Long range planning of biologics process development and clinical trial material supply process

Abstract

This thesis investigates the feasibility of using a complex model with a Monte Carlo simulation model to forecast the financial, personnel, and manufacturing capacity resources needed for biologic drug development. Accurate forecasting is integral across industries in order to make strong longterm, strategic decisions and an area many companies struggle with. The resources required for the development of a biologic drug are especially hard to estimate due to the variability in the time and probability of success of each development phase. However, in the pharmaceutical industry getting products to market faster allows the company more time to recoup the substantial development investments before the patent expires and also potentially has a large impact on a company's market share. For these reasons, Novartis Biologics wanted to develop a simulation model to provide an objective opinion and assist them in their long-range planning. This thesis describes the design, development, and functionalities of the resultant model. During validation runs, the model demonstrated accuracy of greater than 90% when compared against historical data for headcount, number of campaigns, costs, and projects per year. In addition, the model contains Monte Carlo simulation capabilities to allow users to forecast variability and test the sensitivity of the results. This proves the model can be confidently used by project management, operations, and finance to predict their respective future resource needs.