Transcript Ani Bhattacharya - Pharma
Portfolio Management and R & D Productivity
Anirban Bhattacharya, PhD
8th Annual Project and Portfolio Management Forum
Is Pharma “over-spending” in R&D ?
Pharma Industry has one of the most expensive R&D efforts
Company EXXON MOBIL CHEVRON CONOCOPHILLIPS GENERAL ELECTRIC GENERAL MOTORS FORD HEWLETT-PACKARD MCKESSON IBM PROCTER & GAMBLE PFIZER APPLE BOEING MICROSOFT ARCHER-DANIELS-MIDLAND JOHNSON & JOHNSON DELL UNITED TECHNOLOGIES CORP.
DOW CHEMICAL KRAFT FOODS
* Top 20 firms traded on US Exchanges
2010 REVENUE ($ IN MILLIONS)
$341,578 $189,607 $175,752 $149,060 $135,592 $128,954 $126,033 $112,084 $99,871 $78,938 $67,791 $65,225 $64,306 $62,484 $61,682 $61,587 $61,494 $54,326 $53,674 $49,207
2010 R&D SPEND ($ IN MILLIONS)
$1,012 $526 $230 $3,939 $6,962 $5,000 $2,959 $407 $5,720 $1,950 $9,538 $1,782 $4,121 $8,714 $56 $6,844 $661 $1,746 $1,660 $583
2010 R&D SPEND (%of sales)
0.30% 0.28% 0.13% 2.64% 5.13% 3.88% 2.35% 0.36% 5.73% 2.47% 14.07% 2.73% 6.41% 13.95% 0.09% 11.11% 1.07% 3.21% 3.09% 1.18%
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”
OPTIMAL R&D SPEND $136,486 $56,690 $100,350 $19,947 $15,570 $14,405 $43,907 $111,598 $10,359 $7,816 $6,304 $9,468 $8,142 $9,210 $29,947 $5,371 $16,218 $6,192 $9,356 $7,254 Difference
$135,474 $56,163 $100,119 $16,008 $8,608 $9,405 $40,948 $111,190 $4,639 $5,866 ($3,234) $7,686 $4,021 $496 $29,891 ($1,472) $15,557 $4,446 $7,695 $6,671
73% drop in R&D productivity !
From Abundance to Scarcity (in quantity AND quality!)
Golden Period: 1996-2004 “Drought” Period:
2005-2010
NMEs approved per year (average) = 3 Average 5 th 5 th year sales per NME ($ MM) = 515 year sales per year in total ($ Bn) = 18.3
R&D spend per year ($Bn) = 65
5 275 th year sales per 1$Bn R&D spend ($MM) =
NMEs approved per year (average) = 22 Average 5 th year sales per NME ($ MM) = 430 5 th year sales per year in total ($ Bn) = 9.4
R&D spend per year ($Bn) = 125
5 th year sales per 1$Bn R&D spend ($MM) = 75
The 90s remained the most fruitful period in the history of Pharma
PDUFA + “good” FDA behavior (e.g. HIV) Targeting chronic diseases with new MoAs Development of many “fast-followers” Establishment of “surrogate markers” Creation of new diseases (OAB, RLS, etc.) Is this a “rebound effect” or an “R&D productivity crisis”?
What is R&D productivity?
Defining R&D Productivity
Required Investment Created Value
R&D Productivity is an aggregate representation of
: R&D efficiency: ability to translate inputs (ideas, resources, money, etc.) into defined outputs (usually approvals and launches), over a defined period of time; it is simply measured by a “cost per launch” R&D effectiveness: ability to produce outputs with certain intended and desired qualities/outcomes (value to patients, physicians and/or payers; and substantial commercial value); it is simply measured by a “value per launch”
S.M..Paul et al. “How to improve R&D productivity” Nature Reviews/Drug Discovery vol.9; March 2010; p.203-214
R&D Productivity Report Card
The good ……
60 Annual number of NME/NBEs approved by the FDA stable over the last 60 years 50 40 30 20 10 0 19 50 19 54 19 58 19 62 19 66 19 70 19 74 19 78 19 82 19 86 19 90 19 94 19 98 20 02 20 06 20 10 FDA approvals of NME/NBEs
The ugly …… The bad ……
- The number of new drugs approved per US$ 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
NPV for NME is -$65 IRR is 7.5% (less than cost of capital at 10%) Negative trend: IRR was 12% in 1997-2001 Better financials for biologics: IRR at 13% and NPV at $1.26 billion
R&D Efficiency Assessment
Developmental Cost Risk 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%
Declining R&D Success Rates
(adapted from Bain drug economics model, 2003* and from KMR 2007-2011**)
1995-2000* 2000-2003* Preclinical: 7 Phase I: 6 Phase II: 3 Phase III: 1.5
Regulatory: 1.1
Launch: 1.0
Preclinical: 13 Phase I: 9 Phase II: 5 Phase III: 1.6
Regulatory: 1.1
Launch: 1.0
2003-2007** Preclinical: 24 Phase I: 15 Phase II: 7 Phase III: 1.8
Regulatory: 1.2
Launch: 1.0
2007-2011** Preclinical: 30 Phase I: 19 Phase II: 9 Phase III: 1.9
Regulatory: 1.2
Launch: 1.0
Cum. Success Rate: 14 % Cum. Success Rate: 8 % Cum. Success Rate: 4 % Cum. Success Rate: 3 %
2% for Small molecules and 11% for Biologics Small Molecules 43.6 (61%) 26.6 (42%) 11.1 (18%) 2.0 (60%) 1.2 (85%) 1 Biologics 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 •
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 !
Early commercial involvement will help to make the right choices to optimize resource allocation in LO and Early Development !
S.M.Paul et al. “How to improve R&D productivity” Nature Reviews/DrugDiscovery vol.9; March 2010; p.203-214
Decrease of PoS for Ph.II and Phase III are the two most important cost drivers: the critical role of VoI
Impact on the average cost of drug discovery & development ($1,778M) of the ten most important cost drivers
S.M.Paul et al. “How to improve R&D productivity” Nature Reviews/DrugDiscovery vol.9; March 2010; p.203-214
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 $100M Adaptive/seamless ph.II/III trial designs will save time and cost Time/cost of development is “disease-specific” (e.g.CV worse than ID) Optimize PoS Reduce attrition in ph.II/III with early PoC studies (reliable biomarkers) More validated/druggable targets & greater use of translational phenotypic assays Sufficient number of projects by phase to ensure 1 launch/year: If PoS in ph.III increases from 70% to 90% , the number of products entering ph.I can decrease from 9 to 7 Redirecting resources from drugs with low PoS: e.g. 1 ph.III has same cost of 10 Ph.I
Moving from FIPCo (Fully Integrated Pharma Co) to FIPNet (Fully Integrated Pharma Network)
S.M.Paul et al. (Nature Reviews/Drug Discovery, vol 9; March 2010; p.203-214)
R&D Effectiveness Assessment
Pharma needs to create new medicines able to surpass an ever-improving SoC, being the victim of its own success.
No more low-hanging fruits!
Decreasing Sales for New Products
716M 556M 482M 408M
1992-1996 1997-2001 2002-2006 2007-2011
Average Peak Sales for New Products ($ M)
Adapted from Accenture Research Report, based on data from various sources
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: 1 st quartile: 2% of them with IRR of 28% 2 nd quartile: 4% of them with IRR of 12% 3 rd quartile: 40% of them with IRR of 8% 4 th 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
Opportunity Level Achieved by Gold Standard
Efficacy Safety / Tolerability Convenience Mortality Morbidity Cost
Measuring Unmet need using published objective clinical trial data
Efficacy:
symptom relief, slowing of progression, restoring lost function, pharmacokinetics
Side effects: Convenience: Mortality:
frequency and severity of each mode and frequency of dosing age-adjusted excess risk of mortality
Morbidity:
pain, disability, hospitalization, quality of life, complications
Costs: Impact of Efficacy:
Assign a score to each component of unmet need: direct (drug and non-drug) and indirect Impact of efficacy on mortality, morbidity, and cost 0 1 2 3 4 5 No unmet need Substantial unmet need
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 standard
Sources of Difference in Unmet Need between Alpha and Current Gold Standard 2.50
2.45
2.40
2.35
2.30
5.5% 2.48
2.25
2.20
2.15
2.10
Current Gold Standard Efficacy -0.8% 2.5% 0.6% Side Effects Convenience Mortality 1.2% 0.1% 0.4% Morbidity Direct Cost Indirect Cost 2.25
Alpha Unmet need scores range from 0 (no unmet need) to 5 (substantial unmet need) Sum of % differences equals overall relative improvement in unmet need:
9.6%
Note: Bars may not sum to overall % due to rounding
The greater the reduction in medical need, the larger the peak share achieved
100% 90% 80% 70% 60% 50% 40% 30% 20%
Lescol
0%
-15%
Singulair
-10%
Boniva Aricept Lipitor Advair Vfend
-5% 0% 5% 10% 15%
Percent Reduction in Unmet Need
20%
Viagra
25% 30%
Confidential Equinox Group Information
Strategic intent
DOP & TCP to support Go / No-go decisions while achieving efficient ROI
Disease Opportunity Profile Unmet Need Market Overview Competitive Landscape Payer Pressure
Regional Contribution
Target Candidate Profile • Commercial Viability • Identification of
Non-negotiable
Attributes • Additional
value drivers
for potential economic upside Decision Criteria # Killer Experiments # Go / No-go
VOI (Value of Information) to Assess early experiments
70% Positive Result 30% Positive Result Clin.
Trial yes Cost $100M 30% Negative Result
$75M
yes Cost ?
70% Test Negative Result Clin.
Trial no Revenue $ 0
$50M
no no Clin.
Trial yes $100M $ 0 30% 70% Positive Result Negative Result Revenue $500M Revenue $ 0 Revenue $500M $ 0
Adapted from N.Rosati, Expert Rev.Pharmacoeconomics Outcomes Res. 2(2), 2002
Prioritizing the Portfolio
1.
2.
3.
4.
5.
6.
7.
8.
Strategic Fit Core competencies (clinical and commercial) Technical feasibility and complexity Criticality of launch timing PoS (including tractability, target validation, etc.) Clinical cost to launch Commercial opportunity: 1.
Level and prevalence of unmet medical needs 2.
3.
4.
5.
Competitive pressure Product Differentiation and Value proposition Payors’ pressure and P&R risks Expected Peak sales Financials (eNPV, ROI, IRR, payback period, etc.)
Different weights by Phase and BIC vs FIC