BE studies are the cornerstone of generic drug approval. However, failures due to high variability or inappropriate designs can delay time-to-market by years.
Craft crossover and replicate study designs tailored to specific drug characteristics and regulatory requirements.
Apply SABE methods for highly variable drugs to meet bioequivalence criteria effectively.
Utilize simulations to predict study outcomes and adjust parameters proactively.
Leverage existing data to inform study design and anticipate potential challenges.
AI predictive models to estimate success probability and design optimality
Built-in modules for outlier detection, carryover effect analysis, ANOVA modeling, and subject-by-formulation interaction testing.
Decreases the likelihood of failed BE studies, accelerates FDA/EMA approval, and enhances ROI for generic and biosimilar development.