# 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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## 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.

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.

### Keywords

Valuation Licensing deal Drug Royalty data Royalty rate Up-front fee Up-front Payment Milestones Regression Drug class Anticancer Antineoplastics Attrition rate Development phase Licensee Life sciences rNPV eNPV (expected NPV) DCF Multivariable analysis IPC code TCT median value Market Size CAGR IP Revenue Multiple input descriptor Significance level*P-*Value Prediction

## Introduction

### R&D productivity in life sciences and “fail fast, fail cheaply” strategy

### Licensing as good strategy

### 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 up-front 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 up-front 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

Early-stage technology companies

Company | Technology | Disease area | R&D status | Market capitalization |
---|---|---|---|---|

StemCells | Cell therapy | Diabetes, Parkinson’s | Preclinicals | $55 million |

Transition Therapeutics | Biopharma | Diabetes | Phase I | $78 million |

Alteon | Biopharma | Diabetes, Aging | Phase II, Preclinicals | $69 million |

Aradigm | Medical devices | Diabetes | Phase II, III | $134 million |

Aastrom Biosciences | Cell theraphy | Oncology, Dermatology | Phase I | $83 million |

Emisphere Technologies | Medical devices | Diabetes, Blood system | Phase I, II, III | $109 million |

NeoPharm | Biopharma | Oncology | Phase,I, II | $476 million |

ConjuChem | Biopharma | Deabetes, AIDS, CHF | Phase II | $589 million |

Spectrum Pharma | Biopharma | Oncology, Neurology | Preclinicals | $79 million |

Ergo Sciences | Biopharma | Diabetes | Technology sold | $15 million |

The expected effect on patent value according to pharmaceutical industry related factors

Variable | Definition | Expected effect on Patent value | Date source |
---|---|---|---|

CRECEIVE | Number of citations received | + | NBER |

OPPOSITION | The occurrence of opposition (1:yes; 0: no) | + | INPADOC |

CLAIMS | Number of claims | + | NBER |

CMADE | Number of citations mode | + | NBER |

BLOCKBUSTER | Blocbuster drug (1:yes; 0: no) | + | PHARMADL |

PORTFOLIO | Number of patents in a patent portfolio | + | FDA |

NDS | New dosing schedule (1:yes; 0: no) | Unknown | FDA |

NI | New indication (1:yes; 0: no) | Unknown | FDA |

NC | New combination (1:yes; 0: no) | + | FDA |

NCE | New chemical entity (1:yes; 0: no) | + | FDA |

NDF | New dosage form (1:yes; 0: no) | + | FDA |

NP | New product (1:yes; 0: no) | + | FDA |

NS | New strength (1:yes; 0: no) | Unknown | FDA |

OD | Orphan drug (1:yes; 0: no) | - | FDA |

PD | Pediatric drug (1:yes; 0: no) | - | FDA |

GYEAR | Grant year | - | NBER |

## Research design and scope and limitation

### Research design

This research analyzes the anticancer (antineoplastics) dataset, including the royalty-related data like running royalty rate and up-front payment, regarding drug candidates for specific drug class of anticancer, by regression analysis between royalty-related 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 royalty-related 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 royalty-related data such as running royalty rate (back-end payments) and up-front payment (up-front 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 x-axis of regression.

#### Step 1. Collection of data such as the running royalty rate, up-front 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 up-front payment (up-front fee + milestones) and back-end payment (running royalty rate) to prepare the dataset ready for regression are described in our previous paper (Lee et al. 2016).

#### 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 up-front payment (up-front 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 back-end payment (running royalty rate)

Used software: IBM SPSSS Statistics Version 21

Regression 1: X-axis = 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

Y-axis = up-front payment (up-front fee + milestones) [Unit: USD]

Regression 2: X-axis = 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

Y-axis = back-end payment (running royalty rate) [Unit: USD]

### Scope and limitation of research

The scope of this research is to derive the formula to predict royalty-related data, such as running royalty rate (back-end payments) and up-front payment (up-front 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 royalty-related 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 X-axis of regression. This study is limited to the relationship between one drug class of anticancer (antineoplastics) and royalty-related 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 e-NPV or Real Options.

## Analysis of dataset

### Analysis of anticancer (antineoplastics) dataset

## Regression analysis

### Regression analysis of anticancer (antineoplastics) dataset

*P-*Value: 0.001).

*P-*Value = 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.

*P-*Value: 0.288), thus requiring further study.

*P-*Value = 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

*P-*Value: 0.288).

## Discussion

*P-*value 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

*P-*value 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 up-front 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 *P-*value 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 % (*P-*Value: 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 up-front payments, this study developed a prediction model like Eq. 2 having a *P-*value of 0.288 for estimating up-front 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 up-front payments requires further study. Ranking of factors that influence the up-front 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 up-front payment, Up-front payments is in direct proportion to Licensee Revenue and Up-front payments is reverse proportion to Market Size, TCT median value, CAGR and Attrition rate.

Up-front Payment (Up-front + 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 up-front 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 know-how 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 up-front 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

- 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 e-NPV or Real Options.

## Notes

### Acknowledgments

We would like to show our gratitude to Dr. Kee Heon Cho of Korea Valuation Association and Dr. Tae-Eung Sung and Dr. Sang-Kuk 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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