Unlocking the Power of Biomarker-Driven Clinical Insights
Quantifying the impact of biomarkers on clinical outcomes, identifying key pathways and mediators, and enabling "what if" scenarios for informed R&D, combination therapies, and optimal indication selection.
No obligations. Just insights.
Data-Driven Discovery
Revolutionizing Clinical Research with Data-Driven Biomarker Insights
Establish causal links, quantify biomarker impact, uncover key pathways, and simulate “what if” scenarios for R&D and therapy decisions.
Establishes causal links between biomarkers and clinical outcomes
Quantifies the impact of changes in biomarkers on clinical benefit
Identifies potential pathways and mediators influencing clinical outcomes
Allows exploration of “What if” scenarios (combination therapies, populations, etc.) as well as informed discovery/preclinical R&D, and optimal indication selection
Model Progression
Why Causal Modeling?
To predict the probability of trial success, PhaseV implements the following steps:
Develop a Causal Graph
- Model disease progression using a prognostic framework
Build the Control Arm
- Leverage the Causal Directed Acyclic Graph (DAG)
- Integrate Real-World Data (RWD) for robust representation
Incorporate Treatment Dynamics
- Utilize RWD, if available, to account for treatment effects
- Integrate insights from preclinical or early clinical data
Quantitatively Assess Impact
- Analyze changes to key nodes by propagating their effects throughout the graph.
Causal Modeling Use Cases
Accelerate Discovery with Data-Efficient Insights
Unlock therapeutic opportunities, assess patient groups, and guide clinical strategies with limited data. Causal-ML enables benchmarking, biological replication, and confident decision-making
Identify new treatments & combinations
Generate and estimate (new) patient populations
Make informed indication selection and sequencing decisions
Conduct benchmarking and evaluation
Accurately recapitulate biology with small sample size limitations
