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

Model-informed precision dosing: State of the art and future perspectives

Model-informed precision dosing (MIPD) stands as a significant development in personalized medicine to tailor drug dosing to individual patient characteristics. MIPD moves beyond traditional therapeutic drug monitoring (TDM) by integrating mathematical predictions of dosing and considering patient-specific factors (patient characteristics, drug measurements) as well as different sources of variability. For this purpose, rigorous model qualification is required for the application of MIPD in patients. This review delves into new methods in model selection and validation, also highlighting the role of machine learning in improving MIPD, the utilization of biosensors for real-time monitoring, as well as the potential of models integrating biomarkers for efficacy or toxicity for precision dosing. The clinical evidence of TDM and MIPD is discussed for various medical fields including infection medicine, oncology, transplant medicine, and inflammatory bowel diseases, thereby underscoring the role of pharmacokinetics/pharmacodynamics and specific biomarkers. Further research, particularly randomized clinical trials, is warranted to corroborate the value of MIPD in enhancing patient outcomes and advancing personalized medicine.

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.

Evaluation of Limited Sampling Strategies for Bayesian Estimation of Daptomycin Area Under the Concentration-Time Curve: A Short Communication

PURPOSE

Increasing evidence supports daptomycin therapeutic drug monitoring. The author's reference center used to perform therapeutic drug monitoring in patients who receive high-dose daptomycin for bone and joint infections, with a three-sample strategy to estimate the daptomycin daily area under the concentration-time curve (AUC). The objective of this study was to evaluate simpler strategies based on only 2 or 1 sample(s).

METHODS

The authors used the BestDose software to estimate the daptomycin AUC after Bayesian posterior estimation of individual pharmacokinetic (PK) parameters at steady state. The reference AUC (AUCfull) was based on 3 samples obtained predose (T0) and approximately 1 hour (T1) and 6 hours (T6) after the start of a 30-minute infusion of IV daptomycin. It was compared with the AUC based on all possible 2-sample and 1-sample strategies. Bias, imprecision, regression, and Bland-Altman plots were used to assess the performance of the alternative strategies.

RESULTS

Data from 77 patients were analyzed. The mean AUCfull value was 936 ± 373 mg·h/L. The best 2-sample strategy was T0 + T6, with a mean prediction bias of 0.13 mg·h/L and absolute imprecision of 3%. The T0 + T1 strategy also performed well with a mean bias of -10 mg·h/L and imprecision of 3%. The best 1-sample strategy was the T6 sample only with a bias of 2.19 mg·h/L and imprecision of 6%.

CONCLUSIONS

Bayesian estimation of daptomycin AUC based on a two-sample strategy was associated with negligible bias and imprecision compared with the author's usual three-sample strategy. The trough and peak strategy may shorten and simplify patient visits and reduce assay labor and costs.

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.

Impact of Patient-Specific Aminoglycoside Monitoring for Treatment of Pediatric Cystic Fibrosis Pulmonary Exacerbations

OBJECTIVE

Aminoglycosides are frequently used for empiric and definitive treatment of cystic fibrosis (CF) pulmonary exacerbations. Various methods have been described for aminoglycoside therapeutic drug monitoring. The objective of this study is to evaluate the effect of patient-specific pharmacokinetic calculations for aminoglycosides used to treat CF pulmonary exacerbations.

METHODS

Ambidirectional cohort study of patients admitted to a children's hospital from June 1, 2018, through February 28, 2019, and June 1, 2019, through February 8, 2021. The primary outcome was the occurrence of dosing changes after analysis of initial serum concentrations in either group. Secondary outcomes included occurrence of nephrotoxicity, duration of antibiotics, and length of stay.

RESULTS

Twenty-four patients (75%) in the intervention group versus zero in the control group required dosing adjustments after initial analysis of serum concentrations were completed (p &lt; 0.001). There was not a statistically significant between-group difference for duration of antibiotics in days (median, 14 vs 13.5; Z, 1.07; p = 0.29) or length of stay (median, 11 vs 11; Z, −0.31; p = 0.76). There was also not a statistically significant between-group difference in forced expiratory volume in one second (FEV1) change from admission to discharge (11.4% vs 13.9%; t, 0.61; Degrees of Freedom, 39; p = 0.55). Two patients (6.25%) in the intervention group experienced nephrotoxicity compared with zero patients in the control group (risk difference, 6.25%; 95% CI, −2.14 to 14.64; number needed to harm, 16).

CONCLUSIONS

Patient-specific pharmacokinetic monitoring led to significantly more dosing changes and was associated with similar patient outcomes as trough-only monitoring. Further studies are needed to identify methods to optimize aminoglycoside dosing and monitoring for these patients with the goal of reducing toxicities while maximizing efficacy.

Optimized sampling to estimate vancomycin drug exposure: Comparison of pharmacometric and equation-based approaches in a simulation-estimation study

Vancomycin dosing should be accompanied by area under the concentration‐time curve (AUC)–guided dosing using model‐informed precision dosing software according to the latest guidelines. Although a peak plus a trough sample is considered the gold standard to determine the AUC, single‐sample strategies might be more economic. Yet, optimal sampling times for AUC determination of vancomycin have not been systematically evaluated. In the present study, automated one‐ or two‐sample strategies were systematically explored to estimate the AUC with a model averaging and a model selection algorithm. Both were compared with a conventional equation‐based approach in a simulation‐estimation study mimicking a heterogenous patient population (n = 6000). The optimal single‐sample timepoints were identified between 2–6.5 h post dose, with varying bias values between −2.9% and 1.0% and an imprecision of 23.3%–24.0% across the population pharmacokinetic approaches. Adding a second sample between 4.5–6.0 h improved the predictive performance (−1.7% to 0.0% bias, 17.6%–18.6% imprecision), although the difference in the two‐sampling strategies were minor. The equation‐based approach was always positively biased and hence inferior to the population pharmacokinetic approaches. In conclusion, the approaches always preferred samples to be drawn early in the profile (<6.5 h), whereas sampling of trough concentrations resulted in a higher imprecision. Furthermore, optimal sampling during the early treatment phase could already give sufficient time to individualize the second dose, which is likely unfeasible using trough sampling.

External evaluation of population pharmacokinetic models of imatinib in adults diagnosed with chronic myeloid leukaemia

AIMS

Imatinib is considered the standard first-line treatment in newly diagnosed patients with chronic-phase myeloid leukaemia (CML). Several imatinib population pharmacokinetic (popPK) models have been developed. However, their predictive performance has not been well established when extrapolated to different populations. Therefore, this study aimed to perform an external evaluation of available imatinib popPK models developed mainly in adult patients, and to evaluate the improvement in individual model-based predictions through Bayesian forecasting computed by each model at different treatment occasions.

METHODS

A literature review was conducted through PubMed and Scopus to identify popPK models. Therapeutic drug monitoring data collected in adult CML patients treated with imatinib was used for external evaluation, including prediction- and simulated-based diagnostics together with Bayesian forecasting analysis.

RESULTS

Fourteen imatinib popPK studies were included for model-performance evaluation. A total of 99 imatinib samples were collected from 48 adult CML patients undergoing imatinib treatment with a minimum of one plasma concentration measured at steady-state between January 2016 and December 2020. The model proposed by Petain et al showed the best performance concerning prediction-based diagnostics in the studied population. Bayesian forecasting demonstrated a significant improvement in predictive performance at the second visit. Inter-occasion variability contributed to reducing bias and improving individual model-based predictions.

CONCLUSIONS

Imatinib popPK studies developed in Caucasian subjects including α1-acid glycoprotein showed the best model performance in terms of overall bias and precision. Moreover, two imatinib samples from different visits appear sufficient to reach an adequate model-based individual prediction performance trough Bayesian forecasting.

Pharmacokinetic modelling to predict risk of ototoxicity with intravenous tobramycin treatment in cystic fibrosis

INTRODUCTION

Further optimization of therapeutic drug monitoring (TDM) for aminoglycosides (AGs) is urgently needed, especially in special populations such as those with cystic fibrosis (CF), >50% of whom develop ototoxicity if treated with multiple courses of IV AGs. This study aimed to empirically test a pharmacokinetic (PK) model using Bayesian estimation of drug exposure in the deeper body tissues to determine feasibility for prediction of ototoxicity.

MATERIALS AND METHODS

IV doses (n = 3645) of tobramycin and vancomycin were documented with precise timing from 38 patients with CF (aged 8-21 years), including total doses given and total exposure (cumulative AUC). Concentration results were obtained at 3 and 10 h for the central (C1) compartment. These variables were used in Bayesian estimation to predict trough levels in the secondary tissue compartments (C2 trough) and maximum concentrations (C2max). The C1 and C2 measures were then correlated with hearing levels in the extended high-frequency range.

RESULTS

Patients with more severe hearing loss were older and had a higher number of tobramycin C2max concentrations >2 mg/L than patients with normal or lesser degrees of hearing loss. These two factors together significantly predicted average high-frequency hearing level (r = 0.618, P < 0.001). Traditional metrics such as C1 trough concentrations were not predictive. The relative risk for hearing loss was 5.8 times greater with six or more tobramycin courses that exceeded C2max concentrations of 3 mg/L or higher, with sensitivity of 83% and specificity of 86%.

CONCLUSIONS

Advanced PK model-informed analysis predicted ototoxicity risk in patients with CF treated with tobramycin and demonstrated high test prediction.

Evaluation of published population pharmacokinetic models to inform tacrolimus dosing in adult heart transplant recipients

BACKGROUND AND AIM

Identification of the most appropriate population pharmacokinetic model-based Bayesian estimation is required prior to its implementation in routine clinical practice to inform tacrolimus dosing decisions. This study aimed to determine the predictive performances of relevant population pharmacokinetic models of tacrolimus developed from various solid organ transplant recipient populations in adult heart transplant recipients, stratified based on concomitant azole antifungal use. Concomitant azole antifungal therapy alters tacrolimus pharmacokinetics substantially, necessitating dose adjustments.

METHODS

Population pharmacokinetic models of tacrolimus were selected (n = 17) for evaluation from a recent systematic review. The models were transcribed and implemented in NONMEM version 7.4.3. Data from 85 heart transplant recipients (2387 tacrolimus concentrations) administered the oral immediate-release formulation of tacrolimus (Prograf) were obtained up to 391 days post-transplant. The performance of each model was evaluated using: (i) prediction-based assessment (bias and imprecision) of the individual predicted tacrolimus concentration of the fourth dosing occasion (MAXEVAL = 0, FOCE-I) from 1-3 prior dosing occasions; and (ii) simulation-based assessment (prediction-corrected visual predictive check). Both assessments were stratified based on concomitant azole antifungal use.

RESULTS

Regardless of the number of prior dosing occasions (1-3) and concomitant azole antifungal use, all models demonstrated unacceptable individual predicted tacrolimus concentration of the fourth dosing occasion (n = 152). The prediction-corrected visual predictive check graphics indicated that these models inadequately predicted observed tacrolimus concentrations.

CONCLUSION

All models evaluated were unable to adequately describe tacrolimus pharmacokinetics in adult heart transplant recipients included in this study. Further work is required to describe tacrolimus pharmacokinetics for our heart transplant recipient cohort.

Bayesian Forecasting for Intravenous Tobramycin Dosing in Adults With Cystic Fibrosis Using One Versus Two Serum Concentrations in a Dosing Interval

BACKGROUND

Intravenous tobramycin treatment requires therapeutic drug monitoring (TDM) to ensure safety and efficacy when used for prolonged treatment, as in infective exacerbations of cystic fibrosis. The 24-hour area under the concentration-time curve (AUC24) is widely used to guide dosing; however, there remains variability in practice around methods for its estimation. The objective of this study was to determine the potential for a sparse-sampling strategy using a single postinfusion tobramycin concentration and Bayesian forecasting to assess the AUC24 in routine practice.

METHODS

Adults with cystic fibrosis receiving once-daily tobramycin had paired concentrations measured 2 hours (c1) and 6 hours (c2) after the end of infusion as routine monitoring. AUC24 exposures were estimated using Tucuxi, a Bayesian forecasting application that incorporates a validated population pharmacokinetic model. Simulations were performed to estimate AUC24 using the full data set using c1 and c2, compared with estimates using depleted data sets (c1 or c2 only), with and without concentration data from earlier in the course. The agreement between each simulation condition and the reference was assessed graphically and numerically using the median difference (∆) AUC24 and (relative) root mean square error (rRMSE) as measures of bias and accuracy, respectively.

RESULTS

A total of 55 patients contributed 512 concentrations from 95 tobramycin courses and 256 TDM episodes. Single concentration methods performed well, with median ∆AUC24 <2 mg·h·L-1 and rRMSE of <15% for sequential c1 and c2 conditions.

CONCLUSIONS

Bayesian forecasting implemented in Tucuxi, using single postinfusion concentrations taken 2-6 hours after tobramycin administration, yield similar exposure estimates to more intensive (two-sample) methods and are suitable for routine TDM practice.

Evaluation of two software using Bayesian methods for monitoring exposure and dosing once-daily intravenous busulfan in paediatric patients receiving haematopoietic stem cell transplantation

AIM

To assess the ability of model-based personalised dosing tools to estimate busulfan exposure (i) in comparison to clinically used intensive sampling exposure estimation procedure, (ii) using limited sampling strategies and (iii) to predict changes in busulfan clearance during busulfan treatment.

METHODS

Data on intravenous busulfan dosing for patients with 4 consecutive days were entered into Bayesian forecasting software, InsightRX and NextDose. Prediction of busulfan cumulative exposure was compared to current clinical practice estimation, aiming for pre-defined individualised target of cumulative exposure. Estimation performance was tested given several limited sampling strategies.

RESULTS

Thirty-two paediatric patients (0.2-16.5 years) provided a total of 103 daily exposure measurements estimated using 7 samples taken per day (full sampling), with 19 patients having sampling following all doses administered. Both software tools utilising Bayesian methods provided acceptable relative bias and precision of cumulative exposure estimations under the tested sampling scenarios. Relative bias ranged from median RE of 0.1-14.6% using InsightRX and from 3.4-7.8% using NextDose. Precision ranged from median RMSE of 0.19-0.32 mg·h·L^-1 for InsightRX and 0.08-0.1 mg·h·L^-1 for NextDose. A median reduction in busulfan clearance from day 1 to day 4 was observed in the clinical data (-10.9%), when using InsightRX (-18.6%) and with NextDose (-14.7%).

CONCLUSION

Bayesian methods were shown to have relatively low bias and precisely estimate busulfan exposure using intensive sampling and several limited sampling strategies, which provides evidence for prospective studies to evaluate these tools in clinical practice. A trend to overestimation of exposure using Bayesian methods was observed compared to clinical practice. Reduction of busulfan clearance from day 1 to 4 of once daily dosing was confirmed and should be considered when adjusting doses.

Evaluating the Impact of Education on Pharmacist Tobramycin Dose Recommendations for Cystic Fibrosis and a Review of Perceptions on Pharmacist-Led Charting

BACKGROUND

Pharmacists routinely interpret and optimize tobramycin dosing for people with cystic fibrosis (PwCF).

OBJECTIVES

To determine the impact of tobramycin therapeutic drug monitoring (TDM) education on pharmacist dose recommendations, and to explore nurses' and medical doctors' perceptions toward pharmacist-led TDM charting.

METHODS

This study involved 3 phases: a 12-month retrospective audit of PwCF prescribed tobramycin to identify the appropriateness of pharmacists' dose recommendations, a pharmacist tobramycin educational intervention utilizing a voiceover presentation with pre- and post-online tobramycin TDM assessment (involving multiple choice pharmacokinetics and case-based scenario questions), and a cross-sectional survey of respiratory nurses' and doctors' perceptions toward pharmacist-led TDM charting. The pharmacists' dose recommendations, in the audit and case-based questions, were considered appropriate if subsequent levels achieved the targeted area under the curve (AUC).

RESULTS

Audit results revealed that 44.4% of the 277 pharmacist dose recommendations identified were appropriate. The pre- and post-interventional assessments were completed by 51 and 52 pharmacists, respectively. Post intervention, correct scores were significantly higher than pre-intervention, evident in both the pharmacokinetics (median score 75% vs 100%; P = 0.048) and case-based scenario (median score 60% vs 90%; P < 0.0001) questions. Of the 54 nurses and medical doctors surveyed, 92.6% supported the implementation of pharmacist-led tobramycin charting.

CONCLUSION

The study demonstrated an increased accuracy and appropriateness of pharmacists' tobramycin pharmacokinetics knowledge and TDM dose recommendations post-educational intervention and highlighted nurses' and medical doctors' support of pharmacist-led tobramycin TDM charting.

From Therapeutic Drug Monitoring to Model-Informed Precision Dosing for Antibiotics

Therapeutic drug monitoring (TDM) and model-informed precision dosing (MIPD) have evolved as important tools to inform rational dosing of antibiotics in individual patients with infections. In particular, critically ill patients display altered, highly variable pharmacokinetics and often suffer from infections caused by less susceptible bacteria. Consequently, TDM has been used to individualize dosing in this patient group for many years. More recently, there has been increasing research on the use of MIPD software to streamline the TDM process, which can increase the flexibility and precision of dose individualization but also requires adequate model validation and re-evaluation of existing workflows. In parallel, new minimally invasive and noninvasive technologies such as microneedle-based sensors are being developed, which-together with MIPD software-have the potential to revolutionize how patients are dosed with antibiotics. Nonetheless, carefully designed clinical trials to evaluate the benefit of TDM and MIPD approaches are still sparse, but are critically needed to justify the implementation of TDM and MIPD in clinical practice. The present review summarizes the clinical pharmacology of antibiotics, conventional TDM and MIPD approaches, and evidence of the value of TDM/MIPD for aminoglycosides, beta-lactams, glycopeptides, and linezolid, for which precision dosing approaches have been recommended.

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.

[Therapeutic drug monitoring of antiinfectives in intensive care unit patients - what's new?]

Pharmacokinetic and pharmacodynamic changes in intensive care unit patients can increase the risk for therapeutic failure or adverse effects of anti-infective therapy. Therapeutic drug monitoring (TDM) can inform required dose adaptions. The present article reviews the current practice and outlines modern approaches for decision making such as model-informed precision dosing software using the area-under-the-concentration-time-curve as target in favor of simplistic decision making based on trough concentrations. Moreover, the current recommendations for performing TDM of beta-lactams, aminoglycosides, linezolid, glycopeptides and voriconazole are concisely summarized.

Evaluation of amikacin use and comparison of the models implemented in two Bayesian forecasting software packages to guide dosing

AIMS

Bayesian forecasting software can assist in guiding therapeutic drug monitoring (TDM)-based dose adjustments for amikacin to achieve therapeutic targets. This study aimed to evaluate amikacin prescribing and TDM practices, and to determine the suitability of the amikacin model incorporated into the DoseMeRx® software as a replacement for the previously available software (Abbottbase®).

METHODS

Patient demographics, pathology, amikacin dosing history, amikacin concentrations and Abbottbase® predicted TDM targets (area under the curve up to 24 hours, maximum concentration and trough concentration) were collected for adults receiving intravenous amikacin (2012-2017). Concordance with the Australian Therapeutic Guidelines was assessed. Observed and predicted amikacin concentrations were compared to determine the predictive performance (bias and precision) of DoseMeRx®. Amikacin TDM targets were predicted by DoseMeRx® and compared to those predicted by Abbottbase®.

RESULTS

Overall, guideline compliance for 63 courses of amikacin in 47 patients was suboptimal. Doses were often lower than recommended. For therapy >48 h, TDM sample collection timing was commonly discordant with recommendations, therapeutic target attainment low and 34% of dose adjustments inappropriate. DoseMeRx® under-predicted amikacin concentrations by 0.9 mg/L (95% confidence interval [CI] -1.4 to -0.5) compared with observed concentrations. However, maximum concentration values (n = 19) were unbiased (-1.7 mg/L 95%CI -5.8 to 0.8) and precise (8.6% 95%CI 5.4-18.1). Predicted trough concentration values (n = 7) were, at most, 1 mg/L higher than observed. Amikacin area under the curve values estimated using Abbottbase® (181 mg h/L 95%CI 161-202) and DoseMeRx® (176 mg h/L 95%CI 152-199) were similar (P = .59).

CONCLUSION

Amikacin dosing and TDM practice was suboptimal compared with guidelines. The model implemented by DoseMeRx® is satisfactory to guide amikacin dosing.

How to design a study to evaluate therapeutic drug monitoring in infectious diseases?

BACKGROUND

Therapeutic drug monitoring (TDM) is a tool to personalize and optimize dosing by measuring the drug concentration and subsequently adjusting the dose to reach a target concentration or exposure. The evidence to support TDM is however often ranked as expert opinion. Limitations in study design and sample size have hampered definitive conclusions of the potential added value of TDM.

OBJECTIVES

We aim to give expert opinion and discuss the main points and limitations of available data from antibiotic TDM trials and emphasize key elements for consideration in design of future clinical studies to quantify the benefits of TDM.

SOURCES

The sources were peer-reviewed publications, guidelines and expert opinions from the field of TDM.

CONTENT

This review focuses on key aspects of antimicrobial TDM study design: describing the rationale for a TDM study, assessing the exposure of a drug, assessing susceptibility of pathogens and selecting appropriate clinical endpoints. Moreover we provide guidance on appropriate study design.

IMPLICATIONS

This is an overview of different aspects relevant for the conduct of a TDM study. We believe that this paper will help researchers and clinicians to design and conduct high-quality TDM studies.

Population pharmacokinetics and Bayesian estimators for intravenous mycophenolate mofetil in haematopoietic stem cell transplant patients

AIMS

Intravenous mycophenolate mofetil (IV MMF), a prodrug of mycophenolic acid (MPA), is used during nonmyeloablative and reduced-intensity conditioning haematopoetic stem cell transplantation (HCT) to improve engraftment and reduce graft-versus-host disease. The aims of this study were to develop population pharmacokinetic models and Bayesian estimators based on limited sampling strategies to allow for individual dose adjustment of intravenous mycophenolate mofetil administered by infusion in haematopoietic stem cell transplant patients.

METHODS

Sixty-three MPA concentration-time profiles (median [min-max] = 6 [4-7] samples) were collected from 34 HCT recipients transplanted for 14 (1-45) days and administered IV MMF every 8 hours, concomitantly with cyclosporine. The database was split into development (75%) and validation (25%) datasets. Pharmacokinetic models characterized by a single compartment with first-order elimination, combined with two gamma distributions to describe the transformation of MMF into mycophenolic acid, were developed using in parallel nonparametric (Pmetrics) and parametric (ITSIM) approaches. The performances of the models and the derived Bayesian estimators were evaluated in the validation set.

RESULTS

The best limited sampling strategy led to a bias (min, max), root mean square error between observed and modeled interdose areas under the curve in the validation dataset of -11.72% (-31.08%, 5.00%), 14.9% for ITSIM and -2.21% (-23.40%, 30.01%), 12.4% for Pmetrics with three samples collected at 0.33, 2 and 3 hours post dosing.

CONCLUSION

Population pharmacokinetic models and Bayesian estimators for IV MMF in HCT have been developed and are now available online (https://pharmaco.chu-limoges.fr) for individual dose adjustment based on the interdose area under the curve.

Towards precision dosing of vancomycin: a systematic evaluation of pharmacometric models for Bayesian forecasting

OBJECTIVES

Vancomycin is a vital treatment option for patients suffering from critical infections, and therapeutic drug monitoring is recommended. Bayesian forecasting is reported to improve trough concentration monitoring for dose adjustment. However, the predictive performance of pharmacokinetic models that are utilized for Bayesian forecasting has not been systematically evaluated.

METHOD

Thirty-one published population pharmacokinetic models for vancomycin were encoded in NONMEM®7.4. Data from 292 hospitalized patients were used to evaluate the predictive performance (forecasting bias and precision, visual predictive checks) of the models to forecast vancomycin concentrations and area under the curve (AUC) by (a) a priori prediction, i.e., solely by patient characteristics, and (b) also including measured vancomycin concentrations from previous dosing occasions using Bayesian forecasting.

RESULTS

A priori prediction varied substantially-relative bias (rBias): -122.7-67.96%, relative root mean squared error (rRMSE) 44.3-136.8%, respectively-and was best for models which included body weight and creatinine clearance as covariates. The model by Goti et al. displayed the best predictive performance with an rBias of -4.41% and an rRMSE of 44.3%, as well as the most accurate visual predictive checks and AUC predictions. Models with less accurate predictive performance provided distorted AUC predictions which may lead to inappropriate dosing decisions.

CONCLUSION

There is a diverse landscape of population pharmacokinetic models for vancomycin with varied predictive performance in Bayesian forecasting. Our study revealed the Goti model as suitable for improving precision dosing in hospitalized patients. Therefore, it should be used to drive vancomycin dosing decisions, and studies to link this finding to clinical outcomes are warranted.