Pistoia initiative seeks to counter slow take-up of animal free testing

The organisation, which advocates for greater research collaboration in life sciences, also states that less than a quarter of those asked rated themselves as ‘very familiar’ with any alternative to non-animal models (NAMs). 

Of the 350 survey respondents who participated, representing pharmaceutical companies, regulators, and contract research organisations (CROs), a clear majority of 60% cited regulatory concerns as the reason for their reluctance to seeking non-animal alternatives. 

The news will come as a disappointment to NAM advocates given greater support from authorities and important legislative backing in the form of the United States’ FDA Modernization Act 2.0 intended to encourage preclinical studies to adopt the alternatives. 

“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,” said the group’s project manager Véronique François.

Now, in order to coordinate efforts, the Pistoia Alliance is launching a Non-Animal Models Community, inviting interested parties to participate and fund the venture.

Ellen Berg, CSO of Alto Predict, which promoted the community plan to the alliance, insisted non-animal models held huge potential for transforming clinical development but collaboration would be critical to adoption.

“Companies are excited by the possibility of producing more accurate models than traditional animal testing that could speed up drug development and support their ESG goals. But, as this research shows, there are several regulatory, technological, and ethical hurdles that must be navigated before this becomes a reality,” she explained.

“The Pistoia Alliance is exactly the forum to bridge the gaps between regulators, pharma, CROs and technology experts, so we can accelerate the reduction of animal testing together.” 

Among the sizeable minority of respondents already employing NAMs, the most popular replacements are cell cultures (64%), followed by in silico models (47%) and organoids (36%).

While regulatory issues top the list of adoption concerns, 17% referenced worries over unreliable data in replacement models (17%). This was attributed to the size, diversity and fragmented nature of the assay provider environment. Lack of standardisation and accessibility across platforms complicated data integration and comparison, causing uncertainty about the quality of models concluded the survey.

“Researchers must have confidence their non-animal models are reliable, and that data are in a submission-ready format,” said Pistoia’s François. “The Alliance has a strong track record of creating new data standards. We hope this new community will add another successful example that underpins our commitment to reducing the burden of regulatory submissions.”

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