ANALYSIS
`
`How to improve R&D productivity:
`the pharmaceutical industry’s grand
`challenge
`
`Steven M. Paul, Daniel S. Mytelka, Christopher T. Dunwiddie, Charles C. Persinger,
`Bernard H. Munos, Stacy R. Lindborg and Aaron L. Schacht
`
`Abstract | The pharmaceutical industry is under growing pressure from a range of
`environmental issues, including major losses of revenue owing to patent expirations,
`increasingly cost-constrained healthcare systems and more demanding regulatory
`requirements. In our view, the key to tackling the challenges such issues pose to both the
`future viability of the pharmaceutical industry and advances in healthcare is to substantially
`increase the number and quality of innovative, cost-effective new medicines, without
`incurring unsustainable R&D costs. However, it is widely acknowledged that trends in
`industry R&D productivity have been moving in the opposite direction for a number of years.
`Here, we present a detailed analysis based on comprehensive, recent, industry-wide data
`to identify the relative contributions of each of the steps in the drug discovery and
`development process to overall R&D productivity. We then propose specific strategies
`that could have the most substantial impact in improving R&D productivity.
`
`New molecular entity
`(NME). A medication
`containing an active ingredient
`that has not been previously
`approved for marketing in any
`form in the United States. NME
`is conventionally used to refer
`only to small-molecule drugs,
`but in this article we use the
`term as a shorthand to refer to
`both new chemical entities and
`new biologic entities.
`
`Lilly Research Laboratories,
`Eli Lilly and Company,
`Lilly Corporate Center,
`Indianapolis, Indiana
`46285, USA.
`Correspondence to: S.M.P.
`e-mail:
`smpaulmd@gmail.com
`doi:10.1038/nrd3078
`Published online
`19 February 2010
`
`The pharmaceutical industry is facing unprecedented
`challenges to its business model. Experienced observers
`and industry analysts have even predicted its imminent
`demise1–3. Over the past decade, serious concerns about
`the industry’s integrity and transparency — for example,
`around drug safety and efficacy — have been raised,
`compromising the industry’s image, and resulting in
`increased regulatory scrutiny4,5. This erosion in confi-
`dence in the industry and its products has resonated
`poorly with patients, health-care professionals, payers
`and shareholders. Indeed, the industry’s price/earnings
`ratio, a measure of the current valuation of the industry,
`has decreased below that of the S&P 500 index and has
`remained more or less flat, as have share prices for the
`past 7 years.
`The industry’s profitability and growth prospects
`are also under pressure as healthcare budgets become
`increasingly strained. Generic drugs, although clearly
`helping to keep drug prices in check, are currently
`approaching 70% of all prescriptions written in the
`United States6. Moreover, key patent expirations between
`2010–2014 have been estimated to put more than US$209
`billion in annual drug sales at risk, resulting in $113
`billion of sales being lost to generic substitution7. Indeed,
`for every dollar lost in declining product revenues due
`
`to patent expirations by 2012, it has been estimated
`that large-cap pharmaceutical companies will only be
`able to replace on average 26 cents with new product
`revenues8.
`Simply stated, without a dramatic increase in R&D
`productivity, today’s pharmaceutical industry cannot
`sustain sufficient innovation to replace the loss of rev-
`enues due to patent expirations for successful products.
`A key aspect of this problem is the decreasing number
`of truly innovative new medicines approved by the
`US Food and Drug Administration (FDA) and other
`major regulatory bodies around the world over the
`past 5 years (in which 50% fewer new molecular entities
`(NMEs) were approved compared with the previous
`5 years)9. In 2007, for example, only 19 NMEs (including
`biologics) were approved by the FDA, the fewest
`number of NMEs approved since 1983, and the number
`rose only slightly to 21 in 2008. Of the 21 new drugs
`approved by the FDA in 2008, only 6 were developed by
`the 15 largest pharmaceutical companies and only 29%
`would be considered ‘first-in-class’ medicines. In 2009,
`24 new drugs were approved, 10 of which were devel-
`oped by large pharmaceutical companies and only 17%
`of which could be considered first-in-class. Some have
`argued that the number of approved ‘mechanistically
`
`NATURE REVIEWS | DRUG DISCOVERY
`
` VOLUME 9 | MARCH 2010 | 203
`
`
`
`© 20 Macmillan Publishers Limited. All rights reserved10
`
`Medac Exhibit 2047
`Koios Pharmaceuticals v. Medac
`IPR2016-01370
`Page 00001
`
`

`

`A N A LY S I S
`
`innovative’ and first-in-class NMEs have remained
`stable at about 5–6 per year. However, the number of
`potential revenue-generating drugs (innovative or
`other wise) as a percentage of R&D expenditures has
`undeniably fallen sharply.
`With an estimated $50 billion in collective annual
`R&D spending by the large pharmaceutical companies,
`and appropriate allocation over time to the successful
`discovery and development of NMEs, the average cost
`for these companies to bring an NME to market is now
`estimated to be approximately $1.8 billion (see below for
`details underlying this estimate), and is rising rapidly.
`Moreover, there is little evidence that the average costs
`of successfully launching an NME vary significantly
`between large pharmaceutical or small biotechnology
`companies10,11.
`Although R&D productivity has been declining
`for a number of years2, the unprecedented combina-
`tion of reduced R&D output in the form of success-
`fully launched truly innovative NMEs, coupled with
`diminishing market exclusivity for recently launched
`new medicines and the huge loss of revenues owing to
`generic competition over the next decade, suggest that
`we may be moving closer to a pharmaceutical ‘ice age’
`and the potential extinction of the industry, at least as it
`exists today12,13. Although this might be welcomed by the
`industry’s critics, the impact on the health and well-being
`of patients owing to delayed or even lost opportunities
`to introduce the next generation of innovative medicines
`could be devastating. In this regard, we underscore the
`findings of Lichtenberg14 on the effects of medical inno-
`vation (including controls for the impact of obesity and
`income), which indicate that ~40% of the 2-year increase
`in life expectancy measured from 1986–2000 can be
`attributed to the introduction and use of new drugs. It
`took approximately 3 years for NME launches to have
`their maximal impact on longevity — this effect was
`not observed for non-NME (older) drugs. One can only
`speculate as to the impact on longevity and quality of life
`that new drugs now in clinical development for cancer
`and Alzheimer’s disease might have. Without these new
`medicines, and given the rise in diseases such as diabetes
`and childhood obesity, it is possible that life expectancy
`may actually decrease over time15.
`Among all the challenges faced by the pharmaceutical
`industry, we argue that improving R&D productivity
`remains the most important. The environmental factors
`that are reducing the industry’s profitability can only
`be mitigated by substantially and sustainably increas-
`ing the number and quality of innovative, as well as
`cost-effective, new medicines; but only if accomplished
`at reasonable R&D costs. So, the key questions are
`where, how and by how much can R&D productivity
`be improved? Here, we present a detailed analysis of
`R&D productivity by first defining and modelling the
`essential elements of contemporary drug discovery
`and development that account for the current cost of
`a new medicine, and discuss the rate-limiting steps of
`the R&D process that are contributing to reduced R&D
`productivity. We then propose, and illustrate, ways to
`improve these factors.
`
`How do we define R&D productivity?
`R&D productivity can be simply defined as the relation-
`ship between the value (medical and commercial) created
`by a new medicine (considered here to be an NME)
`and the investments required to generate that medicine.
`However, R&D productivity can in our view best be
`elaborated in two important dimensions: inputs leading
`to outputs, or R&D efficiency; and outputs leading to
`outcomes, or R&D effectiveness (FIG. 1).
`R&D efficiency represents the ability of an R&D
`system to translate inputs (for example, ideas, invest-
`ments, effort) into defined outputs (for example, inter-
`nal milestones that represent resolved uncertainty for
`a given project or product launches), generally over a
`defined period of time. If launching (gaining regulatory
`approval and commercializing) an NME is the desired
`output, how can this be achieved with greater efficiency
`(that is, at a lower cost)?
`R&D effectiveness can be defined as the ability of the
`R&D system to produce outputs with certain intended
`and desired qualities (for example, medical value to
`patients, physicians and payers, and substantial com-
`mercial value). Thus, R&D productivity can be viewed
`as an aggregate representation of both the efficiency and
`effectiveness of the drug discovery and development
`process; the goal of a highly productive R&D system is
`to efficiently translate inputs into the most desired and
`valuable outputs. For a more detailed description of these
`definitions, see Supplementary information S1 (box).
`With this definition of R&D productivity in mind, we
`have further adapted a productivity relationship or
`‘pharmaceutical value equation’, which includes the key
`elements that determine both the efficiency and effec-
`tiveness of the drug discovery and development process
`for any given pipeline (see equation 1).
`WIP (cid:2) p(TS) (cid:2) V
`CT (cid:2) C
`
`P (cid:1)
`
`(1)
`
`R&D productivity (P) can be viewed as a function of the
`elements comprising the numerator — the amount of
`scientific and clinical research being conducted simul-
`taneously, designated here as the work in process (WIP),
`the probability of technical success (p(TS)) and the value
`(V) — divided by the elements in the denominator, the
`cycle time (CT) and cost (C). Each of these parameters
`can be conceptualized and analyzed on a per project
`basis (for example, a single drug candidate or WIP = 1)
`or collectively as a larger portfolio or pipeline of projects
`or drug candidates. In general, increasing the numerator
`relative to the denominator will increase productivity
`and vice versa. Thus, if one could increase the p(TS)
`(that is, reduce attrition) for any given drug candidate
`or ideally for a portfolio of drug candidates at a given
`phase of development, P would increase accordingly.
`Similarly, for any given level of R&D investment, sub-
`stantially reducing CT or lowering C (such as unit costs)
`would increase P.
`However, most of the elements comprising equa-
`tion 1 are inextricably linked to one another and changing
`one element can often adversely or beneficially affect
`
`204 | MARCH 2010 | VOLUME 9
`
` www.nature.com/reviews/drugdisc
`
`
`
`© 20 Macmillan Publishers Limited. All rights reserved10
`
`Page 00002
`
`

`

`Inputs
`
`Outputs
`
`Outcomes
`
`Cost per launch
`
`Value per launch
`
`R&D efficiency
`More affordable drugs
`via less costly R&D
`
`R&D effectiveness
`More value for the patient
`via innovative drugs with
`high-quality information
`Figure 1 | Dimensions of R&D productivity. To improve
`R&D productivity, it is crucial to understand the
`interdependencies between inputs (for example, R&D
`investments), output (for example, new molecular entity
`launches) and outcomes (for example, valued outcomes
`for patients). This figure outlines the key dimensions of
`R&D productivity and the goals tied to R&D efficiency
`and effectiveness. An effective R&D productivity strategy
`must encompass both of these components. Value will
`be created by delivering innovative products with
`high-quality information.
`
`another. For example, as discussed below, having suf-
`ficient pipeline WIP (by phase of development) is
`crucial given the substantial phase-specific attrition
`rates. However, increasing WIP (especially late-phase
`WIP) alone will undoubtedly increase C and may also
`increase CT, which could further reduce P and diminish
`productivity.
`Finally, although carrying out definitive health out-
`come studies on late-stage compounds before approval
`is often highly desirable and increasingly necessary to
`unequivocally demonstrate value (V) for reimbursement
`purposes, such studies can substantially increase CT and
`C, thus also diminishing P. Nevertheless, such studies
`will also increase V, potentially offsetting any decrease,
`or even increasing, P.
`
`A model of R&D productivity
`To inform efforts to increase R&D productivity (P), the
`key questions include: which of the associated elements
`have the greatest impact; how might they be improved;
`and by what magnitude? To help address these questions,
`we have built an economic model of drug discovery and
`development which, using industry-appropriate assump-
`tions, provides the basis for our estimate that the fully
`capitalized cost of an average NME developed by a typi-
`cal large pharmaceutical company is currently ~$1.8
`billion) (see Supplementary information S2 (box) for
`details). The model has been constructed using recently
`available R&D performance productivity data from a
`group of 13 large pharmaceutical companies, provided
`by the Pharmaceutical Benchmarking Forum (PBF)16
`(see Supplementary information S3 (box)), as well as
`our own internal data, to closely approximate the key
`elements of our productivity relationship that underlie
`R&D efficiency — C, WIP, CT and p(TS) — for each
`phase of discovery and development (FIG. 2).
`We recognize that the estimated cost per NME is
`highly dependent on a number of economic or financial
`assumptions. Consequently, for our estimated cost of an
`NME we show both ‘out of pocket’ and ‘capitalized’ costs
`using a cost of capital of 11% (FIG. 2). Our estimate repre-
`sents ‘molecule only’ costs and does not include the costs
`
`A N A LY S I S
`
`of exploratory discovery research (target identification
`and validation) or other ‘non-molecule’ costs (which
`include overheads, such as salaries for employees that
`are not engaged in research and development activities
`but that are otherwise necessary to support the R&D
`organization; these represent approximately 20–30% of
`total costs). We discuss comparisons of our estimates
`with other reported estimates in Supplementary infor-
`mation S2 (box). However, for modelling purposes, the
`exact cost per NME is not crucial as long as our assump-
`tions for each parameter in our model are consistent and
`represent reasonable estimates. Each R&D organization
`can (and should) build a similar model based on their
`own data, which may vary from company to company.
`The exact output of the model — the desired number
`of new launches (and the estimated commercial value
`per launch) — will depend on business aspirations, ther-
`apeutic focus and absolute level of R&D investments of a
`given company. Nonetheless, based on our model, a few
`key observations can be made.
`First, clinical development (Phases I–III) accounts for
`approximately 63% of the costs for each NME launched
`(53% from Phase II to launch), and preclinical drug dis-
`covery accounts for 32%. However, this represents an
`underestimate of the costs for drug discovery, as we have
`excluded from our model the earliest phase of discovery
`research; that is, that prior to target selection. This is
`because the research required to identify and validate
`a given target is highly variable, making the underlying
`parameters difficult to quantify. However, target selec-
`tion may well be one of the most important determinants
`of attrition (p(TS)) and thus overall R&D productivity
`(discussed below).
`Second, based on realistic and current assumptions
`on C, CT, p(TS) and WIP, only 8% of NMEs will success-
`fully make it from the point of candidate selection (pre-
`clinical stage) to launch (FIG. 2). It has been suggested that
`new biologic drugs have a higher probability of launch
`than small-molecule drugs9,11. For the purposes of our
`model, we have used 7% for small-molecule drugs and
`11% for biologics.
`Third, the process of discovering and developing an
`NME on average required approximately 13.5 years (CT)
`in 2007 (yearly averages ranged from 11.4 to 13.5 using
`the PBF study data across 2000–2007). This includes
`regulatory review but not the time it takes to fully identify
`and validate a drug target16.
`Fourth, based on our model, the number of mol-
`ecules entering clinical development every year must be
`approximately 9 (or 11 if all small molecules) to yield a
`single NME launch per year. Most large companies aspire
`for 2–5 launches per year and therefore 18–45 Phase I
`starts (and resulting WIP) would be required annually.
`However, such numbers are rarely, if ever, achieved even
`in very large companies. If sustained over several years,
`this WIP deficit will result in a substantial pipeline gap. If
`it takes approximately 9 Phase I drug candidates annually
`to launch 1 NME per year and if these derive exclusively
`from a given company’s internal discovery efforts, then
`the number of discovery projects (WIP) from target-to-
`hit, hit-to-lead and lead optimization is approximately 25,
`
`Capitalized cost
`This is the out-of-pocket cost
`corrected for cost of capital,
`and is the standard accounting
`treatment for long-term
`investments. It recognizes the
`fact that investors require a
`return on research investments
`that reflects alternative
`potential uses of their
`investment. So, the capitalized
`cost per drug launch increases
`out-of-pocket costs by the cost
`of capital for every year from
`expenditure to launch.
`
`Out-of-pocket cost
`This is the total cost required
`to expect one drug launch,
`taking into account attrition,
`but not the cost of capital.
`
`Cost of capital
`This is the annual rate of return
`expected by investors based
`on the level of risk of the
`investment.
`
`NATURE REVIEWS | DRUG DISCOVERY
`
` VOLUME 9 | MARCH 2010 | 205
`
`
`
`© 20 Macmillan Publishers Limited. All rights reserved10
`
`Page 00003
`
`

`

`A N A LY S I S
`
`Target-to-hit
`
`Hit-to-lead
`
`Lead
`optimization
`
`Preclinical
`
`Phase I
`
`Phase II
`
`Phase III
`
`Submission
`to launch
`
`p(TS)
`WIP needed for 1 launch
`Cost per WIP per Phase
`Cycle time (years)
`Cost per launch (out of pocket)
`% Total cost per NME
`Cost of capital
`Cost per launch (capitalized)
`
`80%
`24.3
`$1
`1.0
`$24
`3%
`11%
`$94
`
`75%
`19.4
`$2.5
`1.5
`$49
`6%
`
`$166
`
`85%
`14.6
`$10
`2.0
`$146
`17%
`
`$414
`
`69%
`12.4
`$5
`1.0
`$62
`7%
`
`$150
`
`54%
`8.6
`$15
`1.5
`$128
`15%
`
`$273
`
`34%
`4.6
`$40
`2.5
`$185
`21%
`
`$319
`
`70%
`1.6
`$150
`2.5
`$235
`27%
`
`$314
`
`Launch
`
`1
`
`$873
`
`$1,778
`
`91%
`1.1
`$40
`1.5
`$44
`5%
`
`$48
`
`Development
`Discovery
`Figure 2 | R&D model yielding costs to successfully discover and develop a single new molecular entity. The model
`defines the distinct phases of drug discovery and development from the initial stage of target-to-hit to the final stage, launch.
`The model is based on a set of industry-appropriate R&D assumptions (industry benchmarks and data from Eli Lilly and
`Company) defining the performance of the R&D process at each stage of development (see Supplementary information S2
`(box) for details). R&D parameters include: the probability of successful transition from one stage to the next (p(TS)), the phase
`cost for each project, the cycle time required to progress through each stage of development and the cost of capital,
`reflecting the returns required by shareholders to use their money during the lengthy R&D process. With these inputs (darker
`shaded boxes), the model calculates the number of assets (work in process, WIP) needed in each stage of development to
`achieve one new molecular entity (NME) launch. Based on the assumptions for success rate, cycle time and cost, the model
`further calculates the ‘out of pocket’ cost per phase as well as the total cost to achieve one NME launch per year (US$873
`million). Lighter shaded boxes show calculated values based on assumed inputs. Capitalizing the cost, to account for the cost
`of capital during this period of over 13 years, yields a ‘capitalized’ cost of $1,778 million per NME launch. It is important to
`note that this model does not include investments for exploratory discovery research, post-launch expenses or overheads
`(that is, salaries for employees not engaged in R&D activities but necessary to support the organization).
`
`20 and 15 respectively (FIG. 2). We will discuss the need
`for sufficient discovery investments and output (WIP)
`to achieve the level of drug candidates necessary below.
`In this model, in the absence of sufficient acquisition
`of drug candidates, especially late-phase compounds,
`achieved as one-off in-license deals or through mergers
`and acquisitions (M&A), most companies are simply
`unable to achieve (or afford) the numbers of compounds
`distributed across the phases of discovery and develop-
`ment they require to achieve their goals for new NMEs
`launched without a substantial increase in productivity.
`Encouragingly, recent benchmark data on Phase I
`WIP across the industry indicate that most companies
`have begun to substantially increase investments in the
`earlier stages of drug discovery; this is reflected by the
`number of candidates entering Phase I trials, which
`has increased significantly9,17,18. However, based on the
`benchmark data, for most companies, the number of
`NMEs entering clinical development and progressing
`to Phase II and III are still insufficient to achieve 2–5
`launches per year9; this reflects many years of operating
`at WIP levels below what would be required in the ear-
`lier stages of drug discovery and development. Thus,
`inevitable pipeline gaps will arise (as they have) and
`given the CT of the process (FIG. 2), such gaps cannot be
`filled quickly through traditional means.
`
`Finally, we suggest that based on this model, many
`companies would find that their R&D operating
`expenses are not appropriately distributed across the
`various phases of drug discovery and development. Too
`many resources are often applied to late-stage develop-
`ment of drug candidates with relatively low p(TS) and/
`or post-launch support of marketed products. This may
`be the root cause of the current drought of new medi-
`cines and the business challenges most companies are
`experiencing.
`
`Key areas for improving R&D productivity
`Using our model (FIG. 2, Supplementary information S2
`(box)) and starting from a baseline value for the estimated
`capitalized cost of a single NME of ~$1.78 billion, we can
`investigate which parameters contributing to this cost are
`the most important. To achieve this, we have varied the
`parameters p(TS), CT and C for different phases of the
`overall process across a realistic range of possibilities
`(reasonable estimates of industry highs and lows for each
`parameter) to identify parameters for which changes
`would have the greatest impact on R&D efficiency, and
`the extent of the impact in each case (FIG. 3).
`As is evident from FIG. 3, attrition — defined as
`1– p(TS) — in the clinical phases of development (espe-
`cially Phase II and III) remains the most important
`
`206 | MARCH 2010 | VOLUME 9
`
` www.nature.com/reviews/drugdisc
`
`
`
`© 20 Macmillan Publishers Limited. All rights reserved10
`
`Page 00004
`
`

`

`A N A LY S I S
`
`p(TS): Phase II
`p(TS): Phase III
`Cost: lead optimization
`Cycle time: Phase III
`p(TS): Phase I
`p(TS): submission to launch
`Cycle time: Phase II
`Cost: Phase II
`Cost: Phase III
`Cycle time: submission to launch
`Cost: Phase I
`p(TS): preclinical
`Cost: hit-to-lead
`p(TS): lead optimization
`Cycle time: Phase I
`Cost: preclinical
`Cycle time: lead optimization
`Cost: target-to-hit
`Cycle time: preclinical
`p(TS): hit-to-lead
`Cost: submission to launch
`Cycle time: hit-to-lead
`p(TS): target-to-hit
`Cycle time: target-to-hit
`
`50%
`
`25%
`
`80%
`$5
`1.25
`65%
`100%
`1.25
`$20
`$75
`0.75
`$7.5
`80%
`$1.25
`95%
`0.75
`$2.5
`1.0
`$0.5
`0.5
`85%
`$20
`0.75
`90%
`0.5
`
`60%
`
`$15
`3.75
`45%
`80%
`
`3.75
`$60
`$225
`2.25
`$22.5
`60%
`$3.75
`75%
`2.25
`$7.5
`3.0
`$1.5
`1.5
`65%
`$60
`2.25
`70%
`1.5
`
`34%
`70%
`$10 million
`2.5 years
`54%
`91%
`2.5 years
`$40 million
`$150 million
`1.5 years
`$15 million
`69%
`$2.5 million
`85%
`1.5 years
`$5 million
`2 years
`$1 million
`1 year
`75%
`$40 million
`1.5 years
`80%
`1 year
`
`$1,200
`
`$1,400
`
`$2,200
`
`$2,400
`
`$1,600
`$1,800
`$2,000
`Baseline value
`Parameter
`Capitalized cost per launch (US$ millions)
`Figure 3 | R&D productivity model: parametric sensitivity analysis. This parametric sensitivity analysis is created
`from an R&D model that calculates the capitalized cost per launch based on assumptions for the model’s parameters
`(the probability of technical success (p(TS)), cost and cycle time, all by phase). When baseline values for each of the
`parameters are applied, the model calculates a capitalized cost per launch of US$1,778 million (see Supplementary
`information S2 (box) for details). This forms the spine of the sensitivity analysis (tornado diagram). The analysis varies each
`of the parameters individually to a high and a low value (while holding all other parameters constant at their base value)
`and calculates a capitalized cost per launch based on those new values for that varied parameter. In this analysis, the
`values of the parameters are varied from 50% lower and 50% higher relative to the baseline value for cost and cycle time
`and approximately plus or minus 10 percentage points for p(TS). Once cost per launch is calculated for the high and low
`values of each parameter, the parameters are ordered from highest to lowest based on the relative magnitude of impact
`on the overall cost per launch, and the swings in cost per launch are plotted on the graph. At the top of the graph are the
`parameters that have the greatest effect on the cost per launch, with positive effect in blue (for example, reducing cost)
`and negative effect in red. Parameters shown lower on the graph have a smaller effect on cost per launch.
`
`determinant of overall R&D efficiency. In our baseline
`model, Phase II p(TS) is 34% (that is, 66% of compounds
`entering Phase II fail prior to Phase III). If Phase II attri-
`tion increases to 75% (a p(TS) of only 25%), then the
`cost per NME increases to $2.3 billion, or an increase of
`29%. Conversely, if Phase II attrition decreases from 66%
`to 50% (that is, a p(TS) of 50%), then the cost per NME
`decreases by 25% to $1.33 billion. Similarly, our baseline
`value of p(TS) for Phase III molecules is 70%; that is,
`an attrition rate of 30%. If Phase III attrition increases
`to 40%, then the cost per NME will increase by 16% to
`$2.07 billion. Conversely, if Phase III attrition can be
`reduced to 20% (80% p(TS)), then the cost per NME
`will be reduced by 12% to $1.56 billion (FIG. 3).
`Combining the impact of these increases or decreases
`in Phase II and Phase III attrition illustrates the profound
`effect of late-stage attrition on R&D efficiency. At the
`higher end of the Phase II and III attrition rates discussed
`above, the cost of an NME increases from our baseline
`case by almost $0.9 billion to $2.7 billion, whereas at the
`lower end of the above attrition rates for Phase II and III,
`the cost per NME is reduced to $1.17 billion.
`It is clear from our analyses that improving R&D effi-
`ciency and productivity will depend strongly on reducing
`Phase II and III attrition. Unfortunately, industry trends
`
`suggest that both Phase II and III attrition are increas-
`ing9,19–21, given both the more unprecedented nature of
`the drug targets being pursued, as well as heightened
`scrutiny and concerns about drug safety and the necessity
`of demonstrating a highly desirable benefit-to-risk ratio
`and health outcome for new medicines. However, main-
`taining sufficient WIP while simultaneously reducing CT
`and C will also be necessary to improve R&D efficiency.
`We discuss these aspects first, before considering strategies
`to reduce attrition in depth.
`Work in process (WIP). We have already emphasized
`the importance of having sufficient WIP at each phase
`of drug discovery and development, and have suggested
`that insufficient WIP, especially in discovery and the
`early phases of clinical development has contributed
`to the decline in NME approvals. To further illustrate
`this point and again demonstrate the impact of Phase II
`and Phase III attrition on Phase I WIP requirements, we
`have carried out another sensitivity analysis using these
`three parameters alone. FIG. 4 shows the impact of varying
`Phase II and III attrition on the number of Phase I entries
`per year required to launch a single NME annually. If the
`p(TS) in Phase II and Phase III are 25% and 50% respec-
`tively, approximately 16 compounds must enter Phase I
`
`NATURE REVIEWS | DRUG DISCOVERY
`
` VOLUME 9 | MARCH 2010 | 207
`
`
`
`© 20 Macmillan Publishers Limited. All rights reserved10
`
`Page 00005
`
`

`

`A N A LY S I S
`
`50%
`60%
`70%
`80%
`90%
`
`Phase III
`p(TS) (%)
`
`25%
`
`Estimate used
`in the model
`
`50%
`
`20
`
`18
`
`16
`
`14
`
`12
`
`10
`
`8
`
`246
`
`Phase I entries (FHDs)
`
`0
`
`15
`
`20
`
`25
`
`30
`
`50
`
`55
`
`60
`
`65
`
`45
`40
`35
`Phase II p(TS) (%)
`Figure 4 | Effect of Phase II and III probability of technical success on the number
`of Phase I entries required for one successful launch of a new molecular entity.
`This analysis shows the number of Phase I entries (first human dose; FHDs) annually
`required to achieve one new molecular entity (NME) launch per year as a result of
`modelling baseline assumptions of the probability of technical success (p(TS)) for the
`stages of Phase I and submission-to-launch (54% and 91% respectively) over a range of
`p(TS) for Phase II and Phase III. Each curve represents a different assumption for the
`Phase III p(TS) over the range of 50% to 90%, and the x axis represents varying p(TS) for
`Phase II. The number of Phase I entries (FHDs) annually needed to produce one NME
`launch per year can be viewed on the y axis for any combination of Phase III (individual
`curve) and Phase II (x axis) p(TS). For example, at a 70% Phase III p(TS) (black curve) and
`a 35% Phase II p(TS) (on x axis), the required number of Phase I entries is about 8.5.
`
`in Phase III (or even Phase IV). Given the C and CT of
`a single Phase III unit of WIP ($150 million), almost 10
`Phase I molecules ($15 million) can be developed for the
`same cost, ideally through to proof-of-concept (POC;
`see discussion of p(TS) below). Reducing late-phase
`attrition through early POC studies (ideally in Phase I)
`is therefore crucial to implement this partial solution.
`The resources (C) saved by lowering Phase III attrition,
`however, must be redirected to fund sufficient discovery
`and Phase I/II WIP. Most importantly, advancement into
`Phase III should be pursued only for those compounds
`with established efficacy (ideally POC in Phase I and
`confirmed in Phase II) and a well-defined margin of
`safety. Ideally, attrition in Phase III should be due pri-
`marily to the emergence of relatively rare and unforeseen
`adverse events. Thus, the key is to have sufficient WIP
`in the early phases of clinical development to effectively
`triage and select molecules that will have a higher p(TS)
`in late-stage development.
`The question of how to affordably increase WIP,
`p(TS) and V without substantially increasing C or
`increasing CT due to capacity constraints and lack of
`focus is in our view paramount to improve R&D pro-
`ductivity. This could be accomplished by transforming
`the R&D enterprise from one that is predominantly
`‘owned’, operated and fully controlled by a given com-
`pany (Fully Integrated Pharmaceutical Company or
`FIPCo) to one that is highly networked, partnered and
`leveraged (Fully Integrated Pharmaceutical Network or
`FIPNet). Traditionally, large pharmaceutical companies
`have pursued the discovery, development, manufacture
`and commercialization of their medicines largely by
`owning and controlling each component. In part, past
`reliance on the FIPCo model was as much a necessity
`as a choice. Today, however, the opportunity to partner
`virtually all elements of R&D through a coordinated and
`global network or FIPNet could (if effectively managed)
`substantially improve R&D productivity by affordably
`enhancing the pipeline from early discovery through to
`launch. A FIPNet will theoretically allow greater access
`to intellectual property, molecules, capabilities, capital,
`knowledge and, of course, talent24–26. Thus, operated as
`a FIPNet, a given R&D organization will be able to ‘play
`bigger than its size’ and