Phase Advance’s Predictive Technology Platform is a modern sieve of AI & mathematical algorithms to identify medicines that will cure disease and become blockbusters – known as good black swan events – 7 to 14 years before clinical trials.
Our nonlinear modeling mimics biological systems, creating a biomechanistic engine from the molecular to the whole-body level, from cellular to tissue to organ to system to entire body, as well as their complex interactions. This virtual cradle-to-grave modeling simulates patient development and disease progression from birth through adulthood, enabling dynamic prediction of therapeutic responses.
Clinical trial success can be affected by unique population differences such as genomics and even diet. An Alzheimer’s patient with particular biomarkers could respond differently to the same drug that benefits a patient from another genetic background. Our multiscale data integration combines genetic, clinical, pharmacometric, and population data in one seamless analytical framework.
Phase Advance’s lifetime models require immense computing power to calculate 10,000 virtual patient’s progression from birth, to adulthood, including disease emergence, and on to death. These models are our core technology.
Machine learning (ML, a form of AI) ensembles accelerate these models for timely results, while agentic AI functions seek out patterns like novel biomarkers and adverse events or off-target effects sequestered in certain genetic groupings. Our AI is custom and proprietary for our platform, and it cannot “hallucinate”.
Unlike technologies borrowing off-the-shelf Large Language Model (LLM) AI, Phase Advance does not consume large data sets in order to deliver predictive insights. Instead, we deliver clients an FDA-level data set in the format of a Phase III clinical trial Case Report Form (CRF).
Many technologies are employing LLMs to help with challenges in drug discovery, target selection, and clinical trial site selection. These LLMs are licensed from partners who also power generative AI tools you may commonly use: Claude, or ChatGPT, for example.
Such models differ from Phase Advance in that they apply LLMs to massive clinical datasets and ask it to extrapolate, for example how proteins might fold, or which trial sites have patient populations with high incidence of a certain disease. They depend on big data to function, and they can produce erroneous output.
Instead, we used applied nonlinear mathematics to model human biological systems — from subcellular to full body level and populations — and our AI is layered over it.
Whether clients use our models just after discovery or after preclinical work, we can compare the future trial endpoints to market leaders, help design better trials, and reduce the target sample size. This translates to compressed development timelines and efficient clinical trials.
Phase Advance models can forecast with minimal details about the molecule or biologic in question, regardless of therapy class.
Post PRE-CLINICAL DRUG Development Phase
Immediately Post discovery
Our disease models align with the most in-demand therapeutic areas in biotech today, and are expanding continuously. Models are disease-state agnostic and drug class-agnostic.
