Benchmarking therapeutic drug monitoring software: a review of available computer tools

Population Pharmacokinetic/Pharmacodynamic Models for P2Y12 Inhibitors: A Systematic Review and Clinical Appraisal Using Exposure Simulation

BACKGROUND AND OBJECTIVE

Recent research indicates a correlation between plasma concentration of P2Y12 inhibitors and clinical events, particularly bleeding, which significantly impeded their clinical therapeutic performance. It is therefore vital to delve into the factors that might affect the plasma concentration. The study aims to summarize population pharmacokinetics/pharmacodynamics (PopPKPD) models for commonly prescribed P2Y12 inhibitors (clopidogrel, prasugrel, and ticagrelor) and assess bleeding risk in specific individual groups.

METHODS

The PopPKPD models of P2Y12 inhibitors were collected and summarized based on predetermined inclusion and exclusion criteria. The collected models were replicated in simulations, which were used to assess factors affecting plasma concentrations of P2Y12 inhibitors. Simulation results for special populations were compared to therapeutic window based on reported exposure-effect relationships (PK/PD-related bleeding and thrombotic clinical outcomes) to predict bleeding risk in special populations with different dosing regimens and cumulative covariates.

RESULT

Finally, 12 studies were included for PK simulation, 7 of which that also included PD data were subjected to further analysis, with the majority being based on Phase I or II trials. Simulations showed that several covariates such as female gender, weight, elderly can significantly impact on exposure, with special populations reaching up to 179% of the general population. However, after dose adjustment, blood concentrations for special populations can reach approximately ±20% of general population exposure. Therefore, lowering the maintenance dose of ticagrelor from 90 to 60 mg bid was first recommended to reduce bleeding risk without significantly increasing ischemic risk, particularly in elderly, small-weight Asian females.

CONCLUSION

Lowering the maintenance dose of ticagrelor from 90 to 60 mg bid effectively reduces bleeding risk without increasing thrombotic infarction risk in elderly, small-weight Asian females.

Implementation and validation of a Bayesian method for accurately forecasting duration of optimal pharmacodynamic target attainment with dalbavancin during long-term use for subacute and chronic staphylococcal infections

Dalbavancin is increasingly being used for long-term treatment of subacute and chronic staphylococcal infections. In this study, a new Bayesian model was implemented and validated using MwPharm software for accurately forecasting the duration of pharmacodynamic target attainment above the efficacy thresholds of 4.02 mg/L or 8.04 mg/L against staphylococci. Forecasting accuracy improved substantially with the a posteriori approach compared with the a priori approach, particularly when two measured concentrations were used. This strategy may help clinicians to estimate the duration of optimal exposure with dalbavancin in the context of long-term treatment.

Ceftriaxone Pharmacokinetics and Pharmacodynamics in 2 Pediatric Patients on Extracorporeal Membrane Oxygenation Therapy

BACKGROUND

Critically ill patients with cardiac or respiratory failure may require extracorporeal membrane oxygenation (ECMO). Antibiotics are frequently administered when the suspected cause of organ failure is an infection. Ceftriaxone, a β-lactam antibiotic, is commonly used in patients who are critically ill. Although studies in adults on ECMO have suggested minimal impact on ceftriaxone pharmacokinetics, limited research exists on ceftriaxone pharmacokinetics/pharmacodynamics (PK/PD) in pediatric ECMO patients. We report the PK profiles and target attainment of 2 pediatric patients on ECMO who received ceftriaxone.

METHODS

Ceftriaxone concentrations were measured in 2 pediatric patients on ECMO using scavenged opportunistic sampling. PK profiles were generated and individual PK parameters were estimated using measured free ceftriaxone concentrations and a published population PK model in children who are critically ill, using Bayesian estimation.

RESULTS

Patient 1, an 11-year-old boy on venovenous ECMO for respiratory failure received 2 doses of 52 mg/kg ceftriaxone 12 hours apart while on ECMO and additional doses every 12 hours off ECMO. On ECMO, ceftriaxone clearance was 13.0 L/h/70 kg compared with 7.6 L/h/70 kg off ECMO, whereas the model-predicted mean clearance in children who are critically ill without ECMO support was 6.54 L/h/70 kg. Patient 2, a 2-year-old boy on venoarterial ECMO due to cardiac arrest received 50 mg/kg ceftriaxone every 12 hours while on ECMO for >7 days. Only clearance while on ECMO could be estimated (9.1 L/h/70 kg). Trough concentrations in both patients were >1 mg/L (the breakpoint for Streptococcus pneumoniae ) while on ECMO.

CONCLUSIONS

ECMO increased ceftriaxone clearance above the model-predicted clearances in the 2 pediatric patients studied. Twelve-hour dosing allowed concentrations to remain above the breakpoint for commonly targeted bacteria but not 4 times the breakpoint in one patient, suggesting that precision dosing may be beneficial to ensure target attainment in children on ECMO.

The clinical application of beta-lactam antibiotic therapeutic drug monitoring in the critical care setting

Critically ill patients have increased variability in beta-lactam antibiotic (beta-lactam) exposure due to alterations in their volume of distribution and elimination. Therapeutic drug monitoring (TDM) of beta-lactams, as a dose optimization and individualization tool, has been recommended to overcome this variability in exposure. Despite its potential benefit, only a few centres worldwide perform beta-lactam TDM. An important reason for the low uptake is that the evidence for clinical benefits of beta-lactam TDM is not well established. TDM also requires the availability of specific infrastructure, knowledge and expertise. Observational studies and systematic reviews have demonstrated that TDM leads to an improvement in achieving target concentrations, a reduction in potentially toxic concentrations and improvement of clinical and microbiological outcomes. However, a small number of randomized controlled trials have not shown a mortality benefit. Opportunities for improved study design are apparent, as existing studies are limited by their inclusion of heterogeneous patient populations, including patients that may not even have infection, small sample size, variability in the types of beta-lactams included, infections caused by highly susceptible bacteria, and varied sampling, analytical and dosing algorithm methods. Here we review the fundamentals of beta-lactam TDM in critically ill patients, the existing clinical evidence and the practical aspects involved in beta-lactam TDM implementation.

A Drug Safety Concept (I) to Avoid Polypharmacy Risks in Transplantation by Individual Pharmacotherapy Management in Therapeutic Drug Monitoring of Immunosuppressants

For several, also vital medications, such as immunosuppressants in solid organ and hematopoietic stem cell transplantation, therapeutic drug monitoring (TDM) remains the only strategy for fine-tuning the dosage to the individual patient. Especially in severe clinical complications, the intraindividual condition of the patient changes abruptly, and in addition, drug-drug interactions (DDIs) can significantly impact exposure, due to concomitant medication alterations. Therefore, a single TDM value can hardly be the sole basis for optimal timely dose adjustment. Moreover, every intraindividually varying situation that affects the drug exposure needs synoptic consideration for the earliest adjustment. To place the TDM value in the context of the patient's most detailed current condition and concomitant medications, the Individual Pharmacotherapy Management (IPM) was implemented in the posttransplant TDM of calcineurin inhibitors assessed by the in-house laboratory. The first strategic pillar are the defined patient scores from the electronic patient record. In this synopsis, the Summaries of Product Characteristics (SmPCs) of each drug from the updated medication list are reconciled for contraindication, dosing, adverse drug reactions (ADRs), and DDIs, accounting for defined medication scores as a second pillar. In parallel, IPM documents the resulting review of each TDM value chronologically in a separate electronic Excel file throughout each patient's transplant course. This longitudinal overview provides a further source of information at a glance. Thus, the applied two-arm concept of TDM and IPM ensures an individually tailored immunosuppression in the severely susceptible early phase of transplantation through digital interdisciplinary networking, with instructive and educative recommendations to the attending physicians in real-time. This concept of contextualizing a TDM value to the precise patient's condition and comedication was established at Halle University Hospital to ensure patient, graft, and drug safety.

Model-Informed Precision Dosing Software Tools for Dosage Regimen Individualization: A Scoping Review

BACKGROUND

Pharmacokinetic nomograms, equations, and software are considered the main tools available for Therapeutic Drug Monitoring (TDM). Model-informed precision dosing (MIPD) is an advanced discipline of TDM that allows dose individualization, and requires a software for knowledge integration and statistical calculations. Due to its precision and extensive applicability, the use of these software is widespread in clinical practice. However, the currently available evidence on these tools remains scarce.

OBJECTIVES

To review and summarize the available evidence on MIPD software tools to facilitate its identification, evaluation, and selection by users.

METHODS

An electronic literature search was conducted in MEDLINE, EMBASE, OpenAIRE, and BASE before July 2022. The PRISMA-ScR was applied. The main inclusion criteria were studies focused on developing software for use in clinical practice, research, or modelling.

RESULTS

Twenty-eight software were classified as MIPD software. Ten are currently unavailable. The remaining 18 software were described in depth. It is noteworthy that all MIPD software used Bayesian statistical methods to estimate drug exposure and all provided a population model by default, except NONMEN.

CONCLUSIONS

Pharmacokinetic software have become relevant tools for TDM. MIPD software have been compared, facilitating its selection for use in clinical practice. However, it would be interesting to standardize the quality and validate the software tools.

Therapeutic drug monitoring of carbapenem antibiotics in critically ill patients: an overview of principles, recommended dosing regimens, and clinical outcomes

INTRODUCTION

The importance of antibiotic treatment for sepsis in critically ill septic patients is well established. Consistently achieving the dose of antibiotics required to optimally kill bacteria, minimize the development of resistance, and avoid toxicity is challenging. The increasing understanding of the pharmacokinetic and pharmacodynamic (PK/PD) characteristics of antibiotics, and the effects of critical illness on key PK/PD parameters, is gradually re-shaping how antibiotics are dosed in critically ill patients.

AREAS COVERED

The PK/PD characteristics of commonly used carbapenem antibiotics, the principles of the application of therapeutic drug monitoring (TDM), and current as well as future methods of utilizing TDM to optimally devise dosing regimens will be reviewed. The limitations and evidence-base supporting the use of carbapenem TDM to improve outcomes in critically ill patients will be examined.

EXPERT OPINION

It is important to understand the principles of TDM in order to correctly inform dosing regimens. Although the concept of TDM is attractive, and the ability to utilize PK software to optimize dosing in the near future is expected to rapidly increase clinicians' ability to meet pre-defined PK/PD targets more accurately, current evidence provides only limited support for the use of TDM to guide carbapenem dosing in critically ill patients.

Precision Oncology by Point-of-Care Therapeutic Drug Monitoring and Dosage Adjustment of Conventional Cytotoxic Chemotherapies: A Perspective

Therapeutic drug monitoring (TDM) of conventional cytotoxic chemotherapies is strongly supported yet poorly implemented in daily practice in hospitals. Analytical methods for the quantification of cytotoxic drugs are instead widely presented in the scientific literature, while the use of these therapeutics is expected to keep going for longer. There are two main issues hindering the implementation of TDM: turnaround time, which is incompatible with the dosage profiles of these drugs, and exposure surrogate marker, namely total area under the curve (AUC). Therefore, this perspective article aims to define the adjustment needed from current to efficient TDM practice for cytotoxics, namely point-of-care (POC) TDM. For real-time dose adjustment, which is required for chemotherapies, such POC TDM is only achievable with analytical methods that match the sensitivity and selectivity of current methods, such as chromatography, as well as model-informed precision dosing platforms to assist the oncologist with dose fine-tuning based on quantification results and targeted intervals.

The Current Status and Future Perspectives of Beta-Lactam Therapeutic Drug Monitoring in Critically Ill Patients

Beta-lactams (BL) are the first line agents for the antibiotic management of critically ill patients with sepsis or septic shock. BL are hydrophilic antibiotics particularly subject to unpredictable concentrations in the context of critical illness because of pharmacokinetic (PK) and pharmacodynamics (PD) alterations. Thus, during the last decade, the literature focusing on the interest of BL therapeutic drug monitoring (TDM) in the intensive care unit (ICU) setting has been exponential. Moreover, recent guidelines strongly encourage to optimize BL therapy using a PK/PD approach with TDM. Unfortunately, several barriers exist regarding TDM access and interpretation. Consequently, adherence to routine TDM in ICU remains quite low. Lastly, recent clinical studies failed to demonstrate any improvement in mortality with the use of TDM in ICU patients. This review will first aim at explaining the value and complexity of the TDM process when translating it to critically ill patient bedside management, interpretating the results of clinical studies and discussion of the points which need to be addressed before conducting further TDM studies on clinical outcomes. In a second time, this review will focus on the future aspects of TDM integrating toxicodynamics, model informed precision dosing (MIPD) and “at risk” ICU populations that deserve further investigations to demonstrate positive clinical outcomes.

Population Pharmacokinetics and Dosage Optimization of Vancomycin in Pediatric Patients with Skin and Soft Tissue Infections, Bone, and Joint Infections

Vancomycin is recommended for the treatment of skin and soft tissue infections (SSTI) and bone and joint infections (BJI). However, a detailed investigation of the pharmacokinetic profile and optimal dosing regimens of vancomycin in pediatric patients with SSTI and BJI is lacking. We successfully developed a new PopPK model of vancomycin in this population by using scavenged blood samples with the typical values for clearance (CL) of 0.14 L/h/kg and volume of distribution (V) of 0.5 L/kg. Body weight was confirmed as the significant covariate on CL and V. The optimal dosing regimens of 75 mg/kg/day and 80 mg/kg/day were recommended for this specific population.

Clinical Relevance of a Vancomycin 24 h Area under the Concentration-Time Curve Values Using Different Renal Function Equations in Bayesian Dosing Software

With the updated 2020 vancomycin therapeutic drug monitoring (TDM) guidelines suggesting a ratio of area under the curve over 24 h to a minimum inhibitory concentration (AUC24/MIC) as a target from the Infectious Diseases Society of America, an accurate estimation of AUC24 has become more critical. We aim to compare the AUC24 using Bayesian dosing software according to various estimated glomerular filtration rate (eGFR) equations in order to analyze the clinical impact of eGFR in vancomycin TDM. We reviewed the TDM dataset of 214 adult patients and analyzed the AUC24 values from various renal function equations, including the Cockcroft-Gault (C-G), the modification of diet in renal disease (MDRD), the chronic kidney disease epidemiology collaboration (CKD-EPI), and the revised Lund−Malmö. The AUC24/MIC results (assuming a MIC of 1 mg/L) were divided into three groups as follows: <400, 400−600, and >600. Additionally, we compared the group agreement between the C-G and the three eGFR formulas. Although there was a statistically significant difference in the AUC24 of the MDRD and the CKD-EPI formulas compared to the C-G, the group concordance rate of the eGFR formula was 95.2−100%, which indicates no clinical significance. The clinical impact of the eGFR formula type on drug dosing recommendations in vancomycin TDM using Bayesian software was insignificant in clinical practice.

A Brief Review of Pharmacokinetic Assessments of Vancomycin in Special Groups of Patients with Altered Pharmacokinetic Parameters

Vancomycin is considered the drug of choice against many Gram-positive bacterial infections. Therapeutic drug monitoring (TDM) is essential to achieve an optimum clinical response and avoid vancomycin-induced adverse reactions including nephrotoxicity. Although different studies are available on vancomycin TDM, still there are controversies regarding the selection among different pharmacokinetic parameters including trough concentration, the area under the curve to minimum inhibitory concentration ratio (AUC24h/MIC), AUC of intervals, elimination constant, and vancomycin clearance. In this review, different pharmacokinetic parameters for vancomycin TDM have been discussed along with corresponding advantages and disadvantages. Also, vancomycin pharmacokinetic assessments are discussed in patients with altered pharmacokinetic parameters including those with renal and/or hepatic failure, critically ill patients, patients with burn injuries, intravenous drug users, obese and morbidly obese patients, those with cancer, patients undergoing organ transplantation, and vancomycin administration during pregnancy and lactation. An individualized dosing regimen is required to guarantee the optimum therapeutic responses and minimize adverse reactions including acute kidney injury in these special groups of patients. According to the pharmacoeconomic data on vancomycin TDM, pharmacokinetic assessments would be cost-effective in patients with altered pharmacokinetics and are associated with shorter hospitalization period, faster clinical stability status, and shorter courses of inpatient vancomycin administration.

Everolimus pharmacokinetics and exposure-response relationship in Japanese patients with advanced breast cancer

Background: Everolimus is one of the key drugs for the treatment of advanced breast cancer. The optimal target concentration range for everolimus therapy in patients with breast cancer has not yet been established. This study aimed to characterize everolimus pharmacokinetics (PK) and determine the relationship between blood concentration and efficacy as well as adverse events in patients with breast cancer.

Methods: This was a prospective, observational PK study. Patients receiving everolimus between November 2015 and November 2018 at our hospital were enrolled in this study. The whole blood samples for the everolimus assay were collected at least two weeks after initiation of treatment or the last everolimus dose change. PK parameters were estimated using Bayesian analysis. Statistical differences in everolimus trough concentrations between patient cohorts were assessed using the Mann–Whitney test. Progression-free survival was assessed using the Kaplan-Meier method and the log-rank test.

Results: Eighteen patients were enrolled in the study. The median follow-up period was 35 months. The most frequently observed adverse event was stomatitis (all grade 94%). There was high inter-individual variation in PK parameters such as clearance [range: 5.1–21.3 L/h/70 kg and co-efficient of variation (CV): 38.5%] and volume of distribution of the central compartment (range: 9.9–103.6 L/70 kg and CV: 57.8%). The trough concentrations at dose-limiting toxicities were significantly higher than trough concentrations in the absence of these toxicities (p = 0.0058). Progression-free survival was significantly longer in the 10–20 ng/ml group than in the other groups (p = 0.0078).

Conclusion: This study characterized the everolimus PK parameters in Japanese patients with breast cancer. High everolimus exposure was found to be associated with poor tolerability. Based on our data, trough concentrations in the range of 10–20 ng/ml may be associated with prolonged progression-free survival. Thus, determining the blood concentration of everolimus and subsequent dose adjustments will potentially reduce side effects and enhance the therapeutic effect in Japanese patients with advanced breast cancer.

Population Pharmacokinetic Models for Direct Oral Anticoagulants: A Systematic Review and Clinical Appraisal Using Exposure Simulation

Available data have shown an association between direct oral anticoagulant (DOAC) plasma concentration and clinical, particularly bleeding, events. Factors that may influence DOAC plasma concentration are therefore the focus of particular attention. Population pharmacokinetic (PopPK) analyses can help in identifying such factors while providing predictive models. The main aim of the present study was to identify all the PopPK models to date for the four most frequently used DOACs (dabigatran, apixaban, rivaroxaban, and edoxaban). The secondary aim was to use these models to simulate different DOAC plasma concentration–time profiles in relevant clinical scenarios. The results of our model‐based simulations confirm the clinical relevance of the known major factors influencing DOAC exposure and support the current approved dose adaptation, at least for atrial fibrillation. They also highlight how the accumulation of covariates, not currently considered for dose adaptation due to their seemingly minor influence on DOAC exposure, lead to supratherapeutic blood concentrations and could thus enhance the risk of major bleeding. The present results therefore question DOAC dose adaptation in the presence of these covariates, such as drug–drug interaction or genotypes, alongside the known existing covariates. As the overall effect of accumulation of several covariates could be difficult to apprehend for the clinicians, PopPK modeling could represent an interesting approach for informed precision dosing and to improve personalized prescription of DOACs.

Multi-center prospective population pharmacokinetic study and the performance of web-based individual dose optimization application of intravenous vancomycin for adults in Hong Kong: A study protocol

A recent consensus guideline recommends migrating the therapeutic drug monitoring practice for intravenous vancomycin for the treatment of methicillin-resistant Staphylococcus aureus infection from the traditional trough-based approach to the Bayesian approach based on area under curve to improve clinical outcomes. To support the implementation of the new strategy for hospitals under Hospital Authority, Hong Kong, this study is being proposed to (1) estimate and validate a population pharmacokinetic model of intravenous vancomycin for local adults, (2) develop a web-based individual dose optimization application for clinical use, and (3) evaluate the performance of the application by comparing the treatment outcomes and clinical satisfaction against the traditional approach. 300 adult subjects prescribed with intravenous vancomycin and not on renal replacement therapy will be recruited for population pharmacokinetic model development and validation. Sex, age, body weight, serum creatinine level, intravenous vancomycin dosing records, serum vancomycin concentrations etc. will be collected from several electronic health record systems maintained by Hospital Authority. Parameter estimation will be performed using non-linear mixed-effect modeling techniques. The web-based individual dose optimization application is based on a previously reported application and is built using R and the package shiny. Data from another 50 subjects will be collected during the last three months of the study period and treated as informed by the developed application and compared against historical control for clinical outcomes. Since the study will incur extra blood-taking procedures from patients, informed consent is required. Other than that, recruited subjects should receive medical treatments as usual. Identifiable patient data will be available only to site investigators and clinicians in each hospital. The study protocol and informed consent forms have been approved by the Joint Chinese University of Hong Kong–New Territories East Cluster Clinical Research Ethics Committee (reference number: NTEC-2021-0215) and registered at the Chinese Clinical Trial Registry (registration number: ChiCTR2100048714).

Augmented Renal Clearance in Severe Infections-An Important Consideration in Vancomycin Dosing: A Narrative Review

Vancomycin is a hydrophilic antibiotic widely used in severe infections, including bacteremia and central nervous system (CNS) infections caused by Gram-positive bacteria such as methicillin-resistant Staphylococcus aureus (MRSA), coagulase-negative staphylococci and enterococci. Appropriate antimicrobial dosage regimens can help achieve the target exposure and improve clinical outcomes. However, vancomycin exposure in serum and cerebrospinal fluid (CSF) is challenging to predict due to rapidly changing pathophysiological processes and patient-specific factors. Vancomycin concentrations may be decreased for peripheral infections due to augmented renal clearance (ARC) and increased distribution caused by systemic inflammatory response syndrome (SIRS), increased capillary permeability, and aggressive fluid resuscitation. Additionally, few studies on vancomycin’s pharmacokinetics (PK) in CSF for CNS infections. The relationship between exposure and clinical response is unclear, challenging for adequate antimicrobial therapy. Accurate prediction of vancomycin pharmacokinetics/pharmacodynamics (PK/PD) in patients with high interindividual variation is critical to increase the likelihood of achieving therapeutic targets. In this review, we describe the interaction between ARC and vancomycin PK/PD, patient-specific factors that influence the achievement of target exposure, and recent advances in optimizing vancomycin dosing schedules for severe infective patients with ARC.

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.

Optimizing Vancomycin Dosing and Monitoring in Neonates and Infants Using Population Pharmacokinetic Modeling

We determined optimal vancomycin starting dose regimens in infants ≤180 days of age to achieve the highest probability of target attainment with an area under the concentration-time curve for 24 h (AUC₂₄) of ≥400 using population pharmacokinetic (PK) modeling. Secondarily, determination of the relationship between serum creatinine (SCR) and vancomycin clearance in neonates was done. A retrospective population PK study was designed and included pediatric patients ≤180 days old who had received vancomycin and had a serum vancomycin concentration sampled. A population PK model was developed using Pumas (v1.0.5). Simulation was performed with various dosing regimens to evaluate the probability of AUC₂₄ target attainment and probability of trough of ≤20 mg/liter, and comparison to published models was performed. Individual clearance estimates, obtained from the final model, were plotted against SCR and faceted by age quartiles to assess the relationship between SCR and vancomycin clearance. A total of 934 patients were included in the study (58.6% male; median age, 43.6 days [range of 0 to 184]; median number of concentration samples, 1 [range of 1 to 29]). A one-compartment model was developed with body weight (WT), SCR, and postmenstrual age (PMA) identified as significant covariates on clearance. Plotting vancomycin clearance versus SCR demonstrated no clear relationship between the two at <10 days postnatal age (PNA). Dosing regimens to attain AUC₂₄ and trough targets were stratified according to SCR for ≥10 days PNA and PMA for <10 days PNA. A vancomycin population PK model was developed for pediatric patients <180 days of age incorporating WT, SCR, and PMA. The relationship between vancomycin clearance and serum creatinine is not clear at <10 days PNA.

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.

Development and Validation of Open-Source R Package HMCtdm for Therapeutic Drug Monitoring

Most therapeutic drug monitoring (TDM) packages are based on the maximum a posteriori (MAP) estimation. In this study, HMCtdm, a new TDM package, was developed using a Hamiltonian Monte Carlo (HMC) simulation. The estimation process of HMCtdm for the drugs amikacin, vancomycin, theophylline, and phenytoin was based on the R package Torsten. The prior pharmacokinetic (PK) models of the drugs were derived from the Abbottbase^® pharmacokinetics systems (PKS) program. The performance of HMCtdm for each drug was assessed through internal and external validations. The internal validation results of the HMCtdm were compared with those of a MAP-based estimation. The developed open-source HMCtdm package is user friendly. The validation results were reviewed and interpreted using the mean percentage error and root mean squared error. The successful transplantation of the prior PK structures (used in PKS) was confirmed by comparing the validation results with a MAP estimation. An open-source HMC-based TDM package was also successfully developed in this study, and its performance was evaluated. This package can be operated by users unfamiliar with C++ and can be further developed for various applications.

Tacrolimus Therapy in Adult Heart Transplant Recipients: Evaluation of a Bayesian Forecasting Software

BACKGROUND

Therapeutic drug monitoring is recommended to guide tacrolimus dosing because of its narrow therapeutic window and considerable pharmacokinetic variability. This study assessed tacrolimus dosing and monitoring practices in heart transplant recipients and evaluated the predictive performance of a Bayesian forecasting software using a renal transplant-derived tacrolimus model to predict tacrolimus concentrations.

METHODS

A retrospective audit of heart transplant recipients (n = 87) treated with tacrolimus was performed. Relevant data were collected from the time of transplant to discharge. The concordance of tacrolimus dosing and monitoring according to hospital guidelines was assessed. The observed and software-predicted tacrolimus concentrations (n = 931) were compared for the first 3 weeks of oral immediate-release tacrolimus (Prograf) therapy, and the predictive performance (bias and imprecision) of the software was evaluated.

RESULTS

The majority (96%) of initial oral tacrolimus doses were guideline concordant. Most initial intravenous doses (93%) were lower than the guideline recommendations. Overall, 36% of initial tacrolimus doses were administered to transplant recipients with an estimated glomerular filtration rate of <60 mL/min/1.73 m despite recommendations to delay the commencement of therapy. Of the tacrolimus concentrations collected during oral therapy (n = 1498), 25% were trough concentrations obtained at steady-state. The software displayed acceptable predictions of tacrolimus concentration from day 12 (bias: -6%; 95%confidence interval, -11.8 to 2.5; imprecision: 16%; 95% confidence interval, 8.7-24.3) of therapy.

CONCLUSIONS

Tacrolimus dosing and monitoring were discordant with the guidelines. The Bayesian forecasting software was suitable for guiding tacrolimus dosing after 11 days of therapy in heart transplant recipients. Understanding the factors contributing to the variability in tacrolimus pharmacokinetics immediately after transplant may help improve software predictions.

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

INTRODUCTION

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Therapeutic drug monitoring in oncology: International Association of Therapeutic Drug Monitoring and Clinical Toxicology consensus guidelines for imatinib therapy

Although therapeutic drug monitoring (TDM) is an important tool in guiding drug dosing for other areas of medicine including infectious diseases, cardiology, psychiatry and transplant medicine, it has not gained wide acceptance in oncology. For imatinib and other tyrosine kinase inhibitors, a flat dosing approach is utilised for management of oral chemotherapy. There are many published studies examining the correlation of blood concentrations with clinical effects of imatinib. The International Association of Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT) determined that there was a need to examine the published literature regarding utility of TDM in imatinib therapy and to develop consensus guidelines for TDM based on the available data. This article summarises the scientific evidence regarding TDM of imatinib, as well as the consensus guidelines developed by the IATDMCT.

Comparing Vancomycin Area Under the Curve With a Pharmacist Protocol that Incorporates Trough and Maximum Doses at a Children's Hospital

OBJECTIVE

Updated vancomycin guidelines suggest dose adjustment based on area under the curve in a 24-hour period (AUC₂₄). This study aims to determine whether a pharmacist managed vancomycin protocol that incorporates maximum dosing paired with trough monitoring can achieve appropriate vancomycin AUC₂₄ exposures.

METHODS

A retrospective review was performed evaluating vancomycin usage from October 2018 through September 2019 at a children's hospital. Patients with less than 4 doses or lack a trough concentration were excluded. Vancomycin AUC₂₄ were estimated using 2 calculations: 1) the Le method, incorporating age and serum creatinine, and 2) the trapezoidal method based upon population data and patient-specific trough. Target AUC₂₄ ranges were assessed. AUC₂₄ goals were 400 to 600 mg·hr/L, but due to known variations between calculations, a variance of 20 mg·hr/L was allowed for each end of the goal. Secondary analyses included evaluations of efficacy and toxicity.

RESULTS

Two-hundred twenty-three patients were included. Initial doses were estimated to meet AUC₂₄ goals in only 63%. After trough-based dose modification, 81% achieved a therapeutic AUC₂₄. Using the trapezoidal method, therapeutic concentrations were found in 51% of patients based on the initial dose and 77% after dose modification. Only 6.3% of patients had kidney injury with only 1 of those patients having any calculated AUC₂₄ > 600 mg·hr/L and none above 620 mg·hr/L. No clinical failures were identified.

CONCLUSIONS

Increased initial dosing in infants and children is needed to result in AUC₂₄ exposures recommended in the guidelines. Maximum dosing paired with trough monitoring may be an alternative to AUC₂₄ monitoring in areas that are unable to perform AUC₂₄ calculations. Prospective data are needed to validate these conclusions.

Administration and Therapeutic Drug Monitoring of β-lactams and Vancomycin in Critical Care Units in Colombia: The ANTIBIOCOL Study

Therapeutic drug monitoring (TDM) and continuous infusion strategies are effective interventions in clinical practice, but these practices are still largely unknown in Colombia, especially in the critical care setting. This study aims to describe the practices involved in the administration and TDM of β-lactams and vancomycin reported by specialists in critical care in Colombia and to explore the factors that are related to the use of extended infusion. An online nationwide survey was applied to 153 specialists, who were selected randomly. A descriptive, bivariate analysis and a logistic regression model were undertaken. In total, 88.9% of the specialists reported TDM availability and 21.57% reported access to results within 6 h. TDM was available mainly for vancomycin. We found that 85.62% of the intensivists had some type of institutional protocol; however, only 39.22% had a complete and socialized protocol. The odds of preferring extended infusions among those who did not have institutional protocols were 80% lower than those with complete protocols, OR 0.2 (95% CI: 0.06−0.61). The most important perceived barriers to performing continuous infusions and TDM were the lack of training and technologies. This pioneering study in Colombia could impact the quality of care and outcomes of critically ill patients in relation to the threat of antimicrobial resistance.

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

BACKGROUND AND AIM

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

Therapeutic drug monitoring of antimicrobial drugs in neonates. An opinion paper

PURPOSE

Neonatal infections are associated with high morbidity and mortality rates. Optimal treatment of these infections requires knowledge of neonatal pharmacology and integration of neonatal developmental pharmacokinetics of antimicrobial drugs in the design of dosing regimens for use with different gestational and postnatal ages. Population pharmacokinetic (PK) and pharmacodynamic (PD) models are used to personalize the use of these drugs in these fragile patients. The final step to further minimize variability in an individual patient is therapeutic drug monitoring (TDM), where the same population PK/PD models are used in concert with optimally drawn blood samples to further fine-tune therapy. The purpose of this manuscript is to describe the present status and future role of model-based precision dosing and TDM of antimicrobial drugs in neonates.

METHODS

PubMed was searched for clinical trials or clinical studies of TDM in neonates.

RESULTS

A total of 447 papers were retrieved, of which 19 were concerned with antimicrobial drugs. Two papers (one aminoglycoside and one vancomycin) addressed the effects of TDM in neonates. We found that, in addition to aminoglycosides and vancomycin, TDM also plays a role in beta-lactam antibiotics and antifungal drugs.

CONCLUSION

There is a growing awareness that, in addition to aminoglycosides and vancomycin, the use of beta-lactam antibiotics, such as amoxicillin and meropenem, and other classes of antimicrobial drugs, such as antifungal drugs, may benefit from TDM. However, the added value must be shown. New analytical techniques and software development may greatly support these novel developments.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Bayesian clinical decision support-guided versus clinician-guided vancomycin dosing in attainment of targeted pharmacokinetic parameters in a paediatric population

OBJECTIVES

To compare a Bayesian clinical decision support (CDS) dose-optimizing software program with clinician judgement in individualizing vancomycin dosing regimens to achieve vancomycin pharmacokinetic (PK)/pharmacodynamic (PD) targets in a paediatric population.

METHODS

A retrospective review combined with a model-based simulation of vancomycin dosing was performed on children aged 1 year to 18 years at the University of California, San Francisco Benioff Children's Hospital Mission Bay. Dosing regimens recommended by the clinical pharmacists, 'clinician-guided', were compared with alternative 'CDS-guided' dosing regimens. The primary outcome was the percentage of occasions predicted to achieve steady-state trough levels within the target range of 10-15 mg/L, with a secondary outcome of predicted attainment of AUC24 ≥400 mg·h/L. Statistical comparison between approaches was performed using a standard t-test.

RESULTS

A total of n=144 patient occasions were included. CDS-guided regimens were predicted to achieve vancomycin steady-state troughs in the target range on 70.8% (102/144) of occasions, as compared with 37.5% (54/144) in the clinician-guided arm (P<0.0001). An AUC24 of ≥400 mg·h/L was achieved on 93% (112/121) of occasions in the CDS-guided arm versus 72% (87/121) of occasions in the clinician-guided arm (P<0.0001).

CONCLUSIONS

In a simulated analysis, the use of a Bayesian CDS tool was better than clinician judgement in recommending vancomycin dosing regimens in which PK/PD targets would be attained in children.

Should Therapeutic Drug Monitoring Based on the Vancomycin Area Under the Concentration-Time Curve Be Standard for Serious Methicillin-Resistant Staphylococcus aureus Infections?-No

In this counterpoint we critically appraise the evidence supporting therapeutic drug monitoring based on the vancomycin 24-hour area under the concentration-time curve (AUC24) for serious methicillin-resistant Staphylococcus aureus infections. We reveal methodologically weaknesses and inconsistencies in the data and suggest that, in the absence of clear and convincing evidence of benefit compared with modestly reducing trough targets, alternative strategies are more likely to result in superior safety and efficacy. These include focusing on fundamental antibiotic stewardship to limit vancomycin exposure overall, achieving earlier and more complete source control, and establishing alternative therapeutic options to vancomycin. Implementation of AUC24-based therapeutic drug monitoring will take resources away from these more promising, alternative solutions.

Therapeutic Drug Monitoring of Targeted Anticancer Protein Kinase Inhibitors in Routine Clinical Use: A Critical Review

BACKGROUND

Therapeutic response to oral targeted anticancer protein kinase inhibitors (PKIs) varies widely between patients, with insufficient efficacy of some of them and unacceptable adverse reactions of others. There are several possible causes for this heterogeneity, such as pharmacokinetic (PK) variability affecting blood concentrations, fluctuating medication adherence, and constitutional or acquired drug resistance of cancer cells. The appropriate management of oncology patients with PKI treatments thus requires concerted efforts to optimize the utilization of these drug agents, which have probably not yet revealed their full potential.

METHODS

An extensive literature review was performed on MEDLINE on the PK, pharmacodynamics, and therapeutic drug monitoring (TDM) of PKIs (up to April 2019).

RESULTS

This review provides the criteria for determining PKIs suitable candidates for TDM (eg, availability of analytical methods, observational PK studies, PK-pharmacodynamics relationship analysis, and randomized controlled studies). It reviews the major characteristics and limitations of PKIs, the expected benefits of TDM for cancer patients receiving them, and the prerequisites for the appropriate utilization of TDM. Finally, it discusses various important practical aspects and pitfalls of TDM for supporting better implementation in the field of cancer treatment.

CONCLUSIONS

Adaptation of PKIs dosage regimens at the individual patient level, through a rational TDM approach, could prevent oncology patients from being exposed to ineffective or unnecessarily toxic drug concentrations in the era of personalized medicine.

Development of a Population Pharmacokinetic Model for Cyclosporine from Therapeutic Drug Monitoring Data

Aim

To develop a population pharmacokinetic model for Uruguayan patients under treatment with cyclosporine (CsA) that can be applied to TDM. Patients and Methods. A total of 53 patients under treatment with CsA were included. 37 patients with at least one pharmacokinetic profile described with four samples were considered for model building, while the remaining 16 were considered for the assessments of predictive performances. Pharmacokinetic parameter estimation was performed using a nonlinear mixed effect modelling implemented in the Monolix® software (version 2019R1, Lixoft, France); meanwhile, simulations were performed in R v.3.6.0 with the mlxR package.

Results

A two-compartment model with a first-order disposition model including lag time was used as a structural model. The final model was internally validated using prediction corrected visual predictive check (pcVPC) and other graphical diagnostics. A total of 621 CsA steady-state concentrations were analyzed for model development. Population estimates for the absorption constant (ka) and lag time were 0.523 h⁻¹ and 0.512 h, respectively; apparent clearance (CL/F) was 30.3 L/h (relative standard error [RSE] ± 8.25%) with an interindividual variability of 39.8% and interoccasion variability of 38.0%; meanwhile, apparent clearance of distribution (Q/F) was 17.0 L/h (RSE ± 12.1%) with and interindividual variability of 53.2%. The covariate analysis identified creatinine clearance (ClCrea) as an individual factor influencing the Cl of CsA. The predictive capacity of the population model was demonstrated to be effective since predictions made for new patients were accurate for C1 and C2 (MPPEs below 50%). Bayesian forecasting improved significantly in the second and third occasions.

Conclusion

A population pharmacokinetic model was developed to reasonably estimate the individual cyclosporine clearance for patients. Hence, it can be utilized to individualize CsA doses for prompt and adequate achievement of target blood concentrations of CsA.

Model-Informed Therapeutic Drug Monitoring of Meropenem in Critically Ill Patients: Improvement of the Predictive Ability of Literature Models with the PRIOR Approach

BACKGROUND AND OBJECTIVE

To improve the predictive ability of literature models for model-informed therapeutic drug monitoring (TDM) of meropenem in intensive care units, we propose to tweak the literature models with the "prior approach" using a subset of the data. This study compares the predictive ability of both literature and tweaked models on TDM concentrations of meropenem in critically ill patients.

METHODS

Blood samples were collected from patients of an intensive care unit treated with intravenous meropenem. Data were split six times into an "estimation" and a "prediction" datasets. Population pharmacokinetic (popPK) models of meropenem were selected from literature. These models were run on the "estimation" dataset with the $PRIOR subroutine in NONMEM to obtain tweaked models. The literature and tweaked models were used a priori (with covariate only) and with Bayesian fitting to predict each individual concentration from the previous concentration(s). Their respective predictive abilities were compared using median relative prediction error (MDPE%) and median absolute relative prediction error (MDAPE%).

RESULTS

The total dataset was composed of 115 concentrations from 58 patients. For each of the six splits, the "estimation" and the "prediction" datasets were respectively composed of 44 and 14 patients or 45 and 13 patients. Six popPK models were selected in the literature. MDPE% and MDAPE% were globally lower for the tweaked than for the literature models, especially for a priori predictions.

CONCLUSION

The "prior approach" could be a valuable tool to improve the predictive ability of literature models, especially for a priori predictions, which are important to optimize dosing in emergency situations.

Evaluation of Bayesian Forecasting Methods for Prediction of Tacrolimus Exposure Using Samples Taken on Two Occasions in Adult Kidney Transplant Recipients

BACKGROUND

Bayesian forecasting-based limited sampling strategies (LSSs) for tacrolimus have not been evaluated for the prediction of subsequent tacrolimus exposure. This study examined the predictive performance of Bayesian forecasting programs/services for the estimation of future tacrolimus area under the curve (AUC) from 0 to 12 hours (AUC0-12) in kidney transplant recipients.

METHODS

Tacrolimus concentrations were measured in 20 adult kidney transplant recipients, 1 month post-transplant, on 2 occasions one week apart. Twelve samples were taken predose and 13 samples were taken postdose at the specified times on the first and second sampling occasions, respectively. The predicted AUC0-12 (AUCpredicted) was estimated using Bayesian forecasting programs/services and data from both sampling occasions for each patient and compared with the fully measured AUC0-12 (AUCmeasured) calculated using the linear trapezoidal rule on the second sampling occasion. The bias (median percentage prediction error [MPPE]) and imprecision (median absolute prediction error [MAPE]) were determined.

RESULTS

Three programs/services were evaluated using different LSSs (C0; C0, C1, C3; C0, C1, C2, C4; and all available concentrations). MPPE and MAPE for the prediction of fully measured AUC0-12 were <15% for each program/service (with the exclusion of when only C0 was used), when using estimated AUC from data on the same (second) occasion. The MPPE and MAPE for the prediction of a future fully measured AUC0-12 were <15% for 2 programs/services (and for the third when participants who had a tacrolimus dose change between sampling days were excluded), when the occasion 1-AUCpredicted, using C0, C1, and C3, was compared with the occasion 2-AUCmeasured.

CONCLUSIONS

All 3 Bayesian forecasting programs/services evaluated had acceptable bias and imprecision for predicting a future AUC0-12, using tacrolimus concentrations at C0, C1, and C3, and could be used for the accurate prediction of tacrolimus exposure in adult kidney transplant recipients.

AUCs and 123s: a critical appraisal of vancomycin therapeutic drug monitoring in paediatrics

The revised vancomycin guidelines recommend implementing AUC24-based therapeutic drug monitoring (TDM) using Bayesian methods in both adults and paediatrics. The motivation for this change was accumulating evidence showing aggressive dosing to achieve high troughs, as recommended in the first guidelines for adults and extrapolated to paediatrics, is associated with increased nephrotoxicity without improving clinical outcomes. AUC24-based TDM requires substantial resources that may need to be diverted from other valuable interventions. It can therefore be justified only after certain assumptions are shown to be true: (i) there is a clear relationship between vancomycin efficacy and/or toxicity and the proposed therapeutic range; and (ii) maintaining exposure within the target range with AUC24-based TDM improves clinical outcomes and/or decreases toxicity. In this review, we critically appraise the scientific basis for these assumptions. We find studies evaluating the relationship between vancomycin AUC24/MIC and efficacy in adults and children do not offer strong support for the recommended lower limit of the proposed therapeutic range (i.e. AUC24/MIC ≥400). Nephrotoxicity in children increases in a stepwise manner along the vancomycin exposure continuum but it is unclear if one parameter (AUC24 versus trough) is a superior predictor. Overall, evidence in children suggests good-to-excellent correlation between AUC24 and trough. Most importantly, there is no convincing evidence that the method of vancomycin TDM has a causal role in improving efficacy or reducing toxicity. These findings question the need to transition to resource-intensive AUC24-based TDM over retaining trough-based TDM with lower targets to minimize nephrotoxicity in paediatrics.

Advances in Therapeutic Drug Monitoring in Biologic Therapies for Pediatric Inflammatory Bowel Disease

In the current era of treat-to-target strategies, therapeutic drug monitoring (TDM) has emerged as a potential tool in optimizing the efficacy of biologics for children diagnosed with inflammatory bowel disease (IBD). The incorporation of TDM into treatment algorithms, however, has proven to be complex. "Proactive" TDM is emerging as a therapeutic strategy due to a recently published pediatric RCT showing a clear benefit of "proactive" TDM in anti-TNF therapy. However, target therapeutic values for different biologics for different disease states [ulcerative colitis (UC) vs. Crohn's disease (CD)] and different periods of disease activity (induction vs. remission) require further definition. This is especially true in pediatrics where the therapeutic armamentarium is limited, and fixed weight-based dosing may predispose to increased clearance leading to decreased drug exposure and subsequent loss of response (pharmacokinetic and/or immunogenic). Model-based dosing for biologics offers an exciting insight into dose individualization thereby minimizing the chances of losing response. Similarly, point-of-care testing promises real-time assessment of drug levels and individualized decision-making. In the current clinical realm, TDM is being used to prolong drug durability and efficacy and prevent loss of response. Ongoing innovations may transform it into a personalized tool to achieve optimal therapeutic endpoints.

A Critique of Pharmacokinetic Calculators for Drug Dosing Individualization

The 'one-dose-fits-all' approach where drug dosing regimen is prescribed according to recommendations from a summary of product characteristics is not appropriate for many patients whose clinical characteristics significantly differ from the most frequent ones in a population, as it cannot guarantee optimal exposure of target tissues to the drug. Our aim here is to provide a concise review of pharmacokinetic calculators currently available for clinical use and, at the same time, to suggest the minimum standards that they should satisfy to be routinely used in clinical practice. A systematic search of Medline, Ebsco, Scopus, Scindeks, Cochrane Library and Google Scholar was performed to find publications about available pharmacokinetic calculators for drug dose individualization. Theoretically well-founded and mathematically correct calculators for many drugs are available, but only a few calculators for specific drugs have been validated in clinical practice or through clinical trials, and the results published in peer-reviewed journals. The majority of available pharmacokinetic calculators for drug dosing individualization remain unvalidated, i.e., there is no evidence of their efficacy and safety in real-life clinical settings. Pharmacokinetic calculators for drug dose individualization are irreplaceable tools for achieving precision medicine, where dosing regimens are tailored to the needs and personal characteristics of each patient, maximizing efficacy and minimizing toxicity.

Changing the Clinical Paradigm of Hydroxyurea Treatment for Sickle Cell Anemia Through Precision Medicine

Sickle cell anemia (SCA) is a common and devastating inherited blood disorder, affecting millions of people across the world. Without treatment, SCA results in tremendous morbidity and early mortality. Hydroxyurea is the primary and most well-established pharmacologic therapy with proven benefits to ameliorate the clinical course of SCA, primarily due to its ability to increase the expression of fetal hemoglobin (HbF), which prevents sickling of red blood cells. The optimal induction of HbF depends upon selection and maintenance of the proper dose that maximizes benefits and minimizes toxicity. Due to the significant interpatient variability in hydroxyurea pharmacokinetics, pharmacodynamics, and dosing, most patients treated with hydroxyurea receive suboptimal doses and have only modest treatment responses. Recognizing this variability, using a precision medicine approach, we developed and prospectively evaluated an individualized dosing model for children with SCA, designed to optimize the hydroxyurea dose and clinical response. We utilize novel laboratory methods and a sparse sampling strategy requiring only 10 μL of blood collected 15 minutes, 60 minutes, and 180 minutes after a test dose. We use Bayesian adaptive control to estimate hydroxyurea exposure and to select an individual, optimal starting dose. This dosing model has resulted in HbF responses >30-40%, levels beyond what is achieved with traditional weight-based dosing and trial and error dose escalation. This hydroxyurea dosing strategy, if widely implemented, has the potential to change the treatment paradigm of hydroxyurea therapy and improve outcomes for the millions of patients with SCA across the world.

Some Suggestions from PK/PD Principles to Contain Resistance in the Clinical Setting-Focus on ICU Patients and Gram-Negative Strains

The containment of the phenomenon of resistance towards antimicrobials is a priority, especially in preserving molecules acting against Gram-negative pathogens, which represent the isolates more frequently found in the fragile population of patients admitted to Intensive Care Units. Antimicrobial therapy aims to prevent resistance through several actions, which are collectively known as “antimicrobial stewardship”, to be taken together, including the application of pharmacokinetic/pharmacodynamic (PK/PD) principles. PK/PD application has been shown to prevent the emergence of resistance in numerous experimental studies, although a straight translation to the clinical setting is not possible. Individualized antibiotic dosing and duration should be pursued in all patients, and even more especially when treating intensive care unit (ICU) septic patients in whom optimal exposure is both difficult to achieve and necessary. In this review, we report on the available data that support the application of PK/PD parameters to contain the development of resistance and we give some practical suggestions that can help to translate the benefit of PK/PD application to the bedside.

Caspofungin Weight-Based Dosing Supported by a Population Pharmacokinetic Model in Critically Ill Patients

The objective of this study was to develop a population pharmacokinetic model and to determine a dosing regimen for caspofungin in critically ill patients. Nine blood samples were drawn per dosing occasion. Fifteen patients with (suspected) invasive candidiasis had one dosing occasion and five had two dosing occasions, measured on day 3 (±1) of treatment. Pmetrics was used for population pharmacokinetic modeling and probability of target attainment (PTA). A target 24-h area under the concentration-time curve (AUC) value of 98 mg·h/liter was used as an efficacy parameter.

The objective of this study was to develop a population pharmacokinetic model and to determine a dosing regimen for caspofungin in critically ill patients. Nine blood samples were drawn per dosing occasion. Fifteen patients with (suspected) invasive candidiasis had one dosing occasion and five had two dosing occasions, measured on day 3 (±1) of treatment. Pmetrics was used for population pharmacokinetic modeling and probability of target attainment (PTA). A target 24-h area under the concentration-time curve (AUC) value of 98 mg·h/liter was used as an efficacy parameter. Secondarily, the AUC/MIC targets of 450, 865, and 1,185 were used to calculate PTAs for Candida glabrata, C. albicans, and C. parapsilosis, respectively. The final 2-compartment model included weight as a covariate on volume of distribution (V). The mean V of the central compartment was 7.71 (standard deviation [SD], 2.70) liters/kg of body weight, the mean elimination constant (K_e) was 0.09 (SD, 0.04) h⁻¹, the rate constant for the caspofungin distribution from the central to the peripheral compartment was 0.44 (SD, 0.39) h⁻¹, and the rate constant for the caspofungin distribution from the peripheral to the central compartment was 0.46 (SD, 0.35) h⁻¹. A loading dose of 2 mg/kg on the first day, followed by 1.25 mg/kg as a maintenance dose, was chosen. With this dose, 98% of the patients were expected to reach the AUC target on the first day and 100% of the patients on the third day. The registered caspofungin dose might not be suitable for critically ill patients who were all overweight (≥120 kg), over 80% of median weight (78 kg), and around 25% of lower weight (≤50 kg). A weight-based dose regimen might be appropriate for achieving adequate exposure of caspofungin in intensive care unit patients.

Clinical implementation of pharmacogenetics and model-informed precision dosing to improve patient care

Providing maximal therapeutic efficacy without toxicity is a universal goal of rational drug therapy. However, substantial between-patient variability in drug response often impedes such successful treatments and brings the necessity of tailoring drug dose to individual needs for more precise therapy. In many cases plenty of patient characteristics, such as body size, genetic makeup and environmental factors, need to be taken into consideration to find the optimal dose in clinical practice. A pharmacokinetics and pharmacodynamics (PK/PD) model-informed approach offers integration of various patient information to provide an expectation of drug response and derive practical dose estimates to support clinicians' dosing decisions. Such an approach was pioneered in the late 1970s, but its broad clinical acceptance and implementation have been hampered by the lack of widespread computer technology, including user-friendly software tools. This has significantly changed in recent years. With the advent of electronic health records (EHRs) and the ubiquity of user-friendly software tools, we now experience a convergence of clinical information, pharmacogenetics, systems pharmacology and pharmacometrics, and technology. Advanced pharmacometrics research is now more appliable and implementable to improve health care. This article presents examples of successful development and implementation of pharmacogenetics-guided and PK/PD model-informed decision support to facilitate precision dosing, including the development of an EHR-embedded decision support tool. Through the integration of clinical decision support tools in EHRs, clinical pharmacometrics support can be brought directly to the clinical team and the bedside.

Right Dose, Right Now: Development of AutoKinetics for Real Time Model Informed Precision Antibiotic Dosing Decision Support at the Bedside of Critically Ill Patients

Introduction

Antibiotic dosing in critically ill patients is challenging because their pharmacokinetics (PK) are altered and may change rapidly with disease progression. Standard dosing frequently leads to inadequate PK exposure. Therapeutic drug monitoring (TDM) offers a potential solution but requires sampling and PK knowledge, which delays decision support. It is our philosophy that antibiotic dosing support should be directly available at the bedside through deep integration into the electronic health record (EHR) system. Therefore we developed AutoKinetics, a clinical decision support system (CDSS) for real time, model informed precision antibiotic dosing.

Objective

To provide a detailed description of the design, development, validation, testing, and implementation of AutoKinetics.

Methods

We created a development framework and used workflow analysis to facilitate integration into popular EHR systems. We used a development cycle to iteratively adjust and expand AutoKinetics functionalities. Furthermore, we performed a literature review to select and integrate pharmacokinetic models for five frequently prescribed antibiotics for sepsis. Finally, we tackled regulatory challenges, in particular those related to the Medical Device Regulation under the European regulatory framework.

Results

We developed a SQL-based relational database as the backend of AutoKinetics. We developed a data loader to retrieve data in real time. We designed a clinical dosing algorithm to find a dose regimen to maintain antibiotic pharmacokinetic exposure within clinically relevant safety constraints. If needed, a loading dose is calculated to minimize the time until steady state is achieved. Finally, adaptive dosing using Bayesian estimation is applied if plasma levels are available. We implemented support for five extensively used antibiotics following model development, calibration, and validation. We integrated AutoKinetics into two popular EHRs (Metavision, Epic) and developed a user interface that provides textual and visual feedback to the physician.

Conclusion

We successfully developed a CDSS for real time model informed precision antibiotic dosing at the bedside of the critically ill. This holds great promise for improving sepsis outcome. Therefore, we recently started the Right Dose Right Now multi-center randomized control trial to validate this concept in 420 patients with severe sepsis and septic shock.

Software Tools for Model-Informed Precision Dosing: How Well Do They Satisfy the Needs?

Model-informed precision dosing (MIPD) software tools are used to optimize dosage regimens in individual patients, aiming to achieve drug exposure targets associated with desirable clinical outcomes. Over the last few decades, numerous MIPD software tools have been developed. However, they have still not been widely integrated into clinical practice. This study focuses on identifying the requirements for and evaluating the performance of the currently available MIPD software tools. First, a total of 22 experts in the field of precision dosing completed a web survey to assess the importance (from 0; do not agree at all, to 10; completely agree) of 103 pre-established software tool criteria organized in eight categories: user-friendliness and utilization, user support, computational aspects, population models, quality and validation, output generation, privacy and data security, and cost. Category mean ± pooled standard deviation importance scores ranged from 7.2 ± 2.1 (user-friendliness and utilization) to 8.5 ± 1.8 (privacy and data security). The relative importance score of each criterion within a category was used as a weighting factor in the subsequent evaluation of the software tools. Ten software tools were identified through literature and internet searches: four software tools were provided by companies (DoseMeRx, InsightRX Nova, MwPharm++, and PrecisePK) and six were provided by non-company owners (AutoKinetics, BestDose, ID-ODS, NextDose, TDMx, and Tucuxi). All software tools performed well in all categories, although there were differences in terms of in-built software features, user interface design, the number of drug modules and populations, user support, quality control, and cost. Therefore, the choice for a certain software tool should be made based on these differences and personal preferences. However, there are still improvements to be made in terms of electronic health record integration, standardization of software and model validation strategies, and prospective evidence for the software tools’ clinical and cost benefits.

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.

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.