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The Value of Building Flexibility into Product
Portfolios:
A Single Site Case Study
A Research Report presented to
In partial fulfilment of the requirements for the
Masters of Business Administration Degree
By
Dale Edwards (EDWDAL001)
and
Kate Turner-Smith (TRNKAT002)
December 2005
Supervisor: Dr Evan Gilbert
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Research Report
The case data presented in this research report is confidential and remains the property
of the Biovac Institute of Southern Africa. The data may not be used by the Graduate
School of Business for a period of 12 months post external examination without
written consent from the Biovac Institute. This report will not be made public prior to
1 January 2007.
The authors wish to thank Selwyn Kahanovitz, and the staff of the Biovac Institute for
their valuable contributions in data which provided the basis of this research. We also
appreciate the access to information which we were given by the Biovac Institute. We
further wish to thank the associates of the Biovac Institute (Anna Blanca of Heber
Biotech, Jean Petre of Bionet, Stephen Jarret of UNICEF) who participated in the
interviews used to generate assumptions and data for the analysis. The authors
appreciate the provision of industry reports by Cape Biotech and the Innovation Fund
which allowed us to review the vaccine industry in preparation for this research.
The authors certify that this report is their own work and all references used are
accurately reported.
Signed:
Dale Edwards (EDWDAL001) Kate Turner-Smith (TRNKAT002)
_____________________________ ____________________________
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Research Report
The Value of Building Flexibility into Product
Portfolios:
A Single Site Case Study
Abstract
This report documents the analysis of portfolios of vaccine research and development
(R&D) projects available to a South African vaccine producer (Biovac Institute) using
both Net Present Value (NPV) methodology and Real Options Analysis (ROA)
methodology in order to determine whether building flexibility into project portfolios
adds value to firms operating in the highly uncertain environment of R&D. R&D
projects have independent technological and market uncertainties, which affect
project and portfolio valuations. Both sources of uncertainty need to be taken into
account when managers select projects with which to populate their portfolios. A
custom built ROA tool was developed in order to model the uncertainties and value
the defer, abandon or switching options available to the Biovac Institute in terms of its
portfolio creation. After analysing a number of portfolios, it was concluded that
significant value can be added to the firm if flexibility, particularly in the form of
being able to switch from the production of one product to another while utilising the
same production platform, is included in the design of the optimal portfolio. It is
further concluded that the model designed for this analysis is an appropriate model to
use to inform management of optimal portfolio design.
Key Words:
Real Options Analysis, Portfolio, Monte Carlo Simulation, Vaccine, Research and
Development
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Table of Contents Abstract .................................................................................................................................................... 3 Table of Contents ..................................................................................................................................... 4 List of Illustrations ................................................................................................................................... 5 List of Tables ............................................................................................................................................ 5 List of Equations....................................................................................................................................... 5 Glossary .................................................................................................................................................... 6 1. Introduction .......................................................................................................................................... 7
1.1. Background to the Research .......................................................................................................... 7 1.2. The Research Problem ................................................................................................................... 7 1.3. The Purpose of the Research Report.............................................................................................. 8 1.4. Limitations of the Research ........................................................................................................... 9 1.5. Layout of the Report .................................................................................................................... 10
2. Literature Review ............................................................................................................................... 11 2.1. The Vaccine Industry ................................................................................................................... 11
2.1.1. Industry Trends in the Developed World .............................................................................. 12 2.1.2. Industry Trends in the Developing World ............................................................................ 17 2.1.3. The South African Vaccine Industry .................................................................................... 19 2.1.4. The Biovac Institute .............................................................................................................. 20
2.2. Portfolio Evaluation Methods- in Context ................................................................................... 24 3. Data Gathering.................................................................................................................................... 28
3.2. Data Gathering and Interviews .................................................................................................... 28 3.3. Data Structuring ........................................................................................................................... 29
4. Data Analysis (Methodology and Findings) ....................................................................................... 32 4.1. Base Case NPV Analysis ............................................................................................................. 32
4.1.1. Assumptions ......................................................................................................................... 33 4.1.2. NPV results ........................................................................................................................... 34
4.2. Product Decision Trees ................................................................................................................ 34 4.3. Monte Carlo Simulation to model uncertainties .......................................................................... 38
4.3.1. Base case PVs and static rates of return .............................................................................. 39 4.3.2. Defining Assumptions and Forecast Variables ..................................................................... 39 4.3.3. The Simulation ..................................................................................................................... 43 4.3.4. Test for significance ............................................................................................................. 44 4.3.5. Results of the Simulation ...................................................................................................... 44 4.3.6 . Sensitivity analysis .............................................................................................................. 45
4.4. Real Option Valuations ............................................................................................................... 48 4.4.1. Data required before construction ......................................................................................... 48 4.4.2. Construction of the binomial lattices .................................................................................... 49 4.4.2. Results of the Real Options Analysis ................................................................................... 53
5. Portfolio Construction ........................................................................................................................ 53 5.1. Portfolio 1: Rabies, Pentavalent, HPV ......................................................................................... 53 5.2. Portfolio 2: Rabies, Pentavalent, Hib (without the switching option) ......................................... 54 5.3. Portfolio 3: Rabies, Pentavalent, Hib (with a switching option) .................................................. 55 5.4. Portfolio 4: Pentavalent, HPV, Hib (without a switching option) ............................................... 55 5.5. Portfolio 5: Pentavalent, HPV, Hib (with a switching option) .................................................... 56 5.6. Portfolio Comparison .................................................................................................................. 56
6. Biovac Feedback session .................................................................................................................... 58 7. Discussion and Conclusions ............................................................................................................... 59 8. Recommendations .............................................................................................................................. 60
Optimal Projects ................................................................................................................................. 60 The use of Real Options ..................................................................................................................... 60
9. Appendices ......................................................................................................................................... 61 10. References ........................................................................................................................................ 69
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List of Illustrations Figure 1: The Structure of the Report ..................................................................................................... 10 Figure 2: The Four Step Process (Copeland and Antikarov, 2003; p220) .............................................. 32 Figure 3: Illustrative Project stages and Objective Probabilities ........................................................... 35 Figure 4: Rabies decision tree ............................................................................................................... 36 Figure 5: HPV decision tree ................................................................................................................... 36 Figure 6: Pentavalent decision tree ........................................................................................................ 37 Figure 7: Hib and Meningitis decision trees with switching options ...................................................... 38 Figure 8: Distribution Assigned to the Coverage Assumption. ........................................................... 40 Figure 9: Distribution Assigned to the Hib Bulk Price Assumption ................................................... 40 Figure 10: Distribution Assigned to the Hib Internal Transfer Price Assumption ............................. 41 Figure 11: Distribution Assigned to the Hib COGS Assumption ....................................................... 41 Figure 12: Distribution Assigned to the Meningococcus Vaccine Price Assumption ......................... 41 Figure 13: Distribution Assigned to the Pentavalent Vaccine Price Assumption ............................... 42 Figure 14: Distribution Assigned to the Rabies Vaccine Price Assumption ....................................... 42 Figure 15: Distribution Assigned to the SADC Births/year Assumption ............................................ 43 Figure 17: Sensitivity chart, Hib ............................................................................................................. 45 Figure 18: Sensitivity chart, HPV ........................................................................................................... 46 Figure 19: Sensitivity chart, Meningitis ................................................................................................. 47 Figure 20: Sensitivity Chart, Pentavalent ............................................................................................... 47 Figure 21: Sensitivity chart, Rabies ........................................................................................................ 48 Figure 22: The Process of Constructing Additive Binomial Lattices ..................................................... 52 Figure 23: Comparison of Portfolios using Net Present Value and Real Options Analysis ................... 57
List of Tables Table 1: Biovac Employees Interviewed during the gathering of data ................................................... 28 Table 2: Other Interviewees and their affiliations .................................................................................. 29 Table 3: Data Gathered and used to build models .................................................................................. 30 Table 4: Structure of Project Data for Analysis ...................................................................................... 31 Table 5: Project NPVs ........................................................................................................................... 34 Table 6: Project Present Values at time point 1 and 2 and the Volatility Estimates (Copeland and Antikarov, 2003)..................................................................................................................................... 39 Table 7: Trial Validity in the Monte Carlo Simulation ........................................................................... 43 Table 8: T-stats Achieved during a Crystal Ball Simulation .................................................................. 44 Table 9: Standard Deviations of the Mean Return Values ..................................................................... 44 Table 10: ROA Results ........................................................................................................................... 53 Table 11: Valuation of portfolio 1 (no switching option) ....................................................................... 54 Table 12: Valuation of Portfolio 2 (No switching option) ...................................................................... 54 Table 13: Valuation of portfolio 3 (with a switching option) ................................................................. 55 Table 14: Valuation of Portfolio 4 (without a switching option) ............................................................ 56 Table 15: Valuation of Portfolio 9 (with a switching option) ................................................................. 56 Table 16: Comparative Valuation of a Portfolio containing Meningitis with a Switching Option ......... 57
List of Equations Equation 1: Calculation of the Cash Flows ............................................................................................ 33 Equation 2: Volatility Estimate of Expected Returns (Copeland and Antikarov, 2003) ........................ 38 Equation 3: Test for significance (Gilbert, 2005) ................................................................................... 44 Equation 4: Calculation of the up movement for a Multiplicative Binomial Lattice .......................... 49 Equation 5: Calculation of the up movement for an Additive Binomial Lattice ................................. 49 Equation 6: Calculation of the down movement for an Additive Binomial Lattice ............................ 49 Equation 7: Calculation of the Present Values used in the Binomial Lattice ......................................... 50 Equation 8: Calculation of the Value of the Project including Flexibility .............................................. 52
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Glossary
DST Department of Science and Technology
IDC Industrial Development Corporation
PPP Public Private Partnership
BRIC Biotechnology Regional Innovation Centre
GMP Good Manufacturing Practice
EPI Expanded Program for Immunization
DoH Department of Health
BEE Black Economically Empowered
R&D Research and Development
NPV Net Present Value
ROA Real Options Analysis
Biovac The Biovac Institute
WHO World Health Organisation
CJD Creutsveld Jacob Disease
BSE Bovine Spongiform Encephalitis (Mad Cow Disease)
HPV Human Papillomavirus
DTP Diptheria Tetanus Pertussis
Hib Haemophilus Influenzae
HepB Hepatitis B
UCT University of Cape Town
GAVI Global Alliance for Vaccines and Immunization
TB Tuberculosis
DCF Discounted Cash Flow
ROA Real Options Analysis
NPV Net Present Value
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1. Introduction
1.1. Background to the Research
The company described in this proposal, The Biovac Institute, is faced with a number
of difficult decisions with regards to selecting a portfolio of projects that is aligned
with the business strategy, that will satisfy the stakeholders and that will ensure that
the company becomes profitable in the future. A traditionally financial analysis tool
was applied in an effort to assist the company in making some of these decisions.
Some of the information presented in this document is sensitive and therefore should
be kept confidential as far as possible.
1.2. The Research Problem
The research question around which this study is focused is: Considering the resource
constraints the Biovac Institute is exposed to, is it preferable to implement a portfolio
of diverse projects with little interdependency and flexibility or to implement a
portfolio of synergistic projects where the switching of products is possible, albeit at a
price?
The Biovac Institute has a number of product choices available to it:
The company has been working on a rabies vaccine for a number of years. The
product has proved illusive, although there is significant experience within the
organisation, which makes this project a relatively low risk project. It has a small
potential market, however, and is produced using a unique production platform.
In 2003, Biovac began to collaborate with the University of Cape Town on the
development of a Human Papillomavirus (HPV) vaccine. The group is developing a
novel product and is a long way from having a product on the market. The risks
associated with this product are high; however, the potential returns are also high.
HPV vaccines are also expected to be produced using a discrete production platform.
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In 2004 the company began the development of a combination vaccine (Pentavalent)
against Diphtheria, Tetanus, Pertussis, Hepatitis B and Haemophilus Influenzae (Hib).
This product is strategically very important both to the company and to South Africa.
This vaccine forms part of the EPI and every child is expected to be vaccinated
against these diseases. Currently South African children get these vaccinations
separately because the world supply of combination vaccine is limited to the
developed world. Combining these antigens requires complex chemical formulation.
A critical element of the Pentavalent vaccine is the Hib antigen. Therefore in order to
secure supply, it is important for Biovac to manufacture its own Hib. On its own, Hib
has limited market potential; however, as part of the Pentavalent vaccine, it is
extremely valuable. The Hib antigen is manufactured via fermentation and then a
conjugation process. An advantage is that there are additional vaccines that require the
same technology platform for manufacture. These include: Meningitis
(Meningococcus), Typhoid Fever, Pneumococcus and Cholera. Should Biovac be
successful at developing a Hib vaccine, they will have the opportunity of diversifying
into these alternative products with minimum investment in the technology. The cost
will be the loss of production during the switch-over time.
During this research project, the authors focused on establishing whether or not
building this technological flexibility into the portfolio of Biovacs projects accounts
for any additional value in the portfolio. When evaluating the value of switching,
however, the study was limited to evaluating the switch between Hib and Meningitis.
The additional products were not considered for the purposes of this report.
1.3. The Purpose of the Research Report
The study focuses on looking at how creating flexibility within vaccine R&D adds
value to the firm. In many instances, accounting for flexibility in projects adds costs
to the project. The authors set out to establish whether or not the extra cost creates
extra value. Five possible portfolios of vaccine projects were evaluated; two where
there is flexibility in terms of products produced using the same production
technology and three where there is little flexibility in terms of switching between
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products. In all portfolios there is tremendous uncertainty; therefore this study lent
itself to the use of Real Options Analysis as an analysis tool. A secondary objective
was to establish whether or not it is feasible to use Real Options Analysis routinely as
a project selection or portfolio design tool for a company such as Biovac.
1.4. Limitations of the Research In order to build a meaningful Real Options Analysis model, additive, recombining
binomial lattices had to be constructed. The nature of these R&D projects is such that
the present values of the project need to become negative as it is unrealistic to expect
all R&D projects to produce positive values even in the down state of the world.
Multiplicative, recombining binomial lattices do not allow the present values to
become negative, the present values merely tend towards zero. In constructing the
additive binomial lattices for this project, the authors were confronted with a distinct
lack of literature pertaining to valuing options presented in additive binomial lattices
including technological uncertainty. All examples containing technological
uncertainty examined valued options in multiplicative binomial lattices. Although the
lack of literature was not a limitation of the research, it proved to be a source of
frustration for the authors.
In many cases, at the time of interviewing interviewees, we were uncertain of all the
information required. Because the interviewees who participated in the study are all
extremely busy executives, we found it difficult to secure second interviews with the
participants. In some cases, therefore, we had to make assumptions with a relative
lack of information.
Due to the fact that the models took so long to finalise, we were unable to present the
results in the form of a workshop to Biovac. We therefore are speculating about
whether or not the tool developed will be useful to them on the basis of one
presentation to the R&D Manager. We are, however, fairly certain that the results of
the study will be beneficial to Biovac.
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1.5. Layout of the Report
The report is structured in such a way that the theory behind the research (The
Literature Review) is presented first in order to position the reader. Following the
Literature Review, the Data Gathering methodology and findings are presented. The
Data Analysis section contains both the methodology and the findings so as to guide
the reader though the section. The construction of the portfolios is then described
along with the findings. Finally the results will be discussed, conclusions drawn and
recommendations are suggested to the sponsoring company in terms of portfolio
construction and the use of Real Options Analysis therein. The following schematic
represents visually the structure of the report.
ed in such a way that the theory behind the research (The
Literature Review) is presented first in order to position the reader. Following the
Literature Review, the Data Gathering methodology and findings are presented. The
Data Analysis section contains both the methodology and the findings so as to guide
the reader though the section. The construction of the portfolios is then described
along with the findings. Finally the results will be discussed, conclusions drawn and
recommendations are suggested to the sponsoring company in terms of portfolio
construction and the use of Real Options Analysis therein. The following schematic
represents visually the structure of the report.
Introduction
Literature Review
Research Problem
Vaccines
Biovac
Portfolio Analysis
Data Gathering
Data Analysis
Decision Trees
NPV
Monte Carlo
Real Options
Portfolio Analysis
Discussions
Conclusions and Recommendations
Introduction
Literature Review
Research Problem
Vaccines
Biovac
Portfolio Analysis
Data Gathering
Data Analysis
Decision Trees
NPV
Monte Carlo
Real Options
Portfolio Analysis
Discussions
Conclusions and Recommendations
Figure 1: The Structure of the Report Figure 1: The Structure of the Report
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2. Literature Review
2.1. The Vaccine Industry
New developments in vaccine technology that have emerged over the past decade
have transformed this relatively lack-lustre, price-competitive market sector into a
technology-driven industry characterised by increasing annual growth rates that
approached 10% during the late 1990s (Business Communications Company Inc,
2001). Prophylactic or preventative vaccines have been the focus of this market,
which has grown tremendously due to expanding global vaccine coverage (Frost and
Sullivan, 2001). Also contributing to market growth are developments in new vectors,
non-injectable vaccines, DNA vaccines and other novel vaccination approaches.
The development and introduction of new vaccines is a costly and time-consuming
process. Unfortunately, those most in need individuals in developing countries are
the last to receive these prophylactic and therapeutic pharmaceuticals. From the time a
vaccine is first licensed in a developed country to the time most of the poor in
developing countries have access to the vaccine can be as long as 20-30 years (Sabin
Vaccine Institute, 2002). For this reason it is important that Vaccine producers in
developing countries, such as Biovac in South Africa, succeed.
In 1998, the global vaccine industry was worth US$1 billion, Frost and Sullivan
predict that today (2005), it is worth US$6.5 billion (Frost and Sullivan 2005). These
sales comprise 5.4 billion doses selling at an average price of US$ 1.11 per dose
(Unicef, 2004). Revenues are growing by 10% annually, and have been doing so since
1992. This growth in the industry has come about mainly as a result of the
development of newer vaccines against diseases such as Influenza, Hepatitis B and
Haemophilus Influenzae B. Currently, vaccines to protect against HIV, cancer, and
other diseases are under development, the success of any of these vaccines will result
in further growth of the market, possibly to US$12-25 billion by 2010. Developed
nations consume 12% of vaccines by volume, but produce 82% of revenues (Frost and
Sullivan, 2001). Thus there is a rapid move towards two distinct vaccine industries,
one aimed at low volume, high margin therapeutic vaccines for industrialised
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countries and the other at high volume, low margin prophylactic vaccines for
developing countries.
The global vaccine industry has experienced significant changes over the past 10
years. The development of new vaccines and improved production techniques has
resulted in the environment becoming more and more competitive. Increased
competition has, in many cases, led to a downward pressure being placed on vaccine
prices. This in turn has resulted in a move towards specialisation in order for
producers to maintain their profitability. Large manufacturers in the developed world,
in particular, have ceased production of the commodity vaccines that have come under
significant price pressure with the entrance of new players in the industry. These
manufacturers have instead focused on niche products aimed at the less price sensitive
target market. Unfortunately, this move away from the production of commodity
vaccines has left the world in short supply of vaccine products such as DTP, Polio and
HepB. The price sensitive customers in the developing world have been hardest hit by
this shift. However, this has introduced an opportunity for producers in the developing
world to produce vaccines for their home markets without having to necessarily
compete with first world producers. Many producers have taken advantage of this
opportunity and have scaled up their operations in order to supply vaccines at low
prices. This is particularly evident in India and China (Harjee Interview, 2005).
2.1.1. Industry Trends in the Developed World As technology improves and vaccines are becoming more effective, so the developed
world is looks for more and more sophisticated vaccines. Being totally assured of
having access to the basic EPI regime, developed world customers are turning towards
the prevention of the lifestyle diseases such as obesity, cancer and the like. These
products, being targeted at a wealthier niche market can command far higher prices
than those for infectious diseases. The developed world manufacturers are obliging
their customers as it assures them of maintaining higher profit margins. Examples of
some of these typically developed world products are described below.
Travel Vaccines: The travel vaccines market is populated with as many as 27
producers and or developers, most of which are large manufacturers situated in
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the developed world (Frost and Sullivan, 1999a). The typical vaccines that fall
into the travel vaccine description are described below.
o Hepatitis A: In their 1999a report, Frost and Sullivan report that
revenues generated by the Hepatitis A vaccine amounted to US$172.8
million in 1998 and that this estimate is likely to be understated given
the large population of travellers into endemic areas as well as the fact
that this population is aging and vaccine compliant. There are 8
companies that sell HepA vaccines, the largest market participant
being GlaxoSmithKline. The average price of a HepA vaccine in the
US in 1998 was US$59.30 and in Australia was US$30.50. The value
of this vaccine is high because treatment options for HepA are limited.
Antibiotics are not effective against this viral disease. In the 1999a
report, Frost and Sullivan predicted that the HepA market would
increase to US$370 million in 2005.
o Typhoid Fever: Typhoid fever vaccines are thought to have generated
$68.8 million in 1998 (Frost and Sullivan, 1999a). There are three
different types of typhoid fever vaccines, a whole cell vaccine prepared
from Salmonella Typhi, a subunit vaccine prepared from purified
bacterial coat protein, and an attenuated strain of Salmonella Typhi
which is given orally. The pathogens that cause Typhoid Fever are
becoming increasingly more resistant to antibiotics which increases the
demand for vaccines as treatment options are reduced. The average
price of a dose of typhoid fever vaccine is $25-32 and the market
leader in this market is Swiss Serum and Vaccine Institute. There is
tremendous focus on moving away from injectable vaccines to oral
equivalents. In the 1999a report, Frost and Sullivan predicted that the
Typhoid Fever market would increase to US$239.4 million in 2005.
o Yellow Fever: The Yellow Fever Vaccine market was estimated to be
valued at US$75.7 million in 1998 (Frost and Sullivan, 1999a).
Because a Yellow Fever vaccination is a prerequisite when travellers
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are entering an endemic area, market penetration of this vaccine has
been higher than most others. Yellow Fever is transmitted by a
mosquito and there is no effective treatment for the disease. This
increases the demand for effective vaccines against this disease. The
average price of a Yellow Fever Vaccine ranges from US$48-52 per
dose; however, in Japan the price is as low as US$3.4. The market
leader in this market is Sanofi Pasteur. In their 1999a report, Frost and
Sullivan predicted that the Yellow Fever market would increase to
US$215.1 million in 2005.
o Japanese Encephalitis: The market for JE vaccines was estimated to
be US$14.3 million in 1998 (Frost and Sullivan, 1999a). The
penetration for this vaccine has been low due to the fact that the
historical 3-dose regimen can cause local reactions in patients. In
addition, the disease is caused by the bite of a mosquito endemic to the
rice-growing areas of the world. The average price for a dose ranges
from US$30 -70 due to the relatively low demand. Sanofi Pasteur are
the market leaders and are currently developing and testing a single
dose vaccine formulation. Frost and Sullivan predicted that the JE
market would increase to US$36.3 million in 2005.
o Cholera: The market for Cholera vaccines was estimated to be US$8
million in 1998 (Frost and Sullivan, 1999a). A reason for this could be
that until recently, the WHO has not encouraged the use of cholera
vaccines due to lack of performance. More efficient vaccines are
currently under development; however, since cholera is transmitted via
contaminated water, it is thought that the risk of exposure to travellers
is less than 1 in 2000. The average price per dose is therefore between
US$7 and 21. This would be too high for use in developing countries
where the risk of exposure to the inhabitants is significantly higher.
Frost and Sullivan predicted that the Cholera market would increase to
US$15.7 million in 2005.
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o Malaria: There is currently no malaria vaccine available. There are
hopes that one will be licensed for sale in 2010
(http://news.bbc.co.uk/2/hi/health/3742876.stm; 15/10/2004). Because
of its importance in the travel vaccines market, much emphasis is being
placed on the development of this vaccine. GlaxoSmithKline and
others are in the process of testing candidate vaccines (Frost and
Sullivan, 1999a).
o Other potential travel vaccines that have not yet reached the market
include:
Dengue (www.cdc.gov; 23/11/05)
ETEC (www.co-gastroenterology.com; 23/11/05)
Shigella (www.who.int; 23/11/05)
Speciality Vaccines: In 1998 the sale of world speciality vaccines were
estimated to have totalled US$287.1 million (Frost and Sullivan, 1999b).
Speciality Vaccines are described as those vaccines which are developed for
specific uses, which often include filling immunisation schedules. There were
28 organisations developing speciality vaccines in 1998, however, as the
industry consolidates, so this number is decreasing. The following vaccines
fall under the term speciality vaccines.
o B19 Parvovirus: No human vaccine available (www.stanford.edu;
23/11/05)
o Rabies: There are a number of rabies vaccines on the market. The
WHO has recommended that the use of neural tissue vaccines (the
traditional vaccine) is ceased, which has resulted in a move by all
major producers to cell culture derived vaccines. This has caused the
price of rabies vaccines to increase and the demand to decrease. The
producers have suffered from a loss of economies of scale and the
industry is in the process of repositioning itself (Petre Interview, 2005).
The market for rabies vaccine in 1998 was estimated to be US$252.5
million (Frost and Sullivan, 1999b). Rabies vaccines are usually given
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therapeutically (after an individual is exposed), therefore in the
developed world they are able to command a high price (up to US$130
in the USA). The developing world, however, sees 99% of the worlds
rabies deaths (www.who.int) which hinders growth in the rabies
market. Currently the market leader in the rabies market is Chiron.
o Staphlococcus Aureus: There is currently no vaccine available for this
disease, which is responsible for most of the complications that occur
in dialysis patients. Since approximately half of the worlds dialysis
patients reside in the USA (Frost and Sullivan, 1999b), this presents an
attractive market opportunity for developed world manufacturers.
There is, however, a product in phase 3 trials at present
(www.nabi.com).
o Tuberculosis: Although TB is a worldwide problem and vaccination
against TB is highly recommended, the majority of TB cases occur in
Africa and South East Asia (Frost and Sullivan, 1999b). The current
vaccine for TB is BCG, which has limited efficacy, and there has been
an increase in the incidence of multi-drug resistant TB, which has
resulted in tremendous impetus in the developed world to develop new
and improved vaccines (Katz Interview, 2005). Because BCG was
developed in the 1920s and is produced using relatively old
technology, the vaccine is sold for as little as 4c by UNICEF (Frost and
Sullivan, 1999b). This makes producing BCG a very unattractive
opportunity for many developed world producers. This role, therefore,
has largely fallen to the developing world producers (Petre Interview,
2005). Currently the market leader in this industry is Pasteur Merieux,
who is focusing strongly on developing new TB vaccines (Frost and
Sullivan, 1999b).
o Lymes Disease: The first Lymes Disease vaccine (developed by
GlaxoSmithKline) went on sale in the USA in 1999 (Frost and
Sullivan, 1999b), however little information exists on its rate of
penetration in the market. This disease is the most commonly reported
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vector-bourn disease in the USA and therefore market growth in the
US is expected.
Lifestyle Vaccines: The following list of vaccines is in development for use
primarily in the developed world. To date none have been successfully
released, but they remain the focus of a number of biotech and pharma
companies.
o Allergies
o Atherosclerosis
o Cocaine addiction (Frost and Sullivan, 1999b)
o Obesity: An obesity vaccine entered trials in May 2005
(www.cytos.com).
o Cancer: As of 2005, there were 5 cancer vaccines in or approaching
clinical trials (Frost and Sullivan, 2005).
2.1.2. Industry Trends in the Developing World Developing countries consume 80% of the worlds vaccine supply, yet contribute a
fraction of the revenue generated by the sale of vaccines (Fitzgerald, 1999). Despite
the large volumes of vaccines consumed by the developing world, vaccine coverage
ratios do not even remotely compare to their developed world counterparts. The
coverage in the developing world tends to respond more to supply side dynamics than
demand dynamics (Milstein and Candries, 2002). There is thus a significant
motivation for countries that require vaccines which are no longer produced by the
developed world manufacturers to develop their own vaccine manufacturing facilities
and secure their supply of vaccines (Wilde, 2001).
The buyers of vaccines in the developing world are limited primarily to humanitarian
organisations such as WHO, UNICEF and USAID (Kahanovitz and Jarret Interview,
2005). These organisations tend to tender for supply contracts, purchase large
volumes of vaccines and hence control the price as well as the supply of these
vaccines (Jarret Interview, 2005). This presents the requirement for substantial
expertise in low cost production and economies of scale in vaccine manufacturing.
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Developing world manufacturers therefore require an injection of technical capability
as well as capital to build the required capacity in vaccines manufacturing and meet
the low cost requirement of the purchasers of vaccines.
A number of developing countries have ramped up their capacity in terms of vaccine
manufacturing to become significant players in the global industry. A brief look at the
rise of China and India provides some insights as to how this may be possible in
South Africa and for Biovac.
Vaccine production in China is growing at 15% p.a. (China Daily, 2003). This is 5%
faster then the total industry growth (Frost and Sullivan, 2005). According to China
Daily (2003), the bottleneck that still exists for Chinas ongoing growth in the vaccine
industry is a shortage of financing. The private market for vaccines in China is
currently insufficient to support ongoing growth in the industry at its current rate. The
Chinese government pledged support to the biotech industry and supported the
conversion of many public research institutes into enterprises for the manufacture of
medicines (Zhenzhen et al, 2004). This benefited Health biotechnology and vaccine
industry tremendously. The large domestic market for home grown products
provided an added contribution to the rapid expansion of the industry as well as
government initiatives to foster an innovation system that promotes private sector
growth (Zhenzhen, et al, 2004).
The Indian biotechnology industry was kick-started by a domestic need. India has a
tremendous Hepatitis B problem (www.wockhardt.com). The government committed
to an immunisation program in 1982 (Chaturvedi and Pandey, 1995) as a result
thereof, which in turn caused a 10% increase in demand for low cost HepB vaccines
and an influx of vaccine producers into India (www.iornet.com). The Indian vaccine
market was estimated to be US$100 million in 1999 and growing at 20% p.a. (Nayak,
1999). In 2002, the Canadian Agrifood Trade Service estimated the Indian vaccine
market to be US$150 million, which amounted to 57% of the total Indian
Biopharmaceutical market (Kennedy School of Government, Harvard University).
The Indian government, in addition to supporting immunisation programs, supported
the local industry by giving preference to local manufacturers (Kennedy School of
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Government) and established the Department of Biotechnology (Nayak, 1999) in
1996 to support medical biotechnology and research and to manage regulatory affairs.
The Department of Biotechnology, not only initiated several vaccine research
projects, but also assisted with building local R&D capacity in terms of vaccine
production (Chaturvedi and Pandey, 1995). The focus of R&D in India is not on new
product development but rather on the development of new and more cost effective
methods of production (Usdin, 2001).
In summary, the successes of the industries in China and India were largely due to
political support, a large local market, and a focus on improving the effectiveness and
economics of existing production methodologies.
2.1.3. The South African Vaccine Industry The South African Department of Health supported the State Vaccine Institute (SVI)
until its acquisition by Biovac in 1999. The SVI produced BCG, Smallpox and Rabies
vaccines as well as Fetal Calf Serum (a biological product required for cell culture).
The technology and products produced by the SVI were outdated and once the
regulatory requirements in South Africa caught up with global standards, the SVI had
to cease production and upgrade the facility. The Department of Health still supports
South African Vaccine Producers (SAVP), which produces snake anti-venom and
specific pathogen free mice for research purposes. Thus, there currently are no
vaccine producers in South Africa. Nevertheless, the Department of Health is still
committed to upgrading the industry, hence, their willingness to establish a public
private partnership with Biovac to upgrade the SVI.
In order to assist Biovac in its transformation of the SVI into a vaccine producing
entity, the Department of Health granted Biovac a supply monopoly to the
Department of Health until the end of 2007. In addition, the Department of Science
and Technology have committed significant funds to the promotion of biotechnology
in South Africa, much of which has been committed to the development of vaccines
and vaccine production capacity.
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It therefore appears that South Africa has learned from the Chinese and Indian
successes, however, the country still has a way to go before it can declare the
biotechnology and vaccine industry a success.
2.1.4. The Biovac Institute 2.1.4.1. The History of the Biovac Institute
Since 1999, the South African Government has strategically focussed on building the
South African Biotechnology Industry. This focus was brought about by the belief
that a core competency such as biotechnology within South Africa would provide the
country with access to improved and more cost effective healthcare solutions as well
as improved production technologies, food and job security. Thus as part of the
biotechnology strategy, the Department of Health called for proposals to establish a
public private partnership (PPP) between the Department of Health and a private
organisation to privatise the States Vaccine Assets. The States Vaccine Assets
consisted of a campus, the State Vaccine Institute, situated in Pinelands, Cape Town
that had historically produced the following products:
1. Small pox vaccine: manufactured 40 million doses per year up to 1978, the year
small pox was declared eradicated. A batch of smallpox vaccine for emergencies
remains at the Institute.
2. Rabies Vaccine: manufactured for clinical trials only until 1994. Production was
halted because of lack of Good Manufacturing Practices (GMP) manufacturing
facility.
3. Percutaneous BCG vaccine: manufactured for 13 years until 2000, due to the
Expanded Program for Immunisation (EPI) moving from percutaneous to
intradermal formulations.
4. Research and Development Projects at the time: Rabies new manufacturing
process.
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Biovac Holdings, an established South African (Black Economically Empowered)
vaccine distribution company submitted an expression of interest and was awarded the
PPP as a result. The PPP is now known as the Biovac Institute.
The Department of Health specified a number of strategic imperatives to which the
Biovac Institute is required to adhere. These include:
To establish manufacturing capacity
To formulate and fill bulk vaccines
To partake in vaccine R&D
To focus on diseases relevant to South Africa
The Biovac Institute has a number of R&D opportunities available to it and limited
resources with which to operate. Even though the Institute has recently been awarded
over R100 million worth of debt and equity investment from the IDC and Cape
Biotech (a DST funded Biotechnology Regional Innovation Centre), the Biovac
Institute is faced with severe resource constraints and a significant task in terms of
creating infrastructure and delivering on its obligations. Reports (Goosner, 2004)
indicate that it costs approximately US$800 million to bring a drug to market once all
the failures and regulatory hurdles have been overcome. With the commercialisation
of vaccines, this cost is not likely to be as high as US$800 million, but will
nevertheless be significant. This is a mammoth task which requires that the Biovac
Institute select and prioritise its projects carefully.
2.1.4.2. The Products Biovac has a number of projects in its current portfolio, many of which remain in the
portfolio for legacy reasons. Due to the method of project uptake, many of these
projects were opportunistically taken on. However, more recently, as a result of a
strategic review, there has been a move towards focusing on a group of projects
sharing similar technology platforms the conjugated and combination vaccines.
Below are brief descriptions of the projects that Biovac currently has in its portfolio.
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Rabies Vaccine
Biovac has been working on the development of a rabies vaccine for the past fourteen
years. Rabies vaccine was traditionally produced in the brains of suckling mice. With
the health concerns pertaining to brain material conferring prion diseases such as
Scrapie, CJD and BSE, the WHO has passed a ruling declaring that nerve tissue
vaccines must be replaced with cell culture produced vaccines by 2008. Biovac
therefore adapted the Pitman Moore Strain of rabies virus to human lung fibroblasts to
create a high quality cell culture vaccine. The virus is produced in cell factories. This
is a very traditional cell culture technology which could potentially be updated to
bioreactor production. The bioreactors to be used are, however, fairly specialised. The
R&D group has been developing the downstream processing for this vaccine since
1993. It is important to note that rabies is a high risk virus and that once contracted
rabies cannot be cured. Therefore this pathogen has to be contained in Biological
Safety Level Three facilities. This precludes using the rabies production facility for
the production of any other vaccine. This vaccine therefore is a discrete product that
does not allow for inter-dependencies with other project. It therefore does not present
Biovac with flexibility in terms of switching products.
The rabies vaccine has been through clinical trials and has been licensed as a
prophylactic vaccine. Since most rabies vaccines are given after exposure, it is
important that the vaccine is effective as a therapeutic. This trial still needs to be done
at the phase III level.
Human Papillomavirus Vaccine
Human Papillomavirus types 16 and 18 are associated with cervical cancer. This type
of cancer is a severe problem in the developing world and the most cost effective
method of treatment is, in fact, prevention i.e. vaccination. UCTs Vaccine Group
has been developing several HPV vaccine candidates in collaboration with Biovac.
Biovacs major contribution to this project is developing the production and
purification methodology. The methods of production under development are:
Production in plants which is an inexpensive production method, but
requires significant purification
Production in insect cell culture in a bioreactor
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No clinical trials have been done on these vaccine candidates and therefore it is
uncertain as to whether they are safe or effective. There are currently no HPV
vaccines on the market, which means that there is uncertainty about how potential
candidates will be priced, however both Merck and GlaxoSmithKline are filing for
registration in 2005/6 (www.medicalnewstoday.com; 23/11/05). Furthermore, since
the production methodology has not yet been finalised, there is also little information
as to what potential vaccine candidates may cost to produce.
Haemophilus Influenzae
Haemophilus Influenzae (Hib) is a paediatric bacterial disease that infects 3 million
children annually, causing approximately 700 000 deaths. Effective vaccination for
the disease is achieved by the administration of 3 doses of a conjugated Hib vaccine
(WHO, 1998). Unfortunately the conjugation processes currently in use have
particularly low yields, resulting in between 5% and 20% recovery of the final
conjugated vaccine (Petre Interview, 2005). This low recovery results in a particularly
expensive vaccine, putting it beyond the reach of most developing countries, and is
the main reason the vaccine is yet to be incorporated into the WHO EPI programme.
Biovac and its collaborators have access to a license that will improve the conjugation
yields and thereby reduce the cost of production of this vaccine. Hib vaccines are not
administered independently; rather they are incorporated into combination vaccines
consisting of DTP and/or HepB. Although this project has not yet begun at Biovac, it
is a strategically important project because:
1. Its production technology (fermentation and conjugation) can be used to
produce other vaccine products such as Typhoid, Meningococcus (Meningitis)
and Pneumococcus,
2. It is an essential ingredient of one of Biovacs other products (The Pentavalent
Vaccine).
The Hib project therefore is extremely uncertain, yet has the potential to build in
flexibility in terms of product alternatives.
Tetravalent and Pentavalent Vaccines
The tetravalent vaccine project started in 2003. It is a complex formulation of DTP
and HepB. There is currently one producer of a patented Pentavalent vaccine. The
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method of combining the antigens in this patent is via aluminium phosphate
adsorption. To avoid this patent, Biovac had to develop a non-adsorbed DTP-HepB
vaccine. Once this has been achieved, the Hib antigen will be added to the DTP-HepB
to produce the Pentavalent vaccine.
2.1.4.3. The Technology Biovac has focused on fermentation and chemical conjugation as its technologies of
choice for the short term. This provides the opportunity for producing any conjugated
and complexly formulated vaccines. Conjugated vaccines include: Hib, Meningitis,
Pneumococcus, Typhoid and Cholera. All these vaccines, prior to being conjugated
require the bacterial strains to be grown up via fermentation.
Complex formulation is required for the production of combination vaccines such as
the Tetravalent and Pentavalent vaccines. This means that employing these
technologies allows Biovac to produce a number of alternative vaccines off shared
production platforms. The benefit of being able to use the same production platform
to produce different products is the focus of this research report.
2.2. Portfolio Evaluation Methods- in Context
Often financial analysis methodology is used to manage portfolios because of the
importance to firms that each project yield as attractive a return as possible to the
firm. Tools such as NPV, IRR and Real Options have traditionally been used as
project selection methodologies. Practitioners are still striving to find a methodology
that is universally applicable because none of the currently used methodologies are
without fault.
Although Net Present Value models are widely used in industry to value projects, and
have been described as the preferred decision rule (Firer et al, 2004, p275), when
compared to methods such as Internal Rate of Return and Profitability Index; NPV
has some major shortcomings in terms of valuing long term or highly risky projects.
Firstly, NPV makes no allowance for the flexibility inherent in large projects
(Copeland and Antikarov, 2003), secondly it makes no recognition of the input that
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managers make as the project progresses. Lastly, NPV assumes that the state of the
world will not change during the life of the project.
Managers have realised these limitations of NPV and Discounted cash flow analyses
in the capital budgeting process and have tried to overcome it using tools such as
what if analysis and scenario analyses (Lander and Pinches, 1998). These types of
analyses certainly do allow managers to deal with a certain amount of flexibility, but
they are generally linear and assume that the environment is unchanging. They
become extremely cumbersome when managers try to model the effects of various
projects on one another.
For this reason, attention has been given to the enhanced decision-making framework
- Real Options Analysis (Lander and Pinches, 1998). Real options.allow
managers to add value to their firm, by acting to amplify good fortune or to mitigate
loss (Brealey and Myers, 1991, p511). Real Options are said to contain value
because they allow for the flexibility managers have in terms of taking advantage of
various opportunities. Such opportunities can be exploited to increase profits or
avoided to decrease losses. Because of the flexibility that is built into an uncertain
framework, the value of Real Options may significantly contribute to an investment
opportunitys total value. Some Real Options occur naturally, however, managers are
increasingly trying to build Real Options into investment opportunities to better cope
with the uncertain future developments (Lander and Pinches, 1998).
Benninga and Tolkowsky (2002) address the issue of resource allocation in the
pharmaceutical industry using Real Options Analysis. They state that resource
allocation is both a strategic and a financial task. They suggest that applying the
Options approach to the capital budgeting problem significantly contributes to the task
of valuation. These authors used the Black-Scholes approach with the following
options:
The option to defer or wait
Staged investments
Option to alter scale
The option to abandon
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The growth option
They conclude that valuation using ROA is superior to using Discounted Cash Flow
valuation in the pharmaceutical environment.
Therefore, using Real Options to value biotechnology and pharmaceutical firms is not
a novel concept. Because biotechnology and pharmaceutical research and
development is so uncertain, Real Options have found purchase among managers who
struggle to make informed decisions in these environments. Biotechnology firms have
been known to have high value prior to generating any cash flow. This is due to the
potential that exists within the firms to produce block buster products. Analysts
have found that using Real Options Analysis assists them in valuing such firms.
Kellogg and Charnes (2000) describe how often Real Options is used to value
individual projects but that it becomes more complicated to value a firm as a portfolio
of projects. They attempt to illustrate that ROA can be used for financial analysis by
valuing Agouron Pharmaceuticals using decision tree analysis as well as binomial
lattice methods. The authors concluded that using ROA can be a powerful addition to
a security analysts toolbox. Clearly this approach is designed to assist potential
investors in the companys stocks to make better informed investment decisions.
A number of attempts at using ROA as a portfolio risk minimising tool for the
pharmaceutical industry have been made (Rogers et al, 2003 and Solo and Paich,
2004). Both of these sets of authors resorted to portfolio specific software to assist
their analysis. While they concluded that the approaches indeed assist in the
construction of minimal risk portfolios, the use of these programs reduces the practice
to black box. This fact results in managers not being as prepared to use the
methodology as they would be if they clearly understood the basis of the valuation.
Herath and Bremser (2001) have developed a framework based on Real Options that
measures Strategic Value Added in R&D projects. The framework provides periodic
performance measurement benchmarks and focuses on risk management in R&D
projects. The authors argue that the Real Options thinking environment motivates
goal oriented thinking. This improves the performance of the projects.
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An analysis was performed on Medigene using ROA (Aranda and Trigeoris, 2002).
They formulated a Strategic Real Options model in which a portfolio of options and
decisions represented Medigene. They built the model based on the generic drug
development stages; Research and discovery, Preclinical, Clinical Trials and
Regulatory approval. They named their approach the soup to Nuts methodology and
used ROME (Real Options Modular Engine) software to apply the data to the model.
They also used binomial lattices with the standard Cox-Ross-Rubinstein scheme.
They valued all the companys projects independently, summed them and divided by
the number of outstanding shares to get a target share price. Although their calculated
share price was lower than the actual share price, the authors conclude that ROA can
be used to value a pharmaceutical company.
An interesting argument is presented by Bowman and Moskowitz (2001), who used
the Black-Scholes model to calculate the value of the options available to Merck.
Merck needed to justify an investment in a particular R&D project. The authors
discuss three problems that they faced during the analysis; finding a model whose
assumptions match those of the project being analysed, determining the inputs to the
model, and being able to solve the mathematical algorithm to get an answer. The
Black-Scholes model presented a problem to Merck because it encompasses
assumptions about future stock prices which meant that the longer Merck could wait
to exercise the option, the more valuable the option became. This did not assist in the
justification of the investment. The authors do, however, describe some key insights
into the nature of the investment that performing the exercise brought to their
attention.
In this report, the authors describe a custom-built Real Options model that was
developed specifically to value Biovacs vaccine projects and construct a portfolio
that maximises the firms value in the light of a relative resource shortage.
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3. Data Gathering
Data was gathered to build and populate the analysis models through primary and
secondary research. Prior to interviewing members of staff at Biovac and other
opinion leaders, background research was conducted.
3.1. Background Research The background research performed during this project was predominantly secondary
research. A detailed understanding of the international and local vaccine industry was
achieved through researching industry reports (Frost and Sullivan etc.), the internet,
and discussions with opinion leaders in the industry. Insight into project selection and
portfolio establishment processes in a risky environment such as this led us to
research methodology such as NPV analysis as well as Real Options Analysis. This
was textbook, internet and literature-based research. The results of this phase of the
research were presented primarily in the Literature Review of this report; however,
the underlying learnings were built and drawn upon throughout the analysis of the
case data.
3.2. Data Gathering and Interviews Interviews were held with individuals from Biovac to ascertain company history,
project details and details of specific R&D risks. The following table (Table 1)
presents the interviewees and their roles in Biovac.
Table 1: Biovac Employees Interviewed during the gathering of data Interviewee Position Role in Biovac
Selwyn Kahanovitz CEO Business Development and
overall leadership
Martin Kahanovitz CFO Financial Management
Patrick Tippoo R&D Manager Project Selection, Management
of all R&D projects
Eleanor Prendergast Acting site Manager Management of Operations and
development of processes
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Biovac has commissioned a number of reports to be written detailing
commercialisation and operational strategies for the organisation. Much of the data,
such as product pricing and costing was gathered from such reports. Consultants
integral in the derivation of these documents include:
Dr Lorraine Thiel (independent consultant)
Phakamisani Venture Finance (Nick Allen and Kate Turner-Smith)
Tech Forward (Tai Scheirenberg)
Interviews were held with other opinion leaders and associates of Biovac to gather
more specific information about industry trends, pricing of products, market potential
and other data used in the generation of the assumptions required for the analysis.
Table 2 below presents a list of these interviewees, their affiliations and their roles in
providing data for this project.
Table 2: Other Interviewees and their affiliations Name Organisation Area of Expertise
Anna Blanca Heber Biotech (Cuba) Technology Transfer, cost of development
Stephen Jarret Unicef Vaccine Supply and demand trends
Jean Petre Bionet Vaccine development; demand for vaccines
Nadir Harjee Private Consultant Licensing of vaccines
Lorraine Thiel Private Consultant Demand for vaccines, Cost of Production
The results were then presented to Biovacs R&D Manager for feedback both on the
results and the method of analysis used.
3.3. Data Structuring
The information gathered through this process was used as a set of assumptions which
formed the basis of both the NPV and Real Options models. The data was structured
in such a way that the projects could be analysed individually using NPV analysis,
Monte Carlo Analysis and Real Options Analysis. The data is presented in the
following table along with the source. All prices and costs presented in this report are
presented in US$.
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Table 3: Data Gathered and used to build models
Assumption Expected Value Reference
Wastage ratio 3.80% www.who.intSA Population growth 0.25% www.Indexmundi.comUnicef doses sold in Africa 25% African Population (Stephen Jarret - Unicef)SA Births/year 858,346 www.Indexmundi.comSADC Births/year 27,316,800 www.unu.eduROA Births/year 8,634,600 www.delmar.edu/socsciPAHO Purchases 13,700,000 Jean Petre Interview (Bionet)Africa population growth 2% http://www.eia.doe.gov/emeu/cabs/subafricaenv.htmlMarket Share variance factor 1.00 Coverage 30% www.who.intDiscount Rate 14.10% Calculated CAPMRf 7.50% R153 - Business DayRm 5.50% Enrico UlianoBeta (Aspen pharmacare 04/10/2005) 1.20 Daniel Bradford, Financial Risk ServicesSalvage value as % of PPE book value 33% Assumed (Turner-Smith and Edwards)Capex allocation per project 33% Assumed (Turner-Smith and Edwards)Delta t 2 Depreciation rate 20% Assumed (Turner-Smith and Edwards)Tax rate 29% SARSPPE Start Value ($ quotes 2005) 1,692,300 Biovac Data
Vaccine PricesRabies Vaccine Price (SA in US$) 8.00 Biovac Sales DataPentavalent Vaccine Price (Unicef in US$) 3.65 Stephen Jarret - UnicefHuman Pappilomavirus Vaccine Price (US$) 15.00 Assumed (Turner-Smith and Edwards)Haemophilus Influenza Bulk Price (US$) 1.20 Interview Jean Petre (Bionet)Haemophilus Influenza Internal transfer Price (US$) 0.18 Interview Jean Petre (Bionet)Meningococcus Vaccine Price (US$) 1.20 Interview Jean Petre (Bionet)Meningococcus Vaccine Internal transfer Price (US$) 0.18 Interview Jean Petre (Bionet)
SA Premium over Unicef 50% Comparisons of Biovac sales data with UNICEF pricingOther market premia over Unicef 20% Comparisons of Biovac sales data with UNICEF pricingPrice erosion -2.50% Stephen Jarret - Unicef
Cogs (US$)Rabies 0.77 Biovac DataPentavalent (external Hib) 3.38 Biovac DataPentavalent (own Hib) 1.69 Biovac DataHPV 0.77 Assumed same as rabiesHib 0.06 Biovac DataMen 0.06 Assumed same as Hib
Capex Saving for shared production platform 33.33% Assumed (Turner-Smith and Edwards)Working Capital Requirements (% of COG) 80% Assumed (Turner-Smith and Edwards)% of indirect costs to be carried by each project 33% Assumed (Turner-Smith and Edwards)
R&D Probabilities of ProgressingRabiesR&D 70% Patrick Tippoo, Biovac R&D ManagerPreclinical 70% Patrick Tippoo, Biovac R&D ManagerPhase 3 80% Patrick Tippoo, Biovac R&D ManagerRegistration 95% Patrick Tippoo, Biovac R&D ManagerProduction 99% Patrick Tippoo, Biovac R&D ManagerAfter (not variable) 100%PentaR&D 100% Patrick Tippoo, Biovac R&D ManagerPreclinical 40% Patrick Tippoo, Biovac R&D ManagerPhase 1 60% Patrick Tippoo, Biovac R&D ManagerPhase 2 65% Patrick Tippoo, Biovac R&D ManagerPhase 3 65% Patrick Tippoo, Biovac R&D ManagerRegistration 90% Patrick Tippoo, Biovac R&D ManagerPre-production 95% Patrick Tippoo, Biovac R&D ManagerProduction 99% Patrick Tippoo, Biovac R&D ManagerHPVR&D 10% Patrick Tippoo, Biovac R&D ManagerPreclinical 45% Patrick Tippoo, Biovac R&D ManagerPhase 1 45% Patrick Tippoo, Biovac R&D ManagerPhase 2 55% Patrick Tippoo, Biovac R&D ManagerPhase 3 63% Patrick Tippoo, Biovac R&D ManagerRegistration 87% Patrick Tippoo, Biovac R&D ManagerPre-production 85% Patrick Tippoo, Biovac R&D ManagerProduction 100% Patrick Tippoo, Biovac R&D ManagerMen/HibR&D 80% Patrick Tippoo, Biovac R&D ManagerPreclinical 80% Patrick Tippoo, Biovac R&D ManagerPhase 1 85% Patrick Tippoo, Biovac R&D ManagerPhase 2 85% Patrick Tippoo, Biovac R&D ManagerPhase 3 90% Patrick Tippoo, Biovac R&D ManagerRegistration 95% Patrick Tippoo, Biovac R&D ManagerProduction 100% Patrick Tippoo, Biovac R&D Manager
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The population sizes, growth, vaccine coverage and wastage ratios were used to
determine the potential market size for each vaccine. The prices were used in
conjunction with the market sizes to determine the forecasted revenues for each
product. The cost of goods sold was used to determine the gross profit in the cash
flow forecasts.
For the NPV analysis, it was assumed that each project would be successful and the
analysis was carried out to 2015. In the Real Options Analysis, each project was
assigned either a simple abandonment option or alternatively simultaneous options of
abandonment and switching. All the projects have a built-in deferral option. The
switching option was limited to those projects which result in products that can be
produced off the same production line (namely Meningitis and Hib). The table below
describes the structure of the data analysed.
Table 4: Structure of Project Data for Analysis Project NPV ROA Option Type/s
Rabies Abandon
Pentavalent Abandon
HPV Abandon
Hib Abandon
Hib/Men X Abandon and switch
Men Abandon
Men/Hib X Abandon and switch
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4. Data Analysis (Methodology and Findings) The method used for the analysis of the data is the 4-Step process as described by
Copeland and Antikarov (2003, p220) and shown below. The process starts with
calculating the NPV of each project without any flexibility.
Step 1
Model uncertainty using event
trees
Compute base case
without flexibility
Identify and incorporate managerial flexibilities to create a
decision tree
Calculate the Real Option value
Step 2 Step 3 Step 4
Objectives : Identify major uncertainties in each stage understand how these uncertainties affect the Present Value
Value the total project using simple algebraic methodology
Analyse the event tree to identify and incorporate managerial influence
Compute base case present value without flexibility at t=0
Overall approach, the four step process
Figure 2: The Four Step Process (Copeland and Antikarov, 2003; p220)
4.1. Base Case NPV Analysis
A 10 year Discounted Cash Flow (DCF) model was constructed for each of the
projects to determine NPV values. The 10 year models were divided into 6 month
time periods, giving each model 20 periods. This was done for two reasons:
1. In order to increase accuracy in the ROA stage, and
2. Some of the stages in the R&D of each project only require 6 months. As each
of these stages has an objective probability linked to it, it was important to
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show the various stages separately for use in the binomial lattice as described
in section 4.4.
The structure of each DCF was as follows:
Revenues Less Cost of Goods Less Direct costs Less Indirect Costs Equals Gross Profit Less Depreciation Equals Operating Profits Pre Tax Less Tax Equals Income after Tax Add Depreciation Equals Cash flow incd depr Less Capex Less Working Capital Rabies Less Working capital (delta) Add salvage value Equals Net cash flow
Equation 1: Calculation of the Cash Flows
These net cash flows were discounted using the discount rate described below to
determine a Net Present Value for each project.
4.1.1. Assumptions Most of the data used in the NPVs was forecasts of future prices, costs and other
influencing market forces. These forecasts were based on either historical data or
information received from interviewees as presented in the section 3.
The only variable derived by the authors was the Discount rate. This was calculated
using a Risk Free rate equal to the R153, a market risk premium of 5.5% (Uliano,
2005) and a Beta of 1.2 taken from Aspen Pharmacare (Daniel Bradford, Financial
Risk Services), a comparable firm in the Biotech industry.
The full list of variables is shown in table 3 (section 3.3)
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4.1.2. NPV results The full DCFs for each project are shown in Appendices 1 to 5. The results of the NPV analysis (in order of NPV) can be summarised as follows: Table 5: Project NPVs
Individual project values NPV
HIB (4,358,545) HPV 1,526,952 Meningitis 3,250,237 Pentavalent 2,191,217 Rabies 2,116,292
Although these values would traditionally be used for determining which projects to
undertake, they do not reflect any of the flexibility or risk inherent in the projects. The
project with the highest NPV would typically be selected by management as the most
attractive project for investment. As discussed in the Literature Review, the limitation
of this approach is the assumption that all future cash flows run as presented in the
NPV model. There is no allowance for changing market conditions and/or technical
failure.
4.2. Product Decision Trees Product Decision Trees are used to understand where the uncertainties in each project
will occur. Each of these projects is comprised of two main phases, R&D and
Production. The R&D phase is further divided into a series of shorter stages as shown
in the diagram that follows. The number and length of these shorter stages will vary
according to the project, but they are typically comprised of:
Pre-clinical trials before testing an experimental vaccine in humans
extensive pre-clinical testing must be completed to establish initial parameters
for safety and efficacy.
Phase I clinical studies - includes the initial introduction of an a new vaccine
into humans.
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Phase II clinical studies early controlled clinical studies conducted to obtain
some preliminary data on the effectiveness of the vaccine for a particular
indication or indications in patients with the disease or condition.
Phase III clinical studies expanded controlled and uncontrolled trials.
Progression from one stage to the next is not always guaranteed; the chance of
progressing can be described in terms of a percentage and is known as the objective
probability of proceeding. This probability will be used during the ROA phase to
calculate the present value of the project at each point in time.
Figure 3: Illustrative Project stages and Objective Probabilities
Phase 1
ProductionPre -clinical
Phase 2
Phase 3
Registration
Chance of advancing45%40% 60% 75% 100%
R & D
35%
R& D Phase of Project Production Phase
Decision trees also allow us to see the options available to the decision maker at any
point during the projects lifespan. This may include the options to abandon, switch
to another project or defer a decision. Each of these options will be valued during
ROA, the decision tree merely allows us to identify what the options are at each node.
The decision trees for the Rabies, Pentavalent and HPV projects are reasonably simple
in that they only have the ability to progress or abandon at each stage. Their decision
trees are as follows.
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Figure 4: Rabies decision tree
Rabies Production
Production
Production and Registration Abandon
Registration Abandon
Phase 3 testing Abandon
Pre clinical trials Abandon
R&D success Abandon
Abandon
Figure 5: HPV decision tree
HPV Production
Production
Production and
RegistrationAbandon
Phase 3 testing Abandon
Phase 2 testing Abandon
Phase 1 testing Abandon
Pre clinical trials Abandon
R&D success Abandon
Abandon
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Figure 6: Pentavalent decision tree
Pentavalent
Production
Production
Production and
RegistrationAbandon
Phase 3 testing Abandon
Phase 2 testing Abandon
Pre clinical trials Abandon
R&D Abandon
Abandon
The Hib and Meningitis projects share a common production platform and the
opportunity exists for the management to switch production from one to the other
should the market for either product change. This is known as a switching option.
The opportunity to abandon the project at any point still exists. The decision trees for
Meningitis and Hib can be represented as shown as follows.
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Figure 7: Hib and Meningitis decision trees with switching options
HIB Phase 4 testing Production
Production
Production and
RegistrationProduction Abandon
Phase 3 testing Abandon
Phase 2 testing Abandon Abandon Switch to Men Production
Phase 1 testing Abandon Switch to Men Production
Pre clinical trials Abandon
Switch to Men Production
Switch to Men Production
R&D Abandon
Abandon
Meningitis Phase 4 testing Production
Production
Production and
RegistrationProduction Abandon
Phase 3 testing Abandon
Phase 2 testing Abandon Abandon Switch to HIB Production
Phase 1 testing Abandon Switch to HIB Production
Pre clinical trials Abandon
Switch to HIB Production
Switch to HIB Production
R&D success Abandon
Abandon
4.3. Monte Carlo Simulation to model uncertainties In order to construct a binomial lattice, the volatility of the expected returns needs to
be determined. This is achieved by using the formula (Copeland & Antikarov, 2003:
246);
rtPVPVt =
0
ln where t=1
Equation 2: Volatility Estimate of Expected Returns (Copeland and Antikarov, 2003)
Using Crystal Ball (a software package that performs Monte Carlo Simulations) to
determine a range of values for PVt, and holding the PV0 constant, a standard
deviation for r (the expected returns) can be determined. This standard deviation is
given the label z. In practice, this process required a number of discrete steps to be
completed and these can be described as follows:
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4.3.1. Base case PVs and static rates of return The PV0 values were calculated using discounted cash flow analysis. The PV at time
period 1 was also calculated, and using the formula above, the static estimated returns
(r) were calculated for each project.
Table 6: Project Present Values at time point 1 and 2 and the Volatility Estimates (Copeland and Antikarov, 2003)
PV of FCF (1) Rabies 2 736 802 PV of FCF (2) Rabies 3 653 196 Volatility Est Rabies 28.88% PV of FCF (1) Penta 2 811 727 PV of FCF (2) Penta 3 815 297 Volatility Est Penta 30.52% PV of FCF (1) HPV 2 147 462 PV of FCF (2) HPV 3 022 308 Volatility Est HPV 34.17% PV of FCF (1) Hib (3 738 035)PV of FCF (2) Hib (3 196 223)Volatility Est Hib -15.66% PV of FCF (1) Men 3 870 747 PV of FCF (2) Men 4 948 978 Volatility Est Men 24.57%
4.3.2. Defining Assumptions and Forecast Variables Before running the simulation, each variable that had an impact on the project was
set-up as a defined assumption in Crystal Ball. The most relevant assumptions (i.e.
those that had the greatest impact on the standard deviations) are presented in the
following set of figures. The full list of assumptions is shown in appendix 6. For
each of the variables, the expected value (likeliest) is the one received from interviews
or other data. The upper and lower limits were, for the most part, best guess estimates
on the part of the authors, one of whom has significant experience in the field of
vaccines.
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Assumption: Coverge Cell: B12
Triangular distribution with parameters:Minimum 10%Likeliest 30%Maximum 40%
10% 16% 22% 28% 34%
Figure 8: Distribution Assigned to the Coverage Assumption.
Coverage refers to the percentage of the population that receives immunisation. The
WHO estimates that the coverage ratio for Africa is 30%. The lower limit assigned to
this triangular distribution was chosen because there are areas in Africa where the
coverage is even lower than the 30% estimate. It was felt also, that it is unlikely that
the coverage ratio would exceed 40%. Assumption: Haemophilus Influenza Bulk Price (US$) Cell: B28
Triangular distribution with parameters:Minimum 0.70 Likeliest 1.20 Maximum 1.50
0.70 0.86 1.02 1.18 1.35
Figure 9: Distribution Assigned to the Hib Bulk Price Assumption
This assumption refers to the price at which Biovac would sell Hib to other
manufacturers producing combination vaccines. The expected price is based on a
quote from a competing Hib supplier and the limits of the triangular distribution were
chosen so that US$1.2 would be close to the mean.
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Assumption: Haemophilus Influenza Internal transfer Price (US$) Cell: B29
Triangular distribution with parameters:Minimum 0.11 Likeliest 0.18 Maximum 0.40
Correlated with: CoefficientMarket Share variance factor (B11) -0.40
0.11 0.17 0.23 0.29 0.34
Figure 10: Distribution Assigned to the Hib Internal Transfer Price Assumption
The Internal transfer price is the price which the Pentavale
