R&D Productivity The Road to Positive Returns Rodney W. - - PowerPoint PPT Presentation

R&D Productivity – The Road to Positive Returns

This report is solely for the use of client personnel. No part of it may be circulated, quoted, or reproduced for distribution outside the client

• rganization without prior written approval from McKinsey & Company.

This material was used by McKinsey & Company during an oral presentation; it is not a complete record of the discussion.

Rodney W. Zemmel, PhD. McKinsey & Company CONFIDENTIAL May 26, 2010

2

Summary

• R&D productivity is failing. This is not “new

news”, but impending patent expiries bring it into sharp focus

• The search for the “new paradigm” of

scientific solutions risks missing the point

• Management solutions focused on cost,

speed, and decision-making are sufficient to improve returns to a level where R&D is value creating, not value-destroying

• Valuations can be increased 15%+ by

increasing output within realistic ranges, or else by cutting expenditures if management teams do not see productive routes to improve

3

Ouch! Exponential growth in inputs with no numerical increase in outputs

1 Includes NCEs and BLAs. BLAs included 1986 onward; biologics approvals in prior years assumed negligible Source: NME data for 1966-1971 from Peltzman, S. (1973) Journal of Political Economy 81, no. 5: 1049–91. NME data for 1972-1979 as reported in Hutt, P.B. (1982) Health Affairs 1(2) 6-24. NME Data for 1980-2007 from Parexel’s Pharmaceutical R&D Statistical Sourcebook 2008/2009, Food and Drug Administration, and Pharmaceutical Research and Manufacturers of America. Industry R&D spend data from the Pharmaceutical Research and Manufacturers of America, PhRMA Annual Membership Survey, 2008

5 10 15 20 25 30 35 40 45 10 20 30 40 50 60 FDA NME approvals1 Number Industry R&D spend $ Billions 2005 2000 1995 1990 1985 1980 1975 1970

4

Growth in talk of new research paradigms has matched decline in NME approvals

5 10 15 20 25 30 35 40 5 10 15 20 25 30 35 40 PubMed occurrences of “new pharmaceutical research paradigm” Number FDA NME approvals Number 2008 2003 1998

Source: NME data from Parexel’s Pharmaceutical R&D Statistical Sourcebook 2008/2009, PubMed search for “new pharmaceutical research paradigm”. The term “cardiac surgery” was used as a control over the same time period to ensure trends were not simply due to changes in the number of publications available in PubMed over time

5

Worldwide total prescription drug sales 2000-14

Pharmacos revenues are under pressure…

SOURCE: EvaluatePharma; team analysis

707 691 674 663 637 612 616 572 521 441 398 345 315 290 9.0 9.0 15.0 9.0 100 200 300 400 500 600 700 800

• 2

2 4 6 8 10 12 14 16 Sales growth Percent $ Billions +2.3% CAGR 2008-14 +2.3% CAGR 2008-14 2014 13 12 11 10 09

• 0.7

08 8.0 07 10.0 06 05 479 04 11.0 03 02 10.0 01 2000 7.0

4.0 4.0 2.0 3.0 2.0 Analyst forecasts

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5.9 9.9 8.4 16.4 22.9 12.4 17.2 11.0 15.6 18.7 25.3

2009 sales at risk due to U.S. patent expiries between 2010-17 $ Billions

…due to an unprecedented period of loss-of-exclusivity

Share of 2009 total sales1 at risk due to U.S. patent expiry 2010-17 Percent 40 52 79 72 43 42 34 30 39 45 54 Ø 48

SOURCE: Evaluate 01/14/2010 1 Pharma sales only

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Traditional growth levers such as promotional spend are experiencing negative marginal returns

An increase $1.8 billion in promotional spend between 2002 and 2006 yielded 100 million fewer TRx An increase $1.8 billion in promotional spend between 2002 and 2006 yielded 100 million fewer TRx Change in TRx (2006-2002)

• 100 million

TRx +$1.8 billion

Change in Share

• f Voice

(2006-2002)

1 Based on aggregate of 14 companies: Amgen, Merck, GSK, J&J, Eli Lilly, Schering Plough, Roche, Astra-Zeneca, BMS, Pfizer, Abbott, Wyeth, Sanofi- Aventis, Novartis SOURCE: IMS; Evaluate Pharms

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Management is rethinking total R&D spend

SOURCE: EvaluatePharma; team analysis

142 138 136 132 128 123 123 85 76 67 59 53 10.0 14.0 14.0 50 100 150 2 4 6 8 10 12 14 R&D Spend growth Percent $ Billions ~2% CAGR 2008-14 ~2% CAGR 2008-14 2014 13 12 11 10 09 08 7.0 07 116 11.0 06 104 12.0 05 93 04 11.0 03 02 01 11.0 2000 Worldwide pharma R&D spend, 2000-14

Note: This does not include any impact from Healthcare Reform

4.0 3.0 3.0 2.0 3.0 Analyst forecasts

9

Pharmacos responding with many initiatives

Improve R&D productivity Reduce R&D total spend Improve R&D governance IT enablement Increase variabilization

• f costs and
• ffshoring

Push hard

• n cost

and speed Increase risk sharing and minority stakes Virtual trials Value based incentives Market forces Prediction markets R&D leadership development New performance metrics Manage external spend Lean labs Make

• rganization

more nimble Reduce trial complexity

Maybes Sure bets

Source: McKinsey analysis

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Ultimately, fewer launches lead to more company consolidation

59 44 38 16 20 15 25 100% = Other Top 20 Top 10 Post-2009 mergers $725B 47 1997 $244B 36 1987 $93B Consolidation by major pharmacos Percent of global sales

Source: IMS World review; team analysis

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Small molecules Small molecules Biologics Biologics

We created a “per drug” model of industry productivity: revenue example

Note: Year 1-6 post-launch sales projections are based on historic performance of drugs launched in 2000-06; year 7-10 sales based on historic sales of drugs that were in last 4 years of exclusivity in 2000-06; sales during first 4 years post-LOE (years 11-14) based on historic data on drugs that lost patent protection in 2000-06; sales for year 15

• nward expected to decline at 6% per year, based on historic performance of drugs that were post-LOE for 4+ years

in 2000-06 Source: EvaluatePharma; team analysis

Patented sales Off-patent sales

166 176 190 249 529 490 454 420 389 336 262 191 111 34 414 Y15 Y14 Y13 Y12 Y11 Y10 Y9 Y8 Y7 Y6 Y5 Y4 Y3 Y2 Y1 654 692 772 858 964 1,024 926 837 757 684 578 460 326 199 75 Y15 Y14 Y13 Y12 Y11 Y10 Y9 Y8 Y7 Y6 Y5 Y4 Y3 Y2 Y1 $ Millions Post-LOE decay assumes competition from biosimilars

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The average IRR of a typical biopharmaceutical portfolio is in decline …

• Declining IRR driven by

– Increasing attrition rates (~5 percentage points) – Increasing development times (~12-18 months) – Cost increases

• Portfolios more heavily

weighted towards biologics will generate somewhat greater returns, driven largely by higher sales post patent expiry For a typical R&D portfolio of 50 compounds … … projected returns have declined ~30% over the last several years Assume the portfolio is 75% small molecules and 25% biologics Number of compounds Assume the portfolio is distributed across phases as follows Number of compounds 50 38 12 Biologic Total Small Molecule 50 13 25 8 4 Phase II Phase IIITotal Phase I Pre- Clin 1997- 2001 2002- 2006 14-15 9-10

1 Assumes 0% contribution margin in yrs 1-2, 50% in yrs 3-4, 70% in yrs 5-10; assumes margins on biologics are 5% lower than for small molecules; Year 10 value of post LOE contributions discounted to year 10 at IRR; sales ramps, decay curves, and peak sales based on average historic sales of all compounds launched between 2000 and 2006 (see appendix for details) Source: Evaluate Pharma; team analysis

Portfolio IRR1 Percent

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… and the average small molecule compound no longer returns its cost

• f capital

Source: Team analysis

Typical pharma portfolio IRR 9-10% Biologic Small molecule IRR = 7.5% NPV = -65 million IRR = 13% NPV = $1.26 billion Returns for an average compound Are biologics alone the answer? Probably not …

• Not enough of them to compensate for declining small molecule returns

(~20-25% of patented molecule sales)

• Higher average peak sales and slow post-patent decay both likely to erode

as biosimilar competition grows

• Declining POS (~7 percentage points), especially in new biologic

categories (e.g., cellular therapy, gene therapy, RNAi)

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Pulling Multiple Levers Can Help Move the Needle on Returns From R&D

Reduce

• verall

cost per molecule by 15% Reduce time-to- launch by 18 months Shift 4%

• f

molecules from fourth quartile to first quartile Increase phase III POS 10% by “taking” attrition in Phase II 13.0 1.5 2.0 7.5 New IRR for average small molecule Decision making 2 1.0 Decision making 1 1.0 Speed Cost Current IRR for average small molecule IRR = WACC = 9.5%

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• Focus on select metrics, balanced

across dimensions (e.g., leading vs. lagging, speed vs. cost)

• Achieve simplicity in measurement

and communication

• Define metrics consistently and

establish primary data sources

• Establish clear accountability
• Monitor performance regularly
• Determine root causes of

performance Guiding principles to measure and manage performance

McKinsey’s Clinical Trial Impact cross- industry diagnostic tool

• Develops customized scorecards for

clinical operations based on an objective fact-base

• Enables issue-based deep dives to

facilitate decision-making to prioritize improvement opportunities

• Collaborate with and learn from peers

Achieving Cost and Speed Improvements Requires Transparent and Simple Metrics

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How Our Clients are Using McKinsey’s Clinical Trial Impact

Set operational targets Clinical Trial Impact findings highlight improvement potential and help calibrate performance goals Prioritize areas of focus Findings used to facilitate internal discussions around which priority initiatives to invest in Assess on-going performance Clinical Trial Impact metrics incorporated into the performance scorecard for monitoring and tracking progress against improvement areas Understand best practices Expert discussions explore how other companies have optimized performance in key areas Motivate and energize different functional areas Workshop on findings creates opportunity to bring together managers across the organization

• 40,000 sites from 85 countries

across 7 regions More than 280,000 enrolled and 100,000 completed patients Mixture of in house and outsourced protocols More than 900 protocols from 10 TAs 11 current participants representing $25 billion in R&D spend

NOT EXHAUSTIVE

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Example analyses from Clinical Trial Impact industry report (1/2)

Example 2. India and China have 1.5-3.5 fold more patients per site than the industry while France and Italy have 30% fewer

Source: McKinsey & Company

Example 1. Primary care protocols have more low performing sites than specialty care (10% more sites recruit 0 patients). One-third

• f sites overall have fewer than 2 patients

Example 3. Increasing the number of sites does not always improve the recruitment speed (e.g., patients recruited per month)

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Example analyses from Clinical Trial Impact industry report (2/2)

Source: McKinsey & Company

Example 5. Monitors in Japan oversee 3x fewer patients than global; both sites/monitor and patients/site are lower in Japan Example 4. Outsourced protocols take an additional month to recruit vs insourced, but do have 5-10% higher retention rates Example 6. N.A. has over 3.5x higher PI grant costs per patient and 10- 15% lower retention rates than Asia Pacific

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Decision-making example: attrition has been worsening

Company Success Rate Phase I: Launch1 Percent Success Rate by Phase for Top 102 Percent probability of moving to next phase 1996-2001 2002-07 Delta 0.75 0.70 0.65 0.60 03 2002 06 2007 Phase I Phase II Phase III 0.30 0.35 0.40 0.45 0.50 0.55 04 05

• 12.0
• 10.6

0.5

• 3.7
• 6.9
• 11.9
• 14.8
• 7.7
• 0.9
• 15.6
• 20.3

4.2 8.8 16.0 8.1 7.2 10.8 8.4 13.8 4.4 14.6 8.0 7.9 17.9 22.0 Top 10 18.5 9.2 17.3 12.5 20.0 10.4 24.7 29.4 18.4 23.0 20.0 Company A Company B Company C Company D Company E Company F Company G Company H Company I Company J Company K

1 Product of phase attrition rates that year; using 90% assumption for regulatory/launch success; for all originated and partnered drugs 2 Based on a three year rolling average across Big Pharma Source: Pharmaprojects (excluding formulations)

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But companies can manage phase III risk more effectively

Note: Includes aggregate attrition rates for following TAs: CNS, Endocrine, CV, ID, Oncology, and Respiratory. All figures are rounded Source: Evaluate; Pharmaprojects; Factiva; literature search; team analysis

Established MOA? Yes No Less Objectivity

• f endpoints

More Attrition rate 63% Attrition rate 37% Quadrant D Quadrant A Quadrant B Quadrant C Attrition rate 70% Attrition rate 25% Efficacy vs. placebo failures 69% Efficacy vs. placebo failures 34%

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And back commercial “winners” more effectively

Methodology: Sales for successfully launched drugs by quartile are based on historic sample of successfully launched drugs; NPV is calculate assuming 0% contribution margin for first 2 years, 50% margin for years 3-4, 70% for years 6-10, and 60% thereafter; 9.5% discount rate. Only R&D costs directly associated with launched molecules reflected in NPV (cost of attrition accounted for elsewhere)

Successfully launched small molecules, IRR by sales quartile Percent 2 Percentage of drugs in each sales quartile 54 4 40 28 12 8 6 Fourth quartile Third quartile Second quartile First quartile IRR = WACC = 9.5%

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Implications for valuations

SOURCE: Productivity model

ILLUSTRATIVE MODELING

Management teams should choose which path to pursue Enterprise value before R&D improvement, typical large pharma $100 billion Enterprise value after R&D improvement, typical large pharma $115-120 billion Spend 30% less on R&D. Achieve same output Launch two more “average” drugs every 4 years Increase long-term revenue growth expectations by 1% a b c

• r
• r

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The long view: pharma still a source of tremendous value creation…

5 10 15 20 25 30 35 40 45 Top 15 Pharma1 1992 1996 2000 2004 1988 Industry Aggregate2 2008 Nestle IBM Intel 20 year average ROIC (%) 31 30 18 13 9 ROIC, percent (5 year rolling average)

1 Johnson & Johnson, Pfizer, Bayer, Roche, Novartis, Sanofi-Aventis, GSK, Astrazeneca, Abbott, Merck, Wyeth, BMS, Lilly, Schering-Plough, Takeda 2 Based on the rolling average of Top 1000 companies by 2008 Sales Source: CPAT

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The really long view: shift happens – evolve and survive !!!

Earth formed 4.5 billion years ago 3.5 billion years ago 2 billion years ago First life 400 million years ago 300 million years ago 200 million years ago 100 million years ago

Cambrian Explosion

• Tremendous genetic diversity in a ~10

million year period

• Every major animal phylum originated
• Numerous evolutionary dead ends

Nucleated cells 1 billion years ago Eukaryotic cells ~530 million years ago Insect Plants Fish Dinosaurs Wooly mammoth ~540 million years ago