SBIR Phase II: nCapsule Personalized Cancer Screening for Colorectal Cancer Treatment Regimens

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project lies in its ability to transform personalized cancer treatment by providing a rapid and precise method for predicting the effectiveness of various treatment options on individual patients. Current treatment selection methods rely on an empirical, trial-and-error approach, often resulting in unnecessary toxicity, increased healthcare costs, and suboptimal patient outcomes. This project seeks to enhance treatment success rates, reduce unnecessary treatment cycles, and ultimately lower the financial burden on both patients and the healthcare system. The commercial potential of this technology is substantial, as it addresses a $2 billion U.S. market in colorectal and pancreatic cancer treatment, with a broader global market exceeding $30 billion. By reducing the likelihood of ineffective treatments and drug resistance, this innovation has the potential to improve survival rates, decrease side effects, and pave the way for more effective, data-driven decision making in oncology. Additionally, this project contributes to STEM workforce development, fostering industry academic collaborations and broader participation in biomedical research and technology. This Small Business Innovation Research (SBIR) Phase II project aims to develop a precision oncology platform that integrates microfluidics, biochips, bioreactors, and 3D tumor models to predict patient-specific cancer drugs responses. The proposed technology utilizes patient-derived biopsy specimens to create tumor models, allowing oncologists to test multiple treatment options before treatment begins. By maintaining tumor heterogeneity and integrating immune and stromal cells, this system closely mimics in vivo tumor behavior. The research objectives include optimizing microfluidic biochips for enhanced tumor growth and viability, automating real-time data collection, and validating clinical predictions through