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Optimizing Genomics Asset Management for High ROI - Evaluating Strategies to Support the Bio/Pharma Value Chain
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Description: |
The need to overcome the pharma industry’s pipeline productivity crisis is forcing the genomics innovators and their partners to reshape their portfolio management. New asset allocation strategies and valuation methodologies are needed to support the evolution to a new business model emphasizing investment in technological diversification.
This report considers the evolution of the genomics sector, through an evaluation of the trends extracted from the key licensing, drug development and M&A deals. We reveal the challenges lying ahead for both innovators and their partners. Vital steps to integrate newly diversified genomics assets into the bio/pharma value chain are introduced followed by a novel way to assess the value of genomics innovation. The new asset valuation platform is based on blending quantitative real option methodologies and qualitative multidimensional asset analyses.
Scope of report:
- Insight into the dynamics of the genomics sector, as it diversifies to meet the challenge of the evolving bio/pharma supply chain
- Survival strategies for potential postgenomics drug developers and successful exit strategies for failed genomics businesses
- Novel portfolio management and option based asset valuation to support postgenomics drug developers, genomics licensees and innovators
Highlights:
- The early wave of the genomics revolution witnessed a spectacular stock value decline. This was caused by the lack of transparency between the industrys innovating, investing and licensing arms, and by a high degree of speculation related to unrealistic market generation targets and lack of understanding of the technological complexities.
- The new wave of innovation is focusing on gene functionalities, population genomics, chemo- and pharmacogenomics. Drug developers should continue integrating well-defined genomics assets into their supply chains, work to maximize successful target validation and optimize alliance networks to ensure asset co-evolution and diversification.
- Conventional discount flow-based asset valuation cannot describe the complexity of each genomics asset. Therefore, blending quantitative real option based and qualitative multifactorial asset analysis models can offer flexible long-term asset allocation and a number of capital growth opportunities hidden in genomics assets.
Reasons to Purchase:
- Understand the dynamics behind the early genomics devaluation, and develop winning business survival and successful postgenomics exit strategies
- Gain insight into how the new wave of genomics diversification helps the bio/pharma supply chain to recover from its current productivity crisis
- Improve genomics project valuation by combining novel multidimensional asset analysis for less supply chain complexity, with real option based methods
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Contents: |
EXECUTIVE SUMMARY 8
Scope and structure 8
Facta non verba: stock devaluation of innovative players is unavoidable when based on speculation 9
Well diversified and specialized asset portfolios can enhance value 9
Blending options-based asset valuation and unfolding asset complexity with multifactorial structural modeling offers an alternative methodology for genomics valuation 10
CHAPTER 1 THE EVOLUTION OF GENOMICS BUSINESS STRATEGIES 11
Introduction 11
A rising pipeline productivity crisis 11
Transforming the 20th Century’s medicine to 21st Century Biomedicine 12
The evolution of the genomics-oriented sector 13
The genomics value matrix: evolving towards prosperity 13
Two evolution waves: from speculation to depression, realization and diversification 14
The early wave: from speculation to realization 17
Key factors that formed the early evolution wave 19
The early wave of alliance strategies 24
MLNM case study: Genomics assets used to boost integration 27
Bayer case study: Genomics assets not enough to boost growth 31
The early wave of M&A strategies 34
Merck case study: the acquisition of Rosetta 35
The new wave of genomics: investing in diversification 37
Supporting supply chain integration with diversification 38
Adding value through vertical integration – assessing recent deal value 40
Accessing the best opportunities: key recommendations for success in accessing genomics 42
Survival strategies for the horizontal genomics innovators 48
Exit strategies for failed horizontal genomics innovators 59
CHAPTER 2 THE EVOLUTION OF GENOMICS PORTFOLIO VALUATION 63
A novel view on the genomics business valuation landscape 63
What forces the need for exploring new asset valuation methods? 65
Evolving new genomics asset valuation methodologies 68
Revealing the role of complexity in genomics business valuation 73
Introducing a novel structural model to unfold genomics asset complexity 73
Adding more depth to the proposed structural model 77
Evolving the comlex dynamic asset structures 80
Evolving hybrid models: real options plus structural equation 84
CHAPTER 3 APPENDIX 89
LIST OF FIGURES
Figure 1: Approach to analyzing the genomics sector 8
Figure 2: NDAs and NMEs approved by the FDA (1996 – 2003) 11
Figure 3: Genomics perceived value matrix 13
Figure 4: The evolution of the Genomics’ index share value, January 1999- July 2004 15
Figure 5: The evolution of the Genomics business models according to asset and investment growth 16
Figure 6: The early wave of the Genomics market evolution 17
Figure 7: The key elements of the Genomics business model evolution 19
Figure 8: R&D investment trends of the US publicly traded Genomics sector, 1993-1999 20
Figure 9: Geographic distribution of Genomics firms 21
Figure 10: Genomics patent filing volume trends, 2002 22
Figure 11: Key trends from the early wave of genomics alliances 26
Figure 12: Development of Millennium’s strategic alliances during the early wave of alliances 28
Figure 13: Bayer’s genomics and genomics-related agreements 31
Figure 14: In 2010, Bayer is forecast to derive 71.8% of sales from mature drugs 33
Figure 15: Genomics’ Index for the new wave of innovation, 2002-04 37
Figure 16: The new wave of genomics assets and their contribution to the biopharma supply chain 38
Figure 17: Adding value through vertical integration: analysis of 12 recent genomics alliances by value 41
Figure 18: Key trends extracted from the new wave of alliances 44
Figure 19: Key trends extracted from the new wave of genomics M&A deals 50
Figure 20: Key extracted trends from the new wave of chemogenomics alliances 54
Figure 21: Key extracted trends from the new wave of pharmacogen-omics/enetics alliances 58
Figure 22: How realization and integration moves closer to capitalization 59
Figure 23: Nuvelo’s (merger of Hyseq and Variagenics) historical share prices 1999-2004 61
Figure 24: A generalized view on the genomics business landscape 64
Figure 25: Schematic representation of the main biotech asset valuation methods according to their ability to extract critical asset information for high ROI and the size of risk tolerability that they can undertake 70
Figure 26: Real options decision making in genomics value chain 72
Figure 27: Multidimensional approach in revealing the key elements that govern the valuation of each genomic asset. 73
Figure 28: Increasing depth in the asset valuation model using a number of sub-elements to support the basic ten-elements structural model of Genomics Success Index 77
Figure 29: Applying variation in genomics assets evolution to achieve high ROI 81
Figure 30: Applying variation in genomics assets evolution to achieve high ROI 83
Figure 31: The dynamics of biotech innovation: from a tendency towards high-ROI asset clusters within the biotech business model to the formation of biotech company clusters 84
Figure 32: Evolving the “fittest” assets or identify your competitor’s advantages 85
Figure 33: Genomics Success Index linked to real option methodology 86
Figure 34: Evaluation of each stage of the option decision process during a genomics-driven drug development project 87 |
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The Evolution of Genomics Business Strategies
The evolution of the genomics-oriented sector
The genomics value matrix: evolving towards prosperity
Figure 3: Genomics perceived value matrix (Shown in Final Report)
Our strategic analysis has identified four different “environments” that affect genomics oriented business model evolution. These include:
suppression (perceived low risk-low return environment: limited to low profitmargin growth);
depression (perceived high risk-low return: stock devaluation, increasing losses and absence of growth);
prosperity (perceived high to medium risk-high return: sustainable growth and increasing investor confidence);
Using a cost of capital of 7% per annum and discounting by 10 years, Merck would need to generate additional profits of $890.44m (452x(1.07)10) for the acquisition of Rosetta to be cost-effective. Any further profits realized after the first $890m would mean that the acquisition was overall beneficial (excluding the opportunity cost of investing in other forms of R&D). Even for a company like Merck, additional profit of $900m is a substantial target. Assuming, therefore, that Merck’s pharmaceutical products generally offer an operating profit margin of approximately 30%, and will continue to do so, Merck will need to generate additional revenues of at least $1,514m from products derived from Rosetta’s technology. This is equivalent to bringing at least one or, more realistically, two or three successful products to market as a result of the Rosetta purchase.
The new wave of genomics: investing in diversification
Figure 15: Genomics’ Index for the new wave of innovation, 2002-04 (Shown in Final Report)
Looking at our Genomics index in the past two years, the new wave of genomic innovation is split into a period of “realization” between January 2002 and January 2003 followed by a period showing some early signs of recovery related to
The Evolution of Genomics Portfolio Valuation
Figure 26: Real options decision making in genomics value chain (Shown in Final Report)
The first option includes the innovator’s or its partner’s decision to invest in a wide selection of genomics tools that can help the players identify new drug targets. Within
this option the project manager can decide to halt the project and not to proceed to the next option or wait until the opportunity arises to move on. It is very important to
note that as the players or partners move from option to option the value of the project should be higher. The second option includes investing in the exploration of human databases and gene functionality-oriented assets that will enable the validation of the identified target. The third option includes the right to invest in assets that can identify certain indications where the validated target could provide useful leads and drug development opportunities. At each stage, the innovator and its partner hold the right to halt or wait for other opportunities before moving on to the next option.
It is should also be noted that during a partnered project where two players, a genomics innovator and its vertically integrated licensee are gathered together to
evolve and increase the value of their project, the common use of the same optionbased model is vital. Failing to operate using the same valuation method, supported by the highest degree of transparency in knowledge exchange, can severely damage the options-based decision process.
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