An Integrated Clinical and Genetic Prediction Model for Tacrolimus Levels in Pediatric Solid Organ Transplant Recipients
Open accessAbstract
BACKGROUND: There are challenges in achieving and maintaining therapeutic tacrolimus levels after solid organ transplantation (SOT). The purpose of this genome-wide association study was to generate an integrated clinical and genetic prediction model for tacrolimus levels in pediatric SOT. METHODS: In a multicenter prospective observational cohort study (2015-2018), children <18 years old at their first SOT receiving tacrolimus as maintenance immunosuppression were included (455 as discovery cohort; 322 as validation cohort). Genotyping was performed using a genome-wide single nucleotide polymorphism (SNP) array and analyzed for association with tacrolimus trough levels during 1-y follow-up. RESULTS: Genome-wide association study adjusted for clinical factors identified 25 SNPs associated with tacrolimus levels; 8 were significant at a genome-wide level (P < 1.025 × 10-7). Nineteen SNPs were replicated in the validation cohort. After removing SNPs in strong linkage disequilibrium, 14 SNPs remained independently associated with tacrolimus levels. Both traditional and machine learning approaches selected organ type, age at transplant, rs776746, rs12333983, and rs12957142 SNPs as the top predictor variables for dose-adjusted 36- to 48-h posttacrolimus initiation (T1) levels. There was a significant interaction between age and organ type with rs776476*1 SNP (P < 0.05). The combined clinical and genetic model had lower prediction error and explained 30% of the variation in dose-adjusted T1 levels compared with 18% by the clinical and 12% by the genetic only model. CONCLUSIONS: Our study highlights the importance of incorporating age, organ type, and genotype in predicting tacrolimus levels and lays the groundwork for developing an individualized age and organ-specific genotype-guided tacrolimus dosing algorithm.
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Using combination of genomic data and clinical factors was shown more important than the choice of algorithm.
“Using combination of genomic data and clinical factors was shown more important than the choice of algorithm.”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [31]
All three studies comparing logistic regression to machine learning algorithms for predicting stable tacrolimus dose in kidney transplant patients showed a superior predictive ability of machine learning tools over linear regression models, albeit with a relatively small difference.
“All studies showed a superior predictive ability of machine learning tools over the linear regression models, albeit with a relatively small difference.”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [31]
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Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation
Peloso, A., Moeckli, B., Delaune, V., Oldani, G., Andres, A., & Compagnon, P. (2022) · Transplant International · doi:10.3389/ti.2022.10640 · Cited by 67 · see citing works
- Authors
- Min, S., Papaz, T., Lambert, A. N., Allen, U., Birk, P., Blydt-Hansen, T., Foster, B. J., Grasemann, H., Hamiwka, L., Litalien, C., Ng, V., Berka, N., Campbell, P., Daniel, C., Saw, C. L., Tinckam, K., Urschel, S., Van Driest, S. L., Parekh, R., & Mital, S.
- Affiliations
- Hospital for Sick Children (CA)
- Vanderbilt University Medical Center (US)
- University of Toronto (CA)
- Health Sciences Centre (CA)
- Division of Pediatric Nephrology, University of British Columbia, Vancouver, BC, Canada.
- Montreal Children's Hospital (CA)
- University of Calgary (CA)
- Centre Hospitalier Universitaire Sainte-Justine (CA)
- University of Alberta (CA)
- Institut National de la Recherche Scientifique (CA)
- McGill University Health Centre (CA)
- University Health Network (CA)
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN.
- Corresponding author
- Mital, Seema
- Venue
- Transplantation
- Published
- 2021-02-22
- Volume
- 106
- Issue
- 3
- Pages
- 597-606
- ISSN
- 0041-1337 (ISSN-L)
- PMC
- PMC8862776
- OpenAlex
- W3135603479
- Cited by
- 18
- References
- 46
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 6 · 2023: 6 · 2022: 3 · 2021: 1
- FWCI
- 3.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Transplantation (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- EspaceINRS (National Institute for Scientific Research (Canada)) (submittedVersion)
- Funders
- Burroughs Wellcome Fund (1015006, IRSA 1015006)
- Heart and Stroke Foundation of Canada
- Vanderbilt University (UL1 RR024975, UL1‐TR000445)
- Vanderbilt University Medical Center (UL1TR000445, S10RR025141, UL1 RR024975, UL1 TR002243)
- Canada Research Chairs
- Astellas Pharma
- Hospital for Sick Children
- National Institutes of Health (UL1TR000445, UL1TR002243)
- Canadian Institutes of Health Research (,TFU 127880)
- National Heart, Lung, and Blood Institute (T32HL105334)
- National Center for Research Resources (S10RR025141, UL1RR024975)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
- Health Sciences › Medicine › Transplantation › Renal Transplantation Outcomes and Treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- tacrolimus, pediatric solid organ transplantation, pharmacogenetics, trough concentration, pharmacokinetic prediction, single nucleotide polymorphisms, rs776746, age at transplantation, genotype-guided dosing, maintenance immunosuppression
- MeSH terms
- Adolescent, Child, Dose-Response Relationship, Drug, Genotype, Humans, Immunosuppressive Agents, Prospective Studies, Organ Transplantation, Tacrolimus, Polymorphism, Single Nucleotide, Cytochrome P-450 CYP3A, Genome-Wide Association Study, Transplant Recipients
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