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Regression analysis of oncology drug licensing deal values

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, September 2006. / "August 2006." / Includes bibliographical references (leaves 37-38). / 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. / by Paul Allen Hawkins. / S.M.

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/37980
Date January 2006
CreatorsHawkins, Paul Allen
ContributorsT. Forcht Dagi., Harvard University--MIT Division of Health Sciences and Technology., Harvard University--MIT Division of Health Sciences and Technology.
PublisherMassachusetts Institute of Technology
Source SetsM.I.T. Theses and Dissertation
LanguageEnglish
Detected LanguageEnglish
TypeThesis
Format38 leaves, application/pdf
RightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission., http://dspace.mit.edu/handle/1721.1/7582

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