Simulations Plus enters funded research collaboration with Lonza, FDA

Simulations Plus (SLP) announced a funded research collaboration with Lonza Group (LZAGY) and the U.S. Food and Drug Administration to develop and validate a mechanistic, predictive framework for assessing the in vivo performance of amorphous solid dispersion drug products. ASDs are among the most powerful yet complex oral drug delivery systems, with performance influenced by factors such as food intake, gastric pH, formulation composition, and manufacturing processes. Current regulatory approaches often require multiple clinical bioequivalence studies, which can be resource-intensive while still carrying uncertainty. The collaboration evaluates whether advanced in vitro dissolution systems-particularly those incorporating dynamic gastrointestinal physiology-combined with mechanistic physiologically based biopharmaceutics modeling, can reliably predict key in vivo outcomes, including food effects and the impact of elevated gastric pH conditions. By establishing and validating these predictive capabilities, the collaboration aims to provide a scientific foundation for reducing reliance on certain clinical BE studies while maintaining the rigor and transparency required by regulators. The collaboration brings together complementary capabilities across experimental science and computational modeling. Lonza will lead experimental work, including in vitro dissolution testing under fasted, fed, and elevated gastric pH conditions using advanced systems such as Controlled Transfer Dissolution, as well as the characterization and, where needed, manufacturing of ASD formulation variants. Simulations Plus will lead the development and validation of in vitro-in vivo extrapolation frameworks using its DDDPlus and GastroPlus platforms, translating experimental data into predictions of in vivo pharmacokinetics and supporting virtual bioequivalence assessments. At the same time, it creates new opportunities to extend these capabilities into grounded AI-enabled workflow environments, where data, mechanistic models, and simulation outputs will be more directly connected. The Company will also contribute to interpretation within a regulatory context, ensuring alignment with evolving expectations for model-informed drug development.