Biopharmaceutical drug development modeling and portfolio management
Current pressures of cost and speed to market are driving the need for more effective means of assessing the value and risks of drug portfolios. This thesis presents research to generate a prototype computer-aided tool to predict the process and business outcomes for portfolios of biopharmaceutical drugs proceeding through the development pathway. The tool was built using a discrete-event simulation package, thus facilitating the dynamic nature of drug development decisions to be captured. The framework uses a hierarchical approach to incorporate the interactions between drug development activities, the available resources and databases of information. In addition to the business and process issues, the risks involved in the process of drug development have also been incorporated into the tool. The application of the tool for assessing drug portfolios under uncertainty is demonstrated via case studies. In the first, the tool was used to perform sensitivity and scenario analysis on the portfolio net present value (NPV). Contour plots were generated that provide the ability to plan for a range of contingencies including uncertainties in manufacturing efficiencies, product demand and the market share captured. The second case study was used to assess the impact of different manufacturing strategies on the portfolio NPV under uncertainty. This example was based on a biopharmaceutical company considering whether to risk building a facility for the commercial manufacture of its antibodies and if so, when to start building, or whether to rely on a contract manufacturer throughout the development cycle and market manufacture. The effects of uncertainties were analysed using Monte Carlo simulation methods. The study highlighted the benefits of incorporating uncertainties when ranking different strategies. The third case study looked at the selection of drug candidates for a drug portfolio. The risk and reward of different portfolios were computed using Monte Carlo simulations. The 'Efficient Frontier' method was used to select an optimal portfolio. The thesis illustrate the benefits of using such a tool to investigate the uncertainty and value of different development strategies and to assist in the process of decision making in the context of both business and process aspects.