Comparing Dosage Adjustment Methods for Once-Daily Tobramycin in Paediatric and Adolescent Patients with Cystic Fibrosis

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.

Personalized Dosing of Infliximab in Patients With Inflammatory Bowel Disease Using a Bayesian Approach: A Next Step in Therapeutic Drug Monitoring

Although biological agents have revolutionized the management of inflammatory bowel diseases (IBDs), a significant proportion of patients show primary non-response or develop secondary loss of response. Therapeutic drug monitoring (TDM) is advocated to maintain the efficacy of biologic agents. Reactive TDM can rationalize the management of primary non-response and secondary loss of response and has shown to be more cost-effective compared with empiric dose escalation. Proactive TDM is shown to increase clinical remission and the durability of the response to a biologic agent. However, the efficacy of proactive and reactive TDM has been questioned in recent studies and meta-analyses. Hence, we need a different approach to TDM, which addresses inflammatory burden, the individual patient, and disease factors. Bayesian approaches, which use population pharmacokinetic models, enable clinicians to make better use of TDM for dose adjustment. With rapid improvement in computer technology, these Bayesian model-based software packages are now available for clinical use. Bayesian dashboard systems allow clinicians to apply model-based dosing to understand an individual's pharmacokinetics and achieve a target serum drug concentration. The model is updated using previously measured drug concentrations and relevant patient factors, such as body weight, C-reactive protein, and serum albumin concentration, to maintain effective drug concentrations in the serum. Initial studies have found utility for the Bayesian approach in induction and maintenance, in adult and pediatric patients, in clinical trials, and in real-life situations for patients with IBD treated with infliximab. This needs confirmation in larger studies. This article reviews the Bayesian approach to therapeutic drug monitoring in IBD.

Personalized tobramycin dosing in children with cystic fibrosis: a comparative clinical evaluation of log-linear and Bayesian methods

BACKGROUND

Children with cystic fibrosis (CF) pulmonary exacerbations receive IV tobramycin therapy, with dosing guided by either log-linear regression (LLR) or Bayesian forecasting (BF).

OBJECTIVES

To compare clinical and performance outcomes for LLR and BF.

PATIENTS AND METHODS

A quasi-experimental intervention study was conducted at a tertiary children's hospital. Electronic medical records were extracted (from January 2015 to September 2021) to establish a database consisting of pre-intervention (LLR) and post-intervention (BF) patient admissions and relevant outcomes. All consecutive patients treated with IV tobramycin for CF pulmonary exacerbations guided by either LLR or BF were eligible.

RESULTS

A total of 376 hospital admissions (LLR = 248, BF = 128) for CF pulmonary exacerbations were included. Patient demographics were similar between cohorts. There were no significant differences found in overall hospital length of stay, rates of re-admission within 1 month of discharge or change in forced expiratory volume in the first second (Δ FEV1) at the end of tobramycin treatment. Patients treated with LLR on average had twice the number of therapeutic drug monitoring (TDM) blood samples collected during a single hospital admission. The timeframe for blood sampling was more flexible with BF, with TDM samples collected up to 16 h post-tobramycin dose compared with 10 h for LLR. The tobramycin AUC0-24 target of ≥100 mg/L·h was more frequently attained using BF (72%; 92/128) compared with LLR (50%; 124/248) (P < 0.001). Incidence of acute kidney injury was rare in both groups.

CONCLUSIONS

LLR and BF result in comparable clinical outcomes. However, BF can significantly reduce the number of blood collections required during each admission, improve dosing accuracy, and provide more reliable target concentration attainment in CF children.

Individualized precision dosing approaches to optimize antimicrobial therapy in pediatric populations

Introduction:Severe infections continue to impose a major burden on critically ill children and mortality rates remain stagnant. Outcomes rely on accurate and timely delivery of antimicrobials achieving target concentrations in infected tissue. Yet, developmental aspects, disease-related variables, and host factors may severely alter antimicrobial pharmacokinetics in pediatrics. The emergence of antimicrobial resistance increases the need for improved treatment approaches.Areas covered:This narrative review explores why optimization of antimicrobial therapy in neonates, infants, children, and adolescents is crucial and summarizes the possible dosing approaches to achieve antimicrobial individualization. Finally, we outline a roadmap toward scientific evidence informing the development and implementation of precision antimicrobial dosing in critically ill children.The literature search was conducted on PubMed using the following keywords: neonate, infant, child, adolescent, pediatrics, antimicrobial, pharmacokinetic, pharmacodynamic target, Bayes dosing software, optimizing, individualizing, personalizing, precision dosing, drug monitoring, validation, attainment, and software implementation. Further articles were sought from the references of the above searched articles.Expert opinion:Recently, technological innovations have emerged that enabled the development of individualized antimicrobial dosing approaches in adults. More work is required in pediatrics to make individualized antimicrobial dosing approaches widely operationalized in this population.

Mechanistic Modelling Identifies and Addresses the Risks of Empiric Concentration-Guided Sorafenib Dosing

The primary objective of this study is to evaluate the capacity of concentration-guided sorafenib dosing protocols to increase the proportion of patients that achieve a sorafenib maximal concentration (C_max) within the range 4.78 to 5.78 μg/mL. A full physiologically based pharmacokinetic model was built and validated using Simcyp^® (version 19.1). The model was used to simulate sorafenib exposure in 1000 Sim-Cancer subjects over 14 days. The capacity of concentration-guided sorafenib dose adjustment, with/without model-informed dose selection (MIDS), to achieve a sorafenib C_max within the range 4.78 to 5.78 μg/mL was evaluated in 500 Sim-Cancer subjects. A multivariable linear regression model incorporating hepatic cytochrome P450 (CYP) 3A4 abundance, albumin concentration, body mass index, body surface area, sex and weight provided robust prediction of steady-state sorafenib C_max (R² = 0.883; p < 0.001). These covariates identified subjects at risk of failing to achieve a sorafenib C_max ≥ 4.78 μg/mL with 95.0% specificity and 95.2% sensitivity. Concentration-guided sorafenib dosing with MIDS achieved a sorafenib C_max within the range 4.78 to 5.78 μg/mL for 38 of 52 patients who failed to achieve a C_max ≥ 4.78 μg/mL with standard dosing. In a simulation setting, concentration-guided dosing with MIDS was the quickest and most effective approach to achieve a sorafenib C_max within a designated range.

Bayesian estimation of pharmacokinetic parameters: an important component to include in the teaching of clinical pharmacokinetics and therapeutic drug monitoring

Bayesian estimation of pharmacokinetic parameters (PKP), as discussed in this review, provides a powerful approach towards the individualization of dosing regimens. The method was first described by Lewis Sheiner and colleagues and it is well suited in clinical environs where few blood fluid measures of drugs are available in the clinic. This makes it a valuable tool in the effective implementation of therapeutic drug monitoring. The principle behind the method is Bayes theorem, which incorporates elements of variability in a priori-known population estimates and variability in the pharmacokinetic parameters, and known errors intrinsic to the assay method used to estimate the blood fluid drug concentrations. This manuscript reviews the Bayesian method. The literature was scanned using Pubmed to provide background into the Bayesian method. An Add-in for Excel program was used to show the ability of the method to estimate PKP using sparse blood fluid concentration vs time data. Using a computer program, the method was able to find reasonable estimates of individual pharmacokinetic parameters, assessed by comparing the estimated data to the true PKP. Education of students in clinical pharmacokinetics is incomplete without some mention and instruction of the Bayesian forecasting method. For a complete understanding, a computer program is needed to demonstrate its utility.

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.

Area under the curve achievement of once daily tobramycin in children with cystic fibrosis during clinical care

BACKGROUND

The area under the concentration-time curve over 24 hours (AUC₂₄ ) is frequently utilized to monitor tobramycin exposure in children with cystic fibrosis (CF). An understanding of exposure target achievement during clinical implementation of an AUC₂₄ based approach in children is limited.

METHODS

A retrospective chart review was performed in children with CF treated with once daily tobramycin and drug concentration monitoring at a pediatric CF center. During clinical care AUC₂₄ was estimated using a traditional log-linear regression approach (LLR). AUC₂₄ was also estimated retrospectively using a pharmacokinetic model-based Bayesian forecasting approach (BF). AUC₂₄ achievement after both approaches were compared.

RESULTS

In 77 treatment courses (mean age, 12.7 ± 5.0 years), a target AUC₂₄ 100 to 125 mg h/L was achieved after starting dose in 21 (27%) and after initial dose adjustment in 35 (45%). In the first 7 days of treatment, 24 (32%) required ≥3 dose adjustments, and the mean number of drug concentrations measured was 7.1 ± 3.2. Examination of a BF approach demonstrated adequate prediction of measured tobramycin concentrations (median bias -2.1% [95% CI -3.1 to -1.4]; median precision 7.6% [95% CI, 7.1%-8.2%]). AUC₂₄ estimates utilizing the BF approach were higher than the LLR approach with a mean difference of 6.4 mg h/L (95% CI, 4.8 to 8.0 mg h/L).

CONCLUSIONS

Achievement of a narrow AUC₂₄ target is challenging during clinical care, and dose individualization is needed in most children with CF. Implementing a BF approach for estimating AUC₂₄ in children with CF is supported.

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.

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.

An Introduction to Machine Learning

In the last few years, machine learning (ML) and artificial intelligence have seen a new wave of publicity fueled by the huge and ever-increasing amount of data and computational power as well as the discovery of improved learning algorithms. However, the idea of a computer learning some abstract concept from data and applying them to yet unseen situations is not new and has been around at least since the 1950s. Many of these basic principles are very familiar to the pharmacometrics and clinical pharmacology community. In this paper, we want to introduce the foundational ideas of ML to this community such that readers obtain the essential tools they need to understand publications on the topic. Although we will not go into the very details and theoretical background, we aim to point readers to relevant literature and put applications of ML in molecular biology as well as the fields of pharmacometrics and clinical pharmacology into perspective.

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.

Pharmacokinetic-Pharmacodynamic Modeling in Pediatric Drug Development, and the Importance of Standardized Scaling of Clearance

Pharmacokinetic/pharmacodynamic (PKPD) modeling is important in the design and conduct of clinical pharmacology research in children. During drug development, PKPD modeling and simulation should underpin rational trial design and facilitate extrapolation to investigate efficacy and safety. The application of PKPD modeling to optimize dosing recommendations and therapeutic drug monitoring is also increasing, and PKPD model-based dose individualization will become a core feature of personalized medicine. Following extensive progress on pediatric PK modeling, a greater emphasis now needs to be placed on PD modeling to understand age-related changes in drug effects. This paper discusses the principles of PKPD modeling in the context of pediatric drug development, summarizing how important PK parameters, such as clearance (CL), are scaled with size and age, and highlights a standardized method for CL scaling in children. One standard scaling method would facilitate comparison of PK parameters across multiple studies, thus increasing the utility of existing PK models and facilitating optimal design of new studies.

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.

Optimising antimicrobial therapy through the use of Bayesian dosing programs

The optimisation of antibiotic dosing therapy with therapeutic drug monitoring is widely recommended. The aim of therapeutic drug monitoring is to help the clinician to achieve target pharmacokinetic/pharmacodynamic parameters, maximising efficacy and minimising toxicity. Computerised programs, utilising the Bayesian estimation procedures, are able to achieve target concentrations in a greater percentage of patients earlier in the course of therapy compared to linear regression analysis and population methods. This article summarises various methods for dose optimisation of antibiotics with a focus on Bayesian programs.

An evaluation of the user-friendliness of Bayesian forecasting programs in a clinical setting

AIMS

To evaluate 3 Bayesian forecasting (BF) programs-TDMx, InsightRx and DoseMe-on their user-friendliness and common liked and disliked features through a survey of hospital pharmacists.

METHODS

Clinical pharmacists across 3 Australian hospitals that did not use a BF program were invited to a BF workshop and complete a survey on programs they trialled. Participants were given 4 case scenarios to work through and asked to complete a 5-point Likert scale survey evaluating the program's user-friendliness. Liked and disliked features of each program were ascertained through written responses to open-ended questions. Survey results were compared using a χ² test of equal or given proportions to identify significant differences in response.

RESULTS

Twenty-seven pharmacists, from hospitals, participated. BF programs were rated overall as user-friendly with 70%, 41% and 37% (P = .02) of participants recording a Likert score of 4 or 5 for DoseMe, TDMx and InsightRx, respectively. Participants found it easy to access all required information to use the programs, understood dosing recommendations and visualisations given by each program, and thought programs supported decision-making with >50% of participants scoring a 4 or 5 across the programs in these categories. Common liked features across all programs were the graphical displays and ease of data entry, while common disliked features were related to the units, layout and information display.

CONCLUSION

Although differences exist between programs, all 3 programs were most commonly rated as user-friendly across all themes evaluated, which provides useful information for healthcare facilities wanting to implement a BF program.

What Does it Take to Make Model-Informed Precision Dosing Common Practice? Report from the 1st Asian Symposium on Precision Dosing

Model-informed precision dosing (MIPD) is modeling and simulation in healthcare to predict the drug dose for a given patient based on their individual characteristics that is most likely to improve efficacy and/or lower toxicity in comparison to traditional dosing. This paper describes the background and status of MIPD and the activities at the 1st Asian Symposium of Precision Dosing. The theme of the meeting was the question, "What does it take to make MIPD common practice?" Formal presentations highlighted the distinction between genetic and non-genetic sources of variability in drug exposure and response, the use of modeling and simulation as decision support tools, and the facilitators to MIPD implementation. A panel discussion addressed the types of models used for MIPD, how the pharmaceutical industry views MIPD, ways to upscale MIPD beyond academic hospital centers, and the essential role of healthcare professional education as a way to progress. The meeting concluded with an ongoing commitment to use MIPD to improve patient care.

Individualised medicine: why we need Bayesian dosing

Individualised drug dosing has been shown to improve patient outcomes and reduce adverse drug events. One method of individualised medicine is the Bayesian approach, which uses prior information about how the population responds to therapy, to inform clinicians about how a specific individual is responding to their current therapy. This information is then used to make changes to the dose. Studies using a Bayesian approach to adjust drug dosing have shown that clinicians are able to achieve a therapeutic range quicker than standard practice. If concentration is related to a pharmacodynamic end-point, this means that the drug will be more effective, and the side-effects will be minimised. Unfortunately, the software options to assist with Bayesian dosing in Australia are limited. The aims of this article are to demystify the concepts of Bayesian dosing, set the context of the Bayesian approach using reference to other dosing strategies and discuss its benefits over current dosing methods for a number of drugs. The article is targeted to medical and pharmacy clinicians, and there is a practical clinical case to demonstrate how this method could be used in everyday clinical practice.

Gentamicin Pharmacokinetics and Monitoring in Pediatric Patients with Febrile Neutropenia

BACKGROUND

The pharmacokinetics of gentamicin in pediatric patients with febrile neutropenia is described, and the adequacy of initial dosing of once-daily gentamicin assessed at Queensland's largest Children's Hospital.

METHODS

Data were retrospectively collected from all pediatrics with febrile neutropenia admitted over a 2-year period who had at least 2 gentamicin concentration-time measurements (a paired set within 1 dosing interval). Gentamicin clearance, volume of distribution, area under the concentration-time curve from 0 to 24 hours postdose (AUC0-24), and maximum concentration values were estimated with log-linear regression using each paired set. The percentage of paired sets associated with gentamicin exposure within predefined hospital targets was calculated, and exposure was examined in relation to the bacterial culture status.

RESULTS

Data were collected from 69 patients [median (interquartile range) age 3.7 years (2.2-8.9)] and comprised 121 paired concentration sets characterizing 80 separate admissions. Median (interquartile range) gentamicin clearance and volume of distribution were 8.1 L·h·70 kg (5.8-12.4) and 21.8 L/70 kg (16.9-29.5), respectively. Predefined hospital exposure targets were achieved for both AUC0-24 and maximum concentration for 10% of paired sets; one or the other of these targets were met for 36% of paired sets, and neither target was achieved for 54% of paired sets. Achievement of targets improved with repeated monitoring during the same admission. Median AUC0-24 achieved was significantly higher in patients with a confirmed Gram-negative infection compared with those without 71 (50-91) mg·h·L versus 55 (40.8-67.5) mg·h·L, respectively (P = 0.003). Over the study period, a median gentamicin dose of 10.8 and 6.4 mg/kg was estimated to be necessary to achieve an AUC target of 80 mg·h·L in children ≤10 years and >10 years of age.

CONCLUSIONS

Based on a log-linear method of analysis, current dosing seems to be consistently producing gentamicin exposure below predefined pharmacokinetic targets, suggesting that an increase in the recommended starting dose of gentamicin may be required.

Gentamicin Pharmacokinetics and Monitoring in Pediatric Patients with Febrile Neutropenia

BACKGROUND

The pharmacokinetics of gentamicin in pediatric patients with febrile neutropenia is described, and the adequacy of initial dosing of once-daily gentamicin assessed at Queensland's largest Children's Hospital.

METHODS

Data were retrospectively collected from all pediatrics with febrile neutropenia admitted over a 2-year period who had at least 2 gentamicin concentration-time measurements (a paired set within 1 dosing interval). Gentamicin clearance, volume of distribution, area under the concentration-time curve from 0 to 24 hours postdose (AUC0-24), and maximum concentration values were estimated with log-linear regression using each paired set. The percentage of paired sets associated with gentamicin exposure within predefined hospital targets was calculated, and exposure was examined in relation to the bacterial culture status.

RESULTS

Data were collected from 69 patients [median (interquartile range) age 3.7 years (2.2-8.9)] and comprised 121 paired concentration sets characterizing 80 separate admissions. Median (interquartile range) gentamicin clearance and volume of distribution were 8.1 L·h·70 kg (5.8-12.4) and 21.8 L/70 kg (16.9-29.5), respectively. Predefined hospital exposure targets were achieved for both AUC0-24 and maximum concentration for 10% of paired sets; one or the other of these targets were met for 36% of paired sets, and neither target was achieved for 54% of paired sets. Achievement of targets improved with repeated monitoring during the same admission. Median AUC0-24 achieved was significantly higher in patients with a confirmed Gram-negative infection compared with those without 71 (50-91) mg·h·L versus 55 (40.8-67.5) mg·h·L, respectively (P = 0.003). Over the study period, a median gentamicin dose of 10.8 and 6.4 mg/kg was estimated to be necessary to achieve an AUC target of 80 mg·h·L in children ≤10 years and >10 years of age.

CONCLUSIONS

Based on a log-linear method of analysis, current dosing seems to be consistently producing gentamicin exposure below predefined pharmacokinetic targets, suggesting that an increase in the recommended starting dose of gentamicin may be required.

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.

Accurately Achieving Target Busulfan Exposure in Children and Adolescents With Very Limited Sampling and the BestDose Software

BACKGROUND

Busulfan dose adjustment is routinely guided by plasma concentration monitoring using 4-9 blood samples per dose adjustment, but a pharmacometric Bayesian approach could reduce this sample burden.

METHODS

The authors developed a nonparametric population model with Pmetrics. They used it to simulate optimal initial busulfan dosages, and in a blinded manner, they compared dosage adjustments using the model in the BestDose software to dosage adjustments calculated by noncompartmental estimation of area under the time-concentration curve at a national reference laboratory in a cohort of patients not included in model building.

RESULTS

Mean (range) age of the 53 model-building subjects was 7.8 years (0.2-19.0 years) and weight was 26.5 kg (5.6-78.0 kg), similar to nearly 120 validation subjects. There were 16.7 samples (6-26 samples) per subject to build the model. The BestDose cohort was also diverse: 10.2 years (0.25-18 years) and 46.4 kg (5.2-110.9 kg). Mean bias and imprecision of the 1-compartment model-predicted busulfan concentrations were 0.42% and 9.2%, and were similar in the validation cohorts. Initial dosages to achieve average concentrations of 600-900 ng/mL were 1.1 mg/kg (≤12 kg, 67% in the target range) and 1.0 mg/kg (>12 kg, 76% in the target range). Using all 9 concentrations after dose 1 in the Bayesian estimation of dose requirements, the mean (95% confidence interval) bias of BestDose calculations for the third dose was 0.2% (-2.4% to 2.9%, P = 0.85), compared with the standard noncompartmental method based on 9 concentrations. With 1 optimally timed concentration 15 minutes after the infusion (calculated with the authors' novel MMopt algorithm) bias was -9.2% (-16.7% to -1.5%, P = 0.02). With 2 concentrations at 15 minutes and 4 hours bias was only 1.9% (-0.3% to 4.2%, P = 0.08).

CONCLUSIONS

BestDose accurately calculates busulfan intravenous dosage requirements to achieve target plasma exposures in children up to 18 years of age and 110 kg using only 2 blood samples per adjustment compared with 6-9 samples for standard noncompartmental dose calculations.

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

Several population pharmacokinetic models describe the dose-exposure relationship of tobramycin in pediatric patients. Before the implementation of these models in clinical practice for dosage adjustment, their predictive performance should be externally evaluated. This study tested the predictive performance of all published population pharmacokinetic models of tobramycin developed for pediatric patients with an independent patient cohort. A literature search was conducted to identify suitable models for testing. Demographic and pharmacokinetic data were collected retrospectively from the medical records of pediatric patients who had received intravenous tobramycin. Tobramycin exposure was predicted from each model. Predictive performance was assessed by visual comparison of predictions to observations, by calculation of bias and imprecision, and through the use of simulation-based diagnostics. Eight population pharmacokinetic models were identified. A total of 269 concentration-time points from 41 pediatric patients with cystic fibrosis were collected for external evaluation. Three models consistently performed best in all evaluations and had mean errors ranging from -0.4 to 1.8 mg/liter, relative mean errors ranging from 4.9 to 29.4%, and root mean square errors ranging from 47.8 to 66.9%. Simulation-based diagnostics supported these findings. Models that allowed a two-compartment disposition generally had better predictive performance than those that used a one-compartment disposition model. Several published models of the pharmacokinetics of tobramycin showed reasonable low levels of bias, although all models seemed to have some problems with imprecision. This suggests that knowledge of typical pharmacokinetic behavior and patient covariate values alone without feedback concentration measurements from individual patients is not sufficient to make precise predictions.

Effect of Kidney Function on Drug Kinetics and Dosing in Neonates, Infants, and Children

Neonates, infants, and children differ from adults in many aspects, not just in age, weight, and body composition. Growth, maturation and environmental factors affect drug kinetics, response and dosing in pediatric patients. Almost 80% of drugs have not been studied in children, and dosing of these drugs is derived from adult doses by adjusting for body weight/size. As developmental and maturational changes are complex processes, such simplified methods may result in subtherapeutic effects or adverse events. Kidney function is impaired during the first 2 years of life as a result of normal growth and development. Reduced kidney function during childhood has an impact not only on renal clearance but also on absorption, distribution, metabolism and nonrenal clearance of drugs. 'Omics'-based technologies, such as proteomics and metabolomics, can be leveraged to uncover novel markers for kidney function during normal development, acute kidney injury, and chronic diseases. Pharmacometric modeling and simulation can be applied to simplify the design of pediatric investigations, characterize the effects of kidney function on drug exposure and response, and fine-tune dosing in pediatric patients, especially in those with impaired kidney function. One case study of amikacin dosing in neonates with reduced kidney function is presented. Collaborative efforts between clinicians and scientists in academia, industry, and regulatory agencies are required to evaluate new renal biomarkers, collect and share prospective pharmacokinetic, genetic and clinical data, build integrated pharmacometric models for key drugs, optimize and standardize dosing strategies, develop bedside decision tools, and enhance labels of drugs utilized in neonates, infants, and children.