The Big Question  

What if we could predict drug safety and efficacy accurately before clinical trials even begin? 

The Problem

It currently costs on average $2 billion dollars to get a new drug from discovery, through preclinical testing and clinical trials, and finally to people who need it. Despite advances in research and technology, there has been no increase in the frequency of new drug approvals and no decrease in the drug failure rate in 40 years. Expensive animal models, although our best estimate for safety and efficacy, are not always predictive of a drug’s success in humans. Even approved medications are sometimes pulled from the market because of safety concerns when fully representative patient biology was not accounted for earlier in the drug development process.   

The Current State

The high failure rate of new drugs is extremely costly, in part because preclinical testing fails to accurately predict drug safety and efficacy in humans. Animal models, including pigs, cats, dogs, and non-human primates, are expensive and not predictive of all aspects of human physiology. Therefore, many drugs that initially seem promising after rounds of laboratory testing enter human clinical trials only to fail. The cost is also human: clinical trial participants shoulder the burden of exposure to potentially toxic compounds and patients miss out on therapeutics that don’t reach the market. Individuals with rare diseases and under-represented populations are more acutely affected. Robust computer models of drug performance are not currently a significant part of the way new candidate medicines are evaluated by regulators for human safety. 

The Challenge

The Computational ADME-Tox and Physiology Analysis for Safer Therapeutics (CATALYST) program envisions a future where approval to begin first in-human clinical trials can be based on in silico safety data. The program focuses on developing animal-free, sound experimental practice methods with specific attention to pharmacokinetics, including absorption, distribution, metabolism, and excretion (ADME), and pharmacodynamics, for safety and toxicity. CATALYST will pursue novel technologies that reliably represent human physiology to reduce the failure rate of investigational new drug candidates. Such technologies will ensure that medicines reaching clinical trials have confident safety profiles and better protect diverse trial participants and future patients. 

The Solution

CATALYST invites proposals across three technical areas: data discovery and deep learning methods for drug safety models, living systems tools for model development, and in silico models of human physiology. CATALYST aims to revolutionize preclinical drug safety prediction by developing human-based models that accurately estimate toxicity and safety profiles for drug candidates. If successful, CATALYST will enable safer and faster drug development, particularly for rare disease populations. Robust modeling will also capture more representative physiologies and help to meet the targets of the U.S. Food and Drug Administration’s Modernization Act. In collaboration with regulators, the program will forge a path for drugs to reach clinical trial readiness based on validated, in silico safety data.  

Why ARPA-H

Modern drug development and approval pathways need leading-edge methods for accurately predicting safety and efficacy profiles. CATALYST aims to demonstrate that such digital models are possible while supporting innovations to revolutionize future drug development work.