Regression analysis of oncology drug licensing deal values

Abstract

This work is an attempt to explain wide variations in drug licensing deal value by using regression modeling to describe and predict the relationship between oncology drug deal characteristics and their licensing deal values. Although the reasons for large variances in value between deals may not be immediately apparent, it was hypothesized that objective independent variables, such as a molecule's phase, its target market size and the size of the acquiring/licensor company could explain a significant portion of variation in cancer drug values. This model, although not predictive when used independently, could be used to supplement other discounted cash flow and market based techniques to help assess the worth of incipient oncology therapies. Using regression analysis to study drug licensing deals is not novel: a study was published by Loeffler et al in 2002 that attempted to assess the impact of multiple variables on deal value in a wide range of pharmaceutical indications. The independent variables in Loeffler's work could explain less than 50% of differences in deal values. It was expected that refining the model could lead to improved regression R squared coefficient and, potentially, be a useful tool for managers. This current work is based on the 2002 Loeffler paper, but differs significantly by: * Focusing on just oncology licensing deals instead of deals covering many indications, * Incorporating a measure of the assets of the larger licensee company, * Accounting for the licensing experience of the smaller licensor company, * Factoring in inflation and the years the deals were signed; and * Assessing the impact of primary indication market size. The goal of the thesis was to advance the art of estimating the value of drug licensing deals by assessing the impact of the aforementioned factors.