The Pistoia Alliance has published the findings of a survey revealing that 77% of R&D professionals aren’t using in vitro alternatives to animal testing.
The not-for-profit has also disclosed that only 23% of the 350 survey participants describe themselves as ‘very familiar’ with animal model alternatives.
Regulatory barriers currently hold the industry back
The organisation surveyed representatives from pharmaceutical companies, contract research organisations and regulators, finding that regulations are a significant barrier to non-animal model (NAM) adoption — with 60% of participants voicing their concerns.
This is despite the FDA Modernisation Act 2.0, which actively encourages preclinical studies to adopt NAM, which has been hypothesised to be better at predicting human therapeutic responses compared with traditional animal models.
To mitigate the effects of these industry challenges, The Pistoia Alliance is launching a novel Non-Animal Models community, and is calling on companies to get involved and provide funding to the cause.
A Project Manager at the Pistoia Alliance, Véronique François, commented: “Supporting the 3Rs – reducing, refining and replacing animal testing – depends on companies having quality data. Researchers must have confidence their non-animal models are reliable, and that data are in a submission-ready format,”
“The Non-Animal Models Community will support the Alliance’s priority of making R&D more sustainability driven. ESG is a priority for our members from both a growth and public health perspective, and the industry must ensure that new therapies are backed by ethical, sustainable operations,” comments the Portfolio Lead of the Pistoia Alliance, Thierry Escudier.”
What are the most popular NAMs?
Of the respondents that are already exploring NAMs, the most used replacements were cell cultures (64%), in silico models (47%) and organoids (36%).
Alongside regulatory concerns, respondents also cited unreliable data in replacement models (17%) as a barrier to NAM adoption.
The lack of data standardisation and accessibility across different NAM platforms complicates data integration and comparison, causing researchers to be uncertain about the quality of NAMs and their effective application.
