ORCID Profile
0000-0002-4031-2514
Current Organisation
The University of Auckland
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Publisher: Springer Science and Business Media LLC
Date: 20-07-2018
Publisher: Informa UK Limited
Date: 28-12-2004
Abstract: The aim of this report is to describe the use of WinBUGS for two datasets that arise from typical population pharmacokinetic studies. The first dataset relates to gentamicin concentration-time data that arose as part of routine clinical care of 55 neonates. The second dataset incorporated data from 96 patients receiving enoxaparin. Both datasets were originally analyzed by using NONMEM. In the first instance, although NONMEM provided reasonable estimates of the fixed effects parameters it was unable to provide satisfactory estimates of the between-subject variance. In the second instance, the use of NONMEM resulted in the development of a successful model, albeit with limited available information on the between-subject variability of the pharmacokinetic parameters. WinBUGS was used to develop a model for both of these datasets. Model comparison for the enoxaparin dataset was performed by using the posterior distribution of the log-likelihood and a posterior predictive check. The use of WinBUGS supported the same structural models tried in NONMEM. For the gentamicin dataset a one-compartment model with intravenous infusion was developed, and the population parameters including the full between-subject variance-covariance matrix were available. Analysis of the enoxaparin dataset supported a two compartment model as superior to the one-compartment model, based on the posterior predictive check. Again, the full between-subject variance-covariance matrix parameters were available. Fully Bayesian approaches using MCMC methods, via WinBUGS, can offer added value for analysis of population pharmacokinetic data.
Publisher: Wiley
Date: 04-06-2004
Publisher: Wiley
Date: 29-06-2022
DOI: 10.1111/NEP.14080
Abstract: To examine whether differences in tacrolimus and mycophenolic acid (MPA) pharmacokinetics contribute to the poorer kidney transplant outcomes experienced by Aboriginal Australians. Concentration‐time profiles for tacrolimus and MPA were prospectively collected from 43 kidney transplant recipients: 27 Aboriginal and 16 Caucasian. Apparent clearance (CL/F) and distribution volume (V/F) for each in idual were derived from concentration‐time profiles combined with population pharmacokinetic priors, with subsequent assessment for between‐group difference in pharmacokinetics. In addition, population pharmacokinetic models were developed using the prospective dataset supplemented by previously developed structural models for tacrolimus and MPA. The change in NONMEM objective function was used to assess improvement in goodness of model fit. No differences were found between Aboriginal and Caucasian groups or empirical Bayes estimates, for CL/F or V/F of MPA or tacrolimus. However, a higher prevalence of CYP3A5 expressers (26% compared with 0%) and wider between‐subject variability in tacrolimus CL/F (SD = 5.00 compared with 3.25 L/h/70 kg) were observed in the Aboriginal group, though these differences failed to reach statistical significance ( p = .07 and p = .08). There were no differences in typical tacrolimus or MPA pharmacokinetics between Aboriginal and Caucasian kidney transplant recipients. This means that Bayesian dosing tools developed to optimise tacrolimus and MPA dosing in Caucasian recipients may be applied to Aboriginal recipients. In turn, this may improve drug exposure and thereby transplant outcomes in this group. Aboriginal recipients appeared to have greater between‐subject variability in tacrolimus CL/F and a higher prevalence of CYP3A5 expressers, attributes that have been linked with inferior outcomes.
Publisher: Elsevier BV
Date: 12-1997
Publisher: Springer Science and Business Media LLC
Date: 07-1998
Publisher: Wiley
Date: 21-08-2014
DOI: 10.1111/BCP.12361
Publisher: Elsevier BV
Date: 04-2021
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 10-2019
Publisher: Wiley
Date: 15-03-2013
Publisher: Wiley
Date: 07-2020
DOI: 10.1111/BCP.14434
Abstract: Twenty years ago, target concentration intervention (TCI) was distinguished from therapeutic drug monitoring (TDM). It was proposed that TCI would bring more clinical benefit because of the precision of the approach and the ability to link TCI to principles of pharmacokinetics and pharmacodynamics to predict the dose required by an in idual (1). We examine the theory and clinical trial evidence supporting the benefits of TCI over TDM and conclude that in the digital age TDM should be abandoned and replaced by TCI.
Publisher: Wiley
Date: 14-07-2010
Publisher: Wiley
Date: 16-09-2019
DOI: 10.1002/PSP4.12460
Publisher: MDPI AG
Date: 23-12-1997
DOI: 10.3390/IJMS19010045
Publisher: Springer Science and Business Media LLC
Date: 05-05-2015
DOI: 10.1007/S40262-015-0277-Z
Abstract: Fat-free mass (FFM) is an important covariate for predicting drug clearance. Models for predicting FFM have been developed in adults but there is currently a paucity of mechanism-based models developed to predict FFM in children. The aim of this study was to develop and evaluate a model to predict FFM in children. A large dataset (496 females and 515 males) was available for model building. Subjects had a relatively wide range of age (3-29 years) and body mass index values (12-44.9 kg/m(2)). Two types of models (M1 and M2) were developed to describe FFM in children. M1 was fully empirical and based on a linear model that contained all statistically significant covariates and their interactions. M2 was a simpler model that incorporated a maturation process. M1 was developed to provide the best possible description of the data (i.e. a positive control). In addition, a published adult model (M3) was applied directly as a reference description of the data. The predictive performances of the three models were assessed by visual predictive checks and by using mean error (ME) and root mean squared error (RMSE). A test dataset (90 females and 86 males) was available for external evaluation. M1 consisted of nine terms with up to second-level interactions. M2 was a sigmoid hyperbolic model based on postnatal age with an asymptote at the adult prediction (M3). For the index dataset, the ME and 95 % CI for M1, M2 and M3 were 0.09 (0.03-0.16), 0.24 (0.14-0.33) and 0.29 (0.06-0.51) kg, respectively, and RMSEs were 1.12 (1.03-1.23), 1.58 (1.46-1.72) and 3.76 (3.54-3.97) kg. A maturation model that asymptoted to an established adult model was developed for prediction of FFM in children. This model was found to perform well in both male and female children however, the adult model performed similarly to the maturation model for females. The ability to predict FFM in children from simple demographic measurements is expected to improve understanding of human body structure and function with direct application to pharmacokinetics.
Publisher: Wiley
Date: 22-06-2023
DOI: 10.1111/BCP.15814
Abstract: The UK Prescribing Safety Assessment was modified for use in Australia and New Zealand (ANZ) as the Prescribing Skills Assessment (PSA). We investigated the implementation, student performance and acceptability of the ANZ PSA for final‐year medical students. This study used a mixed‐method approach involving student data ( n = 6440) for 2017–2019 (PSA overall score and 8 domain subscores). Data were also aggregated by medical school and included student evaluation survey results. Quantitative data were analysed using descriptive and multivariate analyses. The pass rate was established by a modified Angoff method. Thematic analyses of open‐ended survey comments were conducted. The average pass rate was slightly higher in 2017 (89%) which used a different examination to 2018 (85%) and 2019 (86%). Little difference was identified between schools for the PSA overall performance or domain subscores. There was low intercorrelation between subscores. Most students provided positive feedback about the PSA regarding the interface and clarity of questions, but an average of 35% reported insufficient time for completion. Further, 70% on average felt unprepared by their school curricula for the PSA, which is in part explained by the low prescribing experience 69% reported completing ≤10 prescriptions during training. The ANZ PSA was associated with high pass rates and acceptability, although student preparedness was highlighted as a concern for further investigation. We demonstrate how a collaboration of medical schools can adapt a medical education assessment resource (UK PSA) as a means for fulfilling an unmet need.
Publisher: Springer Science and Business Media LLC
Date: 10-03-2013
DOI: 10.1007/S00228-013-1478-8
Abstract: The aims of this study were to develop a population pharmacokinetic model for allopurinol and oxypurinol and to explore the influence of patient characteristics on allopurinol and oxypurinol pharmacokinetics. Data from 92 patients with gout and 12 healthy volunteers were available for analysis. A parent-metabolite model with a two-compartment model for allopurinol and a one-compartment model for oxypurinol was fitted to the data using non-linear mixed effects modelling. Renal function, fat-free mass (FFM) and diuretic use were found to predict differences in the pharmacokinetics of oxypurinol. The population estimates for allopurinol clearance, inter-compartmental clearance, central and peripheral volume were 50, 142 L/h/70 kg FFM, 11.4, 91 L/70 kg FFM, respectively, with a between-subject variability of 33 % (coefficient of variance, CV) for allopurinol clearance. Oxypurinol clearance and volume of distribution were estimated to be 0.78 L/h per 6 L/h creatinine clearance/70 kg FFM and 41 L/70 kg FFM in the final model, with a between-subject variability of 28 and 15 % (CV), respectively. The pharmacokinetic model provides a means of predicting the allopurinol dose required to achieve target oxypurinol plasma concentrations for patients with different magnitudes of renal function, different body mass and with or without concomitant diuretic use. The model provides a basis for the rational dosing of allopurinol in clinical practice.
Publisher: Wiley
Date: 15-09-2015
DOI: 10.1111/BCP.12729
No related grants have been discovered for Nick Holford.