ImmunoPrecise Antibodies Announces Its Support For FDA's Recent Decision To Phase Out Animal Testing Requirements For Monoclonal Antibodies And Other Pharmaceutical Products

ImmunoPrecise Antibodies Ltd. (IPA) (NASDAQ:IPA) a leader in AI-driven biotherapeutics, today announced its strong support for the U.S. Food and Drug Administration's (FDA) recent decision to phase out animal testing requirements for monoclonal antibodies and other pharmaceutical products.

The FDA's announcement marks a significant advancement in regulatory modernization and aligns fully with ImmunoPrecise's mission to revolutionize drug discovery and development processes through cutting-edge, human-relevant technologies, including its proprietary AI platform, LENSai™.

"This policy shift reflects a growing global recognition that traditional animal models are often inadequate in predicting human biology," said Dr. Jennifer Bath, CEO of ImmunoPrecise Antibodies. "IPA has long championed the transition toward more ethical, efficient, and accurate methodologies. Our AI-powered LENSai™ platform and suite of integrated discovery tools were designed precisely to support this new era of translational science."

IPA provides validated AI-driven methodologies that directly align with the FDA's shift towards New Approach Methodologies (NAMs). These advanced tools effectively address critical drug development challenges, including safety, toxicity, immunogenicity, and efficacy, by leveraging sophisticated predictive capabilities:

1. Safety and Toxicity Prediction: IPA's LENSai platform utilizes AI analytics to accurately predict potential toxicities by mapping drug interactions across the human proteome, significantly reducing the reliance on animal models.
2. Immunogenicity Screening: The platform identifies immunogenic hotspots and predicts potential adverse immune responses, reducing the risk of anti-drug antibody formation and optimizing patient-specific strategies.
3. Knowledge Graphs for Drug Efficacy: IPA's semantic knowledge graphs integrate biological data and scientific literature, enabling precise predictions of drug efficacy and interactions in human systems.

Through transitioning from animal testing to AI-driven methodologies, IPA aims to streamline preclinical workflows, reduce development costs, uphold higher ethical standards, and support regulatory compliance in line with the FDA's NAMs framework.