Background: Initial algorithm-based dosing appears to be effective in predicting tacrolimus dose requirement. However, achieving and maintaining the target concentrations is challenging. Model... Show moreBackground: Initial algorithm-based dosing appears to be effective in predicting tacrolimus dose requirement. However, achieving and maintaining the target concentrations is challenging. Model-based follow-up dosing, which considers patient characteristics and pharmacological data, may further personalize treatment. This study investigated whether model-based follow-up dosing could lead to more accurate tacrolimus exposure than standard therapeutic drug monitoring (TDM) in kidney transplant recipients after an initial algorithm-based dose. Methods: This simulation trial included patients from a prospective trial that received an algorithm-based tacrolimus starting dose followed by TDM. For every measured tacrolimus predose concentration (C-0,C-obs), model-based dosing advice was simulated using the InsightRX software. Based on previous tacrolimus doses and C-0, age, body surface area, CYP3A4 and CYP3A5 genotypes, hematocrit, albumin, and creatinine, the optimal next dose, and corresponding tacrolimus concentration (C-0,C-pred) were predicted. Results: Of 190 tacrolimus C-0 values measured in 59 patients, 121 (63.7%; 95% CI 56.8-70.5) C-0,C-obs were within the therapeutic range (7.5-12.5 ng/mL) versus 126 (66.3%, 95% CI 59.6-73.0) for C-0,C-pred (P = 0.89). The median absolute difference between the tacrolimus C-0 and the target tacrolimus concentration (10.0 ng/mL) was 1.9 ng/mL for C-0,C-obs versus 1.6 ng/mL for C-0,C-pred. In a historical cohort of 114 kidney transplant recipients who received a body weight-based starting dose followed by TDM, 172 of 335 tacrolimus C-0 (51.3%) were within the therapeutic range (10.0-15.0 ng/mL). Conclusions: The combination of an algorithm-based tacrolimus starting dose with model-based follow-up dosing has the potential to minimize under- and overexposure to tacrolimus in the early posttransplant phase, although the additional effect of model-based follow-up dosing on initial algorithm-based dosing seems small. Show less
Background Iohexol plasma clearance-based glomerular filtration rate (GFR) determination provides an accurate method for renal function evaluation. This technique is increasingly advocated for... Show moreBackground Iohexol plasma clearance-based glomerular filtration rate (GFR) determination provides an accurate method for renal function evaluation. This technique is increasingly advocated for clinical situations that dictate highly accurate renal function assessment, as an alternative to conventional serum creatinine-based methods with limited accuracy or poor feasibility. In the renal transplantation setting, this particularly applies to living renal transplant donor eligibility screening, renal transplant function monitoring and research purposes. The dependency of current iohexol GFR estimation techniques on extensive sampling, however, has limited its clinical application. We developed a population pharmacokinetic model and limited sampling schedules, implemented in the online InsightRX precision dosing platform, to facilitate pragmatic iohexol GFR assessment. Methods Iohexol concentrations (n = 587) drawn 5 min to 4 h after administration were available from 67 renal transplant recipients and 41 living renal transplant donor candidates with measured iohexol GFRs of 27-117 mL/min/1.73 m(2). These were split into a model development (n = 72) cohort and an internal validation (n = 36) cohort. External validation was performed with 1040 iohexol concentrations from 268 renal transplant recipients drawn between 5 min and 4 h after administration, and extended iohexol curves up to 24 h from 11 random patients with impaired renal function. Limited sampling schedules based on one to four blood draws within 4 h after iohexol administration were evaluated in terms of bias and imprecision, using the mean relative prediction error and mean absolute relative prediction error. The total deviation index and percentage of limited sampling schedule-based GFR predictions within +/- 10% of those of the full model (P-10) were assessed to aid interpretation. Results Iohexol pharmacokinetics was best described with a two-compartmental first-order elimination model, allometrically scaled to fat-free mass, with patient type as a covariate on clearance and the central distribution volume. Model validity was confirmed during the internal and external validation. Various limited sampling schedules based on three to four blood draws within 4 h showed excellent predictive performance (mean relative prediction error < +/- 0.5%, mean absolute relative prediction error < 3.5%, total deviation index < 5.5%, P-10 > 97%). The best limited sampling schedules based on three to four blood draws within 3 h showed reduced predictive performance (mean relative prediction error < +/- 0.75%, mean absolute relative prediction error < 5.5%, total deviation index < 9.5%, P-10 >= 85%), but may be considered for their enhanced clinical feasibility when deemed justified. Conclusions Our online pharmacometric tool provides an accurate, pragmatic, and ready-to-use technique for measured GFR-based renal function evaluation for clinical situations where conventional methods lack accuracy or show limited feasibility. Additional adaptation and validation of our model and limited sampling schedules for renal transplant recipients with GFRs below 30 mL/min is warranted before considering this technique in these patients. Show less
Zwart, T.C.; Moes, D.J.A.R.; Boog, P.J.M. van der; Erp, N.P. van; Fijter, J.W. de; Guchelaar, H.J.; ... ; Heine, R. ter 2020
Background and Objective The immunosuppressant everolimus is increasingly applied in renal transplantation. Its extensive pharmacokinetic variability necessitates therapeutic drug monitoring,... Show moreBackground and Objective The immunosuppressant everolimus is increasingly applied in renal transplantation. Its extensive pharmacokinetic variability necessitates therapeutic drug monitoring, typically based on whole-blood trough concentrations (C-0). Unfortunately, therapeutic drug monitoring target attainment rates are often unsatisfactory and patients with on-target exposure may still develop organ rejection. As everolimus displays erythrocyte partitioning, haematocrit-normalised whole-blood exposure has been suggested as a more informative therapeutic drug monitoring marker. Furthermore, model-informed precision dosing has introduced options for more sophisticated dose adaptation. We have previously developed a mechanistic population pharmacokinetic model, which described everolimus plasma pharmacokinetics and enabled estimation of haematocrit-normalised whole-blood exposure. Here, we externally evaluated this model for its utility for model-informed precision dosing. Methods The retrospective dataset included 4123 pharmacokinetic observations from routine clinical therapeutic drug monitoring in 173 renal transplant recipients. Model appropriateness was confirmed with a visual predictive check. A fit-for-purpose analysis was conducted to evaluate whether the model accurately and precisely predicted a futureC(0)or area under the concentration-time curve (AUC) from prior pharmacokinetic observations. Bias and imprecision were expressed as the mean percentage prediction error (MPPE) and mean absolute percentage prediction error (MAPE), stratified on 6 months post-transplant. Additionally, we compared dose adaptation recommendations of conventionalC(0)-based therapeutic drug monitoring andC(0)- or AUC-based model-informed precision dosing, and assessed the percentage of differences between observed and haematocrit-normalisedC(0)( increment C-0) and AUC ( increment AUC) exceeding +/- 20%. Results The model showed adequate accuracy and precision forC(0)and AUC prediction at <= 6 months (MPPEC0: 8.1 +/- 2.5%, MAPE(C0): 26.8 +/- 2.1%; MPPEAUC: - 9.7 +/- 5.1%, MAPE(AUC): 13.3 +/- 3.9%) and > 6 months post-transplant (MPPEC0: 4.7 +/- 2.0%, MAPE(C0): 25.4 +/- 1.4%; MPPEAUC: - 0.13 +/- 4.8%, MAPE(AUC): 13.3 +/- 2.8%). On average, dose adaptation recommendations derived fromC(0)-based and AUC-based model-informed precision dosing were 2.91 +/- 0.01% and 13.7 +/- 0.18% lower than for conventionalC(0)-based therapeutic drug monitoring at <= 6 months, and 0.93 +/- 0.01% and 3.14 +/- 0.04% lower at > 6 months post-transplant. The increment C(0)and increment AUC exceeded +/- 20% on 13.6% and 14.3% of occasions, respectively. Conclusions We demonstrated that our population pharmacokinetic model was able to accurately and precisely predict future everolimus exposure from prior pharmacokinetic measurements. In addition, we illustrated the potential added value of performing everolimus therapeutic drug monitoring with haematocrit-normalised whole-blood concentrations. Our results provide reassurance to implement this methodology in clinical practice for further evaluation. Show less
