1、April 2024Unleash AIs potentialMeasuring the return frompharmaceutical innovation 14th editionForeword 1Executive summary 2Measuring the return from 4 pharmaceutical innovationRealising efficiency opportunities 8Optimising the value of pipelines 14Strategies to improve productivity 18Key questions f

2、or an optimum R&D strategy 24Endnotes 25Contacts 27ContentsForewordWelcome to Unleash AIs potential,Deloittes fourteenth annual report in our Measuring the return from pharmaceutical innovation series.Our report explores the performance of the biopharmaceutical(biopharma)industry and its ability to

3、generate returns from investment in innovative products in the development pipeline.The current biopharma R&D operating model faces several serious challenges,including ongoing regulatory changes,loss of exclusivity of an unprecedented number of high-value assets,and the rapid pace of scientific and

4、 technological advancements.However,advances in digitalisation and artificial intelligence(AI)present new opportunities to improve R&D productivity,paving the way for a new era of innovation and accelerating patient access to new therapies.As always,our report explores the industrys performance,the

5、changing characteristics of the late-stage portfolios and the opportunities for biopharma companies to improve capital returns.Between 2010 and 2023,our report series has provided insights into the productivity of biopharma R&D.In 2010,we analysed the expected return on investment from the late-stag

6、e pipelines of a cohort of 12 large-cap biopharma companies.Over subsequent years,the composition of our cohort has evolved to now include the top 20 companies by R&D spend in 2020.Our analysis in 2023 shows that following a long-term trend of declining returns that there are some welcome signs of i

7、mprovement with the cohorts internal rate of return rising to 4.1 per cent.Insights from our year-on-year analysis have demonstrated that transformational change in R&D productivity is essential if improvements in projected returns across the biopharma industry are to be sustained and grow.Our analy

8、sis this year shows that this conclusion is as relevant as ever given R&D projected returns remain below the cost of capital which will make R&D leaders funding requests continue to be challenging.Today,the industry is increasingly using technology-enabled approaches,including AI,to optimise the use

9、 of a wide range of proprietary R&D data to inform their decision making.However,the full benefits can only be obtained if relevant data are managed,processed and utilised to gain actionable insights.Advances in AI,including generative AI,can enable companies to demystify complex disease biology,exp

10、edite drug discovery,cut study timelines,revitalise the clinical trial experience and improve regulatory success.Ultimately,unleashing AIs potential could be the key to improving longstanding internal and external productivity challenges across the biopharma R&D industry but these activities need fo

11、cus to ensure value is created.We explore these themes in our report and,as always,welcome your feedback and look forward to discussing the implications of our findings.Colin TerryPartnerUK Life Sciences and Healthcare Consulting LeaderKevin DondarskiPrincipalLife Sciences Strategy1Unleash AIs poten

12、tial|Measuring the return from pharmaceutical innovationExecutive summaryInvestments and advances in biopharma research and development(R&D)continue to fuel innovation,improve health outcomes and shape the future of health.This years analysis shows signs of improvement in productivity following a st

13、eep decline in 2022.However,as biopharma companies work to sustain a profitable R&D pipeline and bring new therapeutics to market,they are navigating a complex landscape of regulations,looming patent expiries,technological advancements,and competitive pressures.About this reportSince 2010,our report

14、 series Measuring the return from pharmaceutical innovation has provided insights into the productivity of biopharma R&D.Our inaugural report analysed the return on investment that 12 large-cap biopharma companies might expect to achieve from their late-stage pipelines.Over the past 14 years,the com

15、position of our cohort has now evolved to include the top 20 companies by 2020 R&D spend.This year,we have expanded the scope of our analysis to include an expanded range of assets,label expansions and line extensions and have increased the granularity of our data set.To supplement our data analysis

16、 and understand better the underlying drivers of change in internal rate of return(IRR),this year we interviewed ten R&D leaders as well as drawing on the expertise of Deloitte colleagues who operate in the R&D space and on an extensive literature review.Measuring the return from pharmaceutical inno

17、vationOur analysis over the past 14 years has shown a steady decline in productivity between 2010 and 2019,a short-lived improvement due to the impact of the COVID-19 assets in 2020 and 2021,followed by a dip in 2022,and in the 2023 cycle,we are beginning to see signs of some improvement.This years

18、modelling,based on a dataset which includes an expanded scope of assets and line extensions,calculates that the IRR has risen to 4.1 per cent from 1.2 per cent last year,which was the lowest point for the cohort since our analysis began.IRR depends on both efficiency(cycle times and costs)and value

19、creation(risk-adjusted forecast sales),each of which has multiple parameters that can improve outcomes.It is therefore important to understand both the trends in costs to develop an asset from discovery to launch and also the risk-adjusted forecast revenue of the assets in the pipeline.Total reporte

20、d R&D spend by our cohort has increased from$139.2 billion last year to$145.5 billion in 2023,an increase of 4.5 per cent.The average R&D cost to progress an asset from discovery to launch has remained flat for 2022-2023 at$2,284 million per asset,reflecting an expanded range of assets and line exte

21、nsions in the analysis this year.The cohorts average forecast peak sales per pipeline asset fell from$389 million in 2022 to$362 million in 2023.This continues the decline from the 2021 peak($500 million)that was driven by high value COVID-19 assets.Reflecting the successful approval of high value a

22、ssets which we have observed year-on-year,the total revenue for our cohort continues to trend upwards without interruption with reported top 20 pharma R&D sales increasing by 9.6 per cent in 2023.Improving productivity in biopharma R&D will never be easy given the need to balance efficiency(cost)and

23、 value creation(sales),each of which depends on multiple factors that can influence the drivers of change.This year,regulatory changes,the impending and unprecedented scale of the loss of exclusivity of high value assets for many companies in our cohort,inflationary pressures,the rapid pace of scien

24、tific and technological advances and rising protocol design complexity are all placing significant pressures on the current R&D operating model but are also creating new opportunities to improve R&D productivity.2Unleash AIs potential|Measuring the return from pharmaceutical innovationRealising effi

25、ciency opportunitiesThis rise in R&D costs can be attributed to several factors,including more complex trial requirements,regulatory changes,the impact of inflation,and continuing to operate in functional silos.Despite the increasing expenditure,the cost of a project very rarely deters a biopharma c

26、ompany from pursuing it,as ultimately the primary driver is to develop a successful product that benefits the intended patient population.Long development cycle times have been a challenge for the industry for many years,reflecting the escalating trial complexity in delivering advanced therapies for

27、 niche and rare indications or complex neurological conditions.Developing more flexible and adaptable clinical trial processes can improve productivity and help companies respond more effectively to the rapidly evolving regulatory and commercial landscape,and in turn reduce costs,while bringing prod

28、ucts to market more quickly and effectively.For a modernised trial to be successful,a clean,understandable and efficient process needs to be identified that enables clinical trials to represent the diversity of the intended patient population.Regulatory compliance can be either a barrier to or an en

29、abler of productivity in the highly regulated biopharma industry.Interpreting the new and evolving regulatory expectations and implementing any necessary changes in a coordinated,cost-efficient and timely manner,across a number of business functions,is a significant challenge for the industry.Most o

30、f our interviewees were concerned about the changing regulation landscape,and specifically called out the US Inflation Reduction Act(IRA).However,there are a plethora of other changes in regulatory requirements on clinical endpoints,diversity of trials and sustainability reporting,which differ acros

31、s geographies,and can have a significant impact on R&D costs and productivity.Biopharma companies are increasingly using technology-enabled approaches that use R&D data to inform their decision-making.As a result,the amount of data produced during clinical trials is growing exponentially;but the ben

32、efits can be obtained only if the data is managed,processed and utilised to gain actionable insights.Given the pace of technology innovation,and increasing use of AI-enabled technologies,the time is ripe for the industry to scale the use of digital technology to obtain enduring value.Optimising the

33、value of pipelinesWe consider that the IRA in the US is likely to be an ongoing catalyst for change,rather than a one-time event.Eighteen months after being signed into law,the extent of its impact is yet to be realised,but it is front-of-mind for many across the industry.Despite the IRA being US le

34、gislation,there is and will continue to be a global ripple effect on biopharma strategies due to the international nature of the industry.EU patent law revisions will also impact development and launch strategies across multiple geographies.With the greater incentives and penalties imposed by regula

35、tions,companies should map their commercial strategy for assets as early as possible in the development process.There is also a need to embed flexibility and a dynamic balance of internal and external sourcing in pipeline strategy.These strategies,developed through early cross-department engagement,

36、need to look forward some five to ten years,develop a prediction of the commercial,regulatory and innovation landscape at the anticipated time of launch,and ultimately guide investment into those programmes most likely to be successful and high value.Harnessing advanced analytics in commercial poten

37、tial and technical and regulatory risk assessments will support the development of these strategies and the go or no-go decisions at various stages across the R&D cycle.Strategies to improve productivityAI-enabled digital transformation is fast becoming a strategic imperative for leaders in life sci

38、ences.The biopharma industry is on the brink of large-scale disruption driven by interoperable data,advances in AI and analytics,open and secure platforms and patient-centric care,which have the potential to deliver less costly and more productive drug development.When developing the business case f

39、or investment in digital and AI,the short-term costs need to be balanced against the long-term efficiency gains.Executing large-scale strategies requires setting up a governance function for making investments,assessing value realised,and monitoring ethical and legal risks from use of AI.Competitive

40、 intensity,scientific breakthroughs and regulatory incentives have skewed R&D spending toward certain areas,particularly oncology and rare diseases.By 2023,39 per cent of late-stage development programmes for our cohort were focused on oncology and the proportion has been consistently greater than a

41、 third of the pipeline since 2020.At the same time,a third of the cohorts development programmes were targeted at rare diseases in 2023.As competition in over-concentrated therapeutic areas heats up and the focus of payers on the equitable allocation of health care spending rises,the current dynamic

42、 could change.Ultimately,transforming the productivity of R&D will require companies to work entirely differently,drawing on change management skills,as well as partnerships and collaborations.If biopharma succeeds in capitalising on AIs potential,the internal and external productivity challenges dr

43、iving the decline in the IRR of biopharma innovation will be reversed and the industry will thrive.We outline the key questions for R&D leaders to consider in establishing a resilient and cost-effective technology-enabled R&D strategy in the final section of this report.3Unleash AIs potential|Measur

44、ing the return from pharmaceutical innovationMeasuring the return from pharmaceutical innovationOur annual report series Measuring the return from pharmaceutical innovation analyses the projected IRR that biopharma companies can expect to earn from their late-stage pipelines.The past 14 years have d

45、emonstrated that transformational change in R&D productivity is required to reverse the declining trends in returns across the biopharma industry while continuing to deliver innovation to patients.Our latest analysis shows that this conclusion is more relevant than ever,given that companies are faci

46、ng an evolving regulatory landscape,growing cost pressures,declining peak sales and difficulties in replenishing their pipelines,with the result that projected R&D returns continue to remain well below the cost of capital.About the reportOur report series Measuring the return from pharmaceutical inn

47、ovation has provided insights into the productivity of biopharma R&D since 2010.Our inaugural report analysed the return on investment that 12 large-cap biopharma companies might expect to achieve from their late-stage pipelines.Over the past 14 years,the composition of our cohort has now evolved to

48、 include the top 20 companies by 2020 R&D spend.MethodologyThis year,we have expanded the scope of our analysis to include an expanded range of assets,label expansions and line extensions and have increased the granularity of our data set.For each of the companies in our cohort,our new data provider

49、,Evaluate,provides sales forecasts for assets and indications in the late-stage pipelines,estimates of the probability of technical and regulatory success(PTRS)and pipeline composition data such as therapy areas,modalities and the source of innovation.We continue to use the same objective methodolog

50、y,which focuses on each companys late-stage pipeline,using multiple inputs to calculate the IRR,which is our measure of R&D productivity.The inputs to our calculation include:the total R&D expenditure incurred by a company in bringing its assets to launch(based on publicly available information from

51、 audited annual reports and readily available data from third-party data providers)the impact of in-licensing and mergers and acquisitions(M&A)on R&D costs forecast estimates of the future revenue that will be generated from the launch of the late-stage assets(revenue forecasts provided by Evaluate)

52、success rates in late-stage development to risk-adjust projections the cost of failure due to the inherent risks in undertaking R&D the impact of clinical cycle times.We consider the late-stage pipeline to be assets in phase II with pivotal or breakthrough designation,in phase III,or filed for regul

53、atory approval.As assets are approved,their forecast revenues move out of the late-stage pipeline into the commercial portfolio,and in doing so move out of scope of our analysis.At the same time,as assets progress through the development cycle into the late-stage pipeline,they enter the scope of our

54、 analysis.We are continually working to improve the methodology,modelling and scope of our analysis to ensure greater accuracy and more comprehensive insights while ensuring that a consistent and objective approach is applied across all companies each year.To supplement our data analysis and underst

55、and better the underlying drivers of change in IRR,this year we interviewed ten R&D leaders,nine from companies in our cohort and one from a R&D platform technology company,as well as drawing on the expertise of Deloitte colleagues who operate in the R&D space and on an extensive literature review.F

56、or more details of the methodology and companies included in our cohort,see methodology annex.4Unleash AIs potential|Measuring the return from pharmaceutical innovationProjected returns from innovation have improved this yearLast years modelling of R&D productivity(as measured by IRR)showed that 202

57、2 marked the lowest point for the cohort since our analysis began,as the result of the successful approvals and commercialisation of several high value forecast assets which left the scope of our analysis.This years modelling,based on a dataset which included an expanded scope of assets and line ext

58、ensions,calculates that the IRR has risen to 4.1 per cent,as shown in Figure 1.The distribution of IRRs across our cohort of 20 companies narrowed in 2023,with the lowest outlier sitting well above the large negative IRRs we witnessed for some companies across 2019-2022.Additionally,the bottom of th

59、e interquartile range in 2023 has a positive IRR for the first time in five years,excluding the 2021 COVID-19 skew.In 2023,as in 2022,several high value forecast assets were approved,and on entering the commercial portfolio left the scope of our analysis.These include GLP-1 receptor agonists used fo

60、r the treatment of type 2 diabetes,novel oral medication for plaque psoriasis,and the first single-dose preventative option for respiratory syncytial virus(RSV).However in 2023 we also saw a number of new high value forecast entries to our late-stage pipeline that target large patient populations.Th

61、ese include multiple single and combination GLP-1 receptor agonists targeting chronic weight management,monoclonal antibodies for early Alzheimers disease,as well as infectious disease vaccines utilising mRNA platforms.The scientific breakthroughs enabling the targeting of these large patient popula

62、tion indications has the potential to reignite the landscape of blockbuster assets and impact positively the health outcomes of a greater proportion of the global population.Nevertheless,as we discuss later in the report,executing sustainable pipeline replenishment remains a nuanced and complex stra

63、tegy for biopharma companies.The IRR depends on both efficiency(cycle times and costs)and value creation(risk-adjusted forecast sales),each of which has multiple parameters that can improve outcomes.It is therefore important to understand both the trends in costs to develop an asset from discovery t

64、o launch and the risk-adjusted forecast revenue of the assets in the pipeline.Figure 1.Return on late-stage pipeline,2013-202305101520-25-20-15-10-52530STATIC IRR(%)20132014201520162017201820192020202120222023TopperformerBottomperformerThird quartileMeanFirst quartileMedianNote:2013-2022 calculated

65、from GlobalData dataset,2023 data point calculated from Evaluate datasetSource:Deloitte analysis,2024.6.57.26.15.55.43.61.52.36.81.24.15Unleash AIs potential|Measuring the return from pharmaceutical innovationWhile the total R&D expenditure has increased,the average cost to develop a pipeline asset

66、is unchangedFigure 2 shows that the average R&D cost to progress an asset from discovery to launch has remained flat for 2022-2023 at$2,284 million per asset.However,this plateau results from the larger number of assets in the 2023 portfolio due to the increase in the scope of assets and line extens

67、ions.When looking at the cost of R&D from discovery to launch all assets in their late-stage portfolio,the average company spend increased from$31.75 billion in 2022 to$48.54 billion in 2023.Total reported R&D spend by our cohort increased from$139.2 billion in FY2021 to$145.5 billion in FY2022,an i

68、ncrease of 4.5 per cent.Three of the companies in our cohort increased their pharma R&D spend by over 25 per cent between FY2021 and FY2022.The average forecast peak sales per asset has decreasedIn 2023 only one of the companies in our analysis is predicted to achieve forecast peak sales per asset o

69、f more than$1 billion.The cohorts average forecast peak sales per pipeline asset decreased from$389 million in 2022 to$362 million in 2023,as shown in Figure 3.This continues the decline from the 2021 peak($500 million)that was driven by high value COVID-19 assets.The distribution of the average for

70、ecast peak sales for the companies continues to converge after the disparate effect caused by high value COVID-19 assets of some companies.Reflecting the successful approval of high value assets which we have observed year-on-year,the total revenue for our cohort continues to trend upwards without i

71、nterruption.Reported top 20 pharma R&D sales in FY2022 increased by 9.6 per cent,to$719.2 billion from$656.2 billion in FY2021.Figure 2.Average R&D cost to develop an asset from discovery to launch,2013-2023$6,000$8,000$10,000$0$2,000$4,000$12,000COST TO LAUNCH($M)20132014201520162017201820192020202

72、120222023TopperformerBottomperformerThirdquartileMeanFirstquartileMedianNote:2013-2022 based on GlobalData dataset,2023 data point calculated from Evaluate datasetSource:Deloitte analysis,2024.$1,986$2,364$2,284$2,284$1,296$1,349$1,511$1,824$2,202$2,432$1,513Figure 3.Average forecast peak sales per

73、pipeline asset,2013-2023$2,500$2,000$3,000$3,500$0$1,000$500$1,500AVERAGE PEAK SALES($M)20132014201520162017201820192020202120222023TopperformerBottomperformerThirdquartileMeanFirstquartileMedianNote:2013-2022 based on GlobalData dataset,2023 data point calculated from Evaluate datasetSource:Deloitt

74、e analysis,2024.$500$398$366$512$555$517$573$520$463$389$3626Unleash AIs potential|Measuring the return from pharmaceutical innovationDrivers of the change in R&D productivityOur analysis over the past 14 years has shown a steady decline in productivity(IRR)between 2010-2019,a short-lived improvemen

75、t due to the impact of the COVID-19 assets in 2020 and 2021,followed by a dip in 2022,and in the 2023 cycle,we are beginning to see signs of some improvement.However,improving productivity in biopharma R&D will never be easy given the need to balance efficiency(cost)and value creation(sales),each of

76、 which depends on multiple factors that can influence the drivers of change.This year,regulatory changes,the impending and unprecedented scale of the loss of exclusivity of high value assets for many companies in our cohort,inflationary pressures,the rapid pace of scientific and technological advanc

77、es and rising protocol design complexity are all placing significant pressures on the current R&D operating model,but are also creating new opportunities to improve R&D productivity,see Figure 4.In the rest of this report,we will explore each of these productivity drivers in detail,and dive into the

78、 levers that biopharma companies can pull to improve their rate of return on investment,and also the many issues that need to be addressed before a clear path ahead can be forged.Source:Deloitte analysis,2024.Figure 4.Opportunities to tackle the drivers of IRR and improve productivity IRR driverRisi

79、ng R&D costsDeclining peak salesStrategies to improve productivityRealise efficiency opportunities across the drug development paradigmOptimise commercial value against ever-changing market dynamics Scale the use of AI and digital technologies Overhaul the clinical trial experience Optimise complian

80、ce management Understand the strategic impacts of regulatory reforms Engage teams across the biopharma value chain from the outset Review pipeline replenishment and therapy area focus GenAI has the potential to transform R&D Data driven early-stage R&D Look beyond overconcentrated therapy areas and

81、modalities Questions for R&D leadersAimActionsOur analysis over the past 14 years has shown a steady decline in productivity(IRR)between 2010-2019,a short-lived improvement due to the impact of the COVID-19 assets in 2020 and 2021,followed by a dip in 2022,and in the 2023 cycle,we are beginning to s

82、ee signs of some improvement.7Unleash AIs potential|Measuring the return from pharmaceutical innovationRising R&D costs are attributable to several factors As stated previously,the R&D spend by our top 20 cohort increased by 4.5 per cent to$145.5 billion in FY2022,from$139.2 billion in FY2021.This r

83、ise in R&D costs can be attributed to several factors,including more complex trial requirements,regulatory changes,the impact of inflation,and continuing to operate in functional silos.Despite the increasing expenditure,the cost of a project very rarely deters a biopharma company from pursuing it,as

84、 ultimately the primary driver is to develop a successful product that benefits the intended patient population.While long development cycle times have been a challenge for the industry for many years,our interviewees reflected that the expectations of the clinical trial experience are growing as tr

85、ials become much more sophisticated.Long cycle times also reflect the escalating trial complexity in delivering advanced therapies for niche and rare indications or complex neurological conditions like Alzheimers,which present challenges in agreeing endpoints.In 2023,half of the development programm

86、es in our dataset involved advanced therapies and biologics,including cell and gene therapies,monoclonal and recombinant antibodies,protein and peptide therapies,and plasma-derived therapies.Additionally,30 per cent of the development programmes of our cohort in 2023 are targeted at rare indications

87、.The need to recruit more specialised and diverse sub-populations for complex studies has compounded recruitment challenges,lengthening trial timelines.Added to this is the lingering issue of high dropout rates due to poor participant experience,leading to loss of valuable trial data and additional

88、costs and time to recruit replacements.But the challenges are not limited to patient recruitment.Our interviewees commented on the numbers of skilled medical professionals leaving the industry since the pandemic,leading to a shortage of experienced site staff required to conduct trials.The ripple ef

89、fect has only intensified the struggle to retain skilled and experienced staff vital to R&D productivity,which in turn adds to the costs.Realising efficiency opportunities While R&D executives prioritise expediting the time to market for drugs targeting unmet needs,they also have pressing concerns a

90、bout the consistently high expenditure and rising costs of R&D.By scaling end-to-end digital transformation and the use of AI and other technology tools,companies have the potential to increase drug development efficiencies dramatically.However,investment in data infrastructure and AI capabilities n

91、eeds to recognise the importance of maintaining the human in the loop in realising value and efficiency gains.“Were now having to elucidate more and more complex science and do more and more complex studies.Recruiting patients for complex studies takes a long time.We are setting higher expectations

92、of how patients should experience a clinical study.And hospital systems and medical systems dont want to share the data except for monetary reward.All these things lead to higher costs,if you are a company that is committed to innovative medicines.”Former Vice President,R&D IT,Top 20 Biopharma8Unlea

93、sh AIs potential|Measuring the return from pharmaceutical innovationR&D executives are most concerned about changing regulations We asked our interviewees to rank their concerns about factors affecting drug development.Figure 5 shows that R&D executives were more concerned with changing regulations

94、such as the Inflation Reduction Act(IRA)than with rising R&D costs and increasing cycle times.In fact,more challenging regulatory requirements which require biopharma companies to increase the quality and quantity of evidence generated during clinical trials are impacting the complexity of clinical

95、trial design and development and therefore the costs and productivity of R&D.Developing more flexible and adaptable clinical trial processes can improve productivity and help companies respond more effectively to the rapidly evolving regulatory and commercial landscape,and in turn reduce costs,while

96、 bringing products to market more quickly and effectively.Ultimately,while rising R&D costs need to be tackled,its important to strike a balance between cost considerations,the experience of patients and site staff,and the need to innovate and bring new products to market more quickly.Effective mana

97、gement of regulatory complianceRegulatory compliance can be a barrier to or an enabler of productivity in the highly regulated biopharma industry.It is a fundamental requirement for the safety and efficacy of product development,and provides a framework in which commercial objectives and patient acc

98、ess can be optimised.However,interpreting the new and evolving regulatory expectations and implementing any necessary changes in a coordinated,cost-efficient and timely manner,across a number of business functions,is a challenge for the industry.Most of our interviewees were concerned about the chan

99、ging regulation landscape,and specifically called out the US IRA.However there are a plethora of other changes in regulatory requirements on clinical endpoints,diversity of trials and sustainability reporting,which differ to varying degrees across geographies,but which can have a significant impact

100、on R&D costs and productivity.While the US Food and Drug Administration(FDA)and the European Medicines Agency(EMA)are often considered as regulatory counterparts,our interviewees noted that they diverge in their requirements.Changing regulations like the IRA,if implemented as intended,is likely to i

101、ntroduce further misalignment across the major regulatory agencies,and pose challenges in clinical execution,hindering innovation and increasing the cost and time taken to ensure compliance.Figure 5.Factors that are of most concern to R&D executives 1 least concerned3245 most concernedNote:N=9Questi

102、on:On a scale of 1 to 5,how concerned are you about the following factors impacting R&D productivity?Source:Deloitte analysis,2024.Rising R&D costsIncreasing cycle timeChanging regulations44%11%33%11%11%11%44%33%50%25%25%“I see changing requirements globally that do not always align across regulator

103、y bodies.More and more individual countries have specific requirements which is complicating development programmes.Sometimes you have to make tactical choices,deselecting specific countries or pushing intended commercial opportunities to a later time.”Executive Vice President,Development,Top 20 Bio

104、pharma 9Unleash AIs potential|Measuring the return from pharmaceutical innovationIn 2023,half the development programmes in our dataset involved biologics and advanced therapies,including cell and gene therapies,monoclonal and recombinant antibodies,protein and peptide therapies,and plasma-derived t

105、herapies.Advanced therapies have created challenges for regulators and pharma companies in agreeing surrogate endpoints,and also in design,manufacturing,and supply complications in the conduct of the trials themselves.The resultant complexities in clinical trial design can increase cycle times,but u

106、ncertainty about regulatory requirements can cause further delays and increase costs.When specific regulatory requirements do not align with clinical practice,this can be a disincentive to innovation and decrease the willingness to invest.Companies are increasingly partnering and working more closel

107、y with regulatory bodies and relevant patients to develop surrogate endpoints during the early R&D stages that can be agreed for individual development programmes.We will return to the impact of changing regulations,particularly the IRA,on the potential commercial value of assets in the next section

108、 of this report.“We havent figured out a way to have surrogate endpoints agreed by regulatory authorities.How are we going to move the needle in Alzheimers?Its clear that earlier treatment will be better.But who can sign up for many thousands of patients for 15 years?Its more of a policy barrier tha

109、n a scientific one because we actually know what to do.”Executive Vice President,Research and Development,Top 20 Biopharma The US Inflation Reduction Act(IRA)The IRA,passed in 2022,has empowered the Center for Medicare and Medicaid(CMS)to negotiate directly with manufacturers on the price of some hi

110、gh cost Medicare drugs.It caps out-of-pocket spending at$2,000 per year,and puts penalties in place for drug manufacturers that increase their Medicare prices by more than the rate of inflation.1 Prior to the IRA,Medicare was forbidden from negotiating drug prices.Now,the CMS is empowered to negotia

111、te the maximum fair prices for biologics 13 years after approval and small molecule drugs nine years post-approval.2 We have calculated that it takes an average of over eight years for a small molecule to recoup investment cost and over seven years for a biologic.iIn August 2023,the CMS released a l

112、ist of the 10 drugs,that are no longer covered by patent exclusivity,for the first round of price negotiation.Together these 10 drugs accounted for$50.5 billion in Medicare spending from June 2022 to May 2023.3 This could shorten the economic product life cycle,impact future revenue,and drive change

113、s in R&D and commercial strategy.Expert analysis of the likely response of companies is that they may rebalance portfolios towards biologics and single indication orphan drugs.4 i Assuming40percentoperatingexpense,averageR&Dcostperassetat$2.3billionandrecoupingR&Dinvestmentcostfromanetcashflowperspe

114、ctive.10Unleash AIs potential|Measuring the return from pharmaceutical innovationScaling the use of AI and digital technologies The traditional linear,randomised clinical trial process is inherently labour-intensive(using high cost human capital),complex,and highly regulated.We have highlighted in p

115、revious reports that digital technologies,automation tools,and patient-experience solutions can reduce the need for manual activities,and in turn reduce the overall cycle time and cost.However,these digital tools have so far had only an incremental,rather than transformational,impact on the producti

116、vity of clinical trials.The potential wide-scale adoption of generative AI(GenAI)to process and learn from terabytes of structured and unstructured data is seen by many as a potential game-changer in R&D.We cover this in our final section,Strategies to improve productivity.Today,pharma companies are

117、 increasingly using technology-enabled approaches that use R&D data to inform their decision-making.As a result,the amount of data produced during clinical trials is growing exponentially;but the benefits can be obtained only if the data is managed,processed and utilised to gain actionable insights.

118、In 2021,according to Tufts Center for the Study of Drug Development(CSDD),Phase III clinical trials generated an average of 3.6 million data points,three times the amount of data collected by late-stage trials ten years ago.5 There is growing evidence of successes in applying AI and digital technolo

119、gies,all with humans in the loop,to the massive amounts of data that are collected during clinical development.This covers the entire R&D value chain from expediting target discovery,helping identify and select potential site and trial participants,recruiting and retaining trial participants,aggrega

120、ting and analysing patient-generated data,and automating document generation,such as protocols and case safety reports,through to helping compile the dossiers for regulatory approval.Nevertheless,to ensure that differentiation and value are obtained from digitalisation,decisions need to be made abou

121、t the type,scale and reliability of clinical data collected as well as how this data is to be managed,stored and utilised so that it can be optimised for research purposes.Digital clinical trial recruitment solutions,data platforms and AI-enabled tools contribute improvements to clinical research te

122、chniques,promoting a more patient-centric,cost-effective and manageable approach.Companies across our cohort are already realising the efficiency potential:TrialHub is a data intelligence platform for clinical trial planning that uses large language models and natural language processing to improve

123、data usability,compatibility and flexibility.TrialHub provides a centralised access point for all participating teams in the trial planning phase to dependable data,ensuring it can be tailored for their unique purposes,thus avoiding fragmented analysis and siloed decision making and has been used in

124、 the planning of more than 6,000 trials:a top 10 contract research organisation reported an increase in the speed of collecting standard of care insights by 20 times and save 170,000 hours of manual research.a top 10 biopharma company avoided at least one substantial amendment and months of unsucces

125、sful patient recruitment,all estimated at$1.6 million.a consultancy company and their biopharma client needed a rescue strategy for a Phase III study and were able to develop a plan for the best countries,sites and DCT elements that can make the patient experience better and ended up having three ti

126、mes faster patient recruitment compared to before.6 For the first time,an AI-designed drug targeting idiopathic pulmonary fibrosis,developed by Insilico Medicine,is in the second phase of clinical trials.The drug(ISM018_055)involved multiple AI-methods throughout its entire development process.Using

127、 Insilio Medicines drug design platform Pharma.AI,the team used multiple AI methods to find a potential target for the disease and then generated promising drug candidates.ISM018_055 stood out for its ability to reduce scarring in cells and in animal models.Last year,the drug successfully completed

128、a Phase I clinical trial in 126 healthy volunteers in New Zealand and China.The drug has reached this stage after three-and-a-half years compared to the normal timeline of around seven years from finding a target to completion of Phase I clinical trials.The company launched Phase II clinical trials

129、in June 2023,which will further investigate the drugs safety and begin to test its efficacy in people with the disease.7 AI can also reduce the number of patients needed for a trial.Unlearn,a start-up,creates digital twins of patients in clinical trials.Based on an experimental patients data at the

130、start of a trial,researchers can use the twin to predict how the same patient would have progressed in the control group and compare outcomes.This method typically reduces the number of control patients needed by between 20 and 50 per cent.Digital twins benefit not only researchers,but also patients

131、 who enrol in trials,because they have a lower chance of receiving the placebo.811Unleash AIs potential|Measuring the return from pharmaceutical innovationGiven the pace of technology innovation,and increasing use of AI-enabled technologies,the time is ripe for the industry to scale the use of digit

132、al technology to obtain enduring value.Our interviewees considered that despite the heavy investment into applications of AI,it has not yet become a game-changer and a full return is yet to be seen.This can be attributed to most companies investing in AI on an ad hoc basis,and they are yet to determ

133、ine a clear,long-term strategy.Many interviewees acknowledged the hard work that is beginning to get enduring value from AI,but that deciding what to scale is a challenge.“With the level of investment that has gone into digitalisation,I dont think the full return has been seen.There have been a lot

134、of failures.While we see continuous progress,its not dramatic progress.Weve had in the last half decade or so an enormous number of very fun and illuminating pilot experiments in the industry.But the number of things that have been scaled is far fewer.”Executive Vice President,Research and Developme

135、ntSuccess requires a strategic focus and enterprise-level buy-in,both top-down and bottom-up,but our interviewees also recognised that its hard for digitalisation to be top priority,given the explosion in science that is happening.Despite the lack of dramatic progress,interviewees recognised that co

136、ntinuous progress is being made across the industry,which is now accelerating with the adoption of GenAI and its potential to improve the productivity of R&D.We consider the impact of GenAI in the final section of this report,Strategies to improve productivity.Overhauling the clinical trial experien

137、ceTodays standard patient-centric approaches are insufficient to resolve the recruitment and retention challenges that add to trial timelines and costs.Our interviewees recognised that there are higher expectations across the industry about how patients should experience a clinical study.By adopting

138、 a human-centred approach,companies can draw on patient needs,experiences,and community relationships to tackle unique issues with awareness,access,and trust.Building community relationships can help biopharma companies cultivate trust in medical research,reduce hesitancy to participate in research,

139、and improve trial diversity.9 Large language model-based apps that enable conversational interfaces with patients can help answer questions about studies at pivotal moments,such as during recruitment or consent,resulting in increased engagement and compliance.One aspect that is often overlooked is t

140、he immense burden that clinical trials can place on investigators,site staff and patients,creating an unwillingness to participate in future research.Decentralised clinical trials(DCTs)were used during the pandemic,and although scaled adoption has yet to be realised,there is transformative potential

141、 for DCTs,or hybrid trial models that incorporate elements of decentralisation,such as e-consent,telehealth and sensors for virtual check-ins and remote assessments.Patient engagement in trials can be greatly enhanced,but this requires trial sponsors and site staff to adapt how they conduct clinical

142、 trials to serve patients better,without compromising data collection or evaluation tools,offering solutions such as mobile clinical research units to improve patient centricity,diversity and inclusion.10,11DCT capabilities include virtual training,telehealth,direct-to-patient shipments,patient reim

143、bursement,connected devices,and image capture focused on meeting study participants in or near their homes,instead of requiring them to travel to trial sites for evaluation.An increase in the use of wearables that enable access to a wider range of communities across diverse age groups,ethnicities,an

144、d locations,could dramatically increase the flexibility of how trials are executed and the collection of real-world trial data.Devices like the Apple Watch,the Oura Ring,and smart clothing enable data collection in ways that increase accuracy and minimise historical trial burdens that participants h

145、ave to navigate.Going forward we expect to see patient-centric DCT solutions incorporated as part of study design,which in turn should improve protocol compliance,patient recruitment and retention,and R&D productivity.12 Our interviewees recognised that there are higher expectations across the indus

146、try about how patients should experience a clinical study.12Unleash AIs potential|Measuring the return from pharmaceutical innovationHowever,remote monitoring and decentralisation is not a straightforward solution.Aspects of decentralisation can be burdensome.Recent research by the Tufts Center for

147、the Study of Drug Development,which conducted an online survey among clinical research sites worldwide and gathered 355 responses,found that a high percentage of investigative sites(50.5 per cent)have had no experience with DCT solutions and only a small percentage(6.6 per cent)have participated in

148、fully decentralised clinical trials.13 Overall,half of respondents view DCT solutions as more burdensome than traditional clinical trials.12 In general,activities related to operational and managerial aspects of trial implementation were viewed as less burdensome when done remotely,while clinical pr

149、ocedures or elements that require study team-patient interactions were viewed as more burdensome when using DCT approaches rather than in-person or traditional methods.However,DCTs can still be operationally very complex when multiple systems and platforms are used.While it is critical to balance th

150、e additional site burden against decreasing the patient burden,the industry can reduce complexity through other means,such as creating synthetic control arms,which can increase speed and support the inclusion of under-represented groups.13 Importantly,when it comes to clinical trials saving time tra

151、nslates into saving lives,or at least improving them,through faster availability of treatments.While these innovations enhance speed,precision,and cost-efficiency in drug development,patient safety remains paramount,necessitating a human-in-the-loop and equity centred design approach.This requires c

152、ompanies to invest in agile data processing,engage with regulators early,collaborate with external partners and prioritise portfolio management.The combination of GenAI,machine learning(ML),deep learning,and data analytics is positioned to enhance time to value across biopharma R&D,with a myriad of

153、opportunities to enhance speed,productivity,quality and sustainability.Combining next-generation AI technologies with rich multi-omics data capturing the language of life can close the loop across the R&D pipeline,with automated generation and testing of hypotheses from bench to bedside.15For a mode

154、rnised trial to be successful,a clean,understandable and efficient process needs to be identified that enables clinical trials to represent the diversity of the intended patient population.As identified in previous research reports,the clinical trial of tomorrow will be built on:purpose-led digital

155、innovation establishing clinical trial networks an ingrained focus on sustainability(sustainability by design)extensive collaboration extensive data interoperability application of FAIR(findability,accessibility,interoperability and reusability)data standards and robust protection and privacy(securi

156、ty by design).16,17,1813Unleash AIs potential|Measuring the return from pharmaceutical innovationThe R&D executives we interviewed are more concerned with changing regulations such as the IRA than rising R&D costs and increasing cycle times.This is due,in part,to the relatively unknown impact of the

157、se regulations and the fact that other regulators,for example the EMA,are also introducing new and more stringent regulations impacting the revenue potential of assets.Having explored the impact of regulations on R&D costs and clinical trials in the previous section,we will look in this section of t

158、he report at the impact of regulation on optimisation of the commercial value of assets in development.Regulatory reforms are likely to impact on indication expansion strategiesWe consider that the IRA in the US is likely to be an ongoing catalyst for change,rather than a one-time event.19 The ultim

159、ate goal of the IRA is to lower healthcare costs for American citizens through price negotiations and higher inflationary cap prices.However,the changes it will enact on Medicare prescription drug pricing will have far-reaching effects on the global biopharma industry.20 Eighteen months after being

160、signed into law,the extent of the impact is still yet to be realised,but it is front-of-mind for many across the industry.At present,the scope of the IRA is limited to ten drugs,taking effect from 2026,but these drugs are all blockbusters in our cohorts commercial portfolios.21 By 2030,the number of

161、 drugs approved for price negotiations is expected to reach 80.22In terms of launch and commercial strategies,many of our interviewees reflected on the difficult decisions around multi-indication launches that the IRA has the potential to impose on the industry.The traditional strategy for blockbust

162、er assets is to market quickly with a narrow indication and then follow with much larger indications two or three years post-launch.However,biopharma companies may want to avoid triggering the IRA clock,the reduced time period before price negotiations,too long before the blockbuster indication laun

163、ch.The alternative route of launching the biggest possible indication so the pre-negotiation window is maximised carries far more risk,with the potential for high investment followed by failure.There is no one-size-fits-all answer.To mitigate exposure to negotiation,manufacturers may begin to favour

164、 drugs likely to be blockbusters outside the current scope of the Medicare market.This could entail a shift to drugs targeting conditions impacting younger people,as Medicare is primarily for the over-65s,or a greater focus on single indication orphan biologics which are out of scope of the IRA nego

165、tiations.A further strategy may be to not pursue post-approval trials to expand indications to avoid potentially being eligible.23 There is much more uncertainty if,or more likely when,the scope of the IRA expands.Our interviewees raised concerns that the IRA may stifle innovation because the incent

166、ives will be lower and profitability will be reduced,so that biopharma companies are likely to take fewer and smaller risks when progressing assets.However,this is not to say that biopharma R&D will involve fewer risks in total.Instead,those fewer assets progressed through the development cycle will

167、 likely be pressured into accelerated timelines,with clinical development efforts run in parallel in order to reduce time to market and recoup the cost before opening to price negotiations.Lessons from the very short development timelines of the COVID-19 therapeutics and vaccines,as covered in our 2

168、021 and 2022 Measuring the return from pharmaceutical innovation reports,can be applied across portfolios to progress assets at greater pace.24,25Optimising the value of pipelinesAs biopharma companies work to sustain a profitable R&D pipeline and bring new therapeutics to market,they navigate a com

169、plex landscape of regulations,looming patent expiries,technological advancements,and competitive pressures.Today the IRA,EU patent laws and the rapid advent of AI across the industry are demanding fast-paced,flexible and collaborative R&D operating models to stay ahead of the curve.“The IRA has quit

170、e a limited scope on a small number of drugs.Its only the very largest drugs.Its probably an incremental impact and headwind,but its not a real game-changing headwind.But if they scale that up,doing it for all drugs,were going to have a shorter period of exclusivity.It becomes a slippery slope to mo

171、re and more negotiation by Medicare.Then I think it will really stifle all kinds of innovation.”Executive Vice President,R&D IT,Top 20 Biopharma14Unleash AIs potential|Measuring the return from pharmaceutical innovationDespite the IRA being US legislation,there is and will continue to be a global ri

172、pple effect on biopharma strategies due to the international nature of the industry.However,it is not the only impending change for the industrys regulations.The European Commission proposed in April 2023 to revise the EUs pharmaceutical legislation,described as the largest reform in over 20 years.2

173、6 Similarly to the IRA,the proposed EU revisions focus on improving access to medicines while cutting down on market exclusivity and ultimately championing multi-indication products incentivised by a one year extension to patents if the product targets more than one indication.Products will also rec

174、eive a patent extension of two additional years if the products are launched across all the EUs member states(targeting the disparity in access to medicines across the EU).Engage launch and commercial teams from the outsetWith the greater incentives and penalties imposed by regulations,companies sho

175、uld map their commercial strategy for assets as early as possible in the development process.Novel scientific breakthroughs are not sufficient alone to guarantee market success.Many of our interviewees highlighted the operational silos between teams across the biopharma value chain.Without insights

176、from across the company about the product,market landscape,regulations and patient population characteristics that will drive success if the drug progresses through to launch,strategic decisions about asset progression and launch and commercial strategies may not be as fully informed as they should

177、be.Early cross-department engagement and determination of the commercial value earlier in the development cycle,then following through with an aligned therapy area strategy and individual study design to ensure tracking to the market,will enable a more successful launch and commercial outcome to be

178、achieved.Ultimately,if companies understand the commercial potential of a drug at the earliest possible stage in R&D,they can focus their resources on those drugs that are most likely to succeed.Traditionally,large biopharma companies have relied on high value blockbuster assets to keep the industry

179、 afloat and counteract the 90 per cent of assets which fail during the development cycle and the high proportion which dont recoup the cost expended to get them to market.One-third of todays drug launches miss analysts forecast expectations.27 Additionally,the nature of patents,and the eventual loss

180、 of exclusivity and the development of lower cost generics,mean that biopharma companies rely on producing a steady stream of blockbusters to replace lost revenues.The loss of exclusivity is likely to have a bigger impact on our cohort over the next few years.28 Deloittes 2023 Life Sciences M&A tren

181、ds report highlighted that M&A deal value grew by 46 per cent in 2023 and predicts that with big pharma continuing to face loss of exclusivity events across various therapeutic areas,2024 will be another strong year for big pharma to plug portfolio gaps through M&A.29 Big pharma are suspected to con

182、tinue to target late-stage development and early-stage commercial assets that can contribute material revenue growth in 2026 through to 2030.30 The strategy for launching blockbusters is likely to change.However,in a shifting market with a growing focus on targeted therapeutics,assets that meet unme

183、t needs while having the potential to generate big profits are likely to remain a priority.Companies that engage commercial teams to develop therapy area strategies to guide R&D investments will support the identification and successful progression of high value assets.These strategies need to look

184、forward some five to ten years,develop a prediction of the commercial,regulatory and innovation landscape at the anticipated time of launch,and ultimately guide investment into those programmes most likely to be successful and high value.Harnessing advanced analytics in commercial potential and tech

185、nical and regulatory risk assessments will support the development of these strategies and the go or no-go decisions at various stages across the R&D cycle.15Unleash AIs potential|Measuring the return from pharmaceutical innovationAdopt sustainable pipeline replenishment strategiesBy 2030,patents wi

186、ll have expired for 190 drugs,69 of them current blockbusters,and almost every major pharma company will be impacted.31 This equates to$236 billion in pharma sales at risk before 2030.32 For an industry that invests around 20 per cent of its revenues in R&D,the need to replenish the commercial portf

187、olio to maintain the pace of innovation is clear.33 When combined with the potential impact of IRA price controls,the projections indicate that there will be almost$160 billion less to invest in R&D at a time when the opportunities for breakthroughs have never been greater.34 Therefore,biopharma com

188、panies should implement flexible sourcing strategies to plug revenue gaps,explore opportunities beyond over-concentrated therapy areas to minimise potential IRA impacts and implement data-driven R&D strategies to increase the likelihood of success.To fuel sustainable pipeline replenishment,companies

189、 need to consider the trade-offs between internal and external sourcing.Internal innovation,as interviewees pointed out,has become increasingly risky,as companies target complex unmet needs.To develop first-in-class therapies,companies need to invest in understanding biology,finding targets,and buil

190、ding modalities and tools biomarkers,assays,and endpoints all in parallel.Validation of these efforts occurs in late-stage clinical studies,where asset termination rates have been rising.For our cohort,the number of terminated late-stage assets more than tripled between 2021 and 2023.However,interna

191、l asset development may lend itself to greater success,given stringent asset progression guidelines,and enable the creation of deep internal expertise.We have looked at the composition of the cohorts pipeline with our new dataset back to 2020.For these companies,the proportion of expected revenue fr

192、om internally sourced assets has remained relatively steady since 2021,at just over half the proportion of forecast revenue 50 per cent,see Figure 6.Figure 6.Proportion of late-stage pipeline forecast sourced from internal and external sources,2020-23PERCENTAGE OF FORECAST LATE-STAGE PIPELINE REVENU

193、E BASED ON NON-RISK ADJUSTED TOTAL INFLOWS(%)0%20%40%60%80%100%2023202220212020ExternalInternalSource:Deloitte analysis,2024.46.55%53.45%43.71%46.13%56.29%53.87%59.06%40.94%“To source external innovation,you want enough proof.When the seller gets that much proof,the price skyrockets.The entire indus

194、try is always trying to figure out how far to go.Companies are willing to pay a lot more than they were.And prices on acquisition have really skyrocketed to the point where it becomes hard to understand how to get the return on their investments.”R&D Chief of Staff and Vice President,Portfolio Progr

195、am Management,Top 20 BiopharmaTo fuel sustainable pipeline replenishment,companies need to consider the trade-offs between internal and external sourcing.16Unleash AIs potential|Measuring the return from pharmaceutical innovationIn the past decade,biopharma companies have relied on external innovati

196、on to refuel pipelines.In terms of volume,externally sourced assets(assets acquired through M&A,purchases,joint ventures,or licensing)formed around 60 per cent of the cohorts late-stage pipelines since 2020.The upcoming patent cliff is also likely to increase the focus on external sourcing as compan

197、ies look to plug portfolio gaps and supplement the loss of commercial inflows.35 Externally sourced assets require the development of internal teams or finding the right partners to progress to commercialisation,depending on when in the development cycle they are acquired.Our interviewees also cauti

198、oned that an over-reliance on acquiring late-stage de-risked assets reduced the return on investment due to soaring acquisition prices.Striking a balance between high likelihood of success and paying a high acquisition price depends on having knowledgeable teams in place to agree the deal at the piv

199、otal point.M&A will continue to play a critical role in the biopharma industry and the upcoming patent cliff will remain a crucial driver.As companies seek to build therapy area depth,the competition for high value acquisitions validated with strong data will only intensify.Indeed,we analysed the M&

200、A deals executed by our cohort of companies in 2022 and 2023.The value of deals executed by the top 20 biopharma companies doubled from 2022 to 2023,as shown in Figure 7.In 2023,the companies allocated a higher proportion of capital to acquiring later-stage R&D assets,particularly those in phase II

201、and phase III,increasing from 17 per cent of deal value in 2022 to 45 per cent in 2023.For long-term success,there is a need to embed flexibility and a dynamic balance of sourcing in pipeline strategy alongside building and maintaining a strong knowledge base and expertise in therapy areas of strate

202、gic focus.Such a knowledge base hinges on the ability to attract and retain talent with years of expertise to guide decisions such as acquiring assets earlier before valuations rocket.Most interviewees indicated their companies are struggling with this challenge due to typical career patterns of tod

203、ay that involve working in multiple companies with much greater diversification of roles.It is no longer standard for scientists to stay 30 to 40 years with one company and build deep knowledge of a single mechanism in one therapy area.“I have seen companies moving into being almost 100 per cent dep

204、endent on external innovation.I think thats the road towards failure.However,I dont think you can or should do everything in-house.But if you dont have a clear foundation and capability for innovation in-house,including deep knowledge of what to acquire,I dont think you will be successful.”Executive

205、 Vice President,Development,Top 20 BiopharmaPhase IIPhase IIICommercialFigure 7.Top 20 biopharma M&A activities by leading phase,2022-2023Deal volumeDeal value($mn)0%20%40%60%80%100%2023202220232022Pre-clinicalPhase INote:Reflects biopharmaceutical-related deals only and excludes transactions withou

206、t publicly disclosed deal valueSource:Capital IQ,company websites,Deloitte primary research and analysis.3%24%21%49%3%14%6%1%2%14%17%28%76%21%21%37%11%26%5%21%1929$69,551$144,83617Unleash AIs potential|Measuring the return from pharmaceutical innovationThe life sciences and health care industries ar

207、e on the brink of large-scale disruption driven by interoperable data,open and secure platforms and patient-centric care,which have the potential to deliver less costly and more productive drug development.AI will accelerate the identification of new,more precise and targeted therapeutics,designed t

208、o be highly specific,precise and with less risk of side effects.Indeed AI,together with enhanced computer simulations and advances in personalised medicine,will lead to in-silico trials,which use advanced computer modelling and simulations in the development and regulatory evaluation of a drug.The p

209、otential of AI to improve the patient experience will also help deliver the ambition of biopharma to embed patient-centricity more fully across the entire R&D process.Consequently,AI-enabled digital transformation is fast becoming a strategic imperative for leaders in life sciences.GenAIs transforma

210、tive potential in R&DThere is a growing consensus that AI,and specifically,GenAI,may finally be the transformative technology innovation that accelerates decision making.In 2020,our report Intelligent clinical trials explored the role that AI was beginning to play in enhancing clinical trial product

211、ivity,improving the patient experience and accelerating regulatory decision-making.36 Today,continuing developments in GenAI,together with machine learning and predictive analytics,have the potential to go much further and create an end-to-end business value streamfrom clinical study startup through

212、 clinical study close-out.R&D presents the top value creation opportunity by scaling the use of AI,followed by commercial,manufacturing&supply chain,then enabling areas according to our recent research.37 To date,the most common use of GenAI is in transforming the way pharma companies decide which d

213、isease areas to invest in.It is also being used to identify targets,develop molecules,and improve the accuracy,predictability and speed of drug discovery.38 Research shows that GenAI currently accounts for approximately 16 per cent of drug discovery efforts and its use is predicted to grow by 106 pe

214、r cent over the next three to five years.39 Moreover GenAI,with the human in the loop,has the potential to slow rising costs and accelerate tasks across the entire R&D value chain and bring a greater proportion of services back within the four walls of biopharma companies,while improving experiences

215、 for internal staff and patients alike,and ultimately contributing to more efficacious therapies.40 Deloitte experts note that many companies in our cohort have built internal capabilities and partnerships to explore how this technology could impact various operational processes.But how can GenAI be

216、st be leveraged as a strategic tenant for transformational change?Figure 8 highlights some strategic applications that could enable companies to demystify complex disease biology,expedite drug discovery,cut study timelines,and revitalise the clinical trial experience.Strategies to improve productivi

217、tySince 2010,our cohort of companies have struggled to replenish their R&D pipeline with new assets at the same pace,and to the same value,as the assets leaving the pipeline due to successful regulatory approval or late-stage termination.With rising costs,long cycle times,looming patent expiries,a c

218、omplex M&A landscape and changing regulations,biopharma is nearing the point where the commercial portfolio is unable to sustain innovative R&D and support long-term growth.18Unleash AIs potential|Measuring the return from pharmaceutical innovationWhen developing the business case for investment in

219、digital and AI,the short-term costs need to be balanced against the long-term efficiency gains.Executing large-scale strategies requires setting up a governance function for making investments,assessing value realised,and monitoring ethical and legal risks from the use of AI.Our recent publication h

220、ighlights the need for cultural,leadership,and mindset within biopharma companies to scale transformation through AI and GenAI use.41 Successfully scaling technology adoption also entails addressing user anxiety,scepticism,and resistance to integrating these technologies into established workflows.F

221、requent demonstrations to users explaining how the technology helps to overcome frustrations in workflow and actively responding to user feedback during scale-up can help encourage wide-spread adoption.Source:Deloitte analysis,2024.Figure 8.Strategic applications of AI across the R&D value chainEnha

222、ncing participant experiences with stategic nudges to revolutionise recruitment and retention strategies Reduced drop out rate Faster recruitment Lower terminations for insufficient recruitmentRole of AIValue leversParticipant experiencesIdentify regulatory requirements across geographies,generate d

223、rafts of dossiers,and understand competitor regulatory strategy Higher regulatory successRegulatory intel and submission excellenceCollate and standardise trial data elements to create analysis-ready data sets and to auto-populate tables and charts in trial artifacts(e.g.,case report forms)Reduced t

224、otal time per phase On-time database lock Faster documentation creationDigital data flowAutomated protocol generation,drafting of study documents(consent form,agreements)and regulatory submissions Lower average protocol authoring time Lower average time to first enrollmentRapid design and startupOpt

225、imise target and biomarker identification and shortlisting candidates while assessing toxicity and therapeutic efficacy Improved clinical success rate Lower failure rates Higher number of NDASAl-driven drug discoveryPerform meta-analysis of clinical trial and research data to generate high quality h

226、ypothesis for drug repurposing Reduced pre-clinical costs Reduced time to market Higher NDAsDrug repurposing“I think the companies that are most successful and getting the most from their data are the ones that are investing in building mature capabilities for data architecture and the implementatio

227、n of FAIR principles in their data architecture.”Former Vice President,R&D IT,Top 20 Biopharma19Unleash AIs potential|Measuring the return from pharmaceutical innovationData-driven early-stage R&D to enhance likelihood of successDespite the large investments in time and money needed to develop a dru

228、g,nine out of ten drug candidates that enter trials fail to make it to market.42 Research shows that the cost of trial failure represents some 60 per cent of all development costs.43 However,recent research by IQVIA suggests the potential for improvement with the composite success rate reaching 10.8

229、 per cent,the highest since 2018.44 While not sufficient alone,improving the analytical insights from early stage R&D could enable companies to progress a greater volume and proportion of high quality candidates.As innovation becomes increasingly complex,investments in translational science can enab

230、le companies to better understand target protein binding,safety profiles,and efficacy,and to validate proof of mechanism pre-clinically or in early-stage R&D.“So if we could get beyond the eight per cent to 10 per cent success rate of drugs entering human testing that actually make it across the fin

231、ish line Just imagine if we failed 80 per cent of the time,we would be the best by a mile.”Executive Vice President,Research and Development,Top 20 BiopharmaAnalysing this depth of knowledge through quantitative decision frameworks could enable R&D organisations to reach go or no go decisions earlie

232、r and with deeper insights,and ultimately reach a stage where phase III studies are confirmatory rather than pivotal.Our interviewees predict that over the next few years,AI and ML algorithms will expedite drug discovery activities(from target identification,molecular design,and lead validation)maki

233、ng them increasingly commoditised.The differentiator is likely to be the quality and comprehensiveness of proprietary data these algorithms are trained on and continue to use.Building analytical engines that make it easy to manage internal clinical trial data,publicly available multi-omics datasets,

234、and patient-generated data(e.g.data from wearables)are likely to transform how companies use data for insight generation.Companies that invest today in building data architecture to use and securely share multiple data sets could realise significant benefits.Developing digital human cell models that

235、 closely mirror the biology of human health and disease can also enable an earlier and better understanding of the safety and efficacy of drug candidates.Furthermore,simulating outcomes from clinical studies can improve drug development by allowing researchers and scientists to test different scenar

236、ios and outcomes in a controlled and efficient manner.By using computer models,researchers can simulate the effects of different doses,treatment regimens,and patient populations on a drugs safety and efficacy.This can help identify potential safety concerns and optimise dosing and treatment strategi

237、es before clinical trials are conducted.Models can also be used to create digital twins of trial participants and simulate what would happen if a trial participant was in the control group thereby reducing the numbers of control candidates needed.20Unleash AIs potential|Measuring the return from pha

238、rmaceutical innovationPERCENTAGE OF LATE-STAGE PIPELINE Figure 9.Late-stage pipeline composition by therapy area,2020-2023Source:Deloitte analysis,2024.OncologyInfectious diseaseCardiovascularHematologyCentral nervous systemGastrointestinalImmunologyEndocrine and MetabolismRespiratoryGenito-urinaryO

239、ther90%80%70%60%50%40%30%20%10%0%100%2023202220212020Looking beyond over-concentrated therapy areas and modalitiesCompetitive intensity,scientific breakthroughs and regulatory incentives have skewed R&D spending toward certain areas,particularly oncology and rare diseases.By 2023,39 per cent of late

240、-stage development programmes for our cohort were focused on oncology and the proportion has been consistently greater than a third of the pipeline since 2020,see Figure 9.At the same time,a third of the cohorts development programmes were targeted at rare diseases in 2023.One interviewee highlighte

241、d the remarkable similarity of portfolio strategies across companies,that has driven competition to buy and develop similar assets within specific therapy areas.As competition in over-concentrated therapeutic areas heats up and the focus of payers on the equitable allocation of health care spending

242、rises,the current dynamic could change.We expect the focus on high burden and large patient population diseases such as diabetes,cardio-metabolic disorders,and mental health conditions to increase.The aging populations in many countries could also add to the demand for therapies in areas such as neu

243、rodegenerative diseases,cardiovascular diseases,and non-rare cancers to enhance both longevity and quality of life.“The challenge is that there are a lot of me-too.The 9th anti-PD1 got approved.Is it productive for a company to put out money and for regulators to approve another anti-PD1 when we alr

244、eady have so many?It doesnt help the field because you get a lot of me-toos or theres no innovation because youre all struggling in the same pool,but youre not sharing information.”Senior Director,Top 20 Biopharma21Unleash AIs potential|Measuring the return from pharmaceutical innovationOur M&A anal

245、ysis shows the continued focus on oncology,mirroring the late-stage pipeline composition,see Figure 10.However,there is also considerable investment across other therapy areas,notably immunology and CNS with 23 per cent and 14 per cent respectively of deal value in 2023.Recent breakthroughs have spu

246、rred greater interest and investments in certain disease areas.For instance,the approval of Novo Nordisks anti-obesity drug put obesity in the limelight as a public health emergency and a lucrative franchise,creating a race to bring a new class of anti-obesity medications to market.45 In 2023,antibo

247、dy-drug conjugate has become a centrepiece of platform-driven acquisitions,25 per cent of deals by our cohort targeted endocrine&metabolic assets.Looking at the top-grossing drugs,many were not planned from the outset,but were serendipitous findings that companies had the experience and ability to c

248、apitalise on.When opportunities arise,pipeline replenishment efforts are likely to be the most successful,when underpinned by flexible operating models and supported by a versatile toolbox to make investments across multiple disease and therapy areas.Beyond therapy area focus,late-stage pipelines to

249、day contain advanced modalities created from a handful of once-novel platform technologies(including cell therapies,RNA interference,and m-RNA).Interviewees expressed a view that there is an over-concentration of capital in many of these advanced modalities.As the next wave of technologies and techn

250、ology platforms evolves(such as gene editors and microbiome therapeutics),being purposefully selective could enable companies to be differentiated while building capabilities to tackle hard-to-drug targets.One company executive highlighted how their company chose strategically to stay away from cell

251、 and gene therapies while building capabilities through partnerships in anti-sense RNA.Such trade-offs are likely to be essential as companies choose bespoke partnerships and build roadmaps to explore new technology platforms.Endocrine&metabolicImmunologyOtherFigure 10.Top 20 biopharma M&A activitie

252、s by therapeutic area,2022-2023Deal volumeDeal value($mn)0%20%40%60%80%100%2023202220232022OncologyCNSNote:Reflects biopharmaceutical-related deals only and excludes transactions without publicly disclosed deal valueSource:Capital IQ,company websites,Deloitte primary research and analysis9%8%14%23%4

253、6%19%50%16%24%10%24%15%21%21%21%26%42%11%1929$69,551$144,83622Unleash AIs potential|Measuring the return from pharmaceutical innovationActions for R&D leadersBiopharma companies face serious headwinds:delays in technology adoption,looming patent expiries and increasing regulatory complexities.As the

254、 pace of scientific and technological advancements accelerates,from gene therapy to AI,the industry continues to face the same challenges in clinical research,including:recruiting and retaining a representative patient population delayed response to operational problems reliance on incomplete or un-

255、insightful data sources.These challenges will continue to plague the industry,unless a way is found to transform the existing culture of competitive behaviours to develop and share open source access to centralised operational data from across the entire industry,including academic medical centres a

256、nd site networks,contract research organisations(CROs),trial sponsors,and patients.Ultimately,transforming clinical trials will require companies to work entirely differently,drawing on change management skills,as well as partnerships and collaborations.This will require companies to develop highly

257、skilled interdisciplinary leadership and AI experts who can innovate,organise and guide others,as well as AI-friendly CEOs and board members to push for the adoption of AI.If biopharma succeeds in capitalising on AIs potential,the internal and external productivity challenges driving the decline in

258、the IRR of biopharma innovation will be reversed and the industry will thrive.However,before adopting AI solutions,there are a number of key questions that biopharma companies need to consider carefully in developing their strategies.23Unleash AIs potential|Measuring the return from pharmaceutical i

259、nnovationWhat are the main cost drivers of your clinical trial process?Where could AI have the most impact?Patient recruitment and data management are among the largest cost drivers of clinical trials and are currently whereAIshowsthemostpromise.How are you adapting your M&A strategy?There is a need

260、 to balance sustainable long-term portfolio value and near-term revenue needs and at the same time account for regulatory scrutiny,such as theIRAandEUpatentlaws.Have you developed a robust and sustainable AI strategy for clinical development and considered the extent to which you should partner with

261、 leading AI companies for drug discovery and development?Biopharmacompaniesbenefitfromselecting reliable partners to leverage theirextensiveknowledgeandexpertisegained from repeated experience,and inhavingAIsolutionsthatarespecifictotheirownproprietarydata.Have you prioritised consideration of how y

262、ou can both incorporate patient perspectives throughout study design and also deploy open communication channels during study execution?AI-enabled engagement during and after theconclusionofthestudywillattract,engage,and improve the retention of committed patients throughout the study.Have you estab

263、lished effective strategies for engaging with regulators,and is your regulatory function seen as a strategic asset?As the variety,velocity and volume of RWD submitted to regulators increase,regulatorswillalsoincreasetheuseofAItools in their processes.For biopharma,earlyengagementwithregulatoryauthor

264、ities to align on objectives,study design and use of digital biomarkers or surrogateendpointswillbeofcriticalimportance.Regulatory relationships needtobebasedonawin-winapproach.Do you understand the legal and compliance requirements needed to protect the increasing volume of R&D data?GDPR compliance

265、 in Europe and similar requirementsintheUSandelsewherewillbeimportant,especiallyasfailuretocomplycouldhavesignificantfinancialand reputational consequences.Moreover,biopharma companies need to ensure that any patient data used has specificconsentforthespecifiedpurposeand that it remains private and

266、secure.Do you have a strategy for developing the future workforce that includes the necessary skills and talent to integrate AI technologies into your clinical development?TheadoptionofAIinnovationwillrequirean internal team of experts,comprising biologists,chemists,engineers,data scientistsandbioin

267、formaticians,workingin cross-functional teams.The aim should be to promote an intrapreneurship culture,providingtheseteamswiththe freedom and resources to create innovative solutions.As discrete tasks areincreasinglyshiftedtowardsusingGenAI,youwillneedtoputestablishedguardrails in place to ensure th

268、e integrity of the outputs,including human validation of outputs.Do you have a clear understanding of the completeness,accuracy and potential bias in historical trial data?Clinical development has historically faced challenges in creating diverse clinical trial cohorts,meaning that leaders in R&D ne

269、ed to take care not to highly rely on historical clinical data or risk amplifying biases inherent in existing data sets.Ensuring that trustworthyAIframeworksandgovernance arrangements are in place can mitigate the potential for bias and unintended outcomes.Before embarking on study startup have you

270、automated document generation activities,to increase productivity?Using previous examples of clinical trial protocols,site contracting agreements,clinical report forms,and other key pieces ofpaperworkrequiredtojumpstartclinical trials,biopharma organisations canquicklydraftandrefinethedocumentation

271、required to establish newtestsites.Thiscanbeacriticalstepincreatingdiversitywithinpatientcohorts,allowingsitesinunder-servedgeographies to be established more quicklyandforlesseffort.Key questions for an optimum R&D strategy24Unleash AIs potential|Measuring the return from pharmaceutical innovationE

272、ndnotes1 InflationReductionActandMedicare,CentersforMedicare&MedicaidServices.Accessedon1April2024.Seealso:https:/www.cms.gov/inflation-reduction-act-and-medicare2 HowtheInflationReductionActWillImpacttheBiopharmaceuticalIndustry,LEK,9September2022.Seealso:https:/ HHSselectsthefirstdrugsformedicared

273、rugpricenegotiation,U.S.DepartmentofHealthandHumanServices(HHS),August29,2023.Seealso:https:/www.hhs.gov/about/news/2023/08/29/hhs-selects-the-first-drugs-for-medicare-drug-price-negotiation.html4 HowtheIRAwillaffectdrugdevelopment,BioSpace,6April2023.Seealso:https:/ Risingprotocoldesigncomplexityis

274、drivingrapidgrowthinclinicaltrialdatavolume,accordingtoTUFTScenterforthestudyofdrugdevelopment,GlobeNewswire,12January2021.Seealso:https:/ CaseexampledevelopedwithproprietaryTrialHubdata.Seealso:https:/ AnAI-designeddrugismovingtowardapprovalatanimpressiveclip,SingularityHub,18March2024.Seealso:http

275、s:/ HowAIisbeingusedtoaccelerateclinicaltrials,NatureIndex,13March2024.Seealso:https:/ Broadeningclinicaltrialparticipationtoimprovehealthequity,Deloitte,accessedon1April2024.Seealso:https:/ Seizethedigitalmomentum:Measuringthereturnfrompharmaceuticalinnovation2022,Deloitte,January2023.Seealso:https

276、:/ Whatisthefutureofvirtualclinicaltrials,Deloitte,13September2022.Seealso:https:/ Ibid.13 Florez,M.,Smith,Z.,Olah,Z.etal.QuantifyingSiteBurdentoOptimizeProtocolPerformance.TherInnovRegulSci58,347356(2024).Seealso:https:/doi.org/10.1007/s43441-023-00602-514 NewHorizons:PioneeringPharmaceuticalR&Dwit

277、hGenerativeAIfromlabtotheclinicanindustryperspective,Arxiv,21December2023.Seealso:https:/arxiv.org/pdf/2312.12482.pdf15 Ibid.16 EmbeddingenvironmentalsustainabilityintopharmasDNA,Deloitte,October2022.Seealso:https:/ Predictingthefutureofhealthcareandlifesciencesin2025,Deloitte,accessedon1April2024.S

278、eealso:https:/ Seizethedigitalmomentum:Measuringthereturnfrompharmaceuticalinnovation2022,Deloitte,January2023 https:/ BiopharmasIRAreadiness:FromWhat?ToNowwhat?,Deloitte,22June2023.Seealso:https:/ HowmightIRAsdrug-pricingprovisionsaffectstakeholders?,Deloitte,28September2023.Seealso:https:/ HHSSele

279、ctstheFirstDrugsforMedicareDrugPriceNegotiation,U.S.DepartmentofHealthandHumanServices(HHS),August29,2023.Seealso:https:/www.hhs.gov/about/news/2023/08/29/hhs-selects-the-first-drugs-for-medicare-drug-price-negotiation.html22 WhatdoestheUSInflationReductionActmeanforpharma,LGT,26September2023.Seeals

280、o:https:/ StrengtheningTheInflationReductionActByPredictingDrugmakersAvoidanceAndGaming,HealthAffairs,2March2023.Seealso:https:/www.healthaffairs.org/content/forefront/lawmakers-can-strengthen-inflation-reduction-act-predicting-pharmaceutical-companies24 Nurturinggrowth:Measuringthereturnfrompharmac

281、euticalinnovation2021,Deloitte,January2022.Seealso:https:/ AIs potential|Measuring the return from pharmaceutical innovation25 Seizethedigitalmomentum:Measuringthereturnfrompharmaceuticalinnovation2022,Deloitte,January2023 https:/ EuropeanHealthUnion:Commissionproposespharmaceuticalsreformformoreacc

282、essible,affordableandinnovativemedicines,EuropeanCommission,26April2023.Seealso:https:/ec.europa.eu/commission/presscorner/detail/en/IP_23_184327 Druglaunchesreflectoverallcompanyperformance,DeloitteInsights,accessedon1April2024.Seealso:https:/ BiopharmaFacingTwoMajorCrisesAHugePatentCliffAndPriceCo

283、ntrols,Forbes,26June2023.Seealso:https:/ LifesciencesM&A:2023trends,Deloitte,accessedon1April2024.Seealso:https:/ Ibid.31 Howsteepispharmaspatentcliff,PharmaVoice,14June2024.Seealso:https:/ Ibid.33 R&Dspendingasrevenueshareofleading10pharmaceuticalcompaniesin2022,Statista,23October2023.Seealso:https

284、:/ BiopharmaFacingTwoMajorCrisesAHugePatentCliffAndPriceControls,Forbes,26June2023.Seealso:https:/ Ibid.36 Intelligentclinicaltrials:TransformingthroughAI-enabledengagement,DeloitteInsights,accessedon2April2024.Seealso:https:/ RealisingtransformativevaluefromAI&generativeAIinLifeSciences,Deloitte,Ap

285、ril2024.Seealso:https:/ Intelligentdrugdiscovery:PoweredbyAI,DeloitteInsights,accessedon1April2024.Seealso:https:/ LeveragingAIandMLinDrugR&D:CurrentandFutureImpactsontheLifeSciencesIndustry,LifeSciSearch,9October2023.Seealso:https:/ CanlifesciencescompaniesunlockthefullvalueofGenAI,Deloitte,3Octobe

286、r2023.Seealso:https:/ Algorithmtolab,exploregenerativeAIsimpactinlifesciences,Deloitte,accessedon1April2024.Seealso:https:/ ProbabilityofSuccessinDrugDevelopment,ASCPT,19April2022.Seealso:https:/ Achievingend-to-endsuccessintheclinic:PfizerslearningsonR&Dproductivity,ScienceDirect,March2022.Seealso:

287、https:/doi.org/10.1016/j.drudis.2021.12.010.44 GlobalTrendsinR&D2024:Activity,productivity,andenablers,IQVIA,22February2024.Seealso:https:/ WorldPreview2023:PharmasAgeofUncertainty,Evaluate,14August2023.Seealso:https:/ AIs potential|Measuring the return from pharmaceutical innovationContactsAuthorsE

288、mily MayManager,UK Centre for Health Solutions+44(0)20 7007 5694elmaydeloitte.co.ukKaren Taylor Director,UK Centre for Health Solutions+44(0)20 7007 3680kartaylordeloitte.co.ukLeena GuptaSenior Manager,US Center for Health Solutions+1(212)436 Wendell MirandaDeputy Manager,US Center for Health Soluti

289、ons+1(615)209 Deloitte EMEA contactsColin TerryUK Life Sciences&Healthcare Consulting Leader+44(0)20 7007 0658colterrydeloitte.co.ukNaveed PanjwaniClinical R&D Consulting Lead,UK Life Sciences&Healthcare+44(0)20 7007 6272npanjwanideloitte.co.ukHanno RonteBiopharma Leader,North,South Europe and UK+44

290、(0)20 7007 2540hrontedeloitte.co.ukAlexander MirowSwitzerland Life Sciences Industry Consulting Lead+41 5 279 6708alexmirowdeloitte.chDoug McKinnelPartner,Life Sciences Regulatory Risk+41 58 279 9146dmmckinnelldeloitte.chDeloitte US contactsJay BhattManaging Director,US Center for Health Solutions a

291、nd Health Equity Institute+1(215)446 Kevin DondarskiPrincipal,Life Sciences Strategy+1(215)997 Vicky LevyPrincipal,Global Life Sciences Sector Leader+1(617)437 Deloitte project teamWe would like to acknowledge the significant contribution of the following people to the analysis,research and drafting

292、 of this report,without which this report would not have been possible:Shreya Nagpal,Victoria Wu,Apoorva Singh,Gargi Khandelwal,Asef Reza Ahmed,Gillian Walsh,Abhishek Rai Saxena,Hitesh Bhatia,Akshaya P V,Chetna Luthra,Muskan Agrawal,Vadey Satya Prashanth,Ajay Jain,Kumud Mandal and Sampriti Gupta.Eva

293、luate contributorsWe would like to acknowledge the contribution of Mark Lansdell,Natasha Choukkar,Jack Sweeting and Tobias Brady.Contact informationTo see more research and sign up for future publications visit:www.deloitte.co.uk/centreforhealthsolutions 27Unleash AIs potential|Measuring the return

294、from pharmaceutical innovationNotes28Unleash AIs potential|Measuring the return from pharmaceutical innovationThis publication has been written in general terms and we recommend that you obtain professional advice before acting or refraining from action on any of the contents of this publication.Del

295、oitteLLP accepts no liability for any loss occasioned to any person acting or refraining from action as aresult of any material in this publication.Deloitte LLP is alimited liability partnership registered in England and Wales with registered number OC303675 and its registered office at 1New Street

296、Square,London EC4A 3HQ,UnitedKingdom.Deloitte LLP is the United Kingdom affiliate of Deloitte NSE LLP,amember firm of Deloitte Touche Tohmatsu Limited,aUK private company limited by guarantee(“DTTL”).DTTLand each of its member firms are legally separate and independent entities.DTTLand Deloitte NSE LLP do not provide services to clients.Pleasesee to learn more about our global network of memberfirms.2024 Deloitte LLP.Allrights reserved.Designed and produced by 368 at Deloitte.J31736-2