050616ISP

The valuation of part-developed projects in the pharmaceutical sector

Masters of BioScience Enterprise Sangeeta Puran Fitwilliam College June 2005

Cambridge Healthcare & Biotech Ltd 18 Duxford Road Whittlesford Cambridge CB2 4ND, UK Tel: Fax (Europe):

+44 (0) 1223 839503 +44 (0) 870 751 7505

http://www.chandb.com e-mail: info@chandb.com Registered in England number 4700145 registered office as above

Masters of BioScience Enterprise

Sangeeta Puran Fitzwilliam College

The valuation of part-developed projects in the pharmaceutical sector

Sponsored by:

Cambridge Healthcare and Biotech

Company Supervisor Martyn Postle

University Supervisor

Jochen Runde

Date of Submission 16th June 2005

Word Count

10256 (Excluding all material prior to page 9, figures, tables, references, and appendices)

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Do you give permission for a PDF version to be placed on the Intranet, where only members of the Institute of Biotechnology can download it?

Signed by Student: Signed on behalf of Company

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Preface

The study reported in this dissertation was undertaken from the offices and under the supervision of Cambridge Healthcare and Biotech of 18 Duxford Road, Whittlesford, Cambridge, CB2 4ND, UK, from the period of 11 April to 27 May 2005. None of the work contained in this dissertation has been submitted for any other degree. This dissertation expressly acknowledges those who have helped with or sponsored this study. The work submitted under the auspices of this dissertation consists of the author’s own work.

Confirmed by the author Sangeeta Puran

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Acknowledgements relating to this study

I would like to thank Martyn Postle from whom the idea of this study originated – and to the rest of the Cambridge Healthcare and Biotech team. I am extremely grateful for all your support and for making me feel so at home on Duxford Road.

I would also like to thank Jochen Runde, my academic supervisor on this study.

To Dr Geraldine Rodgers, thank you for all your hard work in organising the internship programme.

I would also like to express my utmost gratitude to all those who participated in this study, including Chandu Ammini, John Aston, Stewart Atkins, Dr Sally Bennett, Sally Brashears, Anthony Gemmell, Alla Grabovska, Enda Gribbon, Paul Hodgkinson, Tom Kassberg, Stuart Kynoch, Alan Lamont, Jonathan Lane, Richard Mason, Richard Parkes, Alex Pasteur, Jonathan Pearce, Dr Cathy Prescott, Stephen Thompson, Richard Wehby, Rupert Winckler and the rest of the participants who could not be mentioned for confidentiality reasons.

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Acknowledgments relating to the MBE programme

I would like to thank Professor Chris Lowe, Dr Jim Murray, Dr Geraldine Rodgers and Ms Sabine Deering and the lecturers involved in the programme. You have provided a truly unparalleled learning experience.

To my peers on the programme, it has been an honour. I look forward to hearing all about your exciting careers and lives from this moment onwards.

Above all, I would like to thank Drazen for making me tea when I needed it most, and patiently waiting for me to finish.

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Abstract

This study considers the approach used by biotech business development to value part-developed projects (as defined by this study), and the extent to which such differs from the approach of pharma business development, analysts and venture capitalists. A combined quantitative and qualitative approach was employed, using interview (face to face and by telephone) and survey techniques. Our findings indicate the risk-adjusted net present value, discounted cashflow and comparables comprise the methodologies most used by all groups to value part-developed projects. Notwithstanding the application of common methodologies by all groups, differences were found between biotech business development and other groups in valuation proficiency. Differences were also observed between biotech business development and the other groups in respect of the application of specific valuation parameters (e.g. discount rates, forecast periods, expenditure assessment) and the qualitative factors and subjective criteria which its relevant partners and investors considered when determining the worth of part-developed projects. On the basis of these findings, recommendations are made to biotech firms on valuation issues to be considered when dealing with commercial partners and investors.

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Executive Summary

This study was conducted from 11 April 2005 to 27 May 2005 in conjunction with Cambridge Healthcare and Biotech. The broad objective of this study was to consider the methods currently used to quantitatively value drug development programmes falling within part-developed projects as defined by this study; to examine the different ways in which these methods are applied and hence understand from the perspective of biotech firms, how different values of a part-developed project can be arrived at. The study involved participants, comprising biotech business development (biotech), pharma business development (pharma), financial analysts (analysts) and venture capitalists (VCs) involved in the valuation of part-developed projects. A combined quantitative and qualitative approach using interview (face to face and by telephone) and survey techniques was employed. Risk-adjusted net present value was the method most used by all groups (including biotech) except for VCs. However, unlike other groups, biotech’s preference was driven largely by a lack of familiarity with less conventional methods such as real-options and Monte Carlo simulation. Biotech adopted the same approach (i.e., the bottom-up approach) as the other groups in terms of assessing sales potential. Biotech, together with pharma and VCs, differed from analysts in the period over which cashflows were assessed. In particular, the analyst period did not commence until launch, consistent with the finding that analyst interest primarily lies in projects in advanced clinical development stage. Biotech also differed from pharma insofar as the detail expended in assessing downstream expenditures (e.g., marketing and promotional expenditures). In this regard, pharma hold informational advantages enabling more detailed assessments of such expenditures. In terms of discount rates, biotech used higher rates than pharma and analysts, and lower rates than VCs. Economic parameters (i.e., market size and pricing) posed the greatest sensitivities insofar as NPV modelling by biotech, suggesting greater scope for the application of real-options, which the literature describes as having been specifically developed to address economic sensitivities. Based on the key findings of this study, recommendations are provided to biotech firms in respect of developing proficiency across a wider range of methods and understanding the quantitative parameters and qualitative factors that commercialisation partners and investors are likely to be most influenced by when negotiating deal values.

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Table of ContentsPage

Chapter 1 – Outline……………………………………………………………................................... 9 1.1 Definition of part-developed project …………………………................................. 9 1.2 Objectives and structure………………………………………………………….. 9

Chapter 2 – Introduction to valuation methods……………………………………………………... 13

2.1 Discounted cashflows……………………………………………………………... 13 2.2.1 Forecasting cashflows……………………………………………………………... 13

(a) Forecast period…………………………………………………………………… 15 (b) Investments……………………………………………………………………….. 16 (c) Revenues………………………………………………………………………….. 18

2.2.1 Discounting……………………………………………………………………….. 21 2.2 DCF critiqued…………………………………………………………………….. 25 2.3 DCF does not properly account for technical risk………………………………… 25

2.3.1 Limitations of rNPV……………………………………………………………… 28 2.4 DCF does not account for different outcomes……………………………………. 28

2.4.1 Scenario analysis………………………………………………………………….. 29 2.4.2 Discount-tree modelling…………………………………………………………... 29 2.4.3 Monte Carlo simulation…………………………………………………………... 31 2.4.4 Limitations……………………………………………………………………….. 33

2.5 DCF does not properly account for the value of managerial flexibility in the face of economic uncertainty……………………………………………………………...

33

2.5.1 Valuation of options ……………………………………………………………... 34 2.5.2 Limitations of real-options……………………………………………………….. 39

2.6 NPV modelling is theoretical……………………………………………………... 39 Chapter 3 – Methodology…………………………………………………………………………… 41

3.1 Participants……………………………………………………………………….. 41 3.2 Analysis…………………………………………………………………………… 43

Chapter 4 – Findings………………………………………………………………………………... 44

4.1 Methods used to value part-developed projects…………………………………… 44 4.2 NPV parameters………………………………………………………………….. 50

4.2.1 Revenue – Forecast of sales potential…………………………………………….. 50 4.2.2 Forecast period…………………………………………………………………… 52 4.2.3 Investments – Forecast of expenditure …………………………………………… 56 4.2.4 Discount rate……………………………………………………………………... 59 4.2.5 Probability of success…………………………………………………………….. 63 4.2.6 Sensitivities………………………………………………………………………. 64

Chapter 5 – Conclusion…………………………………………………………………………….. 67 References………………………………………………………………………………………….. 72

Appendix 1 …………………………………………………………………………………… 75 Appendix 2 …………………………………………………………………………………… 76 Appendix 3 …………………………………………………………………………………… 79 Appendix 4 …………………………………………………………………………………… 81 Appendix 5 …………………………………………………………………………………… 83 Appendix 6 …………………………………………………………………………………… 90

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List of Figures and Tables

Figures Page Figure 1 A summary of the stages of drug development……………………………………… 10 Figure 2 A project lifecycle curve from a cashflow perspective……………………………….. 14 Figure 3 Example of standard sales evolution curves…………………………………………. 20 Figure 4 Example of DCF calculation for Project X………………………………………….. 24 Figure 5 Example of rNPV calculation for Project X…………………………………………. 27 Figure 6 Outline of decision-tree modelling of the impact of technical and success ………….. 30 Figure 7 Outline of Monte Carlo simulation………………………………………………….. 32 Figure 8(a) Example of Real-options calculation for Project X: Step 1 – Construction of binomial

tree………………………………………………………………………………….. 36

Figure 8(b) Example of Real-options calculation for Project X: Step 2 – Calculation of the value of end states …………………………………………………………………………

37

Figure 8(c) Example of Real-options calculation for Project X: Step 3 – Calculation of the value of internal states……………………………………………………………………...

38

Figure 9 Distribution of participants by reference to the following groups: biotech, pharma, analyst and VC……………………………………………………………………….

42

Figure 10 Methods used by biotech, pharma, analysts and VCs to value part-developed projects 44 Figure 11 Approach used by biotech, pharma, analysts and VCs to forecast sales potential…….. 50 Figure 12 Forecast periods used by biotech, pharma, analyst and VCs in NPV based methods 52 Figure 13 Level of detailed assessment by biotech of expenditures relevant to NPV modelling 56 Figure 14 DRs used by biotech, pharma, analysts and VCs in NPV modelling…………………. 59 Figure 15 The NPV methods (rNPV and DCR) and the discount rates used by biotech………. 62 Figure 16 Sensitivities relevant to NPV modelling by biotech…………………………………. 64 Figure 17 The effects of discount rate changes on capitalised pre-clinical, clinical and total costs

per approved new drug ……………………………………………………………. 80

Figure 18 Example of detailed cashflow ………………………………………………………. 82 Figure 19 Probability of success rates used in NPV modelling…………………………………. 96

Tables Table 1 Assumptions relating to post-clinical marketing costs……………………………….. 17 Table 2 Advantages of NPV based methods methods ……………………………………… 91 Table 3 Disadvantages of NPV based………………………………………………………. 92 Table 4 Advantages of Comparables………………………………………………………… 93 Table 5 Disadvantages of Comparables……………………………………………………… 93 Table 6 Probability of success rates ………………………………………………………… 96

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Chapter 1 Outline

Drug development follows a sequence of distinct stages, each of which aims to generate “economically valuable specific knowledge” about the drug candidate in question (Arojarvi, 2001). In this way, the implementation of drug development1 programmes generates intellectual assets capable of transfer (Rzakhanov, 2004). Determining the monetary worth of such intellectual assets, though complicated, is central to internal research programme prioritisation, licensing negotiations, investor funding decisions and equity analysis in the pharmaceuticals sector. A range of methods, each with differing computational complexities and limitations, can be used to assign a value to development programmes. However, complex science, long development times, high risk of technical failure and changing regulatory and market conditions, make valuing development programmes a significant challenge. The principal objective of this study is to consider the methods currently used to value development programmes comprising part-developed projects as defined in Section 1.1 below; to examine the different ways in which these methods are applied and hence understand how different values can be arrived at. 1.1 Definition of “part-developed project” For the purposes of this study, a part-developed project is defined as a development programme that is yet to complete all the following stages of the well defined paradigm of drug development: discovery, pre-clinical, phase I, phase II, phase III, regulatory review and commercial launch. Once the product in development is launched, the programme is taken to fall outside the definition of a part-developed project. Each of the relevant stages is summarised in Figure 12.

1 Drug development in this context is taken to include both drug discovery and development. 2 Figure 1 is based on Charnes et al (2000) and DiMasi et al (2003). A more detailed overview is provided in Appendix 1

Purpose

Totalpopulation

Years

Launchcommercial salesof drug product

Obtain regulatoryapproval tosupply interritories ofinterest

Establish statisticallysignificant efficacyand monitorpossible adversereactions

Test drug candidate inpatients. Verify safetyand obtain preliminaryefficacy data

Establish safedosages andassess metaboliceffects

Assesssafety andefficacy profile

Discover drugcandidate

1000 to 3000patient volunteers

100 to 300 patientvolunteers

20 to 80 healthyvolunteers

Laboratory andanimal studies

Laboratory studies

1-22-41-21-2

Commerciallaunch

Regulatoryreview

Phase IIIPhase IIPhase IPre-clinicalDiscovery

Clinical trials

Figure 1 - A summary of the stages of drug development

Early stage

Duration difficult to estimate

To this end, the focus of this study is on projects involving the development of prescription pharmaceutical products. Projects involving the development of medical devices and platform technologies are not considered. 1.2 Objectives and structure Most valuation methods stem from the traditional net present value method, commonly referred to as the discounted cashflow (DCF) method. This study considers DCF and the following other methods stemming from DCF, commonly cited3 as relevant to valuing part-developed projects: risk-adjusted net present value method (rNPV); scenario analysis; decision-tree modelling; Monte Carlo simulation; real-options and comparables. The Economist4 recently reported that biotech firms in 2004 accounted for more new drug approvals from the U.S. Food and Drug Administration than did large pharmaceutical firms, highlighting the importance of part-developed projects sourced from the biotech sector. There are now more than 4,400 biotech firms in the world, the vast majority still privately owned5.

The specific objectives of this study are to consider: • The methods used by biotech firms to value part-developed projects; • The application of parameters relevant to the predominant methods used by biotech

firms to value part-developed projects; and • The key differences in the valuation approaches between biotech firms and large

pharma firms, financial analysts and venture capitalists. The findings of the study will contribute to the information currently relied upon by investors to assess the value proposition offered by funding drug development programmes, and by senior management of biotech firms to prioritise projects that are most likely to

3 Bennett et al 2004, Borissiouk, 2001, Charnes et al, 2000, Stewart, 2002 4 The Economist June 4th 2005, p.79 5 Refer to the footnote above.

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maximise firm value. These findings will also be relevant to understanding the commercial value attributed to part-developed projects in licensing and other commercialisation arrangements with commercial partners, and to financial analysts assessing the implications of part-developed projects on equity prices and public flotations. Following this outline, Chapter 2 introduces the relevant valuation methods. The presentation below commences with an introduction to DCF, followed by a critique of its limitations and the alternate methods that have been developed to overcome such limitations. Chapter 3 describes the methodology of this study. Chapter 4 presents the findings and Chapter 5 concludes by presenting recommendations to biotech firms based on such findings.

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Chapter 2 Introduction to valuation methods6

2.1 DCF Valuation generally involves either a market, cost or income approach. By focusing on the cashflow opportunities of a project, DCF methodology incorporates the income approach. In short, the future revenues and investments7 associated with the project are modelled into a net cashflow, which is then discounted in accordance with finance theory to derive the net present value of the project.

2.1.1 Forecasting cashflows Modelling cashflows involves forecasting investments and revenues over a forecast period. Figure 2 presents the typical project lifecycle from a cashflow perspective8. During the early research stage, project cashflows tend to be negative. Early stage research can take several years, but is not as expensive as the clinical trial stages. The product is launched upon regulatory approval being issued, followed by relatively fast market penetration. A stable period of revenue generation follows. Finally, revenues start to decline as patent expiry approaches, with rapid decline on actual expiry. A consequence of the project lifecycle is that while the term of patent protection usually lasts 20 years, the effective period of monopoly is reduced to the patent term remaining following the issuance of regulatory approval9.

6 The reader should note that the field of valuation is extensive and the presentation below is not intended to be an exhaustive discussion of valuation theory. 7 Investments in this regard are taken to mean all costs and expenditures associated with drug development. 8 Adapted from Arojarvi (2001) 9 Note that there is significant commentary arguing that the project lifecycle can be managed by ‘evergreening’ practices (e.g., seeking additional patent protection on the basis of product enhancements to in effect extend patent term).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27Cash

flows

Years

Early research

Advanced clinical trials

Patent period Patent expiry

Figure 2 - A project lifecycle curve from a cashflow perspective

Adapted from Arojarvi (2001)

Launch

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(a) Forecast period

Financial models can vary on how far into the project lifecycle they forecast. Arojarvi (2001) notes patent expiry is a common endpoint. The launch of generic versions after patent expiry is a key consideration in whether or not to use patent expiry as an endpoint. Recent history contains examples of rapid market share erosion of blockbuster drugs following generic launch. A famous example is the case of Eli Lilly whose US patent for its antidepressant ProzacR, expired in 2001. This paved the way for the regulatory approval of Barr Laboratories’ generic version, Fluoxetine. ProzacR reportedly lost 73% of market share within two weeks of generic launch (Tuttle et al, 2004)10.

The impact of generic competition can vary. Tuttle et al (2004) suggests market share erosion depends on product attributes (e.g., the degree of manufacturing difficulty)11.Chandon (2004) suggests that a product may operate in a niche category that is too small, or with a brand presence too strong, to attract competition upon patent expiry. In the case of biologics, whether there can be generics that are directly substitutable for patented biologics is yet to be resolved. Bennett et al (2004) also considers the possibility of a product experiencing revenue decline due to competition before patent expiry. The endpoint of a forecast period may also be selected as the point beyond which information required to forecast is unavailable or unreliable (Frei et al, 2004). Selecting an endpoint ultimately remains arbitrary and at such endpoint, the question of residual value arises12.

10 The following commentary, released at the end of 2004, contains even more recent examples: “… GlaxoSmithKline… and AstraZeneca … faced patent expiry dates for some of their biggest selling drugs and the subsequent generic competition has torn apart sales. AstraZeneca’s blockbuster stomach ulcer drug Prilosec saw sales drop by 70% in 2003. The company lost $2.6 billion in sales to generic competition. Rival Glaxo is facing a difficult 2004 as the brunt of generic erosion of antidepressant Paxil bites. The company admitted 40% of Paxil sales were lost within weeks of the launch of generic competition.”

[Carter Nield of OrbiMed Advisors, accessed on 22 May 2005 from http://www.sharesmagazine.com/sharesmag/edition59/?page=feature1] 11 Tuttle et al (2004) also refer to generic competition being dependent on therapeutic class (e.g., the extent to which there are a significant number of generic players already in a therapeutic class). 12 A terminal value may be calculated to assess such terminal value. Refer to Section 2.1.2 below.

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(b) Investments According to DiMasi et al (2003), the average capitalised out of pocket investment for developing a new product to the point of regulatory review is $US 802M13, which is based on totalling the costs found by the authors to be incurred in pre-clinical development ($US 335M14) and clinical development ($US 467M15). The authors also highlighted potential investment variation depending on the therapeutic indication in question, with increased costs likely to be associated with chronic and degenerative diseases16. Such variation is driven by the number of patients needed in a clinical trial and by the treatment costs per patient (e.g., outpatient versus intensive care treatment, cost of diagnostic procedures and co-medications, durations of treatment and requirements of follow-up).

13 The figure is provided in 2000 dollars and is based on information provided by firms, which at the time of the study, accounted for 42% of the US pharmaceutical industry research and development. The estimated average out of pocket costs per drug is $US 403M (2000 dollars). Capitalising out of pocket costs to the point of marketing approval at a real discount rate of 11% yields a total pre-approval cost of $US 802 M (2000 dollars). Appendix 2 contains a graph reflecting the effect of changes in discount rates. 14 Pre-clinical costs were taken to include all costs incurred from the commencement of a project up to completion of the pre-clinical stage. The authors found that assessment of pre-clinical costs is made difficult by the fact that pre-clinical costs are commonly incurred as part of wider research and development programme involving multiple projects. However, by construing a pre-clinical to clinical costs ratio, the authors inferred average pre-clinical out of pocket capitalised costs per approved drug of $US 335M. 15 Aggregating across all clinical phases, DiMasi et al (2003) found out of pocket capitalised clinical costs per approved drug to be in the order of $US 467M. Clinical costs include costs relating to trial design, patient recruitment15, investigator and clinician costs, monitoring costs, data analysis, close out and reporting results, infrastructure costs, and costs related to the production of the clinical trial supplies and animal testing during the clinical period. 16 Bode-Greuel et al (2005) also observed that the costs for clinical development beyond phase I are difficult to estimate because they differ considerably depending on therapeutic indications.

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The costs of regulatory submission17 aside, the projection of post-clinical marketing costs tend to be based on conventional assumptions such as those outlined in Table 1. Nevertheless, it is necessary to consider the specificities of the target market (Bode-Greuel et al, 2005). For instance, hospital products are characterised by lower marketing costs than products promoted to specialists or primary physicians18.

Table 1 – Assumptions relating to post-clinical marketing costs

Item Assumption

Cost of revenue 25.5 % of revenue

Marketing expense Year 1 after launch Year 2 after launch Years 3–4 after launch Years 5–13 after launch

100 % of revenue 50 % of revenue 25 % of revenue 20 % of revenue

General and administrative expenses 11 % of revenue

Tax rate 35 % of profit

Working capital 17 % of revenue

Source: Charnes et al (2000)

17 These costs are determined by on a territorial basis. In general, most drugs will aim for U.S. Food and Drug Administration (FDA) approval. The costs of seeking FDA approval are estimated at $US 0.8 -1.8M + $US300,000 for the Prescription Drug User Fee and the remainder for the preparation of the New Drug Application. Preparation costs are highly variable and depend largely on the amount and the quality of data to be presented. (Frei et al, 2004). 18 The increase is driven by the required number of physician contacts. Costs may also depend on the competitiveness of the market, such as in the case of the oncology market, where marketing expenses have considerably increased in the last few years due to increasing numbers of companies becoming active in the field.

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(c) Revenues

The discussion of revenue forecasting is based on the approach presented by Porter (1993). Porter’s assessment of the potential revenues associated with a project comprises three limbs: size of target market, market share likely to be penetrated by the product in development and subsequent market growth. Starting with the assessment of market size, Porter outlines the “bottom-up” and “top- down” approaches. The bottom-up approach19 calculates market size as follows: Market Size = Number of patients * Number of patients receiving treatment * Price

of treatment per patient While incidence and prevalence information can usually be obtained from national health statistics, not all patients are necessarily diagnosed or treated. Price estimation, as will be considered shortly, is also complex. In contrast, the top-down approach20 extrapolates from existing sales data of product(s) in the same therapeutic class as the product in development. Relevant sales data can be obtained from information services21 and competitor firm reporting. The second limb of revenue forecasting considers market share. Porter identifies pricing, competition, dosage and formulation, efficacy, sales detailing and patient product loyalty amongst the factors influencing a new product’s ability to penetrate the identified market. The distinction between volume market share (based on number of treatments) and value market share (based on sales value) is also relevant to the evaluation of market share. Bennett et al (2004) also notes that the most reliable way to estimate market share is to perform quantitative market research22.

19 Also referred to as an epidemiological-based approach 20 Also referred to as a market-based approach 21 For example, IMS [refer to www.imshealth.com] 22 The author states that this ascertains the likely demand for the product based on its attributes such as efficacy, dosing, side-effect profile and so on. The market research can range from fairly basic, such as interviewing a handful of clinicians, up to conducting several hundred physician interviews to power what is known as a conjoint model.

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Pricing is a key consideration in revenue forecasting. Earl (2003) provides an illustration of the complexities relevant to pricing. The author begins with the observation that novel products, that are more efficacious than existing products, are typically priced at a premium. However, the number of participants who will switch to a more expensive product also needs to be determined. Further, during the forecast period, other products may lose patent protection and become subject to generic competition. Patient switch again needs to be considered. The preceding is by no means exhaustive of the factors relevant to pricing (e.g., reimbursement policies are often cited in pricing discussions). The third limb of revenue forecasting considers market growth. The current market growth will only be a guide to future growth prospects. The factors behind market growth need to be identified and again, the distinction between sales volume growth and sales value growth is relevant. Sales volume growth will be affected by changes in population growth, spread of an illness, frequency of occurrence, frequency of diagnosis, and treatment practice. Sales value growth will depend on changes in pricing and product mix (older products may have significantly lower prices than newer, more efficacious ones).

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Falling outside of Porter’s presentation is the use of standard sales evolution curves to forecast revenues. By looking at historical peak sales of drug products, the rate of ramp-up to peak sales and the rate of market erosion, different sales evolution curves have been developed. Figure 3 illustrates a sample of such curves. An obvious limitation of such approach is that the sales lifecycle of the product in development may not fit within the limited standard curve options offered.

Figure 3 – Example of standard sales evolution curves

0

100

200

300

400

500

-3 -1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31Year from launch

Sales

US$

milli

on

F1: fast ramp fast erosion F2: fast ramp medium erosion S1: slow ramp fast erosionS2: Slow ramp medium erosion R: Tail product, growth T: Tail product, stable

F1

F2S1

S2

R

T

Source: Lehman Brothers23

23 Figure is provided as part of an internal confidential document. Accordingly, the relevant document containing such figure is not referenced in the Reference section.

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2.1.2 Discounting An amount of money received today is worth more than the same nominal amount of money received in the future. Conversely, money received tomorrow is worth less than a dollar received today. Application of this principle to forecasted cashflows means that not only are future revenues worth less today than in the future, but also future investments will “cost” less today. Finance theory requires that a discount rate (DR) be used to translate the future cashflows of a project to today’s value. From the perspective of investors financing a drug development project, the DR reflects the opportunity cost of capital. From the perspective of the entrepreneurs seeking to fund such project, the DR reflects the cost of capital. The textbook computations of the DR to be applied to a project (rProject) use the capital asset pricing model (CAPM), which prescribes the following:

rProject = rF + βProject * (rM- rF)Where:

• rF is the risk–free rate; • βProject is the beta value of the project; and • rM-rF is the difference in the expected return on the market and the risk-free

rates. rF represents investor requirements that a project generate at least the same return as would be expected from investing in risk-free investments24. CAPM also assumes that a risk premium (i.e., rM - rF) is requested by investors for accepting the risk to invest in assets whose value is highly volatile. The risk premium in the case of the project in question is given an appropriate weight through βProject

25.

Although CAPM is not the only way of determining DRs26, it is a framework allowing the DR of a project to be derived in an objective and replicable way. Notably, Randerson (2001) observed that none of the methods for calculating DRs would generate the DRs 24 In practice, this tends to be based on short term treasury rates. 25 The beta is company specific and describes the co-variance of the company’s equity with the market 26 The weighted average cost of capital (WACC) is also cited in discussions of DR assessment. This model typically deals with firm DRs. If the firm has issued debt, an extension to CAPM is applied, reflecting the usually lower requirement of debt. Please refer to Bode-Greuel et al (2005) for further discussion.

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(in the 30% - 70%27 range) typically advocated by venture capitalists (VCs). VCs tend to use DRs that represent the internal rate of return (IRR) expected by their fund investors. While the IRR does not qualify as a DR in the strict textbook sense, their use by VCs is well established. The reasons provided by VCs for applying such high DRs include the high risk associated with drug development investments, the low liquidity of the underlying intellectual asset, and compensation for management services provided by VCs (Dittman et al, 2000). Once a DR has been identified, the present value of each time point of the net cashflow is calculated as follows:

PVt = Ct /(1 + rProject)t

Where: • Ct is the net cashflow at period t (usually yearly intervals).

If residual value is considered to subsist in a project beyond the forecast period, a terminal value for the project can be calculated as follows28:

Terminal valueproject = CEP / rProject (1 + rProject)tEP

Where: • CEP is the cashflow as valued at the period representing the endpoint of the

forecast; and • tEP is the period at such endpoint.

27 Average DRs applied by VCs as published by the Harvard Business School:

• Start up: 33-46% • First stage: 26-39% • Second and third stage: 23-33% • Fourth stage: 20-26% • IPO: 16-23%

Average DRs required by VCs in the UK as published by the London School of Business: • Seed: 33-67 • Start up/early stage: 27-49 • Expansion/later stage: 20-34% • Buy-out: 17-23%

(Randerson, 2001) 28 Multiples methodology may also be used to calculate a terminal value. Given the limited scope of the present discussions, this methodology will not be considered here, however, relevant discussions can be found in most finance texts.

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Finally, the net present value (NPV) of the project is the sum of the present values at each time point and, where relevant, the terminal value – i.e.,:

NPVproject = Co + C1 /(1 + rProject)1 + C2 /(1 + rProject)2 + … + CEP/ (1 + rProject)tEP +CEP/rProject (1 + rProject)tEP

A positive NPV is usually taken to indicate that a project is likely to create value and is worth funding (with preference for higher NPV projects), with a negative NPV usually indicating the converse. As indicated above, the field of valuation is computation intensive. To avoid undertaking the present discussion completely in a theoretical vacuum, examples are provided. Figure 4 considers the application of DCF to a published example, which will hereafter be referred to as Project X (Villiger et al, 2005).

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Figure 4 – Example of DCF calculation for Project X

The following example presents a very basic cashflow forecasted on a yearly basis for a pharmaceutical project right before the start of phase II, which isscheduled to last two years. The model assumes that the phase III trial will take three years, after which an application for regulatory approval can be submitted,with approval to follow after one year. Typically, a far greater level of detail will be presented. Elements such as depreciation, amortisation, tax and debtconsiderations can also be addressed29. Note also that Project X is presented as a self conducted project. If, however, commercialisation of the project is to be byout-licensing, assumptions, for instance, relevant to licence fees, milestone payments and royalty rates further refine the projected revenues. The focus of thepresent discussion is on projected revenues attributable to the project as a whole. Accordingly, the impact of commercialisation strategies will not be considered.Following the computation of a net cashflow stream, a DR of 30% is used to calculate a net present value for Project X. In this regard, it is not unusual for the DRto be presented as part of a discount factor. No terminal value has been calculated.

29 Appendix 3 contains an example of a more detailed cashflow model.

-1.9M56-0.9-42-15.0.2.3.60.8Discount factor

6520Years from now

280-3-70-15Net cashflow

490000Revenues

-210-3-70-15Investments ($US M)LaunchFDAPh IIIPh II

Forecast revenues

Forecast investments

Net present value

Calculate net cashflow

Discount factor(1/(1 + rProject)t)

Forecast period

25

2.2 DCF critiqued DCF has many advantages, including simplicity in application and wide acceptance30. Villiger et al (2005) argue that DCF articulates each element of value in a form that can be adjusted to determine the impact of alternative views on key assumptions31. Criticism of DCF however is equally prominent. The rest of this chapter will consider some of the commonly cited limitations of DCF, and the valuation methods that have been developed to address such limitations. 2.3 DCF does not properly account for technical risks The use of DRs in DCF methodology to simultaneously adjust for time and technical risks has been shown to penalise long term projects relative to short term projects (Randerson, 2001)32.

The risk-adjusted NPV method (rNPV) was developed specifically to deal with technical risk. More precisely, it takes technical risk33 outside DRs, instead accounting for such risk by adjusting cashflows of each stage of development by probability rates based on the technical risks associated with such stage34. In turn, a lower DR is also used, with the literature

30 Generally, DCF methodology is said to be used by virtually every major corporation doing a variety of business tasks, including capital investment decisions, operations analysis and business reporting. It is used in business in various forms – under the names such as free cashflow, capital cashflow, shareholder value analysis, flows to equity and internal rate of return. In each form, minor technical differences exist; but the fundamental principles of the analysis remain the same and are consistent with the NPV principles as outlined above. The method is also used routinely by major management consulting firms, investment banks and government policy makers. It is taught in universities all over the world. Indeed the method is so central that some textbooks treat value as synonymous with “discounted cashflows”. The method has found considerable support in legal proceedings where it has been used widely to measure the value of lost opportunities. 31 Thus if one assumption or finding comes into question, the calculation can be modified with relative ease. 32 In this regard, the high rates applied by VCs are considered to invariably render research and development programmes to a negative value. 33 Also referred to as technological or scientific risk 34Example of industry average rates of success (phase to market) published by Bennett et al (2004): Pre-clinical -3%, Phase I – 6%, Phase II – 18%, Phase III – 42%, Regulatory review – 90%, Launch -100%.

26

publishing rates in the range of 9-15% (Randerson, 2001). Figure 5 applies rNPV to Project X, replicating an example recently published by Villeger et al (2005)35.

35 The author has simply copied the example published by Villiger et al (2005) and has not undertaken an analysis of the correctness or otherwise of the calculations. It is beyond the current scope to undertake such analysis however, the principles employed in the relevant example mirror the principles employed by Borrissiouk et al (2001). An Excel sheet of the example can be found on http://www.nature.com/nbt/journal/v23/n4/suppinfo/nbt0405-423_S1.html

-280-3-70-15Net cashflow

-1.842.7-0.6-28.9-15.056%62%83%100%Discount factor

6520Years from now27%30%50%100%Probability to achieve state

90%60%50%Success rate

490000Revenues-210-3-70-15Investments ($M)

sumlaunchFDAphase IIIphase II

Figure 5 – Example of rNPV calculation for Project X

Risk adjustednet present value

This refers to the probability of the project reaching launch from the start of therelevant stage. Standard rates can be sourced from literature, with noted sourcesincluding those published by The Tufts Centre for the Study of Drug Developmentand The Centre of Medical Research. In the case of Project X, at the start of phaseII, there is a 50% probability of reaching launch.

Project X is just about to enter into phase II, hence there is a100% probability of achieving the relevant success rate state ( i.e.,50%).

The net cashflow of each stage is adjusted by the correspondingprobability to achieve state.

In the case of each stage following the initial stage, the probabilityto achieve state at the start of the stage is cumulative of thesuccess rate and probability to achieve state of the precedingstate.

By way of example, in the case of phase III of Project X, theprobability to achieve is calculated by:

Success rate (phase II) *Probability to achieve state (phase II)

50% *100% = 50%

The net cashflow of phase III is then adjusted by 50%.

A discount rate of 10% is used. The risk-free rate isnot applied because there are risks in thedevelopment process in addition to technical risks.

28

2.3.1 Limitations of rNPV Bennett et al (2004) observes a continued practice of applying high DRs to rNPV modelling, indicating rNPV is susceptible to misuse. The calculation of probability rates remains problematic, particularly in respect of pre-clinical stages. Many unsuccessful pre-clinical projects are quietly discontinued. Additionally, probability rates tend to be presented as industry averages. The challenge of applying such rates to a specific therapeutic indication is well documented. For instance, Bennett et al (2004) explains that where the drug mechanism is well understood (such as in hypertension, diabetes or asthma) the relevant probabilities of technical success are likely to be higher than industry averages. Similarly, projects dealing with lesser understood diseases (such as cancer) may be associated with lower probabilities of technical success36.

2.4 DCF does not account for different outcomes DCF valuation is in effect based on a single projection of inputs (Remer et al, 2001). The relevant inputs are, however, impossible to calculate with any certainty. Sensitivity analysis can be undertaken by flexing individual inputs in isolation (Bennett et al, 2004). By comparison, scenario analysis, decision-tree modelling and Monte Carlo simulation seek to deliver a range of NPVs based on likely variations to more than one input.

36 Randerson (2001) refers to the likelihood of rates between biologics and new chemical entities differing.

29

2.4.1 Scenario analysis Scenario analysis models the outcomes of events (i.e., scenarios) on NPV. For instance, Figure 5 is based on a revenue projection of $US 490 M. However, this is only one possible revenue scenario. Clearly, the product may attain different revenues37. The relevant scenarios, based on the probabilities of such scenarios eventuating, can be modelled to examine effects on NPV.

2.4.2 Decision-tree modelling Decision-tree modelling considers the impact on project value of different scenarios at nominated decision points along the development path. Typically, decision points occur at the completion of each stage relevant to the development path. Figure 6 outlines decision-tree modelling to consider the impact of technical failure or success on project NPV. In this way, the relevant impact on value can also be pictorially represented, together with relevant pay-offs if the project is abandoned at any decision point in the event of technical failure.

37 In certain cases, the likely outcomes or scenarios have to be estimated as in the discussed example, while in others they are binary (such as product failure or success in clinical trials). Bennett et al (2004) outlines the following examples of scenarios that can be readily modeled: the outcome of a product failure or success, the entry of a new drug in the clinic, the entry of existing drugs into new indications, additional sales potential, the outcome of fundraising and the outcome of a royalty dispute.

Begin phase II

phase III

regulatory submission

launch

Abandon

Abandon

Abandon

Succeeds50%

Succeeds60%

Succeeds90%

Fails50%

Fails40%

Fails10%

NPV of proceeding to launch

Figure 6 – Outline of decision-tree modelling of the impact of technical and success

NPV of proceeding to regulatorysubmission

NPV of proceeding to phase III

31

2.4.3 Monte Carlo Monte Carlo methodology simulates adjustments to multiple inputs (e.g., market size, expenditures, pricing, time to market) to produce an overall distribution of possible outcomes. This is achieved, in the first instance, by defining the statistical probability distribution of each uncertain input of interest. The type of distribution will depend upon the conditions surrounding that input. Software simulation is then used to repeatedly sample values from the probability distributions of each input. Each simulation generates a single NPV estimate. The end result of the repeated simulation is a range of possible NPVs and their respective probabilities of occurrence38. Figure 7 outlines Monte Carlo simulation.

38 Another advantage is that the method allows the use of a lower DR because the uncertainties (failure possibilities) are incorporated into the valuation model.

Market size

Expenditures

Pricing

Time to market

SimulationRepeated

sampling fromthe probability

distributions of eachinput of uncertainty

NPVproject

Probability

Figure 7 – Outline of Monte Carlo simulation

Example of uncertain inputs

ProbabilityProbability

ProbabilityProbability

33

2.4.4 Limitations The methods essentially supplement DCF and rNPV, and to that extent propagate a number of the limitations associated with such methods. The value derived also depends on the choice of scenarios and the associated probabilities of occurrence, which in large part are determined subjectively. Further, although the methods are useful for assessing the spread of NPVs for a project depending on the various outcomes, they still do not assist in yielding a more reliable single NPV. 2.5 DCF does not properly account for the value of managerial flexibility in the face

of economic uncertainty 39

In addition to the earlier mentioned technical risks, part-developed projects face economic uncertainty. Remer et al (2001) identify economic uncertainties that affect all projects (e.g., in respect of the size and scope of the market, the regulatory issues, intellectual property rights regimes, competition through generic drugs) and project specific uncertainties (e.g., lack of organisational and financial resources). Real-options methodology aims to address the impact of economic uncertainties on project value by applying financial options theory to the drug development process. The notion of value subsisting in a financial option is illustrated by the simple hypothetical presented in the footnote40. By way of a basic overview, an option grants the option-holder a right (as opposed to an obligation) to exercise certain rights in respect of an asset. The relevant option derives its value from this right. The parameters relevant to financial options analyses

39 Market uncertainty is also referred to as ‘economic uncertainty’. 40 Consider a firm that has a present value of $100 M for operating an existing line of business into the future. There is the possibility that the firm can extend into a new opportunity with a present value of $50M if favourable conditions develop. Suppose that the likelihood of such conditions developing is 50%. According to Frei et al (2004), a business with this option is worth more than the $100M because rational investors will place a present value of $25M on this option. While the future outcome is uncertain – either $50M or $0M – the present value of $25M on this option is not uncertain. Investors will pay $25M today for a 50% risk neutral probability to receive a present value of $50M from the project. Once the value of the optional project is known by the market, the stock of this business would rise to $125M to reflect the full value of the firm. If the uncertainty over the project is resolved, the value of the business will change accordingly: either by rising to $150M if the favourable conditions materialise or by dropping to $100M if they do not.

34

include the price payable to be the option-holder (i.e., the exercise price) and the underlying asset to which the option provides rights to. Real-options theory models the drug development process as embedding a series of options41 in the face of unpredictable economic developments. For example, Borissiouk et al (2001) explain that once a project has passed phase I, the option-holder has the option to invest in phase II. The start of phase II will require an investment outlay, which is the exercise price for that option. If the option-holder decides to invest, it will acquire the option to invest in phase III, together with an option on the future commercialisation of the project42. However it may be that technical success in phase III is accompanied by unfavourable market conditions. The option-holder in such circumstances may abandon the project. Rational investors are assumed to be willing to place a value on such options43.Consequently, under real-options methodology, the value of the project is linked not only to its cashflows but also to the presence of options. 2.5.1 Valuation of options Villiger et al (2005) explain, while there are alternative methods to value options44 embedded in a part-developed project, the binomial tree45 “is accessible even to managers without math skills”. The construction of a binomial tree is still formula driven and includes steps beyond succinct summation here. In the interests of simplicity, real-options valuation by binomial tree construction is considered in series in Figures 8(a) to 8(c) by replicating the Project X

41 The existing literature provides six categories of real-options based upon the types of managerial flexibility: option to defer, option to expand or contract, option to abandon or license, option to switch, compound option, and option to grow. 42 This option tends to be referred to as a compound option. 43 The option to wait to invest until relevant economic uncertainty resolves itself is also referred to as a learning option. Projects can be modelled to embed several other options of choice, including the options of expansion, deferral and licensing. 44 Villiger et al (2005) observe that the valuation of options is a daily business and there exist various methods for the valuation. Nevertheless, not all are suitable for the real option valuation. The often mentioned Black-Scholes formula, the standard valuation method for financial options, cannot be used in the context of real-options, because of its inability to handle staged investments. Some alternative methods like finite difference are complicated, requiring sophisticated mathematics and computing. 45 The binomial model is also referred to as the true discrete binomial lattice option pricing model or the lattice algorithm methodology.

35

example published by Villiger et al (2005)46. Readers will note that the example begins by computing the underlying assets over the forecast period. This depends on a parameter referred to as volatility (δ), which represents the standard deviation of project revenues due to economic uncertainty. Each underlying assets computation comprises a node of the binomial tree. The next step involves calculating the NPV of each node. Compare the NPV of -$1.8 M for Project X derived using rNPV to the NPV of $5.5 M derived using real-options. The difference in value is described as the flexibility value captured in real-options valuation. More precisely, the relevant increase results from the ability at any node of the binomial tree to set a floor of zero for negative values. This zero value limits the downside exposure to the exercise price of the option. The binomial tree meanwhile still retains the benefit of the upside exposure. In comparison, DCF assumes that once a decision is made to invest, all investments occur.

46 The author has simply copied the example published by Villiger et al (2005) and has not undertaken an analysis of the correctness or otherwise of the calculation. It is beyond the current scope to undertake such analysis however, the principles employed in the relevant example mirror the principles employed by Borrissiouk et al (2001). The example contained in Figures 8(a) to 8(b) was also recommended to us by a participant in this study. An Excel sheet of the example can be found on http://www.nature.com/nbt/journal/v23/n4/suppinfo/nbt0405-423_S1.html

S

uS

dS

udS

d2S

u2S

u3S

u2dS

ud2S

d3S

u5S

u4dS

u4S

u3dS

u2d2S

ud3S

d4S

u3d2S

u2d3S

d5S

ud4S

u6S

d6S

ud5S

u2d4S

u3d3S

u4d2S

u5d2S

Year 1

Year 2

Year 3

Year 4

Year 5

Year 6

Year 7

490

661

363

893

490

269

1205

661

363

199

1627

893

490

269

148

2196

1205

661

363

199

109

2964

1627

893

490

269

148

81

Figure 8(a) – Example of real-options calculation for Project XStep 1 – Construction of binomial tree

This figure sets out how to construct a binomial tree for Project X bycomputing the underlying assets over the forecast period outlined inFigure 4.

A binomial tree commences by focussing on the underlying asset at timezero (initial state), which in the case of a part-developed project is therevenue forecasted as at time zero.

From this initial state (i.e., initial node), the projected revenue can moveto a state of up or down depending on the direction in which relevanteconomic uncertainty resolves itself. By computing the up and downstates relevant to each yearly interval of the forecast period, thesubsequent nodes of a binomial tree can be completed.

The multipliers relevant to calculating the up (u) and down (d) statesdepend on a parameter referred to as volatility (δ). The volatilityrepresents the standard deviation of the project revenues due toeconomic uncertainty. Volatility estimates in the range of 30%-50% havebeen published.

The specific relationship between u, d and δ is as follows:

u = eWhere δ is the volatility in cashflow and δt is the length of eachtime stepd = 1/u

Based on the following parameters specific for Project X, the binomialtree on the right is constructed:

Volatility = 30%Initial state (S) = $US490 Mu = 1.35d = 0.74

tδδ

2964

81

148

269

490

893

1627

2754

1417

683

280

59

0

0

Figure 8(b) – Example of real-options calculation for Project XStep 2 – Calculation of the value of end states

The calculation of net present value of a project starts at the end of thebinomial tree. The present value of each end state (launch state) iscalculated as follows:

V launch(i) = Max (S launch(i) – K launch;0)

[Where K launch is the investment associated with launch, being $ 210M asoutlined in Figure 4]

The present value of each end state, as calculated by Villiger et al (2005),of Project X is shown (refer to numbers in red).

If the net present value of an end node is negative such as in the case ofthe bottom two end states, the option holder has the right to abandon thelaunch of Project X. The abandonment of the project allows the optionholder to save launch costs. The opportunity to abandon Project X isincorporated in the calculation by defining present value as the maximumof the present value of the underlying asset or zero.

i.e., V1627 = Max [(1627 –210);0] = [1417;0] = 1417

i.e., V148= Max [(148 –210);0] = [-62;0] = 0

Year 7

490

661

363

490

269

893

1205

661

363

199

2196

1205

1627

893

490

269

148

661

363

109

199

2964

81

148

269

490

893

1627

-210-3

-70

-15

Investment

7100%Launch690%FDA

560%4100%3100%Ph III250%1100%Ph II

YearProbability of successStage

5.5

43.9

6.7

180

34

0

402

182

65

15

631

303

123

34

4

1622

811

366

122

18

0

2754

1417

683

280

59

0

0

Figure 8(c) – Example of real-options calculation for Project XStep 3 – Calculation of the value of internal states

Once the values of each end state of the binomial tree has beencalculated, the present value of each state one stage immediately back(i.e., FDA) is calculated.

Using the upper most state of the FDA column (S2196) as an example, thenet present value is calculated as follows:

V2196 = ([1/(1+r)1 * PFDA * (p V launch up + (1-p) V launch down)] - SFDA; 0)

Where:p = 1-d/u-d ( [ 1-0.74/1.35 -0.74] ) (risk neutral probabilities)r = discount rate (10%) of one year back

PFDA = Probability of success for FDA stage (90%)V launch up = 2754V launch down = 1417SFDA = Investment of FDA review ($3M)

In this way, by working backwards to the initial state, the net presentvalue of a part-developed project can be calculated.

Using the parameters in the table below, the net present value of each ofthe internal states is calculated by Villiger et al (2005) (refer to thenumbers in red), resulting in a net present value of $US 5.5M for ProjectX.

39

2.5.2 Limitations of the real-options Even proponents acknowledge that the real-options approach is difficult to model with much work remaining in developing a practical application of real-options theory (Villiger et al, 2005). Difficulties have also been encountered in accurately estimating the volatility parameter. Benninga et al (2002) argue that the market data needed to guide such estimation is unavailable, and there is also an assumption that volatility remains constant over time. This is unlikely to be the case for drug development projects. The longer a project survives, the project becomes less risky and the variance in its potential revenue is lower. Stewart (2002) considers while the real-options approach has a strong basis in theory, in practice many of the “options” relied upon are not necessarily options that are exercisable in practice. For example, real-options assign value to the ability to pull back from a project should revised market projections be unfavourable. For this option to be exercised, revised market projections would have to be trusted more than the original projections and then possibly a project in late-stage development may need to be stopped. Such an option is unlikely to be exercisable in practice. 2.6 NPV modelling is theoretical A number of limitations have been identified in respect of the methods considered thus far. In general, these methods depend on projecting cashflows, which are impossible to calculate with certainty. To compensate, parameters such as DRs, probability of success rates and volatility are used. However, these parameters are also difficult to determine, depending on subjective assumptions. Against this background, there exists support for using the comparables method, which by assigning values on the basis of analysing valuations of comparable projects (typically implicit in commercialisation or financing deal values) is considered a more accurate and reliable measure of project value. Comparables methodology, however, presents its own challenges. Finding comparable projects is difficult for novel products that have no obvious counterpart. Even if a comparable project can be identified, care must be exercised in drawing valuation

40

information from it (for example, the market conditions and bargaining powers of relevant parties may have been different). The methodology also assumes that the comparable project has been properly valued (Frei et al, 2004).

41

Chapter 3 Methodology

This study was conducted from 11 April 2005 to 27 May 2005 in conjunction with Cambridge Healthcare and Biotech. The study used a combined quantitative and qualitative approach using interviews (face-to-face and by telephone) and survey techniques with the participants identified below. In the first week of the study, preliminary interviews were conducted to assist in designing a detailed interview guide/questionnaire (questionnaire). These interviews suggested that rNPV and DCF (NPV based methods) were the predominant methods being used to value part-developed projects. The questionnaire was developed to consider key parameters of NPV modelling in addition to more general methodology considerations. In addition, an email and cover note explaining the study, the benefits of participation, the response deadline and confidentiality issues were also developed. A copy of the questionnaire together with the relevant email and cover note are annexed to Appendix 447.

3.1 Participants In the initial consideration of study design, the study took into account the possibility that approaches to valuation may differ in terms of the objectives for which and by whom the valuation is undertaken. This led to the study seeking a diverse range of participants representing biotech business development (biotech), large pharma business development (pharma), financial/investment analysts (analysts) and venture capitalists (VCs) involved in the valuation of part-developed projects. Individuals from the EU48 and US were selected from a database compiled by the sponsor firm. Before any participant was invited to participate, the website of the relevant participant firm was checked to ensure that the firm was involved in the evaluation of part-developed projects as defined by this study.

47 Due to time constraints, the questionnaire was piloted with only one individual. 48 Including UK

42

Accessing willing participants for the study by the relevant deadline was perhaps the major challenge of this study49. A total of 129 individuals were approached by email to participate in either an interview or, if unable to do this, to simply complete the questionnaire. From this, 12 individuals agreed to participate in an interview. The 12 completed questionnaires returned before 27 May 2005 were analysed. Figure 9 illustrates the distribution of participants by each group. Notwithstanding the lower representation of pharma and analyst groups, the relevant contributions still provide insights into the approaches of such groups, given that these sectors comprise fewer players relative to the other groups.

Figure 9 – Distribution of participants by reference to the following groups: biotech, pharma, analyst and VC

152

2

5

BiotechPharmaAnalystVC

49 While literature recommends following up in order to increase response rates, time constraints prevented such being undertaken in this study.

43

3.3 Analysis The questionnaire sought to obtain both quantitative and qualitative data (Hartley, 1994). Questions 1 to 8 of the questionnaire provided quantitative data. Questions 9 to 11 provided qualitative data. Questions 1 to 8 of the questionnaire asked participants to nominate from specified responses50. In respect of Questions 1-3 and 5-7, a score of 1 was assigned each time a particular response was identified. The scores in respect of each response category were then summed. Questions 4 and 8 sought responses on a scale of 1 to 5. Where multiple participants from a group had responded, the responses for each category were averaged51.

Findings were compared across groups and discussed. A number of limitations (including those noted in the footnote52) arose in respect of the study methodology. While a complete discussion cannot presently be undertaken, such limitations need to be carefully considered when relying on the findings of this study.

50 Note that participants could in most cases identify more than one response and provide additional responses. 51 Microsoft Excel was used in respect of all computations. 52 The low response rate generally, and the disproportionately small sample size of the pharma and analyst groups have already been noted. Limitations arising out of the design of questions are considered in the context of analysing the findings in Chapter 4. The study incorporates both qualitative and some quantitative data obtained from the interviews and the questionnaires. Aside from the fact that each participant did not necessarily answer each question, the qualitative element has meant that the information collected is not consistent across each participant involved in the study. In other words, some participants have provided in depth data on certain issues while others may have provided relatively limited or no information on those same issues.

44

Chapter 4 Findings

4.1 Methods used to value part-developed projects

The participants were asked to specify which of the methods identified in Figure 10 they used to value part-developed projects.53 As shown below, rNPV received the highest score amongst biotech, followed by DCF and then comparables. Scenario analysis, decision-tree modelling and real-options next achieved equal scores. Monte Carlo simulation scored the lowest.

Figure 10 – Methods used by biotech, pharma, analysts and VCs to value part-developed projects

0

2

4

6

8

10

12

14

16

18

rNPV DCF Comparables Scenario Decision Tree Real options Monte CarloMethods

Scor

e

VCAnalystPharma Biotech

53 Refer to Question 1 of the questionnaire. For ease of reference, the relevant methods comprised DCF, rNPV, scenario analysis, decision-tree modelling, Monte Carlo simulation, real-options and comparables

45

rNPV, DCF, scenario analysis and decision-tree modelling achieved the highest scores amongst pharma54. Given the group comprised only of two participants, representation across all methodologies is notable. Unlike the other groups, the predominant method used by VCs was comparables. The findings also showed analysts as the most limited in their use of methods (i.e., rNPV and to a more limited extent, scenario analysis55). We also asked participants to identify the advantages and disadvantages of the methods they used56. Given the predominance of rNPV, DCF and comparable users, majority of the responses related to the advantages and disadvantages of these methods. A sample of the responses is tabled in Appendix 5. rNPV predominance The predominance of rNPV amongst biotech is consistent with the findings of an earlier study showing rNPV as the predominant tool used by European biotechnology managers to value research and development programmes (Remer et al, 2001). Biotech participants in this study considered rNPV to be (amongst other things) computationally simple and most easily understood by senior management. While some biotech participants were clearly proficient in the lower scoring methods (e.g., real-options), the overwhelming number of biotech participants explained their reluctance to use such methods stemming from a lack of proficiency (including within senior management levels). The lack of uptake of alternate methods is somewhat surprising given the extensive literature advocating the application of such methods. The low scoring of real-options is particularly interesting. Recently, Lewis et al (2004) observed the inconsistency between the practical application of real-options and its popularity in literature. Consistent with present findings, 54 One of the pharma participants also indicated that the formulation of the assumptions relevant to modelling and the actual construction of the financial model were split between individuals. Amongst the other groups, these tasks tended to be presented as a single exercise conducted by the same individual, suggesting that pharma may also differ in the way in which they carry out financial valuations. 55 In this regard, the analyst participant identifying using scenario specified that such analysis was used less frequently than rNPV. 56 Participants could also specify any additional method(s) they used to value part-developed projects. The following methods were identified primarily by the pharma participants: conjoint analysis using tornado diagrams and payback periods. These responses confirm pharma’s apparent sophistication across valuation methods. More generally, they suggest that the specified methods do not represent the totality of methods used to value part-developed projects.

46

the authors reported lack of expertise as the primary barrier to real-options usage in the biotechnology sector. It is also noteworthy that the authors of a study in 2001 predicted in view of the shortcomings of NPV based methods, such methods were expected to be replaced by real-options as the dominant biotechnology valuation tool over the next 5 years (Remer et al, 2001). Although participants in this study were aware of the shortcomings of NPV methodology57, the present findings do not suggest a trend consistent with such expectations. Any efforts towards popularising real-options will need to careful consider how best to persuade biotech senior management of the merits of real-options analysis. Management are likely to only approve numbers based on valuation analyses that they understand. Notably, the lack of familiarity and proficiency observed amongst the biotech group was not disclosed by the other groups. The reasons underlying their methodology preferences are explained during the course of this chapter. Early research stage An exception to the overall preference for NPV based methods came from those participants involved in valuing part-developed projects in the early research stage (early stage projects). Such participants formed a large part of the VC group. Uncertainty associated with forecasting at such an early stage was given as the predominant reason against using NPV based methods58 to value early stage projects.

“We do not have enough information to put together NPV. If you do NPV for early stage, you would not get out of bed.”(VC)

Similar sentiments have also been expressed outside this study. Frei et al (2004) viewed the application of cashflow based methods to early stage projects as “technical overkill” given the variable quality of assumptions made about the future cashflows of the project and the lack of tangible results on which to base calculations.

57 Refer to Table 3 in Appendix 5 for a list of disadvantages identified by participants. 58 Or any of the other cashflow based methods for that matter.

47

The above views also highlight a limitation in the design of this study. Part-developed projects as defined by this study cover projects in a broad spectrum of development stages. A limitation of such broad coverage is that any valuation nuances specific to a development stage could not be identified other than when comments such as the above were provided. The VC valuation perspective on investing in early stage projects presents an interesting study in its right. In this context, it is helpful to think of VC considerations as relating to part-developed projects encompassed in the vehicle of a private company, rather than a project in isolation.

“What we look at is how novel is the science, is it addressing a major market sector, what freedom do you have to operate in that sector, is it a really hot target … We really look at management. Are these people who have been there and done it before? Is management going to be able to adapt and change? …. Do regulatory issues raise an additional cost burden? What are the clinical issues? What is the business model?” (VC)

The importance of downstream considerations was also explained. For instance, when considering the business model relevant to a project, if the product in development is aimed at a cure rather than a treatment, VCs will extrapolate this as meaning no co-development interest from pharma. This, in turn, downgrades VC interest. When interest in a project is established, negotiation of the financing arrangement is driven by IRR requirements, comparable financing deals and largely what the VC can get away with - constrained only by what is needed to maintain the entrepreneur’s interest in developing the project to the point of VC exit. In this way, VC decisions are based mainly on subjective criteria and qualitative factors, and the value assigned to a project is implicit in the financing provided relative to the private equity sought. Multiple method approachA number of the participants described using a multiple methodology approach.

48

“Any one model is just one picture. We typically use several models for one project, to capture any variables particularly important to project and decision making process.” (biotech)

Notably, though, when it came to presenting quantitative valuations to senior management, the multiple methodology proponents acknowledged adhering to the relevant “company” method (predominantly being the NPV based methods). Value negotiatedParticipants from all groups were keen to stress that the emphasis on quantitative valuation alone risks implying part-developed projects have a “stand alone” value. In the context of negotiating financing or commercialisation partnerships, the theoretical value derived from applying valuation methodology will be secondary to relevant strategic interests.

“Remember valuing a partly developed project should be for a reason. If it is to sell the programme to a partner, it can only be a starting point. Purchasers … will pay what they need to strategically (which is not the same as NPV).” (VC)

“NPV or expected NPV is only one of the inputs that needs to be considered when making an investment decision, Other important factors include return on investment, cashflow implications, strategic/portfolio fit (e.g. therapeutic area, sales force requirements, anticipated launch, revenue, geographic rights” (biotech)

Such perspectives suggest that in the context of deal negotiations, quantitative valuation becomes merely one of the tools by which to understand the sources of value and risk in part-developed projects. The diminished importance of quantitative valuation in deal negotiations also seemed to be accompanied by increased reliance on comparables during negotiations with partners and investors.

“Biotech seem to know exactly what we paid for the last deal” (pharma)

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In this regard, a multiple methodology approach is not necessarily restricted to multiple methods being applied at the same time. It may also encompass shifts in methodology depending on negotiation stage. In this context, the limitations of comparables also become important (refer to Table 559).

“There are few comparators. Consider accessing pool of knowledge in the US – longer history and larger pool – however would also have to apply caveats when transferring data from one territory to another.” (VC)

The above suggests that key to using comparables is having access to sources of comparables information including those sourced from previous dealings, “bio-world” networks and public sources – and also possessing an understanding of the caveats to be applied when applying comparables.

59 Appendix 5

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4.2 NPV parameters 4.2.1 Revenue - Forecast of sales potential Participants were asked to identify which of the approaches listed in Figure 11 they used to assess market size60.

Figure 11 – Approach used by biotech, pharma, analysts and VCs to forecast sales potential

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The bottom-up approach received the highest score amongst biotech. This approach was also the highest scoring approach for pharma and VCs. Analysts used the approach on an equal basis to standard sales evolution curves. Preference for the bottom-up approach is also

60 Refer to Question 2 of the questionnaire. For ease of reference, relevant approaches comprised the bottom-up approach, the market based approach and standard sales evolution curves.

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found in literature. For instance, Bode-Greuel et al (2005) described the approach as “trustworthy” and more rigorous compared to other approaches – because the bottom-up approach involves constructing a market from its base components; also lending itself to be adjusted to analyse changes in such base components.

The market assessment approach unique to the travel vaccine market was also presented. Assessment begins by considering the number of travellers travelling to regions where the targeted disease is endemic, followed by refinement by reference to “softer” factors61 that qualify the number of doses actually supplied. It is noteworthy that this approach also re-constructs the market from relevant base components. The pricing peculiarities of the vaccine market were also outlined. If vaccines are to be supplied as part of government contracts (tending to often be the case), pricing is set by the relevant government purchaser and effectively non-negotiable. Suppliers, however, can look to private markets (such as travel clinics) to make up revenue margins. The market-based approach was advocated in circumstances where an already well defined market existed. An example of a well defined market provided by one of the biotech participants was the chronic obstructive pulmonary disease market, where the success of the leading SprivaR product has been well researched.

In the context of revenue projection generally, preference for the bottom-up approach was not the only consistency observed between the groups. Interviewee participants from each group identified sales forecasting as the aspect of cashflow modelling, they most wanted to improve. Interestingly, despite being perceived by the other groups as possessing informational advantages, the forecasting of revenues also posed difficulties for pharma.

“Often you have to go on 20 years gut feel … All companies are struggling.” (pharma)

Participants identified particular challenges relating to: the lack of clinical data and information on competitor products and activity; obtaining data on prevalence and 61 The examples of softer factors given included referrals from GPs and rural/urban travellers. Drawing parallels with other vaccine products was also referred to.

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incidence; determining pricing; predicting peak sales; assessing the percentage of patients diagnosed or on therapy; and the significant expenses associated with undertaking market research.

4.2.2 Forecast period Participants were asked to identify which of the forecast periods specified in Figure 12 they used62.

Figure 12 – Forecast periods used by biotech, pharma, analyst and VCs in NPV based methods

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In the case of biotech, the forecast period from now to patent expiry achieved the highest score. Further, the use of a starting point of “now” was visibly more popular than “launch”. 62 Refer to Question 3 of the questionnaire. Participants were asked identify the relevant starting point as either from now (meaning at the time of valuation) or launch, and the relevant endpoint as less than 10 years, 10-15 years, greater than 15 years or patent expiry. The relevant ranges were selected on the basis of preliminary discussions Participants were also asked to specify any other forecast periods they used. No additional periods were specified. Note also, while there was the potential for “now” to coincide with “launch”, the question was aimed at deciphering the participant’s general approach and in this regard, a clear delineation between which of the two points best described the participant’s approach was assumed.

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In the case of pharma, “now to patent expiry” was equally popular with “launch to patent expiry”. These periods in turn were equally popular with the periods of “now to 10-15 years” and “launch to 10-15 years”. The important feature was that in the case of endpoints nominated by the pharma participants, patent expiry and “10-15 years” were used concurrently, i.e., the forecast was to either of these endpoints, whichever occurred later. The responses of VCs were concentrated in periods forecasting from “now”, while the analysts forecasted solely from “launch”. The difference in the starting point of analysts from the rest of the groups provided the opportunity to discuss the valuation nuances specific to analysts. Analysts apply valuation tools as part of setting target share prices. In this study, the analyst valuation of part-developed projects was discussed in two specific contexts; where “projects of interest” involving “firms of interest” form the basis of a licence or other collaborative arrangement and initial public offerings (IPOs). In the first context, the analysts specified that “projects of interest” tend to only include projects in the advanced clinical stage of development and “firm(s) of interest” comprised mainly pharma and to a more limited extent, the more prominent listed biotechnology firms. This particular focus was explained to be partly a function of the difficulties in assessing the risk of pre-clinical projects, and partly also a function of the historical volume and maturity of projects warranting valuation. In regards to the latter, the following shift was noted:

“Whole area of preclinical discovery has sprung up because early stage companies are now doing deals” (analyst)

Upon the registration of interest, analyst focus turns to when the product in development will be launched and when it will reach peak sales.

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Biotech and VCs also articulated the nuances of analysts.

“They will only try to value products after Phase I.” (VC)

“I don’t think that investment analysts are all that interested in the value of the pipeline products, they tend to place more emphasis on actual revenues.” (biotech)

“Investment analysts are very interested in price over the next year (short time horizon)” (VC)

The analysts also described adjusting computed values by reference to wider considerations, including those relating to the biotechnology sector and public markets generally. In the context of IPOs, differences between analysts and VCs, whose valuations are superseded by the former, were presented. As explained by one of the VC participants, when a company seeks to list, analysts will refer to the amount raised by the project/company at the last round of private funding. From the VC perspective however, such amount incorporates a discount63 for a perceived lack of liquidity in the private stock at that time. At the time of listing, advancement of the company and the project is considered by VCs to increase the liquidity of such stock – and for which VCs expect a premium. Analysts, however, still consider the application of a discount.

“Discount reflects that they are burning cash and have not established themselves as a business” (analyst)

Nomination of patent expiry as the predominant endpoint is consistent with the literature64.With the imminent patent expiry of a number of blockbuster drugs65 and government policies increasingly supportive of generic launch66, the emphasis on patent expiry is likely to continue. Interestingly, while the literature emphasises circumstances warranting the 63 Discount in this context is distinct from notion of DR discussed earlier as parameter of DCF methodology. 64 Section 2.1.1(a) 65 There is significant commentary indicating that branded products with aggregate sales of more than $US 15 – 20 billion facing patent expiry between 2004 and 2006. Information sources from http://www.novartis.com/annual_reports/2004/march.shtml.66 This includes the governments in the US and EU countries.

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modification of an endpoint inside or outside of patent expiry, none of the participants mentioned the routine consideration of such modifications when selecting the forecast period. The earlier findings of a limited number of participants using standard evolution sales curves, arguably provides evidence that such considerations are being undertaken indirectly by at least those participants. Forecasting sales potential by such approach requires considering revenue lifecycle curves characterised by varying scenarios of market share erosion.

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4.2.3 Investments - Construction of expenditure line On a scale of 1 to 5 (1 being broad brush estimate and 5 being detailed forecast), the participants were asked to assess the level of detail they devoted to assessing the expenditures identified in Figure 1367.

Figure 13 : Level of detailed assessment by biotech of expenditures relevant to NPV modelling

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Clinical expenditure

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Average level of detail on scale of 1 to 5 (1 being broad brush estimate and 5 being highly detailed)

As shown, biotech on average undertook a more detailed assessment of clinical, pre-clinical and manufacturing expenditures compared to regulatory submission and marketing and

67 Refer to Question 4 of the questionnaire. For ease of reference, relevant expenditures were pre-clinical, clinical, regulatory submission, manufacturing, and marketing and promotional activities.

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promotional expenditures (downstream expenditures)68. The responses of VCs mirrored this 69.

Pharma indicated overall, all expenditures associated with a project were equally considered. At any particular time, however, a more detailed assessment is made of the expenditures to be incurred in the short term.

“We do not go into marketing costs (i.e. number of reps, cost of reps) until the product is very close to market stage.”(pharma)

The analysts indicated relying primarily on standard assumptions to estimate expenditures70,preferring instead to undertake detailed assessments of sales potential. Although the reasons for the low ranking of downstream expenditures were not specifically the subject of this study, it is nonetheless possible to proffer explanations based on the traditional division of responsibilities in drug development71. The biotech business model of entering into partnerships with a pharma partner pursuant to which the pharma partner assumes responsibility for regulatory submission, launch and subsequent marketing activities is well established. The model is reinforced by pharma firms continuing to have, above biotech firms, the experience and economies of scale required to conduct the downstream stages of drug development (Nicholson et al, 2004). Importantly for present purposes, the low ranking of downstream expenditures may be explained by the lack of biotech involvement in such activities. Notably, the few biotech participants who had been involved in co-promotional deals indicated undertaking detailed assessment of marketing and promotional expenditures of the project in question. In making ongoing extrapolations by 68 Biotech: Clinical expenditures (4.2), Pre-clinical expenditures (4.1), Manufacturing expenditures (3.9), Regulatory submission expenditures (3.2), Marketing & promotional expenditures (2.8). 69 VCs: Clinical expenditures (3.8), Pre-clinical expenditures (3.3), Manufacturing expenditures (3.3), Regulatory submission expenditures (3), Marketing & promotional expenditures (2.3). 70 We were unable to obtain the precise response in respect of this question by either of the analysts. We were however able to obtain comments on their general approach to forecasting expenditures which is presented as above.71 Other explanations can also be proffered. For instance, a number of the biotech participants agreed with the statement that the background of the individual constructing the model influences the relevant expenditures that the model most focuses on. Also as cash burning firms, biotech may understandably be more concerned with short term expenditures.

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reference to the activities in which biotech are involved, any shifts in the current delineation of responsibilities between biotech and pharma will be relevant.

“Given that IPO and trade sales seem to be taking place later, biotechs may be forced to deal with such costs” (VC)

Information asymmetry may also be relevant to explaining the findings presented in Figure 13. Interviews revealed a strong perception amongst biotech, analysts and VCs that pharma hold clear informational advantages on expenditures associated with drug development.

“They [pharma] have much inside knowledge of historical expenditures” (VC)

Such informational advantages are unsurprising given the traditional division of responsibilities. Information asymmetries between pharma and the rest of the drug development sector, as a result of such have previously been suggested (Nicholson et al, 2004). In present discussions, the low ranking of downstream expenditures may be due to biotech being unable to access the information required to undertake detailed assessment of such expenditures. The practice of estimating downstream expenditures as revenue margins72 is worth revisiting. One of the analysts using such approach indicated that such margins are difficult to predict over the product lifecycle, with for instance, how much promotion a product is given varying from year to year at the discretion of management73.The adequacy of the key mechanism used to deal with the relevant information asymmetry hence is questionable. In this context, participants were asked whether they shared their valuation models in deal negotiations. The predominant response was that models were not shared. However, participants acknowledged sharing certain key assumptions behind the parameters in their models. Reasons given against sharing included preserving sensitive information and the risk that sharing may result in parties arguing about the minutiae of relevant models. In

72 Refer to Section 2.2.1(b) 73 There is also commentary suggesting that such an approach of estimation has lead to downstream expenditures often being underestimated in the biotechnology industry (Bode-Greuel, 2005).

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comparison, the sharing of key assumptions is consistent with, as previously discussed, the use of valuation methodology to understand the key drivers of project value and risk.

4.2.4 Discount rate The participants were asked to identify which of the ranges74 identified in Figure 14 the DRs they use in NPV modelling come within.

Figure 14 – DRs used by biotech, pharma, analysts and VCs in NPV modelling

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74 For ease of reference, these comprised: <10%; 10-13%; 14-17%; 18-20%; >20% and discount rate varies depending on the development stage.

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Amongst biotech, DRs of 14-17% received the highest score, followed by 10-13%. Notably, DRs of >20% achieved the highest score amongst the VCs. In contrast, analysts used lower DRs (10-13%) than biotech. Only one of the pharma disclosed its DR (10%). While the derivation of DRs was not directly explored in this study, comments provided by the analysts, biotech and VCs who were interviewed nevertheless provided the insights discussed below. In this regard, neither of the pharma participants commented on the derivation of the DRs used other than indicating that DRs were used in accordance with corporate guidelines.

• Analyst:

“We use a 10% ‘risk free’ discount rate to discount back to all earnings: it is appropriate that this is higher than long dated treasury yields as earnings from marketed drugs are not fully risk-free, as even old and widely used drugs may occasionally be withdrawn. Earnings from yet to be marketed products are discounted separately by the probability of getting to market.”

• VCs:

“Note that as an investor I use the VC’s cost of capital which is 40% … That is for the investment in the company and not necessarily the company… what we are judged on is the internal rate of return of the fund”

Unlike the analysts, whose DRs were based on returns on public stock dealing with established firms, the VCs’ (higher) DR are based on the IRR that its fund investors expect from high risk early stage investment.

• Biotech:

No uniform basis for determining DRs was observed amongst biotech. Some of the interviewee participants indicated using the firm cost of capital. A participant

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representing a listed firm indicated using the DRs applied by analysts. Another participant from a listed firm however specifically identified using a DR that was different to that used by analysts. Notwithstanding textbook emphasis on the use of project specific DRs 75, the only evidence of textbook compliance came from the two biotech participants who identified using a DR that varied with the stage of development.

75 In this regard, one of the biotech participants indicated that the DR used by the firm had been adjusted as the firm has developed, with the firm no longer being forced into using the higher DRs imposed by VCs. Notably, however, such adjustment remains non-project specific.

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In order to further understand discounting by biotech, the relevant DRs were reconciled with the particular NPV methods in which they were used. The results of such analysis are shown below.

Figure 15 – The NPV methods (rNPV and DCR) and the discount rates used by biotech

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The analysis showed a number of the biotech participants identifying only one DR notwithstanding identifying using both rNPV and DCF methods. Participants using DCF methods were using DRs of 10-13% whereas majority of the participants applying rNPV were using the higher rates of 14-17%. The rNPV method specifically takes technical risk outside of the DR parameter, thereby warranting a lower DR than that used for DCF. The above analysis suggests a misapplication of DRs consistent to that observed by Bennett et al (2004)76.

76 Refer to Section 2.3.2

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4.2.5 Probability of success The participants were asked to identify the probability of success rates, and the source of such rates they used in respect of each stage of development. The groups applied similar rates in respect of each stage of development. All groups also relied predominantly on external sources. This arguably explains the consistency observed amongst groups. Further, most of the interviewee participants demonstrated an awareness of the limitations of using industry average rates. Given the apparent consensus between groups insofar as probability of success rates, the relevant findings are considered in Appendix 6.

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4.2.6 Sensitivities On a scale of 1 to 5 (1 being least sensitive and 5 being highly sensitive), participants were asked to rank the sensitivity of their NPV models to changes in the parameters listed in Figure 16 77.

Figure 16 – Sensitivities relevant to NPV modelling by biotech

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Average of sensitivity rankings given on scale of 1 to 5 (1 being least sensitive and 5 being highly sensitive)

Biotech on average considered NPV modelling to be most sensitive to changes in price and market size. The literature refers to such sensitivities as economic sensitivities (Borrissouk et al 2001). Sensitivity to probabilities of success followed such economic sensitivities. Part-

77 Refer to Question 8 of the questionnaire. For ease of reference, the relevant parameters were market size, price, pre-clinical costs, clinical costs, manufacturing costs, sales and marketing costs and probability of success rates.

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developed projects were found to be less sensitive to the expenditures78. Averaging the VC responses provided the same ranking of sensitivities79.

Only one pharma participant responded, nominating probability of success rates on par with the economic sensitivities as posing the greatest sensitivities80.

Further, only one of the analysts provided information on sensitivities. The relevant analyst did so by providing his own list of sensitivities: Probability of success, launch year, peak sales, year of peak, product lifecycle, average post-tax margin. While the list differs from the list of sensitivities identified in the questionnaire, the emphasis on post-launch parameters is consistent with the earlier finding81 that analysts predominantly focus on actual revenues. An interesting observation arises when Figure 16 is compared with the results in respect of expenditure assessment in Figure 13. Whilst biotech undertook less detailed assessment of downstream expenditures, insofar as sensitivities to expenditures were concerned, these expenditures were almost on par with clinical expenditures – and posed greater sensitivities than pre-clinical expenditures. Given that biotech appears to recognise the sensitivity implications of downstream expenditures, arguably relevant information asymmetries are more likely to account for the less detailed assessment of such expenditures.

The participants were not asked to identify the basis on which they assigned scores. However, some participants did comment on such, suggesting an assessment based on experiences of running various sensitivity simulations and common sense.

“… if you run a 10%- 20% range on development costs, it does not make much of difference…if you run the same on price, makes a huge difference…” (biotech participant who nominated NPV modelling to be highly sensitive to changes in pricing)

78 Biotech: Market size (4.3), Price (4), Probability of success rates (3.7), Clinical expenditure (3.1), Manufacturing expenditure (3), Sales and marketing expenditure (2.8), Pre-clinical expenditure (1.6) 79 Market size (4.8), Price (4.3), Probability of success rates (4), Clinical expenditure (3), Sales and marketing expenditure (2.3), Manufacturing expenditure (2), Pre-clinical expenditure (1.3) 80 Market size (5), Price (5), Probability of success rates (5), Sales and marketing expenditures (4), Pre-clinical expenditure (2), Clinical expenditure (2), Manufacturing expenditures (2) 81 Refer to discussion in Section 4.2.3

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“Costs are something that you might never get to spend. Expenditure depends on whether you get there, i.e. your probability of successfully getting there” (pharma explaining the low ranking of the expenditure parameters)

This result of ranking market size and pricing as the top sensitivities is consistent with the literature identifying market and future pricing uncertainties as the key sources of economic uncertainties influencing NPVs of projects (Borrissouk et al, 2001). Importantly, the same commentary recommends real-options as the method most suited to evaluating economic uncertainties. Real-options, however, was one of the lesser used methods amongst biotech. The present findings suggest a potentially greater scope for the application of real-options.

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Chapter 5 Conclusion

5.1 Familiarity across methods A key finding of this study was that most biotech did not use the more sophisticated valuation approaches such as real-options and Monte Carlo. Many of these participants were limited to using the more conventional methods of rNPV, DCF and comparables due to a lack of understanding within their firms of the more sophisticated methods. The literature indicates that each method considered in this study seeks to address unique sources of value and uncertainties that subsist in a part-developed project. For example, real-options is the only method seeking to address the value of managerial flexibility in the face of economic uncertainties. On this basis, the lack of familiarity in a particular method risks translating to a lack of familiarity with the relevant sources of value and uncertainties evaluated by such method. From the perspective of biotech firms, an understanding of as many possible sources of value and uncertainty is important to decreasing the risk of underselling a project when negotiating with commercial partners and investors. It should be noted that in advocating familiarity with the methods found to be less used in this study, such methods are not being advocated as more “correct” ways to value part-developed projects compared to the more commonly used methods. Rather, the focus is on using each method as a tool to gather insights into as many possible sources of project value and uncertainty. 5.2 Improving existing proficiencies The findings of this study also indicate that there is scope for biotech firms to improve their application of the more popular conventional methods. For instance, very few biotech participants appeared to use DRs in a way that accorded with textbook requirements. The lack of emphasis observed in respect of modifying forecast periods to inside or outside of patent expiry suggests another area warranting improvement. Given the participant

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backgrounds, the valuation approaches considered in this study were limited to those relevant in commercialisation and investor contexts. Individuals managing internal research programmes were not included. Further study on the valuation approaches relevant in such context is recommended. 5.2 Broad approach In pursuing any strategy to improve current valuation proficiency, developing criteria that guides users on the valuation methods most suited to particular stages of development should also be considered. The issues raised by participants in respect of valuing early stage research highlight valuation nuances dependent on the stage of development. In view of the observed support for a multiple methodology approach, criteria on when and how to use such an approach and guidelines on how to reconcile any differences in end results would also be useful. 5.3 “Educate” and “sell” From the perspective of biotech firms, a potentially greater scope for the application of the less used methods such as real-options to value part-developed projects was suggested by:

• The disadvantages of using conventional methods as identified by biotech. • The theoretical literature identifying alternate methodologies that seek to overcome

such disadvantages. • The present findings on sensitivities showing NPV modelling to be most sensitive to

changes in economic sensitivities, which in turn is addressed specifically by real-options.

To the extent, the less used methods do produce compellingly better results, biotech firms should carefully consider the strategies by which senior management can be “educated” and partners and investors can be “sold” on valuations derived from applying such methods.

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5.4 Differences in application Despite all groups applying the same NPV based methods to value part-developed projects, differences were found in the way the groups applied such methods. From the perspective of biotech firms, these differences are key to understanding the value that a partner or investor will assign to a part-developed project. For instance, in the context of licensing and other collaborative negotiations with pharma, the present findings indicate that pharma are likely to be proficient in several valuation methods. They are also likely to hold informational advantages, which enable more sophisticated modelling of, for example, marketing and promotional expenditures. The value of a part-developed project is clearly a function of both short and long term cashflows – as are likely to be the deal terms presented by pharma partners. Accordingly, it is recommended that biotech firms consider strategies to improve assessment of long term cashflows. Biotech firms should also note that pharma may not necessarily hold informational advantages in the context of revenue forecasting to the extent perceived. An implication of this can be that contractual terms presented by pharma based on such forecasts are open to negotiation to a greater extent than those terms based on subject matter in which pharma holds clear informational advantages.

In the context of funding negotiations with VCs, biotech firms need to understand the VC view on DRs, and develop strategies to rebut the application of high internal rates of return. In the context of share price setting by analysts, the present findings suggest that analyst interest in projects approaching advanced clinical stages is specifically on when the project will result in a launched product and the time to peak sales, highlighting from a biotech firm perspective, the importance of having in place strategies and practices that best emphasize the sales potential associated with its part-developed projects.

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5.5 Qualitative factors and subjective criteria The theoretical value derived from the application of valuation theory, when considered in isolation, assumes an intrinsic value. However, as a number of the participants pointed out, the actual realised value of part-developed projects will be determined by qualitative factors and subjective criteria specific to the relevant negotiating partner or investor. In the case of prospective pharma partners, biotech firms should consider the strategic factors and synergies that the particular pharma partner may wish to exploit in respect of the part-developed project in question. The present findings indicate that these elements determine what the worth of the part-developed project will be to the pharma partner. In the case of early stage projects, the present findings indicate that VCs will value part-developed projects on the basis of qualitative factors such as the quality of management. From the perspective of biotech firms seeking to raise VC funding, this emphasises “business plan” like factors rather than quantitative forecasting. The present findings also indicate that the amount of equity sought by VCs in the company through which the project will be conducted, relative to the financing offered, will be determined largely by what VCs are able to get away with. From a biotech perspective, the importance that VCs place on maintaining sufficient incentives for biotech entrepreneurs to develop the project to a stage of VC exit becomes important.

The shift from quantitative evaluation also indicated a greater role for the application of market-based comparables. Biotech firms should have in place information systems that allow them to access comparables information. Biotech firms should also develop a thorough understanding of the caveats that apply when applying comparables to a specific project. The sector should also consider supporting a study into the valuation nuances specific to biotech-biotech dealings.

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5.6 Implication of preceding values The disconnect observed in this study between VCs and analysts in the context of an IPO highlights the importance of understanding the future implications of values agreed to early in the development process. At the time of IPO, the preceding VC valuation may act to constrain the value assigned by the analyst. Accordingly, where biotech firms wish to IPO, it is important to work with analysts to identify any issues likely to stem from the valuation implicit in the last round of VC funding. Addressing such issues early gives biotech firms time to address any discontinuities and maximise the success of the eventual IPO. It is equally important that biotech firms consider alongside with VCs, the future implications of the terms of the financing arrangements when negotiating such arrangements.

……o0o……

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Gibson, L.[Year not provided] Implementing the SEC Risk Quantification. Requirements to improve shareholder value. [URL: http://www.garp.com/library/Articles/implment.doc]. Hartley, J., 1994. Case studies in organisational research in Cassell, C. & Symon, G. (Eds) Qualitative Methods in Organisation Research (Sage, London). Kermani, F., 2002. The Rise of Generic Drugs. Business Briefing: Pharma Generics. [URL: http://www.chiltern.com/_data/Articles/14.pdf]. Lewis, N., Enke, D,. & Spurlock, D (2004) The Staging Option and Drug Development. [URL: http://web.umr.edu/~enke/main_publications.html] Nicholson, S., Danzon P.M. & McCullough J., 2004. Biotech-Pharmaceutical Alliances as a Signal of Asset and Firm Quality. [URL: http://hc.wharton.upenn.edu/nicholson/pdf/biodeals.pdf]. Pitkethly, R., 1997. The Valuation of Patents: A review of patent valuation methods with consideration of option-based methods and the potential for further research. [URL: http://www.oiprc.ox.ac.uk/RPWP0599.pdf]. Porter, A.D., 1993. Pharmaceutical Equities: Evaluation and Trading. (Woodhead Publishing Limited, Cambridge, UK). Randerson, D., 2001. Forensic Accounting Special Interest Group Valuing a Biotechnology Company. Acuity Technology Management Pty Ltd, Melbourne [URL: http://www.icaa.org.au/upload/download/Valuing%20biotech%20companies.doc.pdf].

Remer, S., & Baden-Fuller, C., 2001. Dealing with uncertainties in the biotechnology industry: the use of real options reasoning. Journal of Commercial Biotechnology, Vol. 8(2), pp. 95-105. Rzakhanov, Z., 2004. Innovation, Product Development and Market Value: Evidence from the Biotechnology Industry. Econ. Innov. New Techn, Vol. 13(8) (December), 747-760. Stewart, J., 2002. Biotechnology Valuations for the 21st Century. Policy Brief No. 27 (April), Milken Institute.[URL: http://www.milkeninstitute.org/publications/publications.taf?function=detail&ID=167&cat=PBriefs] Stewart, J.J., Allison, P.N., & Johnson, R.S., 2001. Putting a price on biotechnology. Nature Biotechnology, Vol. 19 (September), 5-9.

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Sudarsanam, S., Sorwar, G. & Marr, B., 2003. Valuation of Intellectual Capital and Real Option Models. Paper presented to PMA Intellectual Capital Symposium, 01-02 October 2003 at Cranfield University: [URL: http://www.realoptions.org/papers2004/SudarsanamIntellCap.pdf]. Tuttle, E., Parece, A. & Hector, A., 2004. Your patent is about to expire: what now? Pharmaceutical Executive,November. [URL: http://www.analysisgroup.com/Pharma_Exec_November_2004.pdf].

Villiger, R. & Bogdan, B., 2005. Getting real about valuations in biotech. Nature Biotechnology, Vol. 23, 423-428.

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Appendices

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Appendix 1Chapter 1 – Section 1.1

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Outline of the stages of drug development

(a) Discovery This refers to the screening process of potential new chemical or biological molecules, leading to the discovery of a drug candidate. This process also involves an understanding of the molecular mechanisms provoking the target disease. (b) Pre-clinical The drug candidate is investigated for pharmacological activity and toxicity in both laboratory environments and animal models to ascertain the preliminary safety and efficacy profile. The pre-clinical and the preceding discovery stages are also referred to as early stage research. The length of early stage research is difficult to estimate, as the time of such activities is almost unpredictable. (c) Phase I Testing of the drug candidate is conducted in a small number (calculated in tens) of healthy volunteers to establish safe dosages and to gather information on the metabolic effects82 of the drug candidate in the body. A Phase I trial may last one to two years. The territory in which the relevant trial is to occur may require regulatory approval to be obtained before testing on humans can occur83.

(d) Phase II Phase II clinical studies represent the first time the drug candidate is tested in patients of the targeted disease. Testing occurs among 100-300 patients and may last for one or two years. Successful Phase II trials verify safety of the drug candidate and also provide preliminary data on efficacy.

82 Metabolic effects include information on the absorption, distribution and excretion of the drug candidate. 83 For instance, in the United States, an Investigational New Drug Application must be submitted to the US Food and Drug Administration before clinical testing can occur.

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(e) Phase III Phase III involves large scale trials on patients to obtain additional evidence on efficacy. The aim is to find statistically significant benefits and to observe any possible adverse reactions that may occur infrequently in patient populations. Consisting of 1000 to 3000 patients, this phase is the most costly part of new drug development (i.e. pre-launch), and may last two to four years. (f) Regulatory review Regulatory authorities will review whether the drug is to be licensed for supply upon being satisfied of evidence on the safety and efficacy of the drug. The agency responsible for approval in the US is the U.S. Food and Drug Administration, and the agency responsible for approval in the EU is the European Medical Evaluation Agency. The review process may take one year.84

(g) Commercial Launch The drug will be launched for supply in the relevant markets upon notification of regulatory approval. Preparation for market launch (including sales force construction) is usually carried out in Phase III to ensure immediate sales upon regulatory approval being issued.

84 After drug approval, a Phase IV study may take place. This phase will not be considered in this dissertation because the literature tends not to be include such phase in the valuation of drug development programmes.

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Appendix 2[Section 2.1.1(b) – Projecting investments]

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Figure 17 – The effects of discount rate changes on capitalised pre-clinical, clinical and total costs per approved new drug

Source: Dimasi et al (2003)

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Appendix 3[Section 2.1.2 - Figure 4]

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Figure 18 – Example of detailed cashflow

Source: Borrissiouk et al (2001)

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Appendix 4[Section 3.2 – Questionnaire (including accompanying email and cover page]

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Dear [ ] I am a graduate student in the Master's in BioScience Enterprise programme at the University of Cambridge,UK - a programme in bio-entrepreneurship developed in conjunction with the MIT in Cambridge, Massachusetts. As part of the programme, I am currently undertaking a dissertation project entitled “Methodology to value intellectual property and other rights subsisting in partly developed pharmaceutical technologies and products”. In order for me to undertake the project, I have to obtain information on the tools that business development professionals like yourself use to value part-developed projects during the negotiation of commercialisation deals. The sponsor of my project, Cambridge Healthcare and Biotech, has suggested that I contact you. I would be grateful if you or a representative from your company could take the time to complete the attached questionnaire. The questionnaire should not take more than 10-15 minutes to complete. I will ensure that all contributors receive an analysis of the results. The analysis will provide an insight into the way the industry is applying valuation tools and may assist you in your future assessments, and the maximisation, of the value of your projects. I am also happy to conduct a telephone interview if that would be preferable. I look forward to hearing from you. If you have any questions, please do contact me. Many thanks in advance for any assistance you are able to offer. Sangeeta Puran

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A questionnaire on methods used to value partly developed projects in the biotechnology sector

Thank you for taking the time to complete the attached questionnaire. Analysis of the results of the questionnaire forms part of a dissertation project currently being undertaken by Sangeeta Puran, a graduate student in the Master’s in BioScience Enterprise programme at the University of Cambridge, UK. This one-year multidisciplinary Master's degree in bioentrepreneurship has been developed by the Graduate School of Biological, Medical and Veterinary Sciences of the University of Cambridge, in conjunction with the MIT in Cambridge, Massachusetts. Further information on the programme can be found at www.biot.cam.ac.uk.

The dissertation project seeks to consider the different methods currently used to assess the value of partly developed projects in the project portfolio of biotechnology companies. The project considers the UK biotechnology industry and the US biotechnology industry. The project is sponsored by Cambridge Healthcare and Biotech, a UK based strategic consultancy specializing in licensing. Further information on Cambridge Healthcare and Biotech can be found at www.chandb.com.

Please email the completed questionnaire to isp21@cam.ac.uk by 20 May 2005. Your responses will be treated as confidential. Please tick the following:

I would like the results of the project sent to me. I am happy to be expressly thanked in the dissertation.

If you have any questions or concerns, please contact:

Sangeeta Puran Email: isp21@cam.ac.ukPhone: + 44 (0) 7910 493 257 Postal: Fitzwilliam College Cambridge CB3 0DG UK

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General

1. What method(s) do you use to value partly developed projects85? Please tick all boxes that apply.

Standard net present value (NPV)86

Risk-adjusted NPV87 Scenario analysis Decision-tree analysis Monte Carlo Real-options Comparables

Other(s) – Please specify below.

Relevant NPV parameters[If you do not use NPV based methods, please proceed to Question 10]

2. How do you forecast product sales? Using a bottom up approach (e.g., based on epidemiology) Using a market based approach Using forecast peak sales and standard sales evolution curves

Other(s) – Please specify below.

3. What forecast period do you use?

From now <10 yrs 10-15 yrs >15 yrs Patent expiry From launch <10 yrs 10-15 yrs >15 yrs Patent expiry

Other(s) - Please specify below.

4. On a scale of 1 to 5 - with 1 being broad brush estimate and 5 being detailed analysis - how much analysis goes into the construction of the expenditure lines of your NPV based model? Please tick a box in respect of each of the expenditures specified below.

Pre-clinical expenditures 1 2 3 4 5Clinical expenditures 1 2 3 4 5Regulatory approval expenditures 1 2 3 4 5Manufacturing expenditures 1 2 3 4 5Marketing and promotional expenditures

1 2 3 4 5

Other(s) – Please specify below. 1 2 3 4 5

1 2 3 4 5

85 Partly developed projects are to be taken to mean pre-launch/market projects 86 Also referred to as discounted cashflow methodology 87 Also referred to as expected net present value methodology

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5. What discount rate do you use? < 10% 10 -13% 14 -17% 18 -20% >20% Discount rate varies depending on development stage. Please specify below the

relevant range of the discount rate.

6. In the case of risk-adjusted NPV, what probability of success rates (i.e., probability of reaching market from a development stage) do you use? Please tick a box in respect of each of the stages specified below.

Pre-clinical <5% 5-10% >10% Phase I <10% 10-20% >20% Phase II <20% 20-40% >40% Phase III <40% 40-80% >80% Regulatory approval <80% 80-90% >90%

Not applicable

7. Which best describes the probability of success rates you have identified in Question 6?

Internally generated and project specific Internally generated and used generally for all projects Based on external source(s). Please specify the source(s) below.

Other description. Please specify below.

8. On a scale of 1 to 5 - with 1 being not very sensitive and 5 being very sensitive - how sensitive is your model to the relevant parameters? Please tick a box in respect of each of the parameters specified below.

Market size 1 2 3 4 5Price 1 2 3 4 5Pre-clinical development expenditures

1 2 3 4 5

Clinical development expenditures 1 2 3 4 5Manufacturing expenditures 1 2 3 4 5Sales and marketing expenditures 1 2 3 4 5Probability of success rates 1 2 3 4 5

Other(s) – Please specify below. 1 2 3 4 5

1 2 3 4 5

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9. What is the key advantage and disadvantage of your NPV based method? Please specify below.

Key advantage

Key disadvantage

Other methods and comments

10. If you use a method(s) other than or in addition to NPV based methods, what is the key advantage and disadvantage of such method(s) over NPV based methods? Please specify - including the relevant method(s) used - below. Key advantage

Key disadvantage

11. How do you believe the approach of biotechnology business development to valuing partly developed projects differs (if at all) from that used by - Investment analysts

“Big pharma” business development

VCs

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o0o

Thank you again for taking the time to complete this questionnaire. Please feel free to use the space below to provide any additional comments.

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Appendix 5[Section 4.1]

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Advantages and disadvantages of NPV based and comparables methodologies used to value part-developed projects

Table 2 – Advantages of NPV based methods

“Provides a feel for the relative value and risk of internal projects” (biotech) “Most well understood by management” (pharma) “It is a start to bringing parties together for a discussion whether for financing or partnership discussions” (biotech) “It takes all of the relevant information without over thinking the issue” (biotech) “Education tool for shareholders and investors in terms of explaining where one is coming from”(VC) “Useful tool for looking at company programme or product” (VC) “Very simple to use and to explain to management” (biotech) “Can get an estimate of approximate valuation, and which variables have greatest effect upon your forecast, i.e. in a sensitivity analysis” (VC) “Provides sufficient data rapidly for licensing timing and deal term proposals and gives strength in any negotiations” (biotech) “Forces consideration of realistic risks, costs, size of rewards, cashflows and takes account of time value of money. In the end it’s just a check to show whether the gut feel is justified.” (biotech) “It can be adjusted easily for changes in interest rates, although we have consistently used a 10% ‘risk free’ rate” (analyst)

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Table 3 – Disadvantages of NPV based methods

“Not useful for early stage projects” (VC) “Accuracy of method questionable given the need to make so many assumptions” (biotech) “Any one model is just one picture. We typically used several models for one project to capture any variables particularly important to the project and decision-making process.” (biotech) “It can be manipulated easily to give the answer desired with respect to Go/No Go” (biotech) “NPV does not reflect any market sensitivity – for instance, you have market failures and the real value of deals does change. Not reflected in NPV value.” (VC) “Very sensitive to changes in parameters” (biotech) “Only as good as your assumptions: garbage in = garbage out.” (VC) “Ultimately valuation is a very subjective tool. NPV implicitly assumes that there is an intrinsic value. In reality valuation depends on what the market is doing at that time – otherwise there would be no bubbles and no burst bubbles.” (VC) “Ball park figure only” (biotech) “Not really applicable before the end of Phase I” (VC) “The inputs are often crude estimates. For example, risk varies considerably from project to project” (biotech) “As constructed now, this approach cannot differentiate between NPVs of products made on the basis of forecasts made with relatively high degree of certainty … and NPVs of drugs in new areas where there is much uncertainty. For both types of products, our central case of sales and margins et may the same, but each one may have a much wider range of possible outcomes.” (biotech) “Another weakness is that the outcome is a point estimate of value. Considering the uncertainty of R&D projects the output should be a range that highlights the possible outcomes.” (biotech) “NPV implicitly assumes there is an “intrinsic” value. I don’t believe in intrinsic value. Valuation depends on what the market is doing at that time…otherwise there would be no bubbles or bursts.” (VC)

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Table 4 – Advantages of Comparables

“Comparators are perceived as being real – market price as opposed to theoretical price. Allows you to point to real situation which achieved X – this puts across a stronger statement than my spread sheet says X” (VC) “Also comparables is fast and if often a good enough for investment management, investment banking and venture capitalist.” (VC) “Provides deal trends” (biotech) “Comparison of the price paid for similar projects at a similar stage is the only way of estimating the market value of a project, which can be very different to the theoretical value obtained through NPV” (biotech) “Comparables tell you what the market is doing now” (VC)

Table 5 – Disadvantages of Comparables

“There are few comparators. Consider accessing pool of knowledge in the US – longer history and larger pool – however would also have to apply caveats when transferring data from one territory to another.” (VC) “Comparable deals are not always available” (VC) “Deal structures are often very complicated and the data for comparables is very difficult to cleanse” (biotech) “Relies on other companies success in licensing” (biotech) “Comparables not useful for a new market. No one would have predicted a Viagra market” (VC) “There may not be a comparable deal to compare with or the details of price may not be available. Also importantly, the technology may have been grossly under or over valued in the comparable deal.” (biotech) “Comparables are insensitive to future events and unique factors that may apply to the company at hand, that will persuade investors to either over or undervalue a project and/or a company” (biotech)

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Appendix 6Section 4.2.5 – Probabilities of success rates

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Probability of success rates In Question 6 of the questionnaire, the participants using rNPV were asked to identify in respect of each of the following development stages, which of the following probability of success rates (i.e., probability of reaching market from a development stage) they used in their rNPVs:

Pre-clinical <5% 5-10% >10% Phase I <10% 10-20% >20% Phase II <20% 20-40% >40% Phase III <40% 40-80% >80% Regulatory approval <80% 80-90% >90%

Table 6 illustrates the findings. In the case of the pre-clinical stage, the predominant rate specified by biotech and pharma was 5-10%. Analysts and VCs participants all specified using a probability of success rate of <5%. This suggests that biotech and pharma tend to be more optimistic about the probability of candidates in pre-clinical development successfully progressing to market. Similar optimism was observed in respect of regulatory approval. Table 6 – Probability of success rates

Biotech Pharma* Analysts* VCs

Pre-clinical 5-10% 5-10% <5% <5%

Phase I 10-20% 10-20% 10-20% 10-20%

Phase II 20-40% 20-40% 20-40% 20-40%

Phase III 40-80% 40-80% 40-80% 40-80%

Regulatory approval

>90% >90% 80-90% 80-90%

* Based on response of 1 participant

Again, there was a perception amongst some of the participants that pharma would be in position to apply the most accurate rates based on experience. However, responses from both the pharma participants indicated that their firms relied primarily on industry averages.

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Given the findings in respect of sensitivities, it is recommended that differences amongst the groups be considered on the basis of narrower categories, particularly in relation to phases I to III. Source of ratesQuestion 7 of the questionnaire asked participants whether the rates they had identified were internally generated and project specific, internally generated and used generally for all projects or based on external sources. In the event that participants selected the last category, respondents were asked to specify the source. Figure 19 illustrates that the majority of the participants identified that the probability of success rates used were based on external sources.

Figure 19 – Probability of success rates used in NPV modelling

0

2

4

6

8

10

12

Based on external source Internally generated and project specific internally generated and generalSource

Scor

e

VCAnalystPharmaBiotech

The relevant external sources specified were as follows: Centre for Medicine Research, general guidelines from academic, banking and consulting company research on the pharma and biotech industries, Tufts Centre for Drug Development, Ernst Young Biotech Report,

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Boston Consulting, McKinsey, Lehman, Accenture Speed to Value, CMR Survey PWC Pharma 2005 Report.

The predominant use of external sources may explain the consistency of rates with industry averages. Notwithstanding the predominant reliance on industry rates, there was evidence that certain participants used internally generated and project specific rates. Furthermore, there was a general awareness amongst participants of the limitations of applying industry average benchmarks to the development of a product in a specific therapeutic class. A few of the participants also raised other limitations that had not been reported in the literature considered in this study. One participant commented that probability of success rates used in rNPV modelling was based on the classic drug development risk profile. Such a profile is not necessarily applicable in the case of products such as isomer products, which develop new products from existing drugs. Another participant pointed out that historical rates are based on pharmaceuticals. In the case of products such as vaccines that cannot be categorised as pharmaceuticals, applying such rates may not be accurate. The relevant participant commented that whilst, on the one hand, the efficacy of a vaccine is likely to be well established during clinical stage, on the other hand, a live viral vaccine tends to be associated with increased safety issues. One of the analysts also indicated that his firm routinely adapted rates depending on the level of novelty and therapeutic area.

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