Volume 27, Issue 3 e14460
ORIGINAL ARTICLE

Noninvasive testing for mycophenolate exposure in children with renal transplant using urinary metabolomics

Khalid Taha

Khalid Taha

Department of Pediatrics, University of British Columbia, BC Children's Hospital Vancouver, Vancouver, British Columbia, Canada

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Atul Sharma

Atul Sharma

Department of Pediatrics and Child Health, University of Manitoba, Children's Hospital at Health Sciences Center, Winnipeg, Manitoba, Canada

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Kristine Kroeker

Kristine Kroeker

Centre for Healthcare Innovation, University of Manitoba, Winnipeg, Manitoba, Canada

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Colin Ross

Colin Ross

Faculty of Pharmaceutical Sciences, University of British Columbia, BC Children's Hospital Vancouver, Vancouver, British Columbia, Canada

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Bruce Carleton

Bruce Carleton

Department of Pediatrics, University of British Columbia, BC Children's Hospital Vancouver, Vancouver, British Columbia, Canada

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David Wishart

David Wishart

Departments of Computing Science and Biological Sciences, University of Alberta, Edmonton, Alberta, Canada

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Mara Medeiros

Mara Medeiros

Departamento de Farmacología, Facultad de Medicina, Universidad Nacional Autónoma de México, Ciudad de México, Mexico

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Tom D. Blydt-Hansen

Corresponding Author

Tom D. Blydt-Hansen

Department of Pediatrics, University of British Columbia, BC Children's Hospital Vancouver, Vancouver, British Columbia, Canada

Correspondence

Tom D. Blydt-Hansen, MDCM, FRCPC Associate Professor, University of British Columbia, K4-149, 4480 Oak Street, Vancouver, British Columbia V6H 3V4, Canada.

Email: [email protected]

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First published: 29 December 2022
Citations: 3

Abstract

Background

Despite the common use of mycophenolate in pediatric renal transplantation, lack of effective therapeuic drug monitoring increases uncertainty over optimal drug exposure and risk for adverse reactions. This study aims to develop a novel urine test to estimate MPA exposure based using metabolomics.

Methods

Urine samples obtained on the same day of MPA pharmacokinetic testing from two prospective cohorts of pediatric kidney transplant recipients were assayed for 133 unique metabolites by mass spectrometry. Partial least squares (PLS) discriminate analysis was used to develop a top 10 urinary metabolite classifier that estimates MPA exposure. An independent cohort was used to test pharmacodynamic validity for allograft inflammation (urinary CXCL10 levels) and eGFR ratio (12mo/1mo eGFR) at 1 year.

Results

Fifty-two urine samples from separate children (36.5% female, 12.0 ± 5.3 years at transplant) were evaluated at 1.6 ± 2.5 years post-transplant. Using all detected metabolites (n = 90), the classifier exhibited strong association with MPA AUC by principal component regression (r = 0.56, p < .001) and PLS (r = 0.75, p < .001). A practical classifier (top 10 metabolites; r = 0.64, p < .001) retained similar accuracy after cross-validation (LOOCV; r = 0.52, p < .001). When applied to an independent cohort (n = 97 patients, 1053 samples), estimated mean MPA exposure over Year 1 was inversely associated with mean urinary CXCL10:Cr (r = −0.28, 95% CI −0.45, −0.08) and exhibited a trend for association with eGFR ratio (r = 0.35, p = .07), over the same time period.

Conclusions

This urinary metabolite classifier can estimate MPA exposure and correlates with allograft inflammation. Future studies with larger samples are required to validate and evaluate its clinical application.

DATA AVAILABILITY STATEMENT

Patient level data are not publicly available. Secondary analysis or further validation of data used in the publication is available by contacting the corresponding author.

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