For an investment discussion, please email Dr Mike Tremblay at mike_tremblay@skythunder.net
What underlies the applications?
The applications use predictive modelling and machine learning to support defined pharmaceutical decisions.
The core intellectual property lies in the methodology for converting complex clinical, economic and market evidence into usable decision-support tools. This includes model design, scenario testing, uncertainty analysis, evidence integration, explainability and decision-focused outputs.
What has been built?
The portfolio includes applications at different stages of development.
Some are interactive simulations that define functional requirements and demonstrate how the application would support a pharmaceutical decision. Others are closer to minimum viable product stage, with elements already implemented in Python.
What has been tested?
Most applications have been developed and tested using synthetic datasets, simulated patient cohorts and modelled commercial or clinical scenarios.
This has allowed the decision logic, assumptions, outputs and response to changing evidence to be examined before access to proprietary pharmaceutical, payer or real-world datasets.
What evidence remains to be generated?
The principal evidence requirements are:
- predictive performance, calibration and accuracy;
- validation using real-world pharmaceutical or healthcare data;
- usefulness in supporting actual commercial, clinical or safety decisions;
- value for money;
- adoption within existing pharmaceutical workflows;
- comparison with current analytical and decision-making methods;
- robustness across products, markets and therapeutic areas.
The required evidence programme will depend on the application, intended user and regulatory or commercial context.
Who pays?
The expected users and purchasers include:
- pharmaceutical and biotechnology companies:
- market-access teams;
- health economics and outcomes research teams;
- pharmacovigilance and patient-safety functions;
- medical-affairs teams;
- pricing and reimbursement teams;
- clinical-development, CROs, etc..
The commercial value may arise through:
- better pricing and reimbursement decisions;
- stronger HTA submissions;
- earlier identification of evidence gaps;
- improved market-access strategy;
- reduced clinical and commercial risk;
- more efficient use of research and analytical resources;
- improved pharmacovigilance and risk management;
- better targeting of further evidence generation.
What is the commercial offer?
Applications may be acquired through:
- outright purchase;
- licensing;
- royalty arrangements;
- product-specific development;
- co-development;
- integration with existing analytical platforms;
- investment in further development.
Commercial terms would be negotiated for each application.
Cassis also welcomes investment in additional pharmaceutical predictors and in the further development of reusable foundation tools that support multiple products, markets and therapeutic areas.