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 and that translation from in vitro to in vivo and ultimately to clinical application is enabled through a re- optimization process. This input?output relationship that is always based on experimental data in lieu of predicted responses may also lead to a straightforward solution for handling human diversity in cancer therapeutics, among other diseases. In these series of studies they will test the hypothesis that PPM can be developed and validated for clinical use by using it to find novel drug combinations of repurposed/repositioned drugs to treat hepatocellular carcinoma. The goal is that by the end of year 3 of this project, they will be able to initiate a clinical trial using these novel combination or combinations. This group has previously used PPM-based optimization to find novel drug combinations in in vitro and in vivo models of cancer and infection. They have shown that this approach was able to markedly improve the efficacy of colorectal cancer therapy in vivo in mouse models. Translationally in a first-in-human clinical trial, they recently completed a prospective clinical study involving 4 PPM-dosed patients and 4 control (standard of care dosed) patients. They calculated the tacrolimus dosing regimen using the PPM process. Because PPM does not require a priori knowledge of disease mechanism and because it is a dynamic process that can accommodate a changing system, it can efficiently find personalized drug dosing over a varying range of time, having a profound stabilizing effect on the tacrolimus trough levels. For this application, they have selected hepatocellular carcinoma (HCC, liver cancer) to be the human disease for PPM application. The key rationale for this clinical selection is that they have a wealth of in vitro data on HCC, an active HCC tumor biorepository, and a large clinical volume of patients with HCC. These existing resources, both in vitro and clinical, allow for the immediate exploration of combination discovery followed by a clinical validation of discovered combination candidates in patients with unresectable HCC.

Public Health Relevance

This project applies a computational platform (Phenotypic Precision Medicine) to optimize combination therapy for the treatment of liver cancer. Using empiric measurements of the responses to a given treatment regimen by liver cancer cell lines and mouse models, they will demonstrate a computational approach to finding and optimizing novel drug combinations using repurposed/repositioned drugs. First they will demonstrate the ability to devise novel combination therapies and then they will start planning for a clinical trial to test the feasibility and efficacy of this approach to the treatment of liver cancer.

National Institute of Health (NIH)
National Center for Advancing Translational Sciences (NCATS)
Exploratory/Developmental Cooperative Agreement Phase II (UH3)
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Special Emphasis Panel (ZTR1)
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Mount, Bobbie Ann
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University of Florida
Schools of Medicine
United States
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