Valuation for new drug: Data-driven predictive modeling and real option analysis with Monte-Carlo simulation

Abstract

In this study, we developed the valuation model for new compounds of drug candidate by utilizing historical drug development and sales data which includes the target therapeutic areas of the compounds, clinical trial outcomes (success/failure), and post-launch sales data. Furthermore, for economic evaluation, we applied the scenario tree approach with Monte-Carlo simulation, which takes into account the option value of future withdrawal/expansion, instead of the conventional DCF method. Case studies showed the increases of value due to future flexibility of investment decisions and that indicated the scenario tree types simulation is valuable for the valuation of innovation in pharma industry.