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Schrödinger’s Statement Regarding FDA Plan to Phase Out Animal Testing Requirement for Monoclonal Antibodies and Other Drugs

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NEW YORK, April 14, 2025--(BUSINESS WIRE)--Schrödinger, Inc. (Nasdaq: SDGR) strongly supports the U.S. Food and Drug Administration’s (FDA) plan to reduce, refine or potentially replace current animal testing requirements with new approaches designed to improve drug safety and accelerate the evaluation process, while reducing animal experimentation. The FDA’s roadmap encourages a number of computational approaches to predict drug properties. Schrödinger’s widely used computational platform enables highly accurate in silico predictions of key molecular properties for small molecules and biologics and has broad application across all biological targets.

"The role of computational methods is changing rapidly in the pharmaceutical industry, and it is exciting to see these methods recognized as a powerful solution for optimizing drug candidates for both efficacy and safety," stated Ramy Farid, Ph.D, chief executive officer at Schrödinger. "We have been pioneering computational molecular discovery for nearly 35 years and continue to develop new solutions that integrate physics with AI/machine learning to accelerate drug discovery, reduce development risk and lower costs. Importantly, this includes advancing our predictive toxicology initiative. We believe our computational solutions will play a vital role in significantly reducing the use of animal models in preclinical development."

In 2024, Schrödinger announced a major initiative, funded by a grant from the Bill & Melinda Gates Foundation, to predict toxicology risk early in drug discovery. The goal of the initiative is to develop a computational solution designed to improve the properties of novel drug development candidates and reduce the risk of development failure associated with binding to off-target proteins. Schrödinger has already generated computational predictive models for a number of key off-targets. The company’s recent advances characterizing the structure of safety-related proteins such as hERG (recently published in Cell) and cytochrome P450 enzymes are examples of these efforts. The company expects to launch its predictive toxicology solution to customers in the second half of 2025.

Additionally, several computational solutions for small molecule and biologics drug discovery are already available as part of Schrödinger's platform, including solutions to predict protein aggregation, which is critical for assessing developability and potential immunogenicity of biologics, as well as methods for predicting binding affinity, which can be used to evaluate selectivity and the potential for off-target interactions.