Our Approach
Modeling and simulation can provide value across all stages of cardiovascular drug development?for novel compounds with limited prior information as well as nth-in-class compounds vying for differentiation in crowded markets. Cardiovascular drugs with novel mechanisms of action challenge decision makers to balance broad uncertainties against potential payoff.
In early development, new compounds may present no clear mechanistic model to link biologic targets to clinical effects. Empirical models, extrapolating from animal data and prior clinical knowledge in the indication, can quantify uncertainty about expected drug performance. For example, modeling approaches can include the effective identification and use of biomarkers critical for drugs intended to reduce cardiovascular events, where the complexity and scale of multi-year morbidity/mortality trials in early development is impractical. Modeling and simulation can serve as valuable tools to charactertize biomarker-to-precursor event relationships to inform trial design and dose optimization, and can serve as a component of biomarker qualification.
Also in early development, adaptive trial designs can enhance efficiency of the clinical program by providing learning at earlier stages in proof-of-concept and dose finding.
In later phases, simulation provides a low risk means to explore performance of different development strategies. Integrative modeling can evaluate trade-offs in medical, ethical and commercial concerns to quantify net treatment benefit, through measures of clinical utility, and aid in key dose selection and development strategy decisions.
Our modeling and simulation approach is customized to your specific development issues, taking into account the richness of available information about your drug and its competitive environment.
Project deliverables might include:
- Clinical development program design to support informed “go/no-go” decisions
- Model-based meta-analysis to predict drug performance versus competitors (e.g., combining pre-clinical and public-source clinical evidence)
- Integrated dose-response modeling and decision analysis for dose optimization
- Trial design, simulation and analysis to maximize likelihood of success (e.g., including adaptive designs to accelerate learning)
- Post-approval program strategies (e.g., drug-disease modeling and trial design to support label expansion)