The authors have developed a computational platform to rapidly identify optimal drug and dose combinations from the innumerable possibilities. By testing this technique termed Phenotypic Personalized Medicine (PPM) in a diverse number of experimental systems representing different diseases, they have found that the response of biological systems to drugs can be described by a low order, smooth multidimensional surface. The main consequence of this is that optimal drug combinations can be found in a small number of tests. This input?output relationship is always based on experimental data, not modeling, and it would lead to a straightforward solution for handling human diversity in drug dosing needs, among other clinical problems. They will test the hypothesis that PPM can be developed and validated for clinical use by conducting a prospective clinical trial to compare the feasibility and efficacy of this approach to standard of care physician dosing. This group has previously used PPM-based optimization to find novel drug combinations in in vitro and in vivo models of cancer and infection. In a first-in-human study, they recently compared 4 PPM-dosed patients and 4 control (standard of care dosed) patients. They calculated the tacrolimus dosing regimen using the PPM process and showed significant improvement in variability and a trend toward improved efficacy. For this application, they aim to show in a clinical trial, that PPM is more effective than unaided physician dosing. This will allow the generation of data to justify a multi-center confirmatory study and to explore a wider array of clinical outcomes to optimize.

Public Health Relevance

This project uses a computational drug-dose optimization platform (Phenotypic Precision Medicine) to an important problem in transplant immunosuppression, the maintenance of patient tacrolimus blood levels within a clinically desired target range. Using empiric measurements of responses to previously administered doses, the Phenotypic Precision Medicine platform optimizes and suggests subsequent doses. A prospective clinical trial will compare the efficacy, feasibility, and safety of this approach to standard of care physician dosing.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21DK116140-02
Application #
9767781
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Sherker, Averell H
Project Start
2018-09-01
Project End
2020-08-31
Budget Start
2019-09-01
Budget End
2020-08-31
Support Year
2
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Florida
Department
Surgery
Type
Schools of Medicine
DUNS #
969663814
City
Gainesville
State
FL
Country
United States
Zip Code
32611