Despite an FDA green light for the use of animal testing alternatives in drug development, regulatory concerns may be keeping the industry from embracing them, data collected by the R&D organization Pistoia Alliance suggests.
In a July 11 press release, the nonprofit claimed that a survey of 350 life sciences professionals working at pharma companies, regulators and contract research organizations found only 23% considered themselves “very familiar” with any kind of animal model alternative. Meanwhile, 77% said they weren’t using cell-based in vitro non-animal models like cell cultures and organoids in their R&D process.
As for why they haven’t begun using alternatives, 60% of respondents said they were concerned about regulations, even though non-animal models (NAMs) are accepted for preclinical studies under the 2022 FDA Modernization Act 2.0 and in some cases might even be better than traditional animal models at predicting how humans will respond to drugs. Alongside these concerns, 17% worried that NAMs would produce unreliable data, indicating a need for data standardization and accessibility across various platforms.
To that end, the Pistoia Alliance in March launched a project called the Non-Animal Models Community, which will establish what it describes online as “harmonized standards for describing animal alternative methods (assay metadata), their characterization (e.g., performance metrics) and develop best practices for management and analysis of data.” Besides convincing more biotechs to take up NAMs for R&D, standardized data will also have applications in AI and machine learning, the project proposal noted. It is seeking participation and funding from biotechs.
The project was the idea of Alto Predict, a research services firm that works with assay developers, regulators, startups and others on replacing lab animals with human cell-based models. In the release about the survey, Alto's Chief Scientific Officer Ellen Berg, Ph.D., noted that companies are excited by the possibility of having more accurate models, faster development and supporting their environmental, social and governance goals.
“But, as this research shows, there are several regulatory, technological and ethical hurdles that must be navigated before this becomes a reality,” she added in the release.