Multiple factors may affect industry performance, risk, innovation and sustainability in coming years such as long-term economic trends, patents expiration, demographic shifts, and regulatory issues.
Currently available industry-wide prediction frameworks have limited capabilities. They are based on: (1) analysts’ consensus; (2) extrapolation of current trends; (3) financial performance of big pharma only; (4) empirical formulas, etc. Therefore, the need of robust forecasting methodology based on simulation modeling and covering the entire industry portfolio predictions could not be underestimated.
The simulation model utilizes available data about each drug/indication in the industry R&D pipeline, and transforms it using predictive simulation algorithms into a set of metrics characterizing future industry performance.
Industry-wide portfolio simulation model was developed to address short- and long-term portfolio productivity forecasting challenges. The model is drug–centric, it simulates drug development workflow process. The model also incorporates multiple business rules related to drugs interdependence.
The model predicted 2016 drop in NME approvals based on 2014 data. Other modeling experiments include analysis of industry sustainability and innovation strategy, impact of approval rates and likelihood on the variations of clinical trials cycle time, probabilities of success, and FDA approval cycle time.
Mapping current trends in 3D printing and the impact on the recent landscape of drug development research