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1、Reinventing R&D in the age of AIHow intelligent technologies are transforming the Biopharma IndustryContentsPage 3Preface Page 5About the researchPage 12Reinventing the R&D organization Page 30ConclusionPage 6The R&D opportunityReinventing R&D in the age of AI2About the researchThe R&D opportunityCo

2、ntentsConclusionPrefaceReinventing the R&D organizationR&D leaders across the industry are unanimous about the challenges ahead,voicing concerns like:I now have to choose between investing in an asset(a clinical trial or external innovation)vs.an emerging technology.I now have to take bigger risks t

3、o scale new technologies on high value assets because the upside is significant.I now have to consider myriad factors(such as drug access,pricing,reimbursement and manufacturing scale challenges)in conjunction with choices around modality before an asset goes into first-in-human studies.I now have t

4、o make investments that will create value in a short timescale so that I can fund essential technology and capability investments that will be critical to R&D reinvention and pay off in the longer term.PrefaceLife-changing interventions that have previously been pipedreams are now within reach for t

5、he biopharma industry,made possible through an increasingly sophisticated understanding of human biology coupled with unparalleled advances in technology.On the other hand,the industrys research and development(R&D)productivity has remained largely unchanged for a decade,while the complexities of bi

6、opharmas competitive market have continued to evolve.While intelligent technologies promise to accelerate scientific advances and address some of the fundamental R&D challenges that existed for a decade,they require capital investment and a thoughtful approach.Creating therapies from basic science a

7、nd making the best use of advances in technology requires companies to reinvent their R&D organization.Biopharma companies that embrace technology,reinvent their workforces and refocus their organizational priorities will improve R&D efficiency and patient outcomes,and ultimately secure growth in an

8、 increasingly challenging macro environment.3ContentsReinventing R&D in the age of AIPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionAlex BlumbergPharmaceutical Research Lead,Accenture ResearchAuthorsAcknowledgementsTom LehmannManaging Director,Global Lead R&D A

9、ccenture Life SciencesSelen Karaca-GriffinThought Leadership Principal Director Products and Life Sciences,Accenture Research Kailash SwarnaManaging Director,Global Research and Clinical Lead,Accenture Life SciencesThe authors would like to thank the following people for their contributions:Barry He

10、avey for his expertise on developing and manufacturing new types of treatments.Akash Trivedi for his expertise on optimizing clinical trials.Joanna Lisiecka for her support on the AI-mediated drug discovery company analyses.Nicole Crane and Alexandra Brown for their support on the development of the

11、 R&D executive survey.Aran Bahl and Biagio Paoletta for their support on gathering Accenture Subject Matter Expert input.Nicole ParaggioManaging Director,Accenture Strategy Life Sciences4Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizat

12、ionConclusionAbout the researchIn 2021,Accentures“Billions to Millions:Improving R&D Productivity”highlighted the escalating costs and extended timelines of drug development amid profitability challenges.At that time,technologies like cloud computing and AI were just beginning to integrate into biop

13、harma R&D,while intelligent technologies such as generative AI and large language models were in their infancy.Today,these advances,combined with a deeper understanding of biology,have evolved biopharma R&D.Our latest research goes beyond the scope of“Billions to Millions,”which focused solely on ti

14、me,success rates,and potential cost savings.This comprehensive study incorporates factors such as enhanced revenue from extended commercialization periods.Our research methodology was threefold:1.We surveyed 75 R&D executives from the top 20 biopharma companies by annual revenue to understand the cu

15、rrent landscape and the direction of change.2.We conducted in-depth interviews with industry subject matter experts and used our internal expertise.3.We developed custom analyses,including a model to quantify the uplift in future potential and an index to better understand the contributors to R&D su

16、ccess.Our findings reveal that companies outperforming their peers in terms of relative cycle times and probability of technical and regulatory success(PTRS)rates generally experienced larger growth in enterprise values.However,these improved success rates and cycle times are no longer sufficient to

17、 generate future commercial value.To achieve that uplift,careful consideration of future potential and value of the assets must be done at earlier stages than traditional value discussions.5Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organi

18、zationConclusionReinventing R&D in the age of AIThe R&D opportunity6Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusion6Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D o

19、rganizationConclusionAn unprecedented understanding of human biology coupled with new advances in technology are allowing the biopharma industry to create life changing interventions we could only dream of until now.Personalized cell therapies are treating previously intractable cancers and showing

20、potential for managing devastating autoimmune conditions.1 Targeted treatments for ultra-rare diseases and genetic conditions with high unmet needs are emerging through antisense oligonucleotides(ASOs)and gene therapies.2 Additionally,precision medicines developed through antibody drug conjugates(AD

21、Cs)and radioligand therapy have become a reality.However,the industrys R&D productivity has remained largely unchanged for a decade,while the complexities of biopharmas competitive market have continued to evolve.Over half a trillion dollars in revenues will likely be at risk by the end of the decad

22、e,dialing up pressure on biopharma to bring new medicines to marketfaster,at lower cost,and without compromising quality.3,4,5 This is partially due to market forces such as the influence of pharmacy benefit managers and insurance companies on reimbursement,access and pricing.Other factors include g

23、overnment policies like the Inflation Reduction Act in the U.S,and loss of exclusivity on major revenue sources.Technologies such as artificial intelligence(AI),generative AI,machine learning,and next-generation computing are pushing science forward at a pace never seen before,while simultaneously e

24、nabling better business and patient outcomes.Our research indicates that harnessing technology for these outcomes has become the top priority for biopharma CEOs in 2024.All biopharma CEOs agree that R&D presents the most significant opportunity within the biopharma value chain when it comes to creat

25、ing value with intelligent technologies.7Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionIn this new scientific age,huge volumes of high-quality human data from sources such as global biobanks and electronic medical record

26、s(EMR)can revolutionize the drug development process,from target selection to clinical trial design and patient recruitment.Our analysis shows that if intelligent technologies are used at scale and workflows are reinvented appropriately to reduce the cost of failure and shorten discovery and develop

27、ment timelines,companies can bring a new medicine to market four years faster,earning an extra$2 billion per successful drug.6 Typically,the cost of bringing a successful medicine to market is between$2.6 billion and$6.7 billion(including the cost of capital and cost of failure)depending on therapeu

28、tic area,treatment modality and disease complexity.7 We expect this cost to be cut by 3545%using intelligent technologies.8 Executing a reinvention strategy will require dedicating 810%of the annual R&D budget to digital transformation initiatives over the next five year.9 For a$5 billion R&D budget

29、,this equates to$400500 million per year.Its a substantial investment.However,the potential value is even more significant.Even a 1%improvement in clinical success rates would yield hundreds of millions in additional revenue.Key risks include technological,talent,cultural and regulatory complexities

30、.The AI and digital technologies being bet on are still maturing,and there will inevitably be failures and setbacks.To mitigate this,maintaining a balanced portfolio of initiatives by pursuing the right mix of near-term,high-confidence opportunities and longer-term,higher-risk/reward projects is cru

31、cial.If intelligent technologies are used at scale and workflows are reinvented appropriately,companies can bring a new medicine to market four years faster,earning an extra$2 billion per successful drug.Its time to reinvent biopharma R&D.Combined with rapidly advancing technologies,the expected rev

32、enue gap weve described is triggering an intelligent technology wave and creating a palpable urgency to reinvent biopharma R&D.8Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionAn evolved definition of success:In this new e

33、ra of R&D where science and intelligent technologies are rapidly converging,it is essential to reevaluate how value is created and R&D success is defined.(see Figure 1)Measure 1:Probability of technical and regulatory success(PTRS):Unsurprisingly,our recent survey confirms that R&D organizations hav

34、e predominately focused on the PTRS measure.This makes sense,as slight improvements in PTRS lead to a significant reduction in the cost of failures and an uptick in R&D productivity.Measure 2:Cycle times:It is crucial to replenish revenue gaps caused by loss of exclusivity,continuously declining net

35、 prices and government pressure on drug pricing.This acceleration is necessary to keep pace with the competitive market and ensure sustained revenue growth.Based on our survey,this measure is currently underappreciated despite the CEO focus on accelerating R&D.Measure 3:Future potential:Companies mu

36、st carefully consider both commercial and manufacturing aspects of drug candidates early on to select drug candidates with the highest future potential.This is more critical now than ever due to 1)increasing new modalities and the associated manufacturing challenges,2)the economic implications of pu

37、rsuing different modalities and the impact of the Inflation Reduction Act in the US and 3)an increasingly complex access and reimbursement environment requiring early alignment on the clinical and economic value of the product.Figure 1Measure 1:Still one of the most important drivers of enterprise v

38、alue.Needs to be reinforced via selection of high-quality molecules and improving clinical trial design Measure 3:Future potential is one of the biggest drivers of enterprise value.Probability of commercial and manufacturing success needs to be evaluated early in the processMeasure 2:Currently under

39、appreciated despite CEO focus.Only 20%rank it as a top three measure of success Probability of Technical and Regulatory Success(PTRS)Cycle Times Future Potential R&D successR&D organizations must consider three important value levers when defining success.Figure 1:Historically,PTRS has been the prim

40、ary measure of R&D success.While PTRS remains important,there also needs to be a focus on improving speed and future commercial potential.9Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionThe R&D Success IndexOur R&D Succes

41、s Index is a retrospective analysis over the past 10 years that illustrates part of this new model,considering the impact of PTRS and cycle times on enterprise value(Figure 2).10 Notably,companies that outperform their peers in terms of relative cycle times and PTRS have generally experienced larger

42、 growth in enterprise value(over the past five years).Figure 2Large Biopharma-Relative PTRS and Cycle Times vs.Change in Enterprise Value-2.5-0.5-1.50.51.5-2.00-1.01.02.0245678310-1-2-3-4Relative PTRS(%)*Relative Cycle Time(Years)*Positive Change Negative Change(Bubble Size:$100b EV change over last

43、 5 years)Figure 2:The X-axis shows how quickly companies drugs move through the pipeline and the Y-axis shows companies success rates,both of which are relative to their peers and are weighted by company pipeline phases and therapeutic categories.Those farther to the right are faster than expected a

44、nd those farther up have better success rates than expected.This data was derived from PharmaPremia and CapIQ.Bubble Size shows the change in enterprise value over the past five years.Source:Accenture Research.*PTRS and Cycle Times over the last 10 years of the top biopharma companies compared to th

45、e peer group average,weighted by phase and therapeutic category to remove variability.10Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionIntelligent technologies offer all companies an opportunity to enhance PTRS and cycle

46、times further.Additionally,companies must now develop their understanding of the future potential of their pipeline assets pre-IND.To be successful in the future,R&D organizations should adopt a comprehensive approach,prioritizing four areas of reinvention supported by three foundational building bl

47、ocks.This strategy can improve R&D productivity and accelerate the cost-effective delivery of innovative therapies to the market.The rest of this report focuses first on these four key capabilities and,second,on the foundational building blocks that underpin them,setting out a roadmap for biopharma

48、companies to thrive in this rapidly evolving landscape.11Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionReinventing the R&D organization12Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunit

49、yReinventing the R&D organizationConclusion12Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionTo address the challenges faced by CEOs,Heads of R&D and CTOs,reinventing the R&D organization requires prioritizing two overarch

50、ing tasks:1/Identifying reinvention opportunitiesFour key capabilities for significant focus and capital investment to drive competitive advantage for prioritized assets.1.1 Using AI for discovery1.2 Developing and manufacturing new types of treatments1.3 Optimizing clinical trials1.4 Dynamic portfo

51、lio management2/Addressing the imperatives that power reinventionThree foundational building blocks that underpin and guide reinvention strategy.2.1 A secure,AI-ready digital core2.2 Talent and leadership2.3 A culture of continuous reinvention13Reinventing R&D in the age of AIContentsPrefaceAbout th

52、e researchThe R&D opportunityReinventing the R&D organizationConclusionReinventing R&D in the age of AI13ContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionFour key capabilities for significant focus and capital investment to drive competitive advantage for

53、 prioritized assets.Turning new knowledge and technologies into a competitive advantage and future success requires new thinking and new processes.Our experience tells us that the top-performing companies stand out because they invest heavily in four reinvention opportunities.According to our survey

54、,the first three are already viewed as crucial for R&D success;using AI for discovery,developing and manufacturing new types of treatments,and optimizing clinical trials.However,success will be further enhanced by dedicating more focus to a fourth area;dynamic portfolio management.Identifying reinve

55、ntion opportunities1.14Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionReinventing R&D in the age of AI1.1 Using AI for discovery 64%rank new drug discovery methods among the top three most important capabilities for drivi

56、ng future success.61%say it has been a top area of investment.72%rank their capability as mature,but our research finds that while most companies are experimenting with these technologies,they rely on partnerships to scale their use.Source:Accentures Biopharma R&D Executive Survey11AI enables a new

57、way of working,where multidisciplinary teams of computational and scientific experts collaborate,using diverse data sets that are managed and enhanced by AI and machine learning.This makes it possible to identify targets that are more likely to succeed in the clinic and use predictive modeling for l

58、ead identification and optimization.With an AI-led discovery strategy,companies can reduce discovery cycle times by two-thirds and design therapies that have been impossible until now.AI-driven insights also help generate robust,comprehensive pre-clinical data sets to support trials,improving the ch

59、ances of success.Companies must build cross-functional teams and equip them with diverse,well governed data sets to enable efficient target identification and validation,lead optimization and molecule design.Through simulating experiments in silico,scientists can discover new links between genes and

60、 diseases.Further,integrating AI platforms into the scientific operating model will allow companies to create a fully scaled R&D portfolio enhanced by AI-led discovery.Critical to all initiatives that deploy AI and generative AI is a dedication to applying responsible AI principles that manage risks

61、 of data set bias and other unintended consequences.15Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionLeaders must facilitate cross-functional data governance,management,connectivity and a“FAIRification”strategy,which refe

62、rs to data and research that is Findable,Accessible,Interoperable and Reusable.They must also work to build a robust partnership ecosystem that supports niche,highly differentiated capabilities and the strategic sourcing of new data sets to generate insights.All of this comes with a set of challenge

63、s,however.For instance,while AI-mediated drug discovery is growing in importance,the industry doesnt yet have enough people with the right skills to drive progress forward.12 And although quality data is fundamental to developing these models,many companies do not have an AI-enabled,strong digital c

64、ore that allows them to make best use of the data they have,or to capture new data from clinical trials and lab notes.There is a symbiotic relationship between nimble AI-mediated drug discovery companies and large biopharma organizations.These companies excel in developing models and architectures,a

65、nd attracting top AI talent,while biopharma companies have extensive therapeutic area expertise,pipelines and vast amounts of data necessary for creating the most effective models.Collaboration between them is both logical and opportunistic,presenting significant potential for advancing innovation a

66、nd enhancing outcomes.16Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionThe AI-mediated drug discovery landscape continues to grow Defining AI-Mediated Drug Discovery CompaniesAccenture Research conducted an analysis of th

67、e AI-mediated drug discovery landscape.Assets in this analysis include those where the AI-mediated drug discovery company was either the lead or partner.We recognize that there is a considerable amount of variability in the type and sophistication of AI/ML methods used across these companies and a d

68、isagreement on how to define what an AI discovered drug is.Therefore,companies were classified as such if they identified or marketed themselves as leveraging AI in the following areas:lead design and optimization target prediction and identification drug repurposing biomarker identification 66%6%3%

69、9%Small MoleculesMonoclonal Antibodies&PeptidesVaccinesUnknownCell&Gene TherapiesOther OncologyInfectious Disease Central Nervous SystemGastrointestinalMetabolic DisordersGenetic Disorders48%13%9%7%7%5%11%16%(73)(53)(16)(11)(8)(8)(5)(12)(11)(7)(3)(19)Source:Accenture Research 2024 leveraging GlobalD

70、ata Source:Accenture Research 2024 leveraging GlobalData Across all modalities,the focus remains on oncology,accounting for nearly half of the assets in clinical development with 53,followed by infectious disease and CNS with 16 and 11,respectively.Small molecules represent two-thirds of the drugs i

71、n clinical development,with 73 assets,followed by monoclonal antibodies and peptides with 19 assets.Of the small molecules,30 are focused on oncology and 9 are in Central Nervous System(CNS).2023202220202019202120,00015,00010,0005,00002024*Source:Accenture Research 2024 leveraging GlobalData and Cit

72、eline data *2024 Data as of 5/24/2024 Large biopharma companies have struck deals potentially worth over$55b since 2019.Deals for 2024 are on pace to eclipse the last two years.Deal Value($M USD)Marketed Phase II Phase IPhase III 6050403020100Source:Accenture Research 2024 leveraging GlobalData data

73、 and companies websites The majority of assets are in phase I,accounting for 59 of 113.Three assets have reached the market so far.Asset Count 17Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusion1.2 Developing and manufactur

74、ing new types of treatments 69%say that this capability is key to achieving success.75%rank it as a top 3 investment over the past 12 months.92%are focused on creating platforms for the rapid development of next-generation therapies.61%note that the establishment of new modalities and the ability to

75、 support new modalities is most critical.Source:Accentures Biopharma R&D Executive Survey14Developing a consistent and scalable production process for new new medicines is critical once they reach clinical trials.For instance,shortages of radioligand therapies have caused clinical trial delays and a

76、ccess issues for patients already on therapy.As R&D advances,producing more specific and complex drugs(like multi-specific antibodies)leads to more intricate recipes and assays.The increasing speed of clinical trials leaves less time for optimizing and scaling these complex recipes,impacting a drugs

77、 commercial potential.With sales of new modalities projected to grow from$22 billion in 2023 to$152 billion by 203015,the ability to quickly develop and scale production becomes even more crucial.Figure 3Advanced Modality Growth 20272026202420232025120160140100806040200202820292030+32%CAGRWW Revenue

78、s($B)Source:Accenture Research 2024 leveraging Evaluate Pharma data18Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionTo meet this demand,advanced techniques such as computational fluid dynamics must be applied more upstrea

79、m.These methods enhance understanding of the biology and chemistry needed for producing and purifying active pharmaceutical ingredients(APIs).Leveraging increased computational power allows for in silico simulations on producer cell lines for gene therapy or stem cells,reducing the need for wet-lab

80、experiments.This results in optimized recipes with higher yields and consistent processes,ultimately streamlining recipe development and scaling.By reducing delays,clinical testing and commercial availability can be accelerated,ensuring that groundbreaking treatments reach patients faster.The curren

81、t operating model presents another opportunity for improvement.Traditionally,process development teams reside in R&D,while Manufacturing Science and Technology(MSAT)teams sit within commercial manufacturing.This division hinders collaboration and knowledge sharing,leading to inefficiencies and overe

82、xtension of specialized teams during critical development phases.Integrating recipe development within manufacturingrather than keeping it siloed in R&Dcould significantly enhance collaboration and efficiency.A unified Development and Manufacturing team focused on recipe development,scaling,optimiza

83、tion and troubleshooting would provide full lifecycle ownership,better knowledge management,and reduce the likelihood of recipe issues.Tech transfer is another critical phase,vulnerable to problems particularly when manufacturing processes shift between teams or sites.This transition is fraught with

84、 risks at all stages of development,but its especially challenging when moving from clinical to commercial scale.Effective tech transfer requires continuous communication,standardized plans,and clear protocols,including thorough risk assessments and quality control strategies.Prioritizing tech trans

85、fer will speed up development and commercialization,ensuring smoother transitions and reducing the strain on MSAT teams.Outsourcing manufacturing to contract development and manufacturing organizations(CDMOs)is increasingly common,projected to rise from 28%in 2023 to 37%in 203016,with 7080%of ADC de

86、velopment now outsourced.17 While outsourcing provides flexibility and access to specialized equipment,it also introduces risks such as dependence on specific vendors and reduced control over the process.Building strong relationships with CDMOs,maintaining some degree of independence,and having robu

87、st MSAT teams can effectively manage these risks.Strategic in/outsourcing decisions and strong supplier relationships can enhance operational agility,prevent material shortages and ensure timely release of commercial batches.Ultimately,the biopharma industry must adapt to the growing complexity of d

88、rug development and manufacturing.By investing in intelligent technologies to streamline recipe development,adjusting operating models to integrate recipe development within manufacturing,prioritizing tech transfer and making intentional in/outsourcing decisions,the industry can enhance efficiency,r

89、educe risks and ensure the timely delivery of new treatments to the market.Taking these steps very early in both drug design and dynamic portfolio management decisions is crucial for keeping pace with the rapid advancements in drug development and ensuring that innovative treatments are available to

90、 patients when they need them most.19Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusion1.3 Optimizing clinical trials 60%rank clinical trial optimization among the top three most important capabilities for driving future suc

91、cess.68%say it has been a top area of investment.Source:Accentures Biopharma R&D Executive Survey18As the number of clinical trials for smaller,more focused indications increases(clinical trials for rare diseases have increased 77%from over 600 in 2017 to nearly 1,100 in 202319),biopharma companies

92、must design trials that reduce patient burden and attract participants.They must adapt to changing healthcare dimensions and scientific advancements while managing the complexity of large trials in areas like cardiovascular and metabolic diseases.Biopharma companies targeting scientifically challeng

93、ing diseases face extensive procedures,numerous endpoints,and multiple subgroups to analyze.Protocols often exceed 150 pages with intricate requirements,making trials burdensome and sometimes unrealistic.Additionally,the global expansion of trialsaimed at accessing diverse patient populations and re

94、ducing costsadds significant logistical complexity.Stricter regulatory requirements,while essential for patient safety,also increase the cost and duration of trials.Patient recruitment is a major bottleneck,causing delays for nearly 80%of trials.20 Outdated technology exacerbates the problem,as core

95、 technologies for executing clinical trials have not fundamentally improved in two decades,leading to siloed data and manual processes.Economic factors also contribute to high costs,including rising costs at clinical trial sites,higher salaries for clinical research associates(CRAs)and project manag

96、ers,increased costs from contract research organizations(CROs),and macroeconomic impacts like regulatory changes and inflation.Biopharma companies must urgently accelerate clinical programs to minimize impending revenue losses from the expiration of drug patents,market forces and governmental polici

97、es.As patents expire,the loss of exclusivity threatens revenues,making it critical to bring new therapies to market faster.Also,for those companies with an increasing number of molecules resulting from enhanced research productivity and a higher PTRS,a pipeline bottleneck can develop with more molec

98、ules vying for development than there is capacity or budget to handle.Meeting these challenges demands a comprehensive strategy focused on clinical trial design and optimization.This includes establishing a digital transformation office to drive strategy and coordinate initiatives;launching a major

99、data infrastructure program to enable seamless,secure data flow across R&D;developing an enterprise AI platform for shared tools,algorithms and compute infrastructure;implementing a comprehensive master data management strategy to ensure data quality,consistency and governability;implementing an end

100、-to-end digital data flow(see Figure 4);and creating an AI governance board to oversee responsible AI policies.20Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionDigital data flow describes the thread of data that connects

101、all aspects of a clinical trial,from the Integrated Evidence Plan to regulatory submission.This thread contains the context required for automating and augmenting clinical development.As a result,organizations can conduct simulations on study designs(including eligibility criteria,endpoint rationali

102、zation,and assessments),automate clinical IT system builds(such as electronic data capture and clinical databases),perform appropriate SDTM transformations,and generate regulatory documentation.Digital data flow ensures seamless integration,standardized procedures,and accelerated progress from proto

103、col inception to study completion.Figure 4Regulatory PlatformStatistical Platform AI enabled protocol design&trial planning Real-world data-based simulation Real-time probability of technical and regulatory success(PTRS)calculations Accelerate initiation of study start up Start up workflow orchestra

104、tion and automation Data-driven patient identification and site selection Direct-to-Patient engagement and communications Direct-to-Patient trial execution and data acquisition Real-time trial adherence and compliance interventions Clinical Research as a Care Option workflows Protocol-based Care Pla

105、n Data collection/direct data capture of clinical/treatment data Clinical trial data acquisition,cleansing,and management Clinical trial operations management Site-facing portals for data acquisition AI enabled SDTM and ADaM transformations Near-Real Time data processing and data cuts AI enabled sta

106、tistical programing Automated Regulatory Document Authoring Ai enabled regulatory intelligence Real-time regulatory information managementdigital data flow creates dynamic platforms that streamline clinical development operations and continuously improve through a feedback loopClinical Trial StagePl

107、atforms used during a trialFuture capabilities enabled by digital dataflowHistorical Data AnalysisReal World DataOperational DataPatient InsightsWrite Once,read many capabilities for cross-functional collaborationEnd-to-end traceability of dataEase of collaboration across vendors and partners Intell

108、igent automation of processData-driven,contextual insightsValue PotentialClinical Trial PlatformSite Platform*Study Startup Platform Protocol Design Platform Patient Platform Study and Protocol DesignTrial ExecutionBy constantly integrating new dataAnalysisStudy StartupSubmission*The Electronic Medi

109、cal Record system is the platform for Care Delivery/Medical facilitiesAdvanced analytics and predictive analyticsFlexibility to scale,adapt,plug and play21Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionHigh-impact AI proj

110、ects in areas like protocol optimization,patient-trial matching and real-time safety monitoring can make a significant difference.Redesigning processes to embed AI and automation across the clinical development value chain;piloting AI-native trial designs that are faster,more adaptive and more patie

111、nt-centric;and establishing a personalized medicine initiative to use AI and multi-modal data to match patients to therapies can all contribute to improvements.Scaling successful AI pilots into full adoption with robust processes for model validation,monitoring and continuous improvement,can deliver

112、 up to a 30%reduction in nonperforming clinical sites and reduce costs by up to 15%,with study-time improvements of up to 18%in certain trials.21The future of clinical trials will be data-driven,digitally enabled and AI-powered.Technology has the potential to transform clinical trials by optimizing

113、protocol design,identifying the right patients and sites,monitoring risks and analyzing safety signals.However,realizing this potential requires overcoming challenges such as data accessibility,algorithmic bias,explainability,regulatory acceptance,privacy and security,and organizational acceptance.T

114、his is an opportunity to fundamentally transform how clinical trials are designed and conducted.While the road ahead will demand significant investment,unwavering focus and a willingness to disrupt long-standing norms,the potential rewardsfor patients,for business,and for societyare immense.Organiza

115、tions that commit will be prepared for the future and positioned as leaders in the digital age of medicine.22Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusion1.4 Dynamic portfolio management 32%list it as a top capability f

116、or driving future success.35%say it has been a top area of investment.73%of companies rank their capability as mature,but our research reveals significant gaps.Source:Accentures Biopharma R&D Executive Survey22Traditionally,biopharma portfolio prioritization decisions focused on discussions of asset

117、s within a therapeutic area,which ensured a sound strategy but also led to isolated decision-making.Portfolio prioritization discussions are now becoming more comprehensive to encompass bio-platforms,new modalities,AI capabilities,digital health solutions and economic factors like reimbursement path

118、ways,value propositions and evidence generation plans.This shift necessitates a dynamic approach that continuously integrates new data to make informed decisions.Adopting dynamic portfolio management is crucial for future success,especially in an environment of budget restrictions and market fluctua

119、tions.Companies need to be agile and reprioritize their pipelines effectively.C-suite executives face tough decisions on whether to invest in strategic capabilities and technologiessuch as generative AIor drug programs and platforms.Currently,half of C-suite executives do not consider intelligent te

120、chnologies as part of their overall capital allocation decisions.23 By appropriately prioritizing investments,companies can significantly improve efficiency,success rates and trial times,even though these investments may limit funds available for drug programs.Despite current efforts,commercial risk

121、 assessment remains a significant gap in portfolio prioritization across many companies.Biopharma companies often evaluate a drugs commercial potential around phase II or the proof-of-concept stage,using net present value and a mix of epidemiological and real-world data.However,experts we spoke with

122、 suggest starting this evaluation earlier during the creation of the target product profile,integrating access and pricing considerations with market share and epidemiological datawhich often remain siloedto avoid an incomplete understanding of commercial potential.Emerging technologies promise to e

123、nable advanced“what if”analyses with sensitivity models,significantly improving assessments.Using extensive data from real-world evidence,epidemiological studies and market research,these technologies make commercial risk assessments more accurate,enabling companies to make more informed decisions a

124、nd balance immediate pipeline needs with long-term technological investments.23Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionAdditionally,companies must take a holistic approach to financial portfolio management,bringing

125、 together both drug project(direct spend)and non-drug project(indirect spend)considerations.Balancing capital expenditures and operational expenses strategically is crucial to supporting long-term growth and innovation.For example,investing in capabilities to accelerate specific high-priority progra

126、ms within the portfolio can ensure that investments are aligned with strategic goals and deliver maximum impact.And given how quickly both pipeline competitive landscapes and technology advancements are shifting,the status quo of quarterly portfolio assessments is insufficient.Integrating external a

127、ssets into the portfolio management process is another challenge.The industry needs to streamline external scouting efforts and ensure that external opportunities are seamlessly integrated into the portfolio.This requires a more logical and consistent application of external data sets to assess new

128、product portfolio prioritization.Additionally,the lack of an R&D-wide data platform to combine and analyze disparate data sets,including real-world evidence,hinders the ability to make successful PTRS predictions.In addition,most R&D organizations have not digitally captured their historical decisio

129、ns,assumptions and outcomes to augment their future decisions.They continue to rely heavily on instincts and portfolio decision-making based on traditional approaches.The implementation of AI and other advanced technologies is still evolving.While there are promising developments in drug discovery a

130、nd clinical trial design,the application of AI for portfolio optimization and commercial calculations remains in its early stages.Nonetheless,these technologies hold significant potential to improve decision-making processes and overall efficiency,improving commercial potential.To conclude,biopharma

131、 companies should consider these integrated strategies to address the challenges and opportunities:broadening portfolio management to include diverse elements like bio-platforms and AI-driven discovery;adopting dynamic portfolio management for agility;prioritizing technology investments for efficien

132、cy;enhancing commercial risk assessments with advanced AI tools;integrating access and pricing considerations early;streamlining decision-making processes;and taking a holistic approach to financial management.We need to be more dynamic.We need to continue that shift and better optimize the use of r

133、eal-world data.R&D VP,Top 10 Biopharma Company24Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionThree foundational building blocks that underpin and guide reinvention strategy.Addressing the imperatives that power reinvent

134、ionFuture success requires a degree of reinvention for biopharma companies.For their reinvention to succeed,they must be properly supported.The right talent and working processes will drive progress forward efficiently and cohesively.A culture of continuous reinvention nurtures open minds and is imp

135、ortant for enterprise agility and adaptability.And a secure,AI-ready digital core that is fueled by data and supported by a robust technology ecosystem will make the previously impossible a reality.2.25Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing th

136、e R&D organizationConclusionReinventing R&D in the age of AI2.1 A secure,AI-ready digital core 87%say that AI and machine learning are imperative to their organizations success.65%of leaders say that using the right tools and technologies to improve productivity is most important for R&D success,but

137、 only 55%of organizations make it a priority.32%stated that generative AI has been highly or fully adopted for both medical and regulatory affairs,versus 93%for research and discovery and 89%for clinical development.Weve seen companies who embrace a tech-enabled innovation philosophy experience 6.5X

138、 faster growth,2.2X more profitability and 4.2X more cost efficiency.25 Source:Accentures Biopharma R&D Executive Survey24 To take advantage of the latest analytical tools and intelligent technologies,companies need a strong,modern,highly interoperable data and AI foundation,supported by an agile di

139、gital infrastructure.This flexibility will enable the use of both general models accessible to all employees and specialized models for specific tasks.Essential elements of this framework include security,a modern data platform,a model switchboard,domain expertise,and a commitment to responsible AI.

140、A robust digital core brings numerous benefits such as the ability to implement next-generation AI models,streamline operations,and reduce data silos across the organization.As R&D organizations gather more data,building data fabrics and warehouses becomes increasingly important.This data serves bot

141、h internal stakeholders and external partners,especially science-tech companies focused on AI-enabled drug discovery.Generative AI also allows for the creation of synthetic data using the vast amounts collected daily from all areas of R&D.While these ideas arent entirely new,they can now be executed

142、 at incredible scale and speed,thanks to modern digital and AI platforms.Multimodal data types like voice,video,and unstructured text from labs or clinical trials can now be quickly analyzed,opening up a new range of applications.Our report,“Reinventing Life Sciences in the Age of generative AI,expl

143、ores digital core strategy for the biopharma industry in more detail.A robust digital core brings numerous benefits such as the ability to implement next-generation AI models,streamline operations,and reduce data silos across the organization.26Reinventing R&D in the age of AIContentsPrefaceAbout th

144、e researchThe R&D opportunityReinventing the R&D organizationConclusion2.2 Talent and leadership 31%of respondents say their organization ranks the development of and investment in talent as a top three priority for success.100%say their R&D organization is focused on developing AI-and machine learn

145、ing-literate talent.Source:Accentures Biopharma R&D Executive Survey26 Transforming talent is important.Launching an AI/Digital Talent Strategy to assess gaps,upskill existing staff and acquire key talent through hiring and partnerships can drive reinvention.Strategic partnerships with leading acade

146、mic institutions and tech companies can provide access to cutting-edge research and innovation,while a comprehensive change management program can build awareness,engagement,and adoption of the digital transformation agenda.The impact of intelligent technologies on talent in core R&D functions is tr

147、ansforming roles:traditional biology roles have evolved into separate biology and data science roles;lab technicians are now focusing more on automation and robotics rather than routine tests;and protein scientists have shifted from creating new molecules to understanding diseases and their mechanis

148、ms from a data science perspective.New roles are emerging as the lines between traditional science and computational science blur.This convergenceincluding analytics and data scienceis making scientists more effective and productive,with an estimated 20%productivity gain.27 This boost can improve co

149、mpany performance,reduce employee burden,or enable talent upskilling.These changes demand new skillsets.Scientists are no longer just conducting basic research and discovering new moleculesthey now spend about a third of their time managing data and integrating it with their lab experiments,meaning

150、they need to be fluent in both traditional science and data science.The question is whether companies are ready for this shift.This transformation requires a new skill and talent profile(Figure 5).The skills required for a biologist today differ profoundly from those needed just two years ago.Unders

151、tanding biology is no longer enough,as scientists must also understand disease,develop and implement generative AI models,and be able to recognize when a model is faulty.Companies must find this new talent and upskill their current workforce to ensure they have the capabilities to embrace the next g

152、eneration of technology.27Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionFigure 5Talent and New Ways of Working Lead Biologist 201535510755351010201015155555Discovery Biologists Molecule Engineering Data&Analytics Protein

153、 Scientists Lab TechniciansLead Disease Biologists Biology Data ScientistsExperimentalists Robotics&Automation EngineersProtein EngineeringMedicinal ChemistryMolecule Design EngineeringImaging EngineeringData EngineeringData StewardsLLM/GenAI/Prompt EngineeringCollaboration/Alliance ManagersAutomati

154、on ScientistsMedicinal Chemists Computational Scientists Data Scientists Architects Data Stewards Project Managers Reskill Todays Work&Roles:100 Future Work&Roles:83 17Todays RoleFuture RoleRe-SkillKeyMulti-disciplinary talent is essential for driving R&D organizations into the future.Biopharma comp

155、anies must expand their recruitment horizons beyond scientific talent to include those who are skilled in technology and AIa trend that is already gaining speed.Life Sciences technology hires have doubled in the past five years,and AI-specific roles have increased fourfold.28 At the same time,techno

156、logy companies are starting to recruit more scientific talent,making recruitment more competitive.Figure 5:As generative AI and other advanced technologies are increasingly leveraged,new ways of working will emerge,requiring the reallocation of current talent,the creation of entirely new roles,and r

157、eskilling the current workforce.Illustrative Discovery Research organizationSource:Accenture.202428Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusion2.3 A culture of continuous reinvention 99%agree their R&D organization is

158、open to change and embraces creativity,but only 68%say people are encouraged to take risks.Research shows that a safe,open culture can improve productivity by 50%and collaboration by 57%.Source:Accentures Biopharma R&D Executive Survey29Reinvention in the R&D organization shouldnt be seen as a conta

159、ined,one-time initiative.It should be embraced as an ongoing strategy,set in the broader context of the evolving regulatory landscape.Establishing a digital academy to provide tailored training programs for staff at all levelsfrom basic AI literacy to advanced data science certificationsand implemen

160、ting agile ways of working across R&D can foster a culture of innovation.New and complex science,enabled by technology,is driving a scientific renaissance.By continuously reinventing themselves,biopharma companies can improve patient outcomes,streamline clinical trials and increase clinical success

161、rates,boosting their commercial potential.Similarly,building a digital core to support reinvention and create competitive advantage also must be considered an ongoing focus.It requires ongoing investment to incorporate the latest technologies and business capabilities across the R&D organization.If

162、companies treat their digital core as something to be completed,it will quickly become obsolete.They must stay on the leading edge of technology to enhance core aspects of their R&D operations.Continuous adoption of new capabilities and technology is impossible without the right talent,so investing

163、in people is crucial to maintaining a competitive advantage.Modern leaders must have the skills and behaviors to guide their companies through continual reinventionthey must embrace new ways of working and build expertise in technology,data,and analytics.Ongoing investment in new skills both for the

164、mselves and for the broader R&D workforce is essential.29Reinventing R&D in the age of AIContentsPrefaceAbout the researchThe R&D opportunityReinventing the R&D organizationConclusionReinventing R&D in the age of AIConclusionThe biopharma industry is sitting on a proverbial golden egg.Technology now

165、 has an incredible capacity for enhancing human ingenuity on a scale never imagined beforelet alone seen in reality.The potential for life changing and life enriching treatments is broader than ever.To harness this potential fully,biopharma companies must undergo a comprehensive reinvention of their

166、 R&D organizations.This involves embracing intelligent technologies,reimagining work processes and talent,and fostering a culture of continuous innovation.Companies can become more competitive in a rapidly evolving market by prioritizing developing and manufacturing new types of treatments using int

167、elligent technologies for discovery,optimizing clinical trials,and dynamic portfolio management.The companies most able to realize this potential are those that adopt a reinvention strategy encompassing the recommendations laid out in this report.They will be well-positioned to deliver transformativ

168、e therapies,drive substantial growth and establish themselves as leaders in the next generation of medical treatments.30Reinventing the R&D organizationContentsPrefaceAbout the researchThe R&D opportunityConclusion30Reinventing R&D in the age of AIReinventing the R&D organizationContentsPrefaceAbout

169、 the researchThe R&D opportunityConclusionMethodologyR&D Success Index MethodologyAccenture Research developed this success index leveraging PTRS and Duration data from Pharmapremia(January 1,2024 December 31,2023)and Enterprise Value from CapitalIQ(2018-2023).The X-axis shows how quickly companies

170、drug-indication pairs moved through the pipeline and the Y-axis shows companies success rates.Those farther to the right achieved shorter cycle times and those farther up achieved higher success rates relative to their expected performance.For each company,we created an“Expected PTRS”and“Expected Cy

171、cle Time”based on the proportion of their pipeline focused on disease group and phase pairs(for example,phase I oncology,phase II oncology,phase I cardiovascular,and so forth)to normalize for varying levels of duration and success rates across phases and therapeutic areas.We then compared this to th

172、e actual success rates and durations that they experienced.Survey MethodologyWe surveyed 75 R&D executives globally from the top 20 biopharma companies by 2023 annual revenue to understand the current R&D landscape and the direction of change.Roles included Head of R&D/Chief R&D Officer,VP/SVP,Senio

173、r/Executive Director,and Chief Science Officer.The survey was conducted in January 2024.Our primary focuses were on how executives and R&D organizations define R&D success;the factors and capabilities required to achieve that success;and the level at which organizations embraced intelligent technolo

174、gies.Time Savings and Revenue UpliftTime savings based on industry case studies as well as expert and client discussions.Almost three years of time savings during target discovery and validation and lead ID and optimization using AI-methods and workflow reinvention.18 months of time savings during t

175、he clinical trial period that comes from accelerated clinical protocol design and better patient selection and recruitment.Revenue uplift was calculated based on the additional four years of exclusivity period in market with average peak sales of$500 million.This calculation does not take into consi

176、deration other qualitative factors such as first mover advantage.As such,we believe this number to be conservative.Additionally,revenue uplift can vary dramatically depending on indication and potential peak sales.Cost savings are based on Accentures“Taking R&D From Billions to Millions”model.Cost s

177、avings come from the uplift in probability of success of clinical trials based on factors such as better target validation,understanding of the disease mechanisms,trial design and patient selection.Reported cost savings are per successful drug including cost of failure and cost of capital.AI-mediate

178、d Drug DiscoveryLandscape:We initially identified over 200 AI drug discovery companies using Pitchbook and other internal resources.However,we recognize that there is a considerable amount of variability in the type and sophistication of AI/ML methods used across these companies and a disagreement o

179、n how to define what an AI discovered drug is.Therefore,as we refined the list,companies were classified as such if they identified or marketed themselves as leveraging AI in the following areas:lead design and optimization target prediction and identification drug repurposing biomarker identificati

180、onOnce we finalized the list of companies,we extracted all pipeline assets from GlobalData.We focused on assets where the identified AI-mediated drug discovery companies were involved as either the originator/licensor or developer.The analysis is limited by the coverage of our databases.As such,in s

181、ome cases,the role of the AI-mediated drug discovery company may be minimal or not fully detailed.Deals:We compiled a comprehensive list of AI partnership deals from Citelines Biomedtracker and GlobalData leveraging keyword searches.The dataset includes all completed partnership deals from January 1

182、,2019 May 24,2024.From this list,we identified companies classified as AI mediated drug discovery companies based on our previous definition.We further categorized the deals to identify those involving Big Pharma companies,which we defined as the top 20 pharmaceutical companies by 2023 Rx sales acco

183、rding to Evaluate Pharma.31References1.Potential treatment for rare autoimmune disorder adapted from CAR-T therapy2.FDA Approves First Gene Therapies to Treat Patients with Sickle Cell Disease3.Inflation Reduction Acts Price Controls Are Deterring New Drug Development:IRA effects on revenue loss ran

184、ge from$237b(CBO)to$450b 4.Accenture Research Analysis of Evaluate Pharma data as of 6/1/2024:$300 billion+of revenues at risk between 2026-2030 due to loss of exclusivity 5.Four Trends That Will Pop the$250 Billion Gross-to-Net Bubbleand Transform PBMs,Market Access,and Benefit Design6.Accenture Re

185、search analysis leveraging Billions to Millions model.Please see methodology for more details7.Innovation in the pharmaceutical industry:New estimates of R&D costs,DiMasi et al.8.https:/ 9.Based on internal Accenture work,industry case studies,and expert&client discussions 10.Accenture Research anal

186、ysis of top biopharma companies leveraging Pharmapremia and S&P Capital IQ data11.Accenture Resarchs Global Biopharma R&D Executive Survey12.Accenture Life Sciences CEO Forum 2024 research13.Accenture Research analysis.All facts and figures are derived from GlobalData and Citelines Biomedtracker.Ple

187、ase see methodology section for additional details14.Ibid.15.Accenture Research analysis of Evaluate Pharma data16.Goldman Sachs Equity Research,April 29,202417.Jeffries Research,Insights with Expert,January 2,202418.ibid.19.Accenture Research analysis of GlobalDatas Clinical Trial Database.Rare dis

188、eases exclude oncology and were selected using GlobalDatas definition20.ibid.21.ibid.22.ibid.23.Survey of 1000+C Suite Executives,Accentures Innovation Unleashed Report24.ibid.25.ibid.26.ibid.27.Accenture Research analysis leveraging occupation level data from the U.S.Bureau of Labor Statistics and

189、O*NET.28.ibid.32About AccentureAbout Accenture ResearchAccenture is a leading global professional services company that helps the worlds leading businesses,governments and other organizations build their digital core,optimize their operations,accelerate revenue growth and enhance citizen servicescre

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