Posaconazole in paediatric malignancy and haematopoietic stem cell transplant: dosing to achieve therapeutic concentration

OBJECTIVES

Posaconazole is increasingly used for the treatment and prophylaxis of invasive fungal infections in immunocompromised children. We aimed to review evidence for paediatric posaconazole dosing regimens focusing on attainment of target concentrations and frequency of adverse effects.

METHODS

In May 2023, the Cochrane, Embase, MEDLINE and PubMed databases were searched for articles reporting posaconazole dosing in children with malignancy or post-haematopoietic stem cell transplantation. Studies reporting the attainment of target serum concentrations were included.

RESULTS

Overall, 24 studies were included. Eighteen studies of the oral suspension consistently reported poor attainment of target concentrations for prophylaxis (≥0.7 µg/mL, 12%-78%) despite high daily doses of 14-23 mg/kg/day (max. 1200 mg/day). Target attainment was significantly affected by gastric pH and food intake. Six studies of the delayed-release tablet (DRT) reported 58%-94% achieved concentrations ≥0.7 µg/mL, with the majority using lower doses of 4-12 mg/kg/day (max. 300 mg/day). Similarly, one study of powder for oral suspension found 67%-100% achieved target concentrations with a dose of 6 mg/kg/day (max. 300 mg/day). As expected, the IV formulation had high attainment of prophylaxis targets (81%-90%) with 6-10 mg/kg/day (max. 400 mg/day). All formulations were well tolerated, and no relationship between adverse effects and posaconazole concentrations was identified.

CONCLUSIONS

The required posaconazole dose in immunocompromised children varies depending on the formulation. The IV infusion had the highest attainment of therapeutic concentration followed by the DRT and powder for suspension. By contrast, the oral suspension had low attainment of target concentrations despite higher daily doses.

A quantitative pharmacology model for cannabinoid CB1 receptor mediated by Gi/Gs protein competition

BACKGROUND AND PURPOSE

Orthosteric agonism of the CB1 receptor normally associates with Gi signalling resulting in a net inhibition of cAMP production. Empirical evidence shows CB1 causes a net cAMP stimulation through Gs coupling under two conditions: co-stimulation with the D2 receptor and high-level CB1 expression. Two hypotheses have been proposed to account for these paradoxical effects, (1) Gi is consumed by coupling to D2 or extra CB1 and excess CB1 binds to Gs and (2), the formation of dimers CB1 -CB1 or CB1 -D2 switches Gi/Gs preference. This study explored the mechanisms of Gi/Gs preference based on a mathematical model of the CB1 receptor.

EXPERIMENTAL APPROACH

The model was based on Hypothesis 1 and known mechanisms. The model was calibrated to align with multiple types of data (cAMP, Gi dissociation and internalisation). The key step of Hypothesis 1 was examined by simulation from the model. An experiment was proposed to distinguish Hypothesis 1 and 2.

KEY RESULTS

The model successfully descripted multiple types of data under Hypothesis 1. Simulations from the model indicated that precoupling of G protein with receptors is necessary for this hypothesis. The model designed experiments to distinguish Hypothesis 1 and 2 by increasing Gi & Gs in parallel with CB1 overexpression. The two hypotheses result in distinct cAMP responses.

CONCLUSION AND IMPLICATIONS

A mathematical model of CB1 -regulated Gi/Gs pathways was developed. It indicated Hypothesis 1 is feasible and G protein precoupling is a key step causing cAMP signalling switch. The model-designed experiments provided guides for future experimentation.

Oral docetaxel plus encequidar - A pharmacokinetic model and evaluation against IV docetaxel

The development of optimized dosing regimens plays a crucial role in oncology drug development. This study focused on the population pharmacokinetic modelling and simulation of docetaxel, comparing the pharmacokinetic exposure of oral docetaxel plus encequidar (oDox + E) with the standard of care intravenous (IV) docetaxel regimen. The aim was to evaluate the feasibility of oDox + E as a potential alternative to IV docetaxel. The article demonstrates an approach which aligns with the FDA's Project Optimus which aims to improve oncology drug development through model informed drug development (MIDD). The key question answered by this study was whether a feasible regimen of oDox + E existed. The purpose of this question was to provide an early GO / NO-GO decision point to guide drug development and improve development efficiency.

METHODS

 A stepwise approach was employed to develop a population pharmacokinetic model for total and unbound docetaxel plasma concentrations after IV docetaxel and oDox + E administration. Simulations were performed from the final model to assess the probability of target attainment (PTA) for different oDox + E dose regimens (including multiple dose regimens) in relation to IV docetaxel using AUC over effective concentration (AUCOEC) metric across a range of effective concentrations (EC). A Go / No-Go framework was defined-the first part of the framework assessed whether a feasible oDox + E regimen existed (i.e., a PTA ≥ 80%), and the second part defined the conditions to proceed with a Go decision.

RESULTS

 The overall population pharmacokinetic model consisted of a 3-compartment model with linear elimination, constant bioavailability, constant binding mechanics, and a combined error model. Simulations revealed that single dose oDox + E regimens did not achieve a PTA greater than 80%. However, two- and three-dose regimens at 600 mg achieved PTAs exceeding 80% for certain EC levels.

CONCLUSION

 The study demonstrates the benefits of MIDD using oDox + E as a motivating example. A population pharmacokinetic model was developed for the total and unbound concentration in plasma of docetaxel after administration of IV docetaxel and oDox + E. The model was used to simulate oDox + E dose regimens which were compared to the current standard of care IV docetaxel regimen. A GO / NO-GO framework was applied to determine whether oDox + E should progress to the next phase of drug development and whether any conditions should apply. A two or three-dose regimen of oDox + E at 600 mg was able to achieve non-inferior pharmacokinetic exposure to current standard of care IV docetaxel in simulations. A Conditional GO decision was made based on this result and further quantification of the "effective concentration" would improve the ability to optimise the dose regimen.

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.

Optimal sample selection applied to information rich, dense data

Dense data can be classified into superdense information-poor data (type 1 dense data) and dense information-rich data (type 2 dense data). Arbitrary, random, or optimal thinning may be applied to type 1 dense data to minimise computational burden and statistical issues (such as autocorrelation). In contrast, a prospective or retrospective optimal design can be applied to type 2 dense data to maximise information gain from limited resources (capital and/or time). Here we describe a retrospective optimal selection strategy for quantification of unbound drug concentration from a discrete set of plasma samples where the total drug concentration has been measured.

Individualized vancomycin dosing in infants: prospective evaluation of an online dose calculator

BACKGROUND

Empiric vancomycin dosing regimens fail to achieve recommended target trough concentrations of 10-20 mg/L in the majority of infants. This study assessed the performance of a model-based dosing calculator (Vanc App) in achieving target vancomycin concentrations at first steady-state level.

METHODS

This was a multicenter prospective study in four tertiary pediatric hospitals over an 18-month period. Infants aged 0-90 days with suspected Gram-positive sepsis requiring empiric vancomycin treatment were included if they did not meet any of the exclusion criteria: post-menstrual age (PMA) <25 weeks, weight <500 g, glycopeptide allergy, receiving extracorporeal membrane oxygenation, vancomycin use within the previous 72 h, and renal impairment. The Vanc App used a published population pharmacokinetic model to generate a dose based on the infant's PMA, weight, creatinine, and target vancomycin concentration.

RESULTS

A total of 40 infants were included; 40% were female, median (range) weight was 2505 (700-4460) g and median (range) PMA was 37.4 (25.7-49.0) weeks. The median (range) vancomycin dose was 45 (24-79) mg/kg/day. All infants had trough vancomycin concentrations measured at steady-state (24-<48 hours) and 30 (75%) infants achieved target concentrations. Five infants had supratherapeutic (median 25, range 21-38 mg/L) and five had subtherapeutic (median 6, range <5-9 mg/L) concentrations. An area under the concentration-time curve (AUC_0-24) of 400-650 mg/L.h was achieved in 33 (83%) infants. There were no infusion-related reactions or nephrotoxicity.

CONCLUSION

Individualized intermittent vancomycin dosing using a model-based online calculator resulted in 75% and 83% of infants achieving target trough and AUC_0-24, respectively, at first steady-state level. There were no vancomycin-related nephrotoxicity or infusion-related reactions.

Optimal dosing of enoxaparin in overweight and obese children

AIM

Current enoxaparin dosing guidelines in children are based on total body weight. This is potentially inappropriate in obese children as it may overestimate the drug clearance. Current evidence suggests that obese children may require lower initial doses of enoxaparin, therefore the aim of this work was to characterise the pharmacokinetics of enoxaparin in obese children and to propose a more appropriate dosing regimen.

METHODS

Data from 196 unique encounters of 160 children who received enoxaparin treatment doses were analysed. Enoxaparin concentration was quantified using the chromogenic anti factor Xa (anti-Xa) assay. Patients provided a total of 552 anti-Xa samples. Existing published pharmacokinetic (PK) models were fitted and evaluated against our dataset using prediction-corrected visual predictive check plots (pcVPCs). A PK model was fitted using a nonlinear mixed-effects modelling approach. The fitted model was used to evaluate the current standard dosing and identify an optimal dosing regimen for obese children.

RESULTS

Published models of enoxaparin pharmacokinetics in children did not capture the pharmacokinetics of enoxaparin in obese children as shown by pcVPCs. A one-compartment model with linear elimination best described the pharmacokinetics of enoxaparin. Allometrically scaled fat-free mass with an estimated exponent of 0.712 (CI 0.66-0.76) was the most influential covariate on clearance while linear fat-free mass was selected as the covariate on volume. Simulations from the model showed that fat-free mass-based dosing could achieve the target anti-Xa activity at steady state in 77.5% and 78.2% of obese and normal-weight children, respectively, compared to 65.2% and 75.5% for standard total body weight-based dosing.

CONCLUSIONS

A population PK model that describes the time course of anti-Xa activity of enoxaparin was developed in a paediatric population. Based on this model, a unified dosing regimen was proposed that will potentially improve the success rate of target attainment in overweight/obese patients without the need for patient body size categorisation. Therefore, prospective validation of the proposed approach is warranted.

Dose Banding – weighing up benefits, risks and therapeutic failure

Aims

Dose banding is a method of dose individualisation in which all patients with similar characteristics are allocated to the same dose. Dose banding results in some patients receiving less intensive treatment which risks a reduction in therapeutic benefit (iatrogenic therapeutic failure) because of variability not predicted by dose banding. This study aims to explore the effects of dose banding on therapeutic success and failure.

Methods

This was a simulation study. Virtual patients were simulated under a simple pharmacokinetic model where the response of interest is the steady‐state average concentration. Clearance was correlated with a covariate used for dose banding. Dose individualisation was based on: one‐dose‐fits‐all, covariate‐based dosing, empirical dose banding, dose banding optimised for net therapeutic benefit and optimised for both benefit and minimising iatrogenic therapeutic failure.

Results

The lowest and highest probability of target attainment (PTA) were 44% for one‐dose‐fits‐all and 72% for covariate‐based dosing. Neither dosing approach would result in iatrogenic therapeutic failure as lower dose intensities do not occur. Empirical dose banding performed better than one‐dose‐fits‐all with 59% PTA but not as good as either optimised method (64–69% PTA) while carrying a risk of iatrogenic therapeutic failure in 25% of patients. Optimising for benefit (only) improved PTA but carried a risk of iatrogenic therapeutic failure of up to 10%. Optimising for benefit and minimising iatrogenic therapeutic failure provided the best balance.

Conclusion

Future application of dose banding needs to consider both the probability of benefit as well the risk of causing iatrogenic therapeutic failure.

1110. In Vivo Pharmacodynamics of Vancomycin Against Staphylococci in Young Infants

Background

Coagulase-negative staphylococci are the predominant pathogen causing late onset sepsis in young infants, however, the pharmacodynamic target for vancomycin therapy is unknown. This study aimed to determine the pharmacodynamic target of vancomycin in young infants with staphylococcal infections.

Methods

Retrospective data were collected for infants aged 0-90 days with methicillin-resistant Staphylococcus aureus (MRSA or coagulase-negative staphylococci (CoNS) bacteraemia over a 4-year period at the Royal Children’s Hospital Melbourne, Australia. Vancomycin broth microdilution minimum inhibitory concentrations (MIC) were determined. A published pharmacokinetic model was externally validated using the study dataset and a time-to-event pharmacodynamic model developed using non-linear mixed effects modelling, with the event being the first negative blood culture. Simulations were performed to determine the 24-hour trough vancomycin concentration correlating with a 90% probability target attainment (PTA) of the area under the curve in the first 24-hours (AUC_0-24) exceeding the identified target.

Results

Thirty infants, 28 with CoNS and two with MRSA bacteraemia, who had 165 vancomycin concentrations determined were included. The vancomycin broth microdilution MIC was determined for 24 CoNS and one MRSA isolate, both with a median MIC of 1 mg/L (CoNS range 0.5 to 4). An AUC_0-24 ≥3 00 mg/L·h was associated with a 7.8-fold increase in the chance of bacteriological cure for all staphylococci at any time point compared to an AUC_0-24 < 300 mg/L·h (hazard ratio 95% CI: 3.21-18.8). The 24-hour trough concentrations associated with a 90% PTA of achieving this target were > 13-16 mg/L and > 8-12 mg/L for 6 and 12-hourly dosing, respectively.

Conclusion

Our study found that an AUC_0-24 ≥ 300 mg/L·h was associated with a 7.8-fold increase in bacteriological cure in young infants with staphylococcal bloodstream infections.

Disclosures

All Authors: No reported disclosures

Population pharmacokinetics of ivermectin for the treatment of scabies in Indigenous Australian children

Ivermectin is a broad-spectrum antiparasitic agent used for the treatment and control of neglected tropical diseases. In Australia, ivermectin is primarily used for scabies and is licensed in children aged ≥5 years weighing >15 kg. However, young children, aged <5 years, are particularly vulnerable to scabies and its secondary complications. Therefore, this study aimed to determine an appropriate ivermectin dose for children aged 2 to 4 years and weighing ≤15 kg. We conducted a prospective, pharmacokinetic study of ivermectin in Indigenous Australian children aged between 5 and 15 years and weighing >15 kg. Doses of 200 μg/kg rounded to the nearest whole or half 3 mg tablet were given to children with scabies and ivermectin concentrations determined at two time points after dosing. A population pharmacokinetic model was developed using non-linear mixed effects modelling. A separate covariate database of children aged 2 to 4 years and weighing <15 kg was used to generate 1000 virtual patients and simulate the dose required to achieve equivalent drug exposure in young children as those aged ≥5 years. Overall, 26 children who had 48 ivermectin concentrations determined were included, 11 (42%) were male, the median age was 10.9 years and median body weight 37.6 kg. The final model was a two-compartment model with first-order absorption and linear elimination. For simulated children aged 2 to 4 years, a dose of 3 mg in children weighing 10–15 kg produced similar drug exposures to those >5 years. The median simulated area under the concentration-time curve was 976 μg∙h/L. Using modelling, we have identified a dosing strategy for ivermectin in children aged 2 to 4 years and weighing less than 15 kg that can be prospectively evaluated for safety and efficacy.

Ivermectin is an important drug for the control and treatment of neglected tropical diseases. However, despite numerous studies showing that ivermectin is safe and well tolerated in young children, it is not currently recommended in young children <5 years and <15 kg. Therefore, there are no guidelines for the dose of ivermectin in young or small children. In this study, we firstly determined how much ivermectin is present in blood in children aged 5 years and older. We then used this information to model what happens to ivermectin in childrens’ bodies allowing us to calculate the dose required in children aged less than 5 years and weighing under 15 kg. This study provides a new dosing guideline that can now be tested in clinical studies of children <5 years and <15 kg.

The Influence of Haemostatic System Maturation on the Dose-Response Relationship of Unfractionated Heparin

BACKGROUND

Unfractionated heparin (UFH) dosing and monitoring guidelines for children are often extrapolated from adult data. This practice is suboptimal given the inherent differences in haemostatic maturation and drug handling in children compared with adults.

OBJECTIVE

The aim of this work was to investigate the impact of haemostatic system maturation on the dose-response relationship of UFH in children.

METHODS

A quantitative model for haemostasis in adults was adapted to account for maturation in UFH pharmacokinetic (PK) parameters with and without age-related changes in coagulation factor concentrations. The adult and adapted models were used to predict the time courses of anti-factor Xa activity (aXa) and activated partial thromboplastin time (aPTT) in patients receiving UFH infusion. Predictions from both models were compared with observed aXa and aPTT measurements from 31 paediatric patients receiving UFH during extracorporeal membrane oxygenation (ECMO).

RESULTS

The model with maturation for both UFH PK and the haemostatic system had an improved aXa and aPTT predictive performance compared with maturation in UFH PK only and the original adult model. Despite the minor effect of haemostatic system maturation on baseline aPTT, it led to substantial changes in the time course of aPTT sensitivity to UFH. This finding suggests that between-subject variability in clotting factors concentrations is potentially a major contributor to the overall variability of aPTT response to UFH. In addition, time-varying clotting factors concentrations may explain within-subject changes in aPTT sensitivity to UFH.

CONCLUSION

We developed the first quantitative systems pharmacology (QSP) model that provides a mechanistic and quantitative basis for linking physiological and pharmacological maturation to UFH effect and response biomarkers. After appropriate clinical validation, the model could be useful for the development of paediatric-specific individualised UFH dosing recommendations.

Exploring a scalable real-time simulation for interprofessional education in pharmacy and medicine

This article was migrated. The article was marked as recommended. The organisational logistics and cost of interprofessional education (IPE) remain barriers to widespread embedding of IPE activities in health care training. The ability of students to undertake IPE activities in the form of treating a virtual patient independently of an instructor and facilities would be of potential benefit. In this study we explore the feasibility and potential IPE benefits of SimPHARM, a cloud based simulation platform that allows students to treat virtual patients in a real-time simulation. We enrolled three pairs of medical and pharmacy students and after familiarisation with the platform assigned them a 2 day virtual patient. At the end of the case they completed a questionnaire, an interview and we reviewed their SimPHARM log-files. The results supported the logistical feasibility of this approach. We found that most students interacted asynchronously on the platform, leaving notes for each other, in order to arrive at their clinical decisions. The participants found their interactions meaningful in helping them to make clinical decisions and learn from each other.

A minimal model to describe short-term haemodynamic changes of the cardiovascular system

AIMS

Current pharmacokinetic-pharmacodynamic models describing the haemodynamic changes often do not include necessary feedback mechanisms. These models provide adequate description of current data but may fail to adequately extrapolate to additional scenarios. This study aims to develop a minimal model to describe the short-term changes of haemodynamics that can be used as the basis for model development by future researchers.

METHODS

A minimal haemodynamic model was developed to describe the influence of drugs on blood pressure components. The model structure was defined based on known mechanisms and previously published models. The model was evaluated under 2 different simulation settings. The model parameters were calibrated to describe (without estimation) the haemodynamics of 2 antihypertensive drugs with data extracted from the literature. Structural identifiability analysis was done using various combinations of the observed variable.

RESULTS

The proposed model structure includes mean arterial pressure, heart rate and stroke volume and is composed of 4 states described by differential equations. Model evaluation showed flexibility in describing the haemodynamics at different target perturbations. Overlay plots of model predictions and literature data showed a good description without data fitting. The structural identifiability analysis revealed all model parameters and initial conditions were identifiable only when heart rate, mean arterial pressure and cardiac output were measured together.

CONCLUSIONS

A minimal model of the haemodynamic system was developed and evaluated. The model accounted for short-term haemodynamic feedback processes. We propose that this model can be used as the basis for future pharmacometric analyses of drugs acting on the haemodynamic system.

Revisiting the Pharmacology of Unfractionated Heparin

Unfractionated heparin (UFH) is a commonly used anticoagulant therapy for the acute treatment and prevention of thrombosis. Its short duration of action, reversibility of effect by protamine sulfate, and extensive clinical experience are some of the advantages that support its use. However, the choice of dose and dosing regimen of UFH remains challenging for several reasons. First, UFH has a narrow therapeutic window and wide variability in the dose-response relationship. Second, its pharmacodynamic (PD) properties are difficult to characterise owing to the complex multidimensional mechanisms of interaction with the haemostatic system. Third, the complex heterogeneous chemical composition of UFH precludes precise characterisation of its pharmacokinetic (PK) properties. This review provides a comprehensive mechanistic approach to the interaction of UFH with the haemostatic system. The effect of chemical structure on its PK and PD properties is quantitatively described, and a framework for characterisation of the dose-response relationship of UFH for the purpose of dose optimisation is proposed.

Development of a population pharmacokinetic-pharmacodynamic model of a single bolus dose of unfractionated heparin in paediatric patients

BACKGROUND

Unfractionated heparin (UFH) is the anticoagulant of choice in paediatric patients undergoing a variety of cardiac procedures. There are currently no population pharmacokinetic-pharmacodynamic (PKPD) models for UFH in paediatrics.

OBJECTIVE

The aim of the present study was to develop and evaluate a PKPD model of UFH in paediatrics.

METHODS

Data from 64 children who received 75-100 IU kg(-1) of UFH during cardiac angiography were analysed. Five blood samples were collected at baseline and at 15, 30, 45 and 120 min postdose. The UFH concentration was quantified using a protamine titration assay. The UFH effect was quantified using activated partial thromboplastin time (aPTT). A PKPD model was fitted using nonlinear mixed-effects modelling. Patient covariates such as gender, weight (WT) and fat-free mass (FFM) were tested. The final model was evaluated using the likelihood ratio test and visual predictive checks (VPCs).

RESULTS

A one-compartment model with linear elimination provided the best fit for the dose-concentration data. FFM was a significant covariate on clearance. A linear model provided the best fit for the concentration-effect data using aPTT as a biomarker for effect. The models performed well using VPCs. However, when used to simulate UFH infusion (at a much lower dose), the model overpredicted target aPTT responses.

CONCLUSIONS

A PKPD model to describe the time course of the UFH effect was developed in a paediatric population. FFM was shown to describe drug disposition well. However, when applied to smaller UFH infusion doses, the model overpredicted target aPTT responses. This unsuccessful extrapolation may be attributed to a possible nonlinear relationship for heparin PKPD.

Methods and software tools for design evaluation in population pharmacokinetics-pharmacodynamics studies

Population pharmacokinetic (PK)-pharmacodynamic (PKPD) models are increasingly used in drug development and in academic research; hence, designing efficient studies is an important task. Following the first theoretical work on optimal design for nonlinear mixed-effects models, this research theme has grown rapidly. There are now several different software tools that implement an evaluation of the Fisher information matrix for population PKPD. We compared and evaluated the following five software tools: PFIM, PkStaMp, PopDes, PopED and POPT. The comparisons were performed using two models, a simple-one compartment warfarin PK model and a more complex PKPD model for pegylated interferon, with data on both concentration and response of viral load of hepatitis C virus. The results of the software were compared in terms of the standard error (SE) values of the parameters predicted from the software and the empirical SE values obtained via replicated clinical trial simulation and estimation. For the warfarin PK model and the pegylated interferon PKPD model, all software gave similar results. Interestingly, it was seen, for all software, that the simpler approximation to the Fisher information matrix, using the block diagonal matrix, provided predicted SE values that were closer to the empirical SE values than when the more complicated approximation was used (the full matrix). For most PKPD models, using any of the available software tools will provide meaningful results, avoiding cumbersome simulation and allowing design optimization.

Dosing of Gentamicin in Patients With End-Stage Renal Disease Receiving Hemodialysis

The aim of this study was to evaluate dosing schedules of gentamicin in patients with end-stage renal disease and receiving hemodialysis. Forty-six patients were recruited who received gentamicin while on hemodialysis. Each patient provided approximately 4 blood samples at various times before and after dialysis for analysis of plasma gentamicin concentrations. A population pharmacokinetic model was constructed using NONMEM (version 5). The clearance of gentamicin during dialysis was 4.69 L/h and between dialysis was 0.453 L/h. The clearance between dialysis was best described by residual creatinine clearance (as calculated using the Cockcroft and Gault equation), which probably reflects both lean mass and residual clearance mechanisms. Simulation from the final population model showed that predialysis dosing has a higher probability of achieving target maximum concentration (Cmax) concentrations (> 8 mg/L) within acceptable exposure limits (area under the concentration-time curve [AUC] values > 70 and < 120 mg x h/L per 24 hours) than postdialysis dosing.

Development of a Semimechanistic Model to Describe the Pharmacokinetics of Gentamicin in Patients Receiving Hemodialysis

The aim of this study was to ascertain the most suitable dosing schedule for gentamicin in patients receiving hemodialysis. We developed a model to describe the concentration-time course of gentamicin in patients receiving hemodialysis. Using the model, an optimal dosing schedule was evaluated. Various dosing regimens were compared in their ability to achieve maximum concentration (C(max), > or = 8 mg/L) and area under the concentration time-curve (AUC > or = 70 mg x h/L and < or = 120 mg x h/L per 24 hours). The model was evaluated by comparing model predictions against real data collected retrospectively. Simulations from the model confirmed the benefits of predialysis dosing. The mean optimal dose was 230 mg administered immediately before dialysis. The model was found to have good predictive performance when simulated data were compared to data observed in real patients. In summary, a model was developed that describes gentamicin pharmacokinetics in patients receiving hemodialysis. Predialysis dosing provided a superior pharmacokinetic profile than did postdialysis dosing.

General practitioners’ views of pharmacists’ current and potential contributions to medication review and prescribing in New Zealand

INTRODUCTION: Internationally, non-medical practitioners are increasingly involved in tasks traditionally undertaken by general practitioners (GPs), such as medication review and prescribing. This study aims to evaluate GPs’ perceptions of pharmacists’ contributions to those services. METHODS: Semi-structured interviews were carried out in two localities with GPs whose patients had and had not undergone a pharmacist-led adherence support Medication Use Review (MUR). GPs were asked their opinions of pharmacists’ provision of MUR, clinical medication review and prescribing. Data were analysed thematically using NVivo 8 and grouped by strengths, weaknesses, opportunities and threats (SWOT) category. FINDINGS: Eighteen GPs were interviewed. GPs mentioned their own skills, training and knowledge of clinical conditions. These were considered GPs’ major strengths. GPs’ perceived weaknesses were their time constraints and heavy workloads. GPs thought pharmacists’ strengths were their knowledge of pharmacology and having more time for in-depth medication review than GPs. Nevertheless, GPs felt pharmacist-led medication reviews might confuse patients, and increase GP workloads. GPs were concerned that pharmacist prescribing might include pharmacists making a diagnosis. This is not the proposed model for New Zealand. In general, GPs were more accepting of pharmacists providing medication reviews than of pharmacist prescribing, unless appropriate controls, close collaboration and co-location of services took place. CONCLUSION: GPs perceived their own skills were well suited to reviewing medication and prescribing, but thought pharmacists might also have strengths and skills in these areas. In future, GPs thought that working together with pharmacists in these services might be possible in a collaborative setting. KEYWORDS: Community pharmacy services; general practitioners; New Zealand; primary health care; professional role

Population pharmacokinetics and pharmacodynamics of escitalopram in overdose and the effect of activated charcoal

AIMS

To describe the pharmacokinetics and pharmacodynamics (PKPD) of escitalopram in overdose and its effect on QT prolongation, including the effectiveness of single dose activated charcoal (SDAC).

METHODS

The data set included 78 escitalopram overdose events (median dose, 140mg [10-560mg]). SDAC was administered 1.0 to 2.6 h after 12 overdoses (15%). A fully Bayesian analysis was undertaken in WinBUGS 1.4.3, first for a population pharmacokinetic (PK) analysis followed by a PKPD analysis. The developed PKPD model was used to predict the probability of having an abnormal QT as a surrogate for torsade de pointes.

RESULTS

A one compartment model with first order input and first-order elimination described the PK data, including uncertainty in dose and a baseline concentration for patients taking escitalopram therapeutically. SDAC reduced the fraction absorbed by 31% and reduced the individual predicted area under the curve adjusted for dose (AUC(i) /dose). The absolute QT interval was related to the observed heart rate with an estimated individual heart rate correction factor (α= 0.35). The heart rate corrected QT interval (QT(c) ) was linearly dependent on predicted escitalopram concentration [slope = 87ms/(mgl(-1) )], using a hypothetical effect-compartment (half-life of effect-delay, 1.0h). Administration of SDAC significantly reduced QT prolongation and was shown to reduce the risk of having an abnormal QT by approximately 35% for escitalopram doses above 200mg.

CONCLUSIONS

There was a dose-related lengthening of the QT interval that lagged the increase in drug concentration. SDAC resulted in a moderate reduction in fraction of escitalopram absorbed and reduced the risk of the QT interval being abnormal.

A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies

Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models.

Adaptive optimal design for bridging studies with an application to population pharmacokinetic studies

PURPOSE

To develop and evaluate methods for conducting adaptive population pharmacokinetic bridging studies.

METHODS

An adaptive D-optimal design based on optimization of the population Fisher information matrix was used to determine the best sampling schedule for a target-population. Recruitment of the target-population was divided into batches and patients are assumed to enroll by batch. A prior-population model was used to determine the optimal sampling schedule for the first batch and to stabilise the data analysis in the interim iteration. Simulation studies were performed under two scenarios (1) the prior- and target-populations have similar pharmacokinetic profiles and (2) the pharmacokinetic profiles diverge significantly. A design criterion to determine early full enrollment was also proposed.

RESULTS

The target-population estimates obtained using the proposed method were compared to estimates obtained if the target-population was studied with a design optimized based on the prior-population model. The proposed method is shown to be not inferior in scenario (1) and superior in scenario (2). The criterion to determine early full enrollment was proven to be effective.

CONCLUSIONS

An adaptive optimal design method together with an early full enrollment criterion were evaluated and resulted in more accurate estimates for the target-population in bridging studies.

Estimating risk from underpowered, but statistically significant, studies: was APPROVe on TARGET?

WHAT IS KNOWN AND OBJECTIVE

The importance of statistical power is widely recognized from a pre-trial perspective, and when interpreting results that are not statistically significant. It is less well recognized that poor power can lead to inflated estimates of the effect size when statistically significant results are observed. We use trial simulations to quantify this bias, which we term 'significant-result bias'.

COMMENT

Significant-result bias is explained, and simulations are used to estimate possible significant-result bias in the rate of thrombotic events observed in the APPROVe trial. Statistically significant results, on outcomes for which there is empirical evidence of poor power, may provide inflated estimates of the size of effect.

WHAT IS NEW AND CONCLUSION

If independent evidence is available to judge the likely effect size of an underpowered statistical test, trial simulations can provide a method for quantifying significant-result bias.

Current enoxaparin dosing guidelines have dubious credibility

AIM

To assess the prescribing practice of enoxaparin in comparison to dosing guidelines.

METHOD

A prospective observational chart review of patients who received enoxaparin for the treatment of thrombosis at Dunedin Public Hospital between August 2007 and January 2008. Deviations in dose from guidelines were defined and recorded along with various clinical and demographic data of participants.

RESULTS

Fifty-nine patients (62 admissions) were recruited. Dose deviations occurred on 19 (30.7%) occasions. More dose deviations occurred at or close to guideline transition points (total body weight over 90 kg and/or creatinine clearance between 20-40 mL/min).

CONCLUSION

Current enoxaparin dosing guidelines are too simplistic and result in discord between dosing in practice and that approved by Medsafe.

Using disease progression models as a tool to detect drug effect

Generally, information required for approval of new drugs is dichotomous in that the drug is either efficacious and safe or not. Consequently, the purpose of most confirmatory clinical trials is to test the null hypothesis. The primary reasons for designing hypothesis testing trials are to provide the information required for approval using analyses techniques that are relatively straightforward and free of apparent assumptions. However, the information required for approval is very different from that used by prescribers for decision making. In the clinic, decisions must be made about dose adjustment for individual patients in the presence of additional therapies and co-morbidities. Choice of drug and dosing regimen is therefore a classical risk to benefit decision that is often poorly informed from the results of confirmatory trials. Therefore, providing answers to the more difficult question of how to use the drug in a clinical setting is essential.

Some considerations on the design of population pharmacokinetic studies

The goal of this manuscript is to introduce a framework for consideration of designs for population pharmacokinetic orpharmacokinetic-pharmacodynamic studies. A standard one compartment pharmacokinetic model with first-order input and elimination is considered. A series of theoretical designs are considered that explore the influence of optimizing the allocation of sampling times, allocating patients to elementary designs, consideration of sparse sampling and unbalanced designs and also the influence of single vs. multiple dose designs. It was found that what appears to be relatively sparse sampling (less blood samples per patient than the number of fixed effects parameters to estimate) can also be highly informative. Overall, it is evident that exploring the population design space can yield many parsimonious designs that are efficient for parameter estimation and that may not otherwise have been considered without the aid of optimal design theory.

Myocardial infarction and type II diabetes--preferential treatment for high risk patients?

OBJECTIVES

To compare variability of blood glucose concentration in patients with type II diabetes with (cases) and without (controls) myocardial infarction. A secondary objective was identification of predictive factors for higher blood glucose on discharge from hospital.

DESIGN

A retrospective matched case-control study.

PARTICIPANTS

Medical notes of 101 type II diabetic patients admitted with a myocardial infarction (MI) and 101 type II diabetic patients (controls) matched on gender and age with no MI were reviewed. Blood glucose concentrations over two consecutive 48-h periods were collected. Demographic data and therapy on admission/discharge were also collected.

RESULTS

Patient characteristics were comparable on recruitment excluding family history of cardiovascular disease (P=0.003), dyslipidaemia (P=0.004) and previous history of MI (P=0.007). Variability of blood glucose in cases was greater over the first 48 h compared with the second 48 h (P=0.03), and greater when compared with controls over the first 48 h (P=0.01). Cases with blood glucose on discharge >8.2 mmol / L (n=45) were less likely to have a history of previous MI (P=0.04), ischaemic heart disease (P=0.03) or hypertension (P=0.02).

CONCLUSIONS

Type II diabetics with an MI have higher and more variable blood glucose concentrations during the first 48 h of admission. Only cardiovascular 'high risk' patients had target blood glucose set on discharge. The desirability of all MI patients with diabetes, having standardized-glucose infusions to reduce variability of blood glucose, should be evaluated in a randomized controlled trial.

Modeling and stimulation for clinical trial design involving a categorical response: a phase II case study with naratriptan

PURPOSE

The overall aim of the present study was to investigate retrospectively the feasibility and utility of model-based clinical trial simulation as applied to the clinical development of naratriptan with effect measured on a categorical scale.

METHODS

A PK-PD model for naratriptan was developed by using information gathered from previous naratriptan and sumatriptan preclinical and clinical trials. The phase IIa naratriptan data were used to check the PK-PD model in its ability to describe future data. A further PK-PD model was developed by using the phase IIa naratriptan data, and a phase IIb trial was designed by simulation with the use of Matlab. The design resulting from clinical trial simulation was compared with that derived by using D-optimal design.

RESULTS

The PK-PD model showed reasonable agreement with the data observed in the phase IIa naratriptan clinical trial. Clinical trial simulation resulted in a design with four or five arms at 0 mg, 2.5 and/or 5 mg, 10 mg, and 20 mg, PD measurements to be taken at 0, 2, and 4 or 6 h and at least 150 patients per arm. A sub-D-optimal design resulted in two dosing arms at 0 and 10 mg and PD measurements to be taken at 1 and 2 h.

CONCLUSIONS

Clinical trial simulation is a useful tool for the quantitative assessment of the influence of the controllable factors and is the only tool for the quantitative assessment of the uncontrollable factors on the power of a clinical trial.

Development and implementation of the population Fisher information matrix for the evaluation of population pharmacokinetic designs

In population pharmacokinetic studies, the precision of parameter estimates is dependent on the population design. Methods based on the Fisher information matrix have been developed and extended to population studies to evaluate and optimize designs. In this paper we propose simple programming tools to evaluate population pharmacokinetic designs. This involved the development of an expression for the Fisher information matrix for nonlinear mixed-effects models, including estimation of the variance of the residual error. We implemented this expression as a generic function for two software applications: S-PLUS and MATLAB. The evaluation of population designs based on two pharmacokinetic examples from the literature is shown to illustrate the efficiency and the simplicity of this theoretic approach. Although no optimization method of the design is provided, these functions can be used to select and compare population designs among a large set of possible designs, avoiding a lot of simulations.