Individualising tacrolimus therapy in adult heart transplant recipients

Tacrolimus is the key immunosuppressant used in most solid-organ transplant recipients, including heart transplants, to prevent graft rejection. However, tacrolimus dosing strategies are complicated by the narrow therapeutic window and considerable pharmacokinetic variability. Individualising lifelong tacrolimus therapy to avoid graft rejection and minimise adverse effects is essential for heart transplant recipients. This thesis aimed to investigate the individualisation of tacrolimus therapy in adult heart transplant recipients using the pharmacokinetic modelling approach. In Chapter 1, I present an overview of tacrolimus clinical pharmacology, including clinical factors influencing tacrolimus pharmacokinetics (e.g., concomitant azole antifungal therapy). In Chapter 2, I explore tacrolimus dosing and monitoring practices in heart transplant recipients (n=87) at St. Vincent’s Hospital Sydney, a major heart transplant centre in Australia. Additionally, I assess the ability of a Bayesian dosing software, approved by the Therapeutic Goods Administration to predict tacrolimus concentrations in heart transplant recipients. Tacrolimus dosing and monitoring practices were discordant with the hospital guidelines. The population pharmacokinetic model integrated within the software was suitable in guiding tacrolimus dosing only after 11 days of therapy. This finding necessitated the identification of other model(s) that might be more suitable for use in heart transplant recipients, particularly for the immediate post-transplantation phase. In Chapter 3, I conduct a systematic review summarising published population pharmacokinetic models of tacrolimus (n=69) developed from various organ transplant recipient populations. In Chapter 4, I select relevant tacrolimus models (n=17) from the systematic review and evaluated their predictive performance in heart transplant recipients (n=85). The evaluated models displayed poor predictive performances. This finding complements the work from Chapters 2 and 3 highlighting a tacrolimus model for heart transplant recipients is required. In Chapter 5, I successfully develop a tacrolimus population pharmacokinetic model for heart transplant recipients. The model incorporated the effects of concomitant azole antifungal use, haematocrit, and body weight on tacrolimus pharmacokinetics. Model evaluation in an independent heart transplant recipient cohort displayed good model performance. The model can be implemented in clinical practice to individualise tacrolimus dosing in heart transplant recipients. In Chapter 6, I discuss the clinical implication of this work and recommendations for future research

An Evaluation of Consumers’ Perceptions Regarding “Modern Medicines” in Penang, Malaysia

The objective of this study was to evaluate consumers’ perceptions regarding “modern medicines” in Penang, Malaysia. To conduct this exploratory study, qualitative techniques were used. Consumers more than 19 years of age and could speak English, who had visited a pharmacy in the last 30 days, were included from the four major areas of Penang. Eighteen interviews were conducted until the point of saturation. The interviews were audio-taped and then transcribed verbatim for thematic content analysis. Many consumers correctly identified the major characteristics and properties of modern medicines; however, others raised doubts regarding the safety, quality and efficacy of “modern medicines”. There were many misconceptions such as “all modern medicines can cause dependence”, traditional medicines are completely “free of side-effects” and “Western medicines cure while Chinese medicines don’t”. Color was also considered a strong determinant of the safety and characteristics of a medicine. Regarding consumers’ “medicine information seeking behavior”, many consumers would seek information from doctors and pharmacists; however, there were others, who would look for books, or get it from the internet and friends. Of concern many consumers emphasized that while “self-searching for drug information” they would only look for side-effects. Misconceptions regarding medicine-taking behavior, medicine use and compliance were also identified. Though several consumers complied with the medicine-taking instructions, many reported that they would stop taking medicines, once they feel better. Though many consumers correctly identified the characteristics of “modern medicines”, misconceptions regarding "medicine information sources and “medicine-taking behavior” were rampant. The situation demands corrective actions including community-oriented educational campaigns to improve “medicine use” in the society

Evaluation of published population pharmacokinetic models to inform tacrolimus dosing in adult heart transplant recipients

Background and Aim: 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. Methods: 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 individual 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. Results: Regardless of the number of prior dosing occasions (1–3) and concomitant azole antifungal use, all models demonstrated unacceptable individual 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. Conclusion: 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.</p

Adaptation of a population pharmacokinetic model to inform tacrolimus therapy in heart transplant recipients

Aim: 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. Methods: 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 Ka, V2/F, Q/F and V3/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, V2/F, V3/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. Results: 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. Conclusion: 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.</p