Science & Research
1. Transform Toxicology to Enhance Product Safety
In the News
- March 9, 2015 Systematic Review Workshop
- See Complete Web Cast of May 10, 2013 FDA-Cosponsored Workshop:
Microphysiological Systems for Use as Regulatory Tools!
Preclinical testing plays a crucial role in identifying the potential risks associated with new FDA-regulated products. However, serious and unexpected negative side effects are sometimes discovered in clinical trials or once a product is on the market, suggesting that critical gaps exist in our understanding of the relationship between patient response and preclinical toxicology findings.
For example, non-clinical safety assessment is often conducted in normal healthy test systems and tends to be exposure-based; it does not attempt to evaluate the possible risk of rare or idiosyncratic responses that may arise from potential interactions with the presence or progression of disease or a patient's genetic background.
New measurement technologies and increasing knowledge about toxicity mechanisms and pathways offer important opportunities for advanced computational analyses that can promote effectively translating non-clinical findings to the clinical setting.
FDA can close these gaps and improve preclinical safety predictions by further investing in three particular areas of regulatory science:
- evaluating and developing models and assays that better predict patient response
- identificating and evaluating more reliable biomarkers for monitoring toxicities, side effects, and abnormalities, and
- using computational tools to integrate and draw conclusions from a wide range of preclinical safety data types and sources.
By addressing these needs, FDA will be better able to identify, predict, and reduce the magnitude and likelihood of risks associated with products. This will help speed and reduce costs in achieving the delivery of safe and effective new products to market, leading to improved health outcomes and reduced patient risk. By addressing these needs, FDA will be better able to identify and accurately predict and reduce the magnitude and likelihood of risks associated with products.