U.S. flag An official website of the United States government
  1. Home
  2. About FDA
  3. FDA Organization
  4. Oncology Center of Excellence
  5. SmartCore Technology: Using AI and Patient Tissue to Identify Potential Cancer Therapies for Ultra-rare Cancers
  1. Oncology Center of Excellence

SmartCore Technology: Using AI and Patient Tissue to Identify Potential Cancer Therapies for Ultra-rare Cancers

External Institution: Fred Hutchinson Cancer Research Center

External Collaborators: Taran Gujral, PhD; Raymond Yeung, MD; Marina Chan, PhD

FDA Collaborators: Jeffery Summers, MD; Caitlin Tydings, MD; Zhongjun Luo, PhD

Date Started: October 30, 2023

Regulatory Science Challenge

Promoting the development of safe and effective drugs and biologics to treat patients with ultra-rare cancers is a priority for the FDA Oncology Center of Excellence (OCE). Many challenges are involved in drug development for ultra-rare cancers, including but not limited to insufficient understanding of the cancer pathophysiology, molecular characteristics, and natural history, limited or lack of timely access to molecular testing to determine eligibility for treatment with targeted therapies, difficulty enrolling sufficient numbers of patients to clinical trials, and limited financial incentives for drug development. Despite these challenges, novel technologies can advance the understanding of the molecular underpinnings of ultra-rare cancers and provide potential new opportunities for treatment.

Project Goals & Objectives

This project uses an AI-driven drug screening platform called SmartCore, which is designed to test primary human tumor tissue. This technology will identify and repurpose drugs that show activity against fibrolamellar cancer (FLC), which is an ultra-rare form of cancer that develops in the liver and mainly affects adolescents and young adults. Aim 1 is to discover therapeutics for FLC using artificial intelligence (AI)-based chemical screening. Optimal performance of the screening platform will be established using independent FLC patient cohorts obtained from multiple sources. The most promising drug candidates will be validated in established patient-derived xenograft models. Signaling networks and proteins targeted by the candidate drugs will be studied to identify the molecular pathways important in FLC. Aim 2 is to further develop and optimize AI-based chemical screening approach using smaller tissue samples from needle biopsies, which are routinely performed in clinical practice. A set of drugs will be selected for FLC specific testing as the basis for drug prediction using the AI algorithm. Needle biopsies will be compared to larger tissue samples in the FLC cohort to optimize conditions to yield reproducible results. The ultimate goal of the project is to create a drug and target discovery platform for ultra-rare cancers directly linked to clinical application.

Further Information

Back to Top