 Research
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Valuation method by regression analysis on real royaltyrelated data by using multiple input descriptors in royalty negotiations in Life Science areafocused on anticancer therapies
Journal of Open Innovation: Technology, Market, and Complexity volume 2, Article number: 21 (2016)
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 upfront payment using multiple data I can get simply as input.
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 class of anticancer 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, 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 upfront payment is as follows.
<Drug Class: Anticancer activity candidates>
In the case of Equations Equation 1 to estimate the royalty rate, it is statistically meaningful at the significance level of 1 % (PValue: 0.001); however, in the case of Equations Equation 2 to estimate the upfront payment it is statistically not meaningful (PValue: 0.288), thus requiring further study.
Research limitations/implications (if applicable)
This research is limited to the relationship between multiple input variables and royaltyrelated 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.
Introduction
R&D productivity in life sciences and “fail fast, fail cheaply” strategy
Drug development requires a great amount of time and money for each development phase. So drug development is expensive, timeconsuming, complex, and risky (Lee et al. 2016). The global life sciences sector’s general decline in R&D productivity is a frequent topic of conversation among industry stakeholders, investors, and analysts. Total projected value of latestage pipelines for the 12 largest pharmaceutical companies showed a decline from $1,369 billion to $913 billion in 2013. The global life sciences sector in R&D productivity is generally declining. As the drug development costs and duration is bigger if the development phase is late phase, dropping the dug project in the early stage is cheaper. While there has been a decline in drug pipeline volumes and success rates in earlyphase drug development, the number of stopped Phase III projects has also reduced gradually and the submission phase has posted a stable success rate. This is “fail fast, fail cheaply” strategy (Deloitte Centre for Health Solutions, 2015). As shown in Fig. 1, New drug and biologic approvals are not keeping pace with rising R&D costs (Kaitin, 2015). R&D expenditures are constantly increasing, and the service enterprise is aiming to improve product development and the production process by increasing both internal and external R&D activities (Kim, 2016).
Licensing as good strategy
Medtech R&D spend is projected to grow by 4.2 % annually, to $30.5 billion by 2020 and Life sciences R&D spending is projected to grow 2.4 % per year from 2013 to 2020, reaching $162 billion. Some smaller biotech firms with limited R&D budgets are securing financial support from large pharmaceutical companies through licensing and collaborative R&D deals (Deloitte Centre for Health Solutions, 2015). With the recent collapse in the general and biotech equity issuance and IPO markets, biotech companies will have to turn more to partnering, licensing and M&A for funding. Linkages of a firm can take in the form of a joint research project, joint development of a product, personnel exchanges, joint patenting, technology licensing, equipment purchase, and also a variety of other channels (Young, 2016; Patra & Krishna, 2015). Licensing is a good strategy and business model to overcome financial difficulties due to long development period in life science. In many cases, purchasing a biotech firm is a more attractive option than buying the rights to the drugs the firm develops. Such a transaction can be a win for biotech firms, too, because large pharma companies typically possess the manufacturing facilities needed to commercialize drugs, which biotechs often lack. As shown in Fig. 2, Life sciences companies tallied over $300 billion in completed or announced M&A transactions globally for 2014 (Deloitte Centre for Health Solutions, 2015). Figure 3 illustrates the scale of licensing activity within the pharmaceutical industry in the last decade. More than 1,000 product deals (most of them licensing deals) were recorded each year in the PharmaDeals® v4 Agreements database since 2002 (Nigel Borshell & Ahmed 2012).
Demanding valuation in the licensing deal in the life sciences sector
Pharmaceutical companies need to make up for their R&D deficiencies with licensing activities. As soon as it comes to licensing and M&A, companies are in urgent need of a valuation method that displays the correct value of early stage projects.
There are two major quantitative valuation approaches applied in the life sciences sector, DCF and real options. But even experienced licensing staff writhes to attribute the right value to a complex license contract and so the valuation is demanding. Compared to other industries, valuation in life sciences is more demanding due to the inherent complexity and length of R&D. Main concerns are the choice of the right valuation method, the methodology itself, the input parameters and the interpretation of the results (Bogdan & Villiger, 2010).
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 (Lee et al. 2016). This study suggests the way to estimate the proper royalty rate and upfront payment using multiple data descriptor we can get easily as input and can be used as a simple tool to answer the basic question, “What would be the most determining factors if I perform regression analysis using several independent variables?”
Review of preceding research
Lee et al. (2016)’s study was 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, but further indepth research is necessary for investigating the relationship between royaltyrelated data and more input descriptors such as market size, molecular and IP, Market size, licensee revenue, molecular structure, and IP can be converted to numerical value and can be used for the input for prediction (Lee et al. 2016) Fig. 4.
The value of technology depends on a large number of factors. As shown in Fig. 5, these include the target market size for the final therapeutic product, the anticipated clinical qualities of the drug and the extent of competition for the drug. These will include the phase specific success probabilities, development costs and timelines, the expected market size and market share, and the costs of goods, marketing and administration. Add to these the scenarios of product life cycle and commercial performance based on predicted ethical and/or generic competition and the task of calculating the value appears almost impossible (Nigel Borshell & Ahmed 2012).
The most complex method conceptually to valuate is Monte Carlo simulation. Instead of putting in single point estimates of all the inputs to calculate a single value in a model, the Monte Carlo methodology puts in probability distributions for various inputs such as market size, costs, pricing and time to market, and then samples all those distributions to run multiple simulations, each calculating an NPV as shown in Fig. 6 (Pullan, 2014).
There was no perfect correlation between the market sizes of certain therapeutic areas and the market caps of early stage technology companies in the life sciences sector. According to Table 1, valuation of a given stem cell therapy company addressing diabetes appears to be very low. This could be due to conservative assumptions; a market premium for track record and proven capability of the listed companies; key collaborative alliances; and positive news during the product development stage. Given these factors, valuation assumptions also depend on the purpose of the valuation and who is represented in the exercise (Ranade, 2008).
Patents and patent valuation have raised tremendous concerns from the researchbased pharmaceutical industry for a long time. It is demonstrated that PTDI (Pharmaceutical technology details indicators) like NCE actually have significant influence on patent value and, more significantly, enhance the quality of existing valuation methods. NCE actually plays the role of the strongest positive factor influencing the expected patent value. On the contrary, OD(Orphan Drug) and PD(Pediatric Drug) show significantly negative effects, which could be rationally explained by the small patient population for these drugs (Hu et al. 2008) Table 2.
There was a simulation approach to value patents and patentprotected R&D projects based on the Real Options approach and takes into account uncertainty in the cost to completion of the project, uncertainty in the cash flows to be generated from the project, and the possibility of catastrophic events that could put an end to the effort before it is completed. Figure 7 shows the critical cash flows rates (critical costs) for costs between $80 and $100 million (cash flow rates between $9 and $18 million) (Schwartz, 2004). Since Eduardo Schwartz’s paper, patent valuation has increasingly attracted considerable interest of researchers and practitioners. Nevertheless, few of the firms that can benefit from patent valuation have the capability to perform inhouse patent valuation, and even the patent valuation expertise of consultancies and financial institutions seems limited (Carte, 2005; Ernst et al. 2010).
Thus, at present, there are problems and challenging issues for the research on patent valuation. First, among previous studies that provide the excellent overviews about the determinants (indexes) of patent, it was shown that forward citations are significantly correlated with a patent’s market value (Nair et al. 2012). Forward citations are defined in Hu, Rousseau, & Chen’s study as the number of patent citations that an auctioned patent received till the Ocean Tomo date of sale. However, measuring a patent’s market value by simply counting the patent’s forward citations has limitation to reflect the complexity in the networks of patents. Moreover, previous studies have shown that the structural patent indicators of the patent citation networks (PCNs) are correlated with patent value and the correlations are different among the groups of firms (Hu et al. 2012). PCNs are constructed by setting patents as nodes and their citation information as edges. Nevertheless, few efforts have been made to investigate the effect of structural patent indicators in forward citations on patent price. Second, it is difficult to investigate dynamics between patent indicators and patent price because the actual price at which the patent is sold or licensed is often a privately maintained record. To resolve these problems and challenging issues, the paper proposed a systematic approach, which investigates the effect of the structural patent indicators, extracted from forward citations, on patent price from the relationship with firm market value. To explain, first, the paper introduces the forward patent citation networks (FPCNs), from which the structural patent indicators are extracted as a set of features to represent patent price. Thereafter, the panel data econometric approach is applied to examine the relationship between the firmlevel structural patent indicators and enterprise value (EV), selected as firm market value. Finally, dynamics between the structural patent indicators in the FPCNs and patent price are explored by referring to the discovered relationship (Suh, 2015) Fig. 8.
Research design and scope and limitation
Research design
This research analyzes the anticancer (antineoplastics) dataset, including the royaltyrelated data like running royalty rate and upfront payment, regarding drug candidates for specific drug class of anticancer, by regression analysis between royaltyrelated data and multiple input descriptors like the attrition rate for the development phase, market size, TCT median value for the IPC code (IP) of the patent, and the revenue data of the license buyer for deriving the formula to predict royaltyrelated data.
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 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. This study selected the attrition rate for the development phase, market size, CAGR, TCT median value for the IPC code (IP), and the revenue data of the license buyer as descriptors for input xaxis of regression.
The main research procedure is divided into three steps as shown in Fig. 9: data collection, Preparation of dataset, and regression analysis.
Step 1. Collection of data such as the running royalty rate, upfront fee, milestones, licensor, licensee, the revenue of licensee, the corresponding drug subclass, IPC subclass, TCT median value of the patent, market size, and CAGR of the drug subclass, and the development phase in drug licensing deals
This study collected the data for one drug class of anticancer. Data collection is based on the several resources described in our previous study (Lee et al. 2016). Additional resources are: (1) Site for checking the revenue of Licensee: http://www.google.com/finance and http://finance.yahoo.com/; (2) Site to retrieve the market size and CAGR of the corresponding drug subclass: http://www.giikorea.co.kr/ (3) Site for checking the IPC subclass of the patent: www.google.com/patents.
Step 2. Preparation of dataset ready for regression analysis
The procedure and examples of data normalization of upfront payment (upfront fee + milestones) and backend payment (running royalty rate) to prepare the dataset ready for regression are described in our previous paper (Lee et al. 2016).
The procedure to get TCT median Value is divided into three steps as shown in Fig. 10: Patent Navigation, Getting IPC Subclass from the patent, and Getting Technology Cycle Time Median Value.
Figures 11 and 12 show the example to get IPC Subclass from the patent, and to get TCT Median Value (Average) from IPC subclass.
The procedure to get Market size (2015) and CAGR (%) is divided into three steps as shown in Fig. 13: Navigate market information, Convert the currency unit of the market size to million dollar, and Estimate the market size of year 2015 by applying CAGR.
Figure 14 shows the example to get the market size of year 2015 and CAGR (%).
Step 3. Regression analysis to investigate the relationship between multiple independent variables of the attrition rate for the development phase, market size, CAGR, TCT median value for the IPC code (IP), and the revenue data of the license buyer and the dependent variable of upfront payment (upfront fee + milestones) and the relationship between multiple independent variables of the attrition rate for the development phase, market size, CAGR, TCT median value for the IPC code (IP), and the revenue data of the license buyer and the dependent variable of backend payment (running royalty rate)
Used software: IBM SPSSS Statistics Version 21
Regression 1: Xaxis = multiple independent variables of the attrition rate for the development
phase, market size, CAGR, TCT median value for the IPC code (IP), and the revenue
data of the license buyer
Yaxis = upfront payment (upfront fee + milestones) [Unit: USD]
Regression 2: Xaxis = multiple independent variables of the attrition rate for the development
phase, market size, CAGR, TCT median value for the IPC code (IP), and the revenue
data of the license buyer
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) drug class and the revenue data of the license buyer (licensee). Statistically speaking, this research derives the formula to predict royaltyrelated data using multiple independent variables like the attrition rate for the development phase, market size, CAGR, TCT median value for the IPC code (IP), and the revenue data of the license buyer. Also, this research selected the attrition rate for the development phase, market size, CAGR, TCT median value for the IPC code (IP), and the revenue data of the license buyer as descriptors for the input for the Xaxis of regression. This study is limited to the relationship between one drug class of anticancer (antineoplastics) and royaltyrelated data. For further studies, we will cover more detail the relationship for more drug classes using multiple input descriptors and we will cover the comparison of the estimation results between by using the prediction formula derived regression analysis Vs. by using traditional valuation methods like eNPV or Real Options.
Analysis of dataset
Analysis of anticancer (antineoplastics) dataset
Figures 15 and 16 show the analysis result of the drug subclass of Anticancer dataset. As shown in Fig. 15, top 3 ranking in the frequency hit percent of drug subclass is as follows: (1) Cancer Immunotherapies, Lung Cancer (2) Leukemia Therapeutics, Protein Kinase Inhibitor Antineoplastics (3) Breast Cancer, Drug Delivery System, Hematologic malignancies, Liver Cancer, Monoclonal Antibody Antineoplastics, Pancreatic Cancer.
Figure 17 shows the analysis result of IPC code in the corresponding patent in the licensing deal of anticancer drug dataset. As shown in Fig. 17, top 5 ranking in the frequency hit percent of IPC Code is as follows: (1) PREPARATIONS FOR MEDICAL, DENTAL, OR TOILET PURPOSES (A61K) (2) HETEROCYCLIC COMPOUNDS (C07D) (3) SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS (A61P) (4) PEPTIDES (C07K) (5) MICROORGANISMS OR ENZYMES (C12N).
Figures 18 and 19 show the analysis of Market size of Anticancer Drug Subclass of anticancer drug dataset. As shown in Fig. 18, top 5 market in the market size is as follows: (1) Drug Delivery System (2) Hematologic malignancies (3) Monoclonal Antibody Antineoplastics (4) Ovarian Cancer (5) Peptide Therapeutics.
Figure 20 shows the analysis of Licensee (license buyer) Revenue of anticancer drug dataset. The interesting point we found is smallmedium companies occupied 66 % in the licensee percent. The percent of smallmedium companies to participate in the licensing deals is bigger than the one of the big companies.
Figure 21 shows the analysis of Development phase distribution of anticancer drug dataset that reported in our previous paper (Lee et al. 2016). As show in Fig. 21, Phase 2related stage deals occupied over 43 %.
Regression analysis
Regression analysis of anticancer (antineoplastics) dataset
We investigated the relationship between multiple independent variables of the attrition rate for the development phase, market size, CAGR, TCT median value for the IPC code (IP), and the revenue data of the license buyer and the dependent variable of backend payment (running royalty rate); its graph is as shown in Fig. 22, and its prediction formula follows Eq. 1. We found that regression model is statistically meaningful at the significance level of 1 % (PValue: 0.001).
PValue = 0.001
Independent Variables = multiple independent variables of the attrition rate for the development phase, market size, CAGR, TCT median value for the IPC code (IP), and the revenue data of the license buyer.
Dependent Variable = Royalty rates [Unit: USD] Fig. 23.
As shown in Fig. 24, Licensee Revenue is statistically meaningful and has significant positive influence at the significance level of 5 %. The most significant variables affected is Licensee Revenue, because Licensee Revenue has the biggest BValue. Ranking of factors that influence the royalty rate is as follows: (1) Licensee Revenue (+) (2) Market Size (−) (3) TCT median value (−) (4) CAGR (−) (5) Attrition Rate (+). Plus (+) symbol means positive influences and Negative (−) symbol means negative influences. CAGR is statistically meaningful and has significant negative influence at the significance level of 1 %. Attrition Rate, TCT median value, Market Size are not significantly meaningful.
We investigated the relationship between multiple independent variables of the attrition rate for the development phase, market size, CAGR, TCT median value for the IPC code (IP), and the revenue data of the license buyer and the dependent variable of backend payment (running royalty rate); its graph is as shown in Fig. 25, and its prediction formula follows Eq. 2. We found that the regression model is statistically not meaningful (PValue: 0.288), thus requiring further study.
PValue = 0.288
Independent Variables = multiple independent variables of the attrition rate for the development phase, market size, CAGR, TCT median value for the IPC code (IP), and the revenue data of the license buyer
Dependent Variable = upfront payment (upfront fee + milestones) [Unit: USD] Fig. 26
As shown in Fig. 27, Licensee Revenue is statistically meaningful and has significant positive influence at the significance level of 5 %. The most significant variables affected is Licensee Revenue, because Licensee Revenue has the biggest BValue. Ranking of factors that influence the upfront payments is as follows: (1) Licensee Revenue (+) (2) Market Size (−) (3) TCT median value (−) (4) CAGR (−) (5) Attrition Rate (−).Plus (+) symbol means positive influences and Negative (−) symbol means negative influences. However, the regression model is statistically not meaningful (PValue: 0.288).
Discussion
A regression analysis was carried out to estimate upfront payments and royalty rates for one dataset of anticancer (antineoplastics) drug classes. In the case of the prediction of Royalty rates, the models for predicting having a Pvalue of 0.001 for the anticancer (antineoplastics) dataset was obtained through statistical analyses. In case of the prediction of royalty rates, the models for predicting having a Pvalue of 0.288 for the anticancer (antineoplastics) dataset was obtained through statistical analyses. Figure 28 shows the overview of the process of this study.
This study was presented with many limitations to reasonably determine the variables for prediction because upfront payments and royalty rates are determined by highly various environmental variables in the field. However, this study developed a prediction model like Eq. 1 having a Pvalue of 0.001 for estimating royalty rates if multiple independent variable like the attrition rate for the development phase, market size, CAGR, TCT median value for the IPC code (IP), and the revenue data of the license buyer are used. This is a “statistically significant” finding at the significance level of 1 % (PValue: 0.001). Thus, the said variables can be used as the solid basis for evaluating royalty rates in the future. Ranking of factors that influence the royalty rate is as follows: (1) Licensee Revenue (+) (2) Market Size (−) (3) TCT median value (−) (4) CAGR (−) (5) Attrition Rate (+). In the regression model to predict the royalty rate, Royalty Rate is in direct proportion to Licensee Revenue and Attrition Rate and Royalty Rate is reverse proportion to Market Size, TCT median value and CAGR.
Royalty Rate = 9.997 + 0.063 * Attrition Rate + 1.655 * Licensee Revenue − 0.410 * TCT Median − 1.090 * Market Size − 0.230 * CAGR (Eq. 1)
In the case of the prediction of upfront payments, this study developed a prediction model like Eq. 2 having a Pvalue of 0.288 for estimating upfront payments if multiple independent variable like the attrition rate for the development phase, market size, CAGR, TCT median value for the IPC code (IP), and the revenue data of the license buyer are used. This is a “statistically not meaningful” finding. Thus, the above prediction model for upfront payments requires further study. Ranking of factors that influence the upfront payments is as follows: (1) Licensee Revenue (+) (2) Market Size (−) (3) TCT median value (−) (4) CAGR (−) (5) Attrition Rate (−). In the regression model to predict the upfront payment, Upfront payments is in direct proportion to Licensee Revenue and Upfront payments is reverse proportion to Market Size, TCT median value, CAGR and Attrition rate.
Upfront Payment (Upfront + Milestones) = 2.909 − 0.006 * Attrition Rate + 0.306 * Licensee Revenue − 0.74 * TCT Median − 0.113 * Market Size − 0.009 * CAGR (Eq. 2)
Conclusion
In royalty negotiations in the life sciences sector, a manager needs a simple tool to estimate the proper royalty rate and upfront payment. Indeed, developing the right valuation methods is very important for the pharmaceutical company, which wants licensing and M&A (Lee et al. 2016). It is also the reason why many pharmaceutical companies keep their valuation knowhow secret. This exclusivity sometimes hinders brisk licensing and M&A activities. Therefore, developing and sharing the right valuation methods cannot help one specific company’s licensing and M&A but can also help the development of licensing and M&A market itself. It related to realizing the merit of the open innovation, which assumes that sharing ideas can be advantageous for all players (Jeon et al. 2015; Leydesdorf & Ivanova, 2016; Oganisjana, 2015; Yun et al. 2016). For this purpose, we proposed the valuation tools.
First, this study yielded meaningful results to predict the royalty rate by the regression analysis using multiple input descriptors. But in the case of the prediction of upfront payments, it requires further study. This study provides the insight what would be the most determining factors to get appropriate license fee among several multiple factors like development phase, market size of subclass of a drug class, TCT median value (Technology Cycle Time) of IP, and the revenue data of the license buyer which can be expressed in numeric form.
Second, This study yielded meaningful results as it aimed to create a tool to predict royalty rate using knowledge on the development phase and its attrition rate, drug class, TCT median value (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 licensee’s revenue, which can easily be Known.
This study covers only one drug class of anticancer and will be extended to cover more drug classes in the future.
Implications
Valuation of drug specific to drug class can be possible and the royalty rate is in direct proportion to licensee revenue and attrition Rate and is in inverse proportion to Market Size, TCT median value and CAGR in specific drug class.
Topics for further research
Further indepth research is necessary for the following topics in the future.

1.
The relationship for other drug classes and royalty related data regression analysis using multiple input descriptors.

2.
The comparison of the estimation results between by using the prediction formula derived regression analysis Vs. by using traditional valuation methods like eNPV or Real Options.
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Acknowledgments
We would like to show our gratitude to Dr. Kee Heon Cho of Korea Valuation Association and Dr. TaeEung Sung and Dr. SangKuk Kim of KISTI for their guidance, and to KISTI for the data source provided.
Authors’ contributions
JHL primarily worked on the research. BKS, JWL, YI and WL participated in the design of the research and helped to perform the statistical analysis. TK conceived of the research and participated in its design and coordination as the corresponding author. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
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Lee, J.H., KheeSu, B., Lee, J.W. et al. Valuation method by regression analysis on real royaltyrelated data by using multiple input descriptors in royalty negotiations in Life Science areafocused on anticancer therapies. J. open innov. 2, 21 (2016). https://doi.org/10.1186/s4085201600477
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DOI: https://doi.org/10.1186/s4085201600477
Keywords
 Valuation
 Licensing deal
 Drug
 Royalty data
 Royalty rate
 Upfront fee
 Upfront 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
 PValue
 Prediction