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1、LIFE SCIENCES:TRENDS FOR THEFUTURE#BreakthroughsForLife2Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge InstituteKnowledge Institute2Life Sciences:Trends for the Future|External Document 2024 Infosys Limited3 External Document 2024 Infosys Limited|Life Sciences:Tr

2、ends for the FutureKnowledge InstituteTable of contentsKnowledge Institute3Foreword 4Executive summary 6Trend 1:Generative AI promises innovation and enhancements to the life sciences value chain 10Trend 2:AI and behavioral science transform digital therapeutics 18Trend 3:From traveling salespeople

3、to digital feedback loops:The future of life sciences sales and marketing 26Trend 4:The increase in virtual trials tips the balance toward remote interactions,improves patient data,and lowers the cost of clinical trials 34Trend 5:The rise of intelligent pharma manufacturing 42Trend 6:The new pharma

4、supply chain:Collaboration anddata 50Trend 7:Knowledge sharing and data integration build stronger healthcare ecosystems 58 External Document 2024 Infosys Limited|Life Sciences:Trends for the Future4Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge InstituteThe life

5、 sciences industry is on the brink of a new era of innovation.From AI-powered clinical trials to digital patient companions,every industry process and touchpoint is being reimagined or upended.Theres never been a more exciting time in life sciences.Whether youre in the pharmaceutical,biotech,medical

6、 devices,or animal health sector,theres abundant opportunity to catalyze change in every part of the value chain.Data-driven platforms can uncover gene therapies,mobile apps can aid remote patient management,and cloud platforms can drive valuable supply-chain synergies.The biggest needle-mover thoug

7、h,seems to be generative AI.It will drive much-needed efficiency gains in many areas research,drug safety,trial reporting,pricing,patient-centricity,and skills gaps across the enterprise.As well as driving efficiency,generative AI will also enable creativity,helping those working in life sciences to

8、 correlate research outcomes faster and engage with patients better.This isnt just giddy optimism.It is a belief formed by having seen the transformative impact digital,cloud and AI have had for our life sciences clients and their customers.This journal shares our insights and expertise with you.Our

9、 leaders and experts have drawn from their experiences working with industry chiefs to help them build their strategies and navigate their landscapes.This journal delivers our perspective on the technologies we think are profoundly changing the way we in the life sciences sector do our work.Even as

10、digital technology plays a greater role in pioneering innovation,synergizing operations,and personalizing journeys,we believe the essence of the industry remains the same as it has over decades which is to enable breakthroughs for life.It is in this spirit that we seek to serve at Infosys.I hope you

11、 find that spirit in our journal,and that its insights help you respond to the challenges and opportunities that will define the transformation agenda ahead.#BreakthroughsForLifeKnowledge InstituteForewordSubhro MallikExecutive vice president and head of life sciences,Infosys5 External Document 2024

12、 Infosys Limited|Life Sciences:Trends for the FutureKnowledge InstituteKnowledge Institute5 External Document 2024 Infosys Limited|Life Sciences:Trends for the Future6Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge InstituteLife sciences is teeming with innovation

13、,from new techniques for drug discovery and improvements in supply chain and manufacturing to better data integration across the ecosystem.At the same time,drug shortages,high costs of healthcare,and data privacy risks threaten to stifle this growth.Fortunately,digital advances and transformative te

14、chnologies such as artificial intelligence(AI)and intelligent manufacturing are accelerating the evolution of this industry and improving lives of billions.Infosys has created this inaugural life sciences journal to share our perspectives and aid industry leaders on their decisions related to these

15、strategic topics.Through research,workshops,and executive interviews,we identified seven trends at the intersection of digital technologies and life sciences.We additionally surveyed 100 life sciences executives at leading firms on their views for planned investments among these seven trends.Althoug

16、h generative AI is stealing the spotlight,demonstrating the impact it has had in such a short time,each of these seven trends will reshape the life sciences industry over the next two to three years.Nearly three-quarters(73%)of leaders we surveyed say their firms will spend between$10 million and$50

17、 million on each of these areas in the next two years,and an additional 20%will spend more than$50 million.These seven trends will enable leaders to harness rapid change,transform their enterprises,and deliver breakthroughs for life.Knowledge InstituteExecutive summary7 External Document 2024 Infosy

18、s Limited|Life Sciences:Trends for the FutureKnowledge Institute1.Generative AI promises to drive innovation and efficiency,while requiring enterprises to enact rigorous safeguards for responsible and explainable AI.From chatbots and AI-assisted research agents,to sales representatives engaging with

19、 healthcare professionals to create rapid summaries of submission-related content,we anticipate that innovations built with generative AI will impact the entire life sciences value chain with patients being the primary beneficiaries.2.Digital therapeutics will continue to grow rapidly for the next d

20、ecade.These new tools will drive personalization,improve efficacy of existing medicines and devices,increase access to patient data,rely on data across the patient journey,and strengthen brand positioning amid the consumerization of healthcare.3.Virtualization of sales and marketing transformed how

21、organizations engage with healthcare providers.The shift to virtual engagements initially driven by the pandemic has increased the adoption of hybrid sales professionals powered by digital tools.This is leading to better insights and a continuous feedback loop that allow sales reps to meet physician

22、s at their point and time of need first multichannel and then omnichannel.4.Hybrid and virtual clinical trials will become the norm,saving time and money,and creating a diverse patient pool that leads to more accurate results,accelerates enrollment,and reduces patient dropout rates.Advancements in a

23、t-home data-capture technologies,wearables,and remote monitoring will increase patient satisfaction and also create new opportunities from the large volume of data collected.5.Intelligent manufacturing positions life sciences to move from industry tech laggard to leader as production of new therapeu

24、tics drives faster progress than other industries.The application of internet of things,big data,and AI will enable real-time process adjustment capabilities and optimize quality control to increase product efficiency,at the same time enabling simplification of regulatory compliance.6.Supply chain r

25、esilience will become a necessary criterion as organizations manage their diverse portfolio of small-and large-molecule drugs.A shift to enterprise platforms that deliver planning at global scale while ensuring visibility of raw material and drug products at regional scale will enable the vision of

26、a flexible supply chain network meeting their growth needs.Generative AI25%Data and evidence integration18%Digital therapeutics15%Sales and marketing transformation12%Intelligent manufacturing12%Hybrid and virtual clinical trials10%Supply chain optimization9%TrendForecast investments as percentage8L

27、ife Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge Institute7.Data and insight integration across the healthcare ecosystem will lead to transformative new products and commercial successes as data,models,and knowledge are shared across the value chain of an organizati

28、on.AI will play a big role in this integration,especially for data navigation,linkage,and interoperability.Recent years have seen a significant adoption of digital technologies within the life sciences ecosystem,and I am confident that these trends will be a substantial enabler for change within org

29、anizations while enriching the lives of patients.9 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge InstituteKnowledge Institute9 External Document 2024 Infosys Limited|Life Sciences:Trends for the Future10Life Sciences:Trends for the Future|External Document 2024

30、Infosys LimitedKnowledge InstituteTrend 1:Generative AI promises innovation and enhancements to the life sciences value chain Generative AI in life sciences promises to drive both innovation and strong growth,offering faster drug discovery and development,better and earlier diagnosis,and insights fr

31、om diverse sources of data.Life sciences leaders must tread a careful line between pushing for innovation and responding to very real concerns about data privacy and ethics as regulatory regimes around the world set standards and guardrails.If companies embed responsible AI at every stage,we expect

32、that generative AI will significantly reduce time to market and increase effectiveness of R&D by accelerating drug discovery,optimizing clinical trial design,enhancing data analysis,and streamlining regulatory submission.Generative AI burst into the mainstream in 2023,and companies are rapidly exper

33、imenting and developing exciting use cases,such as an AI tool capable of detecting tiny cancers almost invisible to the human eye.Tools like these have tremendous potential to transform life sciences in this decade.We anticipate that innovations built with generative AI will impact the entire life s

34、ciences value chain,from drug discovery to distribution with drug development being the next frontier.Boston Consulting Group Knowledge Institute11 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge Institutehas projected that the generative AI market in healthcare w

35、ill grow from$1 billion now to$22 billion by 2027,a compound rate of 85%.Potential benefits include fewer bottlenecks,lower administrative burdens,faster drug discovery,and improved data analysis.However,generative AI introduces and amplifies ongoing concerns about responsible AI,particularly data q

36、uality,privacy,ethics,and security.In life sciences,these risks could compromise patient records and drug safety.Life sciences companies are likely to face new regulations around AI,evolving as the technology matures and societal implications are better understood.Life sciences firms must walk a tig

37、htrope of simultaneous rapid experimentation and risk mitigation to unlock value responsibly from generative AI.To realize business value while minimizing risk,it is imperative to understand patient impact,ensure ownership of AI models,and maintain strong data and AI governance.Ethics,governance,and

38、 transparency are vital to earn trust from the medical community,regulators,and the general public and they are central to an AI-first approach.Looking ahead,we believe that taking this approach will lead to reduced time to market and will facilitate managing the increasing complexity in drugs,regul

39、atory requirements,and patient expectations.Exciting potentialAs in all industries,generative AI investment in life sciences exploded in 2023.As companies across sectors continue to increase experimentation and use cases,2024 looks to be the year when many get to grips with this transformative techn

40、ology.In March 2024,the UKs National Health Service piloted an AI tool called Mia to detect cancer in the mammograms of more than 10,000 women.It found tiny tumors nearly invisible to the human eye in 11 of the mammograms.In another use case,a deep learning AI algorithm detected autism spectrum diso

41、rder using retinal images with a claimed 100%accuracy.However,since AI could spur overdiagnosis and associated concerns,providers will also need governance and a risk-based approach,such as humans-in-the-loop,to deploy these capabilities responsibly.Transformative use cases such as these indicate ge

42、nerative AI has the potential to impact every part of the life sciences value chain,from drug discovery to manufacturing.For example,our own work with AI assistants in clinical and regulatory operations indicates that companies can significantly reduce time to market.Executives belief in this potent

43、ial is reflected by their increased investment in AI.Enterprise spending on generative AI services,software and infrastructure is anticipated to increase from$16 billion in 2023 to$143 billion by 2027 at a compound annual growth rate of 73.3%.Life sciences and healthcare have increased spending in s

44、imilar fashion.Research and development in generative AI use cases,which included protein and molecule generation and synthetic healthcare datasets,reached$983 million in fundraising in 2023.Meanwhile,$216 million was raised for commercial and medical projects within verticals such as predictive too

45、ls,patient-doctor interaction summaries,and patient data analysis.Life sciences leaders 12Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge Instituteare spending big on generative AI and have plans to accelerate even more.According to an Infosys survey of 100 life s

46、ciences executives,73%of respondents say they will spend$15 million a year or more on generative AI more than they plan to spend on other technology initiatives.Nearly one-third(29%)indicate that their investment in generative AI will grow by more than 40%from 2024 to 2026.As shown in Figure 1,spend

47、ing amounts and projected spend change are beginning to diverge among life sciences businesses.The most popular industry application for generative AI is in drug discovery and early-stage development,which polled as the top choice for investments over the next five years.Generative AI in drug discov

48、eryDrug discovery is a high-risk,high-reward proposition.The discovery process can take up to 14 years,and 97%of potential cancer drugs fail during clinical trials.Generative AI can improve several stages of the discovery process through Figure 1.Although AI spending is up,the cost and complexity of

49、 putting AI to work splits enterprises into distinct cohortsSource:Infosys Knowledge InstituteFor the first time in human history,biology has the opportunity to be engineering,not science.were going to have incredible tools that bring the world of biology into theworld of computer science.And that i

50、s going to be profound.Jensen HuangCEO,NVIDIA,speaking at University of California,BerkeleyGenerative AI spendGenerative AI spend by revenue(%)?Generative AI spend change(%)?Low change(%)High change(%)Lowspend(%)Highspend(%)?applications such as generative models for drug discovery,reinforcement lea

51、rning,and drug-disease associations.Life sciences researchers and companies have 13 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge Institutealready put generative AI to practical use in their fields of expertise.Stanford Medicine has proven this point with Synthe

52、Mol,a generative AI model providing new drugs for antibiotic-resistant bacteria.Insilico developed an anti-fibrotic small molecule inhibitor that has progressed from origins in a generative AI algorithm to a Phase II clinicaltrial.Merck has used a small molecule generative AI tool produced by Variat

53、ional AI to explore a wider range of potentially therapeutic compounds.Insilico managed to develop its pulmonary fibrosis drug candidate within 18 months using Pharma.AI.A year later,in 2022,the company announced that its total time from target discovery to phase I took less than 30 months,thanks to

54、 its generative AI.Within the same sector,firms Ordas Bio and Absci are focusing on the use of generative AI to further pharma breakthroughs within the protein vertical.Ordas Bio is using deep learning and proprietary“generative perception systems”to create mini-proteins for pharma and biotech compa

55、nies.Absci uses zero-shot learning a type of deep learning that makes inferences about concepts it has not been directly shown to validate and design de novo antibody candidates to reduce cycle time for drugdiscovery.NVIDIA is collaborating with Amgen,the pharma giant specializing in biological medi

56、cations through advanced genetics,to build generative AI models for drug discovery.NVIDIA CEO Jensen Huang believes that what he terms“digital biology”will be the“next amazing revolution,”with the revolution already underway in fields such as genomics,cell engineering and synthetic biology.The next

57、frontierOn the development side,generative AI has become the new paradigm,with its significant potential to impact the entire cycle of clinical trials,from study design to patient recruitment to analyzing data.(See also Trend 7).It can also help reduce cost and accelerate clinical trials.(See also T

58、rend 4.)A recent article in Nature describes ways generative AI is revolutionizing clinical trials.In addition to design,recruitment and analysis,generative AI also has the potential to deliver prompts and encouragement that keep trial participants enrolled and engaged in trials,Nature notes.Recruit

59、ment and retention are well-known major challenges in getting a trial to completion.On the analysis front,generative AI and AI algorithms can potentially process and analyze complex datasets quickly,identifying patterns and correlations that might be missed by traditional methods.This capability all

60、ows for more adaptive and flexible trial designs,where adjustments can be made based on interim results.In addition to generative AIs potential to propel clinical trials,it might also allow companies to develop drugs via synthetic data,or computer-generated data used as a replacement of data from hu

61、mans or real-world events.In the quest to treat diseases,clinical research needs high-quality data.14Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge InstituteAs disease study grows more specific,the datasets can grow thin.Here,generative AI applied to quality smal

62、l data sets can be used to synthesize larger datasets that can advance research and treatment.Published research shows success using this approach in studying leukemia.Augmenting existing solutionsWhile more modest than new miracle drugs and game-changing innovations,generative AI also augments exis

63、ting processes.Solutions driven by generative AI to enhance process across pharmacovigilance,clinical trials,personalized medicine,data management and analysis,and administration significantly reduce time to market and costs.As generative AI models are trained to detect adverse events,they can enhan

64、ce pharmacovigilance.Patient reactions are analyzed using data from multiple sources to alert for issues or clinical trial complexities.Data ingestion and trend detection improve pharmacovigilance,understanding patterns that could contribute to adverse reactions.As with the United Kingdoms NHSs succ

65、ess in reading mammograms,generative AI-fueled innovation extends beyond crafting novel therapeutics to diagnostic innovations.A recent study shows that generative AI can detect sepsis in patients,and Paige.AI uses generative AI to improve early detection of prostate cancer.It is the first firm to w

66、in FDA approval for the use of AI in digital pathology.Personalized,precision medicine is another area that directly benefits from the analytical and interpretive capabilities of generative AI.Access to vast quantities of data offers deeper patient insights,allowing for physicians,healthcare organiz

67、ations,and pharmaceutical companies to create treatment plans that reflect the individuality of the patient.Previously,it was not possible to achieve this level of personalization across a detached medical history,and at scale.Patients visiting multiple doctors across different specialties did not r

68、eceive cohesive care due to fractured datasets and limited visibility.However,generative AI through large models creates a holistic patient picture and uses that information to develop a personalized care plan for that individual.While the technology is in place,access to all this patient data is co

69、nstrained by healthcare regulators(e.g.,HIPAA in US,NIH in UK).In a similar vein,generative AI organizes disparate data sources,gives structure to unstructured data,and brings together apparently unrelated information in ways that deliver new meaning.Generative AI has the capacity to absorb and anal

70、yze data from billions of sources,so it could potentially collate data from multiple sources to extract insights,manage administration,refine patient interactions,and collect historical medical knowledge.This is especially valuable for clinical trials.For example,data held within traditionally fract

71、ured sources such as physician notes,laboratory notes,clinical trial insights and 15 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge InstituteEmbedding personal generative AI assistants into everyday work processes to improve speed and productivity is n ot a choic

72、e of“if to do it”but rather“when to do it.”Martin WoergaardChief executive officer,BASE life sciencehistorical medical knowledge could be ingested and analyzed to build health and pharma intelligence.Healthcare providers and pharma companies can then use this intelligence to personalize medicine,dis

73、cover bottlenecks in healthcare or operations,and improve clinical trial outcomes.The Truveta Language Model is a large language model designed to overcome the complexity of siloed and inaccessible healthcare data.Its being used to turn electronic health record(EHR)data into accurate data points for

74、 drug,disease,or device research.Its goal is to simplify access to data and improve patient outcomes.Finally,within the broader generative AI category,Google has consistently worked to build a safe,consumer-focused EHR record since 2008.The companys Google Health project was shut down in 2012,but th

75、e tech giant has continued to work toward more connected health applications through the use of generative AI,including two new models launched toward the end of 2023.These focus on improving administrative tasks and research development.The need for guardrailsTo reap any benefit from generative AI,

76、life sciences companies must put in place guardrails that address the security,privacy,ethical,data quality,and trust concerns its existence brings into play.Life sciences companies must address these concerns before generative AI is implemented at scale.Specific solutions are emerging to address th

77、e sectors major challenges of access to universal data and shared insights.However,even as generative AI solutions offer“black box”solutions,they raise other thorny problems.Any generative AI data solution must answer ethical,accessibility and security questions about non-transparent and potentially

78、 biased data use that could impact patient trust and outcomes.Experts currently advocate for a glass box model that treats data ethically and transparently.Placing humans at the center of these innovations and ethical practices provides a 16Life Sciences:Trends for the Future|External Document 2024

79、Infosys LimitedKnowledge Institutesimple yet effective guide for generative AI implementation and will create a safer path to improve patient and practitioner outcomes.Confidence over ROIThe life sciences and healthcare sectors have struggled to realize benefits from AI,despite heavy investment.In f

80、act,it lags other industries in return on AI investment possibly because of the greater volume of data that must be analyzed.Computer processing power and capacity must increase before complex conditions can be mapped and addressed with confidence.Also,healthcare data requires considerable curation

81、before it can be used in AI models.Generative AI provides an opportunity for life sciences to significantly increase their return on AI investment.As shown in Figure 2,chatbots currently demonstrate the Figure 2.Life sciences leaders are confident generative AI will provide ROI in the next three yea

82、rs Source:Infosys Knowledge InstituteNote.Percentages do not total to 100 due to rounding.DomainGenerative AI use caseRespondents(%)Drug and protein sequence design Personalized drug development Synthetic dataDrug discovery andearly development AI protocol assistance Generate protocol Trial onboardi

83、ng Virtual trialsClinical development Field-force assistance Opinion leader identifcation Personalized marketing Precision targeted materials Virtual sales representativesSales and marketing Connected devices and remote monitoring Digital twins Industrial internet of thingsManufacturing Flexible sup

84、ply chain modeling Sustainability Supply chain resilience Supply chain visibilitySupply chain Chatbots for patients Digital therapeutics Personalized treatment Symptoms tracking and treatmentPatient and healthcare0102030405060708090100Achieving ROIProbably achieve ROIProbably not achieve ROII dont k

85、nowWill achieve ROI17 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge Institutehighest ROI for life sciences enterprises.This is not surprising,as chatbots are a mature technology,developed well before the recent wave of generative AI innovation.Other areas demons

86、trate ROI as well,with sales and marketing as the most productive use cases.In fact,our research shows each area of potential generative AI-assisted innovation demonstrates some return.While this points to a positive future,prioritization and adoption challenges must be overcome to realize this pote

87、ntial.The area most anticipated to increase ROI in the coming years is drug,gene,and protein sequence design,with nearly six in 10 life sciences leaders saying that this area will drive returns.Evolving law and regulationAI legislation and regulation will be codified in the coming years,as countries

88、 take note of the 2024 EU AI Act and study its impacts.In the meantime,guidance is being formulated for the life sciences sector.In the US,the Department of Justice is concerned that the use of generative AI in health records could result in fraud or faulty recommendations,while the Office of the Na

89、tional Coordinator for Health Information Technology has released a draft federal health IT strategic plan that highlights the need for education and transparency around the use of generative AI.The Coalition for Health AI(CHAI)has developed a blueprint for generative AI in healthcare,and its effort

90、s are echoed by the Generative AI Council to Advance Life Sciences Innovation,a group of leaders from pharma,academia,and technology.However,the industry is also wary.Our research shows that life sciences leaders are keenly aware of the barriers that hinder implementation and adoption,based on previ

91、ous technology initiatives and their pervasive regulatory environment.Half of all leaders cited legislation and/or regulatory barriers as either significant or highly significant obstacles to their use of new technologies,including generative AI.This is a looming challenge for industry leaders.Inves

92、tment and innovation with generative AI are accelerating.But trust,regulation and legislation move more slowly,and are perceived as obstacles by half of life sciences leaders.This creates a gap between the confidence to invest in the tech and the confidence to use it in a legally and ethically sound

93、,compliant way.This is particularly relevant in life sciences,where misuse,mistakes,and bias can impact on human life.Guardrails must include accountability,ethics,awareness,transparency,and regulatory compliance at the very least.Designing these now into how organizations use generative AI,at the b

94、eginning of their journey,will embed these principles as part of responsible AI and an AI-first mindset.Generative AI has the potential to dramatically uplift life sciences,improving R&D,engagement,and efficiency.The research insights reinforce the imperative for responsible leadership to guide this

95、 exciting technology.Enthusiastic experimentation will transition to methodical adoption.Enterprises that balance these factors can create significant value while serving patients better.18Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge InstituteTrend 2:AI and beh

96、avioral science transform digital therapeutics The Covid-19 pandemic turbo-charged the emerging trend of digital therapeutics,with practitioners and life sciences leaders increasingly aware of their potential to improve access to better,more personalized treatment for a wide range of conditions.Digi

97、tal therapeutics will surge in effectiveness and in market growth by combining AI and insights from behavioral science.We expect that pharma and medical device companies will continue to evaluate,incubate,promote,and include digital therapeutics to improve efficacy of existing medicines and devices,

98、increase access to patient data across the patient journey,and strengthen brand positioning.Advances in digital therapeutics are changing how and where healthcare is delivered,moving care from the hospital to the home.These advances augment existing therapies by tracking and promoting wellness,detec

99、ting preconditions,and monitoring and managing health conditions digitally.This shift has vast potential to help prevent,manage,and treat diseases and conditions,potentially reducing the burden of disease and health administration costs.It also improves health equity by providing in-home treatment f

100、or hard-to-reach communities,which often have the most unmet needs,Knowledge Institute19 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge Instituteand for groups previously considered niche like women.Further,it has the potential to change the trajectory of healthc

101、are for developing countries in ways uniquely suited to the geographic needs and befitting economic constraints.Digital therapeutics help healthcare providers tailor interventions prescribed to patients and ensure compliance.For maximal effectiveness,digital therapeutics require providers to leverag

102、e insights into peoples behaviors and what influences these behaviors.These insights are uncovered through a combination of behavioral science,AI,and datasets from peoples social lives.However,technology also introduces risks from device and application obsolescence and ecosystem incompatibility.The

103、se risks create additional compliance barriers when vulnerable people use unsupported devices and apps.In addition,risks of data leakage or theft increase when health technology is used outside of a clinical setting.Rapid growth,vast potentialThe rapid growth of medical technology in the past few de

104、cades has given rise to this new,digital category of therapeutic interventions.These include companion software to help treat,manage,or prevent conditions and diseases,and span technologies that include web and mobile apps,wearable and ambient sensors,virtual reality and video games.Many of these te

105、chnologies are enabled by AI and data analytics delivered at the edge.Digital therapeutics now include pills with sensors that transmit information directly to a mobile device to ensure compliance with treatment regimens.For patients with diabetes,these applications sync pharmaceutical therapies,fit

106、ness brands,and health trackers with glucose meters.Another novel product is smart contact lenses that detect glucose levels in tears.Digital therapeutics function most effectively in combination with conventional therapies,often with the intention to improve patient adherence to treatment regimens.

107、Digital therapeutics is a significant growth area in life sciences,with the market expected to jump from$4.8 billion in 2023 to$25.3 billion in 2032.With AIs rapidly increasing capabilities,market growth and efficacy stand to exponentially increase.Indeed,94%of companies in our survey of executives

108、from Figure 1.Almost all companies plan to increase their spending on digital therapeutics in the next two yearsSource:Infosys Knowledge Institute6%Nochange71%Slightincrease23%Signifcantincrease0%Decrease94%20Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge Institu

109、tetop life sciences companies say they plan to increase spending on digital therapeutics in the next two years(Figure 1).With the growing recognition that these technologies improve patient quality of life,a rich partnership ecosystem is developing among the healthcare and pharmaceutical industries,

110、commercial companies,research entities,and technology and service providers.Our survey asked executives to allocate 100 points to indicate investment areas among subtrends enabling the increased impact of digital therapeutics.Priorities were split quite evenly(from 21%to 27%)among integration with t

111、raditional therapies,remote patient monitoring,virtual care platforms,and patient engagement and adherence(Figure 2).These enabling subtrends are also part of a broader move towards decentralized,hybridized healthcare.Virtual care platforms,for instance,provide a range of services to patients to mee

112、t them outside of the physical office and make it easier to get the care they need.In Trend 4 in this journal,we discuss the benefits of combining virtual clinical trials with in-person clinical trials:improved patient engagement,experience,and adherence.For both virtual trials and digital therapeut

113、ics,remote monitoring is a less invasive way for researchers and HCPs to gather patient data.From startup to scaleAs an emerging category within healthcare,digital therapeutics is mostly the domain of small innovative firms and startup ventures.Figure 2.Life sciences companies plan to spend in four

114、critical areasSource:Infosys Knowledge InstituteIntegration withtraditional therapies?Remote patientmonitoring?Patient engagementand adherence?Virtual careplatforms?Note.Percentages do not total to 100 due to rounding.Digital therapeutics increases patient adherence to protocols and improves health

115、outcomes.It complements contemporary treatments with digital options,addressing gaps in an already constrained healthcare ecosystem.Gurdeep S.RoopraiAssociate vice president,Infosys Life Sciences21 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge InstituteBut it is

116、 already very relevant to larger life sciences organizations,pharmaceutical and medical technology alike.Here are two compelling reasons.First,startups will only achieve reach to a large number of patients with the sponsorship of large life sciences organizations.For the big incumbents,early-stage p

117、artnerships or incubator relationships smooth the integration of innovative new digital tools with established processes and systems.Second,as digital therapeutics reach scale,they will spawn more and richer data about patients and patient journeys.The large and established life sciences companies t

118、hat engage with digital therapeutics at the present stage will have access to that data.Rich data then has the potential to influence patient adherence,enhance efficacy and improve patient outcomes.AI and behavioral scienceAn especially promising area is the combination of AI with insights from beha

119、vioral science.For instance,an app can analyze a users behavior and predict when to nudge them to take medication,exercise,or follow their prevention or treatment plan.As described in Trend 4 in this journal,non-invasive monitors collect patient health data,such as facial emotions,gait patterns,and

120、sedentary time.Similarly,data from monitors,fed back into apps,can provide highly personalized prompts and feedback to patients as well as modifications to the engagement interfaces and the health and care plans.With rapid innovations in both AI and digital therapeutics,it is not difficult to imagin

121、e that a company will create a Spotify-like playlist forhealth.Specifically,this would be an interactive,smart system that suggests a personalized series of treatments based on patient(aka user)data,in the same way that streaming services suggest playlists based on a users preferences.This health pl

122、aylist would evolve over time and according to preferences,and how a condition evolves and also based on changes in patient behavior,mood,and environment.A recent study explores how AI and machine learning(ML)can analyze patient engagement the primary indicator of success of digital therapeutics int

123、erventions.Researchers showed that AI and ML can indicate not only the level of engagement but also the quality of Figure 3.Most life sciences firms expect generative AI to provide ROI in digital therapeutics7%Alreadyachieving ROI30%Willachieve ROI63%Will probablyachieve ROI1%Will probablynot achiev

124、e ROINote.Percentages do not total to 100 due to rounding.Source:Infosys Knowledge Institute22Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge Instituteengagement.This and other AI applications that measure engagement and experience detect where engagement plateaus

125、 and then automatically recommend a differentapproach.The role of generative AIIt is early days for generative AI in this field,but 99%of our survey respondents expect generative AI to achieve return on investment in the next three years(Figure 3).In fact,7%say they have already achieved returns for

126、 generative AI in this rapidly progressing field,despite the up-front tech costs and behavior changes required for patients and providers.Generative AI is expected to improve the patient experience perhaps ironically,as AI has the potential to make patients feel more cared for,thanks to personalizat

127、ion via richer,data-based feedback.Practical benefitsDigital therapeutics products offer benefits not only to individuals and their healthcare providers but also to society more broadly.At the individual level,these products help patients manage chronic conditions such as diabetes,autoimmune disease

128、s,and cancer.For instance,digital therapeutics assist with managing complicated dosing requirements and self-administration of treatments as well as complementary changes in patients well-being behaviors,such as nutrition or physical exercise.They also provide a personalized mix of educational mater

129、ials,dosing guidelines,and tools for monitoring side effects.Together,these interventions make it easier for patients to maintain medication regimes.Medication non-adherence is a common problem,with approximately 50%of patients not taking their medications as prescribed.Increasing patient compliance

130、 was identified as“the most impressive application of digital therapeutics”from a diabetologists perspective.The Digital Therapeutics Alliance lists the benefits as increasing access to therapies;offering the convenience and privacy of home treatment;providing therapies in a variety of languages;and

131、 providing results and supplying insights on personalized goals and patient outcomes.These benefits extend beyond the individual by improving access to healthcare and addressing disparities in care at the population level.Digital therapeutics products equalize the rural versus non-rural health divid

132、e,providing patients“asynchronous support and therapy when they are actively experiencing symptoms or are unable to immediately access their healthcare providers.”Digital therapeutics are available or under development for six of the seven causes of death identified by the US government initiative H

133、ealthy People 2020.These products directly address the disease or underlying associated conditions and stand to substantially improve peoples health.Digital therapeutics in actionDigital products are already in use or are under investigation in several areas of the life sciences industry.For example

134、,the complex 23 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge Institutewomens health space has traditionally been left behind,limited by data and research built on male medical profiles.As the femtech market plays catch up,it is expected to grow from$36.5 billio

135、n in 2023 to almost$42 billion in 2028.This growth is fueled by an increasing radical global awareness of womens health disparities,and the market will utilize multiple levers,including AI,data,crowdsourcing,and innovation to address the gender healthcare gap.One healthcare technology company focuse

136、d on improving womens cardiovascular health recently received funding to support a clinical trial for textile-based sensor technology to gather medical-grade data from womens physiology(a smart bra).The trial will address womens low participation rates in cardiac rehabilitation programs and will use

137、 a personalized,data-driven approach to improve patient experience in cardiac rehabilitation.Despite only modest growth in 2024,global wearable technology is forecast to grow in the product areas of smartwatches and smart rings.Some analyses suggest a resurgence of interest in smartwatches,especiall

138、y in emerging markets like India.These portable,wearable medical devices consolidate health data and provide physicians with immediate medical readings from patients.An upcoming special issue in the peer-reviewed journal Sensors explores wearables for neurological conditions.These so-called neurolog

139、ical wearables offer remote assessment,testing,and treatment of neurological injuries and diseases.Wearables capability in neurology and other clinical areas is expected to drive the market segment further,particularly in the ability to assess cognitive capabilities during everyday activities.Beyond

140、 wearables,a wave of innovation also extends to ambient sensors embedded in peoples homes,enabling more continuous monitoring and avoiding the user-dependent challenges of wearables(dead battery,forgetting or simply deciding not to wear them,etc.)Shifting from device types,companies are applying dig

141、ital therapeutics to a widening range of conditions.This includes topics ranging from pain management,anxiety and depression,digestive health and sleep management to cancer treatments,respiratory therapies and treatments for neurodegenerative conditions.Fuller implementationLike other life sciences

142、disciplines,digital therapeutics was catapulted into prominence by the Covid-19 pandemic.Their momentum is extending as they prove their value in multiple areas community health,womens health,neurological conditions,more and better data,and patient experience.However,success also relies on the under

143、standing of and compliance with an increasingly challenging regulatory landscape,and a growing imperative for trust and transparency.Our research shows that regulatory barriers are a priority for life sciences leaders,with 49%indicating that regulations are significant obstacles to implementing new

144、technologies.24Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge InstituteA further 34%say this is a moderately significant barrier.For digital therapeutics,the picture is fragmented the EU has no specific legislation on digital therapeutics.However,Germany has esta

145、blished its own Digital Healthcare Act,which addresses digital therapeutics.There is a pressing need for new regulatory frameworks and to improve regulatory ability to evaluate increasingly complex products,which support rapid updates to their software and use adaptive algorithms that change over ti

146、me as they encounter new data.Other challenges include the difficulties of reimbursement through insurance,lack of development skills for those creating digital products,patient acceptance,devices that become obsolete,and technology ecosystem incompatibility,such as competing operatingsystems).Despi

147、te their promise,digital therapeutics solutions have not yet fully entered mainstream healthcare.This is partly because it is difficult for patients and health practitioners to separate unproven,low-value applications from genuinely valuable,evidence-based products.Finally,as with any device that st

148、ores and transmits patient data,privacy and security are particularly important for digital therapeutics.Another challenge for AI-based digital therapeutics will be evaluating their safety and efficacy in diverse populations.This is crucial to ensure equitable healthcare delivery.Device labeling sho

149、uld clarify how AI models were trained and how they derived their outputs.On the cusp of growthAdvances in AI continue to stir up excitement about digital therapeutics.Its acceleration depends on both expertise about software and patient behavior,and companies navigating an uncertain regulatory land

150、scape.It also depends on trust.Like all interventions,the physician-patient relationship has long been understood as a cornerstone of good outcomes and functional health.This relationship is essential in digital therapeutics too as is trust in technology to provide the expected medical outcomes.Espe

151、cially as digital therapeutics expands into new clinical fields,real-world data and evidence is necessary for this area to reach its full potential.Digital therapeutics thus stands to benefit from the kind of collaborative ecosystem described in Trend 7 of this journal,and in research published in N

152、ature.Clinicians,academics,commercial companies,manufacturers,regulatory bodies,and organizations like the Digital Therapeutics Alliance can together overcome implementation and engagement barriers.These collaborations can also lower costs,as well as historical healthcare bottlenecks and complexitie

153、s.25 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge InstituteKnowledge Institute25 External Document 2024 Infosys Limited|Life Sciences:Trends for the Future26Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge InstituteTrend 3:Fro

154、m traveling salespeople to digital feedback loops:The future of life sciences sales and marketing The shift to virtual engagements driven by the pandemic has increased flexibility for healthcare sales professionals,freeing up their schedules by balancing in-person and remote interactions.Digital too

155、ls lead to better insights and a continuous feedback loop that allow sales reps and healthcare providers to choose the most effective ways to interact with each other,although some providers still struggle with the learning curve inherent in new technologies.We expect that companies that meet health

156、care providers where,when,and how they want will have a tremendous competitive advantage in large part because the digitalization of these interactions creates more touchpoints and more data.Sales and marketing relied on virtual solutions during the pandemic to connect with healthcare practitioners,

157、and to build and maintain relationships.And this door remains open.These practitioners now accept virtual interactions with sales and marketing agents as a viable option that complements,rather than replaces,face-to-face meetings.Organizations discovered that virtual platforms improve customer engag

158、ement through synchronous and asynchronous communication channels and meet physicians at their points of need first multichannel and then omnichannel.Knowledge Institute27 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge InstituteThis can reduce the time burden for

159、 practitioners and sales representatives;improve sustainability through reduced travel;increase reach and opportunities through digital channels;and support the shift toward a service model.In addition,healthcare professionals can more easily bring sales reps into discussions than with in-person mee

160、tings.Most companies have returned to face-to-face interactions,rather than seeking the optimal mix of live and virtual engagements that leads to the greatest impact.But the potential to use digital technology for sales and marketing goes beyond virtual agents.The growing number of data points deriv

161、ed from chatbots and other digital assistants creates a feedback loop to make better decisions,whether its a whisper agent,agent assistant on a call,or simply providing sales with better insights on each individual provider.However,teams must be structured in ways that ensure the data is analyzed an

162、d used,or its value may be lost.Companies are just beginning to realize the potential for remote sales and marketing.For instance,AI can enhance traditional customer relationship management(CRM)systems to automate tasks,gain insights from large datasets,and provide more customized experiences.When c

163、ombined,these solutions allow businesses to offer more support and better access to products and solutions,and to reduce practitioner time requirements.While automation is important,it is not the only goal.To succeed,CRM platforms need to evolve from transactional proof of interactions to managing c

164、ustomer relationships and experiences a true customer relationship management solution.Organizations will benefit by moving beyond compliance systems to something more valuable for all stakeholders.From travel to virtualLife sciences sales reps have traditionally been at home in the waiting rooms of

165、 healthcare providers(HCPs),expecting to develop one-on-one relationships.In 2020,just before Covid-19 became a pandemic,75%of physicians said they preferred on-site visits from their sales reps,compared to remote interactions but a year later,nearly half preferred virtual exchanges.Data from 2023 i

166、ndicates a stronger preference for face-to-face,one-on-one visits(43%for male and 38%for female healthcare professionals).Necessitated by lockdowns,lightning-fast innovation accelerated remote technology and allowed sales reps to move beyond their traditional relationship-based philosophy and ensure

167、 they added value to the healthcare providers.In the process,this digital approach focused on customer experience increased the number of touchpoints.Successfully orchestrating large numbers of customer touchpoints improves the transaction and adds tremendous value to the long-term relationship valu

168、e.In the past few years,sales reps have found that they could meet healthcare providers on their terms,while ensuring that their relationships remained intact and resources accessible.Half of physicians now prefer 28Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge

169、InstituteCompanies must build a service engagement model for their team to continuously add value to HCPs,and the technology should support this path.Luca MorrealeChief commercial officer,BASE life sciencetotally or primarily virtual engagements,compared to 20%pre-pandemic,according to research from

170、 Bain&Company.The preference for virtual engagements is even higher(80%)among healthcare administrators,an increase from 33%before the pandemic.This move away from in-person interactions is just one element challenging sales and reminding life sciences companies that they need to center the preferen

171、ces of the providers.Even before the pandemic,about 40%of physicians refused to see sales reps at all,and the numbers remain high.The factors for not wanting to meet reps include physicians switching from independent practitioners to health network employees,mistrust of content supplied by life scie

172、nces companies,and not wanting to take time away from patients.And so some HCPs are already shifting their scheduling priorities toward medical science liaisons rather than sales representatives.Post-pandemic burnout partly created by overwork,but also by administrative burden continues to be a prob

173、lem in the healthcare field.Burnout is likely another reason physicians do not want to meet with sales reps.A US survey of women in healthcare,including physician assistants and technicians,found that 86%had experienced burnout.And nearly two thirds(64%)said they were at risk of burning out,which is

174、 often influenced by workload.A US federal survey of healthcare workers found that having enough time to do their jobs was an important factor in reducing burnout.With a wide variety of interactions now possible,the outlook can be confusing.Physicians are reluctant to meet with sales reps and often

175、prefer remote communication.Healthcare workers weigh the value of every interaction or activity that takes time away from patient care.However,100%virtual visits are history and can often be limiting despite the time benefits.Meanwhile,sales reps prefer in-person meetings for their tremendous value

176、to develop and maintain relationships.Both sides have to give an inch,or more,to engagement channels at the right time.Then engagement type,whether advisory board or sharing of product launch data,should determine the channel.And for life sciences companies,using data to determine the optimal will b

177、e critical 29 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge InstituteFigure 1.Nearly three-quarters of life sciences leaders plan to increase investment in virtualization of sales and marketing in the next five yearsSource:Infosys Knowledge Instituteas they pivo

178、t from a multichannel to an omnichannelapproach.For the life sciences industry,this balancing act in sales and marketing is critical to their future interactions with clients,and also their spending plans.To support this kind of omnichannel e-detailing,virtualization remains a significant investment

179、 priority in life sciences:25%of industry leaders say they plan to invest between$10 million and$50 million in sales and marketing virtualization between now and 2026.An even larger percentage(75%)expect to significantly increase these investments in the next fiveyears.As life sciences companies see

180、k to balance the virtual and in person,it is often easy to focus on productivity.While that is valuable,impact must remain the most important factor and can be enhanced by small changes.Interactions between the commercial organization and the medical organization,rather than a siloed approach,tend t

181、o bring better results.Adding the market access function to the mix will ensure the coverage of all relevant topics while serving the needs of the HCPs.Benefits of digital engagementTwo-thirds of biopharma sales leaders say they already employ an even mix of virtual and in-person engagements.This hy

182、brid approach is clearly the future,but developing the right mix and the right timing of the mix will be more difficult than anticipated.Phil Benton,partner in AI experience transformation at Infosys Consulting,says that sales leaders need to consider which in-person engagements are better virtually

183、,and how these vary by product lifecycle and physician specialty.For instance,when a product launches,Benton suggests that virtual engagements are best to maximize short-term reach,when HCP interest in the product is high.Virtualization and other emerging technologies require companies to upgrade th

184、eir data and analytics strategies and practices.Life sciences businesses can use advanced analytics to gather raw data from digital interactions.That data generates valuable insights,such as measuring engagement levels and identifying which interactions lead to sales.These insights help sales and ma

185、rketing teams to explain products,sell solutions,and resolve queries.23%Nochange47%Slightincrease27%Signifcantincrease2%Decrease74%30Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge InstituteThis feedback loop enables continuous improvement and enhances the impact

186、of sales and marketing efforts,particularly when the focus is more on the customer than theproduct.In addition,AI augments traditional CRM systems by automating tasks,gaining insights from large datasets,and providing more customized experiences.With the right data,analytics can direct sales and mar

187、keting leaders toward determining the best time to make an in-person visit versus scheduling a virtual meeting,how to best optimize each approach to suit specific individuals needs,and how to plan proactively instead of reactively.Even in situations where some interactions are by their nature reacti

188、ve for example,with medical science liaison visits reps can still plan messages that should be shared in those interactions.Freeing up time from travel schedules allows reps to spend more time on improving the quality of their interactions and focus less on the frequency.Generative AI to connect dot

189、sThe search for the right hybrid balance has arrived at a fortuitous time.The need for technology solutions coincides with the rise of generative AI.When life sciences companies gather all the data they need on marketing and sales efforts,generative AI will allow them to connect the dots more quickl

190、y and effectively.The mix of data needs to include information about events,industry news,market access efforts,and competing products,especially in specific therapeutic areas.In addition,companies need to include patient data,although outside of the US this is difficult since only proxy data is ava

191、ilable.Based on all those data points,you can define the right market mix.“Continuously reviewing your marketing mix model through the lenses of AI to improve the impact could deliver tremendous value and ensure the right message reaches the right HCP at the right time,”says Morreale.Our survey indi

192、cates that generative AI is now delivering ROI for life sciences sales and marketing more than in other areas of the value chain(see Trend 1).Further,four out of five sales and marketing use cases we surveyed achieved ROI for at least 20%of respondents compared with less than 5%for some use cases in

193、 supply chain and manufacturing(Figure 2).Chatbots for patients was an outlier,with nearly 50%of respondents saying they already achieved ROI.The survey did not ask about chatbots in sales and marketing,however.Generative AI-powered segmentation is an area rich for exploring faster,more efficient,an

194、d more granular divisions of customers into different brackets based on specific characteristics.For example,the Veeva Pulse Field Trends Report determined how often various specialties meet on average with reps.Understanding these demographic trends can only help drive better engagement,although de

195、lving further into behavioral segmentation can offer greater value.Data from each companys interactions 31 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge InstituteFigure 2.Generative AI returns positive ROI in sales and marketing in four of five use casesmight of

196、fer different insights or additional nuances particular to a specific field,region,or types of products.Proprietary data can fine-tune messaging,hone interactions,create customized engagements across digital and physical platforms,and improve personalization.Our research has found that life sciences

197、 executives prioritize data-driven personalization as their top investment priority in sales and marketing efforts(Figure 3).Even so,approvals remain a challenge as even advanced companies still struggle with medical,legal,and regulatory bottlenecks.Moreover,data created by virtual tools and other e

198、merging technologies especially those powered by generative AI can institutionalize knowledge that might disappear when a longtime rep leaves the company or the industry.These tools ensure that information about specific HCPs,companies and products remains within the firm and ensures valued relation

199、ships remain intact.This type of knowledge drain is a concern in many industries,including lifesciences.Digital solutions address these challenges by ensuring that behavioral analysis is taken from data and not solely from the reps input.However,many enterprises struggle to adopt these tools,which c

200、an hinder progress.Companies need to ensure that each tool is suited for its specific need.Although these solutions are technology-oriented,the core element of sales is the relationship.Virtual tools and solutions offer significant benefits,many of them costsavings.But the value of authentic,in-pers

201、on engagements cannot be overlooked.Building connections based on integrity and transparency is essential,particularly as it relates to trusting new technologies.We have focused our discussion of the sales and marketing virtualization trend on 1:1?Field-force assistance to answercomplex queries?Opin

202、ion leader identifcationand mapping?Precision targeted materials forcommercial representatives?Virtualization of salesrepresentatives?Personalized marketing tohealthcare professionals?Generative AI use caseSource:Infosys Knowledge InstituteFigure 3.Life sciences leaders prioritize data-driven person

203、alization investmentsSource:Infosys Knowledge InstituteVirtual agents?Data-drivenpersonalization?Integrated experiences forhealthcare personnel?Remote detailing?Note.Percentages do not total to 100 due to rounding.32Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge

204、Institutemeetings between physicians and sales reps,but events are also a tremendous opportunity to mix in-person and virtual modes.How to persuade physiciansTo be more successful,reps can engage with HCPs on their attitudes toward technology,particularly those customers who were traditionally techn

205、ology-averse.The past tense is key here.While some physicians remain resistant to technology encroachment,a recent AMA survey found that many are open to use cases,particularly around AI.Virtual meetings,generative AI,self-service chatbots,and data and analytics tools offer opportunities to ease med

206、ical professionals into additional technologies and help them overcome their hesitancy,so this should be approached on a case-by-case basis,while ensuring that physicians are given the information they need to feel informed andaware.Technology is also a valuable tool in the competition for talent an

207、d to retain the top talent.The 2022 Sales Happiness Index found that 33%of sales professionals leave because they do not have the tools and technology necessary to perform their jobs efficiently.In addition,companies that struggle to attract top talent can invest in technology as an advantage over l

208、arger or better-known competitors.“Leveraging techniques such as gamification could be a very efficient way to ensure team motivation and adoption of the business process for an optimal customer engagement,”says Morreale.“You can engage your team internally to maximize the external engagement.”Sales

209、 reps in the medical field remain in their roles for one to two years,according to research from both Zippia and Hubspot.Reps are expensive to replace and keeping them engaged is critical for company efforts to reduce turnover.This rapid turnover is especially damaging if sales reps take their custo

210、mer relationships with them;healthcare professionals might be resistant to form a relationship with a new sales rep.In addition to retention issues,the life sciences industry must contend with a skills gap created by the rapid advancement of technology.Our survey found that 61%of life sciences leade

211、rs say that lack of skills in the workforce posed a significant or highly significant obstacle to realizing the benefits of emerging technologies.This was the most significant barrier for life sciences organizations efforts to benefit from new technology.Compounding this skills issue is the potentia

212、l for a knowledge drain as employees leave the industry or retire;71%of sales reps in life sciences are 40 or older.Generative AI offers new and creative options to combat this loss by collecting and passing on knowledge from more experienced reps to new ones.“The wealth of knowledge of very experie

213、nced sales representatives is the perfect input data for an AI model that trains 33 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge Institutethe next generation of reps more effectively,more quickly,”Benton says.Tech to enhance relationshipsThe success of sales en

214、gagements typically comes down to building meaningful relationships and the provision of timely,valuable inputs that provide HCPs and decision-makers with the information and support they need.The balance between physical and virtual is keenly felt in these relationships,as reps use digital tools to

215、 enhance their engagements through personalization and segmentation.At the same time,they offer customers access to immediate information and support.By providing accessible data and information to physicians quickly,reps enable them to make better,more informed decisions.It improves access to infor

216、mation and removes the irritation of engaging in a transactional sales approach feeling“sold to.”Instead,life sciences companies can create a system of shared information and insights that feed back into the enterprise customer platform to nurture relationships,trust,and transparency.Companies need

217、to ensure that their sales staff stay current on how these emerging technologies affect their fields of specialization.Investment in talent is required for reps to realize the benefits of new technologies and share and promote that knowledge across their organization.The human touch and personalized

218、 support have always been a cornerstone of effective sales relationships,and particularly important in health orchestration.Leaders must not lose sight of this in the face of virtualization,AI applications,and digital transformation.As emerging technologies become integrated into everyday life,ensur

219、ing that the humans who use them are confident and capable of using them well is crucial.Empowered hybrid sales and marketing teams will convert the increasing investment life sciences leaders make in virtualization into improved customer relationships.The wealth of knowledge of very experienced sal

220、es representatives is the perfect input data for an AI model that trains the next generation of reps more effectively,more quickly.Phil BentonPartner,AI experience transformation,Infosys Consulting34Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge InstituteTrend 4:

221、The increase in virtual trials tips the balance toward remote interactions,improves patient data,and lowers the cost of clinical trials Modern technologies including telemedicine,digital health tools,AI,and machine learning help researchers reach a wider clinical trial participant pool through cost-

222、efficient virtual trials,gather data accurately,and generate richer insights.A hybrid approach that combines both virtual and in-person clinical trials can deliver significant benefits but creates its own challenges,including compliance and potential difficulty interacting with researchers to addres

223、s problems.We expect that hybrid trials will bring beneficial medicines to patients more quickly and less expensively,through robust data governance,transparency,and participant connections that realize efficiency benefits while mitigating risks.Companies are increasingly exploring virtual trials as

224、 a path to reduce drug time to market,an even greater imperative as drug development costs have spiraled into billions of dollars.Virtual trials can reduce patient dropout rates,accelerate enrollment,and increase data collection.A variety of technologies enable this trend,including telemedicine,wear

225、ables,and biometric sensors.Virtual clinical trials also improve Knowledge Institute35 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge Institutehealth equity by increasing access to a broader and more diverse patient population and providing researchers with riche

226、r data.However,the need for in-person clinical trials persists because technological inefficiencies and talent shortages impede the ability to scale virtual trials and patients will also want to see the doctor in person.Many practitioners lack the technology proficiency to conduct virtual trials,and

227、 they do not trust these outcomes as much as in-person trials.Initial virtual trial setup is often more expensive than for traditional trials,though speed to market and other factors do offset these costs.Additionally,many trial designs do not translate well to virtual models,such as for patients wi

228、th Alzheimers disease.Hybrid models combine virtual and in-person trials and can optimize outcomes and reduce costs.Technologies including AI improve hybrid trials by detecting,analyzing,and improving patient clinical trial experience.For example,AI tools can analyze not only patient voice content b

229、ut also their expressions and tone of voice.This behavioral data,combined with other datasets on factors like social determinants of health,improves the richness of data and,potentially,health outcomes.Enhanced patient experienceElements of modern clinical trials have existed for centuries.However,t

230、he gold standard of randomized controlled trials only dates to the 1940s.After decades of fine-tuning these processes,researchers are again discovering new ways to advance medical knowledge,reduce suffering,and save lives.Traditionally,clinical trials have been conducted at hospitals,laboratories,an

231、d other centralized locations all under the watchful eyes of medical professionals.This approach has generally served researchers well.However,it is time-consuming,expensive,and limits the trials potential population,a primary research challenge.For the past decade or so,researchers have experimente

232、d with virtual trials that allow participants and physicians to engage in trials from any location,with data collected and collated digitally.Remote patient monitoring,wearable devices,artificial intelligence,and other virtualization tools allow life sciences companies to create remote clinical tria

233、ls that can be as effective as,and in some cases more effective than,conventional trials.Virtual clinical trials leverage digital technologies across the clinical trial process,from design to patient recruitment to analysis and sponsor reporting.Some technologies enable continuous monitoring using w

234、earables,sensors,health monitors,and even ingestible devices that provide practitioners near real-time visibility.Unlike traditional trials conducted at bricks-and-mortar research facilities,virtual trials allow remote participation in studies using telemedicine and digital health tools.“Virtual tri

235、als aim to make clinical research more patient-centric and inclusive,”says Ian Storr,associate partner,health and life sciences R&D lead,EMEA,Infosys Consulting.(See Figure 1 for a summary of critical differences between traditional and virtual clinical trials.)36Life Sciences:Trends for the Future|

236、External Document 2024 Infosys LimitedKnowledge InstituteVirtual trials have several advantages over traditional trials.First,they are less invasive.Use of sensors in these remote trials can collect minute details such as patterns of patient movements,gait,pace,chest expansion while breathing,steadi

237、ness,and so on all without the patient having to manually record and share that data.A patient just needs to be within a reasonable range of the device thats in their house,according to David Champeaux,lead partner,health and life sciences,EMEA,InfosysConsulting.The sensors that virtual trials use p

238、rovide more and better data that is less prone to bias.This creates an environment conducive to better insights,thanks to continuous monitoring that extends into real-world settings.Data collected in this manner is more accurate and comprehensive.This is data that would not be otherwise captured,and

239、 is continuous,rather than only capturing a single reading at a point in time.Finally,virtual trials increase diversity and inclusion in the sociodemographic sense,but also in that they can include geographically diverse participants,such as those with rare diseases.“Neither patients nor investigato

240、rs of rare diseases are concentrated geographically.A hybrid clinical trial design requires the patient to visit the investigator site only periodically,greatly reducing the time and travel burden,”observes George Hunnewell,senior vice president and general manager,US,BASE lifescience.Researchers ha

241、ve long struggled to create trials that balanced people of various ethnicities,ages,genders,and income levels.The US Food and Drug Administrations 2015-2019 Drug Trials Snapshot found that only 7%of trial participants were Black,slightly more than half their percentage in the overall population.Hisp

242、anic residents in the US were also underrepresented,while white and Asian residents were overrepresented.Figure 1.Differences between traditional and virtual clinical trialsFeatureTraditional clinical trialsVirtual clinical trialsLocationConducted at fixed physical sitesSite-less:Participants engage

243、 remotely from any locationParticipant accessGeographically limitedGeographically openData collectionPeriodically,through in-person visits to trial sitesContinuous and real-timePatient engagementIn personRemote and digitalCostHigher operational costsLower operational costs Regulatory acceptanceEstab

244、lishedEvolvingSource:Infosys Knowledge Institute and Infosys Consulting37 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge InstituteChampeaux notes the importance of identifying and enrolling a diverse group of patients into trials and to connect additional dataset

245、s during clinical trial research.Beyond information from electronic health records,these datasets will help researchers understand social determinants of health,broader social context,and life experiences.Start-up costs are a hurdleFor many the entry point to these virtual trials has been driven by

246、cost.In the US,clinical trials account for about 40%of research and development costs.Researchers found that because of recruitment challenges,more than one fifth of some medical trial categories end early or are closed.Approximately 80%of trials are delayed,at significant cost against expected regu

247、latory filing commitment.The cost of these challenges adds up quickly for life sciences companies.In the US,organizations spend about$6,500 to recruit each patient for a study,and dropout rates often top 30%.When researchers must replace a person for noncompliance,the cost to recruit a new patient a

248、verages$19,533.Costbenefit analysis shiftsDespite the steep initial cost,the life sciences sector is investing heavily in virtual trials,establishing a clear trajectory toward adoption,powered by manufacturers and capital markets.The market was estimated at$8.6 billion in 2023,with an anticipated co

249、mpound annual growth rate of 7.1%from 2024 to 2032.Although virtual trials are expensive to start,one of their key advantages is that they reduce the length of clinical trials reducing the cost of trials.According to one study,the virtual clinical trial can bring down the median duration of the tria

250、l from 16 months to four months.Experts estimate that virtual trials reduce study costs by 25%.With advancements in AI and generative AI,especially its ability to handle data,the cost-benefit analysis of implementing virtual trials will shift.This will lower the cost of clinical trials for companies

251、.Life sciences companies are likely to further increase their investment in virtual clinical trials in the next five to seven Neither patients nor investigators of rare diseases are concentrated geographically.A hybrid clinical trial design requires the patient to visit the investigator site only pe

252、riodically,greatly reducing the time and travel burden.George HunnewellSenior vice president and general manager,US,BASE life science38Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge Instituteyears,with the goal to reduce clinical trial cost from the current 40%to

253、 50%of the overall R&D investment.In our estimate,the increase is expected to contribute approximately 24%to the overall projected growth in the expenditure of clinical trials market over the next two years(Figure 2).Our research backs this up as well.Analyzing our survey data and data from other pr

254、oprietary and public sources found that companies are currently increasing spending in virtual clinical trials.Our conservative estimate anticipates the virtual clinical trials to increase by nearly$1.5 billion in the next two years,with average increase of$750 million each year(Figure 3).This huge

255、investment will most likely come from the largest revenue companies(greater than$10 billion).We also surveyed several subtrends supporting hybrid trials(Figure 4).Of the investment options covered,the highest priorities were decentralization and data-driven patient centricity.However,the distributio

256、n for the top four priorities was tight,with virtual agents and diversity in trials following with slightly lower priority.Home-based clinical services stood out as the lowest investment priority investment area.Our further research indicates that the pharmaceutical industry is committed to increasi

257、ng diversity and inclusion in clinical trials.In 2022,a multi-institutional study of 32,000 people in the US who participated in new drug trials in 2020,only 25%were Black(8%),Asian(6%),or Hispanic(11%)compared with the 40%these ethnic minority groups comprise in the US population.This disparity is

258、relevant because social determinants of health everything from age to ethnicity to geography to environmental conditions affect the way people experience a disease.Looking ahead:AI innovationsTechnologies are evolving to support this changing field,providing the life sciences industry with innovatio

259、ns designed to simplify,enhance,and even transform Figure 2.Virtual clinical trials market expected to increase over the next two yearsFigure 3.Virtual trials to increase by nearly$1.5 billion in the next two years24%76%Increase inclinical trial marketIncrease invirtual trial marketSource:Infosys Kn

260、owledge InstituteSource:Infosys Knowledge InstituteEstimated virtual trial investment(in millions)Life sciences company size$3bn to$10bn$250More than$10bn$1,150Total$1,450$50$1bn to$3bn39 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge Instituteclinical trials.In

261、the next phase of virtual trial development,AI is expected to shape how medications and treatments are created,tested,and approved.Generative AI will enable companies to better understand the patient experience a perennial challenge in clinical trials.Anne Bichteler of Infosys Consulting describes a

262、 virtual trial scenario where AI-enabled voice companions accompany patients throughout their day,asking them how they are feeling and reminding them to perform critical steps to adhere to trial protocol.Then this data and face-reading technology detect and analyze sentiment,providing insights into

263、how people physically experience a clinical trial.When practitioners have access to these nonverbal messages,they can pinpoint early signs that a patient is considering dropping out and suggest solutions.In addition,Bichteler says that natural language processing and sentiment analysis can predict p

264、atient adherence to protocols,dropout,and later behaviors.Combined with behavioral science expertise,researchers can adapt trial elements to provide a frictionless patient experience.Technology can also reinforce positive behaviors and improve patient experience,engagement,and attrition.Life science

265、s companies are also tapping into the strengths of machine learning to find patients for clinical trials.Amgen,Bayer,and Novartis are training AI to scan health records,prescription data,and insurance claims to locate patients quickly and accurately.Virtualization dramatically increases the patient

266、population,so AI is a natural fit to digest this volume of data.If algorithms are designed well,this approach also decreases the risk of physician or researcher bias.In addition,generative AI can be used to normalize electronic health records data that is unstructured and difficult to parse.The Truv

267、eta Language Model transforms electronic health records into clean,accurate data that researchers and physicians can use to improve patient care and trial outcomes.Results are already promising.In our survey of life sciences leaders,19%say that AI or generative AI is currently generating returns on

268、their investment in the virtualization of clinical trials.Another 40%say that it will achieve ROI within the next three years.And a large majority(80%)say the technology will or probably will achieve ROI in trial virtualization Figure 4.Life sciences firms prioritize decentralized trials and data-dr

269、iven patient-centricitySource:Infosys Knowledge Institute?Remote monitoring anddecentralization24%Data-driven patientcentricity23%Inclusion and diversityin drug trials18%Home-based clinicalservices13%Virtual agents and digitalhealth technologies21%Note.Percentages do not total to 100 due to rounding

270、.40Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge Institutein the next three years.These developments not only benefit companies but carry implications beyond virtual trials.Wearable or ingestible devices designed for remote patient monitoring can be adapted for

271、use across digital therapeutics.(See Trend 2.)Challenges for virtual trialsAlthough virtual trials offer significant benefits,they also demand more from patients in some ways and create new challenges while they attempt to solve existing ones.In both in-person and virtual trials,researchers must tra

272、in patients to meet trial expectations,such as dosing compliance and data collection,and minimize errors that result from unfamiliar devices or medication protocols.Patients need comprehensive training to ensure they clearly understand instructions and avoid actions that skew the results.Researchers

273、 must provide patients with regular reminders of study rules.Virtual-only trials also risk creating new biases.Groups less comfortable with technology older people and others that are less tech savvy might not participate.To take part in a virtual trial,participants must learn and engage with the pl

274、atform and perhaps new technology.They might have low confidence in the technology and not trust that their data is secure and private.In these cases,the tools empowering virtualization become a barrier to entry or increase the likelihood that patients will dropout.The distancing effect of technolog

275、y might cause patients to be more candid,while the lack of human interaction might make it easier for others to quit.One-on-one interaction gives physicians and researchers greater ability to answer questions and reassure patients about their concerns.Life sciences companies also must contend with t

276、alent shortages and lack of training,making it more difficult to begin and scale virtual trials.As a result,companies could struggle to get launch trials,and outcomes could be questioned due to technical mistakes from researchers and patients.To overcome these challenges,trials should prioritize tra

277、ining,increase transparency regarding biases and limitations,and build trust with participants.Virtual participant support networks are a mechanism to share information and experiences.Communication between researchers and patients will mitigate attrition,and support network communications should oc

278、cur at each stage of the research cycle.Fortunately,the virtualization model enhances ongoing communication.An effective hybrid approachVirtual and in-person trials each present complexities and challenges,yet they are an opportunity to create a more effective hybrid approach.Blending in-person enga

279、gement and digital interactions creates a trial environment that supports both patient and practitioner needs,while reducing the drawbacks of each.This hybrid approach is particularly valuable in high-risk trials,or 41 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowled

280、ge Institutefor complex procedures.A mixed model also helps reduce dropout.Combined with behavioral science,generative AI analytics,and access to broader datasets,life sciences companies can develop hyperpersonalized trial experiences.As barriers to entry are removed,patients are more likely to enga

281、ge with the trial and enabling technologies.Tools such as wearables and smart devices are unobtrusive and aid patients to stay on track.These tools minimize forgetfulness,improve adherence to trial expectations,and deliver monitoring data to physicians removing much of the administrative burden on t

282、he participant.Virtual clinical trial technologies help life sciences organizations improve trial patient identification and enrollment and create more diverse cohorts.However,this requires connecting more datasets,developing insights from multiple sources,and moving beyond electronic health records

283、.Data must include social determinants of health,gender,and broader social contexts.This technology enhances data quality,access,and insights and supports removal of inherent biases.Generative AI-driven systems automate the patient identification process,which in turn reduces the burden on physician

284、s and mitigates the risk of bias by selecting patients based on established criteria instead of preconceived ideas.As with other initiatives since the end of the pandemic,the initial rush to fully remote methods to conduct trials has since given way to a hybrid approach,which combines the benefits o

285、f in-person interaction with the scale and reach that technology can deliver.However,hybrid and the rollout of technology bring their own issues.These include ensuring that data is used ethically and that robust governance is in place,and that care is taken to address issues of properly informed con

286、sent among patients who might be less familiar with the technologies being used by the trial organizers.Virtual trials aim to make clinical research more patient-centric and inclusive.Ian StorrAssociate partner,health and life sciences R&D lead,EMEA,Infosys Consulting42Life Sciences:Trends for the F

287、uture|External Document 2024 Infosys LimitedKnowledge InstituteThe pharmaceutical industry stands on the brink of a new manufacturing era,propelled by technological advancements in a rapidly evolving and uncertain market landscape.In the previous decade,enterprises rushed to digitally transform thei

288、r operations,creating a technology foundation that freed data and improved core business processes like scheduling and fulfillment.The pandemic and subsequent system shocks forced improvements in production flexibility and resilience,and hybrid models emerged to manage plant operations despite worke

289、r shortages and supply chain breakdowns.As life sciences leaders peer into a future of bold aspirations and huge complexity,a question arises:What role will manufacturing operations play to realize their vision and shape the future of pharma?The answer lies in the convergence of intelligent manufact

290、uring and visionary leadership beyond machines and materials:Reinvigorate the operating model,elevate Trend 5:The rise of intelligent pharma manufacturing Intelligent manufacturing has arrived at the cusp of a new era,as technology advances begin to deliver smart answers to growing complexity.Pharma

291、ceutical manufacturing can be personalized,efficient and dynamic.We expect that a convergence of intelligent manufacturing and visionary leadership will unlock value for the entire sector.Knowledge Institute43 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge Instit

292、utehumans in the age of AI,and aggressively leverage technology breakthroughs.To unlock and create value,even a visionary manufacturing strategy requires an operating model that is up to the task.Business functions are moving to scalable platforms,corporate capabilities are shared across the industr

293、y ecosystem,and technology advancements have created step-function performance improvements.Network orchestration,talent upskilling,and rapid learning models have taken their place as essential components of the modern pharmaceutical operating model.This chapter examines how pharmaceutical manufactu

294、ring can unlock value now while taking steps to realize a vision of operational excellence for the rest of this decade.A vision for manufacturingHow might the manufacturing operating model look?We see a profound shift toward intelligent,agile,and sustainable production operations.The smart factory i

295、s already on the rise:The global smart manufacturing market is expected to increase to$650 billion by 2029,from$277 billion in 2022.Figure 1.Pharmaceutical manufacturing operating modelSource:Infosys Knowledge Institute?44Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnow

296、ledge InstituteBeyond operational performance,manufacturers have an opportunity to revolutionize the way drugs are produced and delivered to patients worldwide.Through emerging manufacturing methods and enabling technologies,companies can achieve new levels of productivity,quality,and compliance(Fig

297、ure 1).Personalized medicineUtilizing advanced analytics and genetic profiling,manufacturers will tailor therapies to individual patient needs,optimizing efficacy and minimizing adverse effects.This shift toward precision medicine requires flexible manufacturing processes capable of producing small

298、batches of customized treatments at scale.For example,the US Food and Drug Administration(FDA)has approved several drugs tailored to genetic mutations,such as pembrolizumab for patients with certain types of tumors with specific biomarkers.Initiatives such as the US federal Precision Medicine Initia

299、tive promise to accelerate the adoption of personalized medicine,largely through the collection and analysis of medical data.Advances are also occurring in precision therapies such as CAR-T(chimeric antigen T-cell receptor)treatments,which are tailored to individual patients.These breakthrough gene

300、therapies treat or cure genetic diseases,diabetes,and blood disorders.A study of 54 patients with hemophilia B found that 51 did not require prophylactic treatment three years after being treated by gene therapy.These drugs are manufactured in small batches and to the highest safety and accuracy sta

301、ndards.However,the cost of these therapies has limited their potential so far.In the US,CAR-T therapy costs$400,000 per dose,largely because of manufacturing costs.Other gene therapies can cost as much as$4.3 million per dose.Fortunately,Indian pharma company ImmunoACT is manufacturing a new CAR-T t

302、herapy called NexCAR19 that is expected to cost$50,000,an encouraging step to make these treatments more widely available.Digital twin technologyDigital twin technology is revolutionizing pharmaceutical manufacturing by enabling Intelligent manufacturing will help bring flexibility in manufacturing,

303、better quality,improved productivity,and provide customization at scale.It will allow companies toproduce personalized medicines at much higher quality.Prabhat KaulVice president,Infosys Life Sciences45 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge Institutevirt

304、ual experimentation and predictive modeling.Manufacturers simulate production processes in real time,optimizing parameters and identifying potential issues before theyoccur.Virtual manufacturing environments serve as testbeds for innovation,accelerating the development and deployment of new drugs an

305、d formulations.Pharmaceutical companies leverage digital twins to simulate production processes,optimize equipment performance,and predict outcomes.For example,Sanofi used virtual twins to simulate manufacturing systems prior to implementation and to refine production processes before deploying them

306、.This enabled Sanofi to accelerate the timeline to launch its new vaccine manufacturing plants.Continuous manufacturingTraditional batch-based manufacturing will give way to continuous manufacturing systems that offer new levels of efficiency,flexibility,and cost-effectiveness.Continuous processes w

307、ill enable real-time monitoring and control,reducing time to market and minimizing waste.By integrating upstream and downstream operations,manufacturers will streamline production processes and enhance product quality and consistency vital for new precision medicines.Pfizer,Vertex Pharmaceuticals,an

308、d others have implemented continuous manufacturing processes for some drugs,reducing production time and improving product quality.Experts also predict that switching to continuous manufacturing plants will decrease production facility size by as much as 70%.Regulatory agencies are also encouraging

309、the adoption of continuous manufacturing through initiatives like the agencys Emerging Technology Program.Research into continuous manufacturing in the US found no significant regulatory barriers,and the accelerated time to market provided an estimated$171 million to$537 million in additional revenu

310、e for pharma companies.Data-driven decisionsData is at the heart of pharma manufacturing,driving informed decision making and process optimization.Large language models will come and go,but enterprise data and knowledge will always be valuable.Advanced analytics and machine learning will unlock insi

311、ghts from vast datasets,meaning manufacturers can enhance productivity,quality,and compliance.Data-driven decision making will lead to innovative business models:1.Dynamic supply chain orchestration.Companies use real-time data and AI to optimize their supply chains,allowing them to adapt rapidly to

312、 market changes and minimize disruptions while reducing inventory costs.2.Real-time demand-supply optimization.The new model enables“buy anywhere,ship anywhere”for 46Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge Institutedistributors,providing a consistent custo

313、mer experience,optimizing warehouse resources,and boosting sales team productivity.Companies such as Merck and Novartis have invested in data analytics platforms to analyze manufacturing data,identify trends,and optimize production processes.Regulatory innovationRegulatory agencies will embrace agil

314、e and risk-based approaches to oversight,fostering innovation and accelerating time to-market for new therapies.Collaborative frameworks and regulatory sandboxes will allow manufacturers to pilot emerging technologies and novel manufacturing processes in a controlled environment.Regulators will prio

315、ritize patient safety while fostering a culture of innovation and continuous improvement within the industry.The European Medicines Agency has already created an Innovation Task Force.This will“invite anyone with a new idea to come to us,talk about it and we try to advise as to what the right path i

316、s,”says Emer Cooke,the agencys director general.In addition,the FDAs Quality Metrics Initiative advises companies on how to measure and improve manufacturing processes and product quality.Regulatory sandboxes,such as the FDAs Digital Health Software Precertification(Pre-Cert)Pilot Program,provide a

317、framework for piloting innovative technologies in a way that complies with regulatory frameworks.Robust frame for innovationTo realize this vision,companies must take full advantage of emerging pharmaceutical manufacturing technologies,such as generative artificial intelligence(AI),the internet of t

318、hings(IoT),advanced analytics,and cloud computing.Growth in these areas is significant;the industrial automation market alone is projected to be worth$115 billion by 2025.Companies already spend heavily on nanotechnology and industrial robotics,with both areas growing at a rapid pace.The UKs Centre

319、for Process Innovation is working with pharma giants to create a cloud-first factory at its Medicines Manufacturing Innovation Centre in Scotland.The publicprivate collaboration plans to use emerging technologies to reduce the operational cost of pharmaceutical manufacturing by as much as 30%and inc

320、rease productivity by 50%.Each emerging technology augments one another,creating not just a foundation for smart manufacturing but also a robust frame to build upon.Generative AI drives advancesGenerative AI algorithms will revolutionize drug discovery and formulation by generating novel molecular s

321、tructures and predicting their properties.Machine learning models will analyze datasets of billions of chemical compounds,accelerating 47 External Document 2024 Infosys Limited|Life Sciences:Trends for the FutureKnowledge Institutethe identification of promising drug candidates and optimizing formul

322、ation parameters.Analysts project that AI in drug manufacturing will grow at a nearly 46%compound annual growth rate to reach$20.8 billion by 2028.Generative AI algorithms are revolutionizing drug discovery and development by accelerating the identification of promising drug candidates.For example,c

323、ompanies such as Atomwise and Insilico Medicine use AI algorithms to design novel molecules with desired properties,expediting the drug discovery process.Furthermore,collaborations between pharmaceutical companies and AI startups are becoming increasingly common,highlighting the industrys interest a

324、round AI in drug discovery and manufacturing.IoT devices to connect dataBiomanufacturing processes are complex,making it difficult to implement robust analytical control infrastructure.IoT devices are ideal for connecting contemporary physical-world data with the infinite data storage and processing

325、 capabilities available in the cloud.For example,companies such as Pfizer and GSK use IoT sensors to collect data on temperature,humidity,and pressure in manufacturing facilities,enabling real-time monitoring and control.Infosys Knowledge Institute research found that IoT was a top pharma investment

326、 priority(Figure 2).Platforms such as PTCs ThingWorx provide connectivity and analytics capabilities tailored to e pharmaceutical industry.Beyond optimization and product quality,IoT-enabled packaging and labeling tracks inventory across manufacturing,transportation,and storage.IoT devices enable mo

327、nitoring of equipment performance,reducing unplanned shutdowns and production issues.Quality by design,focus on data and analytics?Industrial IoT and connected devices?Remote monitoring and control?Virtualization and digital twins?Note.Percentages do not total to 100 due to rounding.Figure 2.IoT and

328、 connected devices are top investment priority in life sciencesSource:Infosys Knowledge Institute48Life Sciences:Trends for the Future|External Document 2024 Infosys LimitedKnowledge InstituteFor example,Novartis uses IoT and AI for predictive maintenance to reduce downtime in their supply chain.Add

329、itionally,IoT devices will be valuable for regulatory compliance.The data collected will support compliance by tracking conditions throughout the manufacturing process.Data integrationThe pharma industry has not made the most of supply chain collaboration thus far,but as contract manufacturing drive

330、s more advanced collaboration models,data integration and interoperability will make collaboration and information sharing across disparate systems and stakeholders easier.Open standards and interoperable platforms enable secure data exchange,boosting innovation and collaboration.Efforts such as the

331、 Pharmaceutical Supply Chain Initiative promote data sharing and interoperability standards among pharmaceutical companies and suppliers.Enterprise software has a role to play as well,as platforms like SAPs Integrated Business Planning for Supply Chain enable data integration and collaboration acros

332、s partners.Essential cybersecurityNo matter how attractive the potential benefits of collaboration,cybersecurity measures will be essential to protect manufacturing operations from threats and to ensure data integrity and confidentiality.Cybersecurity in operational technology is a challenge for eve

333、ry industry.Legacy devices,embedded proprietary software,and 24/7 operations make managing security a challenging task.In pharmaceuticals and medical device manufacturing,managing these challenges properly is even more important thanks to the higher regulatory requirements attached to sensitive personal information and the concurrent risks to patient safety and personal information.Cybersecurity m