Factors Affecting Pricing in Patent Licensing Contracts in the Biopharmaceutical Industry

Jeong Hee Lee, Eungdo Kim, Tae-Eung Sung and Kwangsoo Shin
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Jeong Hee Lee: Graduate School of Health Science Business Convergence, College of Medicine, Chungbuk National University, Cheongju 28644, Korea
Eungdo Kim: Graduate School of Health Science Business Convergence, College of Medicine, Chungbuk National University, Cheongju 28644, Korea
Tae-Eung Sung: Department of Computer and Telecommunications Engineering, College of Science and Technology, Yonsei University, 1 Yonseidae-gil, Wonju 26493, Gangwon-do, Korea
Kwangsoo Shin: Graduate School of Health Science Business Convergence, College of Medicine, Chungbuk National University, Cheongju 28644, Korea

Sustainability, 2018, vol. 10, issue 9, 1-21

Abstract: This paper analyzes factors affecting pricing in patent licensing contracts in the biopharmaceutical industry based on a dataset that includes royalty-related data such as running royalty rate, up-front payment, milestones, and deal value. Data on drug candidates for 11 drug classes is obtained for regression analysis between royalty-related data and multiple input descriptors such as market factors, licensor factors, and licensee factor in order to derive the formula for predicting royalty-related estimates such as royalty rate, up-front payment, milestones, and deal value. Data is gathered from multiple sources including MedTrack and is processed through merging and cleaning. We found that the three most important factors in pricing in patent licensing in the biopharmaceutical industry are CAGR (Compound Annual Growth Rate), PDELR (Previous Deal Experience of Licensor), and AR (Attrition Rate). We found that factors in the formula used to estimate the license fee are totally different by drug class. We found that the three most important factors in the frequency in the formula used to estimate the license fee are PDELR, RnDLR (R&D Costs of Licensor), and PDELE (Previous Deal Experience of Licensee). This study suggests a method of estimating the proper royalty rate, up-front payment, milestones, and deal value of the drug candidates of 11 drug classes by using easily obtained input data.

Keywords: valuation; licensing deal; drug class; royalty rate; up-front fee; milestones; deal value; regression; biotech industry; attrition rate (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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