ORCID Profile
0000-0002-8213-8710
Current Organisation
UNSW Sydney
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Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 12-2021
DOI: 10.1097/FTD.0000000000000909
Abstract: Therapeutic drug monitoring is recommended to guide tacrolimus dosing because of its narrow therapeutic window and considerable pharmacokinetic variability. This study assessed tacrolimus dosing and monitoring practices in heart transplant recipients and evaluated the predictive performance of a Bayesian forecasting software using a renal transplant–derived tacrolimus model to predict tacrolimus concentrations. A retrospective audit of heart transplant recipients (n = 87) treated with tacrolimus was performed. Relevant data were collected from the time of transplant to discharge. The concordance of tacrolimus dosing and monitoring according to hospital guidelines was assessed. The observed and software-predicted tacrolimus concentrations (n = 931) were compared for the first 3 weeks of oral immediate-release tacrolimus (Prograf) therapy, and the predictive performance (bias and imprecision) of the software was evaluated. The majority (96%) of initial oral tacrolimus doses were guideline concordant. Most initial intravenous doses (93%) were lower than the guideline recommendations. Overall, 36% of initial tacrolimus doses were administered to transplant recipients with an estimated glomerular filtration rate of mL/min/1.73 m despite recommendations to delay the commencement of therapy. Of the tacrolimus concentrations collected during oral therapy (n = 1498), 25% were trough concentrations obtained at steady-state. The software displayed acceptable predictions of tacrolimus concentration from day 12 (bias: −6% 95%confidence interval, −11.8 to 2.5 imprecision: 16% 95% confidence interval, 8.7–24.3) of therapy. Tacrolimus dosing and monitoring were discordant with the guidelines. The Bayesian forecasting software was suitable for guiding tacrolimus dosing after 11 days of therapy in heart transplant recipients. Understanding the factors contributing to the variability in tacrolimus pharmacokinetics immediately after transplant may help improve software predictions.
Publisher: Wiley
Date: 27-10-2022
DOI: 10.1111/BCP.15091
Abstract: Identification of the most appropriate population pharmacokinetic model‐based Bayesian estimation is required prior to its implementation in routine clinical practice to inform tacrolimus dosing decisions. This study aimed to determine the predictive performances of relevant population pharmacokinetic models of tacrolimus developed from various solid organ transplant recipient populations in adult heart transplant recipients, stratified based on concomitant azole antifungal use. Concomitant azole antifungal therapy alters tacrolimus pharmacokinetics substantially, necessitating dose adjustments. Population pharmacokinetic models of tacrolimus were selected ( n = 17) for evaluation from a recent systematic review. The models were transcribed and implemented in NONMEM version 7.4.3. Data from 85 heart transplant recipients (2387 tacrolimus concentrations) administered the oral immediate‐release formulation of tacrolimus (Prograf) were obtained up to 391 days post‐transplant. The performance of each model was evaluated using: (i) prediction‐based assessment (bias and imprecision) of the in idual predicted tacrolimus concentration of the fourth dosing occasion (MAXEVAL = 0, FOCE‐I) from 1–3 prior dosing occasions and (ii) simulation‐based assessment (prediction‐corrected visual predictive check). Both assessments were stratified based on concomitant azole antifungal use. Regardless of the number of prior dosing occasions (1–3) and concomitant azole antifungal use, all models demonstrated unacceptable in idual predicted tacrolimus concentration of the fourth dosing occasion ( n = 152). The prediction‐corrected visual predictive check graphics indicated that these models inadequately predicted observed tacrolimus concentrations. All models evaluated were unable to adequately describe tacrolimus pharmacokinetics in adult heart transplant recipients included in this study. Further work is required to describe tacrolimus pharmacokinetics for our heart transplant recipient cohort.
Publisher: Wiley
Date: 06-11-2023
DOI: 10.1111/BCP.15566
Abstract: Existing tacrolimus population pharmacokinetic models are unsuitable for guiding tacrolimus dosing in heart transplant recipients. This study aimed to develop and evaluate a population pharmacokinetic model for tacrolimus in heart transplant recipients that considers the tacrolimus‐azole antifungal interaction. Data from heart transplant recipients ( n = 87) administered the oral immediate‐release formulation of tacrolimus (Prograf®) were collected. Routine drug monitoring data, principally trough concentrations, were used for model building ( n = 1099). A published tacrolimus model was used to inform the estimation of K a , V 2 /F, Q/F and V 3 /F. The effect of concomitant azole antifungal use on tacrolimus CL/F was quantified. Fat‐free mass was implemented as a covariate on CL/F, V 2 /F, V 3 /F and Q/F on an allometry scale. Subsequently, stepwise covariate modelling was performed. Significant covariates influencing tacrolimus CL/F were included in the final model. Robustness of the final model was confirmed using prediction‐corrected visual predictive check (pcVPC). The final model was externally evaluated for prediction of tacrolimus concentrations of the fourth dosing occasion ( n = 87) from one to three prior dosing occasions. Concomitant azole antifungal therapy reduced tacrolimus CL/F by 80%. Haematocrit (∆OFV = −44, P .001) was included in the final model. The pcVPC of the final model displayed good model adequacy. One recent drug concentration is sufficient for the model to guide tacrolimus dosing. A population pharmacokinetic model that adequately describes tacrolimus pharmacokinetics in heart transplant recipients, considering the tacrolimus–azole antifungal interaction was developed. Prospective evaluation is required to assess its clinical utility to improve patient outcomes.
Publisher: EManuscript Technologies
Date: 04-2012
Publisher: Wiley
Date: 05-12-2021
DOI: 10.1002/CPT.2113
Abstract: This study evaluated the ability of a pilot therapeutic drug monitoring (TDM) Advisory Service to facilitate vancomycin therapeutic target attainment within a real‐world clinical setting. The Service provided area under the concentration‐time curve (AUC)–guided vancomycin dose recommendations, using Bayesian forecasting software and clinical expertise, to prescribers at an Australian hospital. A retrospective audit of intravenous vancomycin therapy ( 48 hours) in adults (≥ 18 years old) was undertaken over a 54‐month period to evaluate attainment of established vancomycin pharmacokinetic harmacodynamic targets (AUC over 24 hours / minimum inhibitory concentration: 400–600) before (36‐month period) and after (18‐month period) Service implementation. Interrupted time series analysis was employed to evaluate monthly measures of the median proportion of therapy spent within the target range. Indices of time to target attainment were also assessed before and after Service implementation. The final cohort comprised 1,142 courses of vancomycin (816 patients) 835 courses (596 patients) and 307 courses (220 patients) administered before and after Service implementation, respectively. Prior to piloting the Service, the median proportion of time in the target range was 40.1% (95% CI, 34.3–46.0%) this increased by 10.4% (95% CI, 1.2–19.6%, P = 0.03) after the Service, and was sustained throughout the post‐Service evaluation period. Post‐Service target attainment at 48–72 hours after initiation of therapy was increased (7.8%, 95% CI, 1.3–14.3%, P = 0.02). The findings of this study provide evidence that a consultative TDM Service can facilitate attainment of vancomycin therapeutic targets however, optimization of the Service may further improve the use of vancomycin.
Publisher: Springer Science and Business Media LLC
Date: 11-08-2020
Publisher: Elsevier BV
Date: 05-2016
No related grants have been discovered for Ranita Kirubakaran.