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

The role of population PK-PD modelling in paediatric clinical research

Children differ from adults in their response to drugs. While this may be the result of changes in dose exposure (pharmacokinetics [PK]) and/or exposure response (pharmacodynamics [PD]) relationships, the magnitude of these changes may not be solely reflected by differences in body weight. As a consequence, dosing recommendations empirically derived from adults dosing regimens using linear extrapolations based on body weight, can result in therapeutic failure, occurrence of adverse effect or even fatalities. In order to define rational, patient-tailored dosing schemes, population PK-PD studies in children are needed. For the analysis of the data, population modelling using non-linear mixed effect modelling is the preferred tool since this approach allows for the analysis of sparse and unbalanced datasets. Additionally, it permits the exploration of the influence of different covariates such as body weight and age to explain the variability in drug response. Finally, using this approach, these PK-PD studies can be designed in the most efficient manner in order to obtain the maximum information on the PK-PD parameters with the highest precision. Once a population PK-PD model is developed, internal and external validations should be performed. If the model performs well in these validation procedures, model simulations can be used to define a dosing regimen, which in turn needs to be tested and challenged in a prospective clinical trial. This methodology will improve the efficacy/safety balance of dosing guidelines, which will be of benefit to the individual child

Continuous ambulatory peritoneal dialysis: pharmacokinetics and clinical outcome of paclitaxel and carboplatin treatment

Purpose: Administration of chemotherapy in patients with renal failure, treated with hemodialysis or continuous ambulatory peritoneal dialysis (CAPD) is still a challenge and literature data is scarce. Here we present a case study of a patient on CAPD, treated with weekly and three-weekly paclitaxel/ carboplatin for recurrent ovarian cancer. Experimental: During the first, second and ninth cycle of treatment, blood, urine and CAPD samples were collected for pharmacokinetic analysis of paclitaxel and total and unbound carboplatin-derived platinum. Results: Treatment was well tolerated by the patient. No excessive toxicity was observed and at the e

Phase i trial of axitinib combined with platinum doublets in patients with advanced non-small cell lung cancer and other solid tumours

BACKGROUND: This phase I dose-finding trial evaluated safety, efficacy and pharmacokinetics of axitinib, a potent and selective secondgeneration inhibitor of vascular endothelial growth factor receptors, combined with platinum doublets in patients with advanced non-small cell lung cancer (NSCLC) and other solid tumours. METHODS: In all, 49 patients received axitinib 5mg twice daily (b.i.d.) with paclitaxel/carboplatin or gemcitabine/cisplatin in 3-week cycles. Following determination of the maximum tolerated dose, a squamous cell NSCLC expansion cohort was enroled and received axitinib 5mg b.i.d. with paclitaxel/carboplatin. RESULTS: Two patients experienced dose-limiting toxicities: febrile neutropenia (n¼1) in the paclitaxel/carboplatin cohort and fatigue (n¼1) in the gemcitabine/cisplatin cohort. Common nonhaematologic treatment-related adverse events were hypertension (36.7%), diarrhoea (34.7%) and fatigue (28.6%). No gradeX3 haemoptysis occurred among 12 patients with squamous cell NSCLC. The objective response rate was 37.0% for patients receiving axitinib/paclitaxel/carboplatin (n¼27) and 23.8% for patients receiving axitinib/gemcitabine/cisplatin (n¼21). Pharmacokinetics of axitinib and chemotherapeutic agents were similar when administered alone or in combination. CONCLUSION: Axitinib 5mg b.i.d. may be combined with standard paclitaxel/carboplatin or gemcitabine/cisplatin regimens without evidence of overt drug–drug interactions. Both combinations demonstrated clinical efficacy and were well tolerated.This study was sponsored by Pfizer Inc. Support was provided in part by National Institutes of Health grant P30 CA006927 to the Fox Chase Cancer Center. We thank the patients who participated in this study and the physicians who referred them, as well as the study coordinators and data managers, Shelley Mayfield and Carol Martins at Pfizer Inc. for support of the study conduct, and Gamal ElSawah, Pfizer Medical Affairs, for his review of the manuscript. Medical writing support was provided by Joanna Bloom, of UBC Scientific Solutions (Southport, CT, USA) and Christine Arris at ACUMED (Tytherington, UK) and was funded by Pfizer In

Design of clinical pharmacology trials

1. There are a variety of methods that could be used to increase the efficiency of the design of experiments. However, it is only recently that such methods have been considered in the design of clinical pharmacology trials. 2. Two such methods, termed data-dependent (e.g. simulation) and data-independent (e.g. analytical evaluation of the information in a particular design), are becoming increasingly used as efficient methods for designing clinical trials. These two design methods have tended to be viewed as competitive, although a complementary role in design is proposed here. 3. The impetus for the use of these two methods has been the need for a more fully integrated approach to the drug development process that specifically allows for sequential development (i.e. where the results of early phase studies influence later-phase studies). 4. The present article briefly presents the background and theory that underpins both the data-dependent and -independent methods with the use of illustrative examples from the literature. In addition, the potential advantages and disadvantages of each method are discussed

Prospective evaluation of a D-optimal designed population pharmacokinetic study

Recently, methods for computing D-optimal designs for population pharmacokinetic studies have become available. However there are few publications that have prospectively evaluated the benefits of D-optimality in population or single-subject settings. This study compared a population optimal design with an empirical design for estimating the base pharmacokinetic model for enoxaparin in a stratified randomized setting. The population pharmacokinetic D-optimal design for enoxaparin was estimated using the PFIM function (MATLAB version 6.0.0.88). The optimal design was based on a one-compartment model with lognormal between subject variability and proportional residual variability and consisted of a single design with three sampling windows (0-30 min, 1.5-5 hr and 11 - 12 hr post-dose) for all patients. The empirical design consisted of three sample time windows per patient from a total of nine windows that collectively represented the entire dose interval. Each patient was assigned to have one blood sample taken from three different windows. Windows for blood sampling times were also provided for the optimal design. Ninety six patients were recruited into the study who were currently receiving enoxaparin therapy. Patients were randomly assigned to either the optimal or empirical sampling design, stratified for body mass index. The exact times of blood samples and doses were recorded. Analysis was undertaken using NONMEM (version 5). The empirical design supported a one compartment linear model with additive residual error, while the optimal design supported a two compartment linear model with additive residual error as did the model derived from the full data set. A posterior predictive check was performed where the models arising from the empirical and optimal designs were used to predict into the full data set. This revealed the optimal'' design derived model was superior to the empirical design model in terms of precision and was similar to the model developed from the full dataset. This study suggests optimal design techniques may be useful, even when the optimized design was based on a model that was misspecified in terms of the structural and statistical models and when the implementation of the optimal designed study deviated from the nominal design