TDMx: A novel web-based open-access support tool for optimising antimicrobial dosing regimens in clinical routine

A Single-Run HPLC-MS Multiplex Assay for Therapeutic Drug Monitoring of Relevant First- and Second-Line Antibiotics in the Treatment of Drug-Resistant Tuberculosis

The treatment of drug-resistant Mycobacterium tuberculosis relies on complex antibiotic therapy. Inadequate antibiotic exposure can lead to treatment failure, acquired drug resistance, and an increased risk of adverse events. Therapeutic drug monitoring (TDM) can be used to optimize the antibiotic exposure. Therefore, we aimed to develop a single-run multiplex assay using high-performance liquid chromatography-mass spectrometry (HPLC-MS) for TDM of patients with multidrug-resistant, pre-extensively drug-resistant and extensively drug-resistant tuberculosis. A target profile for sufficient performance, based on the intended clinical application, was established and the assay was developed accordingly. Antibiotics were analyzed on a zwitterionic hydrophilic interaction liquid chromatography column and a triple quadrupole mass spectrometer using stable isotope-labeled internal standards. The assay was sufficiently sensitive to monitor drug concentrations over five half-lives for rifampicin, rifabutin, levofloxacin, moxifloxacin, bedaquiline, linezolid, clofazimine, terizidone/cycloserine, ethambutol, delamanid, pyrazinamide, meropenem, prothionamide, and para-amino salicylic acid (PAS). Accuracy and precision were sufficient to support clinical decision making (≤±15% in clinical samples and ±20-25% in spiked samples, with 80% of future measured concentrations predicted to fall within ±40% of nominal concentrations). The method was applied in the TDM of two patients with complex drug-resistant tuberculosis. All relevant antibiotics from their regimens could be quantified and high-dose therapy was initiated, followed by microbiological conversion. In conclusion, we developed a multiplex assay that enables TDM of the relevant first- and second-line anti-tuberculosis medicines in a single run and was able to show its applicability in TDM of two drug-resistant tuberculosis patients.

Meropenem Pharmacokinetics and Target Attainment in Critically Ill Patients

Purpose

This study aimed to investigate the pharmacokinetics and target attainment of meropenem and compare the effect of meropenem dosing regimens in critically ill patients.

Patients and Methods

Thirty-seven critically ill patients who were administered meropenem in intensive care units were analyzed. Patients were classified according to their renal function. Pharmacokinetic parameters were assessed based on Bayesian estimation. The target attainment of 40%fT > MIC (fraction time that the free concentration exceeds the minimum inhibitory concentration) and 100%fT > MIC with the pathogen MIC of 2 mg/L and 8 mg/L were specially focused. Furthermore, the effects of standard dosing (1g meropenem, 30 min intravenous infusion every 8h) and non-standard dosing (dosage regimens except standard dosing) were compared.

Results

The results showed that the values of meropenem clearance (CL), central volume of distribution (V1), intercompartmental clearance (Q), and peripheral volume of distribution (V2) were 3.3 L/h, 9.2 L, 20.1 L/h and 12.8 L, respectively. The CL of the patients among renal function groups was significantly different (p < 0.001). The tow targets attainment for the pathogen MIC of 2 mg/L and 8 mg/L were 89%, 73%, 49% and 27%, respectively. The severe renal impairment group has higher fraction of target attainment than the other group. The standard dosing achieved the target of 40%fT > 2/8 mg/L (85.7% and 81%, respectively) and patients with severe renal impairment achieved the target fraction of 100% for 40%fT > MIC. Additionally, there was no significant difference between standard and non-standard dosing group in target attainment.

Conclusion

Our findings indicate that renal function is an important covariate for both meropenem pharmacokinetics parameters and target attainment. The target attainment between standard and non-standard dosing group was not comparable. Therefore, therapeutic drug monitoring is indispensable in the dosing adjustment for critically ill patients if it is available.

Bayesian prediction-based individualized dosing of anti-methicillin-resistant Staphylococcus aureus treatment: Recent advancements and prospects in therapeutic drug monitoring

As one of the efficient techniques for TDM, the population pharmacokinetic (popPK) model approach for dose individualization has been developed due to the rapidly growing innovative progress in computer technology and has recently been considered as a part of model-informed precision dosing (MIPD). Initial dose individualization and measurement followed by maximum a posteriori (MAP)-Bayesian prediction using a popPK model are the most classical and widely used approach among a class of MIPD strategies. MAP-Bayesian prediction offers the possibility of dose optimization based on measurement even before reaching a pharmacokinetically steady state, such as in an emergency, especially for infectious diseases requiring urgent antimicrobial treatment. As the pharmacokinetic processes in critically ill patients are affected and highly variable due to pathophysiological disturbances, the advantages offered by the popPK model approach make it highly recommended and required for effective and appropriate antimicrobial treatment. In this review, we focus on novel insights and beneficial aspects of the popPK model approach, especially in the treatment of infectious diseases with anti-methicillin-resistant Staphylococcus aureus agents represented by vancomycin, and discuss the recent advancements and prospects in TDM practice.

Accuracy of 4 Free Online Dosing Calculators in Predicting the Vancomycin Area Under the Concentration-Time Curve Calculated Using a 2-Point Pharmacokinetic Approach

BACKGROUND

Free online adaptive vancomycin dosing calculators are available to estimate area under the concentration-time curve (AUC), but the accuracy of predicting vancomycin AUC using these calculators compared with using a 2-point pharmacokinetic approach has not been described.

OBJECTIVE

To evaluate the accuracy of calculator-predicted AUC (cpAUC) using 4 free online calculators compared with reference AUC (rAUC), and to assess pharmacists' impressions of the ease of use.

METHODS

Vancomycin AUC was estimated using (1) the reference method via the Sawchuk-Zaske method and linear-logarithmic trapezoidal rule using 2 steady-state postdistributional vancomycin serum concentrations and (2) 4 free online vancomycin dosing calculators including ClinCalc, VancoPK, TDMx, and DMC. Accuracy was calculated by dividing cpAUC by rAUC. Ease of cpAUC estimation was determined by using a 10-point Likert scale.

RESULTS

All 4 calculators had a median cpAUC accuracy ranging from 89% to 110%. Concordance between cpAUC and rAUC determinations of AUC <400 and > 600 mg·h/L occurred 63.3% to 71.4% and 74.5% to 78.6% of the time, respectively. Pharmacist investigators agreed that ClinCalc and VancoPK calculators were easiest to use.

CONCLUSION AND RELEVANCE

cpAUC accuracy varied among the 4 calculators, but all consistently identified patients with an rAUC <400 mg·h/L and an rAUC > 600 mg·h/L at comparable frequencies. All 4 calculators demonstrated some imprecision based on their wide 95% CIs and potential inaccuracies in predicting an rAUC <400 mg·h/L or an rAUC > 600 mg·h/L. Clin Calc and VancoPK were most user friendly based on our pharmacists' impressions.

Implementing Vancomycin Population Pharmacokinetic Models: An App for Individualized Antibiotic Therapy in Critically Ill Patients

In individualized therapy, the Bayesian approach integrated with population pharmacokinetic models (PopPK) for predictions together with therapeutic drug monitoring (TDM) to maintain adequate objectives is useful to maximize the efficacy and minimize the probability of toxicity of vancomycin in critically ill patients. Although there are limitations to implementation, model-informed precision dosing (MIPD) is an approach to integrate these elements, which has the potential to optimize the TDM process and maximize the success of antibacterial therapy. The objective of this work was to present an app for individualized therapy and perform a validation of the implemented vancomycin PopPK models. A pragmatic approach was used for selecting the models of Llopis, Goti and Revilla for developing a Shiny app with R. Through ordinary differential equation (ODE)-based mixed effects models from the mlxR package, the app simulates the concentrations' behavior, estimates whether the model was simulated without variability and predicts whether the model was simulated with variability. Moreover, we evaluated the predictive performance with retrospective trough concentration data from patients admitted to the adult critical care unit. Although there were no significant differences in the performance of the estimates, the Llopis model showed better accuracy (mean 80.88%; SD 46.5%); however, it had greater bias (mean -34.47%, SD 63.38%) compared to the Revilla et al. (mean 10.61%, SD 66.37%) and Goti et al. (mean of 13.54%, SD 64.93%) models. With respect to the RMSE (root mean square error), the Llopis (mean of 10.69 mg/L, SD 12.23 mg/L) and Revilla models (mean of 10.65 mg/L, SD 12.81 mg/L) were comparable, and the lowest RMSE was found in the Goti model (mean 9.06 mg/L, SD 9 mg/L). Regarding the predictions, this behavior did not change, and the results varied relatively little. Although our results are satisfactory, the predictive performance in recent studies with vancomycin is heterogeneous, and although these three models have proven to be useful for clinical application, further research and adaptation of PopPK models is required, as well as implementation in the clinical practice of MIPD and TDM in real time.

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."

Multi‐model averaging improves the performance of model‐guided infliximab dosing in patients with inflammatory bowel diseases

Infliximab dosage de‐escalation without prior knowledge of drug concentrations may put patients at risk for underexposure and trigger the loss of response. A single‐model approach for model‐informed precision dosing during infliximab maintenance therapy has proven its clinical benefit in patients with inflammatory bowel diseases. We evaluated the predictive performances of two multi‐model approaches, a model selection algorithm and a model averaging algorithm, using 18 published population pharmacokinetic models of infliximab for guiding dosage de‐escalation. Data of 54 patients with Crohn’s disease and ulcerative colitis who underwent infliximab dosage de‐escalation after an earlier escalation were used. A priori prediction (based solely on covariate data) and maximum a posteriori prediction (based on covariate data and trough concentrations) were compared using accuracy and precision metrics and the classification accuracy at the trough concentration target of 5.0 mg/L. A priori prediction was inaccurate and imprecise, with the lowest classification accuracies irrespective of the approach (median 59%, interquartile range 59%–63%). Using the maximum a posteriori prediction, the model averaging algorithm had systematically better predictive performance than the model selection algorithm or the single‐model approach with any model, regardless of the number of concentration data. Only a single trough concentration (preferably at the point of care) sufficed for accurate and precise prediction. Predictive performance of both single‐ and multi‐model approaches was robust to the lack of covariate data. Model averaging using four models demonstrated similar predictive performance with a five‐fold shorter computation time. This model averaging algorithm was implemented in the TDMx software tool to guide infliximab dosage de‐escalation in the forthcoming prospective MODIFI study (NCT04982172).

Systematic Comparison of Hospital-Wide Standard and Model-Based Therapeutic Drug Monitoring of Vancomycin in Adults

We aimed to evaluate the predictive performance and predicted doses of a single-model approach or several multi-model approaches compared with the standard therapeutic drug monitoring (TDM)-based vancomycin dosing. We performed a hospital-wide monocentric retrospective study in adult patients treated with either intermittent or continuous vancomycin infusions. Each patient provided two randomly selected pairs of two consecutive vancomycin concentrations. A web-based precision dosing software, TDMx, was used to evaluate the model-based approaches. In total, 154 patients contributed 308 pairs. With standard TDM-based dosing, only 48.1% (148/308) of all of the second concentrations were within the therapeutic range. Across the model-based approaches we investigated, the mean relative bias and relative root mean square error varied from −5.36% to 3.18% and from 24.8% to 28.1%, respectively. The model averaging approach according to the squared prediction errors showed an acceptable bias and was the most precise. According to this approach, the median (interquartile range) differences between the model-predicted and prescribed doses, expressed as mg every 12 h, were 113 [−69; 427] mg, −70 [−208; 120], mg and 40 [−84; 197] mg in the case of subtherapeutic, supratherapeutic, and therapeutic exposure at the second concentration, respectively. These dose differences, along with poor target attainment, suggest a large window of opportunity for the model-based TDM compared with the standard TDM-based vancomycin dosing. Implementation studies of model-based TDM in routine care are warranted.

Toward Therapeutic Drug Monitoring of Lenalidomide in Hematological Malignancy? Results of an Observational Study of the Exposure-Safety Relationship

Objective: Continuous lenalidomide (LEN) therapy is important to achieve a therapeutic effect in patients with multiple myeloma (MM) and non-Hodgkin lymphoma (NHL). However, despite dose adjustment according to kidney function, many patients discontinue LEN therapy because of hematological toxicity. To date, therapeutic drug monitoring (TDM) of LEN has not been performed in oncology, and no target concentration level has been yet defined. The aim of this study was to evaluate the exposure-safety relationship of LEN and determine the target concentration for toxicity.

Materials and Methods: A prospective observational study was designed and implemented. Blood samples were collected at 0.5 h (trough concentration, C_min) before oral administration and 1 h (C_1h) thereafter on the day. Clinical data were gathered from patients’ medical records and laboratory reports. Outcome measures of hematological toxicity were defined by the Common Terminology Criteria for Adverse Events. The concentration values were dichotomized by receiver operating characteristic (ROC) curve analysis, and the association between exposure and outcome was determined using the logistic regression model.

Results: Out of the 61 patients enrolled in this study, 40 (65.57%) had MM, and 21 (34.43%) had NHL. Hematological toxicity was reported in 15 (24.59%) patients. The LEN C_min showed remarkable differences (p = 0.031) among patients with or without hematological toxicity, while no association between C_1h values and toxicity was noted (p>0.05). By ROC analysis, a C_min threshold of 10.95 ng/mL was associated with the best sensitivity/specificity for toxicity events (AUC = 0.687; sensitivity = 0.40; specificity = 0.935). By multivariate logistic regression, an LEN C_min below 10.95 ng/mL was associated with a markedly decreased risk of hematological toxicity (<10.95 ng/mL vs. >10.95 ng/mL: OR = 0.023, 95% CI = 0.002–0.269; p = 0.003).

Conclusions: We demonstrate that the LEN trough concentration correlates with hematological toxicity, and the C_min threshold for hematological toxicity (10.95 ng/mL) is proposed. Altogether, LEN TDM appears to be a new approach to improve medication safety and achieve continuous treatment for patients with NHL or MM in routine clinical care.

Evaluation of a Meropenem and Piperacillin Monitoring Program in Intensive Care Unit Patients Calls for the Regular Assessment of Empirical Targets and Easy-to-Use Dosing Decision Tools

The drug concentrations targeted in meropenem and piperacillin/tazobactam therapy also depend on the susceptibility of the pathogen. Yet, the pathogen is often unknown, and antibiotic therapy is guided by empirical targets. To reliably achieve the targeted concentrations, dosing needs to be adjusted for renal function. We aimed to evaluate a meropenem and piperacillin/tazobactam monitoring program in intensive care unit (ICU) patients by assessing (i) the adequacy of locally selected empirical targets, (ii) if dosing is adequately adjusted for renal function and individual target, and (iii) if dosing is adjusted in target attainment (TA) failure. In a prospective, observational clinical trial of drug concentrations, relevant patient characteristics and microbiological data (pathogen, minimum inhibitory concentration (MIC)) for patients receiving meropenem or piperacillin/tazobactam treatment were collected. If the MIC value was available, a target range of 1–5 × MIC was selected for minimum drug concentrations of both drugs. If the MIC value was not available, 8–40 mg/L and 16–80 mg/L were selected as empirical target ranges for meropenem and piperacillin, respectively. A total of 356 meropenem and 216 piperacillin samples were collected from 108 and 96 ICU patients, respectively. The vast majority of observed MIC values was lower than the empirical target (meropenem: 90.0%, piperacillin: 93.9%), suggesting empirical target value reductions. TA was found to be low (meropenem: 35.7%, piperacillin 50.5%) with the lowest TA for severely impaired renal function (meropenem: 13.9%, piperacillin: 29.2%), and observed drug concentrations did not significantly differ between patients with different targets, indicating dosing was not adequately adjusted for renal function or target. Dosing adjustments were rare for both drugs (meropenem: 6.13%, piperacillin: 4.78%) and for meropenem irrespective of TA, revealing that concentration monitoring alone was insufficient to guide dosing adjustment. Empirical targets should regularly be assessed and adjusted based on local susceptibility data. To improve TA, scientific knowledge should be translated into easy-to-use dosing strategies guiding antibiotic dosing.

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Free and Open-Source Posologyr Software for Bayesian Dose Individualization: An Extensive Validation on Simulated Data

Model-informed precision dosing is being increasingly used to improve therapeutic drug monitoring. To meet this need, several tools have been developed, but open-source software remains uncommon. Posologyr is a free and open-source R package developed to enable Bayesian individual parameter estimation and dose individualization. Before using it for clinical practice, performance validation is mandatory. The estimation functions implemented in posologyr were benchmarked against reference software products on a wide variety of models and pharmacokinetic profiles: 35 population pharmacokinetic models, with 4.000 simulated subjects by model. Maximum A Posteriori (MAP) estimates were compared to NONMEM post hoc estimates, and full posterior distributions were compared to Monolix conditional distribution estimates. The performance of MAP estimation was excellent in 98.7% of the cases. Considering the full posterior distributions of individual parameters, the bias on dosage adjustment proposals was acceptable in 97% of cases with a median bias of 0.65%. These results confirmed the ability of posologyr to serve as a basis for the development of future Bayesian dose individualization tools.

Current status and future outlooks on therapeutic drug monitoring of fluorouracil

INTRODUCTION

: Therapeutic drug monitoring (TDM) of the anticancer drug fluorouracil (5FU) as a method to support dose adjustments has been researched and discussed extensively. Despite manifold evidence of the advantages of 5FU-TDM, traditional body surface area (BSA)-guided dosing is still widely applied.

AREAS COVERED

: This review covers the latest evidence on 5FU-TDM based on a literature search in PubMed between June and September 2021. It particularly highlights new approaches of implementing 5FU-TDM into precision medicine by combining TDM with pharmacogenetic testing and/or pharmacometric models. This review further discusses remaining obstacles in order to incorporate 5FU-TDM into clinical routine.

EXPERT OPINION

: New data on 5FU-TDM further strengthen the advantages compared to BSA-guided dosing as it is able to reduce pharmacokinetic variability and thereby improve treatment efficacy and safety. Interprofessional collaboration has the potential to overcome the remaining barriers for its implementation. Pre-emptive pharmacogenetic testing followed by 5FU-TDM can further improve 5FU exposure in a substantial proportion of patients. Developing a model framework integrating pharmacokinetics and pharmacodynamics of 5FU will be crucial to fully advance into the precision medicine era. Model applications can potentially support clinicians in dose finding before starting chemotherapy. Additionally, TDM provides further assistance in continuously improving model predictions.

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.

Easy and reliable maximum a posteriori Bayesian estimation of pharmacokinetic parameters with the open-source R package mapbayr

Pharmacokinetic (PK) parameter estimation is a critical and complex step in the model‐informed precision dosing (MIPD) approach. The mapbayr package was developed to perform maximum a posteriori Bayesian estimation (MAP‐BE) in R from any population PK model coded in mrgsolve. The performances of mapbayr were assessed using two approaches. First, “test” models with different features were coded, for example, first‐order and zero‐order absorption, lag time, time‐varying covariates, Michaelis–Menten elimination, combined and exponential residual error, parent drug and metabolite, and small or large inter‐individual variability (IIV). A total of 4000 PK profiles (combining single/multiple dosing and rich/sparse sampling) were simulated from each test model, and MAP‐BE of parameters was performed in both mapbayr and NONMEM. Second, a similar procedure was conducted with seven “real” previously published models to compare mapbayr and NONMEM on a PK outcome used in MIPD. For the test models, 98% of mapbayr estimations were identical to those given by NONMEM. Some discordances could be observed when dose‐related parameters were estimated or when models with large IIV were used. The exploration of objective function values suggested that mapbayr might outdo NONMEM in specific cases. For the real models, a concordance close to 100% on PK outcomes was observed. The mapbayr package provides a reliable solution to perform MAP‐BE of PK parameters in R. It also includes functions dedicated to data formatting and reporting and enables the creation of standalone Shiny web applications dedicated to MIPD, whatever the model or the clinical protocol and without additional software other than R.

Impact of cumulative fluid balance on the pharmacokinetics of extended infusion meropenem in critically ill patients with sepsis

Background

Meropenem dosing for septic critically patients is difficult due to pathophysiological changes associated with sepsis as well as supportive symptomatic therapies. A prospective single-center study assessed whether fluid retention alters meropenem pharmacokinetics and the achievement of the pharmacokinetic/pharmacodynamic (PK/PD) targets for efficacy.

Methods

Twenty-five septic ICU patients (19 m, 6f) aged 32–86 years with the mean APACHE II score of 20.2 (range 11–33), suffering mainly from perioperative intra-abdominal or respiratory infections and septic shock (n = 18), were investigated over three days after the start of extended 3-h i.v. infusions of meropenem q8h. Urinary creatinine clearance (CL_cr) and cumulative fluid balance (CFB) were measured daily. Plasma meropenem was measured, and Bayesian estimates of PK parameters were calculated.

Results

Eleven patients (9 with peritonitis) were classified as fluid overload (FO) based on a positive day 1 CFB of more than 10% body weight. Compared to NoFO patients (n = 14, 11 with pneumonia), the FO patients had a lower meropenem clearance (CL_me 8.5 ± 3.2 vs 11.5 ± 3.5 L/h), higher volume of distribution (V₁ 14.9 ± 3.5 vs 13.5 ± 4.1 L) and longer half-life (t_1/2 1.4 ± 0.63 vs 0.92 ± 0.54 h) (p < 0.05). Over three days, the CFB of the FO patients decreased (11.7 ± 3.3 vs 6.7 ± 4.3 L, p < 0.05) and the PK parameters reached the values comparable with NoFO patients (CL_me 12.4 ± 3.8 vs 11.5 ± 2.0 L/h, V₁ 13.7 ± 2.0 vs 14.0 ± 5.1 L, t_1/2 0.81 ± 0.23 vs 0.87 ± 0.40 h). The CL_cr and Cockroft–Gault CL_cr were stable in time and comparable. The correlation with CL_me was weak to moderate (CL_cr, day 3 CGCL_cr) or absent (day 1 and 2 CGCL_cr). Dosing with 2 g meropenem q8h ensured adequate concentrations to treat infections with sensitive pathogens (MIC 2 mg/L). The proportion of pre-dose concentrations exceeding the MIC 8 mg/L and the fraction time with a target-exceeding concentration were higher in the FO group (day 1–3 f C_min > MIC: 67 vs 27%, p < 0.001; day 1%f T > MIC: 79 ± 17 vs 58 ± 17, p < 0.05).

Conclusions

These findings emphasize the importance of TDM and a cautious approach to augmented maintenance dosing of meropenem to patients with FO infected with less susceptible pathogens, if guided by population covariate relationships between CL_me and creatinine clearance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03680-9.

Amikacin initial dosage in patients with hypoalbuminaemia: an interactive tool based on a population pharmacokinetic approach

OBJECTIVES

To characterize amikacin population pharmacokinetics in patients with hypoalbuminaemia and to develop a model-based interactive application for amikacin initial dosage.

METHODS

A population pharmacokinetic model was developed using a non-linear mixed-effects modelling approach (NONMEM) with amikacin concentration-time data collected from clinical practice (75% hypoalbuminaemic patients). Goodness-of-fit plots, minimum objective function value, prediction-corrected visual predictive check, bootstrapping, precision and bias of parameter estimates were used for model evaluation. An interactive model-based simulation tool was developed in R (Shiny and R Markdown). Cmax/MIC ratio, time above MIC and AUC/MIC were used for optimizing amikacin initial dose recommendation. Probabilities of reaching targets were calculated for the dosage proposed.

RESULTS

A one-compartment model with first-order linear elimination best described the 873 amikacin plasma concentrations available from 294 subjects (model development and external validation groups). Estimated amikacin population pharmacokinetic parameters were CL (L/h) = 0.525 + 4.78 × (CKD-EPI/98) × (0.77 × vancomycin) and V (L) = 26.3 × (albumin/2.9)-0.51 × [1 + 0.006 × (weight - 70)], where CKD-EPI is calculated with the Chronic Kidney Disease Epidemiology Collaboration equation. AMKdose is a useful interactive model-based application for a priori optimization of amikacin dosage, using individual patient and microbiological information together with predefined pharmacokinetic/pharmacodynamic (PKPD) targets.

CONCLUSIONS

Serum albumin, total bodyweight, estimated glomerular filtration rate (using the CKD-EPI equation) and co-medication with vancomycin showed a significant impact on amikacin pharmacokinetics. A powerful interactive initial dose-finding tool has been developed and is freely available online. AMKdose could be useful for guiding initial amikacin dose selection before any individual pharmacokinetic information is available.

Therapeutic drug monitoring of oral targeted antineoplastic drugs

PURPOSE

This review provides an overview of the current challenges in oral targeted antineoplastic drug (OAD) dosing and outlines the unexploited value of therapeutic drug monitoring (TDM). Factors influencing the pharmacokinetic exposure in OAD therapy are depicted together with an overview of different TDM approaches. Finally, current evidence for TDM for all approved OADs is reviewed.

METHODS

A comprehensive literature search (covering literature published until April 2020), including primary and secondary scientific literature on pharmacokinetics and dose individualisation strategies for OADs, together with US FDA Clinical Pharmacology and Biopharmaceutics Reviews and the Committee for Medicinal Products for Human Use European Public Assessment Reports was conducted.

RESULTS

OADs are highly potent drugs, which have substantially changed treatment options for cancer patients. Nevertheless, high pharmacokinetic variability and low treatment adherence are risk factors for treatment failure. TDM is a powerful tool to individualise drug dosing, ensure drug concentrations within the therapeutic window and increase treatment success rates. After reviewing the literature for 71 approved OADs, we show that exposure-response and/or exposure-toxicity relationships have been established for the majority. Moreover, TDM has been proven to be feasible for individualised dosing of abiraterone, everolimus, imatinib, pazopanib, sunitinib and tamoxifen in prospective studies. There is a lack of experience in how to best implement TDM as part of clinical routine in OAD cancer therapy.

CONCLUSION

Sub-therapeutic concentrations and severe adverse events are current challenges in OAD treatment, which can both be addressed by the application of TDM-guided dosing, ensuring concentrations within the therapeutic window.

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.

Evaluation of Current Amikacin Dosing Recommendations and Development of an Interactive Nomogram: The Role of Albumin

This study aimed to evaluate the potential efficacy and safety of the amikacin dosage proposed by the main guidelines and to develop an interactive nomogram, especially focused on the potential impact of albumin on initial dosage recommendation. The probability of target attainment (PTA) for each of the different dosing recommendations was calculated through stochastic simulations based on pharmacokinetic/pharmacodynamic (PKPD) criteria. Large efficacy and safety differences were observed for the evaluated amikacin dosing guidelines together with a significant impact of albumin concentrations on efficacy and safety. For all recommended dosages evaluated, efficacy and safety criteria of amikacin dosage proposed were not achieved simultaneously in most of the clinical scenarios evaluated. Furthermore, a significant impact of albumin was identified: The higher is the albumin, (i) the higher will be the PTA for maximum concentration/minimum inhibitory concentration (Cmax/MIC), (ii) the lower will be the PTA for the time period with drug concentration exceeding MIC (T_>MIC) and (iii) the lower will be the PTA for toxicity (minimum concentration). Thus, accounting for albumin effect might be of interest for future amikacin dosing guidelines updates. In addition, AMKnom, an amikacin nomogram builder based on PKPD criteria, has been developed and is freely available to help evaluating dosing recommendations.

Role of TDM-based dose adjustments for taxane anticancer drugs

The classical taxanes (paclitaxel, docetaxel), the newer taxane cabazitaxel and the nanoparticle-bound nab-paclitaxel are among the most widely used anticancer drugs. Still, the optimal use and the value of pharmacological personalization of the taxanes is still controversial. We give an overview on the pharmacological properties of the taxanes, including metabolism, pharmacokinetics-pharmacodynamic relations and aspects in the clinical use of taxanes. The latter includes the ongoing debate on the most effective and safe regimen, the recommended initial dose, and pharmacological dosing individualization. The taxanes are among the most widely used anticancer drugs in patients with solid malignancies. Despite their longtime use in clinical routine, the optimal dosing strategy (weekly versus 3-weekly) or optimal average dose (cabazitaxel, nab-paclitaxel) has not been fully resolved, as it may differ according to tumour entity and line of treatment. The value of pharmacological individualization of the taxanes (TDM, TCI) has been partly explored for 3-weekly paclitaxel and docetaxel, but remains mostly unexplored for cabazitaxel and nab-paclitaxel at present.

[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.

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.

Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy

Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health economics, and drug regulations.

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.

How, When, and for Whom Should We Perform Therapeutic Drug Monitoring?

The implementation of therapeutic drug monitoring (TDM) in the inflammatory bowel disease practice has evolved over the years. In the early days, the focus was merely on measuring and reporting drug concentrations. Later, these concentrations were considered in light of target concentrations that are related to clinical response. This not only allowed passively predicting a patient's future response, but it also triggered physicians and pharmacists to actively use the information to optimize the drug dosage to induce and maintain a clinical response in the future. Although reactive TDM, testing at time of loss of response, is widely accepted in practice, especially for anti-tumor necrosis factor antibodies, there are less data for the other monoclonal antibodies belonging to other classes. Besides reactive testing, there is a movement toward proactively adjusting biologic dosing to prevent loss of response, in keeping with the tight control philosophy of inflammatory bowel disease care. This review highlights the various assays available to measure drug concentrations and antidrug antibodies, as well as algorithmic approaches to TDM, the unmet needs and required studies to enable pharmacokinetics principles to be applied in the future.

Mobile Health Apps for Improvement of Tuberculosis Treatment: Descriptive Review

BACKGROUND

Mobile health (mHealth) is a rapidly emerging market, which has been implemented in a variety of different disease areas. Tuberculosis remains one of the most common causes of death from an infectious disease worldwide, and mHealth apps offer an important contribution to the improvement of tuberculosis treatment. In particular, apps facilitating dose individualization, adherence monitoring, or provision of information and education about the disease can be powerful tools to prevent the development of drug-resistant tuberculosis or disease relapse.

OBJECTIVE

The aim of this review was to identify, describe, and categorize mobile and Web-based apps related to tuberculosis that are currently available.

METHODS

PubMed, Google Play Store, Apple Store, Amazon, and Google were searched between February and July 2019 using a combination of 20 keywords. Apps were included in the analysis if they focused on tuberculosis, and were excluded if they were related to other disease areas or if they were games unrelated to tuberculosis. All apps matching the inclusion criteria were classified into the following five categories: adherence monitoring, individualized dosing, eLearning/information, diagnosis, and others. The included apps were then summarized and described based on publicly available information using 12 characteristics.

RESULTS

Fifty-five mHealth apps met the inclusion criteria and were included in this analysis. Of the 55 apps, 8 (15%) were intended to monitor patients' adherence, 6 (11%) were designed for dosage adjustment, 29 (53%) were designed for eLearning/information, 3 (6%) were focused on tuberculosis diagnosis, and 9 (16%) were related to other purposes.

CONCLUSIONS

The number of mHealth apps related to tuberculosis has increased during the past 3 years. Although some of the discovered apps seem promising, many were found to contain errors or provided harmful or wrong information. Moreover, the majority of mHealth apps currently on the market are focused on making information about tuberculosis available (29/55, 53%). Thus, this review highlights a need for new, high-quality mHealth apps supporting tuberculosis treatment, especially those supporting individualized optimized treatment through model-informed precision dosing and video observed treatment.

Integrated Data Analysis of Six Clinical Studies Points Toward Model-Informed Precision Dosing of Tamoxifen

Introduction

At tamoxifen standard dosing, ∼20% of breast cancer patients do not reach proposed target endoxifen concentrations >5.97 ng/mL. Thus, better understanding the large interindividual variability in tamoxifen pharmacokinetics (PK) is crucial. By applying non-linear mixed-effects (NLME) modeling to a pooled ‘real-world’ clinical PK database, we aimed to (i) dissect several levels of variability and identify factors predictive for endoxifen exposure and (ii) assess different tamoxifen dosing strategies for their potential to increase the number of patients reaching target endoxifen concentrations.

Methods

Tamoxifen and endoxifen concentrations with genetic and demographic data of 468 breast cancer patients from six reported studies were used to develop a NLME parent-metabolite PK model. Different levels of variability on model parameters or measurements were investigated and the impact of covariates thereupon explored. The model was subsequently applied in a simulation-based comparison of three dosing strategies with increasing degree of dose individualization for a large virtual breast cancer population. Interindividual variability of endoxifen concentrations and the fraction of patients at risk for not reaching target concentrations were assessed for each dosing strategy.

Results and Conclusions

The integrated NLME model enabled to differentiate and quantify four levels of variability (interstudy, interindividual, interoccasion, and intraindividual). Strong influential factors, i.e., CYP2D6 activity score, drug–drug interactions with CYP3A and CYP2D6 inducers/inhibitors and age, were reliably identified, reducing interoccasion variability to <20% CV. Yet, unexplained interindividual variability in endoxifen formation remained large (47.2% CV). Hence, therapeutic drug monitoring seems promising for achieving endoxifen target concentrations. Three tamoxifen dosing strategies [standard dosing (20 mg QD), CYP2D6-guided dosing (20, 40, and 60 mg QD) and individual model-informed precision dosing (MIPD)] using three therapeutic drug monitoring samples (5–120 mg QD) were compared, leveraging the model. The proportion of patients at risk for not reaching target concentrations was 22.2% in standard dosing, 16.0% in CYP2D6-guided dosing and 7.19% in MIPD. While in CYP2D6-guided- and standard dosing interindividual variability in endoxifen concentrations was high (64.0% CV and 68.1% CV, respectively), it was considerably reduced in MIPD (24.0% CV). Hence, MIPD demonstrated to be the most promising strategy for achieving target endoxifen concentrations.

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

Background

Routine clinical TDM data is often used to develop population pharmacokinetic (PK) models, which are applied in turn for model-informed precision dosing. The impact of uncertainty in documented sampling and infusion times in population PK modeling and model-informed precision dosing have not yet been systematically evaluated. The aim of this study was to investigate uncertain documentation of (i) sampling times and (ii) infusion rate exemplified with two anti-infectives.

Methods

A stochastic simulation and estimation study was performed in NONMEM^® using previously published population PK models of meropenem and caspofungin. Uncertainties, i.e. deviation between accurate and planned sampling and infusion times (standard deviation (SD) ± 5 min to ± 30 min) were added randomly in R before carrying out the simulation step. The estimation step was then performed with the accurate or planned times (replacing real time points by scheduled study values). Relative bias (rBias) and root mean squared error (rRMSE) were calculated to determine accuracy and precision of the primary and secondary PK parameters on the population and individual level. The accurate and the misspecified (using planned sampling times) model were used for Bayesian forecasting of meropenem to assess the impact on PK/PD target calculations relevant to dosing decisions.

Results

On the population level, the estimates of the proportional residual error (prop.-err.) and the interindividual variability (IIV) on the central volume of distribution (V1) were most affected by erroneous records in the sampling and infusion time (e.g. rBias of prop.-err.: 75.5% vs. 183% (meropenem) and 10.1% vs. 109% (caspofungin) for ± 5 vs. ± 30 min, respectively). On the individual level, the rBias of the planned scenario for the typical values V1, Q and V2 increased with increasing uncertainty in time, while CL, AUC and elimination half-life were least affected. Meropenem as a short half-life drug (~1 h) was more affected than caspofungin (~ 9-11 h). The misspecified model provided biased PK/PD target information (e.g. falsely overestimated time above MIC (T > MIC) when true T > MIC was <0.4 and thus patients at risk of undertreatment), while the accurate model gave precise estimates of the indices across all simulated patients.

Conclusions

Even 5-minute-uncertainties caused bias and significant imprecision of primary population and individual PK parameters. Thus, our results underline the importance of accurate documentation of time.

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.

GAPPS (Grading and Assessment of Pharmacokinetic-Pharmacodynamic Studies) a critical appraisal system for antimicrobial PKPD studies - development and application in pediatric antibiotic studies

Introduction: There are limited data on optimal dosing of antibiotics in different age groups for neonates and children. Clinicians usually consult pediatric formularies or online databases for dose selection, but these have variable recommendations, are usually based on expert opinion and are not graded based on the existing pharmacokinetic-pharmacodynamic (PKPD) studies. We describe here a potential new tool that could be used to grade the strength of evidence emanating from PKPD studies.Areas covered: A scoring system was developed (GAPPS tool) to quantify the strength of each PK assessment and rate the studies quality in already published articles. GAPPS was evaluated by applying it to pediatric PKPD studies of antibiotics from the 2019 Essential Medicines List for children (EML_C), identified through a search of PubMed.Expert opinion: Evidence for most antibiotic dose selection decisions was generally weak, coming from individual PK studies and lacked PKPD modeling and simulations. However, the quality of evidence appears to have improved over the last two decades.Incorporating a formal grading system, such as GAPPS, into formulary development will provide a transparent tool to support decision-making in clinical practice and guideline development, and guide PKPD authors on study designs most likely to influence guidelines.

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.

Development of a dosing nomogram for continuous-infusion meropenem in critically ill patients based on a validated population pharmacokinetic model

Background

Optimal antibiotic exposure is a vital but challenging prerequisite for achieving clinical success in ICU patients.

Objectives

To develop and externally validate a population pharmacokinetic model for continuous-infusion meropenem in critically ill patients and to establish a nomogram based on a routinely available marker of renal function.

Methods

A population pharmacokinetic model was developed in NONMEM® 7.3 based on steady-state meropenem concentrations (CSS) collected during therapeutic drug monitoring. Different serum creatinine-based markers of renal function were compared for their influence on meropenem clearance (the Cockcroft-Gault creatinine clearance CLCRCG, the CLCR bedside estimate according to Jelliffe, the Chronic Kidney Disease Epidemiology Collaboration equation and the four-variable Modification of Diet in Renal Disease equation). After validation of the pharmacokinetic model with independent data, a dosing nomogram was developed, relating renal function to the daily doses required to achieve selected target concentrations (4/8/16 mg/L) in 90% of the patients. Probability of target attainment was determined for efficacy (CSS ≥8 mg/L) and potentially increased likelihood of adverse drug reactions (CSS >32 mg/L).

Results

In total, 433 plasma concentrations (3.20-48.0 mg/L) from 195 patients (median/P0.05 - P0.95 at baseline: weight 77.0/55.0-114 kg, CLCRCG 63.0/19.6-168 mL/min) were used for model building. We found that CLCRCG best described meropenem clearance (CL = 7.71 L/h, CLCRCG = 80 mL/min). The developed model was successfully validated with external data (n = 171, 73 patients). According to the nomogram, daily doses of 910/1480/2050/2800/3940 mg were required to reach a target CSS = 8 mg/L in 90% of patients with CLCRCG = 20/50/80/120/180 mL/min, respectively. A low probability of adverse drug reactions (<0.5%) was associated with these doses.

Conclusions

A dosing nomogram was developed for continuous-infusion meropenem based on renal function in a critically ill population.

Development of a dosing algorithm for meropenem in critically ill patients based on a population pharmacokinetic/pharmacodynamic analysis

Effective antibiotic dosing is vital for therapeutic success in critically ill patients. This work aimed to develop an algorithm to identify appropriate meropenem dosing in critically ill patients. Population pharmacokinetic (PK) modelling was performed in NONMEM®7.3 based on densely sampled meropenem serum samples (n_patients = 48; n_samples = 1376) and included a systematic analysis of 27 pre-selected covariates to identify factors influencing meropenem exposure. Using Monte Carlo simulations newly considering the uncertainty of PK parameter estimates, standard meropenem dosing was evaluated with respect to attainment of the pharmacokinetic/pharmacodynamic (PK/PD) target and was compared with alternative infusion regimens (short-term, prolonged, continuous; daily dose, 2000-6000 mg). Subsequently, a dosing algorithm was developed to identify appropriate dosing regimens. The two-compartment population PK model included three factors influencing meropenem pharmacokinetics: the Cockcroft-Gault creatinine clearance (CLCR_CG) on meropenem clearance; and body weight and albumin on the central and peripheral volume of distribution, respectively; of these, only CLCR_CG was identified as a vital influencing factor on PK/PD target attainment. A three-level dosing algorithm was developed (considering PK parameter uncertainty), suggesting dosing regimens depending on renal function and the level (L) of knowledge about the infecting pathogen (L1, pathogen unknown; L2, pathogen known; L3_(-MIC), pathogen and susceptibility known; L3_(+MIC), MIC known). Whereas patients with higher CLCR_CG and lower pathogen susceptibility required mainly intensified dosing regimens, lower than standard doses appeared sufficient for highly susceptible pathogens. In conclusion, a versatile meropenem dosing algorithm for critically ill patients is proposed, indicating appropriate dosing regimens based on patient- and pathogen-specific information.

Exploiting Pharmacokinetic Models of Tamoxifen and Endoxifen to Identify Factors Causing Subtherapeutic Concentrations in Breast Cancer Patients

BACKGROUND AND OBJECTIVES

A better understanding of the highly variable pharmacokinetics (PK) of tamoxifen and its active metabolite endoxifen in breast cancer patients is crucial to support individualised treatment. This study used a modelling and simulation approach to quantitatively assess the influence of cytochrome P450 (CYP) 2D6 activity and other relevant factors on tamoxifen and endoxifen PK to identify subgroups at risk for subtherapeutic endoxifen concentrations.

METHODS

Simulations were performed using two previously published PK models jointly describing tamoxifen and endoxifen with CYP2D6 and CYP3A4/5 enzyme activities implemented as covariates. Steady-state predictions were compared between models and with the literature values. Factors potentially causing between-model discrepancies were explored. A previously published threshold (6 ng/mL) was used to identify patients with subtherapeutic endoxifen concentrations and to perform a dose adaptation study.

RESULTS

Steady-state predictions of tamoxifen and endoxifen were considerably different between the models. The factors, differences in sampling time, adherence and bioavailability, were not able to fully capture between-model variability. Endoxifen steady-state fluctuations within a dosing interval were minimal (<6%). Poor (97%) and intermediate (54%) CYP2D6 metabolisers failed to achieve therapeutic endoxifen concentrations, suggesting adapted doses of tamoxifen 80 and 40 mg, respectively, achieving therapeutic endoxifen concentrations in 89.7% of patients (standard dosing 45.2%). However, interindividual variability remained.

CONCLUSIONS

To achieve therapeutic endoxifen concentrations early in treatment, it is advisable to initiate treatment by CYP2D6 genotype/phenotype-guided dosing, followed by therapeutic drug monitoring at steady-state. We strongly advocate to adequately measure, report and prospectively investigate influential factors (i.e. adherence, bioavailability, time to PK steady-state) in clinical trials.

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.

High voriconazole target-site exposure after approved sequence dosing due to nonlinear pharmacokinetics assessed by long-term microdialysis

Voriconazole, a broad-spectrum antifungal drug used to prevent and treat invasive fungal infections, shows complex pharmacokinetics and is primarily metabolised by various CYP enzymes. An adequate unbound antibiotic concentration-time profile at the target-site of an infection is crucial for effective prophylaxis or therapy success. Therefore, the aim was to evaluate the pharmacokinetics of voriconazole after the approved sequence dosing in healthy volunteers in interstitial space fluid, assessed by microdialysis, and in plasma. Moreover, potential pharmacogenetic influences of CYP2C19 polymorphisms on pharmacokinetics were investigated. The prospective, open-labelled, uncontrolled long-term microdialysis study included 9 healthy male individuals receiving the approved sequence dosing regimen for voriconazole. Unbound voriconazole concentrations were sampled over 84 h in interstitial space fluid of subcutaneous adipose tissue and in plasma and subsequently quantified via high-performance liquid chromatography. For pharmacokinetic data analysis, non-compartmental analysis was used. High interindividual variability in voriconazole concentration-time profiles was detected although dosing was adapted to body weight for the first intravenous administrations. Due to nonlinear pharmacokinetics, target-site exposure of voriconazole in healthy volunteers was found to be highly comparable to plasma exposure, particularly after multiple dosing. Regarding the CYP2C19 genotype-predicted phenotype, the individuals revealed a broad spectrum, ranging from poor to rapid metaboliser status. A strong relation between CYP2C19 genotype-predicted phenotype and voriconazole clearance was identified.

After standard dosage of piperacillin plasma concentrations of drug are subtherapeutic in burn patients

Infections are a major problem in patients with burn diseases. Mortality is high despite antibiotic therapy as studies are controversial concerning drug underdosing. The aims of this prospective, observational study were to monitor plasma concentrations of piperacillin during standard piperacillin/tazobactam treatment in 20 burn patients and 16 controls from the intensive care unit (ICU) and to optimize doses by in silico analyses. Piperacillin/tazobactam (4/0.5 g, tid) was administered over 0.5 h. Blood samples were taken at 1, 4, and 7.5 h after the end of the infusion. Free piperacillin plasma concentrations were determined. Pharmacokinetic parameters and in silico analysis results were calculated using the freeware TDMx. The primary target was defined as percentage of the day (fT_>1xMIC; fT_>4xMIC) when piperacillin concentrations exceeded 1xMIC/4xMIC (minimum inhibitory concentration), considering a MIC breakpoint of 16 mg/L for Pseudomonas aeruginosa. In an off-label approach, two burn patients were treated with 8/1 g piperacillin/tazobactam, 3 h qid. fT_>1xMIC (55 ± 22% vs. 77 ± 24%) and fT_>4xMIC (17 ± 11% vs. 30 ± 11%) were lower in burn than in ICU patients after 4/0.5 g, 0.5 h, tid. In silico analyses indicated that fT_>1xMIC (93 ± 12% burn, 97 ± 4% ICU) and fT_>4xMIC (62 ± 23% burn, 84 ± 19% ICU) values increase by raising the piperacillin dosage to 8/1 g qid and prolonging the infusion time to 3 h. Off-label treatment results were similar to in silico data for burn patients (84%fT_>1xMIC and 47%fT_>4xMIC). Standard dosage regimens for piperacillin/tazobactam resulted in subtherapeutic piperacillin concentrations in burn and ICU patients. Dose adjustments via in silico analyses can help to optimize antibiotic therapy and to predict respective concentrations in vivo. Trial registration: NCT03335137, registered 07.11.2017, retrospectively.

Variable Linezolid Exposure in Intensive Care Unit Patients-Possible Role of Drug-Drug Interactions

BACKGROUND

Standard doses of linezolid may not be suitable for all patient groups. Intensive care unit (ICU) patients in particular may be at risk of inadequate concentrations. This study investigated variability of drug exposure and its potential sources in this population.

METHODS

Plasma concentrations of linezolid were determined by high-performance liquid chromatography in a convenience sample of 20 ICU patients treated with intravenous linezolid 600 mg twice daily. Ultrafiltration applying physiological conditions (pH 7.4/37°C) was used to determine the unbound fraction. Individual pharmacokinetic (PK) parameters were estimated by population PK modeling. As measures of exposure to linezolid, area under the concentration-time curve (AUC) and trough concentrations (Cmin) were calculated and compared with published therapeutic ranges (AUC 200-400 mg*h/L, Cmin 2-10 mg/L). Coadministered inhibitors or inducers of cytochrome P450 and/or P-glycoprotein were noted.

RESULTS

Data from 18 patients were included into the PK evaluation. Drug exposure was highly variable (median, range: AUC 185, 48-618 mg*h/L, calculated Cmin 2.92, 0.0062-18.9 mg/L), and only a minority of patients had values within the target ranges (6 and 7, respectively). AUC and Cmin were linearly correlated (R = 0.98), and classification of patients (underexposed/within therapeutic range/overexposed) according to AUC or Cmin was concordant in 15 cases. Coadministration of inhibitors was associated with a trend to higher drug exposure, whereas 3 patients treated with levothyroxine showed exceedingly low drug exposure (AUC ∼60 mg*h/L, Cmin <0.4 mg/L). The median unbound fraction in all 20 patients was 90.9%.

CONCLUSIONS

Drug exposure after standard doses of linezolid is highly variable and difficult to predict in ICU patients, and therapeutic drug monitoring seems advisable. PK drug-drug interactions might partly be responsible and should be further investigated; protein binding appears to be stable and irrelevant.

Role of renal function in risk assessment of target non-attainment after standard dosing of meropenem in critically ill patients: a prospective observational study

Background

Severe bacterial infections remain a major challenge in intensive care units because of their high prevalence and mortality. Adequate antibiotic exposure has been associated with clinical success in critically ill patients. The objective of this study was to investigate the target attainment of standard meropenem dosing in a heterogeneous critically ill population, to quantify the impact of the full renal function spectrum on meropenem exposure and target attainment, and ultimately to translate the findings into a tool for practical application.

Methods

A prospective observational single-centre study was performed with critically ill patients with severe infections receiving standard dosing of meropenem. Serial blood samples were drawn over 4 study days to determine meropenem serum concentrations. Renal function was assessed by creatinine clearance according to the Cockcroft and Gault equation (CLCR_CG). Variability in meropenem serum concentrations was quantified at the middle and end of each monitored dosing interval. The attainment of two pharmacokinetic/pharmacodynamic targets (100%T_>MIC, 50%T_>4×MIC) was evaluated for minimum inhibitory concentration (MIC) values of 2 mg/L and 8 mg/L and standard meropenem dosing (1000 mg, 30-minute infusion, every 8 h). Furthermore, we assessed the impact of CLCR_CG on meropenem concentrations and target attainment and developed a tool for risk assessment of target non-attainment.

Results

Large inter- and intra-patient variability in meropenem concentrations was observed in the critically ill population (n = 48). Attainment of the target 100%T_>MIC was merely 48.4% and 20.6%, given MIC values of 2 mg/L and 8 mg/L, respectively, and similar for the target 50%T_>4×MIC. A hyperbolic relationship between CLCR_CG (25–255 ml/minute) and meropenem serum concentrations at the end of the dosing interval (C_8h) was derived. For infections with pathogens of MIC 2 mg/L, mild renal impairment up to augmented renal function was identified as a risk factor for target non-attainment (for MIC 8 mg/L, additionally, moderate renal impairment).

Conclusions

The investigated standard meropenem dosing regimen appeared to result in insufficient meropenem exposure in a considerable fraction of critically ill patients. An easy- and free-to-use tool (the MeroRisk Calculator) for assessing the risk of target non-attainment for a given renal function and MIC value was developed.

Trial registration

Clinicaltrials.gov, NCT01793012. Registered on 24 January 2013.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-017-1829-4) contains supplementary material, which is available to authorized users.

A Population Pharmacokinetic Model of Gentamicin in Pediatric Oncology Patients To Facilitate Personalized Dosing

To ensure the safe and effective dosing of gentamicin in children, therapeutic drug monitoring (TDM) is recommended. TDM utilizing Bayesian forecasting software is recommended but is unavailable, as no population model that describes the pharmacokinetics of gentamicin in pediatric oncology patients exists. This study aimed to develop and externally evaluate a population pharmacokinetic model of gentamicin to support personalized dosing in pediatric oncology patients. A nonlinear mixed-effect population pharmacokinetic model was developed from retrospective data. Data were collected from 423 patients for model building and a further 52 patients for external evaluation. A two-compartment model with first-order elimination best described the gentamicin disposition. The final model included renal function (described by fat-free mass and postmenstrual age) and the serum creatinine concentration as covariates influencing gentamicin clearance (CL). Final parameter estimates were as follow CL, 5.77 liters/h/70 kg; central volume of distribution, 21.6 liters/70 kg; peripheral volume of distribution, 13.8 liters/70 kg; and intercompartmental clearance, 0.62 liter/h/70 kg. External evaluation suggested that current models developed in other pediatric cohorts may not be suitable for use in pediatric oncology patients, as they showed a tendency to overpredict the observations in this population. The final model developed in this study displayed good predictive performance during external evaluation (root mean square error, 46.0%; mean relative prediction error, -3.40%) and may therefore be useful for the personalization of gentamicin dosing in this cohort. Further investigations should focus on evaluating the clinical application of this model.

Linezolid in liver failure: exploring the value of the maximal liver function capacity (LiMAx) test in a pharmacokinetic pilot study

Patients in the intensive care unit frequently require antibiotic treatment. Liver impairment poses substantial challenges for dose selection in these patients. The aim of the present pilot study was to assess the novel maximal liver function capacity (LiMAx test) in comparison with conventional liver function markers as covariates of drug clearance in liver failure using linezolid as a model drug. A total of 28 patients with different degrees of liver failure were recruited. LiMAx test as well as plasma, dialysate and urine sampling were performed under linezolid steady-state therapy (600 mg twice daily). NONMEM^® was used for a pharmacometric analysis in which the different clearance routes of linezolid were elucidated. Linezolid pharmacokinetics was highly variable in patients with liver failure. The LiMAx score displayed the strongest association with non-renal clearance (CL_non-renal) [ = 4.46∙(body weight/57.9) ^0.75∙(LiMAx/221.5)^0.388 L/h], which reduced interindividual variability in CL_non-renal from 46.6% to 33.6%, thereby being superior to other common markers of liver function (international normalised ratio, gamma-glutaryl transferase, bilirubin, thrombocytes, alanine aminotransferase, aspartate aminotransferase). For LiMAx < 100 µg/kg/h, 64% of linezolid trough concentrations were above the recommended trough concentration of 8 mg/L, indicating the necessity of therapeutic drug monitoring in these patients. This is the first pilot application of the LiMAx test in a pharmacokinetic (PK) study demonstrating its potential to explain PK variability in linezolid clearance. Further studies with a larger patient collective and further drugs are highly warranted to guide dosing in patients with severe liver impairment.

Utility of Microdialysis in Infectious Disease Drug Development and Dose Optimization

Adequate drug penetration to a site of infection is absolutely imperative to ensure sufficient antimicrobial treatment. Microdialysis is a minimally invasive, versatile technique, which can be used to study the penetration of an antiinfective agent in virtually any tissue of interest. It has been used to investigate drug distribution and pharmacokinetics in variable patient populations, as a tool in dose optimization, a potential utility in therapeutic drug management, and in the study of biomarkers of disease progression. While all of these applications have not been fully explored in the field of antiinfectives, this review provides an overview of how microdialysis has been applied in various phases of drug development, a focus on the specific applications in the subspecialties of infectious disease (treatment of bacterial, fungal, viral, parasitic, and mycobacterial infections), and developing applications (biomarkers and therapeutic drug management).