Phase Advance Predictive Technology Platform

Phase Advance's Predictive Technology Platform is a modern sieve of AI and mathematical algorithms designed to identify medicines that will cure disease and become blockbusters — referred to as "good black swan events" — 7 to 14 years before clinical trials. The platform is built for biopharmaceutical and biotech organizations seeking to de-risk drug development, optimize trial design, and gain a competitive edge from the earliest stages of discovery through preclinical work.

Operating under a Filter to Forecast philosophy, Phase Advance combines nonlinear biomechanistic modeling, multiscale data integration, and population-level personalization into a single predictive framework that spans the full drug development lifecycle.

Cradle-to-Grave Biomechanistic Modeling

• Nonlinear modeling that mimics biological systems, functioning as a biomechanistic engine from the molecular to the whole-body level
• Simulates complexity across cellular, tissue, organ, system, and entire-body scales, as well as their interactions
• Virtual cradle-to-grave modeling simulates patient development and disease progression from birth through adulthood
• Enables dynamic, mechanistic prediction of therapeutic responses across the full human lifespan

Population Personalization and Multiscale Data Integration

• Accounts for unique population differences — including genomics and diet — that can affect clinical trial success
• Recognizes that patients with the same condition (e.g., Alzheimer's disease) may respond differently to the same drug based on their genetic background and biomarkers
• Combines genetic, clinical, pharmacometric, and population data within one seamless analytical framework
• Supports more precise, personalized predictions of drug efficacy and safety across diverse patient populations

Trial Design and Competitive Benchmarking

• Models can be applied immediately post-discovery or following preclinical work, offering flexibility across the development timeline
• Enables comparison of future trial endpoints against market leaders to contextualize a drug's potential
• Helps clients design better-structured clinical trials and reduce required target sample sizes
• Supports earlier, more informed go/no-go decisions relative to the traditional biopharma development timeline

Minimum Required Inputs

Phase Advance models are designed to forecast with minimal data, regardless of therapy class. Inputs vary depending on the stage of development:

• Immediately post-discovery: chemical or antibody structure and class, mechanism of action, affinity and selectivity, molecule-target receptor binding kinetics with human protein, potency and efficacy in in vitro assays, proposed dosing route, predicted pharmacokinetics (if any), metabolic stability, and any other information gathered during design and discovery
• Post preclinical drug development phase: all information required for post-discovery use of virtual models, animal pharmacokinetics (if available), and animal model pharmacodynamic data

Disease Model Coverage

• Lung infections
• Liver diseases, including MASH, viral hepatitis, and drug-induced liver injury
• Alzheimer's disease and related dementias, Parkinson's disease, and ALS
• Immune-based diseases and HIV
• Metabolic diseases, including diabetes, obesity, and high blood pressure
• Cancers currently in biopharma focus

Phase Advance's disease model library is continuously expanding to align with the most in-demand therapeutic areas in biotech, making the platform a scalable solution for organizations working across a broad range of indications.