Predicting In Vivo Efficacy from In Vitro Data: Quantitative Systems Pharmacology Modeling for an Epigenetic Modifier Drug in Cancer

Predicting *in vivo* drug efficacy based on *in vitro* data is a critical step in drug development, offering the potential to reduce animal testing and improve the design of human clinical trials. However, achieving reliable predictions remains a significant challenge. In this study, we developed a quantitative pharmacokinetic/pharmacodynamic (PK/PD) mathematical model capable of predicting *in vivo* efficacy in tumor xenograft models, relying almost exclusively on *in vitro* cell culture data for its training.

We focused on ORY-1001, a chemical inhibitor of Iadademstat lysine-specific histone demethylase 1 (LSD1), an epigenetic regulator, and examined its effects on target engagement, biomarker modulation, and tumor cell growth under various dosing schedules, including pulsed and continuous regimens. The model integrated a PK component describing unbound plasma drug concentrations with an *in vitro* PD model to predict *in vivo* tumor growth dynamics across a range of doses and dosing regimens. Remarkably, scaling the PD model from the *in vitro* to the *in vivo* context required adjusting only a single parameter—the intrinsic tumor growth rate in the absence of treatment.

This approach establishes a framework for leveraging *in vitro* data to predict *in vivo* drug efficacy, offering a robust, resource-efficient alternative to animal studies. By enabling the generation of high-density, time-course, and dose-response data in controlled *in vitro* environments, this method advances drug development while minimizing reliance on animal models.