Luis Quintairos (1,2), Helena Colom (1), Olga Millán (3), Virginia Fortuna (2), Cristina Espinosa (2), Lluis Guirado (4), Klemens Budde (5), Claudia Somerer (6), Mercè Brunet(3).
(1) Department of Pharmacy, Pharmaceutical Technology and Physical Chemistry, Faculty of Pharmacy and Food Sciences, University of Barcelona, Spain; Unit of Biopharmaceutics and Pharmacokinetics, (2) Pharmacology and Toxicology Laboratory, CDB, IDIBAPS, Fundació clÃnic per la recerca biomèdica (FCRB), Hospital Clinic of Barcelona, University of Barcelona, Spain, (3) Pharmacology and Toxicology Laboratory, CDB, IDIBAPS, CIBERehd, Hospital Clinic of Barcelona, University of Barcelona, Spain, (4) Renal Transplant Unit, Nephrology Department, Fundació Puigvert, Barcelona, Spain, (5) Medizinische Klinik mit Schwerpunkt Nephrologie, Charité Universitätsmedizin Berlin, Berlin, Germany, (6) Department of Nephrology, University of Heidelberg, University Hospital of Heidelberg and Mannheim, Heidelberg, Germany
Objectives:
Despite advances in immunosuppression, allograft rejection still remains a challenge in solid-organ transplantation. Micro RNA-155-5p (miRNA155) has been described as a positive regulator of inflammatory responses that participates in adaptive immunogenicity controlling T-CD4+ cells differentiation. Chemokine interferon inducible (CXCL10) is also a promising biomarker of short- and long-term kidney graft function. Previous results of our group and others, showed that urinary pellet levels of miRNA155 and urinary CXCL10 production could play a key role of prognosis and diagnosis in risk of acute rejection in kidney transplantation [1]. The objective of the present study was to develop a binary pharmacodynamic model in adult kidney transplant patients, establishing a relationship between miRNA155 and CXCL10 levels, tacrolimus and MPA exposure and the probability of acute rejection.
Methods:
Data from 58 kidney transplanted patients from the European multicenter study IMAGEN (EudraCT -number: 2013–001817-33) were analyzed in the present work. All[H1] patients received tacrolimus, mycophenolate mofetil (MPA) and methylprednisolone. Details about patients and therapies are specified elsewhere [1]. Samples were obtained at the 1st week and 1st,2nd,3rd and 6th months post-transplantation. Trough tacrolimus and MPA concentrations, urinary pellet expression of miRNA155 and urinary production of CXCL10 were determined on each occasion. The final pooled data set included 193 observations of each biomarker, tacrolimus and MPA. Patient demographic characteristics, as well as occurrence of acute rejection or infection, and cytomegalovirus (CMV) or BK virus (BKV) presence were recorded.
A logistic regression model was used to investigate the relationship between either biomarker or/and drug exposure and the probability of acute rejection occurrence. The efficacy data (rejection event or not) were evaluated as binary data with 0 indicating no rejection and 1 indicating rejection occurrence. The probability of the observed score was linked to biomarkers and drug exposure through the logit transformation, to ensure that the probability falls between 0 and 1. The influence of all physiologically plausible covariates and the effect of time as a linear model, on graft function outcome, were tested. Between subject variability (BSV) was tested on all parameters. NONMEM 7.4 [2] software with FOCE method with Laplacian was applied throughout all building process.
Selection of the best model was performed according to: i) objective function value (MOFV), ii) plausibility of parameter estimates and precision (given a %relative standard error) iii) visual predictive check plots for categorical data after 1000 simulations. The acceptance criteria for a covariate into the model was a ΔMOFV of at least -3.84 (p<0.05) and a reduction or at least no increase in the unexplained variability in the model. R software 3.3 [3] and vpc R package [4] were used as graphical evaluation tools, and Pirana software [5] was also used as a support tool.
Results:
8 out of 58 patients developed an acute rejection event and 4 of them, developed a second acute rejection event during the study. 14, 9 and 34 out of 58 patients developed CMV, BKV and nonspecific infections, respectively, at certain occasions, but no infection episodes were observed at the same time of the any of the acute rejection events.
No statistically significant relationship was found between exposure of tacrolimus or MPA and the clinical outcome. The linear effect of time did not improve the fit. BSV was only included on the baseline (B0) (10.8% (36%RSE)). The Inclusion of miRNA155 significantly improved the baseline model (ΔMOFV[clinic1] -17.658) and the posterior inclusion of CXCL10 (ΔMOFV[clinic2] -7.857) led to a reduction 51.71% in BSV. None of the other tested covariates (infection, CMV and BKV) were identified as significant predictors. The final logit function was as follows:
Log (OR)=B0 + B1*miRNA155 + B2*CXCL10
Being, B0 (-8.3(17%RSE)) the baseline effect and B1(5.7(18%RSE)) and B2(0.0071(30%RSE)) the slopes of the miRNA155 and CXCL10 effects, respectively.
Conclusions:
Both miRNA155 and CXCL10 were identified as predictors of the risk of developing an acute rejection in early kidney post-transplant patients while no influence was found for neither tacrolimus nor MPA, confirming previous results [1]. Further studies with a larger sample size would be required in order to confirm the current findings.
References:
[1] Millán, O., Budde, K., Sommerer, C., Aliart, I., Rissling, O., Bardaji, B., et al. (2017). Urinary miR-155-5p and CXCL10 as prognostic and predictive biomarkers of rejection, graft outcome and treatment response in kidney transplantation. Br. J. Clin. Pharmacol. 10:.
[2] Beal S, Sheiner LB, Boeckmann A, Bauer RJ (2009) NONMEM user’s Guides. (1989-2009). Ellicott City, MD, USA.
[3] R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
[4] Ron Keizer (2018). vpc: Create Visual Predictive Checks. R package version 1.0.1. https://CRAN.R-project.org/package=vpc
[5] Keizer RJ et al.; Comput Methods Programs Biomed 2011 Jan;101(1):72-9; Piraña and PCluster: a modeling environment and cluster infrastructure for NONMEM. PubMed
Reference: PAGE 27 (2018) Abstr 8658 [www.page-meeting.org/?abstract=8658]
Poster: Drug/Disease Modelling - Other Topics