Effective anemia management in End-Stage Renal Disease (ESRD) is a complex task due to large variation in erythropoietic response among the ESRD patients. Evidence shows that excessive doses of Erythropoiesis Stimulating Agents (ESA), used in hypo-responsive patients, coincide with increased cardiovascular complications. In addition to health risks, reimbursement restrictions imposed by Medicare demand additional vigilance in ESA administration. Iron is one of the most important elements affecting the erythropoietic response. The ongoing blood losses associated with hemodialysis treatments, increased risk of gastrointestinal bleeding, and access complications, as well as poor iron absorption lead to iron depletion in ESRD patients. Standard anemia management protocols do not fully address the synergistic nature of interactions between erythropoietin and iron. Personalized strategies for coordinated ESA and iron dosing are therefore necessary to achieve the desired clinical outcome, reduce patient risk exposure, and improve the cost-effectiveness of treatment. Our long-term goal is the advancement of biomedical computing to personalize medicine and pharmacologic management of chronic conditions. The objective of this project is to address the challenge of multiple drug dosing personalization with a specific application to ESA and iron management in anemia of ESRD. The central hypothesis is that a computational approach can be developed to perform coordinated personalized dosing of erythropoiesis stimulating agents and iron in order to achieve desired clinical outcomes and minimize patient exposure resulting in optimal care. The rationale behind the proposed research is that computational models quantifying the interaction between iron and erythropoietin can be applied through the principles of control theory and machine learning, to derive personalized dosing strategies for both agents. The main objective will be accomplished through three specific aims: 1) quantification of the erythropoietin-iron interaction through clinically relevant computational models, 2) application of such models to derive algorithms for coordinated personalized ESA and iron dosing, 3) validation of these algorithms through an "in sillico" trial for a wide range o simulated subjects and clinical conditions. The expected outcome of this project is a new systematic approach to the management of ESRD anemia using multiple agents resulting in improved patient care and decreased cost which will be broadly applicable to other therapeutic areas.

Public Health Relevance

The proposed research is relevant to public health because personalized dosing of multiple therapeutic agents broadly applicable to many chronic conditions is ultimately expected to improve cost-effectiveness of treatment and minimize patient risk. The proposed research is relevant to the NIH mission because it contributes toward advancement of personalized medicine, one of the top priorities of the NIH.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
1R01DK093832-01A1
Application #
8370685
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Bishop, Terry Rogers
Project Start
2012-09-01
Project End
2016-07-31
Budget Start
2012-09-01
Budget End
2013-07-31
Support Year
1
Fiscal Year
2012
Total Cost
$581,158
Indirect Cost
$147,971
Name
University of Louisville
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
057588857
City
Louisville
State
KY
Country
United States
Zip Code
40292
Gaweda, Adam E; Ginzburg, Yelena Z; Chait, Yossi et al. (2015) Iron dosing in kidney disease: inconsistency of evidence and clinical practice. Nephrol Dial Transplant 30:187-96
Gaweda, Adam E; Aronoff, George R; Jacobs, Alfred A et al. (2014) Individualized anemia management reduces hemoglobin variability in hemodialysis patients. J Am Soc Nephrol 25:159-66
Krzyzanski, Wojciech; Brier, Michael E; Creed, Timothy M et al. (2013) Reticulocyte-based estimation of red blood cell lifespan. Exp Hematol 41:817-22