Enhancing productivity in biopharmaceutical R&D · Enhancing productivity in biopharmaceutical R&D 3 Recent discussions and publications have focused on improving the R&D operating

Enhancing productivity in biopharmaceutical R&D

Productivity is a fundamental concept in R&D

Productivity is an important issue for any R&D operation, but in the biopharmaceutical industry it is particularly difficult to predict or control due to the risk, cost and cycle times associated with development of new medicines. It is further complicated by the influence of multiple regulatory authorities with continuously evolving requirements. Consequently, it is difficult to link simplistic measures of productivity that are often referenced (such as new molecular entities – NMEs - per year or peak sales / retrospective R&D spend), to specific aspects that could be changed in order to sustainably improve performance.

To proactively increase productivity in biopharmaceutical R&D we must focus as much as possible on drivers of productivity that can be directly influenced by R&D leaders. We propose 5 dimensions that we believe, if enhanced, will in their entirety significantly improve productivity in R&D - as defined by any reasonable measure.

Several definitions of productivity have been proposedWhile productivity appears to be simple to define – the mathematical quotient of outputs divided by inputs – the reality is far more nuanced. In biopharmaceutical R&D the long medicine development cycle time (an average of 14 years across all therapeutic areas1) results in a significant delay between action and effect. This means it is difficult to directly link success or failure to specific strategies and activities in a learning feedback loop. The industry’s varied approaches to medicine development and the way that each individual company chooses to define R&D productivity also make it challenging to find truly comparable metrics.

Despite the lack of a common definition for productivity, it is widely understood that large increases in biopharmaceutical R&D investment have not led to equivalent increases in the value created for patients and shareholders. The costs associated with bringing a discovery candidate through to a marketed medicine have risen dramatically, leading to widespread references to a ‘productivity crisis in the industry’2, 3. Other reports suggest wide variances in R&D productivity, with some outliers showing enhanced R&D productivity in contrast with the broader industry trend 4, 5, 6.

Enhancing productivity in biopharmaceutical R&D 2

1. Paul et al, How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nature Reviews Drug Discovery 2010, Volume 92. Scannell et al, Diagnosing the decline in pharmaceutical R&D efficiency. Nature Reviews Drug Discovery 2012, Volume 11 3. DiMasi et al, Innovations in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics 2016, Volume 474. Ringel et al, What drives operational performance in clinical R&D? Nature Reviews Drug Discovery 2016, Volume 155. Schulze et al, R&D Productivity: On the Comeback Trail. Nature Reviews Drug Discovery 2014, Volume 136. Schuhmacher et al, Changing R&D models in research-based pharmaceutical companies. Journal of Translational Medicine 2016, Volume 14

The internal rate of return (IRR) likely

to be delivered by company’s late

stage pipeline

The number of FDAapprovals or global

first launches ina given year

WIP x p(TS) x V

CT x CPa

Productivity ratio =

Average peak sales

Average R&D spending

With a four year offset

Figure 1: Examples of R&D productivity definitions used across the industry

3Enhancing productivity in biopharmaceutical R&D

Recent discussions and publications have focused on improving the R&D operating model7, scientific strategy8, company behaviour4 or adoption of country-level life sciences strategy9 to drive productivity improvements. While useful, these proposals tend to be multi-faceted and can lack the ready applicability to pragmatically improve productivity.

R&D productivity must be considered in a holistic wayOver the last decade, various aspects of productivity have been analysed in depth, and tools to effect improvement in areas such as process efficiency, data management or portfolio management are now widely used. However, a more holistic approach that recognises the interdependencies of the system would enable further focus on important drivers such as the quality and speed of decision-making across the R&D organisation – a fundamental driver of productivity in a competitive environment with multiple niche opportunities and scenarios to consider.

The model in Figure 2 builds on previous thinking and is specifically intended to drive productivity from both a value and an efficiency perspective.

More successful R&D programs

Fuelled byeffective decision

making

Maximum value to all external stakeholders

Minimum cycle times

Optimal R&D spend

Effective QMS

Delivering

brought to the market in

and

supported by an

Figure 2: Drivers of productivity in biopharmaceutical R&D

7. Cook et al, Lessons learnt from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nature Reviews Drug Discovery 2014, Volume 138. Tollman et al, Unlocking productivity in biopharma R&D, 2016. https://www.bcg.com/publications/2016/unlocking-productivity- in-biopharmaceutical-rd-the-key-to-outperforming.aspx Accessed January 20189. Life Sciences Industry Strategy – A report to the Government from the life sciences sector. Office for life sciences, August 2017. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/650447/ LifeSciencesIndustrialStrategy_acc2.pdf (accessed May 2018)


1 & 2: Driving more successful R&D programs that deliver maximum value to external stakeholdersThe first two dimensions of the model are considered together as these two questions are often addressed by the same stakeholders in the same decision-making forums and by definition should be interdependent decision-making criteria. The quality and the speed of decision-making in the organisation are therefore key drivers of value across the portfolio.

In an ideal world the organisation generates as many successful programs as possible from the portfolio of options, and designs these programs in a way that maximises the value of the medicine - not only at launch, but through to its loss of patent. Value in this context is contingent on the iterative and cumulative generation of evidence that will support the medicine throughout its lifecycle and enable appropriate positioning of the medicine within the treatment landscape. A typical challenge is therefore to orient investment decisions as early as possible in R&D around long-term patient value creation rather than only around regulatory approval of a medicine. This does not diminish the importance of early termination of programs on the basis of safety and efficacy profiles.

The development process cumulatively builds the evidence which underpins the value of a medicine

Figure 3 represents the evolution of the data value chain as a program approaches its investment decision points. The program team will use internal and external input and analysis to agree the unifying value proposition for patients and hence all external stakeholders downstream. They will then develop the optimal evidence generation strategy and plans for the medicine under development, including the design and placement of pivotal clinical studies as well as the populations and markets to target. The data will be evaluated by internal governance bodies at investment point X. If successfully endorsed, the team executes the approved plans and generates further evidence. This evidence is shared with internal and external stakeholders again, and through a cumulative and iterative process evidence is built up to support the value proposition, hypotheses and proposed strategy at investment point Y.

Anticipating the future needs of external stakeholders is crucial, as those future needs must drive and guide the decision-making from as early as possible in the process. While in theory this is simple, it is also critical in a cross-functional context that insight and rationale are credible and data driven. A series of advisory boards is not sufficient; the addition of real world data and compelling evidence and insight are necessary to unite commercial, R&D and medical decision-makers around a longer-term strategy, notwithstanding potential changes along the way. It is also important to ensure that scientific advice from health authorities is proactively requested and integrated throughout the process.


Effective organisational decision-making is a key driver of value

Decision-making in R&D is an organisational capability. It is reliant on alignment and the maturity of processes and governance to ensure that data and insight inform decision-making, and that the criteria on which decisions are made reflect not only internal priorities but also the anticipated downstream needs of external stakeholders when a medicine is launched. The decision-making criteria are the key levers by which the insights and information supporting proposals are managed.

In addition, transparency of the framework and criteria will lead to more efficient and predictable decision-making i.e. decision-makers will more routinely be presented with the information they require and teams will become more attuned to what is expected and what is likely to be supported. Discussions can then focus predominantly on future value opportunities and scenario planning which is so critical in a dynamic landscape that is influenced by evolving stakeholder needs, competitor activities, emerging evidence as well as precedents and government policies.

Enabling the best decisions

With a clear framework in place, there are four key factors that must be achieved to ensure that the best decisions are made along the continuum of the evidence value chain:

• Simple governance

• Patient value creation as a key decision-making criterion

• Comprehensive and current data to support decisions

• Strategic continuity across the value chain.

Effectivedecisionmaking

PROCESSES

GOVERNANCE

AN

D C

RIT

ERIA

FRA

MEW

ORK DATA

INSIGHT

External input on requirements

e.g. regulators,payors, patients

Anticipated evidence requirements of downstream external stakeholders and decision makers must drive upstream decisions at earlier investment points

Gather outputsfrom external

decision makers

Anticipatedstakeholder

requirements at launch and beyond

Disseminateevidence to

external stakeholders

Define evidence generation strategy, plans and unifying

value proposition

Investment point X

Investment point Y

Input from external stakeholders and data sources

(e.g. regulators, payors, patients)

Input from all available internal sources

Define evidencegeneration strategy,plans, and unifyingvalue proposition

Executeplans andgenerateevidence

Develop greater internal knowledge of the

product or compound

External engagement

Internal assessment

1

1 2

2

Figure 3: Evolution of the data value chain as an R&D program approaches its investment decision points


Simplify the governance model

There are often multiple governance review meetings as R&D program teams approach investment decisions. A typical governance set-up might involve one forum for strategic endorsement, one for funding approval and a third for operational plan review and endorsement. Experience suggests that this is a time- and resource-consuming way of working which leads to a fragmented review and in turn reduces effectiveness, engagement and value creation in decision-making. The aspiration should be to simplify strategic endorsement, funding approval and operational plan review into a single approval process per therapeutic area, and to have one overarching governing body across all therapeutic areas. Inevitably this requires a review of the purpose and charters of governance bodies. Ultimately the organisation will make more timely and clearer decisions, especially as leaders can more easily see the competing priorities across the R&D programs and therefore make trade-offs as appropriate to best support the company. At this level of organisational maturity, there may even be an opportunity to move away from an annual portfolio and budget review exercise and potentially make it more ‘in stream’.

Define patient value creation as a key decision-making criterion

Alongside scientific and net present value (NPV) criteria, broader value criteria should be agreed. The patient value proposition or ‘value through the eyes of the patient’ provides a proxy indicator of value to the broader healthcare ecosystem, and it is also more orientated to the longer-term value throughout the lifetime of the medicine. This concept will not only inform the value proposition for all external stakeholders; it will also reinforce the insight and inputs required.

Figure 4: Patient value drives the healthcare ecosystem

Patient value can be considered in terms of improved patient experience, improved outcomes and better access to medicines. New innovative medicines or solutions must address a patient’s condition more effectively in this regard than currently available treatment regimens. Failure to do so runs the risk of a new medicine being developed that patients either do not want or will not use10, or where the benefits cannot be realised due to patient non-compliance11. These factors are important to consider rigorously in early stage programs, as NPV and ROI are necessarily based on sales forecasts which can be skewed (consciously or subconsciously) to what teams would like the outcome to be, rather than the actual situation post launch.

Patient

HTA proposition

Regulator proposition

Payor proposition

HCP proposition

Patient/carer proposition

Insights Evidence Guidance Service provision

UNIFYING VALUE PROPOSITIONEVIDENCE GENERATION

Payors

RegulatorsHCPs

Policy Makers

10. Schaeffer et al, Back to school: changing the subject. BioCentury 7th September 2015. http://www.biocentury.com/Data/ StaticContent/ContentFiles/090715_Cover_BTS.pdf .biocentury.com/Data/StaticContent/ContentFiles/090715_Cover_BTS.pdf (accessed January 2018)11. Brown & Bussell, Medication adherence: WHO cares. Mayo Clin Proc 2011, Volume 86


Understanding patient value also helps with ongoing medicine lifecycle management, i.e. how the medicine will continue to be differentiated after launch. This must be considered early in development. It then requires continuous learning about the medicine so that further value can be delivered to existing or new patient populations, based on unmet needs in terms of patient outcomes, as well as patient and HCP experience post launch. Specific strategies to support longer-term value creation for a medicine include:

• Segmenting patients using real-world evidence: The use of actual patient experiences andinformation help to (1) identify specific unmet patient needs; (2) create specific solutionsthat address multiple patient needs; and (3) enhance advocacy strategies andinterventions that empower patient self-management.

• Embedding patient centricity into R&D processes: Although much progress has been made(e.g. through Kinapse-, PFMD- and TransCelerate-led initiatives), actual implementation inindividual biopharmaceutical companies has been variable. In most companies there is stillscope to greatly improve the way in which medicines are co-created with adequate patientinput. One concrete approach is through a well-defined patient engagement strategy- a collaboration between the patient community and R&D teams spanning discovery,research, development, distribution and access to medicines12. This leads to better valuecreation for patients and better outcomes for the healthcare system. Though models forpatient engagement vary by company, an approach that has been gaining traction acrossthe industry is a three-part framework which includes patient insights, patient collaboration(the way patient input is integrated into medicine development), and patient enablement (howthe patient community is supported to lower barriers to enable participation13, 14).

• Innovating service packages around the medicine as well as new medicine combinationsand medicine device pairings: Ultimately, the goal is to enhance the patient experiencewith the medicine. This supports disease self-management and treatment compliance.Examples include medication technology (e.g. ingestible sensor pills, patch and iPad appsto track treatment adherence as well as other emerging technologies) and partnershipswith digital health coaching programs (e.g. Omada Heath for pre-diabetic populations).

While these types of initiatives are simple in theory, there still does not appear to be systematic adoption at scale across the industry.

12. Impact of Patient Engagement on Project Valuation, CTTI13. Capturing the Value of Patient Engagement, DIA14. Yeoman et al, Defining patient centricity with patients for patients and caregivers: a collaborative endeavour. BMJ

Innovations 2017


Maximise the availability of internal and external data

Data enables evidence-based, value-creating decision making. Whether this is through real world evidence and advanced analytics, independent internal reviews or through artificial intelligence (AI) tools and data lakes, the aim is to use credible data to reduce the influence of individual bias, assumptions and organisational politics. As achievable as this sounds, there are still some practical challenges that need to be overcome. Access to data for specific populations (e.g. many of the emerging markets) is still limited and there is the practical issue of how the data sets should be set up to allow easy curation and analysis to support decision-making. Increasingly the search functionality and understanding becomes more important than the structure and taxonomy of data sets. As the volume of data grows, further work and capabilities are required in this area. Organisations will also need to respond appropriately to changes in data privacy legislation.

Availability of internal data and information is no trivial issue either, whether this be data from clinical trials or information in previous plans. In large, complex companies it is increasingly important to shift to the management of data rather than documents. The technology systems required for this are well established and structured content management (the ability to discover, reuse, reconfigure and adapt individually authored and approved materials) is not a new concept.

Furthermore, technology-enabled information management would enable valuable retrospective analysis of decisions, i.e. an investment decision ‘audit’. This would not only improve decision-making but also highlight opportunities to repurpose existing data and information.

Figure 5: Structured content management overview

Addresses needs of a heavily regulated industry

DOCUMENTS DATA

KEY DRIVERS FOR CHANGE

ManualDocumentation

Structured ContentManagement

TechnologySupported Data

Information resides across documents and multipleinstances the same data is replicated

Robust standardized,structure and a consistent source of high qualityinformation

Semi-automated exchange of structured data within a business function

Fully-automated exchange of structured data at the enterprise level

Electronic Paper Quality & Consistency Efficiency Industry-leading

Makes it easy to deal with increasing numbers of documents and submissions

Reduces the need to manually author content or enter key information

Enables consistentinformation sharingacross the business

Supports reporting from documents where this is a legal or regulatory requirement

As an industry, biopharmaceuticals are still highly dependant on documents, both within organisations and those that are submitted to regulators and health authorities

As document volumes have increased, there is a necessity to make a transition from a primarily document-driven industry to one predicated on re-usable data

Structured Content Management provides the capability to realise this goal


Ensure strategic continuity throughout the medicine development lifecycle

It is vital that there is strategic continuity along the lifecycle of a medicine. Specific approaches need to be considered as timelines are long and the external landscape is increasingly complex:

• End-to-end budget accountability: Reference to functional ‘silos’ is common withindevelopment and, as long as functional heads continue to exist in the current form, this isinevitable to some degree. Processes, organisational constructs and governance bodies mayprevent fragmentation to a degree but the ownership of budgets is possibly the clearestdefinition of accountability in a complex matrix. One leader in control of one budget drivesend-to-end goal alignment and successful long-term planning. In R&D, this can be appliedboth at the R&D program team leader level and at the R&D governance level.

Team leader accountability through budget control drives performance and allows decisions on innovation and appropriate risk tolerance to be made by those closest to the medicine under development and the population in question, rather than layering these decisions under functional leadership.

At the R&D governance level, there is merit in aligning R&D investment decisions across the full length of the value chain under one committee. This would help to ensure a more holistic strategy based on unmet needs of the population recognising co-morbidities, the importance of integration with the development of devices, lifecycle management opportunities and the ultimate evidence needs of external stakeholders which must be the ‘true north’ throughout the R&D process.

• Truly integrated development plans: While most companies have a process and templatealready in place that creates and documents the outputs of the cross-functional R&Dprogram team, this can often be enhanced in a way that drives the desired thinkingupstream rather than predominantly focusing on the document itself. Cross-functionalco-creation of the document is important, and situations where individual plans or inputsare pasted together must be avoided. While templates themselves rarely generateenthusiasm, comparability of plans is extremely important to the functioning of aneffective review committee where trade-offs need to be made. This will also enablethe necessary focus on the quality of data and insights which underpin assumptions andproposals with the ultimate value creation goals in mind.

• Reward of teams on accurate decision-making: Through reward and recognitionsystems, many companies inadvertently promote medicine progression behaviourover value creation behaviour. As incentives are focused on moving a medicine forwardin the development process, medicines may be advanced even when their continuationis unjustified. Aligning R&D team leader progression to a career path framework thatrewards effective decision-making rather than project progression can result in greaterobjectivity.

• Optimal input into Phase 2a trial design: The cost of a late-stage medicine developmentfailure is extremely high. This could be due to tolerability or efficacy questions, orbecause the value to patients, payers and HCPs is unclear and the launch of themedicine is consequently compromised or delayed due to evidence gaps. Phase 2a trialdesign therefore needs to balance the focus on driving to the next developmentmilestone with consideration of positioning the medicine optimally - in terms ofdemonstrating the value for patients, post authorisation commitments and lifecyclemanagement.


3, 4 & 5: Reducing cycle times, optimising spend and the importance of the quality management system (QMS)Having considered the value dimensions of productivity, we must also consider the dimensions that are primarily related to operations and efficiency.

Resources will always be limited and there are inevitable opportunities to impact productivity through improvements in time, cost and quality. More traditional methodologies have been supplemented by additional approaches, some of which are shown below. While these approaches are being widely adopted, many still tend to be limited to best practices in teams rather than systematised across companies.

Figure 6: Approaches taken by the industry to manage time, cost and quality aspects of R&D

Minimum R&D cycle times

Optimal R&D spend Effective Quality Management System

Enhance R&D productivity bottom line

Identify

Manage Measure

Implementadaptiveplanning

Use innovative clinical trial

design methodologies

Utilise new databases and analytics for trial recruitment /

feasibility

Streamline data management and

review

Apply Ai to accelerate candidate discovery

Focus organisation effort on R&D programs rather than dispersed improvement efforts

Optimise current vendor contract terms with industry

Institute lessons learnt forum

Review functional and cross-functional

resourcing model

Deploy data standards

across company

Implement retrospective review, data reuse

& repackaging

Proactive sponsorship of Q & C function

Establish top down documentation framework

Adopt risk-based audit strategy

Build QMS documentation technology platform

Increasingly transformative


Minimum R&D cycle times

Notwithstanding the development strategy for a medicine, R&D cycle times can be significantly reduced by process improvement and/or by adoption of new technologies. These two aspects are naturally linked and digital technology is less likely to drive significant improvement on its own than when it is coupled with a fairly granular review of the process itself. The introduction of technology provides an opportunity to refine or indeed reengineer processes to make significant efficiency gains. Improvements may relate to faster execution through automation (often with additional quality benefits) or faster and better outcomes through access to and assimilation of large data sets.

Many approaches and use cases focus on the clinical program, where most of the budget in R&D is spent and where potential delays have the greatest impact on the development timeline. Major levers are now feasible in all companies and have been shown to significantly reduce cycle times as well as reduce the number of patients required for clinical programs. These levers include:

• Adopting methods such as adaptive trials, basket trials, virtual trials using wearabletechnology, patient input into protocol design and running patient visit simulations.

• Using better sources of data to make decisions (e.g. technology-enabled deep learning onreal world evidence, amalgamating investigator and site performance data).

• Improving internal processes (e.g. reducing clinical trial data validation and approach todata cleaning and analysis).

Future-state technologies such as digitisation, robotic process automation, machine learning and AI have the potential to further disrupt ways of working. Companies must invest smartly and tie innovation and execution activities into the same budget to help create organisational traction. The success of digital centres of excellence and digital focus groups is variable, and with a topic as broad as this, tying it to specific project team challenges is likely to produce the most demonstrable results. At the same time, initiatives and their implementation must be coordinated and sequenced centrally, otherwise there will be multiple, similar work streams running concurrently within the organisation resulting in duplication, lack of control and organisational fatigue.

Partnerships will also need to change and evolve, and the way that biopharmaceutical companies approach this will be critical. Delivery of the innovation and digital agenda must be consistently fuelled by innovative partners and best-in-breed solutions. At the most transformative end of the spectrum, companies may choose to continue to work across the R&D value chain and harness AI to help select discovery and pre-clinical medicine candidates. Alternatively they may choose to exit discovery and early-stage development entirely, relying on academia or new partners to source their future pipeline.

Optimal R&D spend

Notwithstanding the choice of programs on which investments are focused, optimising R&D spend relates largely to resourcing strategies and the decisions about what to outsource or manage in-house and in which geographies. Sourcing strategies must balance a number of factors and it could be argued that the traditional approach of outsourcing ‘non-core’ activities and executing ‘core’ activities in-house is too simplistic. There are advantages in bundling certain activities; there is a need to engage a range of innovative partners in an increasingly digital environment; the maturity of providers in certain areas is variable; and the opportunities to build flexibility into capacity management are important. However, key sourcing decisions still relate to optimal engagement of CROs to conduct trials, and the use of service providers or in-house centres located off shore to provide functional services such as regulatory submissions, safety reporting and data management, for example.


Outsourcing remains a viable and effective way of managing capacity, controlling costs, accessing capabilities, and/or improving the quality of study execution and data. The ability to outsource effectively is a capability in its own right; otherwise it can be a drain on time and resources, and compromise quality. Key requirements include:

• Understanding of the drivers and objectives for a partnership, the delivery structurethat would help achieve these, and internal capacity and capability requirements. A clearunderstanding of workload and internal benchmark performance data is also essential foreffective management of the partnership.

• Selecting the partner of ‘best fit’ for a mutually-beneficial contract, leading to continuousimprovement by both parties, supported by a well-defined operating model. Contributionto innovation is also an increasingly important factor.

• Onboarding the partner effectively to build mutual understanding, transparency,collaborative values and commitment to the partnership.

• Developing and sustaining genuine collaboration through alignment of incentives.The governance and management of providers must also avoid duplication and overlap.Often the micromanagement of partners prevents sponsors from realising the full value thata provider can offer. Confidence in what the CRO can offer will allow internal resources tofocus on other high-value activities (see Figure 7 for potential areas where partner inputcan add value).

Figure 7: Potential areas where input from partners could add value

Feasible and realistic outsourcing drivers

External perspective on core competencies

Data to quantifyoutsourcing benefits

External perspective on oversight approach

Use of vendor validated processes

Shaping technology landscape

Program strategy design

Clinical Development plans

Operational feasibility of studies

Potential areas where inputs from CROs could add value

1. Outsourcingstrategy

2. Portfoliooperations

3. Programs/ studies

Wage arbitrage is used by sponsors with captive units as well as by service providers. However, low-cost locations will continue to evolve and continuously chasing the next new low-cost location is not a sustainable strategy. The advent of new technologies will potentially result in significant savings as the ratio of headcount to workload reduces.

The adoption of data standards across the industry could also be transformative. International standards such as IDMP and GS1 for product traceability and associated tools such as SPL (Structured Product Labelling), GSRS (Global Substance Registration System) and GINAS (the Global Ingredient Archival System) will minimise and potentially eliminate workload duplication by enabling data consistency across R&D functions, clinical trial sites and manufacturing and supplies. This is expected to result in improved end-to-end transparency, preparedness for regulatory authority scrutiny, and cost-optimised customer interactions.

Effective quality management systems

Despite a focus on addressing regulated quality measures, the annual number of Warning Letters for Drugs issued by the FDA has remained relatively unchanged over the last decade15. An effective quality management system (QMS) is not only essential in enabling the business in a compliant and ethical way, but it also impacts productivity by reducing distraction and re-work and averting the possibility of reputational damage and fines. Optimising the QMS will mitigate the risk of major disruptions, and it will also underpin more risk-based approaches to quality management.

While the QMS for R&D covers an extremely broad area, improvements are often focused initially on streamlining the document management framework. In conjunction with other productivity initiatives, this is ideal if it follows cross-functional process redesign and technology adoption.

Looking ahead, the fundamental drivers remain unchangedThe healthcare ecosystem will become more competitive and complex, the regulatory environment will become more stringent, in-licensing and innovative partnerships will become more prominent, and digital technology will transform the way that R&D is conducted. However, productivity will remain a priority in order to generate greater value for shareholders and for patients.

We continue to see both incremental and transformational opportunities to improve productivity in R&D, but all require a clear objective as well as a holistic appreciation of the interdependencies across the organisation. The quality and speed of decision-making at investment points for a product continue to be key drivers of productivity. In addition, emerging opportunities to improve productivity on a program team basis must not be constrained to pockets of best practice, but must instead be adopted and industrialised across a company.

15. Kinapse analysis of FDA Summary of Inspectional Observations by Fiscal Year. https://www.fda.gov/ICECI/Inspections/ ucm250720.htm (accessed January 2018)



About Kinapse Kinapse is recognised as a leading advisory and operational services provider to the global Life Sciences industry. Founded by professionals from the biopharmaceutical sector, the company provides its services across the full R&D and commercialisation life-cycle, collaborating with its clients to improve the lives of patients, through a unique Advise – Build – Operate delivery model.

19 of the global top 25 life sciences companies rely on the breadth of Kinapse’s world class advisory and operational services to analyse, implement and perform a wide range of projects and programs across global markets, delivering quantifiable business benefits and operational success.

Headquartered in the UK, Kinapse has over 700 staff located in Europe, India and USA.

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Enhancing productivity in biopharmaceutical R&D · Enhancing productivity in biopharmaceutical R&D 3 Recent discussions and publications have focused on improving the R&D operating