Portfolio Management and R & D Productivity

Anirban Bhattacharya, PhD

8th Annual Project and Portfolio Management Forum

Pharma Industry has one of the most expensive R&D efforts

*Top 20 firms traded on US Exchanges

Company 2010 REVENUE($ IN MILLIONS)

2010 R&D SPEND($ IN MILLIONS)

2010 R&D SPEND (%of sales)

OPTIMAL R&D SPEND

Difference

EXXON MOBIL $341,578 $1,012 0.30% $136,486 $135,474 CHEVRON $189,607 $526 0.28% $56,690 $56,163 CONOCOPHILLIPS $175,752 $230 0.13% $100,350 $100,119 GENERAL ELECTRIC $149,060 $3,939 2.64% $19,947 $16,008 GENERAL MOTORS $135,592 $6,962 5.13% $15,570 $8,608 FORD $128,954 $5,000 3.88% $14,405 $9,405 HEWLETT-PACKARD $126,033 $2,959 2.35% $43,907 $40,948 MCKESSON $112,084 $407 0.36% $111,598 $111,190 IBM $99,871 $5,720 5.73% $10,359 $4,639 PROCTER & GAMBLE $78,938 $1,950 2.47% $7,816 $5,866 PFIZER $67,791 $9,538 14.07% $6,304 ($3,234)APPLE $65,225 $1,782 2.73% $9,468 $7,686 BOEING $64,306 $4,121 6.41% $8,142 $4,021 MICROSOFT $62,484 $8,714 13.95% $9,210 $496 ARCHER-DANIELS-MIDLAND $61,682 $56 0.09% $29,947 $29,891 JOHNSON & JOHNSON $61,587 $6,844 11.11% $5,371 ($1,472)DELL $61,494 $661 1.07% $16,218 $15,557 UNITED TECHNOLOGIES CORP. $54,326 $1,746 3.21% $6,192 $4,446 DOW CHEMICAL $53,674 $1,660 3.09% $9,356 $7,695 KRAFT FOODS $49,207 $583 1.18% $7,254 $6,671

Optimal R&D Spend calculated on the basis of RQ (Research Quotient)Article by A.M.Knott on Harvard Review (May 2012): “The trillion-dollar R&D Fix”

Is Pharma “over-spending” in R&D ?

“Drought” Period: 2005-2010

NMEs approved per year (average) = 22Average 5th year sales per NME ($ MM) = 4305th year sales per year in total ($ Bn) = 9.4R&D spend per year ($Bn) = 125 5th year sales per 1$Bn R&D spend ($MM) = 75

Golden Period: 1996-2004

NMEs approved per year (average) = 3Average 5th year sales per NME ($ MM) = 5155th year sales per year in total ($ Bn) = 18.3R&D spend per year ($Bn) = 655th year sales per 1$Bn R&D spend ($MM) = 275

From Abundance to Scarcity (in quantity AND quality!)

73% drop in R&D productivity !

The 90s remained the most fruitful period in the history of Pharma

PDUFA + “good” FDA behavior (e.g. HIV)Targeting chronic diseases with new MoAsDevelopment of many “fast-followers”Establishment of “surrogate markers”Creation of new diseases (OAB, RLS, etc.)

Is this a “rebound

effect” or an “R&D

productivity crisis”?

Required Investment Created Value

Defining R&D Productivity

What is R&D productivity?

R&D Productivity is an aggregate representation of: R&D efficiency: ability to translate inputs (ideas, resources, money, etc.) into defined outputs (usually approvals and launches), over a defined period of time; it is simply measured by a “cost per launch”R&D effectiveness: ability to produce outputs with certain intended and desired qualities/outcomes (value to patients, physicians and/or payers; and substantial commercial value); it is simply measured by a “value per launch”

S.M..Paul et al. “How to improve R&D productivity” Nature Reviews/Drug Discovery vol.9; March 2010; p.203-214

R&D Productivity Report Card

0

10

20

30

40

50

60

FDA approvals of NME/NBEs

- The number of new drugs approved per US$ 1 Bn spent in R&D has halved every 9 years since 1950, falling around 80-fold- The cost of developing one NME raised 38-fold, from $50M in the 50s to $1.8 Bn in the 2000s

The good ……

Annual number of NME/NBEs approved by the FDA stable over the last 60 years

The bad ……

The ugly ……

NPV for NME is -$65IRR is 7.5% (less than cost of capital at 10%)Negative trend: IRR was 12% in 1997-2001Better financials for biologics: IRR at 13% and NPV at $1.26 billion

R&D Efficiency Assessment

Developmental CostRisk

Time

Increased Development Costs

The first randomized controlled trial, published in 1948, recruited 109 patients and randomized 107 of them

Between 1987 and 2001, the number of patients per pivotal trial for anti-hypertensive agents rose from 200 to 450

Between 1993 and 2006, the average number of patients across the pivotal trials in diabetes rose from 900 to over 4,000

The first long-acting insulin analogue, glargine, was approved in 1999 following 3 pivotal trials; the newest long-acting insulin analogue , degludec, was filed in 2011 following 12 pivotal trials

The first pivotal trial for Merck’s simvastatin, published in 1994, recruited 4,400 pts; a pivotal trial for Merck’s Anacetrapib is recruiting more than 30,000 pts

According to a study (E. David et al.), between 1997 and 2010, the cost of development increased by 8%

Preclinical: 7Phase I: 6Phase II: 3Phase III: 1.5Regulatory: 1.1Launch: 1.0

Cum. Success Rate: 14 %

1995-2000*

Preclinical: 13Phase I: 9Phase II: 5Phase III: 1.6Regulatory: 1.1Launch: 1.0

Cum. Success Rate: 8 %

2000-2003*

Preclinical: 24Phase I: 15Phase II: 7Phase III: 1.8Regulatory: 1.2Launch: 1.0

Cum. Success Rate: 4 %

2003-2007**

Preclinical: 30Phase I: 19Phase II: 9Phase III: 1.9Regulatory: 1.2Launch: 1.0

Cum. Success Rate: 3 %

2007-2011**

Declining R&D Success Rates(adapted from Bain drug economics model, 2003* and from KMR 2007-2011**)

Small Molecules 43.6 (61%) 26.6 (42%) 11.1 (18%) 2.0 (60%) 1.2 (85%) 1Biologics 8.8 (75%) 6.6 (56%) 3.7 (44%) 1.6 (79%) 1.3 (79%) 1 PC Ph.I Ph.II Ph.III Reg Launch

2% for Small molecules and 11% for Biologics

• There are other reports with different numbers (e.g. according to E.David et al., between 1997 and 2010, the cumulative PoS lost 5 points in %), however the negative trend remains a constant !

Increased time for ClinDev

While the time spent in Discovery has remained stable over time at 4.5 years, the time spent in development has increased significantly According to E.David et al. , between 1997 and 2010, clinical development time

has increased by 15 months According to KMR, between 1998 and 2011, clinical development time has

increased by 2 years (from 11.5 to 13.7 years) The total time for development currently averages 8 years, with high

variability by TA (adapted from KMR report) Phase I => 2 year Phase II => 3 year Phase III => 3 year

LO, Ph.II & Ph.III as main cost drivers

Even though Phase III has by far the highest average cost per project ($150M), the higher number of projects and the capital cost over time make Phase II and Lead Optimization average costs higher!

S.M.Paul et al. “How to improve R&D productivity” Nature Reviews/DrugDiscovery vol.9; March 2010; p.203-214

Early commercial involvement will help to make the right choices to optimize resource allocation in LO and Early Development !

Decrease of PoS for Ph.II and Phase III are the two most important cost drivers: the critical role of VoI

S.M.Paul et al. “How to improve R&D productivity” Nature Reviews/DrugDiscovery vol.9; March 2010; p.203-214

Impact on the average cost of drug discovery & development ($1,778M) of the ten most important cost drivers

Optimizing R&D Efficiency: the cost of an NME can be cut by 50%

Reduce cost and time of development Trial size, sites/investigators, CRO management, low-cost countries, partnerships Reduction of ph.III from 2.5 to 2 years will reduce the cost by $100M Adaptive/seamless ph.II/III trial designs will save time and cost Time/cost of development is “disease-specific” (e.g.CV worse than ID)

Optimize PoS Reduce attrition in ph.II/III with early PoC studies (reliable biomarkers) More validated/druggable targets & greater use of translational phenotypic assays

Sufficient number of projects by phase to ensure 1 launch/year: If PoS in ph.III increases from 70% to 90% , the number of products entering ph.I can

decrease from 9 to 7 Redirecting resources from drugs with low PoS: e.g. 1 ph.III has same cost of 10 Ph.I Moving from FIPCo (Fully Integrated Pharma Co) to FIPNet (Fully Integrated Pharma

Network)

S.M.Paul et al. (Nature Reviews/Drug Discovery, vol 9; March 2010; p.203-214)

R&D Effectiveness AssessmentPharma needs to create new medicines able to

surpass an ever-improving SoC, being the victim of its own success.

No more low-hanging fruits!

Decreasing Sales for New Products

Adapted from Accenture Research Report, based on data from various sources

716M

556M482M

408M

1992-1996 1997-2001 2002-2006 2007-2011

Average Peak Sales for New Products ($ M)

Variable Return from New products

New drugs launched in 2000-2006 showed an average IRR of 7.5% ; they can be grouped in quartiles based on revenues generated:

1st quartile: 2% of them with IRR of 28% 2nd quartile: 4% of them with IRR of 12% 3rd quartile: 40% of them with IRR of 8% 4th quartile: 54% of them with IRR of 6%

E David et.al., Nature Reviews / Drug Discovery vol.8, Aug.2009 p.509-510)

The questionable value of new medicines: BIC or me-toos? From its inception in 2004, Germany’s IQWIG has classified 70% of

drugs reviewed as drugs with “unproven benefit”; from Jan.2011, Value Dossier required with NDA

According to the French HTA system, in recent years, only 12% of new medicines are bringing significant clinical benefits over SoC; nearly 60% of new medicine had no additional value

Between 1998 and 2008, the UK’s NICE granted restricted or no market access to almost 60% of the drugs launched by the top ten pharma companies

In the US, only one third of the new medicines achieve a formulary listing that allows unrestricted use, with reimbursement

Optimizing R&D Effectiveness: Focused portfolio on Core TAs Several challenges (cheaper generics, more aggressive payors, more difficult

science, etc.) have increased the competitive requirements Companies are realizing they cannot compete effectively in every TA

70% of the BBs launched in 1970-2000 were in TAs where the marketer had significant presence

Category leaders completed 2 times more deals, 70% higher success rate in ClinDev and had 5 times more revenues as compared to other competing firms

More attention to be paid to specialization of capabilities and integration of focus areas from research through to commercialization

Changing mindset: from “playing everywhere” to “play to win”

Optimizing R&D Effectiveness: Darwinian approach to decision making Objective evaluation of projects and elimination of decision-making biases,

allowing only the best program to survive Changing mindset from “win with any innovation” to “raise the bar for

innovation” From “targeting the broadest population in which the new drug has statistically

significant (though often clinically marginal!) benefits” to “targeting patients with the greatest benefit”

Recently launched Pfizer’s crizotinib targets only 5% of lung cancer patients with ALK oncogene, but has very high efficacy: with a target population WW of 50,000 this product will surpass $500MM by 2015

Incentives need to give less weight to milestone accomplishments and more to measures of quality and strategic intensity

Ability to re-allocate resources across different franchises to better invest R&D money

Optimizing R&D Effectiveness: Regain trust of all customers

From PoC to PoC&EB (Proof of Concept and Economic Benefit): providing value to all customers Instead of searching for a gap in the market where to sell a product in development,

design a product to fill a well-identified market gap Gap defined by the needs of the patients, the physicians, the regulators, the HTAs

and the payors From artificial patients identification with surrogates to patient segmentation to

maximize drug value (=>personalized medicine!) Identify and define, before starting clinical development, what outcomes matter

to all customers, including what evidence is required From treating payors as a problem to solving their problems

Assessing the Balance between Efficiency and Effectiveness

The Role of Portfolio Management

R&D Productivity - Implications R&D Productivity grows with:

Number of projects PoS Value of projects

R&D Productivity decreases with: Time to complete projects Cost of projects

But these 5 elements are inter-connected and Portfolio Management, if started early, can help optimize them!

DOP – Disease Opportunity Profile

Available to R&D at Target Identification Defines the “opportunity” and the “challenges” in the marketplace,

clarifying KSFs (Key Success Factors) The DOP is continuously updated and shared with the critical players

in R&D The Assessment of the disease focuses on:

Definition of Target Population Evaluation of the Level unmet medical need Identification of key differentiators from a ‘gold standard” Assessment of the Competitive landscape (incl. LOEs) Understanding of key P&R requirements SWOT analysis

DOP scoring and threshold

Disease Target Assessment quantifies the opportunity in each of the key attributes of medical need

Efficacy Convenience Mortality Morbidity Cost

Opportunity

Level Achieved by

Gold Standard

Safety /Tolerability

Measuring Unmet need using published objective clinical trial data

Efficacy:symptom relief, slowing of progression, restoring lost function, pharmacokinetics

Side effects: frequency and severity of each

Convenience: mode and frequency of dosing

Mortality: age-adjusted excess risk of mortality

Morbidity:pain, disability, hospitalization, quality of life, complications

Costs: direct (drug and non-drug) and indirect

Impact of Efficacy:

Impact of efficacy on mortality, morbidity, and cost

Assign a score to each component of unmet need:

No unmet need Substantial unmet need

0 1 2 3 4 5

TCP – Target Candidate Profile

Available to R&D at Lead Optimization It states the minimal attributes for the new product to be commercially

viable It identifies, among all product attributes, the key “value drivers” It focuses on the key differentiators from SoC It helps the performance of a more effective “Lead Optimization”

phase while offering guidance for GnG decisions It includes a “bucket” forecast with possible upsides It is always complemented by the relevant DOP TCP scoring and prioritization (facilitating the “early kill”)

Profile Alpha is highly innovative vs. current gold standardSources of Difference in Unmet Need between Alpha and Current Gold Standard

Sum of % differences equals overall relative improvement in unmet need: 9.6%Note: Bars may not sum to overall % due to rounding

Unmet need scores range from 0 (no unmet need) to 5 (substantial unmet need)

2.10

2.15

2.20

2.25

2.30

2.35

2.40

2.45

2.50

Unm

et N

eed

Sco

re

Current GoldStandard

2.48

Alpha

2.25

Efficacy

5.5%

Side Effects

-0.8%

Convenience

2.5%

Mortality

0.6%

Morbidity

1.2%

Direct Cost

0.1%

Indirect Cost

0.4%

The greater the reduction in medical need, the larger the peak share achieved

Confidential Equinox Group Information

0%

20%

30%

40%

50%

60%

70%

80%

90%

100%

-15% -10% -5% 0% 5% 10% 15% 20% 25% 30%

Percent Reduction in Unmet Need

Pea

k P

atie

nt S

hare

Singulair

Lescol

Viagra

Aricept

Lipitor

VfendAdvair

Boniva

Unmet Need

Payer Pressure

CompetitiveLandscape

MarketOverview

Disease Opportunity Profile

# Killer Experiments# Go / No-go

Decision Criteria

• Commercial Viability

• Identification of Non-negotiable Attributes

• Additional value drivers for potential economic upside

Target Candidate Profile

Regional Contribution

DOP & TCP to support Go / No-go decisions while achieving efficient ROI

Strategic intent

VOI (Value of Information) to Assess early experiments

Test

yes

no

Positive Result

NegativeResult

30%

70%

Clin.Trial

Clin.Trial

Adapted from N.Rosati, Expert Rev.Pharmacoeconomics Outcomes Res. 2(2), 2002

yes

no

Positive Result

NegativeResult

Cost$100M

70%

30%

Revenue$500M

$ 0

Revenue$ 0

Clin.Trial

no

yes$100M

$ 0

Positive Result

NegativeResult

30%

70%

Revenue$500M

Revenue$ 0

$50M

$75M Cost ?

Prioritizing the Portfolio

1. Strategic Fit 2. Core competencies (clinical and commercial)3. Technical feasibility and complexity4. Criticality of launch timing5. PoS (including tractability, target validation, etc.)6. Clinical cost to launch 7. Commercial opportunity:

1. Level and prevalence of unmet medical needs2. Competitive pressure3. Product Differentiation and Value proposition4. Payors’ pressure and P&R risks5. Expected Peak sales

8. Financials (eNPV, ROI, IRR, payback period, etc.)

Different weights by Phase and BIC vs FIC

George W. Merck, 1950

“ We try never to forget that medicine is for the people. It is not for the profits. The profits follow, and if we have remembered that, they have never failed to appear. The better we have remembered it, the larger they have been.”