Practical therapeutic drug management in HIV-infected patients: use of population pharmacokinetic models supplemented by individualized Bayesian dose optimization

Training the next generation of pharmacometric modelers: a multisector perspective

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.

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

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

Effects of obesity on the pharmacology of proton pump inhibitors: current understanding and future implications for patient care and research

INTRODUCTION

In the United States, obesity affects approximately ⅖ adults and ⅕ children, leading to increased risk for comorbidities, like gastroesophageal reflux disease (GERD), treated increasingly with proton pump inhibitors (PPIs). Currently, there are no clinical guidelines to inform PPI dose selection for obesity, with sparse data regarding whether dose augmentation is necessary.

AREAS COVERED

We provide a review of available literature regarding the pharmacokinetics (PK), pharmacodynamics (PD), and/or metabolism of PPIs in children and adults with obesity, as a step toward informing PPI dose selection.

EXPERT OPINION

Published PK data in adults and children are limited to first-generation PPIs and point toward reduced apparent oral drug clearance in obesity, with equipoise regarding obesity impact on drug absorption. Available PD data are sparse, conflicting, and limited to adults. No studies are available to inform the PPI PK→PD relationship in obesity and if/how it differs compared to individuals without obesity. In the absence of data, best practice may be to dose PPIs based on CYP2C19 genotype and lean body weight, so as to avoid systemic overexposure and potential toxicities, while monitoring closely for efficacy.

Implementation of Model-Based Dose Adjustment of Tobramycin in Adult Patients with Cystic Fibrosis

Therapeutic drug monitoring (TDM) of tobramycin is widely performed in patients with cystic fibrosis (CF), but little is known about the value of model-informed precision dosing (MIPD) in this setting. We aim at reporting our experience with tobramycin MIPD in adult patients with CF. We analyzed data from adult patients with CF who received IV tobramycin and had model-guided TDM during the first year of implementation of MIPD. The predictive performance of a pharmacokinetic (PK) model was assessed. Observed maximal (Cmax) and minimal (Cmin) concentrations after initial dosing were compared with target values. We compared the initial doses and adjusted doses after model-based TDM, as well as renal function at the beginning and end of therapy. A total of 78 tobramycin courses were administered in 61 patients. After initial dosing set by physicians (mean, 9.2 ± 1.4 mg/kg), 68.8% of patients did not achieve the target Cmax ≥ 30 mg/L. The PK model fit the data very well, with a median absolute percentage error of 4.9%. MIPD was associated with a significant increase in tobramycin doses (p < 0.001) without significant change in renal function. Model-based dose suggestions were wellaccepted by the physicians and the expected target attainment for Cmax was 83%. To conclude, the implementation of MIPD was effective in changing prescribing practice and was not associated with nephrotoxic events in adult patients with CF.

Dosage Individualization of Linezolid: Precision Dosing of Linezolid To Optimize Efficacy and Minimize Toxicity

The high interindividual variability in the pharmacokinetics (PK) of linezolid has been described, which results in an unacceptably high proportion of patients with either suboptimal or potentially toxic concentrations following the administration of a fixed regimen. The aim of this study was to develop a population pharmacokinetic model of linezolid and use this to build and validate alogorithms for individualized dosing. A retrospective pharmacokinetic analysis was performed using data from 338 hospitalized patients (65.4% male, 65.5 [±14.6] years) who underwent routine therapeutic drug monitoring for linezolid. Linezolid concentrations were analyzed by using high-performance liquid chromatography. Population pharmacokinetic modeling was performed using a nonparametric methodology with Pmetrics, and Monte Carlo simulations were employed to calculate the 100% time >MIC after the administration of a fixed regimen of 600 mg administered every 12 h (q12h) intravenously (i.v.). The dose of linezolid needed to achieve a PTA ≥ 90% for all susceptible isolates classified according to EUCAST was estimated to be as high as 2,400 mg q12h, which is 4 times higher than the maximum licensed linezolid dose. The final PK model was then used to construct software for dosage individualization, and the performance of the software was assessed using 10 new patients not used to construct the original population PK model. A three-compartment model with an absorptive compartment with zero-order i.v. input and first-order clearance from the central compartment best described the data. The dose optimization software tracked patients with a high degree of accuracy. The software may be a clinically useful tool to adjust linezolid dosages in real time to achieve prespecified drug exposure targets. A further prospective study is needed to examine the potential clinical utility of individualized therapy.

Fused Deposition Modeling (FDM), the new asset for the production of tailored medicines

Over the last few years, conventional medicine has been increasingly moving towards precision medicine. Today, the production of oral pharmaceutical forms tailored to patients is not achievable by traditional industrial means. A promising solution to customize oral drug delivery has been found in the utilization of 3D Printing and in particular Fused Deposition Modeling (FDM). Thus, the aim of this systematic literature review is to provide a synthesis on the production of pharmaceutical solid oral forms using FDM technology. In total, 72 relevant articles have been identified via two well-known scientific databases (PubMed and ScienceDirect). Overall, three different FDM methods have been reported: "Impregnation-FDM", "Hot Melt Extrusion coupled with FDM" and "Print-fill", which yielded to the formulation of thermoplastic polymers used as main component, five families of other excipients playing different functional roles and 47 active ingredients. Solutions are underway to overcome the high printing temperatures, which was the initial brake on to use thermosensitive ingredients with this technology. Also, the moisture sensitivity shown by a large number of prints in preliminary storage studies is highlighted. FDM seems to be especially fitted for the treatment of rare diseases, and particular populations requiring tailored doses or release kinetics. For future use of FDM in clinical trials, an implication of health regulatory agencies would be necessary. Hence, further efforts would likely be oriented to the use of a quality approach such as "Quality by Design" which could facilitate its approval by the authorities, and also be an aid to the development of this technology for manufacturers.

Clinical pharmacology applications in clinical drug development and clinical care: A focus on Saudi Arabia

Drug development, from preclinical to clinical studies, is a lengthy and complex process. There is an increased interest in the Kingdom of Saudi Arabia (KSA) to promote innovation, research and local content including clinical trials (Phase I-IV). Currently, there are over 650 registered clinical trials in Saudi Arabia, and this number is expected to increase. An important part of drug development and clinical trials is to assure the safe and effective use of drugs. Clinical pharmacology plays a vital role in informed decision making during the drug development stage as it focuses on the effects of drugs in humans. Disciplines such as pharmacokinetics, pharmacodynamics and pharmacogenomics are components of clinical pharmacology. It is a growing discipline with a range of applications in all phases of drug development, including selecting optimal doses for Phase I, II and III studies, evaluating bioequivalence and biosimilar studies and designing clinical studies. Incorporating clinical pharmacology in research as well as in the requirements of regulatory agencies will improve the drug development process and accelerate the pipeline. Clinical pharmacology is also applied in direct patient care with the goal of personalizing treatment. Tools such as therapeutic drug monitoring, pharmacogenomics and model informed precision dosing are used to optimize dosing for patients at an individual level. In KSA, the science of clinical pharmacology is underutilized and we believe it is important to raise awareness and educate the scientific community and healthcare professionals in terms of its applications and potential. In this review paper, we provide an overview on the use and applications of clinical pharmacology in both drug development and clinical care.

Target-Controlled Continuous Infusion for Antibiotic Dosing: Proof-of-Principle in an In-silico Vancomycin Trial in Intensive Care Unit Patients

OBJECTIVES

In this in-silico study, we investigate the clinical utility of target-controlled infusion for antibiotic dosing in an intensive care unit setting using vancomycin as a model compound. We compared target-controlled infusion and adaptive target-controlled infusion, which combines target-controlled infusion with data from therapeutic drug monitoring, with conventional (therapeutic drug monitoring-based) vancomycin dosing strategies.

METHODS

A clinical trial simulation was conducted. This simulation was based on a comprehensive database of clinical records of intensive care unit patients and a systematic review of currently available population-pharmacokinetic models for vancomycin in intensive care unit patients. Dosing strategies were compared in terms of the probability of achieving efficacious concentrations as well as the potential for inducing toxicity.

RESULTS

Adaptive target-controlled infusion outperforms rule-based dosing guidelines for vancomycin. In the first 48 h of treatment, the probability of target attainment is significantly higher for adaptive target-controlled infusion than for the second-best method (Cristallini). Probability of target attainments of 54 and 72% and 47 and 59% for both methods after 24 and 48 h, respectively. Compared to the Cristallini method, which is characterized by a probability of attaining concentrations above 30 mg.L^-1 > 65% in the first few hours of treatment, adaptive target-controlled infusion shows negligible time at risk and a probability of attaining concentrations above 30 mg.L^-1 not exceeding 25%. Finally, in contrast to the other methods, the performance of target-controlled infusion is consistent across subgroups within the population.

CONCLUSIONS

Our study shows that adaptive target-controlled infusion has the potential to become a practical tool for patient-tailored antibiotic dosing in the intensive care unit.

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.

Pilot Study of Model-Based Dosage Individualization of Ganciclovir in Neonates and Young Infants with Congenital Cytomegalovirus Infection

Newborns with congenital cytomegalovirus (CMV) infection are at high risk for developing permanent sequelae. Intravenous ganciclovir therapy is frequently used for the treatment of congenital CMV infection. A target area under the concentration-time curve from 0 to 24 h (AUC_0-24) of 40 to 50 μg · h/ml is recommended. The standard dose has resulted in a large variability in ganciclovir exposure in newborns, indicating the unmet need of dosage individualization for this vulnerable population, but the implementation of this strategy remains challenging in clinical practice. We aim to evaluate the clinical utility of model-based dosage individualization of ganciclovir in newborns using an opportunistic sampling approach. The predictive performance of a published ganciclovir population pharmacokinetic model was evaluated using an independent patient cohort. The individual dose was adjusted based on the target AUC_0-24 to ensure its efficacy. A total of 26 newborns with congenital CMV infection were included in the present study. Only 11 (42.3%) patients achieved the target AUC_0-24 after being given the standard dose. For all the subtherapeutic patients (achieving <80% of the target AUC) (n = 5), a model-based dosage adjustment was performed using the Bayesian estimation method combined with the opportunistic sampling strategy. The adjusted doses were increased by 28.6% to 60.0% in these five patients, and all adapted AUC_0-24 values achieved the target (range, 48.6 to 66.1 μg · h/ml). The clinical utility of model-based dosing individualization of ganciclovir was demonstrated in newborns with congenital CMV infection. The population pharmacokinetic model combined with the opportunistic sampling strategy provides a clinically feasible method to adapt the ganciclovir dose in neonatal clinical practice. (This study has been registered at ClinicalTrials.gov under registration no. NCT03113344.).

A Nonparametric Method to Optimize Initial Drug Dosing and Attainment of a Target Exposure Interval: Concepts and Application to Busulfan in Pediatrics

The traditional approach for model-based initial dosing is based on the use of a single vector of typical population parameters for targeting a specific exposure. This approach is theoretically ill-suited for targeting a range of exposure. The objective of this work was to develop a general approach for optimal (OPT) targeting of a drug exposure interval. After methodological purposes, we applied our method to the busulfan case. We used a nonparametric population pharmacokinetic model of intravenous busulfan to estimate the individual pharmacokinetic parameters of 163 bone marrow-transplanted children. Then, an array of 151 doses of busulfan ranging from 0.5 to 2 mg/kg was simulated a priori in each patient. For each dose, 29 possible busulfan plasma concentration profiles, corresponding to the nonparametric prior, each associated with a probability, were obtained. The multiple-model-based, OPT dose was identified as the dose maximizing the a priori probability of achieving the busulfan target area under the concentration-time curve (AUC). Two AUC targets were considered: 900-1500 (conventional) or <1500 µM min^-1. Finally, the OPT dose was individually simulated in each patient. We compared the ability of this method to achieve the target exposure interval with that of three other traditional model-based methods and one based on the non-parametric approach. When targeting the busulfan conventional AUC range, the OPT dose provided better attainment than the best of the three other methods after one dose (82.2 vs. 41.7 %, p < 0.005), two doses (79.1 vs. 65.0 %, p < 0.005), and at the end of therapy (80.4 vs. 76.7 %, p < 0.42). The approach provided a balanced distribution between under- (10.4 %) and overexposure (9.2 %), while other approaches showed higher rates of underexposure (≥19 %). When targeting an AUC <1500 µM min, the OPT dose was successful in minimizing overexposure as 0 % of children showed simulated AUC >1500 µM min^-1. Our approach has been designed to optimize the targeting of an exposure interval. When applied to busulfan in children, it outperformed the traditional model-based dosing approach, with earlier and better achievement of busulfan target AUC. The approach can be applied for OPT dosing of many drugs, when the target objective is an interval.

Therapeutic drug management of linezolid: a missed opportunity for clinicians?

Some studies have shown that adjustments to the linezolid dose guided by therapeutic drug monitoring (TDM) can reduce interindividual variability in drug exposure and improve linezolid tolerability. In this study, 6 years of linezolid TDM, a diagnostic service for our hospital and others in the Milan (Italy) area, is described. Samples were collected immediately before the morning dose intake (trough concentrations) in steady-state conditions. Linezolid concentrations were quantified by a validated high-performance liquid chromatography (HPLC) method. Four hundred linezolid trough concentrations from 220 patients were collected. A 20-fold variability in linezolid levels was observed. Positive and significant correlations between linezolid trough concentrations and patient age (r = 0.325, P <0.01) or serum creatinine (r = 0.511, P <0.01) were found. A progressive increase in linezolid concentrations with time was observed in a subgroup of patients with more than one TDM assessment. Elderly patients, especially those aged >80 years and with impaired renal function, are at a higher risk of overexposure to linezolid. Despite the observed progressive increase in linezolid concentrations over time, most physicians did not change the drug dose according to the TDM results, even in the presence of frank overexposure to linezolid.

Accurately Achieving Target Busulfan Exposure in Children and Adolescents With Very Limited Sampling and the BestDose Software

BACKGROUND

Busulfan dose adjustment is routinely guided by plasma concentration monitoring using 4-9 blood samples per dose adjustment, but a pharmacometric Bayesian approach could reduce this sample burden.

METHODS

The authors developed a nonparametric population model with Pmetrics. They used it to simulate optimal initial busulfan dosages, and in a blinded manner, they compared dosage adjustments using the model in the BestDose software to dosage adjustments calculated by noncompartmental estimation of area under the time-concentration curve at a national reference laboratory in a cohort of patients not included in model building.

RESULTS

Mean (range) age of the 53 model-building subjects was 7.8 years (0.2-19.0 years) and weight was 26.5 kg (5.6-78.0 kg), similar to nearly 120 validation subjects. There were 16.7 samples (6-26 samples) per subject to build the model. The BestDose cohort was also diverse: 10.2 years (0.25-18 years) and 46.4 kg (5.2-110.9 kg). Mean bias and imprecision of the 1-compartment model-predicted busulfan concentrations were 0.42% and 9.2%, and were similar in the validation cohorts. Initial dosages to achieve average concentrations of 600-900 ng/mL were 1.1 mg/kg (≤12 kg, 67% in the target range) and 1.0 mg/kg (>12 kg, 76% in the target range). Using all 9 concentrations after dose 1 in the Bayesian estimation of dose requirements, the mean (95% confidence interval) bias of BestDose calculations for the third dose was 0.2% (-2.4% to 2.9%, P = 0.85), compared with the standard noncompartmental method based on 9 concentrations. With 1 optimally timed concentration 15 minutes after the infusion (calculated with the authors' novel MMopt algorithm) bias was -9.2% (-16.7% to -1.5%, P = 0.02). With 2 concentrations at 15 minutes and 4 hours bias was only 1.9% (-0.3% to 4.2%, P = 0.08).

CONCLUSIONS

BestDose accurately calculates busulfan intravenous dosage requirements to achieve target plasma exposures in children up to 18 years of age and 110 kg using only 2 blood samples per adjustment compared with 6-9 samples for standard noncompartmental dose calculations.

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.

Paediatric models in motion: requirements for model-based decision support at the bedside

Optimal paediatric pharmacotherapy is reliant on a detailed understanding of the individual patient including their developmental status and disease state as well as the pharmaceutical agents he/she is receiving for treatment or management of side effects. Our appreciation for size and maturation effects on the pharmacokinetic/pharmacodynamic (PK/PD) phenomenon has improved to the point that we can develop predictive models that permit us to individualize therapy, especially in the situation where we are monitoring drug effects or therapeutic concentrations. The growth of efforts to guide paediatric pharmacotherapy via model-based decision support necessitates a coordinated and systematic approach to ensuring reliable and robust output to caregivers that represents the current standard of care and adheres to governance imposed by the host institution or coalition responsible. Model-based systems which guide caregivers on dosing paediatric patients in a more comprehensive manner are in development at several institutions. Care must be taken that these systems provide robust guidance with the current best practice. These systems must evolve as new information becomes available and ultimately are best constructed from diverse data representing global input on demographics, ethnic / racial diversity, diet and other lifestyle factors. Multidisciplinary involvement at the project team level is key to the ultimate clinical valuation. Likewise, early engagement of clinical champions is also critical for the success of model-based tools. Adherence to regulatory requirements as well as best practices with respect to software development and testing are essential if these tools are to be used as part of the routine standard of care.

Pharmacokinetic modeling of therapies for systemic lupus erythematosus

With the increasing use of different types of therapies in treating autoimmune diseases such as systemic lupus erythematosus (SLE), there is a need to utilize pharmacokinetic (PK) strategies to optimize the clinical outcome of these treatments. Various PK analysis approaches, including population PK modeling and physiologically based PK modeling, have been used to evaluate drug PK characteristics and population variability or to predict drug PK profiles in a mechanistic manner. This review outlines the PK modeling of major SLE therapies including immunosuppressants (methotrexate, azathioprine, mycophenolate and cyclophosphamide, among others) and immunomodulators (intravenous immunoglobulin). It summarizes the population PK modeling, physiologically based PK modeling and model-based individualized dosing strategies to improve the therapeutic outcomes in SLE patients.

Achieving target voriconazole concentrations more accurately in children and adolescents

Despite the documented benefit of voriconazole therapeutic drug monitoring, nonlinear pharmacokinetics make the timing of steady-state trough sampling and appropriate dose adjustments unpredictable by conventional methods. We developed a nonparametric population model with data from 141 previously richly sampled children and adults. We then used it in our multiple-model Bayesian adaptive control algorithm to predict measured concentrations and doses in a separate cohort of 33 pediatric patients aged 8 months to 17 years who were receiving voriconazole and enrolled in a pharmacokinetic study. Using all available samples to estimate the individual Bayesian posterior parameter values, the median percent prediction bias relative to a measured target trough concentration in the patients was 1.1% (interquartile range, -17.1 to 10%). Compared to the actual dose that resulted in the target concentration, the percent bias of the predicted dose was -0.7% (interquartile range, -7 to 20%). Using only trough concentrations to generate the Bayesian posterior parameter values, the target bias was 6.4% (interquartile range, -1.4 to 14.7%; P = 0.16 versus the full posterior parameter value) and the dose bias was -6.7% (interquartile range, -18.7 to 2.4%; P = 0.15). Use of a sample collected at an optimal time of 4 h after a dose, in addition to the trough concentration, resulted in a nonsignificantly improved target bias of 3.8% (interquartile range, -13.1 to 18%; P = 0.32) and a dose bias of -3.5% (interquartile range, -18 to 14%; P = 0.33). With the nonparametric population model and trough concentrations, our control algorithm can accurately manage voriconazole therapy in children independently of steady-state conditions, and it is generalizable to any drug with a nonparametric pharmacokinetic model. (This study has been registered at ClinicalTrials.gov under registration no. NCT01976078.).

Population pharmacokinetics of abacavir in infants, toddlers and children

AIMS

To characterize the pharmacokinetics of abacavir in infants, toddlers and children and to assess the influence of covariates on drug disposition across these populations.

METHODS

Abacavir concentration data from three clinical studies in human immunodeficiency virus-infected children (n = 69) were used for model building. The children received either a weight-normalized dose of 16 mg kg(-1) day(-1) or the World Health Organization recommended dose based on weight bands. A population pharmacokinetic analysis was performed using nonlinear mixed effects modelling VI. The influence of age, gender, bodyweight and formulation was evaluated. The final model was selected according to graphical and statistical criteria.

RESULTS

A two-compartmental model with first-order absorption and first-order elimination best described the pharmacokinetics of abacavir. Bodyweight was identified as significant covariate influencing the apparent oral clearance and volume of distribution. Predicted steady-state maximal plasma concentration and area under the concentration-time curve from 0 to 12 h of the standard twice daily regimen were 2.5 mg l(-1) and 6.1 mg h l(-1) for toddlers and infants, and 3.6 mg l(-1) and 8.7 mg h l(-1) for children, respectively. Model-based predictions showed that equivalent systemic exposure was achieved after once and twice daily dosing regimens. There were no pharmacokinetic differences between the two formulations (tablet and solution). The model demonstrated good predictive performance for dosing prediction in individual patients and, as such, can be used to support therapeutic drug monitoring in conjunction with sparse sampling.

CONCLUSIONS

The disposition of abacavir in children appears to be affected only by differences in size, irrespective of the patient's age. Maturation processes of abacavir metabolism in younger infants should be evaluated in further studies to demonstrate the potential impact of ontogeny.

Are vancomycin trough concentrations adequate for optimal dosing?

The current vancomycin therapeutic guidelines recommend the use of only trough concentrations to manage the dosing of adults with Staphylococcus aureus infections. Both vancomycin efficacy and toxicity are likely to be related to the area under the plasma concentration-time curve (AUC). We assembled richly sampled vancomycin pharmacokinetic data from three studies comprising 47 adults with various levels of renal function. With Pmetrics, the nonparametric population modeling package for R, we compared AUCs estimated from models derived from trough-only and peak-trough depleted versions of the full data set and characterized the relationship between the vancomycin trough concentration and AUC. The trough-only and peak-trough depleted data sets underestimated the true AUCs compared to the full model by a mean (95% confidence interval) of 23% (11 to 33%; P = 0.0001) and 14% (7 to 19%; P < 0.0001), respectively. In contrast, using the full model as a Bayesian prior with trough-only data allowed 97% (93 to 102%; P = 0.23) accurate AUC estimation. On the basis of 5,000 profiles simulated from the full model, among adults with normal renal function and a therapeutic AUC of ≥400 mg · h/liter for an organism for which the vancomycin MIC is 1 mg/liter, approximately 60% are expected to have a trough concentration below the suggested minimum target of 15 mg/liter for serious infections, which could result in needlessly increased doses and a risk of toxicity. Our data indicate that adjustment of vancomycin doses on the basis of trough concentrations without a Bayesian tool results in poor achievement of maximally safe and effective drug exposures in plasma and that many adults can have an adequate vancomycin AUC with a trough concentration of <15 mg/liter.

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

Therapeutic drug monitoring (TDM) aims to optimize treatments by individualizing dosage regimens based on the measurement of blood concentrations. Dosage individualization to maintain concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculations currently represent the gold standard TDM approach but require computation assistance. In recent decades computer programs have been developed to assist clinicians in this assignment. The aim of this survey was to assess and compare computer tools designed to support TDM clinical activities. The literature and the Internet were searched to identify software. All programs were tested on personal computers. Each program was scored against a standardized grid covering pharmacokinetic relevance, user friendliness, computing aspects, interfacing and storage. A weighting factor was applied to each criterion of the grid to account for its relative importance. To assess the robustness of the software, six representative clinical vignettes were processed through each of them. Altogether, 12 software tools were identified, tested and ranked, representing a comprehensive review of the available software. Numbers of drugs handled by the software vary widely (from two to 180), and eight programs offer users the possibility of adding new drug models based on population pharmacokinetic analyses. Bayesian computation to predict dosage adaptation from blood concentration (a posteriori adjustment) is performed by ten tools, while nine are also able to propose a priori dosage regimens, based only on individual patient covariates such as age, sex and bodyweight. Among those applying Bayesian calculation, MM-USC*PACK© uses the non-parametric approach. The top two programs emerging from this benchmark were MwPharm© and TCIWorks. Most other programs evaluated had good potential while being less sophisticated or less user friendly. Programs vary in complexity and might not fit all healthcare settings. Each software tool must therefore be regarded with respect to the individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Computer-assisted TDM is gaining growing interest and should further improve, especially in terms of information system interfacing, user friendliness, data storage capability and report generation.

High dose of darunavir in treatment-experienced HIV-infected adolescent results in virologic suppression and improved CD4 cell count

We describe an unintentional significant overdose of darunavir in a treatment-experienced adolescent with decreased darunavir susceptibility and prior treatment failure on darunavir therapy. Minimal toxicity and improved virologic suppression observed with an overdose have prompted consideration of the continued use of a higher than recommended dose. Pharmacokinetic and pharmacodynamic evaluations justified the individualized use of high-dose darunavir, which resulted in virologic suppression, improved CD4 cell count, and resolution of toxicity.

Accurate detection of outliers and subpopulations with Pmetrics, a nonparametric and parametric pharmacometric modeling and simulation package for R

INTRODUCTION

Nonparametric population modeling algorithms have a theoretical superiority over parametric methods to detect pharmacokinetic and pharmacodynamic subgroups and outliers within a study population.

METHODS

The authors created "Pmetrics," a new Windows and Unix R software package that updates the older MM-USCPACK software for nonparametric and parametric population modeling and simulation of pharmacokinetic and pharmacodynamic systems. The parametric iterative 2-stage Bayesian and the nonparametric adaptive grid (NPAG) approaches in Pmetrics were used to fit a simulated population with bimodal elimination (Kel) and unimodal volume of distribution (Vd), plus an extreme outlier, for a 1-compartment model of an intravenous drug.

RESULTS

The true means (SD) for Kel and Vd in the population sample were 0.19 (0.17) and 102 (22.3), respectively. Those found by NPAG were 0.19 (0.16) and 104 (22.6). The iterative 2-stage Bayesian estimated them to be 0.18 (0.16) and 104 (24.4). However, given the bimodality of Kel, no subject had a value near the mean for the population. Only NPAG was able to accurately detect the bimodal distribution for Kel and to find the outlier in both the population model and in the Bayesian posterior parameter estimates.

CONCLUSIONS

Built on over 3 decades of work, Pmetrics adopts a robust, reliable, and mature nonparametric approach to population modeling, which was better than the parametric method at discovering true pharmacokinetic subgroups and an outlier.

Population pharmacokinetics and maximum a posteriori probability Bayesian estimator of abacavir: application of individualized therapy in HIV-infected infants and toddlers

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

Abacavir is used to treat HIV infection in both adults and children. The recommended paediatric dose is 8 mg kg(-1) twice daily up to a maximum of 300 mg twice daily. Weight was identified as the central covariate influencing pharmacokinetics of abacavir in children.

WHAT THIS STUDY ADDS

A population pharmacokinetic model was developed to describe both once and twice daily pharmacokinetic profiles of abacavir in infants and toddlers. Standard dosage regimen is associated with large interindividual variability in abacavir concentrations. A maximum a posteriori probability Bayesian estimator of AUC(0-) (t) based on three time points (0, 1 or 2, and 3 h) is proposed to support area under the concentration-time curve (AUC) targeted individualized therapy in infants and toddlers.

AIMS

To develop a population pharmacokinetic model for abacavir in HIV-infected infants and toddlers, which will be used to describe both once and twice daily pharmacokinetic profiles, identify covariates that explain variability and propose optimal time points to optimize the area under the concentration-time curve (AUC) targeted dosage and individualize therapy.

METHODS

The pharmacokinetics of abacavir was described with plasma concentrations from 23 patients using nonlinear mixed-effects modelling (NONMEM) software. A two-compartment model with first-order absorption and elimination was developed. The final model was validated using bootstrap, visual predictive check and normalized prediction distribution errors. The Bayesian estimator was validated using the cross-validation and simulation-estimation method.

RESULTS

The typical population pharmacokinetic parameters and relative standard errors (RSE) were apparent systemic clearance (CL) 13.4 () h−1 (RSE 6.3%), apparent central volume of distribution 4.94 () (RSE 28.7%), apparent peripheral volume of distribution 8.12 () (RSE14.2%), apparent intercompartment clearance 1.25 () h−1 (RSE 16.9%) and absorption rate constant 0.758 h−1 (RSE 5.8%). The covariate analysis identified weight as the individual factor influencing the apparent oral clearance: CL = 13.4 × (weight/12)1.14. The maximum a posteriori probability Bayesian estimator, based on three concentrations measured at 0, 1 or 2, and 3 h after drug intake allowed predicting individual AUC0–t.

CONCLUSIONS

The population pharmacokinetic model developed for abacavir in HIV-infected infants and toddlers accurately described both once and twice daily pharmacokinetic profiles. The maximum a posteriori probability Bayesian estimator of AUC(0-) (t) was developed from the final model and can be used routinely to optimize individual dosing.

Selection of antibiotics for meticillin-resistant Staphylococcus pseudintermedius: time to revisit some old drugs?

The aim of this review is to consider systemic therapy options for meticillin-resistant Staphylococcus pseudintermedius (MRSP). Infections caused by MRSP in small animals--particularly dogs--have been frustrating veterinarians in recent years. After a susceptibility test is performed, veterinarians are left to select from drugs that have not been frequently encountered on a susceptibility report. Some of these are old drugs that have not been used regularly by veterinary dermatologists. As MRSP is, by definition, resistant to all β-lactam antibiotics, including cephalosporins, penicillins and amoxicillin-clavulanate combinations, the β-lactam drugs are not an option for systemic treatment. As most MRSPs are multidrug resistant, familiar drugs, such as trimethoprim-sulfonamides, fluoroquinolones, macrolides and lincosamides (clindamycin), are also not usually an option for treatment. Therefore, veterinarians are left with drugs such as rifampicin, chloramphenicol, tetracyclines, aminoglycosides and vancomycin to choose from on the basis of an in vitro susceptibility test. Some of these drugs were originally approved over 50 years ago and may not be familiar to some veterinarians. Each of these drugs possesses unique properties and has particular advantages and disadvantages. Veterinarians should be particularly aware of the adverse effects, limitations and precautions when using these drugs. New drugs also have been developed for meticillin-resistant Staphylococcus aureus in humans. These include linezolid, ceftaroline, daptomycin and tigecycline. Although these drugs are very infrequently--if ever--considered for veterinary use, the properties of these drugs should also be known to veterinary dermatologists.

The role of therapeutic drug monitoring in the management of patients with human immunodeficiency virus infection

Therapeutic drug monitoring (TDM) is a well-established method to optimize dosing regimens in individual patients for drugs that are characterized by a narrow therapeutic range and large interindividual pharmacokinetic variability. For some antiretroviral drugs, mainly nonnucleoside reverse transcriptase inhibitors and protease inhibitors, TDM has been proposed as a means to improve the response in human immunodeficiency virus-infected patients. In contrast, nucleoside reverse transcriptase inhibitors do not show a predictable plasma concentration-response (toxicity, efficacy) relationship, and intracellular analyses are expensive. Therefore, TDM is generally not recommended for this class of drugs. TDM has been successfully applied in the clinical practice for certain antiretroviral drugs, but there are ongoing research efforts on the use and refinement of TDM for human immunodeficiency virus treatment, and convincing data from randomized trials are still needed. The best pharmacokinetic measures of drug exposure such as trough and peak concentrations or concentration ratios have not been unambiguously established.

Can therapeutic drug monitoring improve pharmacotherapy of HIV infection in adolescents?

Currently, therapeutic drug monitoring (TDM) of antiretroviral therapy (ART) is not performed in the United States as part of routine clinical care of an HIV-infected adolescent patient. TDM is recommended to rule out subtherapeutic drug concentrations and to differentiate among malabsorption, drug interactions, poor adherence, or increased drug metabolism or clearance as possible causes of decreased drug exposure. The use of TDM is also considered to assist in finding the optimal dose of a drug in patients whose virus has shown reduced susceptibility to that drug. The dosing of antiretroviral (ARV) drugs in adolescent patients with HIV infection depends on the chronologic age, weight, height, and the stage of sexual maturation. As a result of the limited data on the pharmacokinetics of ART during puberty, the transition of a dosing regimen from higher pediatric (weight and surface-based) to adult (fixed) range is not well defined. Developmental pharmacokinetic differences contribute to high variability in pediatric and adolescent patients and an increased frequency of suboptimal ARV exposure as compared to in adults. Individualized, concentration-targeted optimal dosing of ARV medications can be beneficial to patients for whom only limited dosing guidelines are available. This article describes three cases of the application of TDM in treatment-experienced adolescent patients whose ART was optimized using ARV TDM. TDM of ARV drugs is useful in managing the pharmacotherapy of HIV in adolescent patients and is well received by the adolescent patients with HIV and their families. Among others, the benefits of TDM provide evidence for adherence interventions and create grounds for enhanced education of the adolescent patient and involved adult caregivers about ART. Finally, TDM in adolescents provides valuable information about the clinical pharmacology of ART during puberty.

Practical therapeutic drug management in HIV-infected patients: use of population pharmacokinetic models supplemented by individualized Bayesian dose optimization

Individualized, model-based, target-oriented optimal concentration-controlled dosing of HIV medications can be beneficial to patients for whom there are limited dosing guidelines, such as children, adolescents, or patients with altered physiologic function. Barriers to this approach include lack of training, expertise, and access to appropriate software to assist the clinician. The authors present 4 illustrative clinical cases of HIV-infected patients whose therapy was optimized using population pharmacokinetic models (here generated from published studies) and supplemented by individualized Bayesian adaptive control of dosage regimens as implemented in the MM-USCPACK software. These 4 cases illustrate how clinicians can maximize therapeutic success in (1) patients with reduced drug clearance, (2) young adolescents transitioning to adult physiology, (3) patients with dose-dependent toxicity, and (4) adolescents with limited therapeutic options.