Several Tac population pharmacokinetic models developed using the non-linear mixed effects methods have been reported in adult renal transplant patients (See Table below). Zhao et al 79 have revised and externally evaluated some of them. This allowed to compare models differing in i) analytical methods used for measurement of concentrations used to develop the model, this being a relevant aspect to be taken into account when a published model is going to be applied as support during the TDM ii) number of Patients/samples included iii) identified covariates as predictors of interindividual variability, among other aspects.
Up until now, the majority of them were based on a two-open compartment model with first order absorption. Some models tried to better characterize the delayed absorption process of Tac using transit compartment models (i.e. Erlang distribution model). Most of the models included inter-occasion variability associated with the main PK parameters to better describe PK variability of parameters from one occasion to another. In general, the models achieved to include all well-known clinically plausible covariates such as demographical (age, weight, fat free mass), biochemical (hematocrit33), the type of Tac formulation77, CYP3A5 41,42,78 and CYP3A446,79,80 genotype, which may explain the Tac PK variability. Some of them also considered the inclusion of post-transplant time (POD) although this variable is considered as a surrogate for many time-dependent factors such as albumin, HCT, corticosteroids dose, among other confusing factors. The dose-dependency in clearance was also incorporated in two of the revised models (posar les dues cites) by Zhao et al79,. Although the real cause of non-linearity could not be elucidated it was rather related to the POD alterations in absorption due to recovery of gastrointestinal function, the activity of P-glycoprotein, and CYPA3 or concentration dependent–binding to erythrocytes among others.
Analyzing all the information which they provide, the most relevant aspects (model type, population PK parameters and model variability explanation) are highlighted in the table below. It is worth noting that the most influential covariate in all of them when assayed
29 was the genetic polymorphisms of CYP3A. The prediction-based and simulation-based performances were evaluated for all the models. The population prediction error calculation as accuracy measure allowed to know the credibility of using the population PK parameters as priors and normalized prediction distribution errors (npdes), indicated the feasibility of the model to be used for new scenario simulations.
According to these results, so far, the population PK model that was reported by Storset et al33 was superior to the others regarding to prediction capability but did not show appropriate capability to be used for simulations. It is worth noting that this model included the most relevant well-known clinically covariates, thus indicating, that dosage of Tac based on hematocrit, CYP3A5 polymorphism and demographic characteristics (fat free mass and/or age) could lead to a better Tacrolimus exposure.
Therefore, further investigation is still required to better explain interindividual variability and to identify the confusing factors leading to the well-known POD influence in Tac PK.
30 Table 2. Summary of the Most Relevant Published Tacrolimus Population Pharmacokinetic Models.
No. of Patients Pharmacokinetic model PK Parameters Model Variability
Benkali et al61
32 model building Absorption: Erlang model with 3 transit compartments.
Disposition: 2-Compartment model with first-order elimination
ktr = 6.5 h-1
Absorption: Erlang model with 3 transit compartments.
Disposition: 2-Compartment model with first-order elimination
ktr = 3.3 h-1 CYP3A5 = 0 for CYP3A5
non-expresser
CYP3A5 = 1 for CYP3A5 expresser
IIV ktr = 52 %
31 Table 2. Summary of the Most Relevant Published Tacrolimus Population Pharmacokinetic Models. (cont.)
No. of Patients Pharmacokinetic model PK Parameters Model Variability
Woillard et al82
73 model building
Absoprtion: Erlang model with 3 transit compartments.
Disposition: 2-Compartment model with first-order elimination
ktr = (θ1 θ2FORM) h-1
FORM = 0 patient received Advagraf®;
FORM = 1; patient received Prograf®
CYP3A5 = 0 for CYP3A5 non-expresser, CYP3A5 = 1 for CYP3A5 expresser
32 Table 2. Summary of the Most Relevant Published Tacrolimus Population Pharmacokinetic Models. (cont.)
No. of Patients Pharmacokinetic model PK Parameters Model Variability
Musuamba et al83
65 model building
Absorption: first order + lag time
Disposition 2-Compartment model + first order elimination
ka = 0.45 h-1
Where CYP3A5 = 0 for CYP3A5 non-expresser CYP3A5 = 15.4 for CYP3A5 expresser ABCB1 = 0 for CC-GG-CC non-carriers ABCB1 = 7.6 for CC-GG-CC carriers
IIV ka =91%
33 Table 2. Summary of the Most Relevant Published Tacrolimus Population Pharmacokinetic Models. (cont.)
No. of
Patients Pharmacokinetic model PK Parameters Model Variability
Zuo et al79
161 (Chinese)
Absorption: first-order absorption
Disposition: 1-Compartment model with first order elimination
ka = 3.09 h-1 (fixed)
- CYP3A = 0.982 for CYP3A5*1/*1, CYP3A5*1/*3 or CYP3A4*1/*1 genotype;
- CYP3A = 0.77 for CYP3A5*3/*3, CYP3A4*1/*1G or CYP3A4*1G/*1G genotype;
- CYP3A = 0.577 for CYP3A5*3/*3 or CYP3A4*1/*1 genotype
IIV CL/F = 24.2 % IIV Vd/F = 58.5 % Prop RE = 19.8 % Add RRE = 1.47 ng/mL
34 Table 2. Summary of the Most Relevant Published Tacrolimus Population Pharmacokinetic Models. (cont.)
No. of Patients Pharmacokinetic model PK Parameters Model Variability
Han et al84
102 Absorption: first-order absorption
Disposition: 1-Compartment model with first order elimination
ka = 3.43 h-1 Lag time = 0.25 h (fixed) CL/F= θ1 (1 + θ2 (POD-9.6)) θ3 CYP3A5 L/h
θ1 = 21.9 θ2 = 0.0119
θ3 = 0.816 Vd/F = 205 L
- CYP3A5 = 1 for CYP3A5 non-expresser - CYP3A5 = 0 for CYP3A5 non-expresser
IIV CL/F = 40.9 % IIV ka =112%
IIV Vd/F=59.1%
Prop RE = (2 = 3.75)
POD: Post-Operative Days
35 Table 2. Summary of the Most Relevant Published Tacrolimus Population Pharmacokinetic Models. (cont.)
No. of
Patients Pharmacokinetic model PK Parameters Model Variability
Storset et al33
69
Absorption: first-order absorption, with a lag- time. A study- specific absorption rate and lag time improved the data fit
of substudy 2
Disposition: 2-Compartment model with and first-order elimination.
36
V2/Fn = θ12 ·(FFM/60) L θ12 = 424
Q/Fn = θ13 ·(FFM/60)0.75 L/h θ13 = 37.3
Where CYP3A5 = 0.51 for CYP3A5 expresser CYP3A5 = 1 for CYP3A5 non-expressers.
See details on paper for Bioavailability (F) coefficients
Tacrolimus whole blood values were standardised to a HCT value of 45 % (see paper for details).
FFM: Fat free mass, POD: Post-Operative Days
37 Table 2. Summary of the Most Relevant Published Tacrolimus Population Pharmacokinetic Models. (cont.)
No. of
Patients Pharmacokinetic model PK Parameters Model Variability
Asberg et al85
69
Absorption: first-order absorption, with a lag- time Disposition: 2-Compartment model with and first-order
elimination.
F = 0.63 (CYP3A5 expressers) F = 1(CYP3A5 non-expressers) ka = 1.04 h-1
Lag time = 1.0 h (first week) Lag time = 0.15 h (week 2–4) Lag time = 0.59 h (after first month)
CL/F = θ1 (FFM/59)0.75 L/h (CYP3A5 expressers) θ1 = 26.7
CL/F = θ2 (FFM/59)0.75 L/h (CYP3A5 non-expressers) θ2 = 21.2 Lag time = 0.57 (after first month) CL/F = 13.2 (CYP3A5 expressers) CL/F = 11.0 (CYP3A5 non-expressers)
V1/F = 295 V2/F = 7736 Q/F = 32.3
FFM: Fat free mass, BMI: Body Mass Index
38 Table 2. Summary of the Most Relevant Published Tacrolimus Population Pharmacokinetic Models. (cont.)
No. of Patients Pharmacokinetic model PK Parameters Model Variability
Bergmann et al86
173
Absorption: first-order absorption, with a lag- time Disposition: 2-Compartment model with and first-order
elimination.
CYP3A5 = 0 for CYP3A5 non-expresser, otherwise CYP3A5 = 1;
POD max at 90 days; PredCmax,unbound is Cmax free prednisolone (nmol/L)
IIV ka = 47.6 %
FFM: Fat free mass; BMI: Body Mass Index; WT: Body Weight; POD: Post-Operative Days;
39
HYPOTHESIS
The calcineurin inhibitor Tac is used to prevent acute rejection after renal transplant.
Unfortunately, the clinical use of Tac is complicated by its considerable toxicity, narrow therapeutic window, and high interindividual pharmacokinetic variability.
Therapeutic drug monitoring is commonly applied to individualize Tac therapy in renal transplant recipients using trough concentrations. When concentrations are out of the target range, the physicians roughly estimate what should be the appropriate change of dose. Despite trough concentrations are the most used exposure parameters, the AUC correlates better with the clinical outcomes. In the clinical setting, an AUC tiered-dosing is not feasible, thus an alternate approach is that based on limited-sampling strategy by means of Bayesian prediction. In this sense, the use of a PPK model can assist for the first dose calculation at the start of treatment but also for dose adaptation based on predefined target by means of MAPB forecasting technique, supporting TDM.
Several Tacrolimus PPK models have been published that includes the CYP3A5 polymorphism to explain part of the interindividual variability. However, recent discovery of new SNPs has led to further investigations on that file aiming to reduce the unexplained interindividual variability in Tacrolimus exposure.
40
OBJECTIVES
The main objective of the present work was to design a population-based Bayesian prediction tool for initial dose calculation and dose adaptation during the post-transplant period through:
1. Characterizing the Tacrolimus population PK using an intensive sampling and confirming the best limiting sampling strategy to be applied during dose adaptation.
2. To deeply Investigate in tacrolimus pharmacogenetic predictors of interindividual variability
3. Implementing new genetic information as well as other clinical factors to generate a refined population pharmacokinetic model reducing unexplained variability.
41