How to finance the biomedical research with securitization techniques, a practical study

How to finance the biomedical research with securitization techniques: a
practical study.
Paolo Vaona, International MBA Candidate 2015, CUOA Business School.
Abstract
The object of this paper is to illustrate how is possible to invest in a competitive and risky environment such as
the biomedical R&D. The securitization techniques represent a step to go beyond the traditional venture
capitalist business model that nowadays rule in the industry. The paper is divided into four sections. In the first
one, an overview of the actual market is presented. In the second a set of data, based on the available
literature, is discussed and validated. In the third section, the concept of “research backed securities” is
illustrated and is outlined the structure of the fund. The performance of the portfolio is discussed analyzing the
main factor that affect the outcomes. In the final section, the conclusions are presented considering how the
theoretical framework applies to firms that are engaged in the buying and selling patents that bear royalties for
a period of time.
Keyword
Biomedical Research,Securitization,Royalties,Business Model.
Market Overview
Principal characteristics of science business
Recent decades have witnessed intensive organizational experimentation in the way science is generated,
diffused, and commercialized1.
The relationship between science and business is not in the objectives of this paper. However, the fact that the
science business boundary has long been blurry should not obscure three salient features of the business of
science in the 20th century. Mainly in the 20th-century large-scale industrial corporation had their research
laboratories in order to find avenues for growth outside the core business. This guaranteed them almost
complete freedom. Secondly, new firms emerged, especially in electronics, commercializing innovation but not
explicitly engaging themselves in research. Finally, although University was involved in this process, they were
not major players in the “science business”.
The science-based companies of biotech engaged directly in research that would normally have been
considered “natural” for a university but not for a for-profit firm, and certainly not for start-up company.
Throughout the history of biotech, starting with monoclonal antibodies, but later with genomics, stem cells,
systems biology and others, entrepreneurial firms (often by academic scientists) engage in “raw” science.
It is interesting consider the history of the biotech pioneer Genentech. Robert Swanson, a venture capitalist,
and Herbert Boyer, a Nobel Laureate biochemist and co‐inventor of a foundational technique for genetic
engineering, founded the firm in 1976. The founding of Genentech is significant, not only because it launched
the biotechnology industry, but also because it put basic science into the organizational framework of a for-
profit firm. Genentech’s first research project, supported by funds raised from venture capitalists, investigated
whether a human protein could be made in a bacterial cell. At the time, this question was a central theoretical
concern in the field of biology.

When it went public in 1980, it had no product revenues. It was still two years away from the launch of its first
product (recombinant human insulin) and five years from the launch of its first wholly owned product.
Genentech demonstrated the feasibility of being a science‐based business, and it created a template for
thousands of entrepreneurial firms and bioscience firms founded over the subsequent thirty‐five years
The science-based activities of the biotech are a novel organizational form. Unlike the corporate labs of
decades past, they face the winds of market forces without the buffer of rich revenue streams and dominant
market positions (the case of monopolistic position that AT&T had). Moreover, unlike the start-ups of
electronics, computer, and other classic “high tech” industries, they face prolonged periods of risky investment
in research.
Risk Integration Trial and error
This organizational form faces distinct economic challenges, namely:
 Risk Problem, basic technological feasibility is not an issue confronting R&D in most industries. This
is not the case in science‐based businesses like biotechnology. Whether a drug emerging from
biotech will be safe and effective can only be truly determined through years (sometimes a decade or
more) of clinical trials. In general predictive models reduce risk, but in biotech the knowledge of the
underlying cause-effect relationships may be lacking or only dimly understood. In these contexts, R&D
is necessarily iterative. This lead that the process is perilous.
 Integration Problem, breakthrough innovation is the result of recombination and integration of
existing bodies of knowledge. Biotechnology is today a term that incorporates an enormous underlying
mosaic of disciplines including molecular biology, cell biology, genetics, bioinformatics, computational
chemistry, protein chemistry, combinatorial chemistry, and many areas of primary medicine. As a
result, one of the biggest challenges of research in these emerging areas is integrating diverse
scientific disciplines.
 Learning Problem, science‐based businesses are at the frontier of knowledge. Technical failure is
the norm, not the exception. What is known pales in comparison to what remains to be discovered.
New hypotheses and new findings must be continuously evaluated, and decisions about what to do
next must be made in the fog of limited knowledge. Knowing the right answer is far less important than
knowing the right experiment to run. When failure is more common than success, the ability to learn
from failure is critical to making progress.
All these facts explain how complex, and challenging is this market. A lot of different studies underline the
need to change the business model. The big promise of the biotechnology was to increase the rate of
innovation but what emerged is that a small biotech firm co-opted the big pharma business models instead of
craft their own. Much interest has grown around the biotech because the results provided globally by these
companies are better than a large company, but at the same time individually they are less reliable.

Finding a way to reduce this variance and risk is an important structural step that could help the whole industry
to attract more capital to pursue researches
Market performance
It is possible to come to the same conclusions of the previous section, having a close look at the data in the
market.The Arca Pharmaceutical Index in the last ten years it had an increase of 84%, compared to an
increase of 131% of Nasdaq Index and 71% of Down Jones index. In the same period, the R&D budget of the
major companies has increased from 68 billion $ to 127 billion $ without showing a substantial increase in
drugs approved in the same period2.
Data from the ThomsonOne database and VentureXpert (VX), indicate that the biotech and healthcare venture
capital (VC) investments have exhibited, over the last decade, significantly lower returns than in the past. In
VX, the biotech sector includes human therapeutic biotechnology, industrial biotechnology, and biosensors,
and the medical/healthcare sector covers pharmaceutical research, therapeutics, diagnostics, and other
healthcare related services. From the graphs below can see how the ten-year IRR have declined during these
years.

As stated at the beginning of the section, it is also interesting to point out that investments in the process
follow a different path depending on the stage of the research.
Industry professionals cite the existence of a “valley of death”, a funding gap between basic biomedical
research and clinical development. For example, in 2010 only $6−7 billion was spent on translational efforts
whereas on basic research was spent $48 billion and on clinical development was spent $127 billion that same
year.
Market environment
On the other end, this market is today rightly looked like one of the most challenging and rewarding
considering all the breakthrough that have characterized it during the previous years.
Of the above-mentioned uncertainties involved in the process it is worth to mention that the market
environment poses other threats to the biotech, such as:
 Decline in prescriptions spending
 Rising drug development cost but shrinking of the disposable budget
 Highly competitive market where is needed to be first or second to go out in the market to have a
competitive advantage3
 Potential health care reform and cost-cutting reform around the world.
 High market volatility
 Venture capitalist disinterested by high volatility and low return.
 The chance of a new “patent cliff” like the one in 2012.
It appears evident that the challenges of founding a science base enterprise is not a simple task due to these
elements that give to it a hybrid structure. The three primary sources of funding for companies that want to
undertake R&D are venture capital/private equity, public equity and monetization of intellectual
property(royalties)

Trend in the market
The sector that shows more interest is the Oncology with a compound annual growth ( CAGR ) of 11% and a
number of markets share. This information should guide the decision on the development of new drugs.
There are also some demographics that could drive the market. As shown from the Population Pyramid of
many Western countries, as well as China, the progressive aging of the population will increase the demand
for neuropsychiatric disorders- including schizophrenia, depression, and Alzheimer’s diseases.
Population Pyramids as 2014 in Germany and China taken from the CIA Factbook4.
Global outbreaks of infectious diseases such as avian flu, SARS, and N1H1, as well as the general antibiotics
resistance of certain bacteria, increase the need for drugs in this field.
Another area of the market that had high potential is the one of the orphan and specialty pharma products. In
part, this market has the benefit from many taxes credits and marketing exclusivities5. This sector has shown
in these years a robust growth, with CAGR of 10% between 2001 and 2010 while it was negative for new
molecular entities as a whole.

The Drug Development process
In the previous section the main steps that involve the research of new drugs had been explained, now the
data for each phase will be analyzed. It is important to understand clearly the rules and factor that affect the
market in order to pursue the best strategy for investing. For sure the pharmaceutical market is from the
beginning of the century in a shifting phase. Although medical science had incredible discovery in these years,
it is also important to note that the complexity of a drug is increasing in these years. Just as an example the
level of complexity of some drugs today could be compared to the standard of complexity of a jet versus a
bike.
The primary metrics that would influence the choice of pursuing a particular research from a company, or a
fund are several, among these the principal factors are:
 Length of development and like hood of approval
 Development costs
 Potential market shares
Much research has been conducted to assess these variables; the principal refers to works of Munros (2009)6,
Di Masi (2010)7, Danzon and Nicholson ( 2009)8, BioMedTraker9. It is important to underline that all these
different research are not exhaustive, and each therapeutic class has strength and weakness.
Length of development
The length of development and the likehood of approval are strictly related to the possibility of being profitable
in the shortest time or being on the market. In recent years, there have been some changes in the FDA
approval process, namely the Prescription Drug Use Fee Act of 1992 (PDUFA) that tried to foster the approval
process. Despite the spike in new molecular entity (NME) approved in 1996, as a result of the clearance of the
backlog of applications, this changes in policies have not shown a significant reduction in approval times.
I could be useful to divide the time between Clinical Phase and Approval Phase. The PDUFA reduced the
average duration from initial submission of a New Drug Application (NDA) or Biological License Application
(BLA). The approval time has been then on average reduced with the PDUFA but at the same time the Clinical
phase has increased its time. Recent data showed that the time for synthesis of a new compound to first
testing in humans increased by 6.6 months on average in the period between 1979 and 1991, due to the
growing complexity of some drugs.The reduction in the approval phase then counterbalance the increase in
the clinical stage, leading to a total time from discovery to approval of about 8.2 years. The complete cycle of
commercialization and additional tests to extend the range of use could result in an 11.8 years time.

A factor that could affect the approval time is the rating value assigned by FDA to the NME at the beginning of
the process. The two categories are P( priority ) ad S( standard ). Results show that being in the priority group
could reduce the approval phase of almost one year.
As mentioned early, orphan diseases had a different regulation, the Orphan Drug Act of 1983 (ODA).
According to the definition, an orphan disease is the one that affect fewer than 200000 individuals. This kind of
conditions is associated with genetic defects hard to identify and thus are present throughout a person's entire
life. According to the latest result, the mean time for the total phases for an orphan drug is approximately 6.6
years.
From the graph below could be seen a parallel between the average duration of development orphans drugs
and priority group. This is explained by the fact that orphan drugs have a rate of precedence from 75% to 85%
of the total NME submitted.
Mean Clinical and Approval phases times for Orphans and Nonorphans NME.
Mean Clinical and Approval phases times for Standard and Priority NME.

Mean Clinical and Approval phases times for different therapeutic class.
Likelihood of Approval
As much important as the time taken to develop the drug there is the likehood of approval. Of course, it is a
value that include in it an enormous amount of variables, some numerical some not. A good indicator of the
chance the research has to be sold in the market. This value could give an initial estimation of the risk of the
project. Since the process involve a series of step we should define a phase success probability, which give
the possibility to move to the next stage, and the likelihood of approval (LOA). At each stage of the pipeline
then we can have the following situation:
The more the drug advance through the pipeline, the more the LOA increase. This graph also explains the
behavior of much big pharmaceutical company which thought M&A process acquire products in the advanced
phase, eliminating in this way the risks.
Several studies have been conducted, and the LOA for the whole process vary from 10% and 20%. Of course,
these statistic vary depending on the available data and the particular compound class.

For example, according to recent studies the LOA for orphan drugs is approximately 22%. In the table, below
are reported the LOA of different type of diseases. Obviously these statistics does not describe the complete
phenomena, but considering the model proposed, to increase the returns of the fund is important to reduce the
correlation and have different time for the development phases.
Cost of development
The development cost for a drug is a theme highly discussed and debated by academics, and it is not the
purpose of this paper to go into deep into the topic. It is worth to note and point out some specifics features of
the cost that could affect the simulation. During each phase, a particular set of expenses is needed to cover
the costs for laboratories, patient scouting and so on. It is not an issue of these years, but it time with high
inflation rate the expenditure during the whole cycle of discovery could vary. The cost related to the failure of
the research is another significant cost that have to be included in the overall costs.
Literature considers the overall cost of development about $1.2 billion in 2011, which bring to $1.3 billion with
2014 dollars. Omitted there are the submission and launch cost ( around $40 million ). According to Danzon
and Nicholson (2012), the value in the preclinical and clinical phase could vary depending on the type of
compound. In a study by Fernandez, Stein and Lo10 cost are evaluated for oncologic drugs, coming up with
values similar to those proposed by Danzon and Nicholson. In the simulations, the same approach used by
Fernandez, Stein and Lo will be used. Then the same method will be used. A mean out of pocket of $276
million ( already adjusted at 2014 value) is considered for the development of the drug from preclinical to
Phase 3. A cap cost for each phase is defined, which yield to a maximum out of pocket cost per compound (
including failures ) of $742 million.
Two main variables could be modified to decrease the cost of the research. One is to increase the success
rate of the compounds, or of the portfolio of compounds. The second variable is the development time. It has
been shown how a decrease in phase length of 10% could reduce approximately the same rate the total costs

Sell of compound
Since in the simulation involve the management of a fund which at each step of the simulation sell and buy
compounds in different clinical phases, it is necessary to define a compound value at each phase. To
accomplish this have been determined a mean value and a maximum value for each stage of the pipeline. The
cost of selling an approved drug depends on the cash flow that commercialization will generate in the future.
This depends upon several variables, and many practitioners refer to the success in the market as a black
swan. According to Munos (2009), the sales forecast for a new product is inaccurate nearly the 80% of the
time. For this reasons, the values have been extrapolated considering the selling or acquisition value of the
company with drugs at different stages. This value is aligned with the ones in the literature.
Stage Preclinical Phase I Phase II Phase III NDA Approved
Mean [ $ million ] 16.5 31 84 440 1560 1920
Max [ $ million ] 102 250 514 1028 2570 5142
Lognormal Mean 2.4 3.0 4.0 5.7 6.9 7.2
Lognormal SD 0.9 0.9 0.9 0.9 0.9 0.9

Creation of a Fund: Simulations
In this section, all the previous assumptions are put together to create a fund composed of drugs at different
stages of development. The use of a numerical simulation allows to test various conditions. The code is the
one provided by Fernandez, Stein and Lo with some modification made in the cost structure and transition
probabilities.
As have emerged from the previous sections, the drug discovery process is an expensive and risky process
that involve few actors. At the same time, this sector is strategic for different players, but the high risk
associated with the process make this kind of investment not suitable for the main investor. Using portfolio
theory and securitization techniques is possible to reduce the risk of the funding, the making it accessible to
different institutional investors. The main advantage of reducing the risk of the fund allow the fund to issuing
bonds backed by the research. The main innovation in issuing bonds is the possibility to access to a large sum
of money, needed to maintain different research projects, instead of relying mainly on government funds and
venture capitalist. In 2014, the size of the US venture capital industry was $180 billion whereas the size of the
bond market is around $38.6 trillion, with $1.5 trillion in asset-backed securities.
US bond market size divided by category
These bonds could be structured to have different priorities, with most senior class rated by credit rating
agencies available to institutional investors. At the same time, the junior tranches and equity could be a
suitable tool for other investors with higher risk tolerance.
Although no specific research exists, this kind of instrument should be less correlated than another sector to
the stock market, representing then a suitable investment in order to diversify the risk.
Securitization, restructuring, and credit enhancement are considered the tools that lead to the financial crisis of
2008. In writer view, every financial instrument is not right or wrong by itself, is right or wrong in the way it is
used. The financial crisis should help to understand and avoid the occurrence of systemic risks, lack of
regulation and inadequate underlying pool assumptions.

Risk reduction and securitization
In order to describe the risk associated with the financing a hypothetical example can be drawn up. Consider a
research project that need $400 million of out of pocket development cost. The project will not generate
revenues for the next eight years, and there is a 10% probability that, at the end of the research, the drug will
be approved. Let’s assume that the drug from the eight year will generate $1 billion of revenues for 12 years till
the patent expires. Under these assumptions, the research project can be seen as a single Bernoulli trial with
a probability of success p=0.1.
Considering a cost of capital of 10%, the annual rate of return for this kind of project would be 9.5% but with an
annualized standard deviation of 181%. Obviously this amount of risk is too high for almost any institutional
investor and probably also for private investors.
Now consider a portfolio made of 100 of these projects and assume them to be independently and identically
distributed. The expected return of the portfolio remain 9.5%, but the standard deviation becomes
181
√100
= 18 %. This value is quite similar to the typical volatility of the stock market.
The value is not entirely correct because it is based on the assumption that 100 projects are uncorrelated. A
real case would have a higher volatility, but still, it would be possible to choose a set of different drugs for
different clinical and orphan diseases that would allow to reduce this correlation. The big tradeoff to be paid for
this volatility reduction is that about 40 $billion would be needed. Lowering the risk allows to finance part of this
capital through debt instead of equity. Under these assumptions, the probability of at least three success
among 100 projects would be 99.4 %. This would give the chance to auction bonds for $20.44 billion with a
probability of default in 8 years of 100-99.4 = 0.6%. The remaining part of the fund could be financed through
equity.

In a very stylized way, this is what happen in a securitization process.
Technically speaking the securitization is the process in which particular type of assets are pooled so that they
can be repackaged into interested bearing securities. Securitization was initially used to finance simple, self-
liquidating assets such as mortgages but any asset with a stable cash flow can in principle be structured into a
reference portfolio that supports securitization debt11. Securities can be backed not only by mortgages but
corporate and sovereign loans, consumer credit, lease/trade receivables, and project finance. The fund that
acquire the research projects become the issuer. The issuer finances the acquisition of the pooled assets by
issuing tradable, interest bearing securities that are sold to capital market investors. The investors received
fixed or floating rate payments by the cash flow generated by the reference portfolio. Securitization represents
an alternative way of financing based on the transfer of credit risk from issuers to investors. It is possible to
refine more the process, dividing the reference portfolio into slices, called tranches, each of which has a
different level of risk associated with it and is sold separately. Return and losses are then allocated among the
various tranches according to the seniority. It could be divided into 2 or 3 tranches: Senior, mezzanine, and
Junior. The junior tranches are usually the smallest one and the ones that bears most of the credit exposure
and receives the highest return.
The securitizations process is not a magical tool that transform the assets value it only redistributes the risk to
different investor. For this reason is fundamental the monitoring the sensitivity of the underlying assets, in this
case, the research projects.

Valuation and cost assumption for the fund
The assets of the Fund are assumed to be new drugs developed by pharma or biotech companies and
focused on the following different pharmaceutical class: Orphan, Infectious, and Neurology.
The choice of the classes has been made considering the opportunity to have classes with a short time and
high LOA, but with not a huge market. Neurology drugs instead have lower success rate and longer
development length but at the same time could have a bigger market share.
N
Preclinical
[ Years ]
Phase I
[ Years ]
Phase II
[ Years ]
Phase III
[ Years ]
NDA
[ Years ]
Total Time
[ Years ]
Orphan 10 1 1.6 2.2 2.1 0.8 7.7
Infectious diseases 60 0.8 2.2 2.6 2 1.4 9
Neurology 20 1.3 3.4 2.9 3.2 1.9 12.7
Total 90 0.9 2.4 2.6 2.3 1.4 9.68
The weighted average of the duration of each phase has then been used in the simulation. The cost of the
development has been reported below. Development costs are modeled as a lognormal distribution. The
standard deviation parameters are maintained the same while the cost have been increased according to the
trends of the market in the last years.
Preclinical Phase I Phase II Phase III Total
Mean Expected cost
[ $millions ]
6.3 20 52.5 197.3 276.1
SD cost/phase 6.3 16.8 49.5 138.5
Max cost/phase
[ $millions ]
21 52.5 126 525 724.5
Lognormal Mean 1.52 2.75 3.64 5.10
Lognormal SD 0.8 0.7 0.8 0.6
The probability of transition from each phase is shown below:
Preclinical Phase I Phase II Phase III NDA LOA
69% 67% 47% 56% 84% 10%

The transitional matrix is build up with these data. Transition matrices specify the probability of moving
between rating states(or, conversely, of remaining in the current state). The transition matrix is the same used
by S&P and others rating agency, to assess the probability that, at each stage, a bond issuer stay with the
same rating or moves. In this case, it describes the likelihood that the compound transit along the different
phases. Rows represent starting stages and columns represent final stages. At each stage the research could
advance stage, remain in the same stage or withdraw. If a compound stays more than the average time of the
phase then, it is withdrawn. The transition through stages is represented as a Markov process, which is the
standard tool applied to systems that transit from state to state. If during this process the compound finish in
the Target phase (which could be defined for each simulation such as Approval, Phase III and so on) it is sold.
In the same way, if it moves to the Discharge stage the compound is dropped from the portfolio. Every time the
compound move into the next stage uses the new probability of that stage.
The development is a complex process which depends on scientific and economic factors and in the discharge
of the project it does not consider the chance to use results from past researchers. During the simulation,
compounds can be sold before the target phase,in order to meet the interest or the management fees
payments according to the capital structure of the Fund. In these case, the sale process occurs according to a
lognormal distribution following the parameters shown in the previous sections. The lognormal distribution tries
to mimic the real market behavior where there is a large number of low-value compound and a small niche of
large valuation drugs. During the sale process, there is a correlation between the costs of different
compounds. This correlation does not affect the mean return of the portfolio but can change inside each
tranches the possibility that there will be many compounds sell for a small value and few sold for higher value.
A Montecarlo Simulation is performed imposing more than 100k simulations or paths allow to simulate a large
number of possible cases. The aggregate results are then presented.
In general the stochastic approach to defining the price when selling the compounds or the cost of the drugs is
useful when there is a common unobservable factor that we are not able to capture with the simulation. For
example, in defining the price to sell a compound there are considerations based on the possible future market
share, a similar compound being developed, and the negotiation power of the parties involved.
Capital structure of the Fund
The capital structure of the Fund is simple for this experiment but more complex structures can be added if
needed. The capital structure is composed of three tranches, senior bond, junior bond and equity. The bonds
receive semiannual coupons and are amortized over different periods of times. Choosing this period also
defines the time duration for the simulation. In the structure, the senior bond owners are the first to receive
coupon and redemption payments ahead of the junior and the equity holders. Junior bond then have a
different maturity, and they are payback before any cash flow accrues to the equity holders.
As a protection for the bond holder, there are two possibilities, an over-collateralization that guarantee the
bondholder from the default and the interest coverage ratio test. The ratio is calculated every iteration, and if it
follows below the index value, compounds have to be sold to bring the IC value back to the target value. This
allows the fund to be sure to have enough found to pay, at each step, servicing, interest, and principal
payments.

The primary tasks in each step of the simulation are schematized in the table below:
Cash Flow Changes in the structure
 At the beginning of each period, if in the past
two semester a compound has been sold,
the revenues are added to the current cash
balance
 Cash account is checked. Then if there is
enough cash in the account payments follow
this order:
-Fund management fee
-Interest on senior bond
-Scheduled principal payment on senior bond
-Interest on junior bond
-Scheduled principal payment on junior bond
 If the fund is in default, which means there is
not enough money to cover all expenses
some bonds are in default and assets are
liquidated. In this case, all cash generate
goes first to senior bondholders, and then to
junior and final to equity.
 If not in default, the IC test is performed on
the funds for the following stages. If the test
fail, compound are sold to meet the ratio.
 Remaining cash is reserved to make service,
interest and principal payments for the
subsequent two periods. Lowering this level
increase the leverage of the fund.
 In the final step, all the assets are liquidated
and returned to equity holders.
 Each compound is tested if to see if it has
transitioned to a new stage. If it is in the
Target phase defined it is sold. The sales
cycle takes two steps, so cash I deferred till
that moment.
 If, after all, the payments any cash remain,
the money are invested in compounds that
have transitioned but have no funds to move
on in the approval process
The flexibility of the program allows to test several combination of investment that could be tested and
observed trying to understand which is the most suitable. Different time horizon and The following simulation
have been tested:
 Venture capital approach.
 Fund with long term approach.
 Fund with short term approach.

Simulation 1: The venture capitalist approach
In this simulation, an initial capital of $1.5 billion is invested over 7.5 years. The portfolio includes 30
compounds in Preclinical phase, and 30 compound in Phase I. The ratios between the different categories is
the same of the timetable. The objective of the fund is to sell the drugs when they reach Phase III. The
simulation includes 1 million paths analyzed. The main results can be seen in the tables below.
DSC PRE P1 P2 P3 NDA APP
Sales 0 0.002 1.922 3.747 3.869 0 0
WD 0 8.826 5.903 4.366 0 0 0
Mean number of compounds in each stage
0 5 10 15
0
2000
4000
6000
Period
0 2 4 6 8 10 12 14
-1
-0.5
0
0.5
1
Period
A1 balance - Quantiles
0
5
10
Mean # compounds sold (green) or withdrawn (red) in phase
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3
0
0.5
1
1.5
2
x 10
4
Frequency
ROE Annualized
0 2 4 6 8 10 12 14
-1
-0.5
0
0.5
1
Period
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15
0
2
4
6
Mean # compounds sold (green) or withdrawn (red) in period

Equity analysis
TOT ANN
E(ROE) 90.2 % 8.3 %
In this case, there is no bond default rate because there are no bonds. The return on equity is 8.3%
annualized. From a scientific point of view in this simulation almost 4 compound on average reach the P3
phase and being sold.
-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3
0
0.5
1
1.5
2
x 10
4
Frequency
ROE Annualized
0 2 4 6 8 10 12 14
-1
-0.5
0
0.5
1
Period
0
2
4
6

Simulation 2: Fund with long term approach
In this simulation, an initial capital of $4 billion is invested over a 10.5 year period. The capital structure include
senior ( 30% ), with coupon of 3% paid out from period 4 to period 12, and junior tranches(20%) with a coupon
of 7% paid out from period 12 to period 18. The remaining part of the capital is made of equity. The portfolio is
composed of 50 compounds in Preclinical phase, 55 compounds in Phase I and 5 in Phase II. The ratios
between the different categories are the same of the timetable. The target for this simulation is to sell the
compound when they reach the approval phase. The simulation includes 1 million paths analyzed. The main
result can be seen in the tables below.
Sales 0 0 0.681 5.951 3.145 0.691 0.504
WD 0 11.586 8.306 6.115 1.003 0.082 0
Mean number of compounds in each stage
0 5 10 15 20 25
0
1
2
3
x 10
4
Period
Cash - Quantiles
0
2
4
6
Frequency
0 2 4 6 8 10 12 14 16 18 20
0
200
400
600
Period
0
100
200
300
400
Period
0
5
10
15
Mean # compounds sold (green) or withdrawn (red) in phase
0
2
4
6
8
25 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4
0
2
4
6
x 10
5
Frequency
ROE Annualized
20 0 2 4 6 8 10 12 14 16 18 20
0
100
200
300
400
Period
S10 S20
0
2
4
6
8

Equity analysis
TOT ANN
E(ROE) 135 % 7.1%
Bond Analysis
Senior Bond Junior Bond
Probability of Default 0.00000 0.00003
Expected Loss 0.00000 0.00000
The results are characterized by two positive aspects. First is the absence of default from the bonds. This
means that, in every simulation, the fund has been able to pay for the interests, thus having a high level of
reliability.
The second is the return on equity, with an annual return of 7.1 %. This is lower than a common stock
investment but still represent a good result for some institutional investor.
The fund, with this time span, is not able to bring to the approval phase a drug. Probably with a higher
investment it would have been possible to finance more research and then come up with an approved drug.
25 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4
0
2
4
6
x 10
5
Frequency
ROE Annualized
18 20 0 2 4 6 8 10 12 14 16 18 20
0
100
200
300
400
Period
APP
phase
S10 S20
0
2
4
6
8

Simulation 3: Fund with short term approach
In this simulation, an initial capital of $1.5 million is invested over a 7.5 years period. In this case the capital
structure include senior bond ( 20% ) with a coupon of 4.5 % paid out from the period 4 to period 8, and junior
bonds (15%) with a coupon of 8% paid out from period 8 to period 12. The remaining part of the capital is
equity. The portfolio is composed of 30 compounds in Approval Phase and 30 compounds in Phase I. The
target for the simulation is to sell the compounds when they complete Phase III. The simulation includes 1
million paths analyzed. The main result can be seen in the tables below.
Sales 0 0.002 5.004 21.157 4.437 0 0
WD 0 9.294 15.094 5.012 0 0 0
0 5 10 15
0
0.5
1
1.5
2
x 10
4
Period
Cash - Quantiles
0 2 4 6 8 10 12 14
0
100
200
300
400
Period
Period
0
10
20
30
Mean # compounds sold (green) or withdrawn (red) in phase0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
0
1
2
3
4
x 10
4
Frequency
ROE Annualized
0 2 4 6 8 10 12 14
0
50
100
150
Period
0
10
20
30

Equity analysis
TOT ANN
E(ROE) 791 % 33.5%
Bond Analysis
Senior Bond Junior Bond
Probability of Default 0.00000 0.00000
Expected Loss 0.00000 0.00000
Also, in this case, the absence of default from the bonds. This means that, in every simulation, the fund has
been able to pay for the interests, thus having a high level of reliability.
The second is the return on equity, with an annual return of 33 %. This is a high value, although in line with the
previous studies of Fagan(2014). This result should be taken carefully because several components of the
selling process are not modeled.
From a scientific point of view, there has been a remarkable success of the research from Phase 1 to Phase 2
with 3 compounds sold in Target Phase 3.
The important outcome of this experiments is that the financing model is feasible, and the security could be
collateralized with success.
5 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
0
1
2
3
4
x 10
4
Frequency
ROE Annualized
4 0 2 4 6 8 10 12 14
0
50
100
150
Period
0
10
20
30

Conclusions
The world of pharmaceutical research is today one of the sectors that more need a change in its practices.
The promise of biopharma, although it has shown to be more efficient in developing drugs, has started to
mimic the old business model. In this scenario, as noted by some authors, there is a need to increase the
financing availability and reduce the cost and the risk of the research not merely externalizing the research12.
Especially in this historically period with interest rate are at an historical low, funding the research with a mix of
equity and debt could be convenient.
The business model of the fund provides the opportunity to combine equity and debt in a practical and
scalable way. Of course, in the model there are some assumptions that in a real case would have to be
studied and discuss more in deep. The first is that the market in which the investment is made is illiquid. For
this reason, some assumptions such as sell the compound in a year, and maybe more than one, could not be
realistic.
The second is that the analysis are based on past data, and the today R&D of drugs is continuously changing
shape and new niches are emerging. This mean the need to test the hypothesis constantly. The third
assumption is that a certain amount of researches exist and can be purchased on the market. This is a strong
assumption because the example assumes that there is a certain amount of different drugs development
available for purchase to be part of the fund.
On the other hand, the fund example shows how this innovation, and the access to new markets could be a
benefit for the research organization and for investors willing to hedge themselves in different market.
This approach could be interesting also for investors that are shareholders in the healthcare process, such as
health insurance company that pay for expensive drugs. Being part of this vertical financial integration could
help them gain competitiveness. Probably the biggest challenge in this process is the finding an organizational
structure able to let professional with scientific, engineering and, business background work together and
leverage the collective intelligence of the single person.
Some of these structures already exist, such as Royalty Pharma, Paul Capital, and DRI Capital. Each fund has
different policies and market strategies, and they do not operate exactly as the fund. The analyzed
performance and their performances are the demonstrations that the fund concept could be a feasible and
profitable alternative for the research market.

References
1 Pisano, G. P. The evolution of science-based business: Innovating how we innovate (2010).
2 World Preview 2014,Outlook to 2020 Evaluate Pharma 2014
3 Rapid Growth in Biopharma , Challenges and Opportunities; Otto, Santagostino; McKinsey
4 https://www.cia.gov/library/publications/the-world-factbook/, CIA Factbook, data at 01/2014.
5 Fagan et al.; Financing drug discovery for orphan diseases, Drug Discovery Today,2014
6 Bernard Munros; Lessons from 60 years of pharmaceutical innovation, Nature Reviews, 2009
7 Di Masi, Kaitin; Pharmaceutical innovation in the 21st Century: New Drug Approvals in the First Decade
2000-2009; Nature ,2010.
8 Danzon, Nicholson; The Oxford Handbook of the economics of the Biopharmaceutical Industry,2012
9 Hay,Thomas,Rosenthal et al.,BioMedTraker Clinical Development Success Rates for Investigational Drugs,
2012.
10 Fernandez,Stein,Lo; Commercializing Biomedical Research Throught Securitization Techniques; Nature
Biotechnology,2012
11 What is Securitization?; Andreas Jobst; Finance and Development IMF, 2008
12 New Frontiers in Financing and collaboration; David,Metha,Norris et al.;Evolution or Revolution, McKinsey
2012

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