 Research
 Open Access
Valuations using royalty data in the life sciences area—focused on anticancer and cardiovascular therapies
 Jeong Hee Lee^{1},
 Youngyong In^{1},
 IlHyung Lee^{2} and
 Joon Woo Lee^{2}Email author
https://doi.org/10.1186/s4085201500255
© Lee et al. 2016
 Received: 23 July 2015
 Accepted: 8 December 2015
 Published: 15 January 2016
Abstract
Purpose
This research seeks to answer the basic question, “How can we build up the formula to estimate the proper royalty rate and upfront payment using the data I can get simply as input?” This paper suggests a way to estimate the proper royalty rate and upfront payment using a formula derived from the regression of historical royalty dataset.
Design/methodology/approach
This research analyzes the dataset, including the royaltyrelated data like running royalty rate (backend payments) and upfront payment (upfront fee + milestones), regarding drug candidates for specific drug classes, like anticancer or cardiovascular, by regression analysis. Then, the formula to predict royaltyrelated data is derived using the attrition rate for the corresponding development phase of the drug candidate for the license deal and the revenue data of the license buyer (licensee). Lastly, the relationship between the formula to predict royaltyrelated data and the expected net present value is investigated.
Findings
In the case of Equations Equation 2 and Equation 4, it is statistically meaningful (R2: 039–0.41); however, in the case of Equations Equation 1 and Equation 3, it has a weak relationship (R2: 022–0.28), thus requiring further study.
Research limitations/implications (if applicable)
This research is limited to the relationship between two drug classes—anticancer (antineoplastics) and cardiovascular—and royaltyrelated data.
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.
Keywords
 Valuation
 Licensing deal
 Drug
 Royalty data
 Royalty rate
 Upfront fee
 Milestones
 Regression
 Drug class
 Anticancer
 Antineoplastics
 Attrition rate
 Development phase
 Licensee
 Life sciences
 rNPV
 eNPV (expected NPV)
 DCF
 QSAR
 Computational chemistry
Introduction
The existing valuation methodologies used in the life sciences area and their strengths and weaknesses
Drug development costs (Bogdan and Villiger 2010)
Phase  Cost 

Lead optimisation  US$ 2–3 mn 
Preclinical phase  US$ 2–3 mn 
Clinical phase 1  US$ 1–5 mn 
Clinical phase 2  US$ 3–11 mn 
Clinical phase 3  US$ 10–60 mn 
Approval  US$ 2–4 mn 
Drug development duration (Bogdan and Villiger 2010)
Phase  Length 

Lead optimisation  20–40 months 
Preclinical  10–12 months 
Clinical phase 1  18–22 months 
Clinical phase 2  24–28 months 
Clinical phase 3  28–32 months 
Approval  16–20 months 
The prediction of the future value of the asset is useful and important to determine economical action (Lee and Lee 2015). Also, licensing deals between pharmaceutical companies and biotech/academia are popular in the life sciences area because drug development is expensive, timeconsuming, complex, and risky.
Main pros and cons of the two main valuation methodologies (Bogdan and Villiger 2010)
Advantages  Disadvantages  

DCF  Easy to implement and to understand  Misses the value of flexibility and market uncertainty 
Standard in all sectors of the economy  Not suitable for risk management  
Real Options  Captures market uncertainty and the management’s ability to react  Relies on more hypothesis and requires more data 
Suitable for risk management  Technical  
Improves strategic thinking if properly understood 
Main pros and cons of the four methods of real options valuation methodologies (Bogdan and Villiger 2010)
Method  Advantages  Disadvantages 

Formula  Easy to use  Calculation process not visible 
Fast  Only for simple option structures  
Sensitivities  Simple assumptions  
Trees  Easy to understand  Rigid 
Visualisation  
can deal with more complicated options  
Simulations  Easy to understand  Time consuming 
Visualisation  Problems with path dependency  
Can deal with more complicated assumptions  
Finite differences  Can deal with more complicated assumptions  Calculation process not visible, hard to understand 
Can deal with more complicated options  Technically demanding 
Review of preceding research
Riskadjusted addedvalue pharmaceutical net present value (NPV) (Blair 2008)
Trial phase  Cumulative risk  NPV ($US million)  Risk adjusted eNPV ($US million)  

II  III  Preregistration  Registration  Basecase  CDx enhanced  
Baseline attrition rates^{a}  
0.52  0.76  0.89  0.94  0.331  892  295  
CDx adjusted attrition rates^{b}  
0.57  0.81  0.4  0.99  0.429  2694  1157 
Attrition rate (DiMasi et al. 2010)
♦ Preclinical to IND: 70 %  Start  End  Probability 
♦ Phase I to Phase 2: 71 %  PC  Approval  13.3 % 
♦ Phase 2 to Phase 3: 45 %  P1  Approval  19.0 % 
♦ Phase 3 to NDA/BLA: 64 %  P2  Approval  26.8 % 
♦ NDA/BLA to approval: 93 %  P3  Approval  59.5 % 
NDA/BLA  Approval  93.0 % 
Manager panel’s ranking of factors that influence the value of a licensing deal (Arnold et al. 2002)
Rank  Driver  Score^{a} 

1  Phase of molecule  1.13 
2  Therapeutic area  1.56 
3  Type of agreement  1.94 
4  Scope of agreement  1.94 
5  Type and reputation of buyer  1.94 
6  Type of molecule  2.19 
Manager panel’s perceptions about the importance of value drivers (Arnold et al. 2002)
Value driver  Percentage of respondents mentioning it as important 

Market, including market size for the licensing agreement, market potential, or patient population  88 % 
Stagephase or stage in the development of the product  69 % 
Strategy, including issues of “fit” of the product in the company’s pipeline and franchises, impact on the current business, and synergies  44 % 
Competitioncompetitive markets, competition from other partners for the product, and competitive products  38 % 
Reputation of the licensee or licensor, including inventor and management talent  31 % 
Investmentfinancial needs to develop the product  25 % 
Intellectual propertygaining key patents or trade secrets  25 % 
Noveltyinnovative merit of the product (revolutionary or evolutionary)  19 % 
Control of the development and commercialization of a product  6 % 
Comparable deal valuations for similar products/technologies  6 % 
Reimbursement–ability or willingness of customers (payers or patients) to pay for the product  6 % 
Main drug classification (Drugs.com 2015)
Drug class Tre view  

1  Allergenics 
2  Alternative medicines 
3  Antiinfectives 
4  Antineoplastics 
5  Biologicals 
6  Cardiovascular agents 
7  Central nervous system agents 
8  Coagulation modifiers 
9  Gastrointestinal agents 
10  Genitourinary tract agents 
11  Hormones 
12  Immunologic agents 
13  Medical gas 
14  Metabolic agents 
15  Miscellaneous agents 
16  Nutritional products 
17  Plasma expanders 
18  Psychotherapeutic agents 
19  Radiologic agents 
20  Respiratory agents 
21  Topical agents 
There is only one case to perform the regression analysis on the work on evaluating pharmaceutical licensing agreements (Arnold et al. 2002; Rogers and Maranas 2005), but the analysis for historical licensing data was for identifying the factors that most affect a deal’s financial terms (Arnold et al. 2002; Rogers and Maranas 2005). In reviewing the preceding research, there have been no cases where a regression analysis could be performed to estimate the proper royalty rate and upfront payment using the formula derived from the regression of the dataset of historical licensing data.
This study is believed to be the first case to estimate the royalty rate and upfront payment using the formula derived from the regression of the dataset of historical licensing data, and can therefore be used as a simple tool to answer the basic question, “How can we find the formula to estimate the proper royalty rate and upfront payment using the data I can get simply as input?” It can also be a good starting point to be referred to by a manager in case of negotiations in pharmaceutical licensing deals for a specific drug class.
Research design and scope and limitation
Research design
The purpose of this research is to derive the formula to predict royaltyrelated data, such as running royalty rate (backend payments) and upfront payment (upfront fee + milestones), using the attrition rate for the corresponding development phase of the drug candidate within a specific drug class, such as anticancer (antineoplastics) or cardiovascular, and the revenue data of the license buyer (licensee) using regression analysis. Another purpose is to find the relationship between the formula to predict royaltyrelated data and eNPV.
According to the preceding research, the main factors to drive the size of licensing deals in the life sciences area are development phase, drug class, contract type, contract scope, licensee, molecular structure, market, strategies, competition, IP, and novelty (Arnold et al. 2002). Market size, licensee revenue, molecular structure, and IP can be converted to numerical value and can be used for the input for prediction for royaltyrelated data such as running royalty rate (backend payments) and upfront payment (upfront fee + milestones). In the case of market size, it requires a great amount of time to estimate the proper market size for the subclass of a drug class (e.g., epidermal growth factor, anticancer immunity, ovarian cancer, alpha interferon as a subclass of the anticancer drug class). In the case of molecular structure, it requires professional chemical software to convert chemical structure into numeric code and requires the collection of molecular structure information for the drug candidate. In the case of IP, identifying what could be the unique descriptor for the drugrelated patents for input for the Xaxis of regression requires more thought (e.g., the technology cycle time median value for the International Patent Classification (IPC) code can be the descriptor candidate). This study selected drug class, licensee revenue, and attrition rate for the development phase as descriptors for the input for the Xaxis of regression.

Step 1. Collection of data such as the running royalty rate, upfront fee, milestones, licensor, licensee, the revenue of licensee, the corresponding drug class, and the development phase in drug licensing deals
This study collected the data for two drug classes in the format of Table 10: anticancer and cardiovascular. Data collection is based on the following resources: ① Book: Royalty Rates for Pharmaceuticals and Biotechnology, 8th Edition Published by IPRA Inc (2012); ② Book: Intellectual Property: Valuation, Exploitation, and Infringement Damages, written by Russell L. Parr and Smith (2005); ③ Site for checking the deal condition: http://www.sec.gov/ (U.S. Securities and Exchange Commission); ④ Site for checking the development phase: https://clinicaltrials.gov; ⑤ Site for checking the revenue of Licensee: http://www.google.com/finance and http://finance.yahoo.com/; ⑥ Site to retrieve the needed data: http://www.google.com.Table 10Example of data collection (IPRA Inc 2012)
#
Development stage
Upfront fee
Milestones
Royalty rate
Contract year
SubClass
1
PreClinical or Phase I
$500,000
4 % of sales
2006
Lyophilized docetaxel
2
Phase I/II
35 M$
372 M$
Low double digit
2009
MEK inhibitors
3
PreClinical
$30,000
$155,000
1 %~2 % of sales
2005
Immunotherapy
4
PreClinical
$100,000
8.5 % of sales
2001
Platinum complex
5
PreClinical
3.5 % of sales
2006
Pseudomonas exotoxin
6
PreClinical
1.5 % of sales
2002
LmLLO cancer
7
Phase II
$3,000,000
10 M$
0
1997
Immune system cancer product
8
Phase II
25 M$
400 M$
35 % of sales
2005
VEGF Trap

Step 3. Regression analysis to investigate the relationship between (attrition rate * licensee revenue) and upfront payment (upfront fee + milestones) and the relationship between attrition rate * licensee revenue and backend payment (running royalty rate)
Used software: ① Preliminary analysis for checking rough type: Microsoft Office Excel 2007
② Main analysis: opensource statistical software
Regression 1: Xaxis = (attrition rate * licensee revenue)/100
Yaxis = upfront payment (upfront fee + milestones) [Unit: USD]

Regression 2: Xaxis = (attrition rate * licensee revenue)/100
Yaxis = backend payment (running royalty rate) [Unit: USD]

Scope and limitation of research
The scope of this research is to derive the formula to predict royaltyrelated data, such as running royalty rate (backend payments) and upfront payment (upfront fee + milestones), using the attrition rate for the corresponding development phase of the drug candidate for the anticancer (antineoplastics) or cardiovascular drug class and the revenue data of the license buyer (licensee), and to investigate the relationship between the formula to predict royaltyrelated data and eNPV. Statistically speaking, this research derives the formula to predict royaltyrelated data using a single independent variable like royalty rate and upfront payment, (attrition rate * licensee revenue)/100]. Also, this research selected drug class, licensee revenue, and attrition rate for the development phase as descriptors for the input for the Xaxis of regression. This study is limited to the relationship between the two drug classes of anticancer (antineoplastics) and cardiovascular and royaltyrelated data. For further studies, it is advised that the relationship be analyzed in more detail to involve more drug classes and royaltyrelated data using several independent variables through software like SPSS or SAS.
Analysis of dataset
Analysis of anticancer (Antineoplastics) dataset
Distribution of the development phase in the anticancer (antineoplastics) dataset
Development phase  No. of hit  % 

in vitro activity  4  5.56 
Preclinical  9  12.50 
Phase I  11  15.28 
Phase I/II  10  13.89 
Phase II  19  26.39 
Phase II/III  2  2.78 
Phase III  14  19.44 
NDA/BLA  3  4.17 
Total deal number  72  100.00 
The average of the upfront fees in the anticancer (antineoplastics) dataset collected is USD 6,123,474, and the average of the milestones in the anticancer dataset is USD 30,088,181. With this, the average royalty rate in the anticancer dataset is 8 % and is slightly higher than average royalty in pharma/biotech license deals in Table 13 and lower than the average royalty rate (11.5 %) of phase II in Table 14.
Industry guideline for royalty rates (Source: 1998, Dr. Michael, CASRIP Newsletter) (IPRA Inc 2012)
Industry  Royalty rate 

Electronics  0.5–5 % 
Machinery  0.33–10 % 
Chemical  2–5 % 
Pharmaceutical  2–10 % 
David Weiler’s royalty rate based on 458 deals in the pharma/biotech license agreement (Source: www.royaltysource.com) (IPRA Inc 2012)
485 deals  Rate 

Average royalty  7 % 
Median royalty  5 % 
Maximum royalty  50 % 
Minimum royalty  0 % 
Medius Associates’ royalty rate by development stage (Source: www.mediusassociate.com) (IPRA Inc 2012)
Development stage  Royalty rate 

Preclinical  0–5 % 
Phase I  5–10 % 
Phase II  8–15 % 
Phase III  10–20 % 
Launched product  20 % + 
Mark G. Edwards’ royalty rate guideline (Source: www.recap.com) (IPRA Inc 2012)
Average royalty by R&D stage  

R&D Stage  Rate 
Discovery  6.4 % 
Lead Molecule  8.1 % 
PreClinical  11.3 % 
Analysis of cardiovascular dataset
Distribution of the development phase in the cardiovascular dataset
Development  No. of hit  % 

in vitro activity  3  9.68 
Preclinical  3  9.68 
Phase I  4  12.90 
Phase II  2  6.45 
Phase III  7  22.58 
NDA/BLA  12  38.71 
Total deal number  31  100.00 
The average of the upfront fees in the cardiovascular dataset collected is USD 10,886,596, and the average of the milestones in the anticancer dataset is USD 10,167,742. The average royalty rate in the anticancer dataset is 10 % and is slightly higher than the average royalty in pharma/biotech license deals in Table 13 and slightly lower than the average royalty rate (15 %) of Phase III; it is much lower than the average royalty rate (20 %+) of launched products in Table 16.
Regression analysis
Preliminary regression analysis of anticancer (Antineoplastics) dataset
To detect and exclude outliers (an observation point that is distant from other observations in statistics) and to find proper regression models for a specific dataset, a preliminary regression analysis using Microsoft Excel is needed. Figures 18 and 19 show some outliers, and a linear regression model does not fit the anticancer dataset.
The deal data from the preliminary regression was excluded if the normalization of the “royalty rate” data is not possible (a total six sets of deal data).

① If “upfront payment” has a value of 0 (zero), it is selected as an outlier (a total of 15 outliers).

② If “(attrition rate * licensee revenue)/100” has a value of more than 5,000,000,000, it is selected as an outlier (a total of 3 outliers),
Thus, the number of deal data used for the main regression analysis to develop the model for prediction is 48.
The deal data from the preliminary regression was excluded if the normalization of the “royalty rate” data is not possible (a total of seven sets of deal data).

① If “(attrition rate * licensee revenue)/100” has a value of more than 1,000,000,000, it is selected as an outlier (a total of eight outliers).
Thus, the number of deal data used for the main regression analysis to develop the model for prediction is 57.
Main regression analysis of anticancer dataset by opensource statistical software
R^{2} = 0.384618
X = (attrition rate * licensee revenue)/100
Y = upfront payment (upfront fee + milestones) [Unit: USD]
R^{2} = 0.223928
X = (attrition rate * licensee revenue)/100
Y = royalty rates [Unit: %]
Preliminary regression analysis of cardiovascular dataset
To detect and exclude outliers (an observation point that is distant from other observations in statistics) and to find proper regression models for a specific dataset, a preliminary analysis using Microsoft Excel is needed. Figures 22 and 23 show some outliers, and a linear regression model does not fit the cardiovascular dataset.
The deal data from the preliminary regression was excluded if the normalization of the “royalty rate” data is not possible (a total of one set of deal data).

① If the “upfront payment” has a value under 50,000, it is selected as an outlier (a total of 12 outliers).

② If the “upfront payment” has a value of more than 190,000,000, it is selected as an outlier (a total of 2 outliers).
Thus, the number of deal data used for the main regression analysis to develop the model for prediction is 16.
The deal data from the preliminary regression was excluded if the normalization of the “royalty rate” data is not possible (a total of one set of deal data).

① If the “royalty rate” has a value of more than 25, it is selected as an outlier (a total of 3 outliers).
Thus, the number of deal data used for the main regression analysis to develop the model for prediction is 27.
Main regression analysis of cardiovascular dataset by opensource statistical software
R^{2} = 0.413879
X = (attrition rate * licensee revenue)/100
Y = upfront payment (upfront fee + milestones) [Unit: USD]
R^{2} = 0.287886
X = (attrition rate * licensee revenue)/100
Y = royalty rates [Unit: %]
Discussion
This study was presented with many limitations to reasonably determine the variables for prediction because upfront payments and royalty rates are determined by various environmental variables in the field. However, this study developed a prediction model having an R^{2} value of about 0.4 if the variables of “attrition rate * licensee revenue” are used. This is a “statistically significant” finding, and it shows the importance of the variables of “attrition rate * licensee revenue” for determining upfront payments. Thus, the said variables can be used as the solid basis for evaluating upfront payments in the future.
In the case of the prediction of royalty rates, this study achieved a low R^{2} value of about 0.25 in the statistical analysis using the variables of “attrition rate * licensee revenue”. This means that it is not possible to perform the analysis reasonably using the said variables only. Because of this, the introduction of other variables is necessary for the analysis of royalty rates.
Conclusion
Summary
This study yielded meaningful results as it aimed to create a tool to predict royalty rate and upfront payment (upfront fee + milestones) only using knowledge on the development phase and its attrition rate, drug class, and licensee’s revenue, which can easily be known.
It is possible to predict rough eNPV using the licensee’s revenue and the licensee’s maximum reserve for the project according to the development phase instead of the attrition rate for the development phase.
Implications
This study allowed valuation of a drug specific to a drug class and proved that the royalty rate can be a variable according to drug class and licensee.
Topics for further research

① The relationship to involve more drug classes and royaltyrelated data.

② Regression analysis using several independent variables through software such as SPSS or SAS.

③ Regression analysis to investigate the relationship between royaltyrelated data and more input descriptors such as market size, molecular structure (numerical code for substructure/fragment), and IP (technology cycle time median value for the IPC code).
Declarations
Acknowledgments
We would like to show our gratitude to Dr. Kee Heon Cho of Korea Valuation Association and Dr. SungJoo Lee of Sanofiaventis Korea for their guidance, and to KISTI for the data source provided.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
References
 Arnold K, Coia A, Saywell S, Smith T, Minick S, Löffler A. Value drivers in licensing deals. Nat Biotechnol. 2002;20:1085–9.View ArticleGoogle Scholar
 Blair ED. Assessing the valueadding impact of diagnostictype tests on drug development and marketing. Mol Diagn Ther. 2008;12(5):331–7.View ArticleGoogle Scholar
 Bogdan B, Villiger R. Valuation in life sciences—a practical guide. 3rd ed. New York: Springer; 2010.View ArticleGoogle Scholar
 Cho BS. Biotech business case study—Merck & Co.: evaluating a drug licensing opportunity. BioWave. 2004 6(21):1–12.Google Scholar
 DiMasi JA, Feldman L, Seckler A, Wilson A. Trends in risks associated with new drug development: success rates for investigational drugs. Clin Pharmacol Ther. 2010;87(3):272–7.View ArticleGoogle Scholar
 Drugs.com. Drug classes. 2015. http://www.drugs.com/drugclasses.html.
 IPRA Inc. Royalty rates for pharmaceuticals & biotechnology, 8th Edition. 2012.Google Scholar
 Kessel M, Frank F. A better prescription for drugdevelopment financing. Nat Biotechnol. 2007a;25(8):859–66.Google Scholar
 Lee SJ. Valuation in life sciences and portfolio management. 2008 Korean Crystal Ball User Conference. 2010.Google Scholar
 Lee S, Lee K. Heterogeneous expectations leading to bubbles and crashes in asset markets: Tipping point, herding behavior and group effect in an agentbased model. J Open Innov. 2015;2015:1–12.Google Scholar
 Parr RL, Smith GV. Valuation, Exploitation, and Infringement Damages. 2005.Google Scholar
 Puran S. The valuation of partdeveloped projects in the pharmaceutical sector. Masters of BioScience Enterprise, Fitzwilliam College, sponsored by Cambridge Healthcare and Biotech. 2005.Google Scholar
 Richards D. Drug development and regulation. Medicine. 2003;31(8):25–31.View ArticleGoogle Scholar
 Rogers MJ, Maranas CD. Valuation and design of pharmaceutical R&D licensing deals. AIChE J. 2005;51(1):198–209.View ArticleGoogle Scholar
 Stewart JJ, Allison PN, Johnson RS. Putting a price on biotechnology. Nat Biotechnol. 2001;19(9):813–7.View ArticleGoogle Scholar