# Utilization of Phenotypic Precision Medicine to Identify Optimal Drug Combinations for the Treatment of Hepatocellular Carcinoma

> **NIH NIH UH3** · UNIVERSITY OF FLORIDA · 2020 · $152,462

## Abstract

PROJECT SUMMARY
 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.

## Key facts

- **NIH application ID:** 9829406
- **Project number:** 4UH3TR002087-03
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Chih-Ming Ho
- **Activity code:** UH3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $152,462
- **Award type:** 4N
- **Project period:** 2017-09-21 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9829406

## Citation

> US National Institutes of Health, RePORTER application 9829406, Utilization of Phenotypic Precision Medicine to Identify Optimal Drug Combinations for the Treatment of Hepatocellular Carcinoma (4UH3TR002087-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9829406. Licensed CC0.

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