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Figs S1-S4. After IIV in pharmacokinetics was accounted for, improvement in model fitting was noted. Distribution of conditional weighted residuals (CWRES) was not different according to time or population prediction and showed no evidence of model misspecification. Prediction-corrected visual predictive check (VPC) plots of TAC, MPA, MPAG, and AcMPAG are presented in Fig. 1. Observed concentrations were overlaid in the confidence interval (CI) of model predicted concentrations, which assures the predictive performance of the final model. Simulation. Steady state concentrations of TAC were simulated in 32 virtual populations. Each population was generated with the combination of the dose of TAC (1 mg bid and 2 mg bid), the dose of MMF (500 mg bid and 1,000 mg bid), CYP3A5 genotype (expresser and non-expresser), SLCO1B1 genotype (T carrier and GG genotype), and UGT2B7 genotype (T carrier and CC genotype). Trough levels of TAC were higher in the combination of CYP3A5 non-expressers and the higher dose of MMF. Trough concentrations per total daily dose (C/D) of
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This study quantifies the interaction between tacrolimus (TAC) and mycophenolate mofetil (MMF) in kidney transplant recipients. Concentrations of TAC, mycophenolic acid (MPA), and metabolites were analyzed and relevant genotypes were determined from 32 patients. A population model was developed to estimate the effect of interaction. Concentrations...
Citations
... The popPK model was used to analyze the interaction. SLCO1B1 genotype (T carrier and GG genotype), UGT2B7 genotype (T carrier and CC genotype) and CYP3A5 genotype have an influence on changes in the concentration of tacrolimus [124]. But, the popPK model was unable to predict the tacrolimus initial dose in pediatric patients [125]. ...
The benefit of personalized medicine is that it allows the customization of drug therapy – maximizing efficacy while avoiding side effects. Genetic polymorphisms are one of the major contributors to interindividual variability. Currently, the only gold standard for applying personalized medicine is dose titration. Because of technological advancements, converting genotypic data into an optimum dose has become easier than in earlier years. However, for many medications, determining a personalized dose may be difficult, leading to a trial-and-error method. On the other hand, the technologically oriented pharmaceutical industry has a plethora of smart drug delivery methods that are underutilized in customized medicine. This article elaborates the genetic polymorphisms of tacrolimus as case study, and extensively covers the diagnostic and therapeutic technologies which aid in the delivery of personalized tacrolimus treatment for better clinical outcomes, thereby providing a new strategy for implementing personalized medicine.
... Most studies used standard twocompartmental model structures, 35,36,[38][39][40][41][42]44,48,49,51,54,56,59,[61][62][63][64][65] with one also proposing a one-compartmental model. 38 Six used two-compartmental models for MPA, combined with metabolite compartments to describe the PK of either MPAG 37,45,47 or MPAG and a second metabolite, MPA acyl-glucuronide (AcMPAG), 50,55,60 coupled to additional intestinal compartments to capture EHC. Given that MPA displays extensive protein binding, six others parameterised their models with the free MPA fraction (fMPA), relying on protein-binding kinetics. ...
... One study described a one-compartmental fMPA model, coupled to MPAG, AcMPAG, and EHC compartments. 53 MPA/fMPA absorption was modelled using time-lagged first-order, 35,[40][41][42][43]45,47,49,50,52,54,57,59,[61][62][63] standard first-order, 37,39,51,53,55,56,60,65 timelagged zero-order, 46 standard zero-order, 36,48,58 Erlang, 64 or single-gamma 38 or double-gamma absorption. 38,44 In most studies, MPA/fMPA elimination was modelled using first-order elimination, [35][36][37][38][40][41][42][43][44][45][46][47][48][49][50][51]53,[55][56][57][58][59][61][62][63][64][65] whereas some used zeroorder 52,54,60 or bi-exponential elimination. ...
... 53 MPA/fMPA absorption was modelled using time-lagged first-order, 35,[40][41][42][43]45,47,49,50,52,54,57,59,[61][62][63] standard first-order, 37,39,51,53,55,56,60,65 timelagged zero-order, 46 standard zero-order, 36,48,58 Erlang, 64 or single-gamma 38 or double-gamma absorption. 38,44 In most studies, MPA/fMPA elimination was modelled using first-order elimination, [35][36][37][38][40][41][42][43][44][45][46][47][48][49][50][51]53,[55][56][57][58][59][61][62][63][64][65] whereas some used zeroorder 52,54,60 or bi-exponential elimination. 39 32 Most studies reported cyclosporine to affect MPA PK (Fig. 3). ...
Immunosuppressive therapy is pivotal for sustained allograft and patient survival after renal transplantation. However, optimally balanced immunosuppressive therapy is challenged by between-patient and within-patient pharmacokinetic (PK) variability. This could warrant the application of personalised dosing strategies to optimise individual patient outcomes. Pharmacometrics, the science that investigates the xenobiotic–biotic interplay using computer-aided mathematical modelling, provides options to describe and quantify this PK variability and enables identification of patient characteristics affecting immunosuppressant PK and treatment outcomes. Here, we review and critically appraise the available pharmacometric model-informed dosing solutions for the typical immunosuppressants in modern renal transplantation, to guide their initial and subsequent dosing.
... 15 The available evidence on associations of ABCC2 variants with MPA PK is contradictory, with a number of studies supporting 357,366,380,382,389,390 but others opposing 355,358,359,361,363,367,381,387,391 such relationships. Similarly, one population pharmacokinetic study in 65 KTR reported ABCC2 variants to affect MPA absorption and clearance, 392 whereas others found no associations between ABCC2 variants and MPA PK. [368][369][370]377,383,384,[393][394][395][396] Of note, the interpretation of associative studies on ABCC2 variants and MPA PK may be complicated in patients receiving concomitant immunosuppressive therapy with cyclosporine A, which is not uncommon in the population receiving MPA. Cyclosporine A exhibits extensive inhibition of ABCC2, which likely masks any impact of ABCC2 variants on MPA PK. 15,374 Aside from ABCC2, it has been suggested that ABCB1 is involved in MPA absorption. ...
... 396 Two other drug transporter genes, SLCO1B1 and SLCO1B3 encoding OATP1B1 and OATP1B3, respectively, are involved in the hepatic uptake of MPAG, contribute to enterohepatic circulation, and exhibit functional genetic variations. 15 For SLCO1B1, 4 studies in solid organ transplant recipients found no associations of any SLCO1B1 variant or haplotype with MPA PK. 363,366,367,399 One population pharmacokinetic study did report lower MPA clearance in SLCO1B1 c.388A.G variant (SLCO1B1*1B; rs2306283, p.Asn130Asp) carriers, 377 whereas 2 others reported no effect from any of the SLCO1B1 variants c.388A.G, SLCO1B1*5 (rs4149056, c.521T.C, p.Val174Ala), SLCO1B1*15 (rs2306283, c.388A.G, p.Asn130Asp/rs4149056, c.521T.C, pVal174Ala), (rs2291073, c.226+89T.G), (rs2291075, c.597C.T, p.Phe199=), (rs2417955, 1883T.A, intronic), (rs3829306, c.-61-2168C.T), (rs4149026, 10169A.C, intronic), or (rs4149058, c.727+1260A.G) on MPA PK. 383,395 In another study, SLCO1B1*15 (rs2306283/ rs4149056) carriers displayed lower MPAG concentrations than noncarriers. 400 Regarding SLCO1B3, 2 studies reported reduced MPA exposure in SLCO1B3 c.334 G (rs4149117) carriers as compared to noncarriers, 363,381 whereas 2 other studies found no associations between SLCO1B3 variants and MPA PK. 382,390 One population pharmacokinetic study reported an increased distribution volume of MPAG for SLCO1B3 c.334T.G (rs4149117) carriers, whereas 4 other studies found no effect of SLCO1B3 on MPA PK. 370,377,383,384,396 PG-PD Relationships MPA exerts its immunosuppressive effect through inhibition of the IMPDH enzyme, which is involved in the de novo purine synthesis. ...
When mycophenolic acid (MPA) was originally marketed for immunosuppressive therapy, fixed doses were recommended by the manufacturer. Awareness of the potential for a more personalized dosing has led to development of methods to estimate MPA area under the curve based on the measurement of drug concentrations in only a few samples. This approach is feasible in the clinical routine and has proven successful in terms of correlation with outcome. However, the search for superior correlates has continued, and numerous studies in search of biomarkers that could better predict the perfect dosage for the individual patient have been published. As it was considered timely for an updated and comprehensive presentation of consensus on the status for personalized treatment with MPA, this report was prepared following an initiative from members of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT). Topics included are the criteria for analytics, methods to estimate exposure including pharmacometrics, the potential influence of pharmacogenetics, development of biomarkers, and the practical aspects of implementation of target concentration intervention. For selected topics with sufficient evidence, such as the application of limited sampling strategies for MPA area under the curve, graded recommendations on target ranges are presented. To provide a comprehensive review, this report also includes updates on the status of potential biomarkers including those which may be promising but with a low level of evidence. In view of the fact that there are very few new immunosuppressive drugs under development for the transplant field, it is likely that MPA will continue to be prescribed on a large scale in the upcoming years. Discontinuation of therapy due to adverse effects is relatively common, increasing the risk for late rejections, which may contribute to graft loss. Therefore, the continued search for innovative methods to better personalize MPA dosage is warranted.
... The research shows that low tacrolimus dosing that does not correspond to the therapeutic range is associated with an increased risk of organ rejection [20] As the concentration of tacrolimus is closely related to graft survival [30,31], it is important to understand the relevant factors, including concomitant drug administration, which influence the variability of tacrolimus and to quantify their effects on the concentration of tacrolimus to assist in drug dosage decisions in patients [32]. M.A. Halim et al. concluded that the CsA dose should be individualized in renal transplant recipients, especially if they have viral hepatitis and a single daily dosing of cyclosporine has the advantage of decreasing dosage and CsA-related adverse effects while maintaining optimal graft function [4]. ...
The AUC (area under the concentration time curve) is considered the pharmacokinetic exposure parameter best associated with clinical effects. Unfortunately, no prospective studies of clinical outcomes have been conducted in adult transplant recipients to investigate properly the potential benefits of AUC(0–12) monitoring compared to the C0-guided therapy. The aim of the present study was to compare two methods, C0 (through level) and AUC(0–12) (area under the concentration time curve), for assessing cyclosporine and tacrolimus concentrations. The study included 340 kidney recipients. The AUC(0–12) was estimated using a Bayesian estimator and a three-point limited sampling strategy. Therapeutic drug monitoring of tacrolimus performed by using AUC(0–12) and C0 showed that tacrolimus in most cases is overdosed when considering C0, while determination of the AUC(0–12) showed that tacrolimus is effectively dosed for 27.8–40.0% of patients receiving only tacrolimus and for 25.0–31.9% of patients receiving tacrolimus with MMF (mycophenolate mofetil). In the 1–5 years post-transplantation group, 10% higher CsA (cyclosporine) dose was observed, which was proportionate with a 10% higher AUC(0–12) exposure value. This indicates good compatibility of the dosage and the AUC(0–12) method. The Bland–Altman plot demonstrated that C0 and AUC(0–12) might be interchangeable methods, while the ROC (receiver operating characteristic) curve analysis of the C0/AUC(0–12) ratio in the tacrolimus-receiving patient group demonstrated reliable performance to predict IFTA (interstitial fibrosis and tubular atrophy) after kidney transplantation, with an ROC curve of 0.660 (95% confidence interval (CI): 0.576–0.736), p < 0.01. Moreover, AUC(0–12) and C0 of tacrolimus depend on concomitant medication and adjustment of the therapeutic range for AUC(0–12) might influence the results.
... Hence, C57BL/6 Ubi-GFP recipients were treated daily with vehicle only (mock group) or 1 mg·kg −1 tacrolimus (T1 group) from day 0 to day 28 post-HTT (figure 2a). Tacrolimus whole-blood concentrations were measured using a liquid chromatography-mass spectrometry (LC-MS) assay at 3 h post-dose (C3) (figure 2b), and showed drug levels similar to those found in transplanted patients [20,21]. After 28 days, histological quantification of epithelial loss and lumen occlusion revealed a significant although heterogeneous effect of tacrolimus in reducing both parameters (figure 2c-e). ...
Bronchiolitis Obliterans Syndrome (BOS) is a fibrotic disease heavily responsible for high mortality rates after lung transplantation. Myofibroblasts are primary effectors of this fibrotic process, but their origin is still under debate. The purpose of this work was to identify the precursors of mesenchymal cells responsible for post-transplant airway fibro-obliteration.
Lineage-tracing tools were used to track or deplete potential sources of myofibroblasts in the heterotopic tracheal transplantation model. Allografts were analysed by histology, confocal microscopy, flow cytometry or single-cell transcriptomic analysis. BOS explants were evaluated by histology and confocal microscopy.
Myofibroblasts in the allografts were recipient-derived. Still, when recipient mice were treated with tacrolimus, we observed rare epithelial-to-mesenchymal transition phenomena and an overall increase in donor-derived myofibroblasts (p=0.0467), but the proportion of these cells remained low (7%). Hematopoietic cells, and specifically the mononuclear phagocyte system, gave rise to the majority of myofibroblasts found in occluded airways. Ablation of Cx3cR1 ⁺ cells decreased fibro-obliteration (p=0.0151) and myofibroblasts accumulation (p=0.0020). Single-cell RNA-sequencing unveiled similarities between myeloid-derived cells from allografts and both murine and human samples of lung fibrosis. Finally, myofibroblasts expressing the macrophage marker CD68 were increased in BOS explants when compared to controls (14.4% versus 8.5% p=0.0249).
Recipient-derived myeloid progenitors represent a clinically-relevant source of mesenchymal cells infiltrating the airways after allogeneic transplantation. Therefore, therapies targeting the mononuclear phagocyte system could improve long-term outcomes after lung transplantation.
Introduction
Mycophenolic acid (MPA) is a widely used immunosuppressant in transplantation and autoimmune disease. Highly variable pharmacokinetics have been observed with MPA, but the exact mechanisms remain largely unknown.
Areas covered
The current review provided a critical, comprehensive update of recently published population pharmacokinetic/dynamic models of MPA (n=16 papers identified from PubMed and Embase, inclusive from January 2017 to August 2021), with specific emphases on the intrinsic and extrinsic factors influencing the pharmacology of MPA. The significance of the identified covariates, potential mechanisms, and comparisons to historical literature have been provided.
Expert opinion
While select covariates affecting the population pharmacokinetics of MPA are consistently observed and mechanistically supported, some variables have not been regularly reported and/or lacked mechanistic explanation. Very few pharmacodynamic models were available, pointing to the need to extrapolate pharmacokinetic findings. Ideal models of MPA should consist of: i) utilizing optimal sampling points to allow the characterizations of absorption, re-absorption, and elimination phases; ii) characterizing unbound/total MPA, MPA metabolites, plasma/urinary concentrations, and genetic polymorphisms to facilitate mechanistic interpretations; and iii) incorporating actual outcomes and pharmacodynamic data to establish clinical relevance. We anticipate the field will continue to expand in the next 5 to 10 years.
Immunosuppressant drugs (ISDs) play a key role in short-term patient survival together with very low acute allograft rejection rates in transplant recipients. Due to the narrow therapeutic index and large inter-patient pharmacokinetic variability of ISDs, therapeutic drug monitoring (TDM) is needed to dose adjustment for each patient (personalized medicine approach) to avoid treatment failure or side effects of the therapy. To achieve this, TDM needs to be done effectively. However, it would not be possible without the proper clinical practice and analytical tools. The purpose of this review is to provide a guide to establish reliable TDM, followed by a critical overview of the current analytical methods and clinical practices for the TDM of ISDs, and to discuss some of the main practical aspects of the TDM.
Mycophenolic acid (MPA) is an immunomodulating agent commonly used in human medicine for the treatment of immune-mediated diseases. There is growing evidence that the immunomodulating properties of mycophenolate mofetil (MMF), a prodrug of MPA, are therapeutically beneficial for the treatment of immune-mediated diseases in dogs. A narrow therapeutic index and high inter-and intra-patient pharmacokinetic (PK) variability complicate the use of MMF. A better characterization of MPA pharmacokinetics is needed to help establish dosing regimens and standardized treatment protocols for canine patients. The purpose of this study was to evaluate the pharmacokinetics of MPA in dogs. MMF oral suspension (10 mg/kg) was administered to five healthy beagle dogs. Serial blood samples were collected from 0 to 18 hours after administration. The simultaneous quantification of MPA, and its metabolites MPA-7-O-glucuronide (MPAG), and acyl glucuronide (AcMPAG) was determined by liquid chromatography (LC)-mass spectrometry (MS)/MS. MPA peak concentrations were achieved rapidly (median Tmax of 0.5 h). Concentrations fell through 3 hours post-dose and then plateaued around 20% of Cmax. The mean elimination half-life was rapid (5.8 hours) and notable variability was observed in all PK parameters. The PK profiles for the MPAG and AcMPAG metabolites followed a similar pattern as MPA concentration. Future repeat-dose studies will be needed to evaluate steady-state PK parameters and to define therapeutic MPA dose levels.
