文章基本信息

标题：Valuation method by regression analysis on real royalty-related data by using multiple input descriptors in royalty negotiations in Life Science area-focused on anticancer therapies
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作者：Jeong Hee Lee ; Jeong Hee Lee ; Bae Khee-Su 等
期刊名称：Journal of Open Innovation: Technology, Market, and Complexity
电子版ISSN：2199-8531
出版年度：2016
卷号：2
期号：1
页码：1-22
DOI：10.1186/s40852-016-0047-7
语种：English
出版社：Springer
摘要：Purpose This research seeks to answer the basic question, “What would be the most determining factors if I perform regression analysis using several independent variables?” This paper suggests the way to estimate the proper royalty rate and up-front payment using multiple data I can get simply as input. Design/methodology/approach This research analyzes the dataset, including the royalty-related data like running royalty rate (back-end payments) and up-front payment (up-front fee + milestones), regarding drug candidates for specific drug class of anticancer by regression analysis. Then, the formula to predict royalty-related data is derived using the attrition rate for the corresponding development phase of the drug candidate for the license deal, TCT (Technology Cycle Time) median value for the IPC code (IP) of the IP, Market size of the technology, CAGR (Compound Annual Growth Rate) of the corresponding market and the revenue data of the license buyer (licensee). Findings For the anticancer (antineoplastics) drug classes, the formula to predict the royalty rate and up-front payment is as follows. Royalty Rate = 9.997 + 0.063 * Attrition Rate + 1.655 * Licensee Revenue ‐ 0.410 * T C T Median $$ \mathrm{Royalty}\ \mathrm{Rate} = 9.997 + 0.063\ *\ \mathrm{Attrition}\ \mathrm{Rate} + 1.655\ *\ \mathrm{Licensee}\ \mathrm{Revenue}\ \hbox{-}\ 0.410\ *\ \mathrm{T}\mathrm{C}\mathrm{T}\ \mathrm{Median} $$ ‐ 1.090 * Market Size ‐ 0.230 * CAGR Formula 1 $$ \hbox{-} 1.090\ *\ \mathrm{Market}\ \mathrm{Size}\ \hbox{-}\ 0.230\ *\ \mathrm{CAGR}\ \left(\mathrm{Formula}\ 1\right) $$ Up ‐ Front Payment Up ‐ front + Milestones = 2.909 ‐ 0.006 * Attrition Rate + 0.306 * Licensee Revenue ‐ 0.74 * T C T Median ‐ 0.113 * Market Size ‐ 0.009 * CAGR Formula 2 $$ \begin{array}{l}\mathrm{Up}\hbox{-} \mathrm{Front}\ \mathrm{Payment}\ \left(\mathrm{Up}\hbox{-} \mathrm{front} + \mathrm{Milestones}\right) = 2.909\ \hbox{-}\ 0.006\ *\ \mathrm{Attrition}\ \mathrm{Rate} + 0.306\ *\ \\ {}\mathrm{Licensee}\ \mathrm{Revenue}\ \hbox{-}\ 0.74\ *\ \mathrm{T}\mathrm{C}\mathrm{T}\ \mathrm{Median}\ \hbox{-}\ 0.113\ *\ \mathrm{Market}\ \mathrm{Size}\ \hbox{-}\ 0.009\ *\ \mathrm{C}\mathrm{AGR}\ \left(\mathrm{Formula}\ 2\right)\end{array} $$ In the case of Equations Equation 1 to estimate the royalty rate, it is statistically meaningful at the significance level of 1 % (P-Value: 0.001); however, in the case of Equations Equation 2 to estimate the up-front payment it is statistically not meaningful (P-Value: 0.288), thus requiring further study. Research limitations/implications (if applicable) This research is limited to the relationship between multiple input variables and royalty-related data in one drug class of anticancer (antineoplastics). Practical implications (if applicable) Valuation for the drug candidate within a specific drug class can be possible, and the royalty rate can be a variable according to drug class and licensee revenue.
关键词：Valuation;Licensing deal;Drug;Royalty data;Royalty rate;Up-front fee;Up-front Payment;Milestones;Regression;Drug class;Anticancer;Antineoplastics;Attrition rate;Development phase;Licensee;Life sciences;rNPV;eNPV (expected NPV);DCF;Multivariable analysis;IPC code;TCT median value;Market Size;CAGR;IP;Revenue;Multiple input descriptor;Significance level;P-;Value;Prediction