re-igniting the r&d engine in a constrained environment

Re-igniting the R&D engine in a constrained environment

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Table of contents

3 Executive summary

4 Finding the formula to succeed in R&D when others are failing

5 The legacy of the Blockbuster

7 Incomplete solutions

11 Redesigning the R&D operating model

15 The business case for R&D transformation

17 Re-igniting the R&D engine

Re-igniting the R&D engine in a constrained environment 3

Current BioPharma R&D operating models lack the flexibility required to effectively apply research capacity to meet shifting demand. As a result, R&D is plagued by operational and governance constraints that drive costs up and limit choices at key stage gates, creating a variable and unsustainable flow of low value products through the pipeline. To re-build sustainable pipelines with high value products, R&D organizations must focus on actively balancing resources across the pipeline to consistently align capacity with demand and create choices that will differentiate products. In many organizations this will require wholesale operational, structural, and cultural transformation. We believe this change needs to occur now, as other options such as M&A, licensing, outsourcing, and cost-cutting alone cannot support long-term growth.

Executive summary

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The challenges facing the BioPharma industry are well documented. Increased competition and regulation, coupled with decreased R&D productivity and differentiation have steadily eroded revenues, while R&D expenses continue to escalate. Based on Deloitte analysis of FTC data, it is estimated that a new drug costs on average $1.1B to develop, ranging from about $0.6B to more than $2.5B depending on the therapeutic area and the company’s cost structure.1 Despite the ever-intensifying attention these problems are receiving, the solutions that have been proposed, and in some cases implemented, have not gained broad traction or shown clear success. Some companies appear to have the appetite to stay in the R&D game, while others are retrenching or even exiting R&D all together.

We do not believe that R&D is dead. In fact, we strongly believe that BioPharma should continue to invest in R&D despite the economic downturn, a sentiment echoed in a recent Harvard Business School article:

“In economic downtimes, businesses are apt to cut R&D projects that don't promise a speedy return on investment. But take a cue from smart science-based businesses, which view the recession as an opportunity to stoke up research and innovation for long-term competitive advantage.”2

Finding the formula to succeed in R&D when others are failing

We see potential for transformation in operating models and mindsets that auger well for organizations willing and able to embrace true transformational change. Our point of view is based on experience working closely with the large and small R&D organizations to help them in their efforts to develop operating strategies designed to position them for long-term success. Our approach to helping these organizations address their challenges is rigorous and data-driven. This has allowed us to identify and quantify root causes of the current lack of R&D productivity and escalating costs, and to define core attributes of consistently high-performing organizations.

Based on these insights, we have developed a framework and implementation approach to help organizations in their efforts to transform R&D operating models and improve performance in a constrained environment. This article describes the key findings and lessons learned on a remarkable journey into the future of R&D operating models that we have taken with our clients.

As used in this document, “Deloitte” means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries.

1 FTC, 2006, Deloitte Analysis. Original estimates in 2000 dollars, adjusted for inflation to represent 2008 dollars.2 “The Challenges of Investing in Science-Based Innovation,” Working Knowledge, Harvard Business School, June 1, 2009. http://hbswk.hbs.edu/item/ 6056.html


Today’s R&D organizations still bear many of the operational hallmarks of an industry enjoying a wide-open market and double-digit growth driven by blockbuster products. Expecting a high level of revenue and opportunities to leverage scale, BioPharma has historically been quick to build major infrastructure to support a single program that shows blockbuster potential. As long as these products were on tap, this behavior was reinforced and accepted as the norm.

Over the past decade, however, the blockbuster product has faded as established markets have become saturated, regulatory scrutiny has increased, and physicians and patients are more demanding. In this environment, BioPharma’s traditional operating model has not only performed poorly, it has added to the industry’s current problems as high fixed costs drag on earnings and revenue lost to patent expiration. Adapting to the current challenges requires a new type of R&D organization, one that has a different set of operational and cultural values.

Root causes of R&D’s malaiseSo why has BioPharma R&D not been able to keep up with the shift in market conditions? What are the operational attributes that have hamstrung the industry?

At the core, traditional operating models have resulted in R&D organizations that are saddled with inflexible capacity and are unable to react to changing demand as corporate and R&D strategies shift from blockbusters to more targeted and specialized products.

This disconnect has created pockets of both stranded capacity and under-capacity across R&D pipelines, driving up costs as assets are not efficiently utilized, and decreasing productivity. As an insufficient number of successful programs progress down the pipeline, flow becomes more variable and corporate demands are not consistently met (Figure 1).

The legacy of the Blockbuster

In many current operating models, R&D output or pipeline flow (“supply”) by phase is not aligned with shifting corporate demand for R&D products. Some R&D phases and activities operate under capacity and are unable to support required levels of pipeline flow. Other phases and activities are over-resourced. This additional capacity is wasted (“stranded”), contributing to increased R&D costs. As a result, overall R&D productivity suffers and R&D output doesn’t meet corporate requirements.

This situation has been further aggravated by the instinctive response of R&D organizations to the breakdown in pipeline flow, which is encapsulated by the adage “Nature abhors a vacuum.” In R&D this means that any stranded downstream capacity tends to be used to “pull” work forward. We have observed that this often involves advancing poor quality programs, which can have the toxic effects of increasing R&D spend, decreasing late stage probability of success (POS), and reducing the potential value of the products when they come under stringent regulatory and commercial evaluation (Figure 2).

Discovery Pre-Clinical Phase I to IIb

Under Capacity

Stranded Capacity

Insufficient Pipeline Output

Corporate Demand

R&D Capacity by Phase

Pipeline Flow (Flow of Successful Programs)

Demand by R&D Phase

Figure 1. Illustrative R&D Pipeline

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Table 1: Key Operational Challenges in R&D

Key Challenges Root Causes Current State

Understanding DEMAND

Inconsistent definition of demand across the •organization

Demand for sub-work products (e.g., •biomarkers) has increased dramatically but is often overlooked when establishing operating targets

Demand is defined solely by •the requirements for end products (drugs, devices, etc.) and not translated into actionable targets

Creating flexible CAPACITY

Market conditions have caused corporate •strategies to shift more quickly, resulting in frequent sudden changes in demand that are not matched by internal capacity

Large inflexible infrastructure creates •sub-optimal outputs

Fixed costs are high •

Capacity is focused on •particular TA’s and phases

ALIGNING Supply and Demand

Increased efficacy, regulatory, and safety •requirements in late phases have increased the burden on R&D

Investment in new technologies have increased •the effort required to integrate across programs

Resources are focused on •activities that deliver limited value and are not aligned with overall priorities

Building CHOICE across the pipeline

Therapeutic markets have become saturated •with generics and follow-on drugs, driving demand for differentiated products

Increased failure rates due to challenging •science have increased R&D expenditure while output continues to stagnate

Resources are not balanced •across the pipeline; poor quality products emerge from under-resourced phases and output of promising products lags targets

The inability of R&D organizations to “kill” unpromising programs early in the development process contributes to an industry-wide failure cost of over 80% of total spend through Phase II, a penalty that is compounded when programs fail in later phases.3

So what is it going to take to get out of this situation? The current problems with R&D operating models reflect a set of core operational challenges that have emerged over the past decade and a half (Table 1). Addressing these challenges can help position an R&D organization to better maintain a sustainable flow of high quality products, and will likely be necessary for them to achieve long-term success.

While organizations may be aware of the current challenges facing them, few have successfully identified and responded to the internal and external root causes that have spawned those challenges.

Phase Preclinical Phase I Phase II

Cumulative R&D Spend

$11.8B $14.7B $20.4B

Cumulative Cost of Failure

$10.6B $13.6B $17.4B

Figure 2. Cumulative Cost of Failure

R&D Spend ($B)

25

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3 “PAREXEL's Bio/Pharma R&D Statistical Sourcebook 2008/2009” on historical phase-specific POS from 1989-Dec 2007; PhRMA 2008 Report on 2006 company-financed phase-specific R&D of PhRMA member companies


An important question at this juncture is why BioPharma has not yet successfully addressed these challenges. The problem of R&D performance has been top-of-mind for years within the executive suite, particularly now that the “Patent Cliff of 2012,” the year in which a number of key blockbuster drugs lose patent protection, looms ever more menacingly over the future of the industry. The ten largest BioPharma companies are expected to lose roughly $73 billion in annual drug sales to generic competition by the end of 2012.4

At some level there are as many answers to this question as there are BioPharma companies. However, a common theme is that R&D organizations have tended to address different parts of the puzzle independently, resulting in an intractable set of inter-dependencies that have not advanced the core issues much. The industry has also been prone to become distracted, pursuing new technologies and other “quick fixes,” while not focusing sufficient attention on its underlying operating model.

We have investigated the merits and short-comings of the major approaches that BioPharma has taken to transform its R&D operations based on a combination of internal and external data. What we found challenges some broadly held assumptions around how R&D operating models should be transformed.

The lost promise of technologyIn the last decade and a half, revolutionary developments in fields such as molecular biology have enticed R&D organizations to spend a significant fraction of their intellectual and financial capital on emerging technologies. Many of these - most notably the “omics” technologies - dramatically increase the amount of information available on biological pathways and disease states. Intuitively it makes sense that more information should lead to more and better drugs, and BioPharma embraced these novel technologies as vehicles to kick-start flagging productivity and drive value creation through innovation.

Despite their promise, however, most discovery technology investments have only produced incremental impacts on R&D. None have yet to open significant market opportunities or prove they can be a viable platform

Incomplete solutions

for developing therapeutics. Even RNAi, perceived by many as the technology most likely to revolutionize drug development (Table 2), has thus far failed to produce broad pipelines.5

Table 2: Major RNA interference (RNAi) investments (2004-present)

Investment Approximate Value ($USB)

Roche and Takeda deals with Alnylam Pharmaceuticals

$ 2.0

Merck acquisition of SiRNA Therapeutics

$ 1.1

GlaxoSmithKline deal with Regulus Therapeutics

$ 0.6

Astra Zeneca deal with Silence Therapeutics

$ 0.4

Total $ 4.1

BioPharma has invested well over $4B in RNAi acquisitions and deals since 2004.

As recently as 2004, it was predicted that the RNAi therapeutic market would reach $10 billion by 2014.6 However, this future looks unlikely, especially considering recent setbacks including the halted phase III trial of OPKO Health’s RNAi therapeutic Bevasiranib (age-related macular degeneration).7 Even the most optimistic projections for RNAi revenue are not sufficient to support the view that it can turn the tide of decreasing BioPharma R&D productivity.

Instead of creating the next generation of blockbusters, it could be argued that the major impact of technology has been to drive the fragmentation of disease markets that now challenges the BioPharma industry. This does not mean that investing in platform technologies was the wrong move for R&D. It has laid the foundation for a possible revolution in disease treatment: rich, personalized information on patient populations and insights into disease etiology. Now BioPharma must focus on transforming its R&D operating models to enable scientists to convert these assets into highly efficacious products.

4 “Big Pharma and Patent Cliffs,” Zacks Investment Research, Mar 9, 2009.5 “The Outlook for RNAi,” Business Insights, 2005.6 “Latest Emerging and Disruptive Market Analysis from Life Science Insights Forecasts the Therapeutic RNAi Market,” Business Wire, Oct 20, 20047 “Opko Halts Phase III Study of siRNA Treatment for AMD on Poor Preliminary Data, “ Genoweb, Mar 12, 2009

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Structural changes alone cannot transform R&DA major focus of operational transformation within BioPharma over the past 5 years has focused on the structure and mission of its research units. Most companies have re-evaluated the size and number of R&D sites within their network, as well as their research focus. Driven in large part by a desire to acquire some of the R&D “mojo” typically associated with small biotechs and research institutions, the general trend has been to create smaller, more focused research groups.

Somewhat surprisingly, there is no clear association between a particular research network structure or focus and a company’s projected R&D productivity. Across the top 15 BioPharma companies,8 examples can be found of successful companies with either few or many research sites, more or less scientists at each site, as well as more focus or more breadth in therapeutic areas (Figure 3) with no indication that any one configuration in particular improves overall R&D productivity.

Even when looking for more subtle correlations between combinations of structural choices and success, such as small sites and more focused research, no clear trends emerge. The message is clear: the major structural attributes of the R&D network alone do not create a high-performing organization. Using “replacement ratio” as a metric9 to define what constitutes a high-performing company, no distinct configuration of R&D sites or research focus correlates highly to long-term success. Similar results were found when looking at other measures of R&D success (e.g. number of new products delivered to market; data not shown).

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8 15 largest BioPharma companies with revenue greater than $12 billion in 2008.9 Source: “Replacement ratios” reflects the proportion of drug sales for products going off patent by 2012 that will be replaced by sales from new products (a value of 1.0 means that 100% of sales will be replaced; higher numbers indicate greater performance). AVOS Life Sciences.

Figure 3. R&D Organizational Structure Correlated to Business Success


“Virtual R&D” is not a stand-alone solutionThere is little debate within the industry that R&D organizations, which have traditionally been highly self-sufficient, will need to look increasingly to external partners to achieve research objectives and bolster their pipeline. Consistent with this, companies across the industry have increased their external spend and now depend much more on partnerships10 to improve performance and shore up their pipelines. We have worked extensively with R&D organizations to help them in their efforts to develop strategic ways to derive high quality benefit from these activities (Table 3).

Table 3. Partnerships as an R&D Value Driver11

Partnerships as R&D Value Drivers

Reduce unit costs and create a more variable cost structure for •BioPharma

Provide the operating agility and flexible capacity required to •support unexpected changes in ‘demand’ due to scientific, regulatory or market shifts

Allow the BioPharma’s most talented scientists, clinicians and •others to focus on innovation

Provide selective access to diverse knowledge bases, emerging •technologies and innovative treatment solutions

As the emphasis on “virtual” R&D increases, companies need to understand the limits of partnering and acquiring assets externally as other industries have discovered recently. For example, Boeing’s experiment with a highly outsourced model (>70%) resulted in loss of control around timing and quality of the supply chain, and ultimately caused significant delay in the launch of the new 787 airplane.12 For Life Sciences companies there are two major considerations that come into play here.

First, what will the “steady state” size and quality of the partnering market be? For products and services, BioPharma depends increasingly on other companies that are subject to many factors that influence how effectively they can meet demand. For example, the recent credit crunch has resulted in big drops in funding for early stage discovery and innovation. As a result the market for quality pipeline assets and research technologies is likely to shrink in the coming years.

Second, what will BioPharma do with the acquired assets? While there are plenty of examples of successful partnerships across the industry, there is no clear indication that a higher level of in-licensing correlates with greater success in getting drugs into the markets (Figure 4). External assets, it appears, are falling victim to many of the same operational problems that plague the development of internal assets. Figure 4. External Drug Development and Success are not Correlated

For the top 50 BioPharma companies, obtaining a larger portion of the pipeline through in-licensing or partnerships does not necessarily assure success in bringing more drugs to market.13

This does not mean that partnering is unwise. Indeed moving towards a more “virtual” R&D model and acquiring external pipeline assets is likely to be a short-term necessity for the industry. However, to sustain long-term growth, companies will need to maintain a certain degree of “research independence” from the market. To derive the most value from both internal and external assets, they will need to continue to support a core internal R&D competency.

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10 In this paper, Deloitte uses the term “partnerships” to describe a broad range of agreements between two or more companies to share materials, resources, and/ or intellectual property, including but not restricted to acquisition and licensing.11 “Strategic Partnerships in Life Sciences R&D,” Deloitte Consulting LLP, 2008.12 “A 787 Supply Chain Nightmare”, Ian McInnes. Aerospace Technology, March 25, 2008.13 IMS. Parexel’s Bio/Pharmaceutical R&D Statistical Sourcebook (2008), Lehman Universe.

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Exiting internal R&D is not an optionThe absence of a clear solution to current problems has caused some companies to decrease, even eliminate, their investment in R&D. In a cost-constrained environment, removing spend from R&D to focus downstream in the value chain is a natural instinct. However, this is a risky move. The correlation between R&D investment and long-term growth is clear (Figure 5). Indications suggest that BioPharma companies must maintain a minimal level of investment in internal R&D, both to drive development of internally discovered products, and to effectively support the acquisition and commercialization of external products. Figure 5. Success in the Market Correlates with R&D Focus

Focus on R&D, here measured as a percent of a company’s total employees dedicated to R&D, correlates with long-term growth.14

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14 The largest 13 out of 15 BioPharma companies with revenue greater than $12 billion in 2008 employ an average of 30% of total staff in R&D functions. Of those companies, those that allocate a greater percentage of staff to R&D appear to have more promising long-term growth potential. Parexel Sourcebook, 2008/2009, company investor reports, Deloitte Analysis.


High performing R&D organizations across the industry differ in size and structure, but share key operational attributes and strategies. At the core, they focus on flexibly, balancing demand and supply, generating choice, and maintaining a sustainable pipeline flow. By working with these organizations we have been able to dig a little deeper, and have found that their operating models achieve the following:

Demand and attrition are accurately defined and •understood, and operating targets are developed based on clear metrics

Resource allocation creates balanced and flexible supply •to support efficient asset utilization

Capacity supports the work required to consistently meet •operating targets and creates a sustainable pipeline flow

Choice is created across the pipeline so that the most •promising products progress and create value

Redesigning the R&D operating model

Based on this we have developed a model for R&D organizations that is based on three management pillars: Demand, Supply, and Alignment of Demand and Supply (Figure 6).

High performing BioPharma companies succeed in properly balancing the demands of the organization and capacity to create sustainable pipeline flow. The focus is on generating more choice for the pipeline whereby poorer quality candidates are killed early and only promising programs flow through to eventually become end products. Throughout the process, management should lead a governance program to make certain that the entire organization adheres to its productivity targets, while also fostering a culture that supports creativity.

Demand

Clearly defined and actionable: Demand is based both on the requirements for end

products and sub-work products

Alignment

Supply balanced with Demand: Resources are balanced across pipeline products,

innovation, and development requirements

Supply

Flexible capacity designed to meet Demand: Fixed costs are lower, with capacity that is fungible across programs and phases

Management & Governance

Processes

Reward creativity

and enable productivity

Choice

Only quality programs and products progress: Pipeline is balanced to make

certain that quality programs can be selected and moved forward in each phase

Sustainable pipeline flow

Figure 6. Core attributes of a successful R&D operating model

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A framework for transformationHigh performing R&D organizations do not occur spontaneously. They are the product of a well-defined strategy that is influenced by sets of internal and external factors, which are individual to each organization. This strategy must be supported by leadership and embraced throughout the organization. Transformation is very challenging and will inevitably require a significant change from “business as usual.”

Working closely with our industry clients, we have developed a framework to support organizations as they transform their R&D organizations in the current financially constrained environment (Figure 7). The key tenets of our framework are:

Consider the organization as a whole•

Tightly link strategic direction and operational execution •

Maximize flexibility•

Focus on creating a sustainable, efficient, and valuable •pipeline

Deloitte’s framework is aimed at transforming the three management pillars: Demand, Capacity and Alignment.

Demand: Establish operating targets that are directly tied to R&D strategyThe first pillar of a successful operating model is Demand. Corporate and R&D strategy needs to be translated into actionable operating targets across the pipeline. These targets must be consistent with the relative investment level for each disease area and modality, and they must be informed by expected attrition rates across the phases of R&D (Figure 8).

Although there are no stock solutions to define Demand, the following actions by successful R&D organizations provide insights into how this might be addressed.

Develop a single currency for demand• . More and more emphasis is placed on biomarkers, translational medicine, the development of novel delivery vehicles and other sub-work products. Often the demand this places on the organization is overlooked. Successful companies are learning how to define and balance demand for pipeline products with the demand for sub-work products.

Demand

Clearly defined and actionable

Alignment

Work balanced across all needs

Capacity

Aligned with demand and flexible

Management & Governance

Processes

Reward creativity

and enable productivity

Choice

Highest quality programs

and products progress


Deloitte Transformation Framework

Capability Management

Partnerships & Sourcing

Network Structure

PipelineManagement

R&D Strategy

Figure 7. The Deloitte Framework for Transforming the R&D Operating Model


Accurately measure attrition• . Attrition rates can vary across disease areas and modalities, even down to the program level. They also fluctuate over time as new approaches and technologies are incorporated. Understanding true attrition rates requires measuring pipeline throughput regularly at a sufficiently granular level. Successful companies are starting to understand not only how many programs are expected to progress through the pipeline, but also the success rate of individual technology platforms and research capabilities.

The successful R&D organization establishes clearly defined and actionable demand for the R&D organization by translating corporate objectives into accurate operating targets for each phase based on anticipated attrition rates.

Supply: Maximize through flexible use of resources and manage costs across different phases The second pillar of a successful operating model is Supply. Supply is derived from the capacity of internal and external assets. Obtaining a careful understanding and efficiently managing capacity has demonstrated to be difficult for many R&D organizations. Some of the main areas where organizations struggle are:

Understanding and managing the ever-evolving maturity •and performance of capabilities. Research capabilities will mature over time, beginning as innovative and evolving towards commoditized, as processes and technologies are refined. These different classes of capabilities have distinct resource requirements as well as performance expectations, and will contribute most effectively in different locations and configurations in the network (e.g., internal vs. sourced). R&D organizations must track and manage capability maturity and performance to make the best use of their capacity.

Efficiently creating and utilizing external capacity. •Although there has been a major push to access capacity externally, most R&D organizations struggle to manage their network of internal and external assets. External capabilities are typically not integrated in a way that maximizes their value to the organization.

We have helped a number of companies in their efforts to develop and implement a capabilities-based view of R&D that allows them to better measure their internal and external capacity and optimize its utilization.15

Figure 8. Establishing Demand

Discovery

Pre-Clinical

Phase I – IIb

Accurate Demand

Accurate Attrition

Accurate Operating Targets

Resources(People, Information, Technology & Spend)

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The mix performance of capauilities should align to the business strategy

The distribution of capabilities across the network should be designed to

maximize performance and innovation

The deployment of capabilities should support execution of the business strategy

Measure Maturity and Performance Define optimal location and distribution Integrate across organization and partners

Discovery Development Clinical Support

Integration Plan

Innovate Experimental Commoditized

Maturity Index

Site or Org A Site or Org B External

Network Map

Figure 9. Capability-Based Management of R&D

15 A Capabilities-Based Approach to Productive and Innovative R&D; Deloitte Consulting, 2008.

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A capabilities-based view of R&D can provide executives with important tools to implement business strategy and drive innovation and productivity.

In this model, each capability is comprised of a discrete grouping of people, processes, and enabling technologies that produce a defined output. This definition of a capability helps deconstruct a complex R&D organization into logical units that can be managed and applied to create flexible capacity (Figure 10).

The successful R&D organization operates with flexible capacity, which allow it to align R&D supply with demand. Resource utilization can be optimized by depending appropriately on internal and external capacity.

Alignment: Manage resources across phases to meet R&D targets and create choiceThe third pillar of a successful operating model is Alignment. Alignment describes how demand and supply are matched by appropriately allocating resources to create pipeline products, as well as to support the additional activities for which R&D is responsible. This “supplementary” work load has become increasingly important as R&D organizations become more involved in

addressing efficacy, regulatory, and safety requirements in late phases, as well as the burden of integrating new technologies and managing new partnerships. A balance must be struck between addressing these growing needs while maintaining and improving the ability to meet demand for new compounds. Successful organizations are able to align capacity with strategic priorities, financial limitations, and decision making, and allocate resources to provide flexible, sustainable pipeline flow and choice (Figure 11).

The successful R&D organization creates choice at each R&D stage gate allowing the organization to move forward with only the most promising programs, and quickly kill bad ones. This supports efforts to increase POS downstream and create additional value for the organization.

Figure 10. Flexible Capacity

Flexible Capacity across phases

Discovery

Pre-Clinical

Phase 1 – 2b


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Figure 11. Creating Choice

Discovery


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Increase Value

Choice

16 A Capabilities-Based Approach to Productive and Innovative R&D; Deloitte Consulting, 2008.


Now that we have presented the “materials and methods” for a set of potential solutions to address R&D challenges, the critical question becomes “what happens when they are implemented?” While the full impact of changes associated with R&D transformation will take years to fully emerge, looking at preliminary results provides a picture of what the range of outcomes might be. This helps motivate change and sets realistic targets to work towards on this long journey.17

The benefits of transformingWorking with our clients, we have been able to develop clear and quantitative understandings of the current state of R&D organizations, create specific operating strategies to succeed in the long-term, and translate these into flexible operating models that are being implemented. Some of the early benefits that we are seeing are summarized in Table 4.

Table 4. Opportunities From R&D Organizational Transformation

Opportunities From Transformation

Improved organizational alignment and decision making• : Emphasis on improved visibility into demand and translation of expectations into actionable targets provides a consistent understanding across the organization. Utilizing a quantifiable framework for capability assessment provides a common platform for decision making, emphasizing the need for information transparency.

Creating a sustainable pipeline flow• : An increased focus on a sustainable and scalable pipeline improves quality and choice of products delivered through the pipeline. Resources can be adapted to changing needs and demands placed on the organization.

Access to external science• : Improved access to partners and external science provides a platform for sustaining innovation, access to expertise and risk sharing. These also provide the ability to tap into innovative technology sources that evolve with changes in the market place.

Improved asset utilization• : Simplified network structure reduces complexity and improves effective utilization of assets. Emphasis on a flexible cost structure allows for variability and effective reallocation of resources.

To understand the potential monetary value of transformation we have explored hypothetical business cases with our clients. As shown in Table 5, the key value drivers that emerge are improved POS and efficiency

The business case for R&D transformation

gains.18 These are achieved through specific R&D transformation objectives, such as increased choice and higher asset utilization. By our estimates, a typical R&D program can expect to see improvements in productivity (measured by annual phase outputs) of up to 50% and cost savings between 20 and 35% by applying the concepts described here.

In these analyses we have restricted ourselves to evaluating value created by “doing the core job of R&D”, i.e. lab and clinical work required to develop drug candidates. We have not included additional benefits of R&D transformation, such as improved governance and decision making, increased speed to market, and enhanced revenue through product differentiation. These can create considerable incremental value.19

Table 5. Value Creation through R&D Transformation

Example Key Value Drivers

Improved POS Efficiency

Gains Cumulative

Benefit

Mechanism Greater •choice of programs

Reduced •execution risk

Improved •resource allocation

Higher asset •utilization

Improved •POS

Efficiency •Gains

Output Increase

25-40% 5-10% 30-50%

Cost Savings

5-10% 15-25% 20-35%

Organizations can expect to see significant productivity gains (phase outputs per year) and cost savings by implementing R&D transformation. Note that the results shown represent average impact of R&D transformation over a multi-year period. In any given year it is likely that an R&D organization will see swings in productivity and cost depending on a myriad of factors, including the nature of the drug programs that are at critical stage gates.

17 “Executing and Sustaining R&D Strategy,” Deloitte Consulting, 2008.18 Key Value Drivers were identified by independently flexing components of R&D transformation framework (Figure 7) between minimum and maximum values observed within client R&D organizations. Impact on productivity (number of phase outputs per year) and cost savings per year were calculated based on industry average spend and POS by phase for drugs, as estimated by Parexel (Source: Parexel‘s Bio/Pharmaceutical R&D Statistical Sourcebook 2008/2009). Phase I: $17M, 81%; Phase II: $34M, 57%; Phase III $27M, 57%19 “Pharma’s New U.S. Commercial Model: Promoting the Science Not the Swag”, Deloitte Consulting, 2009. “Leveraging RAPID to Enhance Clinical Operations”, Deloitte Consulting, 2009.

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Key considerations for R&D transformationThe essential lesson learned from the successful R&D transformations that we have supported is that it is not a simple one step program, but requires a fundamental change in the way the organization thinks and operates. Change touches each segment of the organization – and often many of its partners and other relationships - and must be implemented with a conscious understanding of its impact and potential downstream challenges. A well articulated overarching vision is critical for the different organizational units to identify with and contribute effectively to the transformation. The vision needs to be substantiated with a true business case demonstrating the value that can be derived from the process. This can make the change real for stakeholders and help create the momentum and buy-in required for success.

20 “Executing and Sustaining R&D Strategy,” Deloitte Consulting, 2008.

It is not easy for any organization to manage change and implementation at this scale. This is particularly true for R&D organizations where experience in transformation and necessary resources is often lacking. While there are many ways to address these issues, it is critical that a formal governance structure and operating model are put in place from the beginning to guide actions and decision making. Furthermore, the whole process must be supported by an effective communication strategy that engages key stakeholder groups internal and external to the organization to make certain there is program transparency and buy-in during the various phases.

Table 6. Factors that contribute to a successful R&D transformation.20

Components of a Successful R&D Overhaul

Assemble the correct team• : Involve members from all levels of the organization. Provide teams with necessary resources and mix of skills.

Communicate effectively internally• : Explain changes and rationale to employees at all levels.

Maintain leadership engagement• : Serve as an example and prevent reversion to legacy processes.


The successful R&D organization of the future will continue to be a hub of innovation and creativity. What will change is how it operates. The emphasis will focus on actively balancing resources across the pipeline to consistently align capacity with demand and create choices that will differentiate products.

We believe the principles that we have laid out here provide a framework for successful R&D transformation and a roadmap to achieve the necessary changes. The concepts and approaches apply across the spectrum of Life Sciences companies, from those with R&D budgets north of $1B down to “virtual companies” consisting of a dozen employees and several partnering relationships. Our experience suggests that those companies that attack the problem with commitment and a clear plan will be much better positioned to succeed.

Re-igniting the R&D engine

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For more information on R&D transformation please contact the authors:Jacques MulderPrincipalDeloitte Consulting LLPNew York, NY917 331 [email protected]

Ralph MarcelloSenior ManagerDeloitte Consulting LLPNew York, NY973 943 [email protected]

Tom FezzaSenior ManagerDeloitte Consulting LLPNew York, NY917 539 [email protected]

Luk LaveryManagerDeloitte Consulting LLPBoston, MA774 286 [email protected]

The authors would like to thank the following contributors:Matthew Hefner, Joel Rosenzweig, Ted Keysor

re-igniting the r&d engine in a constrained environment

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