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
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 in...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lee, Jeong Hee [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s). 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of open innovation - Heidelberg : SpringerOpen, 2015, 2(2016), 1 vom: 17. Okt. |
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| Übergeordnetes Werk: |
volume:2 ; year:2016 ; number:1 ; day:17 ; month:10 |
| Links: |
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| DOI / URN: |
10.1186/s40852-016-0047-7 |
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| Katalog-ID: |
SPR037929607 |
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| 041 | |a eng | ||
| 100 | 1 | |a Lee, Jeong Hee |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a 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 |
| 264 | 1 | |c 2016 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a © The Author(s). 2016 | ||
| 520 | |a 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. <Drug Class: Anticancer activity candidates>RoyaltyRate=9.997+0.063*AttritionRate+1.655*LicenseeRevenue‐0.410*TCTMedian%$ \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} %$‐1.090*MarketSize‐0.230*CAGRFormula1%$ \hbox{-} 1.090\ *\ \mathrm{Market}\ \mathrm{Size}\ \hbox{-}\ 0.230\ *\ \mathrm{CAGR}\ \left(\mathrm{Formula}\ 1\right) %$Up‐FrontPaymentUp‐front+Milestones=2.909‐0.006*AttritionRate+0.306*LicenseeRevenue‐0.74*TCTMedian‐0.113*MarketSize‐0.009*CAGRFormula2%$ \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. | ||
| 650 | 4 | |a Valuation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Licensing deal |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Drug |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Royalty data |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Royalty rate |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Up-front fee |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Up-front Payment |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Milestones |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Regression |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Drug class |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Anticancer |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Antineoplastics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Attrition rate |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Development phase |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Licensee |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Life sciences |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a rNPV |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a eNPV (expected NPV) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a DCF |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multivariable analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a IPC code |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a TCT median value |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Market Size |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a CAGR |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a IP |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Revenue |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multiple input descriptor |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Significance level |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Value |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Prediction |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Khee-Su, Bae |4 aut | |
| 700 | 1 | |a Lee, Joon Woo |4 aut | |
| 700 | 1 | |a In, Youngyong |4 aut | |
| 700 | 1 | |a Kwon, Taehoon |4 aut | |
| 700 | 1 | |a Lee, Wangwoo |4 aut | |
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| 856 | 4 | 0 | |u https://dx.doi.org/10.1186/s40852-016-0047-7 |z kostenfrei |3 Volltext |
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10.1186/s40852-016-0047-7 doi (DE-627)SPR037929607 (SPR)s40852-016-0047-7-e DE-627 ger DE-627 rakwb eng Lee, Jeong Hee verfasserin aut 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 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 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. <Drug Class: Anticancer activity candidates>RoyaltyRate=9.997+0.063*AttritionRate+1.655*LicenseeRevenue‐0.410*TCTMedian%$ \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} %$‐1.090*MarketSize‐0.230*CAGRFormula1%$ \hbox{-} 1.090\ *\ \mathrm{Market}\ \mathrm{Size}\ \hbox{-}\ 0.230\ *\ \mathrm{CAGR}\ \left(\mathrm{Formula}\ 1\right) %$Up‐FrontPaymentUp‐front+Milestones=2.909‐0.006*AttritionRate+0.306*LicenseeRevenue‐0.74*TCTMedian‐0.113*MarketSize‐0.009*CAGRFormula2%$ \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. Valuation (dpeaa)DE-He213 Licensing deal (dpeaa)DE-He213 Drug (dpeaa)DE-He213 Royalty data (dpeaa)DE-He213 Royalty rate (dpeaa)DE-He213 Up-front fee (dpeaa)DE-He213 Up-front Payment (dpeaa)DE-He213 Milestones (dpeaa)DE-He213 Regression (dpeaa)DE-He213 Drug class (dpeaa)DE-He213 Anticancer (dpeaa)DE-He213 Antineoplastics (dpeaa)DE-He213 Attrition rate (dpeaa)DE-He213 Development phase (dpeaa)DE-He213 Licensee (dpeaa)DE-He213 Life sciences (dpeaa)DE-He213 rNPV (dpeaa)DE-He213 eNPV (expected NPV) (dpeaa)DE-He213 DCF (dpeaa)DE-He213 Multivariable analysis (dpeaa)DE-He213 IPC code (dpeaa)DE-He213 TCT median value (dpeaa)DE-He213 Market Size (dpeaa)DE-He213 CAGR (dpeaa)DE-He213 IP (dpeaa)DE-He213 Revenue (dpeaa)DE-He213 Multiple input descriptor (dpeaa)DE-He213 Significance level (dpeaa)DE-He213 Value (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Khee-Su, Bae aut Lee, Joon Woo aut In, Youngyong aut Kwon, Taehoon aut Lee, Wangwoo aut Enthalten in Journal of open innovation Heidelberg : SpringerOpen, 2015 2(2016), 1 vom: 17. Okt. (DE-627)833526413 (DE-600)2832108-X 2199-8531 nnns volume:2 year:2016 number:1 day:17 month:10 https://dx.doi.org/10.1186/s40852-016-0047-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2016 1 17 10 |
| spelling |
10.1186/s40852-016-0047-7 doi (DE-627)SPR037929607 (SPR)s40852-016-0047-7-e DE-627 ger DE-627 rakwb eng Lee, Jeong Hee verfasserin aut 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 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 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. <Drug Class: Anticancer activity candidates>RoyaltyRate=9.997+0.063*AttritionRate+1.655*LicenseeRevenue‐0.410*TCTMedian%$ \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} %$‐1.090*MarketSize‐0.230*CAGRFormula1%$ \hbox{-} 1.090\ *\ \mathrm{Market}\ \mathrm{Size}\ \hbox{-}\ 0.230\ *\ \mathrm{CAGR}\ \left(\mathrm{Formula}\ 1\right) %$Up‐FrontPaymentUp‐front+Milestones=2.909‐0.006*AttritionRate+0.306*LicenseeRevenue‐0.74*TCTMedian‐0.113*MarketSize‐0.009*CAGRFormula2%$ \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. Valuation (dpeaa)DE-He213 Licensing deal (dpeaa)DE-He213 Drug (dpeaa)DE-He213 Royalty data (dpeaa)DE-He213 Royalty rate (dpeaa)DE-He213 Up-front fee (dpeaa)DE-He213 Up-front Payment (dpeaa)DE-He213 Milestones (dpeaa)DE-He213 Regression (dpeaa)DE-He213 Drug class (dpeaa)DE-He213 Anticancer (dpeaa)DE-He213 Antineoplastics (dpeaa)DE-He213 Attrition rate (dpeaa)DE-He213 Development phase (dpeaa)DE-He213 Licensee (dpeaa)DE-He213 Life sciences (dpeaa)DE-He213 rNPV (dpeaa)DE-He213 eNPV (expected NPV) (dpeaa)DE-He213 DCF (dpeaa)DE-He213 Multivariable analysis (dpeaa)DE-He213 IPC code (dpeaa)DE-He213 TCT median value (dpeaa)DE-He213 Market Size (dpeaa)DE-He213 CAGR (dpeaa)DE-He213 IP (dpeaa)DE-He213 Revenue (dpeaa)DE-He213 Multiple input descriptor (dpeaa)DE-He213 Significance level (dpeaa)DE-He213 Value (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Khee-Su, Bae aut Lee, Joon Woo aut In, Youngyong aut Kwon, Taehoon aut Lee, Wangwoo aut Enthalten in Journal of open innovation Heidelberg : SpringerOpen, 2015 2(2016), 1 vom: 17. Okt. (DE-627)833526413 (DE-600)2832108-X 2199-8531 nnns volume:2 year:2016 number:1 day:17 month:10 https://dx.doi.org/10.1186/s40852-016-0047-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2016 1 17 10 |
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10.1186/s40852-016-0047-7 doi (DE-627)SPR037929607 (SPR)s40852-016-0047-7-e DE-627 ger DE-627 rakwb eng Lee, Jeong Hee verfasserin aut 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 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 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. <Drug Class: Anticancer activity candidates>RoyaltyRate=9.997+0.063*AttritionRate+1.655*LicenseeRevenue‐0.410*TCTMedian%$ \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} %$‐1.090*MarketSize‐0.230*CAGRFormula1%$ \hbox{-} 1.090\ *\ \mathrm{Market}\ \mathrm{Size}\ \hbox{-}\ 0.230\ *\ \mathrm{CAGR}\ \left(\mathrm{Formula}\ 1\right) %$Up‐FrontPaymentUp‐front+Milestones=2.909‐0.006*AttritionRate+0.306*LicenseeRevenue‐0.74*TCTMedian‐0.113*MarketSize‐0.009*CAGRFormula2%$ \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. Valuation (dpeaa)DE-He213 Licensing deal (dpeaa)DE-He213 Drug (dpeaa)DE-He213 Royalty data (dpeaa)DE-He213 Royalty rate (dpeaa)DE-He213 Up-front fee (dpeaa)DE-He213 Up-front Payment (dpeaa)DE-He213 Milestones (dpeaa)DE-He213 Regression (dpeaa)DE-He213 Drug class (dpeaa)DE-He213 Anticancer (dpeaa)DE-He213 Antineoplastics (dpeaa)DE-He213 Attrition rate (dpeaa)DE-He213 Development phase (dpeaa)DE-He213 Licensee (dpeaa)DE-He213 Life sciences (dpeaa)DE-He213 rNPV (dpeaa)DE-He213 eNPV (expected NPV) (dpeaa)DE-He213 DCF (dpeaa)DE-He213 Multivariable analysis (dpeaa)DE-He213 IPC code (dpeaa)DE-He213 TCT median value (dpeaa)DE-He213 Market Size (dpeaa)DE-He213 CAGR (dpeaa)DE-He213 IP (dpeaa)DE-He213 Revenue (dpeaa)DE-He213 Multiple input descriptor (dpeaa)DE-He213 Significance level (dpeaa)DE-He213 Value (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Khee-Su, Bae aut Lee, Joon Woo aut In, Youngyong aut Kwon, Taehoon aut Lee, Wangwoo aut Enthalten in Journal of open innovation Heidelberg : SpringerOpen, 2015 2(2016), 1 vom: 17. Okt. (DE-627)833526413 (DE-600)2832108-X 2199-8531 nnns volume:2 year:2016 number:1 day:17 month:10 https://dx.doi.org/10.1186/s40852-016-0047-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2016 1 17 10 |
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10.1186/s40852-016-0047-7 doi (DE-627)SPR037929607 (SPR)s40852-016-0047-7-e DE-627 ger DE-627 rakwb eng Lee, Jeong Hee verfasserin aut 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 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 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. <Drug Class: Anticancer activity candidates>RoyaltyRate=9.997+0.063*AttritionRate+1.655*LicenseeRevenue‐0.410*TCTMedian%$ \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} %$‐1.090*MarketSize‐0.230*CAGRFormula1%$ \hbox{-} 1.090\ *\ \mathrm{Market}\ \mathrm{Size}\ \hbox{-}\ 0.230\ *\ \mathrm{CAGR}\ \left(\mathrm{Formula}\ 1\right) %$Up‐FrontPaymentUp‐front+Milestones=2.909‐0.006*AttritionRate+0.306*LicenseeRevenue‐0.74*TCTMedian‐0.113*MarketSize‐0.009*CAGRFormula2%$ \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. Valuation (dpeaa)DE-He213 Licensing deal (dpeaa)DE-He213 Drug (dpeaa)DE-He213 Royalty data (dpeaa)DE-He213 Royalty rate (dpeaa)DE-He213 Up-front fee (dpeaa)DE-He213 Up-front Payment (dpeaa)DE-He213 Milestones (dpeaa)DE-He213 Regression (dpeaa)DE-He213 Drug class (dpeaa)DE-He213 Anticancer (dpeaa)DE-He213 Antineoplastics (dpeaa)DE-He213 Attrition rate (dpeaa)DE-He213 Development phase (dpeaa)DE-He213 Licensee (dpeaa)DE-He213 Life sciences (dpeaa)DE-He213 rNPV (dpeaa)DE-He213 eNPV (expected NPV) (dpeaa)DE-He213 DCF (dpeaa)DE-He213 Multivariable analysis (dpeaa)DE-He213 IPC code (dpeaa)DE-He213 TCT median value (dpeaa)DE-He213 Market Size (dpeaa)DE-He213 CAGR (dpeaa)DE-He213 IP (dpeaa)DE-He213 Revenue (dpeaa)DE-He213 Multiple input descriptor (dpeaa)DE-He213 Significance level (dpeaa)DE-He213 Value (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Khee-Su, Bae aut Lee, Joon Woo aut In, Youngyong aut Kwon, Taehoon aut Lee, Wangwoo aut Enthalten in Journal of open innovation Heidelberg : SpringerOpen, 2015 2(2016), 1 vom: 17. Okt. (DE-627)833526413 (DE-600)2832108-X 2199-8531 nnns volume:2 year:2016 number:1 day:17 month:10 https://dx.doi.org/10.1186/s40852-016-0047-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2016 1 17 10 |
| allfieldsSound |
10.1186/s40852-016-0047-7 doi (DE-627)SPR037929607 (SPR)s40852-016-0047-7-e DE-627 ger DE-627 rakwb eng Lee, Jeong Hee verfasserin aut 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 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2016 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. <Drug Class: Anticancer activity candidates>RoyaltyRate=9.997+0.063*AttritionRate+1.655*LicenseeRevenue‐0.410*TCTMedian%$ \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} %$‐1.090*MarketSize‐0.230*CAGRFormula1%$ \hbox{-} 1.090\ *\ \mathrm{Market}\ \mathrm{Size}\ \hbox{-}\ 0.230\ *\ \mathrm{CAGR}\ \left(\mathrm{Formula}\ 1\right) %$Up‐FrontPaymentUp‐front+Milestones=2.909‐0.006*AttritionRate+0.306*LicenseeRevenue‐0.74*TCTMedian‐0.113*MarketSize‐0.009*CAGRFormula2%$ \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. Valuation (dpeaa)DE-He213 Licensing deal (dpeaa)DE-He213 Drug (dpeaa)DE-He213 Royalty data (dpeaa)DE-He213 Royalty rate (dpeaa)DE-He213 Up-front fee (dpeaa)DE-He213 Up-front Payment (dpeaa)DE-He213 Milestones (dpeaa)DE-He213 Regression (dpeaa)DE-He213 Drug class (dpeaa)DE-He213 Anticancer (dpeaa)DE-He213 Antineoplastics (dpeaa)DE-He213 Attrition rate (dpeaa)DE-He213 Development phase (dpeaa)DE-He213 Licensee (dpeaa)DE-He213 Life sciences (dpeaa)DE-He213 rNPV (dpeaa)DE-He213 eNPV (expected NPV) (dpeaa)DE-He213 DCF (dpeaa)DE-He213 Multivariable analysis (dpeaa)DE-He213 IPC code (dpeaa)DE-He213 TCT median value (dpeaa)DE-He213 Market Size (dpeaa)DE-He213 CAGR (dpeaa)DE-He213 IP (dpeaa)DE-He213 Revenue (dpeaa)DE-He213 Multiple input descriptor (dpeaa)DE-He213 Significance level (dpeaa)DE-He213 Value (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Khee-Su, Bae aut Lee, Joon Woo aut In, Youngyong aut Kwon, Taehoon aut Lee, Wangwoo aut Enthalten in Journal of open innovation Heidelberg : SpringerOpen, 2015 2(2016), 1 vom: 17. Okt. (DE-627)833526413 (DE-600)2832108-X 2199-8531 nnns volume:2 year:2016 number:1 day:17 month:10 https://dx.doi.org/10.1186/s40852-016-0047-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 2 2016 1 17 10 |
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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 Value Prediction |
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Lee, Jeong Hee @@aut@@ Khee-Su, Bae @@aut@@ Lee, Joon Woo @@aut@@ In, Youngyong @@aut@@ Kwon, Taehoon @@aut@@ Lee, Wangwoo @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR037929607</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519160349.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s40852-016-0047-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR037929607</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s40852-016-0047-7-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lee, Jeong Hee</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">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</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s). 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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. <Drug Class: Anticancer activity candidates>RoyaltyRate=9.997+0.063*AttritionRate+1.655*LicenseeRevenue‐0.410*TCTMedian%$ \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} %$‐1.090*MarketSize‐0.230*CAGRFormula1%$ \hbox{-} 1.090\ *\ \mathrm{Market}\ \mathrm{Size}\ \hbox{-}\ 0.230\ *\ \mathrm{CAGR}\ \left(\mathrm{Formula}\ 1\right) %$Up‐FrontPaymentUp‐front+Milestones=2.909‐0.006*AttritionRate+0.306*LicenseeRevenue‐0.74*TCTMedian‐0.113*MarketSize‐0.009*CAGRFormula2%$ \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). 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|
| author |
Lee, Jeong Hee |
| spellingShingle |
Lee, Jeong Hee misc Valuation misc Licensing deal misc Drug misc Royalty data misc Royalty rate misc Up-front fee misc Up-front Payment misc Milestones misc Regression misc Drug class misc Anticancer misc Antineoplastics misc Attrition rate misc Development phase misc Licensee misc Life sciences misc rNPV misc eNPV (expected NPV) misc DCF misc Multivariable analysis misc IPC code misc TCT median value misc Market Size misc CAGR misc IP misc Revenue misc Multiple input descriptor misc Significance level misc Value misc Prediction 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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Lee, Jeong Hee |
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electronic Article |
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2199-8531 |
| topic_title |
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 Valuation (dpeaa)DE-He213 Licensing deal (dpeaa)DE-He213 Drug (dpeaa)DE-He213 Royalty data (dpeaa)DE-He213 Royalty rate (dpeaa)DE-He213 Up-front fee (dpeaa)DE-He213 Up-front Payment (dpeaa)DE-He213 Milestones (dpeaa)DE-He213 Regression (dpeaa)DE-He213 Drug class (dpeaa)DE-He213 Anticancer (dpeaa)DE-He213 Antineoplastics (dpeaa)DE-He213 Attrition rate (dpeaa)DE-He213 Development phase (dpeaa)DE-He213 Licensee (dpeaa)DE-He213 Life sciences (dpeaa)DE-He213 rNPV (dpeaa)DE-He213 eNPV (expected NPV) (dpeaa)DE-He213 DCF (dpeaa)DE-He213 Multivariable analysis (dpeaa)DE-He213 IPC code (dpeaa)DE-He213 TCT median value (dpeaa)DE-He213 Market Size (dpeaa)DE-He213 CAGR (dpeaa)DE-He213 IP (dpeaa)DE-He213 Revenue (dpeaa)DE-He213 Multiple input descriptor (dpeaa)DE-He213 Significance level (dpeaa)DE-He213 Value (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 |
| topic |
misc Valuation misc Licensing deal misc Drug misc Royalty data misc Royalty rate misc Up-front fee misc Up-front Payment misc Milestones misc Regression misc Drug class misc Anticancer misc Antineoplastics misc Attrition rate misc Development phase misc Licensee misc Life sciences misc rNPV misc eNPV (expected NPV) misc DCF misc Multivariable analysis misc IPC code misc TCT median value misc Market Size misc CAGR misc IP misc Revenue misc Multiple input descriptor misc Significance level misc Value misc Prediction |
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misc Valuation misc Licensing deal misc Drug misc Royalty data misc Royalty rate misc Up-front fee misc Up-front Payment misc Milestones misc Regression misc Drug class misc Anticancer misc Antineoplastics misc Attrition rate misc Development phase misc Licensee misc Life sciences misc rNPV misc eNPV (expected NPV) misc DCF misc Multivariable analysis misc IPC code misc TCT median value misc Market Size misc CAGR misc IP misc Revenue misc Multiple input descriptor misc Significance level misc Value misc Prediction |
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misc Valuation misc Licensing deal misc Drug misc Royalty data misc Royalty rate misc Up-front fee misc Up-front Payment misc Milestones misc Regression misc Drug class misc Anticancer misc Antineoplastics misc Attrition rate misc Development phase misc Licensee misc Life sciences misc rNPV misc eNPV (expected NPV) misc DCF misc Multivariable analysis misc IPC code misc TCT median value misc Market Size misc CAGR misc IP misc Revenue misc Multiple input descriptor misc Significance level misc Value misc Prediction |
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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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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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Lee, Jeong Hee |
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Journal of open innovation |
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2016 |
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Lee, Jeong Hee Khee-Su, Bae Lee, Joon Woo In, Youngyong Kwon, Taehoon Lee, Wangwoo |
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Elektronische Aufsätze |
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Lee, Jeong Hee |
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10.1186/s40852-016-0047-7 |
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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 |
| title_auth |
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 |
| abstract |
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. <Drug Class: Anticancer activity candidates>RoyaltyRate=9.997+0.063*AttritionRate+1.655*LicenseeRevenue‐0.410*TCTMedian%$ \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} %$‐1.090*MarketSize‐0.230*CAGRFormula1%$ \hbox{-} 1.090\ *\ \mathrm{Market}\ \mathrm{Size}\ \hbox{-}\ 0.230\ *\ \mathrm{CAGR}\ \left(\mathrm{Formula}\ 1\right) %$Up‐FrontPaymentUp‐front+Milestones=2.909‐0.006*AttritionRate+0.306*LicenseeRevenue‐0.74*TCTMedian‐0.113*MarketSize‐0.009*CAGRFormula2%$ \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. © The Author(s). 2016 |
| abstractGer |
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. <Drug Class: Anticancer activity candidates>RoyaltyRate=9.997+0.063*AttritionRate+1.655*LicenseeRevenue‐0.410*TCTMedian%$ \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} %$‐1.090*MarketSize‐0.230*CAGRFormula1%$ \hbox{-} 1.090\ *\ \mathrm{Market}\ \mathrm{Size}\ \hbox{-}\ 0.230\ *\ \mathrm{CAGR}\ \left(\mathrm{Formula}\ 1\right) %$Up‐FrontPaymentUp‐front+Milestones=2.909‐0.006*AttritionRate+0.306*LicenseeRevenue‐0.74*TCTMedian‐0.113*MarketSize‐0.009*CAGRFormula2%$ \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. © The Author(s). 2016 |
| abstract_unstemmed |
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. <Drug Class: Anticancer activity candidates>RoyaltyRate=9.997+0.063*AttritionRate+1.655*LicenseeRevenue‐0.410*TCTMedian%$ \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} %$‐1.090*MarketSize‐0.230*CAGRFormula1%$ \hbox{-} 1.090\ *\ \mathrm{Market}\ \mathrm{Size}\ \hbox{-}\ 0.230\ *\ \mathrm{CAGR}\ \left(\mathrm{Formula}\ 1\right) %$Up‐FrontPaymentUp‐front+Milestones=2.909‐0.006*AttritionRate+0.306*LicenseeRevenue‐0.74*TCTMedian‐0.113*MarketSize‐0.009*CAGRFormula2%$ \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. © The Author(s). 2016 |
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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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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. <Drug Class: Anticancer activity candidates>RoyaltyRate=9.997+0.063*AttritionRate+1.655*LicenseeRevenue‐0.410*TCTMedian%$ \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} %$‐1.090*MarketSize‐0.230*CAGRFormula1%$ \hbox{-} 1.090\ *\ \mathrm{Market}\ \mathrm{Size}\ \hbox{-}\ 0.230\ *\ \mathrm{CAGR}\ \left(\mathrm{Formula}\ 1\right) %$Up‐FrontPaymentUp‐front+Milestones=2.909‐0.006*AttritionRate+0.306*LicenseeRevenue‐0.74*TCTMedian‐0.113*MarketSize‐0.009*CAGRFormula2%$ \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. 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