Kidney Transplantation Clinical Trial
— EU-TRAINOfficial title:
The EUropean TRAnsplantation and INnovation Consortium for Risk Stratification in Kidney Transplant Patients
Verified date | June 2024 |
Source | Assistance Publique - Hôpitaux de Paris |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
Main objective: To design a precision risk stratification system that predicts individual risk of rejection
Status | Completed |
Enrollment | 558 |
Est. completion date | October 28, 2021 |
Est. primary completion date | October 28, 2021 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years to 100 Years |
Eligibility | Inclusion Criteria: - Men and women, Age = 18 years old at the time of transplantation. - Patients receiving a living or deceased donor kidney allograft. - Patients who signed the informed consent form and willing to comply with study procedures. - Female patients of child-bearing potential must have a negative pregnancy test (serum beta-hCG) and must be practicing an effective, reliable and medically approved contraceptive regimen Exclusion Criteria: - History of multi-organ transplant (interference with rejection natural history). - Participant is unable or unwilling to comply with study procedures (including foreign language speakers who are not assisted by a native language speaker). - Vulnerable participants (minors, protected adults, legally detained) |
Country | Name | City | State |
---|---|---|---|
France | Hôpital du Kremlin Bicêtre | Le Kremlin-Bicêtre | Paris |
France | CHU Nantes | Nantes | |
France | Hôpital Necker | Paris | |
France | Hopital Saint Louis | Paris | Ile De France |
Germany | Hospital La Charité Campus Virchow | Berlin | |
Germany | Hospital La Charité | Berlin-Mitte | Berlin |
Spain | Hospital Bellvitge | Barcelona | |
Spain | Hospital Vall d'Hebron | Barcelona | |
Switzerland | Hôpitaux Universitaires de Genève | Geneva |
Lead Sponsor | Collaborator |
---|---|
Assistance Publique - Hôpitaux de Paris |
France, Germany, Spain, Switzerland,
Ba R, Geffard E, Douillard V, Simon F, Mesnard L, Vince N, Gourraud PA, Limou S. Surfing the Big Data Wave: Omics Data Challenges in Transplantation. Transplantation. 2022 Feb 1;106(2):e114-e125. doi: 10.1097/TP.0000000000003992. — View Citation
Brinas F, Danger R, Brouard S. TCL1A, B Cell Regulation and Tolerance in Renal Transplantation. Cells. 2021 Jun 1;10(6):1367. doi: 10.3390/cells10061367. — View Citation
Danger R, Feseha Y, Brouard S. The Pseudokinase TRIB1 in Immune Cells and Associated Disorders. Cancers (Basel). 2022 Feb 17;14(4):1011. doi: 10.3390/cancers14041011. — View Citation
Danger R, Le Berre L, Cadoux M, Kerleau C, Papuchon E, Mai HL, Nguyen TV, Guerif P, Morelon E, Thaunat O, Legendre C, Anglicheau D, Lefaucheur C, Couzi L, Del Bello A, Kamar N, Le Quintrec M, Goutaudier V, Renaudin K, Giral M, Brouard S; DIVAT Consortium. — View Citation
Danger R, Moiteaux Q, Feseha Y, Geffard E, Ramstein G, Brouard S. FaDA: A web application for regular laboratory data analyses. PLoS One. 2021 Dec 20;16(12):e0261083. doi: 10.1371/journal.pone.0261083. eCollection 2021. — View Citation
Divard G, Raynaud M, Tatapudi VS, Abdalla B, Bailly E, Assayag M, Binois Y, Cohen R, Zhang H, Ulloa C, Linhares K, Tedesco HS, Legendre C, Jouven X, Montgomery RA, Lefaucheur C, Aubert O, Loupy A. Comparison of artificial intelligence and human-based pred — View Citation
Ed-Driouch C, Cheneau F, Simon F, Pasquier G, Combes B, Kerbrat A, Le Page E, Limou S, Vince N, Laplaud DA, Mars F, Dumas C, Edan G, Gourraud PA. Multiple sclerosis clinical decision support system based on projection to reference datasets. Ann Clin Trans — View Citation
Ed-Driouch C, Mars F, Gourraud PA, Dumas C. Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human-Machine Intelligence. Sensors (Basel). 2022 Oct 29;22(21):8313. doi: 10. — View Citation
Girardin FR, Cohen K, Schwenkglenks M, Durand-Zaleski I. Editorial: Pharmacoeconomics in the era of health technology assessment and outcomes research to prioritize resource use, innovation and investment. Front Pharmacol. 2023 May 16;14:1210002. doi: 10. — View Citation
Girardin FR, Nicolet A, Bestard O, Lefaucheur C, Budde K, Halleck F, Brouard S, Giral M, Gourraud PA, Horcholle B, Villard J, Marti J, Loupy A. Immunosuppressant drugs and quality-of-life outcomes in kidney transplant recipients: An international cohort s — View Citation
Kerouac S, Taggart ME, Lescop J, Fortin MF. Dimensions of health in violent families. Health Care Women Int. 1986;7(6):413-26. doi: 10.1080/07399338609515756. No abstract available. — View Citation
Massart A, Danger R, Olsen C, Emond MJ, Viklicky O, Jacquemin V, Soblet J, Duerinckx S, Croes D, Perazzolo C, Hruba P, Daneels D, Caljon B, Sever MS, Pascual J, Miglinas M; Renal Tolerance Investigators; Pirson I, Ghisdal L, Smits G, Giral M, Abramowicz D — View Citation
Montero N, Farouk S, Gandolfini I, Crespo E, Jarque M, Meneghini M, Torija A, Maggiore U, Cravedi P, Bestard O. Pretransplant Donor-specific IFNgamma ELISPOT as a Predictor of Graft Rejection: A Diagnostic Test Accuracy Meta-analysis. Transplant Direct. 2 — View Citation
Tripathi N, Danger R, Chesneau M, Brouard S, Laurent AD. Structural insights into the catalytic mechanism of granzyme B upon substrate and inhibitor binding. J Mol Graph Model. 2022 Jul;114:108167. doi: 10.1016/j.jmgm.2022.108167. Epub 2022 Mar 22. — View Citation
Yoo D, Goutaudier V, Divard G, Gueguen J, Astor BC, Aubert O, Raynaud M, Demir Z, Hogan J, Weng P, Smith J, Garro R, Warady BA, Zahr RS, Sablik M, Twombley K, Couzi L, Berney T, Boyer O, Duong-Van-Huyen JP, Giral M, Alsadi A, Gourraud PA, Morelon E, Le Qu — View Citation
* Note: There are 15 references in all — Click here to view all references
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Capacity of non-invasive biomarkers and intragraft gene expression profiles combined to standard of care data (HLA system, clinical and biological data) | Prognosis value of non-invasive biomarkers and intragraft gene expression profile changes combined to standard of care data changes (HLA system, clinical and biological data), to identify high versus low risk profiles of rejection as measured by DSA characteristics (Donor-Specific Antibody) by Luminex single antigen assay and non DSA characteristics by functional in vitro assay on endothelial targets, alloreactive T and B cells profiles by ELISPOT, blood mRNA expression by NanoString technologies and gene expression on DNA chips. | Day 0, Month 3, Month 12, clinical indication over 12 months | |
Secondary | Correlation of blood biomarkers concentration with allograft rejection (rejection assessed by histopathology) | Blood biomarkers measured: HLA and non-HLA DSA characteristics (by Luminex Single Antigen and functional in vitro assay on endothelial targets), alloreactive T and B cell profiles (by ELISPOT) and candidate gene profiles by NanoString technologies: AKR1C3, CD40, CTLA4, ID3, MZB1, TCL1A, TRIB1, TLR4 TUBA4A, WHAZ, CD3E, CD8A, CD4, MS4A1, FOXP3, GZMB, ENTPD1, POU2AAF1, POU2F1, CD9, IL7R, BLK, MMP9, CXCL9, CXCL10, CXCL11, UPK1A, TGFB1, IL2RA, PRF1, TIMP1, PAI1, FN1, TIGIT and 4 reference genes: HPRT1, B2M, GAPDH and ACTB | Day 0, Month 3, Month12, clinical indication over 12 months | |
Secondary | Correlation of blood biomarker concentrations with allograft function (function measured by the Glomerular Filtration Rate (GFR)). | Blood biomarkers measured: HLA and non-HLA DSA characteristics (by Luminex Single Antigen and functional in vitro assay on endothelial targets), alloreactive T and B cell profiles (by ELISPOT) and candidate gene profiles by NanoString technologies: AKR1C3, CD40, CTLA4, ID3, MZB1, TCL1A, TRIB1, TLR4 TUBA4A, WHAZ, CD3E, CD8A, CD4, MS4A1, FOXP3, GZMB, ENTPD1, POU2AAF1, POU2F1, CD9, IL7R, BLK, MMP9, CXCL9, CXCL10, CXCL11, UPK1A, TGFB1, IL2RA, PRF1, TIMP1, PAI1, FN1, TIGIT and 4 reference genes: HPRT1, B2M, GAPDH and ACTB | Day 0, Month 3, Month12, clinical indication over 12 months | |
Secondary | Assessment of changes in biomarker levels in serial measurements and association with allograft function (function measured by the GFR) | Blood biomarkers measured: HLA and non-HLA DSA characteristics (by Luminex Single Antigen and functional in vitro assay on endothelial targets), alloreactive T and B cell profiles (by ELISPOT) and candidate gene profiles by NanoString technologies: AKR1C3, CD40, CTLA4, ID3, MZB1, TCL1A, TRIB1, TLR4 TUBA4A, WHAZ, CD3E, CD8A, CD4, MS4A1, FOXP3, GZMB, ENTPD1, POU2AAF1, POU2F1, CD9, IL7R, BLK, MMP9, CXCL9, CXCL10, CXCL11, UPK1A, TGFB1, IL2RA, PRF1, TIMP1, PAI1, FN1, TIGIT and 4 reference genes: HPRT1, B2M, GAPDH and ACTB Biomarkers measured in the biopsy: pangenomic approach of gene expression profiles using Affymetrix DNA chips and comparing gene expression in low- and high-risk patients | Day 0, Month 3, Month12, clinical indication over 12 months | |
Secondary | Correlation of gene expression in kidney allografts with allograft rejection (rejection assessed by histopathology) | Biomarkers measured in the biopsy: pangenomic approach of gene expression profiles using Affymetrix DNA chips and comparing gene expression in low- and high-risk patients | Day 0, Month 3, Month 12, clinical indication over 12 months | |
Secondary | Gene expression related risk stratification of response to treatment in kidney allograft rejection (rejection assessed by histopathology) | Blood biomarkers measured: Candidate gene profiles by NanoString technologies: AKR1C3, CD40, CTLA4, ID3, MZB1, TCL1A, TRIB1, TLR4 TUBA4A, WHAZ, CD3E, CD8A, CD4, MS4A1, FOXP3, GZMB, ENTPD1, POU2AAF1, POU2F1, CD9, IL7R, BLK, MMP9, CXCL9, CXCL10, CXCL11, UPK1A, TGFB1, IL2RA, PRF1, TIMP1, PAI1, FN1, TIGIT and 4 reference genes: HPRT1, B2M, GAPDH and ACTB Biomarkers measured in the biopsy: pangenomic approach of gene expression profiles using Affymetrix DNA chips and comparing gene expression in low- and high-risk patients | Day 0, Month 3, Month12, clinical indication over 12 months | |
Secondary | Assessment of the changes of patient's well-being across time and centres | Assessment of the changes of patient's well-being across time and centres using the results of a self health-questionnaire filled in by each patient at each time point. All items are measured on a scale of 1 to 3. The first 3 items measure mobility, self-care and performance of usual activities, with higher values indicating lower mobility and unability to take care of one's self or to perform usual activities, respectively. The 4th item measures pain and discomfort, with higher values indicating extreme pain or discomfort. The last item measures anxiety and depression, with higher values indicating extreme anxiety or depression. A general self-reported health state measures patient's opinion about his/her own health on a scale of 0 to 100, with a 0 score as the worst state and 100 as the best state. | Day 0, Month 3, Month12, clinical indication over 12 months |
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