Literature DB >> 33481955

Early prognostic performance of miR155-5p monitoring for the risk of rejection: Logistic regression with a population pharmacokinetic approach in adult kidney transplant patients.

Luis Quintairos1,2, Helena Colom1, Olga Millán2,3, Virginia Fortuna2, Cristina Espinosa2, Lluis Guirado4, Klemens Budde5, Claudia Sommerer6, Ana Lizana2, Yolanda López-Púa7, Mercè Brunet2,3.   

Abstract

Previous results from our group and others have shown that urinary pellet expression of miR155-5p and urinary CXCL-10 production could play a key role in the prognosis and diagnosis of acute rejection (AR) in kidney transplantation patients. Here, a logistic regression model was developed using NONMEM to quantify the relationships of miR155-5p urinary expression, CXCL-10 urinary concentration and tacrolimus and mycophenolic acid (MPA) exposure with the probability of AR in adult kidney transplant patients during the early post-transplant period. Owing to the contribution of therapeutic drug monitoring to achieving target exposure, neither tacrolimus nor MPA cumulative exposure was identified as a predictor of AR in the studied population. Even though CXCL-10 urinary concentration showed a trend, its effect on AR was not significant. In contrast, urinary miR155-5p expression was prognostic of clinical outcome. Monitoring miR155-5p urinary pellet expression together with immunosuppressive drug exposure could be very useful during routine clinical practice to identify patients with a potential high risk of rejection at the early stages of the post-transplant period. This early risk assessment would allow for the optimization of treatment and improved prevention of AR.

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Year:  2021        PMID: 33481955      PMCID: PMC7822507          DOI: 10.1371/journal.pone.0245880

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  49 in total

Review 1.  Urine proteomics in kidney transplantation.

Authors:  Steven C Kim; Eugenia K Page; Stuart J Knechtle
Journal:  Transplant Rev (Orlando)       Date:  2013-10-24       Impact factor: 3.943

2.  Importance of shrinkage in empirical bayes estimates for diagnostics: problems and solutions.

Authors:  Radojka M Savic; Mats O Karlsson
Journal:  AAPS J       Date:  2009-08-01       Impact factor: 4.009

3.  Differential expression of microRNAs in renal transplant patients with acute T-cell mediated rejection.

Authors:  Ehsan Soltaninejad; Mohammad Hossein Nicknam; Mohsen Nafar; Pedram Ahmadpoor; Fatemeh Pourrezagholi; Mohammad Hossein Sharbafi; Morteza Hosseinzadeh; Farshad Foroughi; Mir Saeed Yekaninejad; Tayyeb Bahrami; Ehsan Sharif-Paghaleh; Aliakbar Amirzargar
Journal:  Transpl Immunol       Date:  2015-05-20       Impact factor: 1.708

4.  Development of a population PK model of tacrolimus for adaptive dosage control in stable kidney transplant patients.

Authors:  Franc Andreu; Helena Colom; Josep M Grinyó; Joan Torras; Josep M Cruzado; Nuria Lloberas
Journal:  Ther Drug Monit       Date:  2015-04       Impact factor: 3.681

5.  Interaction between everolimus and tacrolimus in renal transplant recipients: a pharmacokinetic controlled trial.

Authors:  Julio Pascual; Domingo del Castillo; Mercedes Cabello; Luis Pallardó; Josep M Grinyó; Ana M Fernández; Mercè Brunet
Journal:  Transplantation       Date:  2010-04-27       Impact factor: 4.939

6.  Population pharmacokinetics of tacrolimus in adult kidney transplant patients: impact of CYP3A5 genotype on starting dose.

Authors:  Troels K Bergmann; Stefanie Hennig; Katherine A Barraclough; Nicole M Isbel; Christine E Staatz
Journal:  Ther Drug Monit       Date:  2014-02       Impact factor: 3.681

7.  miRNA profiling discriminates types of rejection and injury in human renal allografts.

Authors:  Julia Wilflingseder; Heinz Regele; Paul Perco; Alexander Kainz; Afschin Soleiman; Ferdinand Mühlbacher; Bernd Mayer; Rainer Oberbauer
Journal:  Transplantation       Date:  2013-03-27       Impact factor: 4.939

8.  Improved prediction of tacrolimus concentrations early after kidney transplantation using theory-based pharmacokinetic modelling.

Authors:  Elisabet Størset; Nick Holford; Stefanie Hennig; Troels K Bergmann; Stein Bergan; Sara Bremer; Anders Åsberg; Karsten Midtvedt; Christine E Staatz
Journal:  Br J Clin Pharmacol       Date:  2014-09       Impact factor: 4.335

9.  A population pharmacokinetic model to predict the individual starting dose of tacrolimus in adult renal transplant recipients.

Authors:  L M Andrews; D A Hesselink; R H N van Schaik; T van Gelder; J W de Fijter; N Lloberas; L Elens; D J A R Moes; B C M de Winter
Journal:  Br J Clin Pharmacol       Date:  2019-01-17       Impact factor: 4.335

10.  A strategy for residual error modeling incorporating scedasticity of variance and distribution shape.

Authors:  Anne-Gaëlle Dosne; Martin Bergstrand; Mats O Karlsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2015-12-17       Impact factor: 2.745

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  2 in total

1.  Significant Correlations between p-Cresol Sulfate and Mycophenolic Acid Plasma Concentrations in Adult Kidney Transplant Recipients.

Authors:  Yan Rong; Penny Colbourne; Sita Gourishankar; Tony K L Kiang
Journal:  Clin Drug Investig       Date:  2022-02-18       Impact factor: 2.859

Review 2.  Epigenetic Regulation in Kidney Transplantation.

Authors:  Xiaohong Xiang; Jiefu Zhu; Guie Dong; Zheng Dong
Journal:  Front Immunol       Date:  2022-04-08       Impact factor: 8.786

  2 in total

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