Real options based analysis of optimal pharmaceutical research and development portfolios

This paper presents a stochastic optimization model (OptFolio) of pharmaceutical research and development (R&D) portfolio management using a real options approach for making optimal project selection decisions. A method is developed to model new product development as a series of continuation/abandonment options, deciding at each stage in pharmaceutical R&D whether to proceed further or stop development. Multistage stochastic programming is utilized to model the flexibility afforded by the abandonment option. The resulting mixed-integer linear programming formulation is applied to a case study involving the selection of the optimal product portfolio from a set of 20 candidate drugs at different stages in the developmental pipeline over a planning horizon of 6 years. This proposed framework provides a road map for future decisions by tracking the decision of abandonment over time and calculating the minimum market value above which development is continued under changing resource constraints and estimated market and technical uncertainty. Results indicate that the riskier the project is, the larger the minimum market value required for continuing testing in future stages. Consequently, the value of the abandonment option increases with rising market uncertainty or decreased probability of clinical trial success. In addition, a framework for incorporating additional managerial choices to the OptFolio model is discussed.