# Population pharmacokinetics of mycophenolic acid and Bayesian estimator in lung transplant adults recipients in the early post-transplant period

Juliette Kauv, Séverine Feuillet, Barrail-Tran Aurélie, Alexandra Delemos, Tiphaine Legrand, Céline Verstuyft, Jérôme Le Pavec, Emmanuelle Comets, Valérie Furlan

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Juliette Kauv, Séverine Feuillet, Barrail-Tran Aurélie, Alexandra Delemos, Tiphaine Legrand, et al. Population pharmacokinetics of mycophenolic acid and Bayesian estimator in lung transplant adults recipients in the early post-transplant period. European Journal of Pharmaceutical Sciences, 2025, 214, pp.107290. 10.1016/j.ejps.2025.107290. hal-05296420

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# Population pharmacokinetics of mycophenolic acid and Bayesian estimator in lung transplant adults recipients in the early post-transplant period

Juliette Kauv<sup>a</sup>, Séverine Feuillet<sup>b</sup>, Aurélie Barrail-Tran<sup>c,d</sup>, Alexandra Delemos<sup>b</sup>, Tiphaine Legrand<sup>a</sup>, Céline Verstuyft<sup>e</sup>, Jérôme Le Pavec<sup>b,f</sup>, Emmanuelle Comets<sup>g,1</sup>, Valérie Furlan<sup>a,1,∗</sup>

## ARTICLE INFO

### Keywords:

Population pharmacokinetics

Mycophenolic acid

Lung transplantation

Early post-transplant period

## ABSTRACT

Mycophenolic acid (MPA), administered as its prodrug Mycophenolate mofetil (MMF), is widely used to prevent rejection after organ solid transplantation. Few population pharmacokinetic studies of MPA in lung transplantation are available, so the present study was designed to describe the population pharmacokinetics (PK) of MPA and develop a maximum a posteriori Bayesian estimator of MPA area under the curve (AUC0-12h) in lung transplant (LT) recipients in the early post-transplant period.

We conducted a single-center retrospective study in the Plessis-Robinson hospital (France), including LT recipients receiving MMF in whom at least one concentration-time profile was available during the first 3 months post-transplantation. MPA was measured before intake and 0.5, 1, 2, 4 ± 6 hours post-administration. Patients were divided into an index group (N=72) and a validation group (N=46). A population PK model and a Bayesian estimator with three sampling times were built with the index dataset and externally evaluated with the validation dataset. Analyses were performed using nonlinear mixed-effects models with Monolix® software.

The PK model was built using 97 MPA profiles in the index dataset. MPA PK was best described using a two-compartment model with a lag time and first-order absorption and elimination. The significant covariates on the MPA apparent clearance were creatinine, body weight and ciclosporin co-administration and the post-transplantation time on the apparent volume of the central compartment. The model was successfully evaluated in the 60 MPA profiles from the validation dataset. The best Bayesian estimator included samples just before intake, 1 and 4 hours post-administration. The prediction of AUC was unbiased in both datasets and had a precision around 20 %. This is the first Bayesian estimator allowing the prediction of AUC in LT patients without cystic fibrosis in the early post-transplant period, providing a tool to improve the therapeutic drug monitoring of MPA.

## 1. Introduction

Mycophenolate mofetil (MMF) is an immunosuppressive drug widely

used to prevent rejection after solid organ transplantation (Bergan et al., 2021). After oral administration, MMF is rapidly and completely converted into mycophenolate acid (MPA), the active metabolite, by

<footnote><sup>a</sup> Laboratoire de Pharmacologie, CHU du Kremlin-Bicêtre, APHP, Université Paris Saclay, Le Kremlin-Bicêtre, France</footnote>

<footnote><sup>b</sup> Service de Pneumologie et Transplantation Pulmonaire, Hôpital Marie-Lannelongue, Le Plessis-Robinson, France</footnote>

<footnote><sup>c</sup> Université Paris-Saclay, CEA, INSERM, UMR1184, Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT Department), Fontenay-aux-Roses/Le Kremlin-Bicêtre, France</footnote>

<footnote><sup>d</sup> Service de Pharmacie, AP-HP. Université Paris-Saclay, Hôpital Bicêtre, Le Kremlin-Bicêtre, France</footnote>

<footnote><sup>e</sup> Service de Génétique Moléculaire et Pharmacogénétique, CHU du Kremlin-Bicêtre, APHP, Université Paris Saclay, Le Kremlin-Bicêtre, France</footnote>

<footnote><sup>f</sup> INSERM UMR_S999, Hôpital Marie-Lannelongue, Le Plessis-Robinson, France</footnote>

<footnote><sup>g</sup> Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME, F-75018 Paris, France, Univ Rennes, Inserm, EHESP, Irset (Institut de recherche en santé, environnement et travail) - UMR_S 1085, F-35000 Rennes, France</footnote>

<footnote>* Corresponding author at: Laboratoire de Pharmacologie, CHU du Kremlin-Bicêtre, APHP, Université Paris Saclay, 78 rue du Général Leclerc, 94270 Le Kremlin-Bicêtre, France.

*E-mail address:* valerie.furlan@aphp.fr (V. Furlan).</footnote>

<footnote n="1">¹ Both authors contributed equally to this work</footnote>
intestinal and hepatic esterases (Benjanuwattra et al., 2020). MPA is a selective, reversible, and non-competitive inhibitor of inosine monophosphate dehydrogenase (IMPDH) involved in the de novo purine synthesis pathway (Bergan et al., 2021). The oral bioavailability of MPA after administration of MMF increases from 80.7 % to 94 % (Staatz & Tett, 2007). MPA is highly bound to plasma proteins, especially to albumin (97 to 99 %) (Monchaud & Marquet, 2009), and is mainly metabolized by UDP-glucuronosyl transferases (UGTs) in the liver, instestine and kidney (Benjanuwattra et al., 2020; Monchaud & Marquet, 2009), leading mainly to an inactive metabolite, MPA-7-O-glucuronide (MPAG) (Bergan et al., 2021). Furthermore, MPA undergoes enterohepatic circulation (EHC), resulting in a second peak of MPA plasma concentration observed 6 to 12 hours after administration (Monchaud & Marquet, 2009). Indeed, MPAG can enter into hepatocytes via OATP1B1 and 1B3 transporters (Benjanuwattra et al., 2020), and is excreted into the bile through MRP2s encoded by ABCC2 (Bergan et al., 2021), present at the apical membrane of hepatocytes (Benjanuwattra et al., 2020). In the gastrointestinal tract, MPAG is then deconjugated by the action of bacterial β-glucuronidases, resulting in MPA which is reabsorbed in the colon administration (Monchaud & Marquet, 2009).

Previous studies in renal transplantation have demonstrated that MPA Area Under the Curve (AUC) may predict the risk of acute rejection and toxicity (Mourad et al., 2001; van Gelder et al., 1999). However, single-point sampling at particular trough levels has shown a poor correlation with the risk of acute rejection and toxicity (Knight & Morris, 2008). The wide variability of MPA and its concentration-effect relationship are considered the most compelling arguments favoring therapeutic drug monitoring (TDM) for MPA, and individualization of MMF dose using TDM based on the MPA AUC (Le Meur et al., 2011). In renal transplantation, a significant decrease in the number of acute rejections during the first year post-transplantation in the patient group was demonstrated with TDM based on MPA AUC<sub>0-12h</sub> (Meur et al., 2007), and the target AUC<sub>0-12h</sub> recommended was between 30 and 60 mg.h/L (Bergan et al., 2021). Currently, there is no therapeutic target in lung transplant recipients for trough plasma concentration or AUC<sub>0-12h</sub> due to the lack of studies (Bergan et al., 2021). Recently, Yabuki et al. showed that the MPA AUC<sub>0-12h</sub> level is associated with the occurrence of graft rejection or infections in lung transplant recipients after three months post-transplantation and reported a target range of MPA AUC<sub>0-12h</sub> between 22.8 and 40.5 mg.h/L which seems to improve clinical outcome (Yabuki et al., 2020).

To our knowledge, contrary to kidney transplantation, population pharmacokinetic (PK) studies of MPA in lung transplant patients remain scarce to date, and the few existing ones were carried out either with a small number of patients (Wang et al., 2015, Wang et al. 2017), or including other types of transplant patients (de Winter et al., 2012). Therefore, we aimed to describe the PK of MPA<sub>0-12h</sub> in lung transplant recipients using a population PK model and to develop a Bayesian estimator of MPA AUC<sub>0-12h</sub> in the early post-transplant period.

## 2. Methods

### 2.1. Patients

This was a single-center retrospective study conducted in the pulmonology department of Marie-Lannelongue Hospital in Le Plessis-Robinson, France, between 2017 and 2021. The study was approved by the Research Ethics Committee of Mondor Hospital (No. 2023-171). Adult patients undergoing lung transplant without cystic fibrosis (CF) were included if they had at least one pharmacokinetic profile of MMF during the first three months post-transplantation. They were excluded if they had received a multi-organ transplantation and if they were treated with enteric-coated mycophenolate sodium. All transplant recipients received triple immunosuppressive therapy, including MMF (Cellcept®) generally administered with the initial dose of 1000 mg BID (500 mg tablets or 250 mg capsules), corticosteroids and tacrolimus at

the initial dose of 0.1 mg kg⁻¹ BID or ciclosporin at the initial dose 5 mg kg⁻¹ BID according to the therapeutic protocol. Ciclosporin was the first-line anti-calcineurin (except in cases of scleroderma). As soon as signs of graft rejection appeared, ciclosporin was replaced with tacrolimus. Demographic, clinical and biological data were collected from patient records. The study protocol was approved by the local ethics Committee for the use of health data and biobanks.

### 2.2. Data collection and assay method

Patients were divided into an index group, included between 2017 and 2019, and a validation group, included between 2020 and 2021. Concentration-time profiles of MPA for each patient were taken into account if they were performed at steady-state and within the first three months after transplantation. Some patients had several MPA concentration profiles, which were treated as different occasions. Blood samples were taken in EDTA at T0 (just before drug intake), 0.5 h, 1 h, 2 h, 4 h and 6 h post intake at steady state (at least 4 to 5 days after treatment initiation or dose change). The 6 hours post intake time was missing in the validation dataset for most patients, except for three concentration-time profiles of MPA of three different patients. Blood samples were rapidly centrifuged to obtain the plasma samples, which were stored at -20°C until analysis. Plasma MPA concentrations were measured using a high-performance liquid chromatography method with ultraviolet detection. This validated and used routinely dosing method was adapted from a published method by Blanchet et al. 2008 (Blanchet et al., 2008). The chromatographic system was a Shimadzu LC-20 Prominence series and the analytical column was a LiChrospher® 100 RP-18 end-capped (125 mm x 4 mm, 5 µm particle size; Merck). The mobile phase was a mixture of orthophosphoric acid (85 %) and acetonitrile (64:36, v/v), in isocratic mode and with a flow rate of 1.2 mL/min. Briefly, 100 µL of a mixture of orthophosphoric acid (85 %), acetonitrile and the internal standard (hexobarbital) was added to 50 µL of plasma sample. After one minute of mixing and 10 minutes of centrifugation at 14,000 rpm, 100 µL of sterile water was added to 100 µL of supernatant. Finally, the injection volume was 50 µL. The calibration range was 0.5 to 25 mg/L.

### 2.3. Statistical analysis

Comparisons between quantitative variables were performed using a Student's t-test. When quantitative variables did not follow a normal distribution, comparisons were realized with the Wilcoxon test. Correlations between the quantitative variables were estimated by the Spearman correlation coefficient for variables with non-normal distribution and p-values less than 0.05 were considered significant. Data were analyzed with the R software (version 4.0.0, 2020).

### 2.4. Population pharmacokinetics analysis

#### 2.4.1. Non-linear mixed effect modelling

All MPA concentration-time profiles of the index dataset, with the exact sampling times provided by the clinical department, were used to build the population pharmacokinetic model. The data were analyzed using nonlinear mixed-effects models with Monolix® software 2019R2 (Lixoft, Antony, France). One-compartment and two-compartment models were tested. A one-compartment model was tested to describe plasma concentrations and estimate the first-order absorption constant (ka), apparent volume of central compartment (V<sub>1</sub>), apparent clearance (CL/F) and their variabilities. Then a two-compartment model with the addition of the apparent volume of peripheral compartment (V<sub>2</sub>) and apparent intercompartment clearance (Q) was explored. Individual variabilities (IIV) and intra-individual variabilities (IOV) of parameters were described with an exponential model, testing for co-variances between the variabilities of the parameters. Concerning the absorption, first order absorption with a lag time (t<sub>lag</sub>) and transit compartment models were explored. Proportional, constant and
combined error models were tested. The comparison between two nested models was based on the difference in the criterion of minus 2 times the log-likelihood between two models (Δ(-2LL)) p-value of 0.05 (3.84 with 1 degree of freedom (df)). If the models were not nested, the model with the lowest Corrected Bayesian Information Criteria (BICc) was chosen.

To explain the influence of factors on the IIV and IOV of the parameters, the following covariates were explored: age, sex, body weight, post-transplantation delay, serum creatinine, uremia, serum protein, CRP, glomerular filtration rate (eGFR) calculated using the CKD-epi formula (Levey et al., 2009), therapeutic combination with calcineurin inhibitors (ciclosporin or tacrolimus). Continuous covariates were added to the model with a multiplicative model centered by the median of the covariate. For example, body weight (BW) on clearance was modelled as:

$$CL_{ik} = TVCLx \left( \frac{BW_i}{BW_{median}} \right) \beta BW \times \exp(\eta_i + \eta_{ik}) \quad (1)$$

where $CL_{ik}$ is the clearance of an individual i (i=1,...N) on occasion k, TVCL the population clearance, BW$_i$ the body weight of individual i, $β_{BW}$ the effect of weight on clearance, $η_i$ is the random effect representing IIV and $η_{ik}$ the random effect representing IOV at occasion k.

For categorical variables (such as calcineurin inhibitors), a multiplicative model was used:

$$CL_{ik} = TVCL \times \beta'_{CSA=1} \times \exp(\eta_i + \eta_{ik}) \quad (2)$$

where $β'_{CSA=1}$ is the effect of ciclosporin on clearance such as $β'_{CSA=1} = \exp(β)$ with $β$ coefficient estimated in the final model, where CSA = 1 if the patient received ciclosporin and 0 otherwise.

Relationships between covariate and parameter were explored and retained for the next step (i) if the p-value of correlation tests of Pearson for continuous covariates or ANOVA for categorical covariates was lower than 0.2 or (ii) if the COSSAC and covSAMBA-COSSAC algorithms in Monolix® proposed this relationship and (iii) if the relationship was biologically plausible. The covariate model was then built using a stepwise procedure. In a first step (forward stepwise), covariates were added one by one to the model in decreasing order of significance according to their Δ(-2LL) (univariate analysis) and retained (i) if Δ(-2LL) decreased by at least 3.84 (p<0.05, 1 df), ii) if the IIV and/or IOV of the parameter decreased, iii) if the associated parameter $β$ was well estimated (Relative standard error (RSE) less than 50 %). Finally in a second step (backward stepwise), covariates in the model were tested one by one and kept if Δ(-2LL) increased by more than 6.63 (p<0.01, 1 df) and if the IIV and/or the IOV of the parameter increased.

### 2.4.2. Evaluation of the final model

The goodness of fit plots from the index group was evaluated by examining observed concentrations versus population predictions, observed concentrations versus individual predictions, Normalized Prediction Distribution Errors (NPDE) versus time, NPDE versus observed concentrations and prediction-corrected visual predictive checks (pcVPC). The final population PK model was evaluated externally with the validation dataset. To this end, all population PK parameters of the final model were fixed in Monolix® with zero iterations for the Stochastic Approximation Expectation-Maximization (SAEM) algorithm to estimate the individual parameters, and the same goodness of fit plots were examined for the validation group.

### 2.5. Development of a maximum a posteriori Bayesian estimator

We then used the model to determine an optimal 3-sample design to estimate the individual parameters, using the PFIM program in R with the model obtained in the index dataset as prior and requesting 3 samples during the first four hours post-intake. We optimised protocols for different clinically relevant combinations of the covariates retained in

the final model. We assessed the ability of these protocols to estimate the AUC, both in the index and validation datasets: first, the individual parameters were estimated in Monolix® for the reduced dataset with the selected 3 sampling times, fixing the population parameters of the final model. Then, the predicted individual $AUC_{0-12h}$ (AUCpred$_{0-12h}$) were calculated by integrating the MPA concentrations as a function of time with the individual PK parameters. Finally, $AUC_{0-12h}$ were compared against the reference $AUC_{0-12h}$ (AUCref$_{0-12h}$) estimated with the individual parameters obtained in the same way using the full dataset.

We calculated the mean error (ME) and mean percentage error (MPE) for accuracy and root mean square relative error (RMSRE) for precision according to the formulas below (Konecki et al., 2021), for the AUC obtained with different protocols:

$$ME = \frac{1}{N} \times \sum_{i=1}^{N} \left( AUC_{ref0-12h,i} - AUC_{pred0-12h,i} \right) \quad (3)$$

$$MPE(\%) = 100 \times \frac{1}{N} \times \sum_{N=1}^{N} \frac{AUC_{ref0-12h,i} - AUC_{pred0-12h,i}}{AUC_{ref0-12h,i}} \quad (4)$$

$$RMSRE(\%) = 100 \times \sqrt{\frac{1}{N} \times \sum_{n=1}^{N} \left( \frac{AUC_{ref0-12h,i} - AUC_{pred0-12h,i}}{AUC_{ref0-12h,i}} \right)^2} \quad (5)$$

## 3. Results

### 3.1. Patients and data collection

During the study period, 97 MPA profiles from 72 patients for the index dataset and 60 MPA profiles from 46 patients for the validation dataset were collected, representing a total of 580 samples. The main characteristics of study population and MPA profiles are presented in Table 1 for the index and validation groups. No significant difference was observed between the two groups for any of the variables. There was no marked difference between patients younger or older than 65, but this was not formally tested as there were only 8 patients in total above 65 years of age. The most common indication in both groups was severe pulmonary hypertension. The number of MPA profiles was balanced over the entire observation period in the index group but there were less visits at M2 and M3 in the validation group.

### 3.2. Population pharmacokinetics analysis

#### 3.2.1. Non-linear mixed effect modelling

MPA PK was best described using a two-compartment model with first-order absorption and elimination and lag-time, and a proportional residual error model. Interindividual variabilities were significant for $t_{lag}$, CL/F, V$_1$, Q. Inter-occasion variabilities were retained for $t_{lag}$, CL/F, V$_1$. Covariances between the variabilities of the parameters were tested, but none were found significant. Shrinkage for all parameters were low, with values of -6.9 %, 1.6 %, 1.1 % and -0.6 % for $t_{lag}$, CL/F, V$_1$ and Q respectively.

Results of covariate selection are presented in Table S1. Covariates which explained part of the variability of the MPA CL/F were body weight and ciclosporin, with a positive influence on CL/F, and the creatinine with an inverse relationship with CL/F. For $V_1$, post-transplant delay was a significant covariate, with $V_1$ during the first month post-transplant lower than the following two months. Indeed, the log-likelihood increased by 20.3, 22.1, 14.3, 10.1 points when body weight, anticalcineurin treatment, creatinine and post-transplant time, respectively, were removed from the final model.

Finally, the IIV on $t_{lag}$ was not correctly estimated (RSE at 385 %) and its removal led to a decrease of 0.2 points of the likelihood function. Results of estimated parameters for the final population
<table><caption>**Table 1**

Characteristics of patients included in index and validation groups and their MPA profiles.</caption><thead><tr><th></th><th>Index group<br>Median [min – max] or number (%)</th><th>Validation group<br>Median [min – max] or number (%)</th></tr></thead><tbody><tr><td>Number of patients, n</td><td>72</td><td>46</td></tr><tr><td>Age (years)</td><td>52 [18–69]</td><td>49 [20–69]</td></tr><tr><td>Over 65 years old, n (%)</td><td>3 (4)</td><td>5 (11)</td></tr><tr><td>Female, n (%)</td><td>34 (47.2)</td><td>24 (52)</td></tr><tr><td>Male n (%)</td><td>38 (52.8)</td><td>22 (48)</td></tr><tr><td>Body weight (kg)</td><td>64 [33–94]</td><td>67 [44–100]</td></tr><tr><td>Female (kg)</td><td>59 [33–90]</td><td>58 [44–86]</td></tr><tr><td>Male (kg)</td><td>69 [44–94]</td><td>76 [48–100]</td></tr><tr><td>Creatinine (µmol/L)</td><td>66 [19–280]</td><td>68 [41–169]</td></tr><tr><td>Pathologies</td><td></td><td></td></tr><tr><td>Severe pulmonary hypertension, n (%)</td><td>42 (58)</td><td>27 (58)</td></tr><tr><td>Pulmonary fibrosis, n (%)</td><td>18 (25)</td><td>10 (21)</td></tr><tr><td>COPD, n (%)</td><td>11 (15)</td><td>9 (19)</td></tr><tr><td>Unknown, n (%)</td><td>1 (2)</td><td>1 (2)</td></tr><tr><td>Number of patients per occasion</td><td></td><td></td></tr><tr><td>One occasion, n (%)</td><td>49 (68)</td><td>35 (76)</td></tr><tr><td>TCL, n (%)</td><td>11 (15)</td><td>16 (35)</td></tr><tr><td>CSA, n (%)</td><td>38 (53)</td><td>19 (41)</td></tr><tr><td>Two occasion, n (%)</td><td>21 (29)</td><td>8 (17)</td></tr><tr><td>TCL, n (%)</td><td>0 (0)</td><td>3 (7)</td></tr><tr><td>CSA, n (%)</td><td>13 (18)</td><td>3 (7)</td></tr><tr><td>Switching from CSA to TCL, n (%)</td><td>8 (11)</td><td>2 (4)</td></tr><tr><td>Three occasion, n (%)</td><td>2 (3)</td><td>3 (7)</td></tr><tr><td>TCL, n (%)</td><td>0</td><td>1 (2)</td></tr><tr><td>CSA, n (%)</td><td>0</td><td>0</td></tr><tr><td>Switching from CSA to TCL, n (%)</td><td>2 (3)</td><td>2 (4)</td></tr><tr><td>Number of MPA profiles by post-transplant period</td><td></td><td></td></tr><tr><td>M1 to M3, total (CSA/TCL)</td><td>97 (74/23)</td><td>60 (30/30)</td></tr><tr><td>M1, total (CSA/TCL)</td><td>35 (33/2)</td><td>31 (25/6)</td></tr><tr><td>M2, total (CSA/TCL)</td><td>35 (25/10)</td><td>19 (5/14)</td></tr><tr><td>M3, total (CSA/TCL)</td><td>27 (16/11)</td><td>11 (0/11)</td></tr></tbody></table>

n : number, COPD : Chronic Obstructive Pulmonary Disease, M : month, CSA : cyclosporin, TCL : tacrolimus

<table><caption>**Table 2**

Parameters estimates in the final population pharmacokinetic model.</caption><thead><tr><th>Parameters</th><th>Estimate</th><th>% RSE</th></tr></thead><tbody><tr><td>t<sub>lag</sub> (h)</td><td>0.25</td><td>18</td></tr><tr><td>ka (h<sup>-1</sup>)</td><td>1.21</td><td>9</td></tr><tr><td>CL/F (L/h)<sup>(a)</sup></td><td>14.3</td><td>18</td></tr><tr><td>β<sub>BW</sub></td><td>1.58</td><td>22</td></tr><tr><td>β<sub>Creat</sub></td><td>- 0.58</td><td>31</td></tr><tr><td>β<sub>CSA=1</sub></td><td>0.52</td><td>32</td></tr><tr><td>V<sub>1</sub> (L)<sup>(b)</sup></td><td>27.4</td><td>28</td></tr><tr><td>β<sub>M≠M1</sub></td><td>0.82</td><td>36</td></tr><tr><td>Q (L/h)</td><td>31.7</td><td>9</td></tr><tr><td>V<sub>2</sub> (L)</td><td>6597</td><td>13</td></tr><tr><td>Between-subject variability (%)</td><td></td><td></td></tr><tr><td>CL/F</td><td>36</td><td>33</td></tr><tr><td>V<sub>1</sub></td><td>70</td><td>30</td></tr><tr><td>Q</td><td>49</td><td>14</td></tr><tr><td>Interoccasion variability (%)</td><td></td><td></td></tr><tr><td>t<sub>lag</sub></td><td>125</td><td>14</td></tr><tr><td>CL/F</td><td>48</td><td>18</td></tr><tr><td>V<sub>1</sub></td><td>63</td><td>23</td></tr><tr><td>Proportional error (%)</td><td></td><td></td></tr><tr><td>B</td><td>36</td><td>4</td></tr></tbody></table>

t<sub>lag</sub> : lag time; ka : first-order absorption rate constant; CL/F : apparent oral clearance; BW : body weight; β<sub>BW</sub> : coefficient of body weight on CL/F; β<sub>Creat</sub> : coefficient of creatinine on CL/F; β<sub>CSA=1</sub> : coefficient of cyclosporin co-treatment on CL/F; V<sub>1</sub> : apparent volume of central compartment; M1 : first month post-transplant; β<sub>M≠M1</sub> : coefficient of post-transplant period on V<sub>1</sub>; Q : apparent intercompartmental clearance; V<sub>2</sub> : apparent volume of peripheral compartment; RSE : relative standard error;

(a) CL/F = 14.3 (BW/64)<sup>βBW</sup> (Creat/67)<sup>βCreat</sup> exp(β<sub>CSA=1</sub>)

(b) V<sub>1</sub> = 27.4 exp(β<sub>M≠M1</sub>)

pharmacokinetic model are presented in Table 2. The parameters had satisfactory estimation precision, with RSE between 4 % and 36 %.

### 3.2.2. Evaluation of the final model

Some examples of individual fits are presented in Fig. S1. The goodness of fit plots for the index dataset (Fig. 1a and Fig. S2) were satisfactory. Concerning the validation dataset, diagnostic plots of observed concentrations versus predicted individual or population concentrations were acceptable with a distribution of data along the identity line (Fig. S2). In the validation dataset, the graph of NPDE versus time showed a tendency to overestimate concentrations measured 2 hours post-intake, also seen in the overestimation of observed concentrations for the highest values, showing the graph of NPDE versus observed concentrations, and in the pcVPC plot for samples measured 2 hours after intake (Fig. 1.b). Moreover, in this last plot, the median curve was at the lower limit of its 90 % prediction interval, suggesting the model slightly overestimates MPA concentrations.

### 3.3. Bayesian estimator

With PFIM, the best 3 samples protocols were 0, 0.5 and 4 hours and 0, 2 and 4 hours post-intake. We also evaluated a protocol with 0, 1 and 4 hours post-intake, which had very similar efficiency when evaluated in PFIM. The performances of these three protocols, when used to predict individual concentrations in the two groups, are summarized in Table 3. The best protocol was 0, 1 and 4 hours post-intake with a MPE of 3.2 % and a RMSRE of 22.4 % for the index dataset and a MPE of 1.5 % and a RMSRE of 18.1 % for the validation dataset. Moreover, there was no significant difference between the predicted AUC<sub>0-12h</sub> and the reference AUC<sub>0-12h</sub> in the two groups. Both groups' Bland-Altman and correlation plots showed no major bias between the predicted AUC<sub>0-12h</sub> and the reference AUC<sub>0-12h</sub> (Fig. 2).

## 4. Discussion

To our knowledge, this study is the first to date to describe the population PK of MPA in LT patients without CF. The covariates retained in the final model were body weight, creatinine, cyclosporin, and the post-transplantation delay. The final population PK model was externally validated. In addition, a maximum a posteriori Bayesian estimator of MPA AUC<sub>0-12h</sub> was developed and validated with sampling time just before intake, 1 and 4 hours post-administration.

The pharmacokinetics of MPA in these patients were best described by a two-compartment model with a first-order absorption and elimination and a lag-time. This model is found in the literature regardless of the type of transplantation (Bergan et al., 2021; Kiang & Ensom, 2018; Zhang et al., 2019). For the absorption phase, more complex models with gamma distribution (Koloskoff et al., 2024) and transit compartments (Nanga et al., 2022) have been proposed but did not describe the data better, as in the study by de Winter et al. 2012 (de Winter et al., 2012) using kidney and lung transplant recipients. The PK profile of MPA presents a second peak of variable intensity between 6 and 12 hours post-intake, corresponding to the EHC (Bergan et al., 2021). Some studies described this EHC, including data for the MPAG metabolite (Wang et al., 2017; Zhang et al., 2019). EHC could not be included in our study, due to the lack of samples beyond six hours post-dose. Moreover, it would probably be minimal because, in the index dataset, the majority of the patients received cyclosporin, known to inhibit the EHC of the MPA through MRP2 carrier, responsible for the elimination of MPAG in the bile (Bergan et al., 2021) and possibly also of OATP1B3, responsible for the uptake of MPAG from the blood by the hepatocytes (de Winter et al., 2012; Sherwin et al., 2011).

In our study, V<sub>1</sub> (27.4 L) was similar to the value estimated in the study by De Winter et al. beyond the first 14 days post-transplantation (27.6 L) (de Winter et al., 2012). Estimated CL/F (14.3 L/h) was also similar to the clearance reported in the study by Wang et al. for LT
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Prediction-corrected Visual Predictive checks for the index (a) and validation (b) dataset. The blue dots represent the data. The blue curves represent the 10th, 50th and 90th percentiles of the data presented. The areas colored in blue correspond to the prediction interval at 90 % of the 10th and 90th percentiles of the predicted data and those in pink to the prediction interval at 90 % of the 50th percentile. Two outliers are not represented on the plots : for the index dataset, a concentration of 66.1 mg/L at 12.5 hours and for the validation dataset a concentration of 32.6 mg/L at 13 hours. The times on the X-axis are given relative to the time of the last dose before the residual concentrations were measured, which was recorded for each individual and used in the analysis.
Please for the above figure the color should be used in print- Thank you</figcaption></figure>

<table><caption>**Table 3**
Results of performance of three protocols.</caption><thead><tr><th></th><th></th><th>n</th><th>ME</th><th>MPE (%)</th><th>RMSE (%)</th><th>p-value</th><th>r</th></tr></thead><tbody><tr><td rowspan="3">Index dataset</td><td>H0H2H4</td><td>97</td><td>2.48</td><td>-15.09</td><td>34.15</td><td>&lt;0.01</td><td>0.909</td></tr><tr><td>H0H1H4</td><td>97</td><td>-5.95</td><td>3.21</td><td>22.36</td><td>0.244</td><td>0.905</td></tr><tr><td>H0H0.5H4</td><td>97</td><td>-5.44</td><td>3.15</td><td>23.64</td><td>0.145</td><td>0.894</td></tr><tr><td rowspan="3">Validation dataset</td><td>H0H2H4</td><td>60</td><td>0.53</td><td>-3.48</td><td>20.08</td><td>0.553</td><td>0.949</td></tr><tr><td>H0H1H4</td><td>60</td><td>-3.41</td><td>1.48</td><td>18.10</td><td>0.235</td><td>0.942</td></tr><tr><td>H0H0.5H4</td><td>60</td><td>0.11</td><td>-5.52</td><td>21.48</td><td>0.024</td><td>0.943</td></tr></tbody></table>

H0H2H4: 0, 2 and 4 hours post-intake, H0H1H4: 0, 1 and 4 hours post-intake, H0H0.5H4: 0, 0.5 and 4 hours post-intake, n: number of MPA profiles, ME: mean error, MPE: mean percentage error, RMSE: root-mean-squared error, p-value: paired Wilcoxon's test between predicted AUC and reference AUC, r: Spearman correlation coefficient

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Bland-Altman plots and correlation plots of predicted AUC₀-₁₂h versus reference AUC₀-₁₂h at sampling time 0, 1 and 4 hours post-administration in index group (a, b) and validation group (c, d). In plots a and c, the black line is the mean and the dotted line the mean ± 1.96 sd. In plots b and d, the black line represents the linear regression and the dashed line the identity line. Unity of AUC is mg.h.L⁻¹.</figcaption></figure>
recipients without CF (14.5 L/h) and lower than that in CF (24.1 L/h) (Wang et al., 2017). De Winter et al. reported a slightly higher estimated CL/F (19.5 L/h), but in their study lung transplant patients with CF were included (de Winter et al., 2012), and CL/F was found to be significantly higher in CF patients compared to non-CF (Stuckey et al., 2014) due to gastrointestinal disturbances, lower serum albumin level and higher P-glycoprotein level among other things (de Winter et al., 2012; Stuckey et al., 2014; Wang et al., 2015).

Inter-individual variabilities for CL/F (36 %), V₁ (70 %) and Q (49 %) estimated in this study are consistent with the results of other studies (Kiang & Ensom, 2018; Zhang et al., 2019) and reflect the wide IIV known about MPA pharmacokinetics (Bergan et al., 2021). The high intra-individual variability of lag-time (125 %) could be explained by the gastroesophageal reflux associated with a hypotensive lower esophageal sphincter in almost 80 % of patients, which was associated with delayed gastric emptying in 44 % of patients (Potestio et al., 2017).

The large variability observed in the clearance of MPA partly stems from the immunosuppressive drugs. Indeed, MPA CL/F was multiplied by 1.62 in the presence of ciclosporin compared to association with tacrolimus. This effect of ciclosporin on CL/F is well known and has been observed in other previous studies (de Winter et al., 2012; Kiang & Ensom, 2018). Ciclosporin is known to decrease MPA exposure by inhibiting the EHC according to the mechanism described above. We also found a positive relationship between body weight and CL/F in the final model, as described in several previous studies (Jiao et al., 2008; Le Guellec et al., 2004; Wang et al., 2015; Yu et al., 2017).

In the present study, we found an inverse relationship between CL/F and creatinine, an unexpected result as MPA is poorly excreted by the kidneys (Joy et al., 2009). Other studies did find a positive relationship between CL/F and creatinine clearance (CLCR), most often in populations with immune pathologies (Romano-Aguilar et al., 2020; Sam & Joy, 2010). Similarly, Czock et al. showed that the CL/F of raloxifene, a substrate also mainly metabolized by UGTs and presenting an EHC, was significantly lower in patients with impaired renal function (median CLCR according to Cockcroft and Gault of 33 ml/min) compared to those without renal impairment (median CLCR of 117 ml/min) (Czock et al., 2005). The studies which found this relationship between creatinine or CLCR and the CL/F of the MPA, hypothesized an inhibition of some drug transport proteins (like OATP carrier) and drug metabolism proteins (UGT) by accumulated endogenous substrates in case of impaired renal function (Barnes et al., 2014; Nolin et al., 2008; Rong & Kiang, 2020). Finally, in our model, the last covariate was the influence of the post-transplantation time in V₁. Indeed, V₁ during the first post-transplant month (49.2 L) was approximately 2.2 times lower than the following two months (109.7 L). De Winter et al. showed in renal and lung transplant recipients that the V₁ was 21 % lower during the first 14 days post-transplantation due to capillary leaks with interstitial edema frequently observed in these patients (de Winter et al., 2012). Another hypothesis could be tissue hypoperfusion and hypovolemia in the early post-transplant period (Potestio et al., 2017).

To the best of our knowledge, there are three studies including lung transplant patients which proposed a reduced sampling schedule for estimate AUC₀-12h of MPA; with a multiple linear regression (MLR) (Tanaka et al., 2019; Ting et al., 2006) or with a MAP Bayesian estimator (MAP-BE) method (de Winter et al., 2012). In our study, the best Maximum a posteriori (MAP) Bayesian protocol with 3 sample times included 0, 1, and 4 hours post-administration. It provided satisfactory performances, with unbiased estimates of the AUC0-12h in both the index and validation datasets and a precision of 20 % (Bergan et al., 2021). It corresponds to the same sampling times as De Winter et al. who estimated the MPA AUC₀-12h from a PK population model including 116 kidney and 41 lung transplant recipients (de Winter et al., 2012). They also used a MAP-BE approach, which allows more flexibility in sampling times and covariate inclusion compared to MLR (Bergan et al., 2021). The proposed MAP-BE for MPA AUC₀-12h was developed based on data collected during the first three months post-lung transplantation which

is considered a critical period. Indeed despite significant advances in immunosuppressive therapy, rejection remains a frequent complication after lung transplantation, occurring in up to 30 % of patients within the first post-transplant year (Chambers et al., 2019). Both acute cellular rejection and antibody-mediated rejection are major risk factors for the subsequent development of chronic lung allograft dysfunction, which represents the leading cause of death beyond the first post-transplant year and remains the primary barrier to improved long-term outcomes (Christie et al., 2024). Therefore, although the clinical benefits of the therapeutic drug monitoring of MPA have not been demonstrated in lung transplantation, optimizing immunosuppressive regimens, particularly through individualized adjustment of mycophenolate mofetil dosing using the proposed MAP-BE, could help mitigate the risk of rejection and improve graft survival.

The present study has some limitations. First, it was a retrospective and monocentric study and cannot be generalized to all LT recipients, especially patients with CF. Second, the datasets were not randomized, but both sets were comparable in terms of inclusion criteria and standard of care including immunosuppressive therapy, as the clinical practice did not change during that time. Third, although BMI has been associated as a covariate of clearance in numerous studies involving different classes of drugs, we could not compute it in this study as patient height was not recorded during the retrospective analysis and we used body weight as a covariate. Total body weight has been shown to be a good measure of body size in non-obese patients (McLeay et al., 2012). Fourth, our study did not include data on polymorphisms of enzymes and transporters such as SLCO1B3, SLCO1B1, UGT2B7 or ABCC2 which could have an impact on the pharmacokinetics of MPA (Na Takuathung et al., 2021; Ruiz et al., 2015). Fifth, albumin has been described like a significant covariate of CL/F in some studies with a negative correlation (Bergan et al., 2021; Le Guellec et al., 2004), but it was not possible to test it in our study because too few patients had albumin measurements. Finally, only the first 3 months post-transplant were considered here, and it might also be interesting to follow MPA profiles for a longer time to know if the PK final model and MAP-BE could be applied for all LT recipients regardless of the post-transplant period.

## 5. Conclusions

This is a study on the population PK of MPA including LT patients in the early post-transplant period and with four covariates: body weight, creatinine, ciclosporin for CL/F, and the post-transplantation time for V/F. To the best of our knowledge, this is the first Bayesian estimator of MPA AUC₀-12h with 3 sample times included 0, 1 and 4 hours post-administration including only LT patients. This Bayesian estimator should facilitate the TDM of these patients.

## Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors

## Ethics approval statement

The study received ethics approval from the Research Ethics Committee of Mondor Hospital under approval number 2023-171

## Patient Consent statement

Not applicable

**Juliette Kauv**: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Writing - original draft. **Séverine Feuillet**: Investigation, Resources. **Aurélie Barrail-Tran**: Writing - review & editing. **Alexandra Delemos**: Data curation, Investigation, Resources. **Tiphaine Legrand**: Writing - review & editing. **Céline Verstuyft**: Writing - review & editing. **Jérôme Le Pavec**: Investigation,
Resources. **Emmanuelle Comets**: Conceptualization, Methodology, Software, Supervision, Writing - review & editing. **Valérie Furlan**: Conceptualization, Supervision, Writing - review & editing.

### CRediT authorship contribution statement

**Juliette Kauv**: Writing – original draft, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Séverine Feuillet**: Resources, Investigation. **Aurélie Barrail-Tran**: Writing – review & editing. **Alexandra Delemos**: Resources, Investigation, Data curation. **Tiphaine Legrand**: Writing – review & editing.
**Céline Verstuyft**: Writing – review & editing. **Jérôme Le Cave**: Resources, Investigation. **Emmanuelle Comets**: Writing – review & editing, Supervision, Software, Methodology, Conceptualization. **Valérie Furlan**: Writing – original draft, Validation, Supervision, Methodology, Conceptualization.

### Declaration of competing interest

The authors declare no conflict of interest.

### Acknowledgments

The authors would like sincerely thank the team of the pulmonology department of Marie-Lannelongue Hospital in Le Plessis-Robinson (physicians and nurses) and the pharmacologist of the laboratory of Kremlin-Bicêtre hospital who analyzed the samples.

### Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ejps.2025.107290.

### Data availability

The data that has been used is confidential.

### Reference

Barnes, K.J., Rowland, A., Polasek, T.M., Miners, J.O., 2014. Inhibition of human drug-metabolising cytochrome P450 and UDP-glucuronosyltransferase enzyme activities in vitro by uremic toxins. Eur. J. Clin. Pharmacol. 70 (9), 1097-1106. https://doi.org/10.1007/s00228-014-1709-7.

Benjanuwattra, J., Puksakorn, D., Koonrungsesomboon, N., 2020. Mycophenolic Acid and Its Pharmacokinetic Drug-Drug Interactions in Humans: Review of the Evidence and Clinical Implications. J. Clin. Pharmacol. 60 (3), 295-311. https://doi.org/10.1002/jcph.1565.

Bergan, S., Brunet, M., Hesselink, D.A., Johnson-Davis, K.L., Kunicki, P.K., Lemaitre, F., Marquet, P., Molinaro, M., Noceti, O., Pattanaik, S., Pawinski, T., Seger, C., Shipkova, M., Swen, J.J., van Gelder, T., Venkataramanan, R., Wieland, E., Woillard, J.-B., Zwart, T.C., Langman, L.J., 2021. Personalized Therapy for Mycophenolate: Consensus Report by the International Association of Therapeutic Drug Monitoring and Clinical Toxicology. Ther. Drug Monit. 43 (2), 150-200. https://doi.org/10.1097/FTD.0000000000000871.

Blanchet, B., Taieb, F., Conti, F., Abbas, H., Seydi, I., Harcouet, L., Dauphin, A., Calmus, Y., Tod, M., 2008. Comparison of a new enzymatic assay with a high-performance liquid chromatography/ultraviolet detection method for therapeutic drug monitoring of mycophenolic acid in adult liver transplant recipients. Liver Transplant.: Off. Publ. Am. Assoc. Study Liver Dis. Int. Liver Transplant. Soc. 14 (12), 1745-1751. https://doi.org/10.1002/lt.21607.

Chambers, D.C., Cherikh, W.S., Harhay, M.O., Hayes, D., Hsich, E., Khush, K.K., Meiser, B., Potena, L., Rossano, J.W., Toll, A.E., Singh, T.P., Sadavarte, A., Zuckermann, A., Stehlik, J., 2019. The International Thoracic Organ Transplant Registry of the International Society for Heart and Lung Transplantation: Thirty-sixth adult lung and heart-lung transplantation Report-2019; Focus theme: Donor and recipient size match. J. Heart Lung Transplant.: Off. Publ. Int. Soc. Heart Transplant. 38 (10), 1042-1055. https://doi.org/10.1016/j.healun.2019.08.001.

Christie, J.D., Van Raemdonck, D., Fisher, A.J., 2024. Lung Transplantation. N. Engl. J. Med. 391 (19), 1822-1836. https://doi.org/10.1056/NEJMra2401039.

Czock, D., Keller, F., Heringa, M., Rasche, F.M., 2005. Raloxifene pharmacokinetics in males with normal and impaired renal function. Br. J. Clin. Pharmacol. 59 (4), 479-482. https://doi.org/10.1111/j.1365-2125.2004.02326.x.

de Winter, B.C.M., Monchaud, C., Prémaud, A., Pison, C., Kessler, R., Reynaud-Gaubert, M., Dromer, C., Stern, M., Guillemain, R., Knoop, C., Estenne, M.,

Marquet, P., Rousseau, A., 2012. Bayesian Estimation of Mycophenolate Mofetil in Lung Transplantation, Using a Population Pharmacokinetic Model Developed in Kidney and Lung Transplant Recipients. Clin. Pharmacokinet. 51 (1), 29-39. https://doi.org/10.2165/11594050-000000000-00000.

Jiao, Z., Ding, J., Shen, J., Liang, H., Zhong, L., Wang, Y., Zhong, M., Lu, W., 2008. Population pharmacokinetic modelling for enterohepatic circulation of mycophenolic acid in healthy Chinese and the influence of polymorphisms in UGT1A9. Br. J. Clin. Pharmacol. 65 (6), 893-907. https://doi.org/10.1111/j.1365-2125.2008.03109.x.

Joy, M.S., Hilliard, T., Hu, Y., Hogan, S.L., Dooley, M.A., Falk, R.J., Smith, P.C., 2009. Pharmacokinetics of Mycophenolic Acid in Patients with Lupus Nephritis. Pharmacotherapy 29 (1), 7-16. https://doi.org/10.1592/phco.29.1.7.

Kiang, T.K.L., Ensom, M.H.H., 2018. Population Pharmacokinetics of Mycophenolic Acid: An Update. Clin. Pharmacokinet. 57 (5), 547-558. https://doi.org/10.1007/s40262-017-0593-6.

Knight, S.R., Morris, P.J., 2008. Does the Evidence Support the Use of Mycophenolate Mofetil Therapeutic Drug Monitoring in Clinical Practice? A Systematic Review. Transplantation 85 (12), 1675. https://doi.org/10.1097/TP.0b013e3181744199.

Koloskoff, K., Benito, S., Chambon, L., Dayan, F., Marquet, P., Jacqz-Aigrain, E., Woillard, J.-B., 2024. Limited sampling strategy and population pharmacokinetic model of mycophenolic acid in pediatric patients with systemic lupus erythematosus: Application of a double gamma absorption model with SAEM algorithm. Eur. J. Clin. Pharmacol. 80 (1), 83-92. https://doi.org/10.1007/s00228-023-03587-0.

Konecki, C., Feliu, C., Cazaubon, Y., Giusti, D., Tonye-Libyh, M., Brixi, H., Cadiot, G., Biron, A., Djerada, Z., 2021. External Evaluation of Population Pharmacokinetic Models and Bayes-Based Dosing of Infliximab. Pharmaceutics 13 (8), 1191. https://doi.org/10.3390/pharmaceutics13081191.

Le Guellec, C., Bourgoin, H., B??chler, M., Le Meur, Y., Lebranchu, Y., Marquet, P., Paintaud, G., 2004. Population Pharmacokinetics and Bayesian Estimation of Mycophenolic Acid Concentrations in Stable Renal Transplant Patients. Clin. Pharmacokinet. 43 (4), 253-266. https://doi.org/10.2165/00003088-200443040-00004.

Le Meur, Y., Borrowes, R., Pescovitz, M. D., Budde, K., Grinyo, J., Bloom, R., Gaston, R., Walker, R. G., Kuypers, D., van Gelder, T., & Kiberd, B. (2011). Therapeutic drug monitoring of mycophenolates in kidney transplantation: Report of The Transplantation Society consensus meeting. *Transplant. Rev.*, 25(2), 58-64. http://doi.org/10.1016/j.tre.2011.01.002.

Levey, A.S., Stevens, L.A., Schmid, C.H., Zhang, Y.L., Castro, A.F., Feldman, H.I., Kusek, J.W., Eggers, P., Van Lente, F., Greene, T., Coresh, J., 2009. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 150 (9), 604-612. https://doi.org/10.7326/0003-4819-150-9.200905050-00006.

McLeay, S.C., Morrish, G.A., Kirkpatrick, C.M.J., Green, B., 2012. The relationship between drug clearance and body size: Systematic review and meta-analysis of the literature published from 2000 to 2007. Clin. Pharmacokinet. 51 (5), 319-330. https://doi.org/10.2165/11598930-000000000-00000.

Meur, Y.L., Büchler, M., Thierry, A., Caillard, S., Villemain, F., Lavaud, S., Etienne, I., Westeel, P.-F., Ligny, B.H.D., Rostaing, L., Thervet, E., Szelag, J.C., Rérolle, J.-P., Rousseau, A., Touchard, G., Marquet, P., 2007. Individualized Mycophenolate Mofetil Dosing Based on Drug Exposure Significantly Improves Patient Outcomes After Renal Transplantation. Am. J. Transplant 7 (11), 2496-2503. https://doi.org/10.1111/j.1600-6143.2007.01983.x.

Monchaud, C., Marquet, P., 2009. Pharmacokinetic optimization of immunosuppressive therapy in thoracic transplantation : Part II. Clin. Pharmacokinet. 48 (8), 489-516. https://doi.org/10.2165/11317240-000000000-00000.

Mourad, M., Malaise, J., Chaib Eddour, D., De Meyer, M., König, J., Schepers, R., Quiffllet, J.P., Wallemacq, P., 2001. Correlation of mycophenolic acid pharmacokinetic parameters with side effects in kidney transplant patients treated with mycophenolate mofetil. Clin. Chem. 47 (1), 88-94.

Na Takuathung, M., Sakuludomkan, W., Koonrungsesomboon, N., 2021. The Impact of Genetic Polymorphisms on the Pharmacokinetics and Pharmacodynamics of Mycophenolic Acid: Systematic Review and Meta-analysis. Clin. Pharmacokinet. 60 (10), 1291-1302. https://doi.org/10.1007/s40262-021-01037-7.

Nanga, T.M., Woillard, J., Rousseau, A., Marquet, P., Prémaud, A., 2022. Population pharmacokinetics and Bayesian estimation of mycophenolate mofetil in patients with autoimmune hepatitis. Br. J. Clin. Pharmacol. 88 (11), 4732-4741. https://doi.org/10.1111/bcp.15389.

Nolin, T.D., Naud, J., Leblond, F.A., Pichette, V., 2008. Emerging evidence of the impact of kidney disease on drug metabolism and transport. Clin. Pharmacol. Ther. 83 (6), 898-903. https://doi.org/10.1038/clpt.2008.59.

Potestio, C., Jordan, D., Kachulis, B., 2017. Acute postoperative management after lung transplantation. In: Best Practice & Research Clinical Anaesthesiology, 31, p. 273-284. https://doi.org/10.1016/j.bpa.2017.07.004.

Romano-Aguilar, M., Reséndiz-Galván, J.E., Medellín-Garibay, S.E., Milán-Segovia, R.D. C., Martínez-Martínez, M.U., Abud-Mendoza, C., Romano-Moreno, S., 2020. Population pharmacokinetics of mycophenolic acid in Mexican patients with lupus nephritis. Lupus. 29 (9), 1067-1077. https://doi.org/10.1177/0961203320931567.

Rong, Y., Kiang, T.K.L., 2020. Mechanisms of Metabolism Interaction Between p-Cresol and Mycophenolic Acid. Toxicol. Sci.: Off. J. Soc. Toxicol. 173 (2), 267-279. https://doi.org/10.1093/toxsci/kfz231.

Ruiz, J., Herrero, M., Bosó, V., Megías, J., Hervás, D., Poveda, J., Escrivá, J., Pastor, A., Solé, A., Alíño, S., 2015. Impact of Single Nucleotide Polymorphisms (SNPs) on Immunosuppressive Therapy in Lung Transplantation. Int. J. Mol. Sci. 16 (9), 20168-20182. https://doi.org/10.3390/ijms160920168.

Sam, W.-J., Joy, M.S., 2010. Population Pharmacokinetics of Mycophenolic Acid and Metabolites in Patients With Glomerulonephritis. Ther. Drug Monit. 32 (5), 594-605. https://doi.org/10.1097/FTD.0b013e3181ee52e2.
Sherwin, C.M.T., Fukuda, T., Brunner, H.I., Goebel, J., Vinks, A.A., 2011. The Evolution of Population Pharmacokinetic Models to Describe the Enterohepatic Recycling of Mycophenolic Acid in Solid Organ Transplantation and Autoimmune Disease. Clin. Pharmacokinet. 50 (1), 1-24. https://doi.org/10.2165/11536640-000000000-00000.

Staatz, C.E., Tett, S.E., 2007. Clinical pharmacokinetics and pharmacodynamics of mycophenolate in solid organ transplant recipients. Clin. Pharmacokinet. 46 (1), 13-58. https://doi.org/10.2165/00003088-200746010-00002.

Stuckey, L., Clark Ojo, T., Park, J.M., Annesley, T., Bartos, C., Cibrik, D.M., 2014. Mycophenolic acid pharmacokinetics in lung transplant recipients with cystic fibrosis. Ther. Drug Monit. 36 (2), 148-151. https://doi.org/10.1097/FTD.0b013e3182a8eea9.

Tanaka, M., Kikuchi, M., Takasaki, S., Hirasawa, T., Sigeta, K., Noda, A., Akiba, M., Matsuda, Y., Oishi, H., Sado, T., Noda, M., Okada, Y., Mano, N., Yamaguchi, H., 2019. Limited Sampling Strategy for the Estimation of Mycophenolic Acid and its Acyl Glucuronide Metabolite Area under the Concentration-Time Curve in Japanese Lung Transplant Recipients. J. Pharm. Pharm. Sci.: Publ. Can. Soc. Pharm. Sci. Soc. Can. Sci. Pharm. 22 (1), 407-417. https://doi.org/10.18433/jpps30505.

Ting, L.S.L., Partovi, N., Levy, R.D., Riggs, K.W., Ensom, M.H.H., 2006. Limited Sampling Strategy for Predicting Area Under the Concentration-Time Curve of Mycophenolic Acid in Adult Lung Transplant Recipients. Pharmacotherapy 26 (9), 1232-1240. https://doi.org/10.1592/phco.26.9.1232.

van Gelder, T., Hilbrands, L.B., Vanrenterghem, Y., Weimar, W., de Fijter, J.W., Squifflet, J.P., Hené, R.J., Verpooten, G.A., Navarro, M.T., Hale, M.D., Nicholls, A.J., 1999. A randomized double-blind, multicenter plasma concentration controlled

study of the safety and efficacy of oral mycophenolate mofetil for the prevention of acute rejection after kidney transplantation. Transplantation 68 (2), 261-266. https://doi.org/10.1097/00007890-199907270-00018.

Wang, X.-X., Feng, M.R., Nguyen, H., Smith, D.E., Cibrik, D.M., Park, J.M., 2015. Population pharmacokinetics of mycophenolic acid in lung transplant recipients with and without cystic fibrosis. Eur. J. Clin. Pharmacol. 71 (6), 673-679. https://doi.org/10.1007/s00228-015-1854-7.

Wang, X.-X., Liu, W., Zheng, T., Park, J.M., Smith, D.E., Feng, M.R., 2017. Population pharmacokinetics of mycophenolic acid and its glucuronide metabolite in lung transplant recipients with and without cystic fibrosis. Xenobiotica 47 (8), 697-704. https://doi.org/10.1080/00498254.2016.1214885.

Yabuki, H., Matsuda, Y., Watanabe, T., Eba, S., Hoshi, F., Hirama, T., Oishi, H., Sado, T., Noda, M., Sakurada, A., Kikuchi, M., Yamaguchi, H., Mano, N., Okada, Y., 2020. Plasma mycophenolic acid concentration and the clinical outcome after lung transplantation. Clin. Transplant 34 (12), e14088. https://doi.org/10.1111/ctr.14088.

Yu, Z., Zhou, P., Wang, X., Françoise, B., Xu, D., Zhang, W., Chen, B., 2017. Population pharmacokinetics and Bayesian estimation of mycophenolic acid concentrations in Chinese adult renal transplant recipients. Acta Pharmacol. Sin. 38 (11), 1566-1579. https://doi.org/10.1038/aps.2017.115.

Zhang, H., Sheng, C., Liu, L., Luo, B., Fu, Q., Zhao, Q., Li, J., Liu, Y., Deng, R., Jiao, Z., Wang, C., 2019. Systematic external evaluation of published population pharmacokinetic models of mycophenolate mofetil in adult kidney transplant recipients co-administered with tacrolimus. Br. J. Clin. Pharmacol. 85 (4), 746-761. https://doi.org/10.1111/bcp.13850.