practice across all survey aspects including dosing and infusion practice. TDM-based dose adjustment in the UK and Ireland was most commonly based only on trough sample collection for avoidance of toxicity, where use of computer programs for targeting both efficacy and toxicity endpoints were most common in Australia and New Zealand. The underlying pharmacokinetic basis of that program was not known by 33% of sites who utilised a computer program for tobramycin dose individualisation.

CONCLUSION

There remains substantial heterogeneity in tobramycin management worldwide. Despite two decades of research into TDM of tobramycin, there has been a slow uptake of new technologies and evolution of practice. An improved understanding of TDM processes is required for translation of evidence-based research into clinical practice. International guidelines require updating due to the advances in research to support confidence in the changes in clinical practice.

Necessity of Tobramycin trough Levels in Once Daily Iv-Treatment in Patients with Cystic Fibrosis

BACKGROUND

Once daily intravenous (iv) treatment with tobramycin for Pseudomonas aeruginosa infection in patients with cystic fibrosis (pwCF) is frequently monitored by measuring tobramycin trough levels (TLs). Although the necessity of these TLs is recently questioned in pwCF without renal impairment, no study has evaluated this so far. The aim of this observational study was to evaluate the frequency of increased tobramycin TLs in pwCF treated with a once daily tobramycin dosing protocol.

METHODS

Patient records of all consecutive once daily iv tobramycin courses in 35 pwCF between 07/2009 and 07/2019 were analyzed for tobramycin level, renal function, co-medication and comorbidity.

RESULTS

Eight elevated TLs (2.9% of 278 courses) were recorded in four patients, two with normal renal function. One of these resolved without adjustment of tobramycin dosages suggesting a test timing or laboratory error. In the other patient the elevated tobramycin level decreased after tobramycin dosage adjustment. Six of the elevated levels occurred in two patients with chronic renal failure. In 15 other patients with reduced glomerular filtration rate (GFR) (36 courses) but normal range creatinine no case of elevated tobramycin trough levels was detected. Neither cumulative tobramycin dosages nor concomitant diabetes or nutritional status were risk factors for elevated TLs.

CONCLUSION

Our data show that elevated tobramycin TLs are rare but cannot be excluded, so determination of tobramycin TLs is still recommended for safety.

Population Pharmacokinetics and Pharmacogenetics Analyses of Dasatinib in Chinese Patients with Chronic Myeloid Leukemia

AIMS

Dasatinib, a second-generation tyrosine kinase inhibitor of BCR-ABL 1, used for first-line treatment of Philadelphia chromosome-positive chronic myeloid leukemia (CML), exhibits high pharmacokinetic (PK) variability. However, its PK data in Chinese patients with CML remains rarely reported to date. Thus, we developed a population pharmacokinetic (PPK) model of dasatinib in Chinese patients and identified the covariate that could explain the individual variability of PK for optimal individual administration.

METHODS

PPK modeling for dasatinib was performed based on 754 plasma concentrations obtained from 140 CML patients and analysis of various genetic and physicochemical parameters. Modeling was performed with nonlinear mixed-effects (NLME) using Phoenix NLME. The finally developed model was evaluated using internal and external validation. Monte Carlo simulations were used to predict drug exposures at a steady state for various dosages.

RESULTS

The PK of dasatinib were well described by a two-compartment with a log-additive residual error model. Patients in the current study had a relatively low estimate of CL/F (126 L/h). A significant association was found between the covariate of age and CL/F of dasatinib, which was incorporated into the final model. None of the genetic factors was confirmed as a significant covariate for dasatinib. The results of external validation with 140 samples from 36 patients were acceptable. Simulation results showed significantly higher exposures in elderly patients.

CONCLUSIONS

This study's findings suggested that low-dose dasatinib would be better suited for Chinese patients, and the dosage can be appropriately reduced according to the increase of age, especially for the elderly.

Population Pharmacokinetic Model of Methotrexate in Brazilian Pediatric Patients with Acute Lymphoblastic Leukemia

OBJECTIVES

Methotrexate (MTX) is subject to therapeutic drug monitoring because of its high pharmacokinetic variability and safety risk outside the therapeutic window. This study aimed to develop a population pharmacokinetic model (popPK) of MTX for Brazilian pediatric acute lymphoblastic leukemia (ALL) patients who attended the Hospital de Clínicas de Porto Alegre, Brazil.

METHODS

The model was developed using NONMEM 7.4 (Icon®), ADVAN3 TRANS4, and FOCE-I. To explain inter-individual variability, we evaluated covariates from demographic, biochemical, and genetic data (single nucleotide polymorphisms [SNPs] related to the transport and metabolism of drugs).

RESULTS

A two-compartment model was built using 483 data points from 45 patients (0.33-17.83 years of age) treated with MTX (0.25-5 g/m2) in different cycles. Serum creatinine (SCR), height (HT), blood urea nitrogen (BUN) and a low BMI stratification (according to the z-score defined by the World Health Organization [LowBMI]) were added as clearance covariates. The final model described MTX clearance as [Formula: see text]. In the two-compartment structural model, the central and peripheral compartment volumes were 26.8 L and 8.47 L, respectively, and the inter-compartmental clearance was 0.218 L/h. External validation of the model was performed through a visual predictive test and metrics using data from 15 other pediatric ALL patients.

CONCLUSION

The first popPK model of MTX was developed for Brazilian pediatric ALL patients, which showed that inter-individual variability was explained by renal function and factors related to body size.

External Evaluation of Population Pharmacokinetic Models of Methotrexate for Model-Informed Precision Dosing in Pediatric Patients with Acute Lymphoid Leukemia

BACKGROUND

Methotrexate (MTX) is a key immunosuppressant for children with acute lymphoid leukemia (ALL), and it has a narrow therapeutic window and relatively high pharmacokinetic variability. Several population pharmacokinetic (PopPK) models of MTX in ALL children have been reported, but the validity of these models for model-informed precision dosing in clinical practice is unclear. This study set out to evaluate the predictive performance of published pediatric PopPK models of MTX using an independent patient cohort.

METHODS

A PubMed literature search was performed to identify suitable models for evaluation. Demographics and measurements of the validation dataset were retrospectively collected from the medical records of ALL children who had received intravenous MTX. Predictive performance for each model was assessed by visual comparison of predictions to observations, median and mean predicted error (PE), and relative root mean squared error (RMSE).

RESULTS

Six models were identified for external evaluation, carried out on a dataset containing 354 concentrations from 51 pediatrics. Model performance varied considerably from one model to another. Different models had the median PE for population and individual predictions at -33.23% to 442.04% and -25.20% to 6.52%, mean PE for population and individual predictions at -25.51% to 780.87% and 1.33% to 64.44%, and RMSE for population and individual predictions at 62.88% to 1182.24% and 63.39% to 152.25%. All models showed relatively high RMSE.

CONCLUSIONS

Some of the published models showed reasonably low levels of bias but had some problems with imprecision, and extensive evaluation is needed before model application in clinical practice.

Predicting Antibiotic Effect of Vancomycin Using Pharmacokinetic/Pharmacodynamic Modeling and Simulation: Dense Sampling versus Sparse Sampling

This study aimed to investigate the effect of a structural pharmacokinetic (PK) model with fewer compartments developed following sparse sampling on the PK parameter estimation and the probability of target attainment (PTA) prediction of vancomycin. Two- and three-compartment PK models of vancomycin were used for the virtual concentration–time profile simulation. Datasets with reduced blood sampling times were generated to support a model with a lesser number of compartments. Monte Carlo simulation was conducted to evaluate the PTA. For the two-compartment PK profile, the total clearance (CL) of the reduced one-compartment model showed a relative bias (RBias) and relative root mean square error (RRMSE) over 90%. For the three-compartment PK profile, the CL of the reduced one-compartment model represented the largest RBias and RRMSE, while the steady-state volume of distribution of the reduced two-compartment model represented the largest absolute RBias and RRMSE. A lesser number of compartments corresponded to a lower predicted area under the concentration–time curve of vancomycin. The estimated PK parameters and predicted PK/PD index from models built with sparse sampling designs that cannot support the PK profile can be significantly inaccurate and unprecise. This might lead to the misprediction of the PTA and selection of improper dosage regimens when clinicians prescribe antibiotics.

A Pharmacokinetic Analysis of Tobramycin in Patients Less than Five Years of Age with Cystic Fibrosis: Assessment of Target Attainment with Extended-Interval Dosing through Simulation

Extended interval dosing of tobramycin is recommended for treatment of pulmonary exacerbations in adults and older children with cystic fibrosis (CF), but data are limited in patients less than 5 years of age. We performed a retrospective population pharmacokinetic (PK) analysis of hospitalized children with CF <5 years of age prescribed intravenous tobramycin for a pulmonary exacerbation from March 2011 to September 2018 at our hospital. Children with normal renal function who had ≥1 tobramycin concentration available were included. Nonlinear mixed effects population PK modeling was performed using NONMEM using data from the first 48 h of tobramycin treatment. Monte Carlo simulations were implemented to determine the fraction of simulated patients that met published therapeutic targets with regimens of 10-15 mg/kg/day once-daily dosing. Fifty-eight patients received 111 tobramycin courses (range 1-9/patient). A two-compartment model best described the data. Age, glomerular filtration rate, and vancomycin coadministration were significant covariates on tobramycin clearance. The typical values of clearance and central volume of distribution were 0.252 L/hr/kg^0.75 and 0.308 L/kg, respectively. No once-daily regimens achieved all pre-specified targets simultaneously in >75% of simulated subjects. A dosage of 13 mg/kg/dose best met the predefined targets of C_max >25 mg/L and AUC₂₄ of 80-120 mg·h/L. Based on our population PK analysis and simulations, once-daily dosing of tobramycin would not achieve all therapeutic goals in young patients with CF. However, extended-interval dosing regimens may attain therapeutic targets in the majority of young patients.

Model-informed precision dosing of vancomycin via continuous infusion: a clinical fit-for-purpose evaluation of published PK models

BACKGROUND

Model-informed precision dosing (MIPD) is an innovative approach used to guide bedside vancomycin dosing. The use of Bayesian software requires suitable and externally validated population pharmacokinetic (popPK) models.

OBJECTIVES

Therefore, we aimed to identify suitable popPK models for a priori prediction and a posteriori forecasting of vancomycin in continuous infusion. Additionally, a model averaging (MAA) and a model selection approach (MSA) were compared with the identified popPK models.

METHODS

. Clinical PK data were retrospectively collected from patients receiving continuous vancomycin therapy and admitted to a general ward of three large Belgian hospitals. The predictive performance of the popPK models, identified in a systematic literature search, as well as the MAA/MSA was evaluated for the a priori and a posteriori scenarios using bias, root mean square errors, normalized prediction distribution errors and visual predictive checks.

RESULTS

The predictive performance of 23 popPK models was evaluated based on clinical data from 169 patients and 923 therapeutic drug monitoring samples. Overall, the best predictive performance was found using the Okada model (bias < -0.1 mg/L), followed by the Colin model. The MAA/MSA predicted with a constantly high precision and low inaccuracy and were clinically acceptable in the Bayesian forecasting.

CONCLUSION

This study identified the two-compartmental models of Okada et al. and Colin et al. as most suitable for non-ICU patients to forecast individual exposure profiles after continuous vancomycin infusion. The MAA/MSA performed equally good as the individual popPK models. Both approaches could therefore be used in clinical practice to guide dosing decisions.

Assessing the predictive performance of population pharmacokinetic models for intravenous polymyxin B in critically ill patients

Polymyxin B (PMB) has reemerged as a last‐line therapy for infections caused by multidrug‐resistant gram‐negative pathogens, but dosing is challenging because of its narrow therapeutic window and pharmacokinetic (PK) variability. Population PK (POPPK) models based on suitably powered clinical studies with appropriate sampling strategies that take variability into consideration can inform PMB dosing to maximize efficacy and minimize toxicity and resistance. Here we reviewed published PMB POPPK models and evaluated them using an external validation data set (EVD) of patients who are critically ill and enrolled in an ongoing clinical study to assess their utility. Seven published POPPK models were employed using the reported model equations, parameter values, covariate relationships, interpatient variability, parameter covariance, and unexplained residual variability in NONMEM (Version 7.4.3). The predictive ability of the models was assessed using prediction‐based and simulation‐based diagnostics. Patient characteristics and treatment information were comparable across studies and with the EVD (n = 40), but the sampling strategy was a main source of PK variability across studies. All models visually and statistically underpredicted EVD plasma concentrations, but the two‐compartment models more accurately described the external data set. As current POPPK models were inadequately predictive of the EVD, creation of a new POPPK model based on an appropriately powered clinical study with an informed PK sampling strategy would be expected to improve characterization of PMB PK and identify covariates to explain interpatient variability. Such a model would support model‐informed precision dosing frameworks, which are urgently needed to improve PMB treatment efficacy, limit resistance, and reduce toxicity in patients who are critically ill.

Population Pharmacokinetics of Meropenem in Critically Ill Korean Patients and Effects of Extracorporeal Membrane Oxygenation

Limited studies have investigated population pharmacokinetic (PK) models and optimal dosage regimens of meropenem for critically ill adult patients using the probability of target attainment, including patients receiving extracorporeal membrane oxygenation (ECMO). A population PK analysis was conducted using non-linear mixed-effect modeling. Monte Carlo simulation was used to determine for how long the free drug concentration was above the minimum inhibitory concentration (MIC) at steady state conditions in patients with various degrees of renal function. Meropenem PK in critically ill patients was described using a two-compartment model, in which glomerular filtration rate was identified as a covariate for clearance. ECMO did not affect meropenem PK. The simulation results showed that the current meropenem dosing regimen would be sufficient for attaining 40%fT_>MIC for Pseudomonas aeruginosa at MIC ≤ 4 mg/L. Prolonged infusion over 3 h or a high-dosage regimen of 2 g/8 h was needed for MIC > 2 mg/L or in patients with augmented renal clearance, for a target of 100%fT_>MIC or 100%fT_>4XMIC. Our study suggests that clinicians should consider prolonged infusion or a high-dosage regimen of meropenem, particularly when treating critically ill patients with augmented renal clearance or those infected with pathogens with decreased in vitro susceptibility, regardless of ECMO support.

Pharmacokinetic and Pharmacodynamic Optimization of Antibiotic Therapy in Cystic Fibrosis Patients: Current Evidences, Gaps in Knowledge and Future Directions

Antibiotic therapy is one of the main treatments for cystic fibrosis (CF). It aims to eradicate bacteria during early infection, calms down the inflammatory process, and leads to symptom resolution of pulmonary exacerbations. CF can modify both the pharmacokinetic (PK) and pharmacodynamic (PD) profiles of antibiotics, therefore specific PK/PD endpoints should be determined in the context of CF. Currently available data suggest that optimal PK/PD targets cannot be attained in sputum with intravenous aminoglycosides. Continuous infusion appears preferable for β-lactam antibiotics, but optimal concentrations in sputum are unlikely to be reached, with some possible exceptions such as meropenem and ceftolozane. Usual doses are likely suboptimal for fluoroquinolones and linezolid, whereas daily doses of 45-60 mg/kg and 200 mg could be convenient for vancomycin and doxycycline, respectively. Weekly azithromycin doses of 22-30 mg/kg could also be appropriate for its anti-inflammatory effect. The difficulty with achieving optimal concentrations supports the use of combined treatments and the inhaled administration route, as very high local concentrations, concomitantly with low systemic exposure, can be obtained with the inhaled route for aminoglycosides, colistin, and fluoroquinolones, thus minimizing the risk of toxicity.

Dosing Recommendations for Lamotrigine in Children: Evaluation Based on Previous and New Population Pharmacokinetic Models

Lamotrigine is a broad-spectrum antiepileptic drug with high interindividual variability in serum concentrations in children. The aims of this study were to evaluate the predictive performance of pediatric population pharmacokinetic (PPK) models published on lamotrigine, to build a new model with our monitoring data and to evaluate the current recommended doses. A validation cohort included patients treated with lamotrigine who had a serum level assayed during therapeutic drug monitoring (TDM). PPK models published in the literature were first applied to the validation cohort. We assessed their predictive performance using mean prediction errors, root mean squared errors, and visual predictive checks. A new model was then built using the data. Dose simulations were performed to evaluate the doses recommended. We included 270 lamotrigine concentrations ranging from 0.5 to 17.9 mg/L from 175 patients. The median (range) age and weight were 11.8 years (0.8-18 years) and 32.7 kg (8-110 kg). We tested 6 PPK models; most had acceptable bias and precision but underestimated the variability of the cohort. We built a 1-compartment model with first-order absorption and elimination, allometric scaling, and effects of inhibitor and inducer comedications. In our cohort, 22.6% of trough concentrations were below 2.5 mg/L. In conclusion, we proposed a PPK model that can be used for TDM of lamotrigine in children. In our population, a high percentage of children had low trough concentrations of lamotrigine. As the intervals of recommended doses are large, we suggest aiming at the higher range of doses to reach the target concentration.

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.

The role of antibiotic pharmacokinetic studies performed post-licensing

Post-licensing pharmacometric studies can provide a better understanding of the pharmacokinetic (PK) alterations in special patient populations and may lead to better clinical outcomes. Some patient populations exhibit markedly different pathophysiology to general ward patients or healthy individuals. This may be developmental (paediatric patients), a manifestation of an underlying disease pathology (patients with obesity or haematological malignancies) or due to medical interventions (critically ill patients receiving extracorporeal therapies). This paper outlines the factors that affect the PK of special patient populations and describes some novel methods of antimicrobial administration that may increase antimicrobial concentrations at the site of infection and improve treatment of severe infection.

Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy

Currently employed methods for qualifying population physiologically-based pharmacokinetic (Pop-PBPK) model predictions of continuous outcomes (e.g., concentration-time data) fail to account for within-subject correlations and the presence of residual error. In this study, we propose a new method for evaluating Pop-PBPK model predictions that account for such features. The approach focuses on deriving Pop-PBPK-specific normalized prediction distribution errors (NPDE), a metric that is commonly used for population pharmacokinetic model validation. We describe specific methodological steps for computing NPDE for Pop-PBPK models and define three measures for evaluating model performance: mean of NPDE, goodness-of-fit plots, and the magnitude of residual error. Utility of the proposed evaluation approach was demonstrated using two simulation-based study designs (positive and negative control studies) as well as pharmacokinetic data from a real-world clinical trial. For the positive-control simulation study, where observations and model simulations were generated under the same Pop-PBPK model, the NPDE-based approach denoted a congruency between model predictions and observed data (mean of NPDE =  - 0.01). In contrast, for the negative-control simulation study, where model simulations and observed data were generated under different Pop-PBPK models, the NPDE-based method asserted that model simulations and observed data were incongruent (mean of NPDE =  - 0.29). When employed to evaluate a previously developed clindamycin PBPK model against prospectively collected plasma concentration data from 29 children, the NPDE-based method qualified the model predictions as successful (mean of NPDE = 0). However, when pediatric subpopulations (e.g., infants) were evaluated, the approach revealed potential biases that should be explored.

Monitoring of Tobramycin Exposure: What is the Best Estimation Method and Sampling Time for Clinical Practice?

OBJECTIVES

The objective of this article is to investigate the influence of blood sampling times on tobramycin exposure estimation and clinical decisions and to determine the best sampling times for two estimation methods used for therapeutic drug monitoring.

METHODS

Adult patients with cystic fibrosis, treated with once-daily intravenous tobramycin, were intensively sampled over one 24-h dosing interval to determine true exposure (AUC_0-24). The AUC_0-24s were then estimated using both log-linear regression and Bayesian forecasting methods for 21 different sampling time combinations. These were compared to true exposure using relative prediction errors. The differences in subsequent dose recommendations were calculated.

RESULTS

Twelve patients, with a median (range) age of 25 years (18-36) and weight of 66.5 kg (50.6-76.4) contributed 96 tobramycin concentrations. Five hundred and eighty-eight estimated AUC_0-24s were compared to 12 measured true AUC_0-24 values. Median relative prediction errors ranged from - 34.7 to 45.5% for the log-linear regression method and from - 14.46 to 11.23% for the Bayesian forecasting method across the 21 sampling combinations. The most unbiased exposure estimation was provided from concentrations sampled at 100/640 min after the start of the infusion using log-linear regression and at 70/160 min using Bayesian forecasting. Subsequent dosing recommendations varied greatly depending on the estimation method and the sampling times used.

CONCLUSION

Sampling times markedly influence bias in AUC_0-24 estimation, leading to greatly varied dose adjustments. The impact of blood sampling times on dosing decisions is reduced when using Bayesian forecasting.

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.

Evaluation of Tobramycin Exposure Predictions in Three Bayesian Forecasting Programmes Compared with Current Clinical Practice in Children and Adults with Cystic Fibrosis

BACKGROUND AND OBJECTIVES

Bayesian forecasting (BF) methods for tobramycin dose individualisation has not seen widespread clinical adoption, despite being endorsed by clinical practice guidelines. Several freeware and commercial programmes using BF methods are available to support personalised dosing. This study evaluated exposure estimates, dose recommendations, and predictive performance compared with current clinical practice.

METHODS

Data from 105 patients (50 adults and 55 children) with cystic fibrosis who received intravenous tobramycin treatment and had paired concentration-time measurements were analysed using (1) log-linear regression analysis, and (2) three BF programmes: TDMx, InsightRX, and DoseMe. Exposure estimates and dose recommendations were compared using the Wilcoxon signed-rank test and Bland-Altman analysis. Predictive performance of BF programmes was compared based on bias and imprecision.

RESULTS

Median estimated tobramycin exposure with current clinical practice was significantly lower (87.8 vs. 92.5, 94.0 and 90.3 mg h l^-1; p ≤ 0.01), hence median subsequent dose recommendations were significantly higher (10.1 vs. 9.4, 9.4 and 9.2 mg kg^-1; p ≤ 0.01) compared with BF programmes. Furthermore, median relative dose-adjustment differences were higher in adults (> 10%) compared with children (4.4-7.8%), and differences in individual dose recommendations were > 20% on 19.1-27.4% of occasions. BF programmes showed low bias (< 7%) and imprecision (< 20%), and none of the programmes made consistently significantly different recommendations compared with each other.

CONCLUSIONS

On average, the predictions made by the BF programmes were similar, however substantial individual differences were observed for some patients. This suggests the need for detailed investigations of true tobramycin exposure.

Identification of Factors Affecting Tacrolimus Trough Levels in Latin American Pediatric Liver Transplant Patients

Tacrolimus is the cornerstone in pediatric liver transplant immunosuppression. Despite close monitoring, fluctuations in tacrolimus blood levels affect safety and efficacy of immunosuppressive treatments. Identifying the factors related to the variability in tacrolimus exposure may be helpful in tailoring the dose. The aim of the present study was to characterize the clinical, pharmacological, and genetic variables associated with systemic tacrolimus exposure in pediatric liver transplant patients. De novo transplant patients with a survival of more than 1 month were considered for inclusion and were genotyped for cytochrome P450 3A5 (CYP3A5). Peritransplant clinical factors and laboratory covariates were recorded retrospectively between 1 month and 2 years after transplant, including alanine aminotransferase (ALT), aspartate aminotransferase, hematocrit, and tacrolimus predose steady-state blood concentrations collected 12 hours after tacrolimus dosing. A linear mixed effect (LME) model was used to assess the association of these factors and the log-transformed tacrolimus dose-normalized trough concentration (logC0/D) levels. Bootstrapping was used to internally validate the final model. External validation was performed in an independent group of patients who matched the original population. The developed LME model described that logC0/D increases with increases in time after transplant (β = 0.019, 95% confidence interval [CI], 0.010-0.028) and ALT values (β = 0.00030, 95% CI, 0.00002-0.00056), whereas logC0/D is significantly lower in graft CYP3A5 expressers compared with nonexpressers (β = -0.349, 95% CI, -0.631 to -0.062). In conclusion, donor CYP3A5 genotype, time after transplant, and ALT values are associated with tacrolimus disposition between 1 month and 2 years after transplant. A better understanding of tacrolimus exposure is essential to minimize the occurrence of an out-of-range therapeutic window that may lead to adverse drug reactions or acute rejection.

Evaluating tacrolimus pharmacokinetic models in adult renal transplant recipients with different CYP3A5 genotypes

PURPOSE

Numerous studies have been conducted on the population pharmacokinetics of tacrolimus in adult renal transplant recipients. It has been reported that the cytochrome P450 (CYP) 3A5 genotype is an important cause of variability in tacrolimus pharmacokinetics. However, the predictive performance of population pharmacokinetic (PK) models of tacrolimus should be evaluated prior to their implementation in clinical practice. The aim of the study reported here was to test the predictive performance of these published PK models of tacrolimus.

METHODS

A literature search of the PubMed and Web of Science databases ultimately led to the inclusion of eight one-compartment models in our analysis. We collected a total of 1715 trough concentrations from 174 patients. Predictive performance was assessed based on visual and numerical comparison bias and imprecision and by the use of simulation-based diagnostics and Bayesian forecasting.

RESULTS

Of the eight one-compartment models assessed, seven showed better predictive performance in CYP3A5 extensive metabolizers in terms of bias and imprecision. Results of the simulation-based diagnostics also supported the findings. The model based on a Chinese population in 2013 (model 3) showed the best and most stable predictive performance in all the tests and was more informative in CYP3A5 extensive metabolizers. As expected, Bayesian forecasting improved model predictability. Diversity among models and between different CYP3A5 genotypes of the same model was also narrowed by Bayesian forecasting.

CONCLUSIONS

Based on our results, we recommend using model 3 in CYP3A5 extensive metabolizers in clinical practice. All models had a poor predictive performance in CYP3A5 poor metabolizers, and they should be used with caution in this patient population. However, Bayesian forecasting improved the predictability and reduced differences, and thus the models could be applied in this latter patient population for the design of maintenance dose.

Bayesian Estimation of Tobramycin Exposure in Patients with Cystic Fibrosis

Fixed tobramycin (mg/kg) dosing is often inappropriate in patients with cystic fibrosis (CF), as pharmacokinetics are highly variable. The area under the concentration-time curve (AUC) is an exposure metric suited to monitoring in this population. Bayesian strategies to estimate AUC have been available for over 20 years but are not standard practice in the clinical setting. To assess their suitability for use in clinical practice, three AUC estimation methods using limited sampling were compared to measured true exposure by using intensive sampling tobramycin data. Adults prescribed once daily intravenous tobramycin had eight concentrations taken over 24 h. An estimate of true exposure within one dosing interval was calculated using the trapezoidal method and compared to three alternate estimates determined using (i) a two-sample log-linear regression (LLR) method (local hospital practice); (ii) a Bayesian estimate using one concentration (AUC₁); and (iii) a Bayesian estimate using two concentrations (AUC₂). Each method was evaluated against the true measured exposure by a Bland-Altman analysis. Twelve patients with a median (range) age and weight of 25 (18 to 36) years and 66.5 (51 to 76) kg, respectively, were recruited. There was good agreement between the true exposure and the three alternate estimates of AUC, with a mean AUC bias of <10 mg/liter · h in each case, i.e., -8.2 (LLR), 3.8 (AUC₁), and 1.0 (AUC₂). Bayesian analysis-based and LLR estimation methods of tobramycin AUC are equivalent to true exposure estimation. All three methods may be suitable for use in the clinical setting; however, a one-sample Bayesian method may be most useful in ambulatory patients for which coordinating blood samples is difficult. Suitably powered, randomized clinical trials are required to assess patient outcomes.

Understanding and applying pharmacometric modelling and simulation in clinical practice and research

Understanding the dose–concentration–effect relationship is a fundamental component of clinical pharmacology. Interpreting data arising from observations of this relationship requires the use of mathematical models; i.e. pharmacokinetic (PK) models to describe the relationship between dose and concentration and pharmacodynamic (PD) models describing the relationship between concentration and effect. Drug development requires several iterations of pharmacometric model‐informed learning and confirming. This includes modelling to understand the dose–response in preclinical studies, deriving a safe dose for first‐in‐man, and the overall analysis of Phase I/II data to optimise the dose for safety and efficacy in Phase III pivotal trials. However, drug development is not the boundary at which PKPD understanding and application stops. PKPD concepts will be useful to anyone involved in the prescribing and administration of medicines for purposes such as determining off‐label dosing in special populations, individualising dosing based on a measured biomarker (personalised medicine) and in determining whether lack of efficacy or unexpected toxicity maybe solved by adjusting the dose rather than the drug. In clinical investigator‐led study design, PKPD can be used to ensure the optimal dose is used, and crucially to define the expected effect size, thereby ensuring power calculations are based on sound prior information. In the clinical setting the most likely people to hold sufficient expertise to advise on PKPD matters will be the pharmacists and clinical pharmacologists. This paper reviews fundamental PKPD principles and provides some real‐world examples of PKPD use in clinical practice and applied clinical research.