# Data-driven QSP software for personalized colon cancer treatment

> **NIH NIH R21** · UNIVERSITY OF MASSACHUSETTS AMHERST · 2020 · $202,044

## Abstract

Abstract
Colon cancer is the third leading cause of cancer-related deaths in the United States in both men and women.
A major clinical challenge is to obtain an effective treatment strategy for each patient or at least identify a subset
of patients who could benefit from a particular treatment. Since each colon cancer has its own unique features,
it is very important to obtain personalized cancer treatments and find a way to tailor treatment strategies for
each patient based on each individual's characteristics, including race, gender, genetic factors, immune response
variations.
 Recently, Quantitative and Systems Pharmacology (QSP) has been commonly used to discover, validate,
and test drugs. QSP models are a system of differential equations that model the dynamic interactions between
drug(s) and a biological system. These mathematical models provide an integrated “systems level” approach to
determining mechanisms of action of drugs and finding new ways to alter complex cellular networks with mono
or combination therapy to obtain effective treatments. Since QSP models are a complex system of nonlinear
equations with many unknown parameters, estimating the values of the model's parameters is extremely difficult.
Existing parameter estimation methods for QSP models often use assembled data from various sources rather
than a single curated dataset. These datasets are usually obtained through various biological experiments, in
vitro and in vivo animal studies, thus rendering QSP models hard to be practicable for personalized treatments.
To the best of our knowledge, no QSP model has been developed for personalized colon cancer treatments.
 In this project, we propose a unique approach to develop a data-driven QSP software to suggest effective
treatment for each patient based on gene expression data from the primary tumor samples. Since signatures of
main characteristics of tumors, such as immune response variations, can be found in gene expression profiling
of primary tumors, we use gene expression data as input. We develop an innovative framework to systematically
employ a combination of data science, mathematical, and statistical methods to obtain personalized colon cancer
treatment. We employ novel inverse problem techniques to estimate the values of parameters of the model and
statistical methods to perform sensitivity analysis. We will use these techniques to propose an optimal treatment
strategy for each patient and predict the efficacy of the proposed treatment. The model might also suggest
alternative therapies in case of low efficacy for some patients.

## Key facts

- **NIH application ID:** 9986715
- **Project number:** 5R21CA242933-02
- **Recipient organization:** UNIVERSITY OF MASSACHUSETTS AMHERST
- **Principal Investigator:** SUVRA PAL
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $202,044
- **Award type:** 5
- **Project period:** 2019-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9986715

## Citation

> US National Institutes of Health, RePORTER application 9986715, Data-driven QSP software for personalized colon cancer treatment (5R21CA242933-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9986715. Licensed CC0.

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