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Aims: We propose using glomerular filtration rate (GFR) as the physiological basis for distinguishing components of renal clearance. Methods: Gentamicin, amikacin and vancomycin are thought to be predominantly excreted by the kidneys. A mixed‐effects joint model of the pharmacokinetics of these drugs was developed, with a wide dispersion of weight, age and serum creatinine. A dataset created from 18 sources resulted in 27,338 drug concentrations from 9,901 patients. Body size and composition, maturation and renal function were used to describe differences in drug clearance and volume of distribution. Results: This study demonstrates that GFR is a predictor of two distinct components of renal elimination clearance: (1) GFR clearance associated with normal GFR and (2) non‐GFR clearance not associated with normal GFR. All three drugs had GFR clearance estimated as a drug‐specific percentage of normal GFR (gentamicin 39%, amikacin 90% and vancomycin 57%). The total clearance (sum of GFR and non‐GFR clearance), standardized to 70 kg total body mass, 176 cm, male, renal function 1, was 5.58 L/h (95% confidence interval [CI] 5.50‐5.69) (gentamicin), 7.77 L/h (95% CI 7.26‐8.19) (amikacin) and 4.70 L/h (95% CI 4.61‐4.80) (vancomycin). Conclusions: GFR provides a physiological basis for renal drug elimination. It has been used to distinguish two elimination components. This physiological approach has been applied to describe clearance and volume of distribution from premature neonates to elderly adults with a wide dispersion of size, body composition and renal function. Dose individualization has been implemented using target concentration intervention. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]