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

Mobile Health Apps for Improvement of Tuberculosis Treatment: Descriptive Review

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Design and Usability of an Electronic Health Record-Integrated, Point-of-Care, Clinical Decision Support Tool for Modeling and Simulation of Antihemophilic Factors

BACKGROUND

With the consequences of inadequate dosing ranging from increased bleeding risk to excessive drug costs and undesirable administration regimens, the antihemophilic factors are uniquely suited to dose individualization. However, existing options for individualization are limited and exist outside the flow of care. We developed clinical decision support (CDS) software that is integrated with our electronic health record (EHR) and designed to streamline the process for our hematology providers.

OBJECTIVES

The aim of this study is to develop and examine the usability of a CDS tool for antihemophilic factor dose individualization.

METHODS

Our development strategy was based on the features associated with successful CDS tools and driven by a formal requirements analysis. The back-end code was based on algorithms developed for manual individualization and unit tested with 23,000 simulated patient profiles created from the range of patient-derived pharmacokinetic parameter estimates defined in children and adults. A 296-item heuristic checklist was used to guide design of the front-end user interface. Content experts and end-users were recruited to participate in traditional usability testing under an institutional review board approved protocol.

RESULTS

CDS software was developed to systematically walk the point-of-care clinician through dose individualization after seamlessly importing the requisite patient data from the EHR. Classical and population pharmacokinetic approaches were incorporated with clearly displayed estimates of reliability and uncertainty. Users can perform simulations for prophylaxis and acute bleeds by providing two of four therapeutic targets. Testers were highly satisfied with our CDS and quickly became proficient with the tool.

CONCLUSION

With early and broad stakeholder engagement, we developed a CDS tool for hematology provider that affords seamless transition from patient assessment, to pharmacokinetic modeling and simulation, and subsequent dose selection.

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

BACKGROUND

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

OBJECTIVES

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

SOURCES

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

CONTENT

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

IMPLICATIONS

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

Identifying Safety Hazards Associated With Intravenous Vancomycin Through the Analysis of Patient Safety Event Reports

Intravenous (IV) vancomycin is one of the most commonly used antibiotics in U.S. hospitals. There are several complexities associated with IV vancomycin use, including the need to have an accurate patient weight for dosing, to provide close monitoring to ensure appropriate drug levels, to monitor renal function, and to continue delivery of the medication at prescribed intervals. There are numerous healthcare system factors, including workflow processes, policies, health information technology, and clinical knowledge that impact the safe use of IV vancomycin. Past literature has identified several safety hazards associated with IV vancomycin use and there are some proposed solutions. Despite this literature, IV vancomycin–related safety issues persist. We analyzed patient safety event reports describing IV vancomycin–related issues in order to identify where in the medication process these issues were appearing, the type of medication error associated with each report, and general contributing factor themes. Our results demonstrate that recent safety reports are aligned with the issues already identified in the literature, suggesting that improvements discussed in the literature have not translated to clinical practice. Based on our analysis and current literature, we have developed a shareable infographic to improve clinician awareness of the complications and safety hazards associated with IV vancomycin and a self-assessment tool to support identification of opportunities to improve patient safety during IV vancomycin therapy. We also recommend development of clear guidelines to optimize health information technology systems to better support safe IV vancomycin use.

The Steps to Therapeutic Drug Monitoring: A Structured Approach Illustrated With Imatinib

Pharmacometric methods have hugely benefited from progress in analytical and computer sciences during the past decades, and play nowadays a central role in the clinical development of new medicinal drugs. It is time that these methods translate into patient care through therapeutic drug monitoring (TDM), due to become a mainstay of precision medicine no less than genomic approaches to control variability in drug response and improve the efficacy and safety of treatments. In this review, we make the case for structuring TDM development along five generic questions: 1) Is the concerned drug a candidate to TDM? 2) What is the normal range for the drug's concentration? 3) What is the therapeutic target for the drug's concentration? 4) How to adjust the dosage of the drug to drive concentrations close to target? 5) Does evidence support the usefulness of TDM for this drug? We exemplify this approach through an overview of our development of the TDM of imatinib, the very first targeted anticancer agent. We express our position that a similar story shall apply to other drugs in this class, as well as to a wide range of treatments critical for the control of various life-threatening conditions. Despite hurdles that still jeopardize progress in TDM, there is no doubt that upcoming technological advances will shape and foster many innovative therapeutic monitoring methods.

External Validation of Model-Based Dosing Guidelines for Vancomycin, Gentamicin, and Tobramycin in Critically Ill Neonates and Children: A Pragmatic Two-Center Study

BACKGROUND

The Dutch Pediatric Formulary (DPF) increasingly bases its guidelines on model-based dosing simulations from pharmacokinetic studies. This resulted in nationwide dose changes for vancomycin, gentamicin, and tobramycin in 2015.

OBJECTIVE

We aimed to evaluate target attainment of these altered, model-based doses in critically ill neonates and children.

METHODS

This was a retrospective cohort study in neonatal intensive care unit (NICU) and pediatric ICU (PICU) patients receiving vancomycin, gentamicin, or tobramycin between January 2015 and March 2017 in two university hospitals. The first therapeutic drug monitoring concentration for each patient was collected, as was clinical and dosing information. Vancomycin and tobramycin target trough concentrations were 10-15 and ≤ 1 mg/L, respectively. Target gentamicin trough and peak concentrations were < 1 and 8-12 mg/L, respectively.

RESULTS

In total, 482 patients were included (vancomycin [PICU] n = 62, [NICU] n = 102; gentamicin [NICU] n = 97; tobramycin [NICU] n = 221). Overall, median trough concentrations were within the target range for all cohorts but showed large interindividual variability, causing nontarget attainment. Trough concentrations were outside the target range in 66.1%, 60.8%, 14.7%, and 23.1% of patients in these four cohorts, respectively. Gentamicin peak concentrations were outside the range in 69% of NICU patients (term neonates 87.1%, preterm infants 57.1%). Higher creatinine concentrations were associated with higher vancomycin and tobramycin trough concentrations.

CONCLUSION

This study illustrates the need to validate model-based dosing advice in the real-world setting as both sub- and supratherapeutic concentrations of vancomycin, gentamicin, and tobramycin were very prevalent. Our data underline the necessity for further individualization by addressing the high interindividual variability to improve target attainment.

The Benefit of Individualized Vancomycin Dosing Via Pharmacokinetic Tools: A Systematic Review and Meta-analysis

Background: Various pharmacokinetic (PK) equations and software have been developed to individualize vancomycin dosing. However, the benefit of using any PK information to guide vancomycin dosing has not been fully elucidated. Objective: To appraise available evidence on the effectiveness and safety of individualized vancomycin dosing via PK tools. Methods: PubMed, EMBASE, the Cochrane Library, and 2 Chinese literature databases were searched through August 1, 2019. Randomized controlled trials (RCTs) and cohort studies that reported the PK and clinical outcomes of individualized vancomycin dosing versus empirical dosing were included. Pooled risk ratios (RRs) and mean differences were calculated for dichotomous and continuous outcomes, respectively. Results: A total of 21 studies involving 4346 patients were finally included, of which 3 were RCTs and 18 were cohort studies. Meta-analysis revealed that PK-guided vancomycin dosing significantly increased the attainment of target trough concentration (RR = 1.59; 95% CI = 1.49-1.70) and decreased the incidence of nephrotoxicity (RR = 0.57; 95% CI = 0.46-0.71). Additionally, the available evidence showed that target area under the curve/minimum inhibitory concentration attainment rate and time to target concentration could improve. However, the evidence on clinical outcomes was scarce, and no significant differences were detected in clinical response rate, microbiological eradication rate, mortality, and length of hospital stay between PK-guided vancomycin dosing and empirical dosing strategies. Conclusion and Relevance: Individualized vancomycin dosing via PK tools significantly increases the attainment of target trough concentration and decreases the incidence of nephrotoxicity. Evidence on clinical effectiveness was limited and showed no significant benefit. Further well-designed studies are warranted to assess its clinical effectiveness and inform routine care.

Optimising antimicrobial therapy through the use of Bayesian dosing programs

The optimisation of antibiotic dosing therapy with therapeutic drug monitoring is widely recommended. The aim of therapeutic drug monitoring is to help the clinician to achieve target pharmacokinetic/pharmacodynamic parameters, maximising efficacy and minimising toxicity. Computerised programs, utilising the Bayesian estimation procedures, are able to achieve target concentrations in a greater percentage of patients earlier in the course of therapy compared to linear regression analysis and population methods. This article summarises various methods for dose optimisation of antibiotics with a focus on Bayesian programs.

An evaluation of the user-friendliness of Bayesian forecasting programs in a clinical setting

AIMS

To evaluate 3 Bayesian forecasting (BF) programs-TDMx, InsightRx and DoseMe-on their user-friendliness and common liked and disliked features through a survey of hospital pharmacists.

METHODS

Clinical pharmacists across 3 Australian hospitals that did not use a BF program were invited to a BF workshop and complete a survey on programs they trialled. Participants were given 4 case scenarios to work through and asked to complete a 5-point Likert scale survey evaluating the program's user-friendliness. Liked and disliked features of each program were ascertained through written responses to open-ended questions. Survey results were compared using a χ² test of equal or given proportions to identify significant differences in response.

RESULTS

Twenty-seven pharmacists, from hospitals, participated. BF programs were rated overall as user-friendly with 70%, 41% and 37% (P = .02) of participants recording a Likert score of 4 or 5 for DoseMe, TDMx and InsightRx, respectively. Participants found it easy to access all required information to use the programs, understood dosing recommendations and visualisations given by each program, and thought programs supported decision-making with >50% of participants scoring a 4 or 5 across the programs in these categories. Common liked features across all programs were the graphical displays and ease of data entry, while common disliked features were related to the units, layout and information display.

CONCLUSION

Although differences exist between programs, all 3 programs were most commonly rated as user-friendly across all themes evaluated, which provides useful information for healthcare facilities wanting to implement a BF program.

A nationwide utilization survey of therapeutic drug monitoring for five antibiotics in South Korea

Purpose

The current status of therapeutic drug monitoring (TDM) assay utilization by clinical laboratories in South Korea remains little known. We investigated the TDM status of five antibiotics known for nephrotoxicity (vancomycin, amikacin, gentamicin, tobramycin, and teicoplanin) for the improvement of TDM in South Korea among patients with infectious diseases using a cross-sectional nationwide survey.

Patients and methods

We developed an online questionnaire and collected responses using a user-friendly web-based platform. The survey included questions about laboratory characteristics, implementation and operation of drug assays, implementation and operation of TDM consulting services, patient needs, and barriers to providing better TDM service including expectations and concerns about other platform-based drug assays.

Results

Among a total of 235 clinical laboratories, 112 (47.7%) responded, and 62 of the responding laboratories (55.4%) possessed drug assay facilities. Only 41.2% to 58.1% of respondents were providing TDM consulting services for each antibiotic. Respondents indicated that there are unmet needs regarding drug assays and TDM consultation as well as barriers to TDM utilization including high operating costs, lack of knowledge about TDM, lack of user-friendly software, lack of medical and laboratory information systems that can access patient information critical for TDM dose calculation, and reimbursement issues.

Conclusion

This study, the first nationwide survey addressing these questions, showed that there are barriers against the utilization of TDM in South Korea. These barriers may be addressed by improving drug assays and TDM consulting services with the goals of new analytical method development, better interpretation of results, consultation services, and quality control.

Individualized dosing of oral targeted therapies in oncology is crucial in the era of precision medicine

PURPOSE

While in the era of precision medicine, the right drug for each patient is selected based on molecular tumor characteristics, most novel oral targeted anticancer agents are still being administered using a one-size-fits-all fixed dosing approach. In this review, we discuss the scientific evidence for dose individualization of oral targeted therapies in oncology, based on therapeutic drug monitoring (TDM).

METHODS

Based on literature search and our own experiences, seven criteria for drugs to be suitable candidates for TDM will be addressed: (1) absence of an easily measurable biomarker for drug effect; (2) long-term therapy; (3) availability of a validated sensitive bioanalytical method; (4) significant variability in pharmacokinetic exposure; (5) narrow therapeutic range; (6) defined and consistent exposure-response relationships; (7) feasible dose-adaptation strategies.

RESULTS

All of these requirements are met for most oral targeted therapies in oncology. Also, prospective studies have already shown TDM to be feasible for imatinib, pazopanib, sunitinib, everolimus, and endoxifen.

CONCLUSIONS

In order to realize the full potential of personalized medicine in oncology, patients should not only be treated with the right drug, but also at the right dose. TDM could be a suitable tool to achieve this.

Handling interoccasion variability in model-based dose individualization using therapeutic drug monitoring data

AIMS

This study aims to assess approaches to handle interoccasion variability (IOV) in a model-based therapeutic drug monitoring (TDM) context, using a population pharmacokinetic model of coagulation factor VIII as example.

METHODS

We assessed 5 model-based TDM approaches: empirical Bayes estimates (EBEs) from a model including IOV, with individualized doses calculated based on individual parameters either (i) including or (ii) excluding variability related to IOV; and EBEs from a model excluding IOV by (iii) setting IOV to zero, (iv) summing variances of interindividual variability (IIV) and IOV into a single IIV term, or (v) re-estimating the model without IOV. The impact of varying IOV magnitudes (0-50%) and number of occasions/observations was explored. The approaches were compared with conventional weight-based dosing. Predictive performance was assessed with the prediction error percentiles.

RESULTS

When IOV was lower than IIV, the accuracy was good for all approaches (50^th percentile of the prediction error [P50] <7.4%), but the precision varied substantially between IOV magnitudes (P97.5 61-528%). Approach (ii) was the most precise forecasting method across a wide range of scenarios, particularly in case of sparse sampling or high magnitudes of IOV. Weight-based dosing led to less precise predictions than the model-based TDM approaches in most scenarios.

CONCLUSIONS

Based on the studied scenarios and theoretical expectations, the best approach to handle IOV in model-based dose individualization is to include IOV in the generation of the EBEs but exclude the portion of unexplained variability related to IOV in the individual parameters used to calculate the future dose.

Therapeutic Drug Monitoring of Beta-Lactams and Other Antibiotics in the Intensive Care Unit: Which Agents, Which Patients and Which Infections?

Antibiotics are among the medications most frequently administered to the critically ill, a population with high levels of intra- and inter-individual pharmacokinetic variability. Our knowledge of the relationships among antibiotic dosing, exposure and clinical effect in this population has increased in recent decades. Therapeutic drug monitoring (TDM) of serum antibiotic concentrations is the most practical means of assessing adequate antibiotic exposure, though until recently, it has been underutilised for this end. Now TDM is becoming more widespread, particularly for the beta-lactam antibiotics, a class historically thought to have a wide therapeutic range. We review the basic requirements, indications, and targets for effective TDM of the glycopeptides, aminoglycosides, quinolones and beta-lactam antibiotics in the adult intensive-care setting, with a special focus on TDM of the beta-lactam antibiotics, the most widely used antibiotic class.

The dosing and monitoring of vancomycin: what is the best way forward?

We have evaluated the literature to review optimal dosing and monitoring of intravenous vancomycin in adults, in response to evolving understanding of targets associated with efficacy and toxicity. The area under the total concentration-time curve (0-24 h) divided by the minimum inhibitory concentration (AUC₂₄/MIC) is the most commonly accepted index to guide vancomycin dosing for the treatment of Staphylococcus aureus infections, with a value of 400 h a widely recommended target for efficacy. Upper limits of AUC₂₄ exposure of around 700 (mg/L).h have been proposed, based on the hypothesis that higher exposures of vancomycin are associated with an unacceptable risk of nephrotoxicity. If AUC₂₄/MIC targets are used, sources of variability in the assessment of both AUC₂₄ and MIC need to be considered. Current consensus guidelines recommend measuring trough vancomycin concentrations during intermittent dosing as a surrogate for the AUC₂₄. Trough concentrations are a misleading surrogate for AUC₂₄ and a poor end-point in themselves. AUC₂₄ estimation using log-linear pharmacokinetic methods based on two plasma concentrations, or Bayesian methods are superior. Alternatively, a single concentration measured during continuous infusion allows simple AUC₂₄ estimation and dose-adjustment. All of these methods have logistical challenges which must be overcome if they are to be adopted successfully.

Suggestions for Model-Informed Precision Dosing to Optimize Neonatal Drug Therapy

Evidence for dosing, efficacy, and safety of most medications used to treat neonates is sparse. Thus, dosing is usually derived by extrapolation from adult and pediatric pharmacologic data with scaling by body weight or body surface area. This may lead to drug dosing that is unsafe or ineffective. However, new strategies are being developed and studied to dose medications in critically ill neonates. Mass spectroscopy technology capable of quickly analyzing drug levels is readily available. Software that integrates population pharmacokinetics and pharmacodynamics with data from sparse samples from neonates allows for timely adjustments of dosing to achieve the desired effect while minimizing adverse outcomes. Some genetic polymorphisms that affect drug response in neonates have also been reported. This review highlights aspects of drug response and how it is impacted by prematurity, assesses pharmacogenomic studies in neonates, and offers suggestions for innovative pharmacokinetic/pharmacodynamic model-based approaches that combine population- or physiology-based pharmacology data, Bayesian analysis, and electronic decision support tools for precision dosing in neonates while illustrating examples where this approach can be used to optimize medical therapy in neonates. Barriers to implementing precision dosing in neonates and how to overcome them are also discussed.

Clinical Pharmacokinetics in Kidney Disease: Application to Rational Design of Dosing Regimens

A change in pharmacokinetics can alter drug exposure and predispose the patient to either over- or underdosing, potentially resulting in adverse drug reactions or therapeutic failure. Kidney disease is characterized by multiple physiologic effects, which induce clinically significant changes in pharmacokinetics. These vary between individuals and may be quantitated in certain instances. An understanding of pharmacokinetic concepts is, therefore, important for a rational approach to the design of drug dosing regimens for the delivery of personalized medical care. Whether kidney disease is acute or chronic, drug clearance decreases and the volume of distribution may remain unchanged or increase. AKI is defined by dynamic changes in kidney function, which complicates attempts to accurately quantify drug clearance. In contrast, changes in drug clearance progress more slowly with CKD. In general, kidney replacement therapies increase drug clearance, but the extent to which this occurs depends on the modality used and its duration, the drug's properties, and the timing of drug administration. However, the changes in drug handling associated with kidney disease are not isolated to reduced kidney clearance and an appreciation of the scale of potential derangements is important. In most instances, the first dose administered in patients with kidney disease is the same as in patients with normal kidney function. However, in some cases, a higher (loading) initial dose is given to rapidly achieve therapeutic concentrations, followed by a lower maintenance dose, as is well described when prescribing anti-infectives to patients with sepsis and AKI. This review provides an overview of how pharmacokinetic principles can be applied to patients with kidney disease to personalize dosage regimens. Patients with kidney disease are a vulnerable population and the increasing prevalence of kidney disease means that these considerations are important for all prescribers.

Clinical applications of population pharmacokinetic models of antibiotics: Challenges and perspectives

Because of increasing antimicrobial resistance and the shortage of new antibiotics, there is a growing need to optimize the use of old and new antibiotics. Modelling of the pharmacokinetic/pharmacodynamic (PK/PD) characteristics of antibiotics can support the optimization of dosing regimens. Antimicrobial efficacy is determined by susceptibility of the drug to the microorganism and exposure to the drug, which relies on the PK and the dose. Population PK models describe relationships between patients characteristics and drug exposure. This article highlights three clinical applications of these models applied to antibiotics: 1) dosing evaluation of old antibiotics, 2) setting clinical breakpoints and 3) dosing individualization using therapeutic drug monitoring (TDM). For each clinical application, challenges regarding interpretation are discussed. An important challenge is to improve the understanding of the interpretation of modelling results for good implementation of the dosing recommendations, clinical breakpoints and TDM advices. Therefore, also background information on PK/PD principles and approaches to analyse PK/PD data are provided.

Population Pharmacokinetics of Vancomycin in Chinese ICU Neonates: Initial Dosage Recommendations

The main goal of our study was to characterize the population pharmacokinetics of vancomycin in critically ill Chinese neonates to develop a pharmacokinetic model and investigate factors that have significant influences on the pharmacokinetics of vancomycin in this population. The study population consisted of 80 neonates in the neonatal intensive care unit (ICU) from which 165 trough and peak concentrations of vancomycin were obtained. Nonlinear mixed effect modeling was used to develop a population pharmacokinetic model for vancomycin. The stability and predictive ability of the final model were evaluated based on diagnostic plots, normalized prediction distribution errors and the bootstrap method. Serum creatinine (Scr) and body weight were significant covariates on the clearance of vancomycin. The average clearance was 0.309 L/h for a neonate with Scr of 23.3 μmol/L and body weight of 2.9 kg. No obvious ethnic differences in the clearance of vancomycin were found relative to the earlier studies of Caucasian neonates. Moreover, the established model indicated that in patients with a greater renal clearance status, especially Scr < 15 μmol/L, current guideline recommendations would likely not achieve therapeutic area under the concentration-time curve over 24 h/minimum inhibitory concentration (AUC24h/MIC) ≥ 400. The exceptions to this are British National Formulary (2016-2017), Blue Book (2016) and Neofax (2017). Recommended dose regimens for neonates with different Scr levels and postmenstrual ages were estimated based on Monte Carlo simulations and the established model. These findings will be valuable for developing individualized dosage regimens in the neonatal ICU setting.

Individualised medicine: why we need Bayesian dosing

Individualised drug dosing has been shown to improve patient outcomes and reduce adverse drug events. One method of individualised medicine is the Bayesian approach, which uses prior information about how the population responds to therapy, to inform clinicians about how a specific individual is responding to their current therapy. This information is then used to make changes to the dose. Studies using a Bayesian approach to adjust drug dosing have shown that clinicians are able to achieve a therapeutic range quicker than standard practice. If concentration is related to a pharmacodynamic end-point, this means that the drug will be more effective, and the side-effects will be minimised. Unfortunately, the software options to assist with Bayesian dosing in Australia are limited. The aims of this article are to demystify the concepts of Bayesian dosing, set the context of the Bayesian approach using reference to other dosing strategies and discuss its benefits over current dosing methods for a number of drugs. The article is targeted to medical and pharmacy clinicians, and there is a practical clinical case to demonstrate how this method could be used in everyday clinical practice.

Therapeutic drug monitoring of antibiotic agents: evaluation of predictive performance

Background

The precision of the population pharmacokinetic model used in therapeutic drug monitoring (TDM) is essential for successful dosage optimisation.

Objective

To evaluate the predictive performance of pharmacokinetic models used in our hospital and to evaluate the possible impact of demographic characteristics or renal function on TDM accuracy.

Methods

We compared a posteriori an adjusted concentration-time curve profile based on the first measured drug concentration with the second measured drug concentration. Linear regression models were used to compare predicted and observed drug serum concentrations, and to evaluate potential relationships between predictive performance and patients´ demographic/clinical features. Predictive performance of TDM was expressed using accuracy, precision, sensitivity and specificity.

Results

One hundred and fifty-two patients were enrolled in the study. All pharmacokinetic models showed good predictive performance expressed by the coefficient of determination (r²) of 0.5642, 0.7263, 0.9001 and 0.9454 for continuous vancomycin, intermittent vancomycin, amikacin and gentamicin, respectively. Accuracy was 93.3%, 91.2%, 113.9% and 130.9% for continuous vancomycin, intermittent vancomycin, amikacin and gentamicin, respectively. Demographic characteristics or renal functions had no substantial impact on the accuracy of TDM.

Conclusion

We found the predictive performance of both aminoglycosides and vancomycin pharmacokinetic models to be satisfactory.

Opportunities for model-based precision dosing in the treatment of sickle cell anemia

Hydroxyurea is the primary pharmacotherapy to prevent complications of sickle cell anemia (SCA). Accumulated clinical experience across multiple age ranges has suggested that the use of an individualized maximum tolerated dose (MTD) will achieve optimal benefit of hydroxyurea treatment. However, the current empirical and trial-and-error approach for dose escalation often results in a lengthy titration process and is not strictly implemented in many clinics. Opportunities exist for pharmacokinetics model-based precision dosing of hydroxyurea to quickly achieve individual MTD. This review intends to introduce the use of a quantitative modeling approach including a Bayesian adaptive control strategy for the precision dosing of hydroxyurea. The rationale and practical considerations for the implementation of this approach are discussed. Future research directions with a focus on integrating specific safety and other clinical outcome endpoints into dose selection decision making are also discussed.

Continuous and Prolonged Intravenous β-Lactam Dosing: Implications for the Clinical Laboratory

Beta-lactam antibiotics serve as a cornerstone in the management of bacterial infections because of their wide spectrum of activity and low toxicity. Since resistance rates among bacteria are continuously on the rise and the pipeline for new antibiotics does not meet this trend, an optimization of current beta-lactam treatment is needed. This review provides an overview of optimization through use of prolonged- and continuous-infusion dosing strategies compared with more traditional intermittent infusions. Included is an overview of the scientific basis for using these nontraditional prolonged- and continuous-infusion-based regimens, with a focus on major areas in which the clinical laboratory can support the clinical use of these regimens.

Personalised dosing of medicines for children

Objectives

Doses for most drugs are determined from population-level information, resulting in a standard ?one-size-fits-all’ dose range for all individuals. This review explores how doses can be personalised through the use of the individuals’ pharmacokinetic (PK)-pharmacodynamic (PD) profile, its particular application in children, and therapy areas where such approaches have made inroads.

Key findings

The Bayesian forecasting approach, based on population PK/PD models that account for variability in exposure and response, is a potent method for personalising drug therapy. Its potential utility is even greater in young children where additional sources of variability are observed such as maturation of eliminating enzymes and organs. The benefits of personalised dosing are most easily demonstrated for drugs with narrow therapeutic ranges such as antibiotics and cytotoxics and limited studies have shown improved outcomes. However, for a variety of reasons the approach has struggled to make more widespread impact at the bedside: complex dosing algorithms, high level of technical skills required, lack of randomised controlled clinical trials and the need for regulatory approval.

Summary

Personalised dosing will be a necessary corollary of the new precision medicine initiative. However, it faces a number of challenges that need to be overcome before such an approach to dosing in children becomes the norm.

Pharmacometrics-based decision tools facilitate mHealth implementation

INTRODUCTION

The healthcare system is experiencing a paradigm shift in delivering its services, evolving from a reactive 'one size-fits-all' structure to a patient-centric model focusing on individualized medicine. This change is driven by scientific progress, including quantitative evaluation and optimization of treatment strategies through pharmacometric approaches, harnessing the power of the digital revolution. Areas covered: This review describes four main steps to apply pharmacometrics-based decision support tools, consisting of validated scientific components, available technical options, consideration of regulatory aspects, and achievement of efficient commercialization. Examples of pharmacometrics-based decision tools that support monitoring of patients and individualization of treatment strategies in neonates, children and adults are presented. Expert commentary: We envision that user-friendly decision support tools will facilitate implementation of mobile health approaches (mHealth) realizing benefits to paediatric and adult patients and their caregivers.

Understanding and applying pharmacometric modelling and simulation in clinical practice and research

Understanding the dose–concentration–effect relationship is a fundamental component of clinical pharmacology. Interpreting data arising from observations of this relationship requires the use of mathematical models; i.e. pharmacokinetic (PK) models to describe the relationship between dose and concentration and pharmacodynamic (PD) models describing the relationship between concentration and effect. Drug development requires several iterations of pharmacometric model‐informed learning and confirming. This includes modelling to understand the dose–response in preclinical studies, deriving a safe dose for first‐in‐man, and the overall analysis of Phase I/II data to optimise the dose for safety and efficacy in Phase III pivotal trials. However, drug development is not the boundary at which PKPD understanding and application stops. PKPD concepts will be useful to anyone involved in the prescribing and administration of medicines for purposes such as determining off‐label dosing in special populations, individualising dosing based on a measured biomarker (personalised medicine) and in determining whether lack of efficacy or unexpected toxicity maybe solved by adjusting the dose rather than the drug. In clinical investigator‐led study design, PKPD can be used to ensure the optimal dose is used, and crucially to define the expected effect size, thereby ensuring power calculations are based on sound prior information. In the clinical setting the most likely people to hold sufficient expertise to advise on PKPD matters will be the pharmacists and clinical pharmacologists. This paper reviews fundamental PKPD principles and provides some real‐world examples of PKPD use in clinical practice and applied clinical research.

Monoclonal antibodies for treating gastric cancer: promises and pitfalls

INTRODUCTION

Gastric cancer (GC) presents dismal prognosis when diagnosed at advanced stages, standard chemotherapy having shown little efficacy. Introduction of biotherapies interfering with novel targets and signaling pathways is currently an emerging strategy.

AREAS COVERED

Only two monoclonal antibodies (trastuzumab and ramucirumab) have been approved, mostly in association with cytotoxics. Conversely, testing other promising biotherapies (panitumumab, cetuximab, bevacizumab, rilotumumab) have yielded conflicting results, since encouraging early clinical trials have failed to be confirmed in larger phase-III studies. Empirical and underpowered strategies when designing combinational studies, lack of comprehensive knowledge of pharmacokinetics/pharmacodynamics (PK/PD) relationships, and underestimation of the large inter-patient variability in drug exposure levels with monoclonal antibodies, could explain the failures in developing biotherapies in gastric cancer. This review covers the achievements and limits of monoclonal antibodies in gastric cancer and proposes clues to overcome current failures.

EXPERT OPINION

Trastuzumab efficacy could be improved thanks to its combination with triplet chemotherapy or with another anti-HER2 agents or in continuation during second-line chemotherapy. Concerning ramucirumab, further studies are necessary to prove its interest in first line treatment of advanced GC, to use the optimal dose in each patient-given the large inter-patients variability, and to find predictive biomarkers of efficacy.

Pharmacometric Approaches to Personalize Use of Primarily Renally Eliminated Antibiotics in Preterm and Term Neonates

Sepsis remains a major cause of mortality and morbidity in neonates, and, as a consequence, antibiotics are the most frequently prescribed drugs in this vulnerable patient population. Growth and dynamic maturation processes during the first weeks of life result in large inter- and intrasubject variability in the pharmacokinetics (PK) and pharmacodynamics (PD) of antibiotics. In this review we (1) summarize the available population PK data and models for primarily renally eliminated antibiotics, (2) discuss quantitative approaches to account for effects of growth and maturation processes on drug exposure and response, (3) evaluate current dose recommendations, and (4) identify opportunities to further optimize and personalize dosing strategies of these antibiotics in preterm and term neonates. Although population PK models have been developed for several of these drugs, exposure-response relationships of primarily renally eliminated antibiotics in these fragile infants are not well understood, monitoring strategies remain inconsistent, and consensus on optimal, personalized dosing of these drugs in these patients is absent. Tailored PK/PD studies and models are useful to better understand relationships between drug exposures and microbiological or clinical outcomes. Pharmacometric modeling and simulation approaches facilitate quantitative evaluation and optimization of treatment strategies. National and international collaborations and platforms are essential to standardize and harmonize not only studies and models but also monitoring and dosing strategies. Simple bedside decision tools assist clinical pharmacologists and neonatologists in their efforts to fine-tune and personalize the use of primarily renally eliminated antibiotics in term and preterm neonates.

Design and Testing of an EHR-Integrated, Busulfan Pharmacokinetic Decision Support Tool for the Point-of-Care Clinician

Background: Busulfan demonstrates a narrow therapeutic index for which clinicians routinely employ therapeutic drug monitoring (TDM). However, operationalizing TDM can be fraught with inefficiency. We developed and tested software encoding a clinical decision support tool (DST) that is embedded into our electronic health record (EHR) and designed to streamline the TDM process for our oncology partners.

Methods: Our development strategy was modeled based on the features associated with successful DSTs. An initial Requirements Analysis was performed to characterize tasks, information flow, user needs, and system requirements to enable push/pull from the EHR. Back-end development was coded based on the algorithm used when manually performing busulfan TDM. The code was independently validated in MATLAB using 10,000 simulated patient profiles. A 296-item heuristic checklist was used to guide design of the front-end user interface. Content experts and end-users (n = 28) were recruited to participate in traditional usability testing under an IRB approved protocol.

Results: Decision support software was developed to systematically walk the point-of-care clinician through the TDM process. The system is accessed through the EHR which transparently imports all of the requisite patient data. Data are visually inspected and then curve fit using a model-dependent approach. Quantitative goodness-of-fit are converted to single tachometer where “green” alerts the user that the model is strong, “yellow” signals caution and “red” indicates that there may be a problem with the fitting. Override features are embedded to permit application of a model-independent approach where appropriate. Simulations are performed to target a desired exposure or dose as entered by the clinician and the DST pushes the user approved recommendation back into the EHR. Usability testers were highly satisfied with our DST and quickly became proficient with the software.

Conclusions: With early and broad stake-holder engagement we developed a clinical DST for the non-pharmacologist. This tools affords our clinicians the ability to seamlessly transition from patient assessment, to pharmacokinetic modeling and simulation, and subsequent prescription order entry.

Clinical Decision Support Tools: The Evolution of a Revolution

Dashboard systems for clinical decision support integrate data from multiple sources. These systems, the newest in a long line of dose calculators and other decision support tools, utilize Bayesian approaches to fully individualize dosing using information gathered through therapeutic drug monitoring. In the treatment of inflammatory bowel disease patients with infliximab, dashboards may reduce therapeutic failures and treatment costs. The history and future development of modern Bayesian dashboard systems is described.

A New Method for Individualized Digoxin Dosing in Elderly Patients

BACKGROUND

Digoxin is a frequently prescribed drug in the elderly population. Estimated glomerular filtration rate is widely used to adjust dosages. The HUGE value is a tool for differentiating the presence or absence of chronic kidney disease in elderly patients. We aimed to investigate the usefulness of the HUGE value to predict the initial dose of digoxin in patients aged older than 70 years.

METHODS

We reviewed retrospectively the medical records of patients aged older than 70 years with serum digoxin concentrations (SDCs) monitored over a 6-month period (63 patients). A linear regression relating the patient's SDC, maintenance dose of digoxin and the HUGE value was estimated to generate a dosage equation. This equation was validated retrospectively (33 patients) and prospectively (35 patients) in comparison with two existing methods based on creatinine clearance.

RESULTS

An equation (HUGE_DIG) was generated to calculate the initial digoxin dose to reach a specific target SDC. Thus, to achieve a SDC of 0.8 ng/mL: Digoxin (mg/day) = 0.091 - 0.006 x HUGE. After retrospective validation, the calculated digoxin doses with this equation were administered in the prospective phase and we did not observe statistical differences between measured and desired SDCs. Moreover, the predictive performance of our equation was better than that obtained with the compared methods.

CONCLUSIONS

We offer a new validated digoxin dosing equation for elderly patients. Our results support the need to perform digoxin dosing in elderly people, bearing in mind the changes in renal physiology secondary to ageing and not merely the estimated glomerular filtration rate.

Improved Tacrolimus Target Concentration Achievement Using Computerized Dosing in Renal Transplant Recipients--A Prospective, Randomized Study

BACKGROUND

Early after renal transplantation, it is often challenging to achieve and maintain tacrolimus concentrations within the target range. Computerized dose individualization using population pharmacokinetic models may be helpful. The objective of this study was to prospectively evaluate the target concentration achievement of tacrolimus using computerized dosing compared with conventional dosing performed by experienced transplant physicians.

METHODS

A single-center, prospective study was conducted. Renal transplant recipients were randomized to receive either computerized or conventional tacrolimus dosing during the first 8 weeks after transplantation. The median proportion of tacrolimus trough concentrations within the target range was compared between the groups. Standard risk (target, 3-7 μg/L) and high-risk (8-12 μg/L) recipients were analyzed separately.

RESULTS

Eighty renal transplant recipients were randomized, and 78 were included in the analysis (computerized dosing (n = 39): 32 standard risk/7 high-risk, conventional dosing (n = 39): 35 standard risk/4 high-risk). A total of 1711 tacrolimus whole blood concentrations were evaluated. The proportion of concentrations per patient within the target range was significantly higher with computerized dosing than with conventional dosing, both in standard risk patients (medians, 90% [95% confidence interval {95% CI}, 84-95%] vs 78% [95% CI, 76-82%], respectively, P < 0.001) and in high-risk patients (medians, 77% [95% CI, 71-80%] vs 59% [95% CI, 40-74%], respectively, P = 0.04).

CONCLUSIONS

Computerized dose individualization improves target concentration achievement of tacrolimus after renal transplantation. The computer software is applicable as a clinical dosing tool to optimize tacrolimus exposure and may potentially improve long-term outcome.