Optimising antimicrobial therapy through the use of Bayesian dosing programs

Feasibility of individualised patient modelling for continuous vancomycin infusions in outpatient antimicrobial therapy, a retrospective study

BACKGROUND

The area under the curve (AUC) to minimum inhibitory concentration (MIC) ratio is proposed as a therapeutic drug-monitoring parameter for dosing vancomycin continuous infusion in methicillin-resistant Staphylococcus aureus (MRSA) infection. Individualised pharmacokinetic-pharmacodynamic (PK/PD) calculation of AUC24 may better represent therapeutic dosing than current Therapeutic Drug Monitoring (TDM) practices, targeting a Steady State Concentration of 15-25 mg/L.

AIM

To compare real world TDM practice to theoretical, individualised, PK/PD target parameters utilising Bayesian predictions to steady state concentrations (Css) for outpatients on continuous vancomycin infusions.

METHOD

A retrospective single centre study was conducted at a tertiary hospital on adult patients, enrolled in an outpatient parenteral antimicrobial therapy (OPAT) program, receiving vancomycin infusions for MRSA infection. Retrospective Bayesian dosing was modelled to target PK/PD parameters and compared to real world data.

RESULTS

Fifteen patients were evaluated with 53% (8/15) achieved target CSS during hospitalisation, and 83% (13/15) as outpatient. Median Bayesian AUC/MIC was 613 mg.h/L with CSS 25 mg/L. Patients suffering an Acute Kidney Injury (33%) had higher AUC0-24/MIC values. Retrospective Bayesian modelling demonstrated on median 250 mg/24 h lower doses than that administered was required (R2 = 0.81) which achieved AUC24/MIC median 444.8 (range 405-460) mg.h/L and CSS 18.8 (range 16.8-20.4) mg/L.

CONCLUSION

Bayesian modelling could assist in obtaining more timely target parameters at lower doses for patients receiving continuous vancomycin infusion as part of an OPAT program, which may beget fewer adverse effects. Utilisation of personalised predictive modelling may optimise vancomycin prescribing, achieving earlier target concentrations as compared to empiric dosing regimens.

The Significance of Bayesian Pharmacokinetics in Dosing for Critically Ill Patients: A Primer for Clinicians Using Vancomycin as an Example

Antibiotic use is becoming increasingly challenging with the emergence of multidrug-resistant organisms. Pharmacokinetic (PK) alterations result from complex pathophysiologic changes in some patient populations, particularly those with critical illness. Therefore, antibiotic dose individualization in such populations is warranted. Recently, there have been advances in dose optimization strategies to improve the utilization of existing antibiotics. Bayesian-based dosing is one of the novel approaches that could help clinicians achieve target concentrations in a greater percentage of their patients earlier during therapy. This review summarizes the advantages and disadvantages of current approaches to antibiotic dosing, with a focus on critically ill patients, and discusses the use of Bayesian methods to optimize vancomycin dosing. The Bayesian method of antibiotic dosing was developed to provide more precise predictions of drug concentrations and target achievement early in therapy. It has benefits such as the incorporation of personalized PK/PD parameters, improved predictive abilities, and improved patient outcomes. Recent vancomycin dosing guidelines emphasize the importance of using the Bayesian method. The Bayesian method is able to achieve appropriate antibiotic dosing prior to the patient reaching the steady state, allowing the patient to receive the right drug at the right dose earlier in therapy.

Analytical and Non-Analytical Variation May Lead to Inappropriate Antimicrobial Dosing in Neonates: An In Silico Study

BACKGROUND

Therapeutic drug monitoring (TDM) of aminoglycosides and vancomycin is used to prevent oto- and nephrotoxicity in neonates. Analytical and nonanalytical factors potentially influence dosing recommendations. This study aimed to determine the impact of analytical variation (imprecision and bias) and nonanalytical factors (accuracy of drug administration time, use of non-trough concentrations, biological variation, and dosing errors) on neonatal antimicrobial dosing recommendations.

METHODS

Published population pharmacokinetic models and the Australasian Neonatal Medicines Formulary were used to simulate antimicrobial concentration-time profiles in a virtual neonate population. Laboratory quality assurance data were used to quantify analytical variation in antimicrobial measurement methods used in clinical practice. Guideline-informed dosing recommendations based on drug concentrations were applied to compare the impact of analytical variation and nonanalytical factors on antimicrobial dosing.

RESULTS

Analytical variation caused differences in subsequent guideline-informed dosing recommendations in 9.3-12.1% (amikacin), 16.2-19.0% (tobramycin), 12.2-45.8% (gentamicin), and 9.6-19.5% (vancomycin) of neonates. For vancomycin, inaccuracies in drug administration time (45.6%), use of non-trough concentrations (44.7%), within-subject biological variation (38.2%), and dosing errors (27.5%) were predicted to result in more dosing discrepancies than analytical variation (12.5%). Using current analytical performance specifications, tolerated dosing discrepancies would be up to 14.8% (aminoglycosides) and 23.7% (vancomycin).

CONCLUSIONS

Although analytical variation can influence neonatal antimicrobial dosing recommendations, nonanalytical factors are more influential. These result in substantial variation in subsequent dosing of antimicrobials, risking inadvertent under- or overexposure. Harmonization of measurement methods and improved patient management systems may reduce the impact of analytical and nonanalytical factors on neonatal antimicrobial dosing.

Implementation of Vancomycin Therapeutic Monitoring Guidelines: Focus on Bayesian Estimation Tools in Neonatal and Pediatric Patients

BACKGROUND

The 2020 consensus guidelines for vancomycin therapeutic monitoring recommend using Bayesian estimation targeting the ratio of the area under the curve over 24 hours to minimum inhibitory concentration as an optimal approach to individualize therapy in pediatric patients. To support institutional guideline implementation in children, the objective of this study was to comprehensively assess and compare published population-based pharmacokinetic (PK) vancomycin models and available Bayesian estimation tools, specific to neonatal and pediatric patients.

METHODS

PubMed and Embase databases were searched from January 1994 to December 2020 for studies in which a vancomycin population PK model was developed to determine clearance and volume of distribution in neonatal and pediatric populations. Available Bayesian software programs were identified and assessed from published articles, software program websites, and direct communication with the software company. In the present review, 14 neonatal and 20 pediatric models were included. Six programs (Adult and Pediatric Kinetics, BestDose, DoseMeRx, InsightRx, MwPharm++, and PrecisePK) were evaluated.

RESULTS

Among neonatal models, Frymoyer et al and Capparelli et al used the largest PK samples to generate their models, which were externally validated. Among the pediatric models, Le et al used the largest sample size, with multiple external validations. Of the Bayesian programs, DoseMeRx, InsightRx, and PrecisePK used clinically validated neonatal and pediatric models.

CONCLUSIONS

To optimize vancomycin use in neonatal and pediatric patients, clinicians should focus on selecting a model that best fits their patient population and use Bayesian estimation tools for therapeutic area under the -curve-targeted dosing and monitoring.

A Free Open-Source Bayesian Vancomycin Dosing App for Adults: Design and Evaluation Study

Background

It has been suggested that Bayesian dosing apps can assist in the therapeutic drug monitoring of patients receiving vancomycin. Unfortunately, Bayesian dosing tools are often unaffordable to resource-limited hospitals. Our aim was to improve vancomycin dosing in adults. We created a free and open-source dose adjustment app, VancoCalc, which uses Bayesian inference to aid clinicians in dosing and monitoring of vancomycin.

Objective

The aim of this paper is to describe the design, development, usability, and evaluation of a free open-source Bayesian vancomycin dosing app, VancoCalc.

Methods

The app build and model fitting process were described. Previously published pharmacokinetic models were used as priors. The ability of the app to predict vancomycin concentrations was performed using a small data set comprising of 52 patients, aged 18 years and over, who received at least 1 dose of intravenous vancomycin and had at least 2 vancomycin concentrations drawn between July 2018 and January 2021 at Lakeridge Health Corporation Ontario, Canada. With these estimated and actual concentrations, median prediction error (bias), median absolute error (accuracy), and root mean square error (precision) were calculated to evaluate the accuracy of the Bayesian estimated pharmacokinetic parameters.

Results

A total of 52 unique patients’ initial vancomycin concentrations were used to predict subsequent concentration; 104 total vancomycin concentrations were assessed. The median prediction error was –0.600 ug/mL (IQR –3.06, 2.95), the median absolute error was 3.05 ug/mL (IQR 1.44, 4.50), and the root mean square error was 5.34.

Conclusions

We described a free, open-source Bayesian vancomycin dosing calculator based on revisions of currently available calculators. Based on this small retrospective preliminary sample of patients, the app offers reasonable accuracy and bias, which may be used in everyday practice. By offering this free, open-source app, further prospective validation could be implemented in the near future.

Chinese ICU physicians' knowledge of antibiotic pharmacokinetics/pharmacodynamics (PK/PD): a cross-sectional survey

BACKGROUND

Patients with sepsis have a high mortality rate, accumulated evidences suggest that an optimal antibiotic administration strategy based on pharmacokinetics/pharmacodynamics (PK/PD) can improve the prognosis of septic patients. Therefore, we assessed Chinese intensive care unit (ICU) physicians' knowledge about PK/PD.

METHODS

In December 2019, we designed a questionnaire focused on Chinese ICU physicians' knowledge about PK/PD and collected the questionnaires after 3 months. The questionnaire was distributed via e-mail and WeChat, and was distributed to ICU doctors in 31 administrative regions of China except Hong Kong, Macao and Taiwan. The passing score was corrected by the Angoff method, and the ICU physicians' knowledge about PK/PD was analysed accordingly.

RESULTS

We received a total of 1,309 questionnaires and retained 1,240 valid questionnaires. The passing score was 90.8, and the overall pass rate was 56.94%. The pass rate for tertiary and secondary hospitals was 59.07% and 37.19%, respectively. ICU physicians with less than 5 years of work experience and resident physician accounted for the highest pass rate, while those with between 5 to 10 years of work experience and attending accounted for the lowest pass rate. The majority of participants in the Chinese Critical Care Certified Course (5C) were from Jiangsu and Henan provinces, and they had the highest average scores (125.8 and 126.5, respectively). For Beijing and Shanghai, the average score was only 79.4 and 90.9, respectively.

CONCLUSIONS

Chinese ICU physicians' knowledge about PK/PD is unsatisfactory. Therefore, it is essential to strengthen ICU physicians' knowledge about PK/PD.

Comparison of Mw\Pharm 3.30 and Mw\Pharm ++, a Windows version of pharmacokinetic software for PK/PD monitoring of vancomycin. Part 1: A-posteriori modelling

BACKGROUND AND OBJECTIVES

For a long time, the Mw\Pharm software suite (MEDI\WARE, Prague, Czech Republic/ Groningen, Netherlands) has been used for PK/PD modelling in therapeutic drug monitoring (TDM). The aim of this study was to find the best model in the newer Windows Mw\Pharm++ 1.3.5.558 version (WIN).

METHODS

25 patients were repeatedly examined for vancomycin (mean age 63±14 years, body weight 88±21 kg, median dose 1 g/12 h). Trough concentrations predicted a-posteriori by WIN models "vancomycin_adult_k_C2", "#vancomycin_adult_C2", "vancomycin_adult_C2" were compared with the measured value and "vancomycin adult" DOS 3.30 model (DOS).

STATISTICS

Percentage prediction error (%PE) calculated as (predicted-measured)/measured values, or WIN-DOS/DOS - data presented as mean±SD, RMSE, Blandt-Altman plot - data presented as bias±SD (95% limits of agreement), Pearson's coefficient of rank correlation (R), Student's t-test. Statistical analysis was performed using GraphPad Prism version 5.00 for Windows.

RESULTS

The mean%PE in vancomycin predicted values varied from -4.5% ± 33.6 to -8.2% ± 39.3. The%PE between WIN and DOS models varied from -0.2% ± 24.5% to 4.4 ± 21.4%. Model "vancomycin_adult_C2" was closest both to measured vancomycin trough concentration and DOS model:%PE -4.5 ± 33.6% vs +4.2 ± 20.3%, RMSE 33.7 vs 20.6, Blandt-Altman bias +2.19 ± 6.17 (-9.9 - 14.3) vs -0.29 ± 3.25 (-6.7 - 6.1), resp. "#vancomycin_adult_C2" model produced largest%PE (-8.2%), RMSE (40.0) as well as Blandt-Altman bias +2.82 ± 6.76 (-10.4 - 16.1). The Pearson's R of predicted and measured vancomycin concentration, and of values predicted by WIN and DOS models, varied from 0.5135 to 0.5854, P<0.0001 and from 0.7869 to 0.8462, P<0.0001, resp.

CONCLUSIONS

Three Windows vancomycin models and one DOS model in the Mw\Pharm software were compared. The best outcomes, i.e. lowest%PE, RMSE and highest Pearson's R, were reached with "vancomycin_adult_C2" model.

Implementation and Comparison of Two Pharmacometric Tools for Model-Based Therapeutic Drug Monitoring and Precision Dosing of Daptomycin

Daptomycin is a candidate for therapeutic drug monitoring (TDM). The objectives of this work were to implement and compare two pharmacometric tools for daptomycin TDM and precision dosing. A nonparametric population PK model developed from patients with bone and joint infection was implemented into the BestDose software. A published parametric model was imported into Tucuxi. We compared the performance of the two models in a validation dataset based on mean error (ME) and mean absolute percent error (MAPE) of individual predictions, estimated exposure and predicted doses necessary to achieve daptomycin efficacy and safety PK/PD targets. The BestDose model described the data very well in the learning dataset. In the validation dataset (94 patients, 264 concentrations), 21.3% of patients were underexposed (AUC_24h < 666 mg.h/L) and 31.9% of patients were overexposed (C_min > 24.3 mg/L) on the first TDM occasion. The BestDose model performed slightly better than the model in Tucuxi (ME = -0.13 ± 5.16 vs. -1.90 ± 6.99 mg/L, p < 0.001), but overall results were in agreement between the two models. A significant proportion of patients exhibited underexposure or overexposure to daptomycin after the initial dosage, which supports TDM. The two models may be useful for model-informed precision dosing.

Optimizing antimicrobial use: challenges, advances and opportunities

An optimal antimicrobial dose provides enough drug to achieve a clinical response while minimizing toxicity and development of drug resistance. There can be considerable variability in pharmacokinetics, for example, owing to comorbidities or other medications, which affects antimicrobial pharmacodynamics and, thus, treatment success. Although current approaches to antimicrobial dose optimization address fixed variability, better methods to monitor and rapidly adjust antimicrobial dosing are required to understand and react to residual variability that occurs within and between individuals. We review current challenges to the wider implementation of antimicrobial dose optimization and highlight novel solutions, including biosensor-based, real-time therapeutic drug monitoring and computer-controlled, closed-loop control systems. Precision antimicrobial dosing promises to improve patient outcome and is important for antimicrobial stewardship and the prevention of antimicrobial resistance.

Personalized Antibiotic Therapy for the Critically Ill: Implementation Strategies and Effects on Clinical Outcome of Piperacillin Therapeutic Drug Monitoring-A Descriptive Retrospective Analysis

Therapeutic drug monitoring (TDM) is increasingly relevant for an individualized antibiotic therapy and subsequently a necessary tool to reduce multidrug-resistant pathogens, especially in light of diminishing antimicrobial capabilities. Critical illness is associated with profound pharmacokinetic and pharmacodynamic alterations, which challenge dose finding and the application of particularly hydrophilic drugs such as β-lactam antibiotics. Methods: Implementation strategy, potential benefit, and practicability of the developed standard operating procedures were retrospectively analyzed from January to December 2020. Furthermore, the efficacy of the proposed dosing target of piperacillin in critically ill patients was evaluated. Results: In total, 160 patients received piperacillin/tazobactam therapy and were subsequently included in the study. Of them, 114 patients received piperacillin/tazobactam by continuous infusion and had at least one measurement of piperacillin serum level according to the standard operating procedure. In total, 271 measurements were performed with an average level of 79.0 ± 46.0 mg/L. Seventy-one piperacillin levels exceeded 100 mg/L and six levels were lower than 22.5 mg/L. The high-level and the low-level group differed significantly in infection laboratory parameters (CRP (mg/dL) 20.18 ± 11.71 vs. 5.75 ± 5.33) and renal function [glomerular filtration rate (mL/min/1.75 m²) 40.85 ± 26.74 vs. 120.50 ± 70.48]. Conclusions: Piperacillin levels are unpredictable in critically ill patients. TDM during piperacillin/tazobactam therapy is highly recommended for all patients. Although our implementation strategy was effective, further strategies implemented into the daily clinical workflow might support the health care staff and increase the clinicians' alertness.

Individualized precision dosing approaches to optimize antimicrobial therapy in pediatric populations

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

To Estimate or to Forecast? Lessons From a Comparative Analysis of Four Bayesian Fitting Methods Based on Nonparametric Models

ABSTRACT

Using pharmacokinetic (PK) models and Bayesian methods in dosing software facilitates the analysis of individual PK data and precision dosing. Several Bayesian methods are available for computing Bayesian posterior distributions using nonparametric population models. The objective of this study was to compare the performance of the maximum a posteriori (MAP) model, multiple model (MM), interacting MM (IMM), and novel hybrid MM(HMM) in estimating past concentrations and predicting future concentrations during therapy. Amikacin and vancomycin PK data were analyzed in older hospitalized patients using 2 strategies. First, the entire data set of each patient was fitted using each of the 4 methods implemented in BestDose software. Then, the 4 methods were used in each therapeutic drug monitoring occasion to estimate the past concentrations available at this time and to predict the subsequent concentrations to be observed on the next occasion. The bias and precision of the model predictions were compared among the methods. A total of 406 amikacin concentrations from 96 patients and 718 vancomycin concentrations from 133 patients were available for analysis. Overall, significant differences were observed in the predictive performance of the 4 Bayesian methods. The IMM method showed the best fit to past concentration data of amikacin and vancomycin, whereas the MM method was the least precise. However, MM best predicted the future concentrations of amikacin. The MAP and HMM methods showed a similar predictive performance and seemed to be more appropriate for the prediction of future vancomycin concentrations than the other models were. The richness of the prior distribution may explain the discrepancies between the results of the 2 drugs. Although further research with other drugs and models is necessary to confirm our findings, these results challenge the widely accepted assumption in PK modeling that a better data fit indicates better forecasting of future observations.

Emerging therapeutic drug monitoring of anti-infective agents in Australian hospitals: Availability, performance and barriers to implementation

AIMS

The purpose of the study was to assess the status of emerging therapeutic drug monitoring (TDM) of anti-infective agents in Australian hospitals.

METHODS

A nationwide cross-sectional survey of all Australian hospitals operating in the public and private health sector was conducted between August and September 2019. The survey consisted of questions regarding institutional TDM practice for anti-infective agents and clinical vignettes specific to β-lactam antibiotics.

RESULTS

Responses were received from 82 unique institutions, representing all Australian states and territories. All 29 (100%) of principal referral (major) hospitals in Australia participated. Five surveys were partially complete. Only 25% (20/80) of hospitals had TDM testing available on-site for any of the eight emerging TDM candidates considered: β-lactam antibiotics, anti-tuberculous agents, flucytosine, fluoroquinolones, ganciclovir, human immunodeficiency virus (HIV) drugs, linezolid and teicoplanin. A considerable time lag was noted between TDM sampling and reporting of results. With respect to β-lactam antibiotic TDM, variable indications, pharmacodynamic targets and sampling times were identified. The three greatest barriers to local TDM performance were found to be (1) lack of timely assays/results, (2) lack of institutional-wide expertise and/or training and (3) lack of guidelines to inform ordering of TDM and interpretation of results. The majority of respondents favoured establishing national TDM guidelines and increasing access to dose prediction software, at rates of 89% and 96%, respectively.

CONCLUSION

Translating emerging TDM evidence into daily clinical practice is slow. Concerted efforts are required to address the barriers identified and facilitate the implementation of standardised practice.

Population pharmacokinetics of daptomycin in patients with bone and joint infection: minimal effect of rifampicin co-administration and confirmation of a sex difference

BACKGROUND

Daptomycin is increasingly used in the treatment of bone and joint infection (BJI), but its pharmacokinetics (PK) and dosage requirements have not been thoroughly investigated in this indication. Daptomycin may be co-administered with rifampicin, which raises questions about a potential drug interaction.

OBJECTIVES

To investigate the population PK and dosage requirements of daptomycin in patients with BJI, and examine the influence of rifampicin co-administration.

METHODS

A population approach was used to analyse PK data from patients who received daptomycin in our regional reference for BJI. We examined the influence of available covariates, including rifampicin co-administration on daptomycin PK. Simulations performed with the final model investigated the influence of dosages and covariates on PTA for both efficacy and safety.

RESULTS

A total of 1303 daptomycin concentrations from 183 patients were analysed. A two-compartment model best described the data. Significant intra-individual variability was observed. Daptomycin clearance was influenced by renal function and sex, with females having a 26% lower typical clearance than males. Central volume of distribution (V1) was influenced by body weight, age, sex and rifampicin co-administration. Typical V1 was 11% lower in patients who were co-administered rifampicin. In PK/PD simulations, sex influenced the probability of AUC24/MIC target attainment, while rifampicin had a marginal effect.

CONCLUSIONS

A daptomycin dosage of 8 mg/kg/24 h in women and 10 mg/kg/24 h in men should optimize efficacy but may lead to excessive trough concentrations in many patients, especially in women. Therapeutic drug monitoring appears necessary for precision dosing of daptomycin.

Pharmacokinetic/Pharmacodynamic Dosage Individualization of Suppressive Beta-Lactam Therapy Administered by Subcutaneous Route in Patients With Prosthetic Joint Infection

Suppressive parenteral antibiotic therapy with beta-lactams may be necessary in patients with Gram-negative bone and joint infection (BJI). Subcutaneous drug administration can facilitate this therapy in outpatient setting, but there is limited information about this practice. We have developed an original approach for drug dosing in this context, based on therapeutic drug monitoring (TDM) and pharmacokinetic/pharmacodynamic (PK/PD) principles. The objective of this study was to describe our approach and its first results in a case series. We analyzed data from patients who received suppressive antibiotic therapy by subcutaneous (SC) route with beta-lactams as salvage therapy for prosthetic joint infection (PJI) and had TDM with PK/PD-based dose adjustment. Ten patients (six women and four men with a mean age of 77 years) were included from January 2017 to May 2020. The drugs administered by SC route were ceftazidime (n = 4), ertapenem (n = 4), and ceftriaxone (n = 2). In each patient, PK/PD-guided dosage individualization was performed based on TDM and minimum inhibitory concentration (MIC) measurements. The dose interval could be prolonged from twice daily to thrice weekly in some patients, while preserving the achievement of PK/PD targets. The infection was totally controlled by the strategy in nine out the 10 patients during a median follow-up of 1,035 days (~3 years). No patient acquired carbapenem-resistant Gram-negative bacteria during the follow-up. One patient presented treatment failure with acquired drug resistance under therapy, which could be explained by late MIC determination and insufficient exposure, retrospectively. To conclude, our innovative approach, based on model-based TDM, MIC determination, and individualized PK/PD goals, facilitates, and optimizes suppressive outpatient beta-lactam therapy administered by SC route for PJI. These encouraging results advocate for larger clinical evaluation.

Impact on Antibiotic Resistance, Therapeutic Success, and Control of Side Effects in Therapeutic Drug Monitoring (TDM) of Daptomycin: A Scoping Review

Antimicrobial resistance (AR) is a problem that threatens the search for adequate safe and effective antibiotic therapy against multi-resistant bacteria like methicillin-resistant Staphylococcus aureus (MRSA), and vancomycin-resistant Enterococci (VRE) and Clostridium difficile, among others. Daptomycin is the treatment of choice for some infections caused by Gram-positive bacteria, indicated most of the time in patients with special clinical conditions where its high pharmacokinetic variability (PK) does not allow adequate plasma concentrations to be reached. The objective of this review is to describe the data available about the type of therapeutic drug monitoring (TDM) method used and described so far in hospitalized patients with daptomycin and to describe its impact on therapeutic success, suppression of bacterial resistance, and control of side effects. The need to create worldwide strategies for the appropriate use of antibiotics is clear, and one of these is the performance of therapeutic drug monitoring (TDM). TDM helps to achieve a dose adjustment and obtain a favorable clinical outcome for patients by measuring plasma concentrations of an administered drug, making a rational interpretation guided by a predefined concentration range, and, thus, adjusting dosages individually.

Model-Optimized Fluconazole Dose Selection for Critically Ill Patients Improves Early Pharmacodynamic Target Attainment without the Need for Therapeutic Drug Monitoring

Fluconazole has been associated with higher mortality compared with the echinocandins in patients treated for invasive candida infections. Underexposure from current fluconazole dosing regimens may contribute to these worse outcomes, so alternative dosing strategies require study. The objective of this study was to evaluate fluconazole drug exposure in critically ill patients comparing a novel model-optimized dose selection method with established approaches over a standard 14-day (336-h) treatment course. Target attainment was evaluated in a representative population of 1,000 critically ill adult patients for (i) guideline dosing (800-mg loading and 400-mg maintenance dosing adjusted to renal function), (ii) guideline dosing followed by therapeutic drug monitoring (TDM)-guided dose adjustment, and (iii) model-optimized dose selection based on patient factors (without TDM). Assuming a MIC of 2 mg/liter, free fluconazole 24-h area under the curve (fAUC24) targets of ≥200 mg · h/liter and <800 mg · h/liter were used for assessment of target attainment. Guideline dosing resulted in underexposure in 21% of patients at 48 h and in 23% of patients at 336 h. The TDM-guided strategy did not influence 0- to 48-h target attainment due to inherent procedural delays but resulted in 37% of patients being underexposed at 336 h. Model-optimized dosing resulted in ≥98% of patients meeting efficacy targets throughout the treatment course, while resulting in less overexposure compared with guideline dosing (7% versus 14%) at 336 h. Model-optimized dose selection enables fluconazole dose individualization in critical illness from the outset of therapy and should enable reevaluation of the comparative effectiveness of this drug in patients with severe fungal infections.

Optimizing the Use of Antibiotic Agents in the Pediatric Intensive Care Unit: A Narrative Review

Antibiotics are one of the most prescribed drug classes in the pediatric intensive care unit, yet the incidence of inappropriate antibiotic prescribing remains high in critically ill children. Optimizing the use of antibiotics in this population is imperative to guarantee adequate treatment, avoid toxicity and the occurrence of antibiotic resistance, both on a patient level and on a population level. Antibiotic stewardship encompasses all initiatives to promote responsible antibiotic usage and the PICU represents a major target environment for antibiotic stewardship programs. This narrative review provides a summary of the available knowledge on the optimal selection, duration, dosage, and route of administration of antibiotic treatment in critically ill children. Overall, more scientific evidence on how to optimize antibiotic treatment is warranted in this population. We also give our personal expert opinion on research priorities.

[Pharmacokinetic modifications and pharmacokinetic/pharmacodynamic optimization of beta-lactams in ICU]

Pharmacokinetic modifications in critically ill patients and those induced by ICU therapeutics raise a lot of issues about antibiotic dose adaptation. Beta-lactams are anti-infectious widely used in ICU. Frequent beta-lactam underdoses induce a risk of therapeutic failure potentially lethal and of emergence of bacterial resistance. Overdoses expose to a neurotoxic and nephrotoxic risk. Therefore, an understanding of pharmacokinetics modifications appears to be essential. A global pharmacokinetic/pharmacodynamic approach is required, including use of prolonged or continued beta-lactam infusions to optimise probability of pharmacokinetic/pharmacodynamic target attainment. Beta-lactam therapeutic drug monitoring should also be considered. Experts agree to target a free plasma betalactam concentration above four times the MIC of the causative bacteria for 100 % of the dosing interval. Bayesian methods could permit individualized doses adaptations.

Vancomycin Area under the Curve and Pharmacokinetic Parameters during the First 24 Hours of Treatment in Critically Ill Patients using Bayesian Forecasting

Background

Currently, the achievement of the target area under the curve (AUC)/minimum inhibitory concentration ratio during the first 24 - 48 h of treatment is associated with reduced 30-day mortality and greater microbiological eradication in patients with methicillin-resistant Staphylococcus aureus bacteremia. This study aimed to determine the AUC and pharmacokinetic parameters on the first day of vancomycin administration based on the Bayesian theorem to optimize the dosing regimen in critically ill patients.

Materials and Methods

This retrospective study included participants meeting the following criteria: 1) ≥18 years old; 2) receipt of at least one dose of vancomycin; 3) measurement of 2 vancomycin serum concentrations during the first 24 h of treatment; and 4) an intensive care unit admission, mechanical ventilator use, or an Acute Physiology and Chronic Health Evaluation II score >15 points. The AUC on day 1 of treatment and the estimated vancomycin pharmacokinetic parameters were measured using PrecisePK software based on the Bayesian theorem.

Results

We obtained 132 vancomycin concentrations from 66 patients. The vancomycin pharmacokinetic parameters were as follows: AUC_0-24, 571.09 (± standard deviation [SD] 188.62) mg/L·h; clearance (CL), 2.97 (± SD 1.81) L/h; volume of distribution (Vd), 50.60 (± SD 13.91) L; elimination rate constant, 0.062 (± SD 0.039) h⁻¹; and half-life, 18.19 (± SD 15.96) h. Focusing on the vancomycin loading dose, AUC_0-24 400 - 600 was achieved in 41.7, 46.1, 44.4, and 26.3% of patients with loading doses of <20, 20 – 24.9, 25 – 30, and >30 mg/kg, respectively. Whereas AUC_0-24 ≥521 was achieved in 50, 50, 77.8, and 84.2% of patients with loading doses of <20, 20 – 24.9, 25 – 30, and >30 mg/kg, respectively. The CL of vancomycin was correlated with creatinine CL, whereas the Vd of vancomycin was significantly correlated with age and body weight.

Conclusion

This study revealed that the higher Vd and CL values on the first day of vancomycin therapy were found in critically ill patients. Additionally, a higher vancomycin loading dose (25 – 30 mg/kg) might be required to achieve target of AUC_0-24 during early phase of administration for critically ill patients.

What Are the Current Approaches to Optimising Antimicrobial Dosing in the Intensive Care Unit?

Antimicrobial dosing in the intensive care unit (ICU) can be problematic due to various challenges including unique physiological changes observed in critically ill patients and the presence of pathogens with reduced susceptibility. These challenges result in reduced likelihood of standard antimicrobial dosing regimens achieving target exposures associated with optimal patient outcomes. Therefore, the aim of this review is to explore the various methods for optimisation of antimicrobial dosing in ICU patients. Dosing nomograms developed from pharmacokinetic/statistical models and therapeutic drug monitoring are commonly used. However, recent advances in mathematical and statistical modelling have resulted in the development of novel dosing software that utilise Bayesian forecasting and/or artificial intelligence. These programs utilise therapeutic drug monitoring results to further personalise antimicrobial therapy based on each patient’s clinical characteristics. Studies quantifying the clinical and cost benefits associated with dosing software are required before widespread use as a point-of-care system can be justified.

The management of anti-infective agents in intensive care units: the potential role of a 'fast' pharmacology

INTRODUCTION

Patients in intensive care units (ICU) are often developing severe infections in which are associated with significant mortality rates. A number of novel technologies for the rapid microbiological diagnosis of these infections have been developed, introducing the era of 'fast microbiology.' Treatment of bacterial and fungal infections in ICU is however complicated by alterations in the pharmacokinetics of antimicrobial agents.

AREAS COVERED

We review novel pharmacologic tools that can be used to optimize anti-infective therapies and patient management in ICU. A MEDLINE Pubmed search for articles published from January 1995 to 2019 was completed matching the terms pharmacokinetics and pharmacology with antimicrobial agents and ICU or critically ill patients. Moreover, additional studies were identified from the reference list of retrieved articles.

EXPERT OPINION

Several tools are in development for the full automation of the analytical methods used for the quantification of antimicrobial concentrations within a few hours after sample collection. Ad hoc software with adaptive feedback is also available for appropriate dose adjustments based on both individual patient covariate data and therapeutic drug monitoring (TDM) data when available. The application of these technological improvements in the clinical practice should open the way to a 'fast pharmacology' at the bedside.