New Approach to Pharma R&D / PPM

Kelvin Stott PhD
Pharma R&D Portfolio Strategy, Risk & Decision Consultant

October 2012

©KelvinStott2012

This presentation explores some of the underlying issues
responsible for declining pharma R&D productivity, and
provides a new, fully integrated approach to reverse this
trend by navigating R&D projects and portfolios through
the risk-return landscape in real time

A case study demonstrates how this system can be used
in practice to improve the risk-return profile of a Phase 2
development project according to risk appetite

This presentation focuses on R&D in the pharmaceutical
industry, however the approach can be used to manage
and optimize the risk-return profile of any business asset
at any stage of its lifecycle, at any level, in any industry

©KelvinStott2012

Pharma R&D productivity crisis
Fundamental issues & insights
Pharma industry challenges
Top 10 organizational issues
Key elements of a complete solution
A new fully integrated approach
Concept
Process
Case study: Phase 2 project
Conclusion & key messages
©KelvinStott2012

New technologies have done
nothing to slow the steady
decline in R&D productivity

Note the
log scale!

Source: Bernstein Research, 2010
©KelvinStott2012

% of preclinical drug candidates successfully passing through each phase
35%

30%

25%
Preclinical
20%
Phase I

15% Phase II
Phase III
10% Approval

5%

0%
1990-94 1995-99 2000-04 2005-09

©KelvinStott2012

More money, capital
… people, FTEs
… skills, know-how
… data, information
… tools, technologies
… disease targets
… projects, partners

So why is Pharma R&D productivity declining so rapidly?

©KelvinStott2012

• Tighter regulations
Decreasing
• Low reimbursement
market
• Pricing pressures
potential

• Increasing R&D Diminishing • Patent expiries
timelines (time profit window • Generics and
to market) (Value/ROI) biosimilars

Decreasing
R&D • Increasing R&D costs
productivity • Higher attrition rates

©KelvinStott2012

Decreasing
Technical quality of new
Decreasing
drug leads
success rates

Increasing
Commercial Increasing Decreasing
standards
& regulatory costs/project productivity
of care

Increasing
Decreasing
timelines
Operational organizational
effectiveness

©KelvinStott2012

Technical accessibility

Potential market by indication
©KelvinStott2012

Current drugs have already exploited the most obvious leads
with the lowest risk and greatest potential
Small molecules based on known active natural
products, ligands, substrates, dietary ingredients & traditional
medicines
Recombinant blood-based proteins to treat deficiencies in simple
metabolic & genetic diseases (e.g., insulin, GH, factor VIII)
Vaccines and antibodies against major pathogens, toxic proteins

Remaining opportunities are technically more challenging or
have limited commercial potential
Highly complex diseases with no clear mechanism, target or lead
Tissue-based diseases with significant barriers to drug delivery
Rare diseases and sub-types with existing drugs or limited market

Consequently, cost and risk have increased, like drilling for oil
from less abundant, less accessible, lower quality sources
©KelvinStott2012

Increasing competition
More safe and effective drugs have become available, raising the
standard to compete for market share
Generic competition is adding further pressure on drug prices

Improving regulatory standards
Regulatory standards are continuously evolving to ensure patient
welfare over current treatments with longer more costly trials, as
regulators learn to avoid past issues (e.g., safety recalls)

Increasing strain on healthcare budgets
Soaring healthcare costs are now considered unsustainable while
payers & providers are under increasing pressure to reduce costs

Consequently, there has been increasing pressure to develop
more safe and effective drugs at lower cost
©KelvinStott2012

Increasing technical risk (innovation)

Revolutionary Incremental Generics &
Combination
first-in-class improvements biosimilars:
therapies and
treatments of existing exact copies of
improved
based on new drugs based on existing drugs
formulations of
& unproven established competing on
existing drugs
target/MoA target/MoA price only

Increasing commercial risk (competition)

Regardless of strategy, overall risk is increasing with time

©KelvinStott2012

1. Too many ambiguous, conflicting goals
2. Misaligned performance metrics
3. Objective bias and illusion of control
4. Fundamental misunderstanding of risk
5. Left brain vs right brain mindset
6. Over-reliance on tools and processes
7. Managing information in silos
8. Ignoring key sources of information
9. Over-modeling discrete scenarios
10. Risk-averse culture and leadership
©KelvinStott2012

Get new products to market
Meet customer needs
Maximize sales/profit/value/ROI/…
Minimize/manage risk
Maximize probability of success
Optimize time, cost, quality
Do the right projects, do them right
Support the strategy
Manage resources effectively
©KelvinStott2012

What are the “right” projects/products?
Which customers? How to balance their needs?
What to maximize: sales, profit, value, ROI?
At what risk? What is “risk”?
How to define & measure success, quality, etc.?
If our goal is to support the strategy, what is the
goal of the strategy? Tail wagging the dog?
How flexible is the current plan/budget?
How to prioritize when goals are not aligned?
©KelvinStott2012

Measured performance Shareholder interests

Balanced
scorecard

No.
≠ Project
Portfolio
Value ± RISK

Project Project
approvals/milestones, time, Value Value Value
cost, “quality”, etc. ± RISK ± RISK ± RISK

©KelvinStott2012

Performance metrics are over-simplified and do
not incorporate all key value/risk drivers
Often based on quantity and defined by simple
short-term operational milestones, e.g.,
No. of approvals/successful phase transitions
No. of milestones achieved on time/budget
Quality is judged subjectively, or measured only
in part, e.g., strategic fit, probability of success
Overall performance is calculated as weighted
average, does not correlate with value and risk
©KelvinStott2012

Value/ROI
± RISK

Sales, P&L, cash flow forecasts ± RISK

Technical &
Operational Commercial BD&L Financial
regulatory
PoS by phase R&D costs Target patients Licensing fees Discount rate
Prob. approval R&D timelines Market share Dev. m’stones DSI, DSO, DPO
Launch date Adoption rate Sales m’stones Exchange rates
Capex Dose & compl. Royalty rates Inflation rates
COGS Net price Tax rates
S&M costs New entrants
G&A costs Generic entry
Other factors
©KelvinStott2012

Perception Reality

Drug has intrinsic
Positive bias
potential to succeed
(“get drug to market”)
(safe & effective)

≠ ?
Drug is inherently
Negative bias
unsafe, ineffective or
(“kill projects early”)
uncompetitive

©KelvinStott2012

Probability
of success Biased objective
to succeed

Costly late-
Perceived stage failure
potential
Unbiased
research
Excess costs saved
for other projects

Intrinsic
potential

Development Time & Cost
©KelvinStott2012

Intrinsic
potential

Unbiased
research
Perceived
potential
Biased objective
to kill project early
Probability
of success Opportunity
cost

Development Time & Cost
©KelvinStott2012

Naturally, project teams want their projects to succeed, and set their
goals to develop a superior drug that is both safe and effective.
However, many seem to forget that the potential of a drug to be safe
and effective is an intrinsic property of the molecule which is already
built into the drug when it comes out of the research labs.
Consequently, teams can over-estimate their own ability to develop a
drug, and may defer or even avoid critical tests to determine its true
potential in case they give a negative result. This results in costly late-
stage failures when the drug’s true potential ultimately reveals
itself, as it always does (sometimes even after approval).
Alternatively, teams may be encouraged to kill their projects early to
avoid such costly late-stage failures, however this can have significant
opportunity costs if good drugs are terminated before sufficient data
is available to confirm their (lack of) intrinsic potential.
Therefore, development teams must learn that their ultimate goal is
to discover the intrinsic potential of a drug candidate, not to make it
succeed or fail. This requires a brave, pioneering mindset to ask the
right questions and test critical hypotheses in an unbiased, objective
manner, in the true spirit of scientific discovery.
©KelvinStott2012

Risk ≠ Prob. of Failure ≠ Potential Loss ≠ Risk-Adjusted Value

Scenario Upside
Value risk

Risk-adjusted Downside
value (eNPV) risk

Probability
Potential Probability
loss of failure

©KelvinStott2012

Shareholders are interested in risk as it relates to
uncertainty (volatility) in their expected return
Risk is a function of both probability and impact
Risk is the expected (probability-weighted average)
deviation (impact) from the expected value/return
across all potential scenarios
Based on uncertainty in all potential value drivers
Probability (PoS) is a key value driver, but a poor
and misleading indicator of risk
PoS does not capture impact of success or failure on
volatility in overall expected value/ROI
Expected value increases linearly with PoS, but risk &
uncertainty are greatest at 50% PoS (coin flip)
©KelvinStott2012

Value

Value

Value
Prob.
Prob. Prob.

0% PoS 50% PoS 100% PoS
min value, no risk mid value, max risk max value, no risk

Value/Risk

Expected Value Downside Risk
= PoS x Upside = PoS x (1-PoS)
Value + (1-PoS) x x (Upside Value -
Downside Value PoS Downside Value)

©KelvinStott2012

Value in $millions Project A Project B
Probability of Success 50% 25%
Upside Value (potential gain) 500 700
Downside Value (potential loss) -300 -100
Expected Value (1 project) 100 100
Expected Loss 150 75
Downside Risk vs EV 200 150
Standard Deviation 400 346
Mean Abs. Deviation 400 300
Expected Value (10 projects) 1,000 1,000
Expected Loss 156 94
Downside Risk vs EV 492 451
Standard Deviation 1,265 1,095
Mean Abs. Deviation 984 901
©KelvinStott2012

4 simple options have the same expected value of $100:
Which is the “MOST RISKY” option?
1. Gain $100 (100% probability)
2. Gain $300 (75%) or lose $500 (25%)
3. Gain $500 (50%) or lose $300 (50%)
4. Gain $700 (25%) or lose $100 (75%)
13% of experts1 chose Option 1 as “most risky”
26% chose Option 2 based on potential loss only
26% chose Option 4 based on probability only
Only 35% chose correct Option 3 based on potential loss
and probability combined2
1. LinkedIn survey of over 800 professional risk managers, financial analysts and investors
2. Greatest risk according to all key risk metrics: expected loss ($150); downside risk vs EV ($200);
standard deviation ($400); mean absolute deviation vs EV ($400)
©KelvinStott2012

Corporate Value ± RISK

R&D portfolio Comm. portfolio
Value ± RISK Value ± RISK

TA portfolio TA portfolio TA portfolio TA portfolio
Value ± RISK Value ± RISK Value ± RISK Value ± RISK

Project Project Project Project Product Product Product Product
Value Value Value Value Value Value Value Value
± RISK ± RISK ± RISK ± RISK ± RISK ± RISK ± RISK ± RISK

©KelvinStott2012

Left &
right brain
work
together

©KelvinStott2012

Detail-oriented, short-term thinking and analysis loses
sight of the big picture and limits creativity & innovation
to incremental improvements on current practice
Implementation of quick fixes and short-term processes
without solving fundamental issues increases risk and
complexity, causes more problems in the long term and
creates a vicious cycle of continuous firefighting
Reliance on analyzing data to predict the future without
incorporating intuition and imagination increases risk by
assuming the future will look like the past
Main risk is that people are too busy watching the dials
to look out the window when the train is about to crash
Humans have evolved to manage risk by applying both
left and right brain thinking; neither has been sufficient
alone to survive, so it is critical to use both together
©KelvinStott2012

Tools and processes are implemented to help an
organization achieve its objectives, however the
organization often ends up supporting its tools &
processes at the expense of its objectives
Tools and processes usually introduce constraints
that help to ensure consistency, however people
tend to switch off their brains because they regard
the tool/process as a substitute for free thinking
Finally, tools & processes are often implemented
without proper foresight and testing, so they end
up causing more problems than they solve
©KelvinStott2012

So what?
Project/Portfolio What if… ?
Manager

Annualised R&D Annualised sales Production cost Annualised S&M BD&L / financial
cost forecasts & forecasts by forecasts by cost forecasts by parameters by
attrition rates country/scenario country/scenario country/scenario country/scenario

R&D teams Bus. Intelligence Tech. Operations Sales & Marketing BD&L / Finance

Discrete Discrete Discrete Discrete Discrete
assumptions assumptions assumptions assumptions assumptions

©KelvinStott2012

In order to manage great complexity, companies
segregate and manage information in separate
parts of the organization, but this creates silos by
reducing connectivity and transparency
Companies then develop more processes and hire
“interface managers” to manage the flow of
information between silos, however this increases
complexity further by adding yet more
interfaces, thus creating a vicious cycle.
Moreover, organizations are primarily designed to
manage people, projects & resources, rather than
information as their ultimate business asset
©KelvinStott2012

Top-down External
analysis advisors

Bottom-up Internal
analysis experts

Mean Spread Skew Shape

©KelvinStott2012

All predictive models are based on assumptions, but the
assumptions are often based on limited data and ignore
key sources of information, for example:
Probabilities of success are often over-estimated by teams
believing that their projects are more likely to succeed than
industry benchmarks, without considering that every team
thinks the same about its own projects, and without asking
external experts for a more balanced objective opinion
Expected costs and timelines are often based on ambitious
project plans, without taking into account a known history
of project delays and budget deviations
Furthermore, the distribution of data is ignored where
discrete assumptions are calculated as average expected
values that are precisely wrong, while any uncertainty
and sensitivities in the assumptions become invisible so
that overall risk cannot be evaluated and managed
©KelvinStott2012

Risk is complex, includes continuous as well as discrete
probability distributions, but models are usually based
only on discrete scenarios & simple probabilities
Such discrete probability models do not include the full
spectrum of potential scenarios, especially the rare and
extreme outliers (black swans) as well as intermediate
scenarios, so the temptation is to model more discrete
scenarios in order to improve accuracy
However, this costs more time and effort, and just adds
more noise and complexity without increasing accuracy
because the underlying data are ultimately limited
Modeling with continuous probability distributions can
solve this problem, but is not common practice
©KelvinStott2012

Discrete

Complex

Continuous

©KelvinStott2012

Perceived
accuracy
Time & effort
Noise and
complexity
Accuracy

Transparency

No. scenarios modelled
©KelvinStott2012

Includes the complete spectrum of all possible
scenarios, including intermediate scenarios and
rare/extreme outliers (“black swan” events)
Simple and transparent, with no more than 4
parameters to define the complete “shape” of
uncertainty in each value/risk driver
Can be easily modeled on historic data as well
as the forward-looking intuition of experts (or
even an integrated combination of the two)
Supports automatic Monte Carlo simulations
©KelvinStott2012

Risk-averse Risk-averse
leaders continue leaders make
to act in a “safe” Risk-averse “safe” decisions
way in order to Risk-averse by following the
decisions
further advance leadership pack, avoiding
their careers in risk in the new
the company RISK-AVERSE and unknown
CULTURE, RESI
STANT TO
Employees are CHANGE Employees act in
hired and then a “safe” manner
promoted based Risk-averse Risk-averse to advance their
on their “safe” hiring & behaviour careers, as risk-
and steady track promotion taking innovators
record, decisions get pushed out –
and behaviour or fed up & quit

©KelvinStott2012

Source: Bernstein Research, 2010
©KelvinStott2012

Bobcat climbs cactus to escape hungry lion

©KelvinStott2012

Information overload
Increasing complexity
Decreasing R&D
Communication issues
productivity as
Data/detail vs insight the right things
are not done at
Reduced transparency
the right time
Decreased objectivity
Slow & poor decisions

©KelvinStott2012

Clear, fully aligned strategic objectives and metrics
based on a solid understanding of value & risk
Shift culture/mindset to more big picture thinking
by integrating and automating information & data
management to provide real insight in real time
Fully integrated system to evaluate and manage risk-
return profile of projects & portfolios in real time
Automatically manages and integrates all key sources
of data, including internal/external expert intuition
Fully consistent, transparent & objective; incorporates
uncertainty in all key value drivers
Strong leadership with a risk appetite to step out
from the pack, challenge current thinking, and try
the new & unknown (especially in recruitment)
©KelvinStott2012

Phase 3
projects

Current
products

Risk BD&L
options
Phase 2
projects

Phase 1
projects

Expected Value/ROI
©KelvinStott2012

-30 -20 -10 0 10 20 30 40 50 60
INPUT
Scenarios & Assumptions Corp. tax rate 7.4
Input Parameter Unit Default Scen. 1 Scen. 2 Scen. 3 Scen. 4 Scen. 5 Scen. 6 SD 7.4
Accounting Tax Corp. tax rate % 30.0%
Prob. deferral
parameters Prob. deferral % Inventory (DSI) 7.4
WCap Inventory (DSI) days
Acc. receivable days Acc. receivable 7.4
7.4

Available
Acc. payable days Acc. payable
NPV Discount rate % 8.5%
Global R&D Preclinical Phase duration mo 18.0 0.0 3.0 Discount rate 7.4
Total costs €M 5.0 0.0 1.0 Phase duration 7.0 7.9
Prob. success % 75.0% 100.0%
Phase 1 Phase duration mo 24.0 0.0 4.0 Total costs 6.7 8.1
Total costs €M 15.0 0.0 3.0
Prob. success 1.0 11.1

options
Prob. success % 65.0% 100.0%
Phase 2 Phase duration mo 30.0 5.0 Phase duration 6.7 8.3
Total costs €M 50.0 10.0
Prob. success % 30.0% Total costs 6.0 8.8
Phase 3 Phase duration mo 42.0 7.0 Prob. success -5.9 17.1
Prob. success % 70.0% Phase duration 6.1 9.0
Registrn. Phase duration mo 18.0 6.0 4.8 10.0
Total costs
Prob. success % 80.0% Prob. success -23.6 54.7
Sales growth Timing Delay to Launch mo
Patients
without special risks Total patient popn. K 1,000.0 100.0 Phase duration 5.0 10.2
& events Annual increase K Total costs 5.9 8.9
Target segment % 100.0%
Peak patient share % 100.0% Prob. success -19.8 25.2
Current share % Phase duration 5.2 10.0
Adoption rate % 40.0% 10.0%
Units Units/patient/year N 1.0 Total costs 7.4
Compliance rate % 100.0%
Price Net price/Unit at L € 400.00 100.00
Prob. success -13.4 17.8
Inflation rate % Delay to Launch 7.4
Special risks & Generics & Event probability % 80.0%
events biosimilars Expected date mo 21.2.2032 36.0 Total patient popn. 3.2 11.6
Volm decrease % 50.0% 10.0% Annual increase 7.4
Price decrease % 20.0% 10.0%
Rate of impact % 50.0% 10.0% Target segment 7.4
Event 2 Event probability % 7.4
Expected date mo
Peak patient share
Volm decrease % Current share 7.4
Price decrease %
Rate of impact % Adoption rate 4.1 9.6
Event 3 Event probability % Units/patient/year 7.4
Expected date mo
Volm decrease % Compliance rate 7.4
Price decrease % -3.0 17.8
Rate of impact %
Net price/Unit at L
Event 4 Event probability % Inflation rate 7.4
Expected date mo
Volm decrease % Event probability 5.2 11.8
Price decrease % Expected date 3.8 10.2
Rate of impact %
Event 5 Event probability % Volm decrease 6.2 8.6
Expected date mo Price decrease 6.6 8.1
Volm decrease %
Price decrease % Rate of impact 7.1 7.9
Rate of impact %
Event 6 Event probability %
Event probability 7.4
Expected date mo Expected date 7.4
Volm decrease %
Price decrease % Volm decrease 7.4
Rate of impact % Price decrease 7.4
Expected date mo Rate of impact 7.4
Volm decrease % 7.4
Event probability

Risk
Price decrease %
Rate of impact % Expected date 7.4
Expected date mo Volm decrease 7.4
Volm decrease % Price decrease 7.4
Price decrease % 7.4
Rate of impact % Rate of impact

appetite
Explicit assumptions & Sensitivity analysis of
probability distributions key value/risk drivers
80.0 1,600.0 25.0 2,500.0
Preclinical Phase 1 Phase 2 Phase 3 Registrn. Phase 4
70.0
1,400.0 1,360.1 1,360.1
20.0 2,000.0
60.0 20.0
1,200.0 18.2
50.0 17.0
1,500.0
40.0 1,000.0 15.0 15.0 15.0
15.0
30.0
800.0 1,000.0
697.0
20.0
10.0
10.0 600.0
487.9 487.9 500.0
0.0
400.0
2012

2014

2016

2018

2020

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

2042

2044

2046

5.0
-10.0 0.0
2.8
200.0 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
-20.0 103.6
61.8
0.5
0.0 0.0 0.0 0.0 0.0 0.0 0.0
-30.0 0.0 0.0 -500.0
2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025
R&D Costs Net Sales G. Margin EBIT Net Profit Cash Flow NPV
R&D Costs Net Sales Margin EBIT Net Profit Cash Flow NPV Value Vmean Vexp Vmed Vmin Vmax Risk threshold

Automatic sales, P&L Cumulative forecasts Risk-adjusted R&D Monte Carlo analysis
& cash flow forecasts over any period costs by phase/year of NPV, any P&L item
©KelvinStott2012

Evaluate Evaluate
risk-return risk-return
profile of EVALUATE profile of
EVALUATE
current OPTIONS options &
PROGRESS
plan scenarios

RISK-RETURN
OPTIMIZATION

Execute MAKE Select best
updated EXECUTE option(s)
DECISIONS
DECISIONS
plan and based on
monitor risk-return
progress profile

©KelvinStott2012

Value
& ROI

Sales, P&L, cash flow forecasts

Technical & Operational Commercial BD&L Financial
regulatory value drivers value drivers value drivers value drivers
PoS by phase R&D costs Target patients Licensing fees Discount rate
Prob. approval R&D timelines Market share Dev. m’stones DSI, DSO, DPO
Launch delay Adoption rate Sales m’stones Exchange rates
Capex Dose & compl. Royalty rates Inflation rates
COGS Net price Tax rates
S&M costs New entrants
G&A costs Generic impact
Other factors
©KelvinStott2012

Value driver Uncertainty Project value/ROI ± risk
Phase 3 PoS

R&D timelines

Phase 2 PoS

Market share

R&D costs

Generic impact

COGS

©KelvinStott2012

EVALUATE
EVALUATE
OPTIONS
PROGRESS

Research Preclinical Clinical Approval Lifecycle
& discovery developmt. developmt. & Launch managemt.

MAKE
EXECUTE
DECISIONS
DECISIONS

©KelvinStott2012

Individual R&D projects (early or late-stage)
Marketed products (branded or generic)
BD&L opportunities (in & out-licensing deals)
Entire portfolios (R&D or commercial)

©KelvinStott2012

Phase 2 NCE for pancreatic cancer, new
MoA, about to enter large Phase 3 trial
Similar safety & efficacy profile vs competitor
Positive eNPV
BUT: current plan feels too risky
Lack of clear dose response in Phase 2
Relatively low PoS (50%) in Phase 3
Significant uncertainty in potential market
Key competitor already in Phase 3

©KelvinStott2012

Phase 3: $120(±10)m, 36(±6)mo, 50% PoS
Approval: $15(±3)m, 12(±3)mo, 90% PoS
Peak sales: $800(±300)m (50% within 24 mo)
Competitor: 50% probability to launch in 2
yrs, reducing peak sales by 65(±10)%
Generics: lose 80(±5)% sales after 12 yrs
10(±2)% COGS, 60(±5)% SG&A costs, 30% tax
8.0% discount rate

©KelvinStott2012

Input Parameter Unit Default Scen. 1 Scen. 2 Scen. 3 Scen. 4 Scen. 5 Scen. 6 ±SD
Accounting Tax Corp. tax rate % 30.0% 250.0
parameters Prob. deferral %
WCap Inventory (DSI) days
Acc/R (DSO) days
Acc/P (DPO) days 200.0

Annual P&L / Cash Flow (millions)
NPV Discount rate % 8.0%
Global R&D Preclinical Phase duration mo
Total costs $M
Prob. success % 150.0
Phase 1 Phase duration mo
Total costs $M
Prob. success %

End of Period
Phase 2 Phase duration mo
Total costs $M
100.0
Prob. success %
Phase 3 Phase duration mo 36.0 6.0
Total costs $M 120.0 10.0
Prob. success % 50.0% 50.0
Registrn. Phase duration mo 12.0 3.0
Total costs $M 15.0 3.0
Prob. success % 90.0%
Sales growth Timing Delay to Launch mo 0.0
without special Patients Total patient popn. K 800.0 300.0
risks & events Annual increase K
Target segment % 100.0%
Peak patient share % 100.0% -50.0
Current share %
Uptake rate (t½) mo 24.0
Units Units/patient/year N 1.0
Compliance rate % 100.0% -100.0
Price Net price/Unit at L $ 1,000.00

2012

2014

2016

2018

2020

2022

2024

2026

2028

2030

2032

2034

2036

2038

2040

2042

2044

2046
Inflation rate %
Special risks & Generics & Event probability % 100.0%
events biosimilars Expected date mo 16/Apr/24
Volm increase % -80.0% 5.0% Net Sales Gross Margin EBIT Net Profit Cash Flow DCF
Price increase %
Impact rate (t½) mo 24.0
Comp. 1 Event probability % 50.0%
Expected date mo 16/Apr/14
Volm increase % -65.0% 10.0%
Price increase %
3,500.0
Impact rate (t½) mo 24.0
Comp. 2 Event probability % 3,078.6
Expected date mo
Volm increase % 3,000.0
Price increase % 2,770.7
Impact rate (t½) mo

Total P&L / Cash Flow (millions)
Tech shift Event probability %
Expected date mo
Price increase %
Expected date mo
Price increase %
Expected date mo
Volm increase %
1,500.0
Price increase %
Expected date mo 1,000.0
Volm increase % 796.1
Price increase %
557.2 557.2
Expected date mo 500.0
Volm increase %
Price increase % 152.8
Prodn costs COGS COGS per Unit $
(or % sales) % 10.0% 2.0% 0.0
Deprecn Depreciation/Unit $ Net Sales G. Margin EBIT Net Profit Cash Flow NPV
(or % sales) %

©KelvinStott2012

New Approach to Pharma R&D / PPM

New Approach to Pharma R&D / PPM

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