Post-transplant renal injury is a mechanistically complex process that leads to progressive, chronic renal insufficiency and constitutes a major clinical barrier to the short- and long-term success of all organ transplants. There is a strong need for non-invasive, predictive and diagnostic biomarkers that can inform therapeutic decisions for Chronic Allograft Nephropathy/Interstitial Fibrosis with Tubular Atrophy (CAN/IFTA) in kidney recipients, Acute Rejection (AR) in both kidney and non-renal recipients and Chronic Kidney Disease (CKD) in non-renal recipients. In collaboration with Northwestern University (NW), and The Scripps Research Institute (TSRI), Rules-Based Medicine (RBM) proposes a quantitative proteomics approach, using comprehensive Multi-Analyte Profiles (MAPs), to compare the protein profiles in plasma samples obtained from kidney, liver and heart transplant patients and identify both common and unique biomarker signatures and mechanisms of immunity, drug toxicity and the concomitant medical risk factors that drive renal injury. A number of research groups are performing detailed studies to evaluate the expression of individual biomarkers associated with renal injury for use as an objective clinical tool. However, the standard method for measuring plasma or serum levels of cytokines, chemokines or other biomarkers is to measure them one at a time using Enzyme-Linked Immunosorbent Assay. One-at-a-time assessment of each putative biomarker incurs considerable time, cost and sample volume. Clearly, no single molecular marker, or small group of markers, will be able to accurately classify individuals at highest risk. The ability to systematically identify protein profiles, predict risk of clinical events, evaluate therapeutic response, and define underlying mechanisms is thereby limited severely. RBM has developed MAPs to screen large numbers of biomarkers in parallel, using bead-based multiplex immunoassays. This technology provides a quantitative evaluation of protein expression patterns using very small sample volumes (10-20 5L) with a dynamic range of fg/mL to mg/mL. This technology is well suited for screening large numbers of markers in parallel to identify protein profiles associated with renal injury. Using this approach in a recent preliminary study, RBM, NW and TSRI have discovered a protein profile for AR with a 79% Predictive Accuracy, and a profile for CAN/IFTA (Banff 1,2,3) with a 91% Predictive Accuracy. We have also discovered a kidney injury panel that has a 94% Predictive Accuracy for kidney patients with transplant dysfunction due to CAN/IFTA, 82% with biopsy-proven AR and 82% for liver transplant recipients with renal insufficiency due to CNI toxicity, hypertension and metabolic syndromes. In this Fast-Track program, we propose to test, refine and validate these profiles. The goal will be to improve the long-term outcome of recipients of thoracic and abdominal organ transplants by developing novel biomarker patterns that clinicians can use to predict, diagnose and monitor transplant outcomes.

Public Health Relevance

Post-transplant renal injury is a mechanistically complex process that leads to progressive, chronic renal insufficiency and constitutes a major clinical barrier to the short- and long-term success of all organ transplants. This program is designed to investigate what is common and what is unique in the biomarker signatures and mechanisms of immunity, drug toxicity and the concomitant medical risk factors that drive renal injury in kidney, liver and heart transplant patients. The goal will be to improve the long-term outcome of recipients of thoracic and abdominal organ transplants by developing novel biomarker patterns that clinicians can use to predict, diagnose and monitor transplant outcomes.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
Project #
1R44AI085819-01
Application #
7804107
Study Section
Special Emphasis Panel (ZRG1-DKUS-K (11))
Program Officer
Prograis, Lawrence J
Project Start
2010-03-01
Project End
2011-02-28
Budget Start
2010-03-01
Budget End
2011-02-28
Support Year
1
Fiscal Year
2010
Total Cost
$100,000
Indirect Cost
Name
Rules-Based Medicine, Inc.
Department
Type
DUNS #
114417327
City
Austin
State
TX
Country
United States
Zip Code
78759