Overview of CATALYST Solicitation
Current in vivo animal model testing methods often fail to predict human drug safety accurately, leading to inefficiencies and high costs. The Health Science Futures (HSF) Office of the Advanced Research projects Agency for Health (ARPA-H) has introduced the CATALYST program to transform drug development. This program will foster the creation of Artificial Intelligence (AI) / Machine Learning (ML)-enabled in silico modeling platforms for Absorption, Distribution, Metabolism, And Excretion (ADME)-Tox simulation, replacing traditional preclinical animal studies.
By leveraging advanced data mining, AI/ML analytics, and innovative instrumentation, CATALYST will develop validated digital platforms for assessing drug safety. This initiative will establish a new sector of “Digital Contract Research Organizations (CROs),” enhancing the CRO landscape.
Key Components of the Opportunity Include
- Data Innovation: Utilizing deep learning to unify diverse data sources and predict drug outcomes.
- Living Systems Emulation: Developing methodologies that accurately replicate human physiology.
- Comprehensive Models: Creating in silico models for thorough preclinical assessments.
All tools must meet Good Laboratory Practice (GLP)-equivalent standards and align with regulatory contexts, facilitating integration into drug approval processes. Through collaboration between methodology developers and product sponsors, CATALYST aims to improve the reliability of drug efficacy predictions, enhance patient safety, and accelerate access to innovative therapies in clinical trials.
CATALYST Objectives
- Data Repository
- Establish a repository for platform improvement and future development.
- Capture relevant cheminformatics, toxicological, and multi-omics data.
- Facilitate stakeholder data sharing.
- Living Systems Tool
- Develop tools to mimic living systems for drug safety testing.
- Integrate high-throughput capabilities and reproducibility.
- Use human-relevant models to enhance reliability.
- AI/ML In Silico Platform
- Create simulation platforms for animal-free Investigational New Drug (IND) data generation.
- Develop user-friendly, scalable Active Pharmaceutical Ingredients (APIs).
- Demonstration of Capabilities
- Conduct a pilot project within in silico and in vitro/ex vivo
- Showcase IND-enabling data generation without animal studies.
- Obtain regulatory approval for Contexts of Use (COU).
- Design First-in-Human (FIH) trials using the in silico
- Transition and Commercialization
- Within 18 months, create a plan for commercializing in silico
- Identify industry partners and address technical and regulatory challenges.
- Expand capabilities to other disease models.
Important Deadlines
- Solution Summaries are due November 25th, 2024, at 5:00PM
- Proposals are due January 31st, 2025, at 5:00PM
If your company has considered applying for ARPA-H funding, your federal funding journey starts here.
For additional information about EverGlade Consulting, reach out to: [email protected]