Impact of Inaccurate Documentation of Sampling and Infusion Time in Model-Informed Precision Dosing

Determination of a vancomycin nephrotoxicity threshold and assessment of target attainment in hematology patients

An area-under-the-curve (AUC24)-based approach is recommended to guide vancomycin therapeutic drug monitoring (TDM), yet trough concentrations are still commonly used despite associated risks. A definitive toxicity target is lacking, which is important for hematology patients who have a higher risk of nephrotoxicity. The aims were to (1) assess the impact of trough-based TDM on acute kidney injury (AKI) incidence, (2) establish a vancomycin nephrotoxicity threshold, and (3) evaluate the proportion of hematology patients achieving vancomycin therapeutic targets. Retrospective data was collected from 100 adult patients with a hematological malignancy or aplastic anemia who received vancomycin between April 2020 and January 2021. AKI occurrence was determined based on serum creatinine concentrations, and individual pharmacokinetic parameters were estimated using a Bayesian approach. Receiver operating characteristic (ROC) curve analysis was performed to assess the ability of pharmacokinetic indices to predict AKI occurrence. The proportion of patients who achieved target vancomycin exposure was evaluated based on an AUC24/MIC ≥400 and the determined toxicity threshold. The incidence of AKI was 37%. ROC curve analysis indicated a maximum AUC24 of 644 mg.h/L over the treatment period was an important predictor of AKI. By Day 4 of treatment, 29% of treatment courses had supratherapeutic vancomycin exposure, with only 62% of courses achieving AUC24 targets. The identified toxicity threshold supports an AUC24 target range of 400-650 mg.h/L, assuming an MIC of 1 mg/L, to optimize vancomycin efficacy and minimize toxicity. This study highlights high rates of AKI in this population and emphasizes the importance of transitioning from trough-based TDM to an AUC-based approach to improve clinical outcomes.

Therapeutic drug monitoring of vancomycin and meropenem: Illustration of the impact of inaccurate information in dose administration time

OBJECTIVES

To illustrate the impact of errors in documented dose administration time on therapeutic drug monitoring (TDM)-based target attainment evaluation for vancomycin and meropenem, and to explore the influence of drug and patient characteristics, and TDM sampling strategies.

METHODS

Bedside observations of errors in documented dose administration times were collected. Population pharmacokinetic simulations were performed for vancomycin and meropenem, evaluating different one- and two-sampling strategies for populations with estimated creatinine clearance (CLcr) of 30, 80 or 130 mL/min. The impact of errors was evaluated as the proportion of individuals incorrectly considered to have reached the target.

RESULTS

Of 143 observed dose administrations, 97% of doses were given within ±30 min of the documented time. For vancomycin, a +30 min error was predicted to result in a 0.1-3.9 percentage point increase of cases incorrectly evaluated as reaching area under the concentration-time curve during a 24-hour period (AUC24)/minimum inhibitory concentration (MIC) >400, with the largest increase for patients with augmented renal clearance and peak and trough sampling. For meropenem, a +30 min error resulted in a 1.3-6.4 and 0-20 percentage point increase of cases incorrectly evaluated as reaching 100% T>MIC, and 50% T>MIC, respectively. Overall, mid-dose and trough sampling was most favourable for both antibiotics.

CONCLUSIONS

For vancomycin, simulations indicate that TDM-based target attainment evaluation is robust with respect to the observed errors in dose administration time of ±30 min; however, the errors had a potentially clinically important impact in patients with augmented renal clearance. For meropenem, extra measures to promote correct documentation are warranted when using TDM, as the impact of errors was evident even in patients with normal renal function.

Model-informed precision dosing of vancomycin for rapid achievement of target area under the concentration-time curve: A simulation study

In this study, we aimed to evaluate limited sampling strategies for achieving the therapeutic ranges of the area under the concentration-time curve (AUC) of vancomycin on the first and second day (AUC0-24 , AUC24-48 , respectively) of therapy. A virtual population of 1000 individuals was created using a population pharmacokinetic (PopPK) model, which was validated and incorporated into our model-informed precision dosing tool. The results were evaluated using six additional PopPK models selected based on a study design of prospective or retrospective data collection with sufficient concentrations. Bayesian forecasting was performed to evaluate the probability of achieving the therapeutic range of AUC, defined as a ratio of estimated/reference AUC within 0.8-1.2. The Bayesian posterior probability of achieving the AUC24-48 range increased from 51.3% (a priori probability) to 77.5% after using two-point sampling at the trough and peak on the first day. Sampling on the first day also yielded a higher Bayesian posterior probability (86.1%) of achieving the AUC0-24 range compared to the a priori probability of 60.1%. The Bayesian posterior probability of achieving the AUC at steady-state (AUCSS ) range by sampling on the first or second day decreased with decreased kidney function. We demonstrated that second-day trough and peak sampling provided accurate AUC24-48 , and first-day sampling may assist in rapidly achieving therapeutic AUC24-48 , although the AUCSS should be re-estimated in patients with reduced kidney function owing to its unreliable predictive performance.

Tobramycin a Priori Dosing Regimens Based on PopPK Model Simulations in Critically Ill Patients: Are They Transferable?

BACKGROUND

In recent years, multiple population pharmacokinetic models have been developed for drugs such as tobramycin that need therapeutic drug monitoring. Some of these models have been used to develop a priori dosing regimens for their respective populations. However, these dosing regimens may not apply to other populations. Therefore, this study aimed to evaluate tobramycin population pharmacokinetic models in critically ill patients and establish an adequate dosing regimen.

METHODS

Evaluated models were identified from a literature review of aminoglycoside population pharmacokinetic models in critically ill patients. After retrospective data collection in 2 Quebec hospitals, external evaluation and model re-estimation were performed with NONMEM (v7.5) to assess imprecision and bias values. Dosing regimens were simulated and compared between the best-performing model and its re-estimated counterparts.

RESULTS

None of the 3 evaluated models showed acceptable imprecision or bias values in the data sets of the 19 patients. Similar percentages of target attainment were obtained for the original and re-estimated models after the dosing regimen simulations.

CONCLUSION

Although the predictive performance evaluation criteria were inadequate, the original and re-estimated models yielded similar results. This raises the question of what a priori bias and imprecision thresholds should be defined as acceptable for the external evaluation of models to be applied in clinical practice. Studies evaluating the impact of these thresholds are needed.

Towards model-informed precision dosing of piperacillin: multicenter systematic external evaluation of pharmacokinetic models in critically ill adults with a focus on Bayesian forecasting

PURPOSE

Inadequate piperacillin (PIP) exposure in intensive care unit (ICU) patients threatens therapeutic success. Model-informed precision dosing (MIPD) might be promising to individualize dosing; however, the transferability of published models to external populations is uncertain. This study aimed to externally evaluate the available PIP population pharmacokinetic (PopPK) models.

METHODS

A multicenter dataset of 561 ICU patients (11 centers/3654 concentrations) was used for the evaluation of 24 identified models. Model performance was investigated for a priori (A) predictions, i.e., considering dosing records and patient characteristics only, and for Bayesian forecasting, i.e., additionally including the first (B1) or first and second (B2) therapeutic drug monitoring (TDM) samples per patient. Median relative prediction error (MPE) [%] and median absolute relative prediction error (MAPE) [%] were calculated to quantify accuracy and precision.

RESULTS

The evaluation revealed a large inter-model variability (A: MPE - 135.6-78.3% and MAPE 35.7-135.6%). Integration of TDM data improved all model predictions (B1/B2 relative improvement vs. A: |MPE|median_all_models 45.1/67.5%; MAPEmedian_all_models 29/39%). The model by Kim et al. was identified to be most appropriate for the total dataset (A/B1/B2: MPE - 9.8/- 5.9/- 0.9%; MAPE 37/27.3/23.7%), Udy et al. performed best in patients receiving intermittent infusion, and Klastrup et al. best predicted patients receiving continuous infusion. Additional evaluations stratified by sex and renal replacement therapy revealed further promising models.

CONCLUSION

The predictive performance of published PIP models in ICU patients varied considerably, highlighting the relevance of appropriate model selection for MIPD. Our differentiated external evaluation identified specific models suitable for clinical use, especially in combination with TDM.

Plasma and Cerebrospinal Fluid Population Pharmacokinetics of Vancomycin in Patients with External Ventricular Drain

Vancomycin is a commonly used antibacterial agent in patients with primary central nervous system (CNS) infection. This study aims to examine predictors of vancomycin penetration into cerebrospinal fluid (CSF) in patients with external ventricular drainage and the feasibility of CSF sampling from the distal drainage port for therapeutic drug monitoring. Fourteen adult patients (9 with primary CNS infection) were treated with vancomycin intravenously. The vancomycin concentrations in blood and CSF (from proximal [CSF_P] and distal [CSF_D] drainage ports) were evaluated by population pharmacokinetics. Model-based simulations were conducted to compare various infusion modes. A three-compartment model with first-order elimination best described the vancomycin data. Estimated parameters included clearance (CL, 4.53 L/h), central compartment volume (Vc, 24.0 L), apparent CSF compartment volume (VCSF, 0.445 L), and clearance between central and CSF compartments (QCSF, 0.00322 L/h and 0.00135 L/h for patients with and without primary CNS infection, respectively). Creatinine clearance was a significant covariate on vancomycin CL. CSF protein was the primary covariate to explain the variability of QCSF. There was no detectable difference between the data for sampling from the proximal and the distal port. Intermittent infusion and continuous infusion with a loading dose reached the CSF target concentration faster than continuous infusion only. All infusion schedules reached similar CSF trough concentrations. Beyond adjusting doses according to renal function, starting treatment with a loading dose in patients with primary CSF infection is recommended. Occasionally, very high and possibly toxic doses would be required to achieve adequate CSF concentrations, which calls for more investigation of direct intraventricular administration of vancomycin. (This study has been registered at ClinicalTrials.gov under registration no. NCT04426383).

Investigating the contribution of residual unexplained variability components on bias and imprecision of parameter estimates in population pharmacokinetic mixed-effects modeling

In a nonlinear mixed-effects modeling (NLMEM) approach of pharmacokinetic (PK) and pharmacodynamic (PD) data, two levels of random effects are generally modeled: between-subject variability (BSV) and residual unexplained variability (RUV). The goal of this simulation-estimation study was to investigate the extent to which PK and RUV model misspecification, errors in recording dosing and sampling times, and variability in drug content uniformity contribute to the estimated magnitude of RUV and PK parameter bias. A two-compartment model with first-order absorption and linear elimination was simulated as a true model. PK parameters were clearance 5.0 L/h; central volume of distribution 35 L; inter-compartmental clearance 50 L/h; peripheral volume of distribution 50 L. All parameters were assumed to have a 30% coefficient of variation (CV). One hundred in-silico subjects were administered a labeled dose of 120 mg under 4 sample collection designs. PK and RUV model misspecifications were associated with relatively larger increases in the magnitude of RUV compared to other sources for all levels of sampling design. The contribution of dose and dosing time misspecifications have negligible effects on RUV but result in higher bias in PK parameter estimates. Inaccurate sampling time data results in biased RUV and increases with the magnitude of perturbations. Combined perturbation scenarios in the studied sources will propagate the variability and accumulate in RUV magnitude and bias in PK parameter estimates. This work provides insight into the potential contributions of many factors that comprise RUV and bias in PK parameters.

Dosing for Personalized Prophylaxis in Hemophilia A Highly Varies on the Underlying Population Pharmacokinetic Models

BACKGROUND

Model-informed personalized prophylaxis with factor VIII (FVIII) replacement therapy aimed at higher trough levels is becoming indispensable for patients with severe hemophilia A. This study aimed to identify the most suitable population pharmacokinetic (PK) models for personalized prophylaxis using various FVIII products and 2 clinical assays and to implement the most suitable one in open-access software.

METHODS

Twelve published population PK models were systematically compared to predict the time above target (TaT) for a reference dosing occasion. External validation was performed using a 5-point PK data from 39 adult patients with hemophilia A with FVIII measured by chromogenic substrate (CSA) and 1-stage assays (OSAs) using NONMEM under 3 different conditions: a priori (with all FVIII samples blinded), a posteriori (with 1 trough sample), and general model fit (with all FVIII samples including the reference dosing occasion provided).

RESULTS

On average, the baseline covariate models overpredicted TaT (a priori; bias -3.8 hours to 49.6 hours). When additionally including 1 previous trough FVIII sample before the reference dosing occasion (a posteriori), only 50% of the models improved in bias (-1.0 hours to 36.5 hours) and imprecision (22.4 hours and 60.7 hours). Using all the time points (general model fit), the models accurately predicted (individual TaT less than ±12 hours compared with the reference) 62%-90% and 33%-74% of the patients using CSA and OSA data, respectively. Across all scenarios, predictions using CSA data were more accurate than those using the OSA data.

CONCLUSIONS

One model performed best across the population (bias: -3.8 hours a priori, -1.0 hours a posteriori , and 0.6 hours general model fit ) and acceptably predicted 44% (a priori) to 90% ( general model fit ) of the patients. To allow the community-based evaluation of patient-individual FVIII dosing, this model was implemented in the open-access model-informed precision dosing software "TDMx."

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

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

Model Re-Estimation: An Alternative for Poor Predictive Performance during External Evaluations? Example of Gentamicin in Critically Ill Patients

Background: An external evaluation is crucial before clinical applications; however, only a few gentamicin population pharmacokinetic (PopPK) models for critically ill patients included it in the model development. In this study, we aimed to evaluate gentamicin PopPK models developed for critically ill patients. Methods: The evaluated models were selected following a literature review on aminoglycoside PopPK models for critically ill patients. The data of patients were retrospectively collected from two Quebec hospitals, the external evaluation and model re-estimation were performed with NONMEM^® (v7.5) and the population bias and imprecisions were estimated. Dosing regimens were simulated using the best performing model. Results: From the datasets of 39 and 48 patients from the two Quebec hospitals, none of the evaluated models presented acceptable values for bias and imprecision. Following model re-estimations, all models showed an acceptable predictive performance. An a priori dosing nomogram was developed with the best performing re-estimated model and was consistent based on recommended dosing regimens. Conclusion: Due to the poor predictive performance during the external evaluations, the latter must be prioritized during model development. Model re-estimation may be an alternative to developing a new model, especially when most known models display similar covariates.

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.

Ciprofloxacin in Patients Undergoing Extracorporeal Membrane Oxygenation (ECMO): A Population Pharmacokinetic Study

Extracorporeal membrane oxygenation (ECMO) is utilized to temporarily sustain respiratory and/or cardiac function in critically ill patients. Ciprofloxacin is used to treat nosocomial infections, but data describing the effect of ECMO on its pharmacokinetics is lacking. Therefore, a prospective, observational trial including critically ill adults (n = 17), treated with ciprofloxacin (400 mg 8–12 hourly) during ECMO, was performed. Serial blood samples were collected to determine ciprofloxacin concentrations to assess their pharmacokinetics. The pharmacometric modeling was performed (NONMEM^®) and utilized for simulations to evaluate the probability of target attainment (PTA) to achieve an AUC_0–24/MIC of 125 mg·h/L for ciprofloxacin. A two-compartment model most adequately described the concentration-time data of ciprofloxacin. Significant covariates on ciprofloxacin clearance (CL) were plasma bicarbonate and the estimated glomerular filtration rate (eGFR). For pathogens with an MIC of ≤0.25 mg/L, a PTA of ≥90% was attained. However, for pathogens with an MIC of ≥0.5 mg/L, plasma bicarbonate ≥ 22 mmol/L or eGFR ≥ 10 mL/min PTA decreased below 90%, steadily declining to 7.3% (plasma bicarbonate 39 mmol/L) and 21.4% (eGFR 150 mL/min), respectively. To reach PTAs of ≥90% for pathogens with MICs ≥ 0.5 mg/L, optimized dosing regimens may be required.

Antifungal Drugs TDM: Trends and Update

PURPOSE

The increasing burden of invasive fungal infections results in growing challenges to antifungal (AF) therapeutic drug monitoring (TDM). This review aims to provide an overview of recent advances in AF TDM.

METHODS

We conducted a PubMed search for articles during 2016-2020 using "TDM" or "pharmacokinetics" or "drug-drug-interaction" with "antifungal," consolidated for each AF. Selection was limited to English language articles with human data on drug exposure.

RESULTS

More than 1000 articles matched the search terms. We selected 566 publications. The latest findings tend to confirm previous observations in real-life clinical settings. The pharmacokinetic variability related to special populations is not specific but must be considered. AF benefit-to-risk ratio, drug-drug interaction (DDI) profiles, and minimal inhibitory concentrations for pathogens must be known to manage at-risk situations and patients. Itraconazole has replaced ketoconazole in healthy volunteers DDI studies. Physiologically based pharmacokinetic modeling is widely used to assess metabolic azole DDI. AF prophylactic use was studied more for Aspergillus spp. and Mucorales in oncohematology and solid organ transplantation than for Candida (already studied). Emergence of central nervous system infection and severe infections in immunocompetent individuals both merit special attention. TDM is more challenging for azoles than amphotericin B and echinocandins. Fewer TDM requirements exist for fluconazole and isavuconazole (ISZ); however, ISZ is frequently used in clinical situations in which TDM is recommended. Voriconazole remains the most challenging of the AF, with toxicity limiting high-dose treatments. Moreover, alternative treatments (posaconazole tablets, ISZ) are now available.

CONCLUSIONS

TDM seems to be crucial for curative and/or long-term maintenance treatment in highly variable patients. TDM poses fewer cost issues than the drugs themselves or subsequent treatment issues. The integration of clinical pharmacology into multidisciplinary management is now increasingly seen as a part of patient care.

Recent advancements in clearance monitoring of monoclonal antibodies in patients with inflammatory bowel diseases

INTRODUCTION

Less than 50% of patients with inflammatory bowel diseases (IBD) receiving monoclonal antibody (mAb) therapy achieve endoscopic remission. Poor outcomes may indicate a need for dose optimization. During therapeutic drug monitoring (TDM), drug concentrations are measured, and when found too low, dosage regimen escalations are performed. To date, benefits of TDM of mAbs in patients with IBD are uncertain.

AREAS COVERED

This review presents an overview of what clearance monitoring is, how it can be performed, and why and when it may be valuable in treating patients with IBD. Virtual patients were used for illustration. A literature search was performed to summarize current evidence for clearance monitoring in IBD and other disease settings.

EXPERT OPINION

During clearance monitoring, mAb clearance is calculated and monitored over time. Higher mAb clearance in patients with IBD has been associated with higher target load (target-mediated drug disposition), protein-losing enteropathy (fecal drug loss), and immunogenicity. Although not prospectively confirmed, clearance monitoring might facilitate identification of (yet) asymptomatic disease flares or presence of (yet) undetectable anti-drug antibodies. Furthermore, clearance monitoring may be used to predict treatment outcomes. Whether dosage regimen adjustments can modify the clearance time course and the treatment outcome is to be determined.

Predictive Value of C-Reactive Protein and Albumin for Temporal Within-Individual Pharmacokinetic Variability of Voriconazole in Pediatric Hematopoietic Cell Transplant Patients

Voriconazole is a widely used antifungal agent in immunocompromised patients, but its utility is limited by its variable exposure and narrow therapeutic index. Population pharmacokinetic (PK) models have been used to characterize voriconazole PK and derive individualized dosing regimens. However, determinants of temporal within-patient variability of voriconazole PK were not well-established. We aimed to characterize temporal variability of voriconazole PK within individuals and identify predictive clinical factors. This study was conducted as a part of a single-institution, phase I study of intravenous voriconazole in children undergoing HCT (NCT02227797). We analyzed voriconazole PK study data collected at week 1 and again at week 2 after the start of voriconazole therapy in 59 pediatric HCT patients (age <21 years). Population PK analysis using nonlinear mixed effect modeling was performed to analyze temporal within-individual variability of voriconazole PK by incorporating a between-occasion variability term in the model. A two-compartment linear elimination model incorporating body weight and CYP2C19 phenotype described the data. Ratio of individual voriconazole clearance between weeks 1 to 2 ranged from 0.11 to 3.3 (-9.1 to +3.3-fold change). Incorporation of covariate effects by serum C-reactive protein (CRP) and albumin levels decreased between-occasion variability of clearance (coefficient of variation: from 59.5% to 41.2%) and improved the model fit (p<0.05). As significant covariates on voriconazole PK, CRP and albumin concentrations may potentially serve as useful biomarkers as part of therapeutic drug monitoring. This article is protected by copyright. All rights reserved.

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

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

Evaluation of the Robustness of Therapeutic Drug Monitoring Coupled with Bayesian Forecasting of Busulfan with Regard to Inaccurate Documentation

Background

Inaccurate documentation of sampling and infusion times is a potential source of error in personalizing busulfan doses using therapeutic drug monitoring (TDM). Planned times rather than the actual times for sampling and infusion time are often documented.

Therefore, this study aimed to evaluate the robustness of a limited sampling TDM of busulfan with regard to inaccurate documentation.

Methods

A pharmacometric analysis was conducted in NONMEM® 7.4.3 and “R” by performing stochastic simulation and estimation with four, two and one sample(s) per patient on the basis of a one-compartment- (1CMT) and two-compartment (2CMT) population pharmacokinetic model. The dosing regimens consisted of i.v. busulfan (0.8 mg/kg) every 6 h (Q6H) or 3.2 mg/kg every 24 h (Q24H) with a 2 h- and 3 h infusion time, respectively. The relative prediction error (rPE) and relative root-mean-square error (rRmse) were calculated in order to determine the accuracy and precision of the individual AUC estimation.

Results

A noticeable impact on the estimated AUC based on a 1CMT-model was only observed if uncertain documentation reached ± 30 min (1.60% for Q24H and 2.19% for Q6H). Calculated rPEs and rRmse for Q6H indicate a slightly lower level of accuracy and precision when compared to Q24H. Spread of rPE’s and rRmse for the 2CMT-model were wider and higher compared to estimations based on a 1CMT-model.

Conclusions

The estimated AUC was not affected substantially by inaccurate documentation of sampling and infusion time. The calculated rPEs and rRmses of estimated AUC indicate robustness and reliability for TDM of busulfan, even in presence of erroneous records.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11095-021-03115-8.

Population pharmacokinetics and target attainment analysis of linezolid in multidrug-resistant tuberculosis patients

AIM

This study investigates the pharmacokinetic/pharmacodynamic (PK/PD) target attainment of linezolid in patients infected with multidrug-resistant (MDR) tuberculosis (TB).

METHODS

A pharmacometric model was developed including 244 timed linezolid concentration samples from 39 patients employing NONMEM 7.4. The probability of target attainment (PTA, PK/PD target: unbound (f) area-under-the-concentration-time-curve (AUC)/minimal inhibitory concentration (MIC) of 119) as well as a region-specific cumulative fraction of response (CFR) were estimated for different dosing regimens.

RESULTS

A one-compartment model with linear elimination with a clearance (CL) of 7.69 L/h (interindividual variability 34.1%), a volume of distribution (Vd) of 45.2 L and an absorption constant (KA) of 0.679 h^-1 (interoccasion variability 143.7%) allometric scaled by weight best described the PK of linezolid. The PTA at an MIC of 0.5 mg/L was 55% or 97% if patients receiving 300 or 600 mg twice daily, respectively. CFRs varied greatly among populations and geographic regions. A desirable global CFR of ≥90% was achieved if linezolid was administered at a dose of 600 mg twice daily but not at a dose of 300 mg twice daily.

CONCLUSION

This study showed that a dose of 300 mg twice daily of linezolid might not be sufficient to treat MDR-TB patients from a PK/PD perspective. Thus, it might be recommendable to start with a higher dose of 600 mg twice daily to ensure PK/PD target attainment. Hereby, therapeutic drug monitoring and MIC determination should be performed to control PK/PD target attainment as linezolid shows high variability in its PK in the TB population.

Model-Informed Precision Dosing during Infliximab Induction Therapy Reduces Variability in Exposure and Endoscopic Improvement between Patients with Ulcerative Colitis

Model-informed precision dosing (MIPD) may be a solution to therapeutic failure of infliximab for patients with ulcerative colitis (UC), as underexposure could be avoided, and the probability of endoscopic improvement (pEI; Mayo endoscopic subscore ≤ 1) could be optimized. To investigate in silico whether this claim has merit, four induction dosing regimens were simulated: 5 mg/kg (label dosing), 10 mg/kg, covariate-based MIPD (fat-free mass, corticosteroid use, and presence of extensive colitis at baseline), and concentration-based MIPD (based on the trough concentration at day 14). Covariate- and concentration-based MIPD were chosen to target the same median area under the infliximab concentration-time curve up to endoscopy at day 84 (AUC_d84), as was predicted from 10 mg/kg dosing. Dosing at 5 mg/kg resulted in a mean ± standard deviation pEI of 55.7 ± 9.0%. Increasing the dose to 10 mg/kg was predicted to improve pEI to 65.1 ± 6.1%. Covariate-based MIPD reduced variability in exposure and pEI (65.1 ± 5.5%). Concentration-based MIPD decreased variability further (66.0 ± 3.9%) but did so at an increased average dose of 2293 mg per patient, as compared to 2168 mg for 10 mg/kg dosing. Mean pEI remained unchanged between 10 mg/kg dosing and MIPD, since the same median AUC_d84 was targeted. In conclusion, quantitative simulations predict MIPD will reduce variability in exposure and pEI between patients with UC during infliximab induction therapy.

Optimal loading dose of meropenem before continuous infusion in critically ill patients: a simulation study

The aim of this study was to investigate optimal loading doses prior to continuous infusion of meropenem in critically ill patients. A previously published and successfully evaluated pharmacokinetic model of critically ill patients was used for stochastic simulations of virtual patients. Maintenance doses administered as continuous infusion of 1.5–6 g/24 h with preceding loading doses (administered as 30 min infusion) of 0.15–2 g were investigated. In addition to the examination of the influence of individual covariates, a best-case and worst-case scenario were simulated. Dosing regimens were considered adequate if the 5th percentile of the concentration–time profile did not drop at any time below four times the S/I breakpoint (= 2 mg/L) of Pseudomonas aeruginosa according to the EUCAST definition. Low albumin concentrations, high body weight and high creatinine clearances increased the required loading dose. A maximum loading dose of 0.33 g resulted in sufficient plasma concentrations when only one covariate showed extreme values. If all three covariates showed extreme values (= worst-case scenario), a loading dose of 0.5 g was necessary. Higher loading doses did not lead to further improvements of target attainment. We recommend the administration of a loading dose of 0.5 g meropenem over 30 min immediately followed by continuous infusion.

Accuracy of documented administration times for intravenous antimicrobial drugs and impact on dosing decisions

AIMS

Accurate documentation of medication administration time is imperative for many therapeutic decisions, including dosing of intravenous antimicrobials. The objectives were to determine (1) the discrepancy between actual and documented administration times for antimicrobial infusions and (2) whether day of the week, time of day, nurse-to-patient ratio and drug impacted accuracy of documented administration times.

METHODS

Patient and dosing data were collected (June-August 2019) for 55 in-patients receiving antimicrobial infusions. "Documented" and "actual" administration times (n = 660) extracted from electronic medication management systems and smart infusion pumps, respectively, were compared. Influence of the day (weekday/weekend), time of day (day/evening/night), nurse-to-patient ratio (high 1:1/low 1:5) and drug were examined. Monte Carlo simulation was used to predict the impact on dose adjustments for vancomycin using the observed administration time discrepancies compared to the actual administration time.

RESULTS

The median discrepancy between actual and documented administration times was 16 min (range, 2-293 min), with discrepancies greater than 60 minutes in 7.7% of administrations. Overall, discrepancies (median [range]) were similar on weekends (17 [2-293] min) and weekdays (16 [2-188] min), and for high (16 [2-157] min) and low nurse-to-patient ratio wards (16 [2-293] min). Discrepancies were smallest for night administrations (P < .05), and antimicrobials with shorter half-lives (P < .0001). The observed discrepancies in vancomycin administration time resulted in a different dose recommendation in 58% of cases (30% higher, 28% lower).

CONCLUSIONS

Overall, there were discrepancies between actual and documented antimicrobial infusion administration times. For vancomycin, these discrepancies in administration time were predicted to result in inappropriate dose recommendations.

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.

A Model Averaging/Selection Approach Improves the Predictive Performance of Model-Informed Precision Dosing: Vancomycin as a Case Study

Many important drugs exhibit substantial variability in pharmacokinetics and pharmacodynamics leading to a loss of the desired clinical outcomes or significant adverse effects. Forecasting drug exposures using pharmacometric models can improve individual target attainment when compared with conventional therapeutic drug monitoring (TDM). However, selecting the "correct" model for this model-informed precision dosing (MIPD) is challenging. We derived and evaluated a model selection algorithm (MSA) and a model averaging algorithm (MAA), which automates model selection and finds the best model or combination of models for each patient using vancomycin as a case study, and implemented both algorithms in the MIPD software "TDMx." The predictive performance (based on accuracy and precision) of the two algorithms was assessed in (i) a simulation study of six distinct populations and (ii) a clinical dataset of 180 patients undergoing TDM during vancomycin treatment and compared with the performance obtained using a single model. Throughout the six virtual populations the MSA and MAA (imprecision: 9.9-24.2%, inaccuracy: less than ± 8.2%) displayed more accurate predictions than the single models (imprecision: 8.9-51.1%; inaccuracy: up to 28.9%). In the clinical dataset, the predictive performance of the single models applying at least one plasma concentration varied substantially (imprecision: 28-62%, inaccuracy: -16 to 25%), whereas the MSA or MAA utilizing these models simultaneously resulted in unbiased and precise predictions (imprecision: 29% and 30%, inaccuracy: -5% and 0%, respectively). MSA and MAA approaches implemented in TDMx might thereby lower the burden of fit-for-purpose validation of individual models and streamline MIPD.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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