《美國信息技術與創新基金會（ITIF）：2024人工智能賦能生物制藥行業創新研究報告（英文版）（47頁）.pdf》由會員分享，可在線閱讀，更多相關《美國信息技術與創新基金會（ITIF）：2024人工智能賦能生物制藥行業創新研究報告（英文版）（47頁）.pdf（47頁珍藏版）》請在三個皮匠報告上搜索。

1、 itif.org Harnessing AI to Accelerate Innovation in the Biopharmaceutical Industry SANDRA BARBOSU|NOVEMBER 2024 AI has the potential to transform drug development by enhancing productivity across the entire development pipeline,boosting biopharmaceutical innovation,accelerating the delivery of new t

2、herapies,and fostering competition to help improve public health outcomes.KEY TAKEAWAYS Drug development is a costly,lengthy,and risky process,marked by declining productivity.Bringing a new drug to market can cost billions of dollars and take over a decade,with fewer than 8 percent of early candida

3、tes succeeding.AI has the potential to boost the efficiency of drug development,accelerating the delivery of new therapies and fostering competition,which could improve public health outcomes.AI applications span the entire drug development pipelinefrom speeding up drug discovery and enhancing clini

4、cal trials to streamlining manufacturing and supply chains.For broader,effective adoption of AI in drug development,key challenges such as data quality and availability,potential for algorithmic bias,workflow integration,and regulatory guidelines must be addressed.Key policy elements to advance AI a

5、doption include public funding for basic research,workforce training,support for privacy-enhancing data sharing and access,and a risk-based regulatory framework based on the risk level each AI use may pose to patients.Such policies are essential to unlocking AIs full potential in drug development,en

6、abling faster delivery of potentially life-saving therapies and expanding patient access.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 2 CONTENTS Key Takeaways.1 Introduction.3 The Drug Development Process.3 The Role of AI in Drug Development.7 Facilitating Drug Discovery.8 Streaml

7、ining Clinical Trials.13 Enhancing Patient Eligibility and Recruitment.13 Optimizing Clinical Trial Design.14 Streamlining Clinical Trial Protocols.15 Optimizing Regulatory Submissions.17 Enhancing Manufacturing and Supply Chains.17 AI in Manufacturing.17 AI in Supply Chains.19 Challenges for AI Ado

8、ption.19 Data Access,Quality,and Privacy.20 Electronic Health Records.21 Pharmaceutical Data.24 Role of Privacy-Enhancing Technologies.25 Secure Multiparty Computation.25 Federated Learning.26 Fully Homomorphic Encryption.26 Differential Privacy.27 Algorithm Validation and Bias Mitigation.27 Efforts

9、 to Reduce Algorithmic Bias.28 Integration With Workflows.29 Policy Recommendations.29 Support for Privacy-Enhancing Data Sharing and Access.30 Public Funding.32 Education and Workforce Development.33 Regulatory Guidelines.34 Conclusion.34 Endnotes.36 INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOV

10、EMBER 2024 PAGE 3 INTRODUCTION Drug development is a lengthy,complex,and costly process marked by extensive clinical trials,substantial risks,and stringent regulations.Artificial intelligence(AI)has the potential to transform the entire drug development processfrom accelerating drug discovery and op

11、timizing clinical trials to improving manufacturing and supply chain logistics.An important challenge for innovation policy is to ensure that spending on medicines drives the greatest return to society.Policies that impose price controls or weaken intellectual property(IP)protections may aim to lowe

12、r drug prices but are known to dampen incentives for future drug research and development(R&D)and fail to improve R&D productivity.A more sustainable approach is to pursue policies that maximize the value of R&D investment.Enhancing R&D productivity,especially in light of declining returns and risin

13、g risks that increase the cost of capitala major factor in drug development costsoffers a more effective path to maximize public health returns,promote equitable access,and drive economic growth.Technological advances hold promise for improving R&D productivity,accelerating access to novel therapies

14、,and promoting health equity and competitiveness,ultimately delivering greater value for resources invested in medicines without stifling innovation.AI applications across various phases of drug development could enhance R&D productivity,increase drug output,and foster competition,driving innovation

15、 and expanding access to novel therapies to improve societal welfare.This increased drug supply can,in turn,drive consumer benefits through market competition.Furthermore,AI could accelerate the discovery of new therapeutic targets and drug candidates for the thousands of diseases that still lack tr

16、eatment options.1 This report explores the potentially transformative role of AI across various phases of drug development,presenting early evidence highlighting how AI can enhance drug discovery,diversify clinical trials,and optimize drug manufacturing processes,ultimately leading to more efficient

17、 development with shorter timelines and improved outcomes.Additionally,the report identifies key challenges to broader AI adoption in drug development and offers several policy recommendations to support effective AI adoption,aimed at fostering innovation while ensuring patient safety.THE DRUG DEVEL

18、OPMENT PROCESS The drug development process is lengthy,risky,and costly.2 While estimates vary widely across therapeutic areas,recent figures suggest that the R&D cost of bringing a new drug to market could be up to$2.83 billion(uncapitalized),factoring in pre-and post-approval R&D,such as new indic

19、ations,patient populations,and dosage formsor,capitalized at an annual rate of 10.5 percent,up to$4.04 billion.3 According to a 2021 Biotechnology Innovation Organization(BIO)report,it takes more than a decade for a drug to reach the market,and only 7.9 percent of Phase I clinical trial candidates u

20、ltimately receive approval.4 Drug development is a complex,multiphase process that transforms scientific discoveries into safe,effective therapies.Key phases include drug discovery,preclinical testing,clinical trials,regulatory review,and manufacturing.(See figure 1.)In the drug discovery phase,scie

21、ntists search for and identify therapeutic targetssuch as proteins,including receptors and enzymesinvolved in a disease.They then screen thousands of potential therapeutic compounds to find those capable of modulating the targets activity.Promising compounds are refined and INFORMATION TECHNOLOGY&IN

22、NOVATION FOUNDATION|NOVEMBER 2024 PAGE 4 advanced to the preclinical testing phase,where lab and animal studies assess safety,efficacy,and pharmacokinetics/dynamics.5 The discovery and preclinical phases together typically take about five to six years.6 After preclinical testing,these candidates mov

23、e on to human clinical trials,which average 9.1 years.7 Drugs that successfully complete clinical trials are submitted to regulatory authorities,such as the Food and Drug Administration(FDA),for approval based on evidence from both preclinical and clinical studies.8 A recent report finds that,on ave

24、rage,it takes 10.5 years for a successful drug candidate to progress from Phase I clinical trials to regulatory approval,following years of early research to discover a therapeutic target and design a drug to effectively modulate it.9 Figure 1:The drug development process10 A prominent method in mod

25、ern drug discovery is the target-based approach,which involves designing drugs that interact with specific therapeutic targets implicated in a disease.This method relies on a deep understanding of a targets role in the disease to develop effective therapies.The process begins with identifying a targ

26、et based on the hypothesis that modifying its activity will impact disease progression.Target identification involves analyzing extensive genomic,proteomic,and other biological data to pinpoint key molecules involved in disease pathways.Target identification is followed by validation through in vivo

27、 and ex vivo models.During this discovery phase,techniques such as high-throughput screening,bioinformatics,and experimental validation are employed to identify and confirm these targets.The process is laborious and complex,requiring significant time and effort.Once validated,drug candidates proceed

28、 to clinical trials to evaluate safety and efficacy in humans.11 Drug development is a complex,multi-phase process that transforms scientific discoveries into safe,effective therapies.Key phases include drug discovery,preclinical testing,clinical trials,regulatory review,and manufacturing.In practic

29、e,drug discovery often integrates target-based and phenotypic approaches.In phenotypic drug discovery,compounds are screened for their effects on disease-relevant traits(phenotypes)without prior knowledge of the exact molecular target or underlying mechanism.This method is particularly useful for co

30、mplex conditions such as cancer and cardiovascular and immune diseases,which involve multiple genes and pathways.By combining these approaches,researchers can leverage their respective strengths to enhance the likelihood of developing effective therapies.12 INFORMATION TECHNOLOGY&INNOVATION FOUNDATI

31、ON|NOVEMBER 2024 PAGE 5 In recent decades,advances in fields such as pharmacology,synthetic biology,and biotechnologycoupled with breakthroughs such as the decoding of the human genome in 2003 and the advent of next-generation sequencing techniques in the mid-2000shave significantly expanded scienti

32、fic understanding of health and disease.Over the past decades,biopharmaceutical R&D investments in the United States surged from$2 billion in 1980 to$96 billion in 2023.13 However,despite the significant increase in resources,several indicators suggest that biopharmaceutical innovation is slowing(or

33、 plateauing).14 Notably,there has as yet been no significant corresponding increase in the number of new drug launches,clinical trial success rates have decreased,and the overall length of drug development has increased.(See figure 2.)15 Moreover,over time,it has become more expensive to develop new

34、 drugs.A Deloitte report notes,“The average cost to develop an asset,including the cost of failure,has increased in six out of eight years.”16 The 2019 version of the report concludes that the average cost of bringing a new biopharmaceutical drug to market has increased by 67 percent since 2010 alon

35、e.17 At the same time,Deloitte found that forecast peak sales per asset have already more than halved since 2010.And significantly,the biopharma industry has experienced a downward trend in returns to pharmaceutical R&D:Deloitte found that the rate of return to R&D in 12 large-cap pharmaceutical com

36、panies declined from 10.1 percent in 2010 to 4.2 percent in 2015 and then to 1.8 percent in 2019.18 Figure 2:FDA new drug approvals,1985202319 Several factors have been proposed to explain the decline in R&D productivity and the growing complexity of biopharmaceutical innovation.20 These include shi

37、fts in the nature of science and technology,more stringent regulatory requirements,and a shift in R&D investments toward novel,high-risk,high-value targets that carry more uncertainty and difficulty.21 Further,advancements 01020304050607019851990199520002005201020152020INFORMATION TECHNOLOGY&INNOVAT

38、ION FOUNDATION|NOVEMBER 2024 PAGE 6 in science,particularly in genetics,molecular biology,and systems biology,have revealed that many diseases are highly complex,resulting from a combination of genetic,environmental,and lifestyle factors.Such complexity often requires sophisticated treatments,such a

39、s targeted therapies or precision medicine,which require additional research and advanced technologies(e.g.,gene editing,biomarker identification,etc.),adding time and cost to drug development.Beyond drug developmentthe process of discovering,designing,and testing new drugs biopharmaceutical manufac

40、turing,which involves producing drugs at scale,has also seen declining productivity.Total factor productivity(TFP),which measures the efficiency of all inputs(labor,capital,and materials)in production,decreased by nearly 2 percent annually from 2010 to 2018,indicating reduced output capacity.(See fi

41、gure 3.)While TFP has since improved,growing at an average annual rate of 2 percent,labor productivitymeasured by output per hour workedhas declined by more than 3 percent per year,pointing to an ongoing challenge.(See figure 4.)This decline could stem from factors such as stricter regulatory demand

42、s,a need for more specialized handling and monitoring,or greater manufacturing complexity,which may be reducing labor input efficiency,even if overall TFP has improved.These factors are likely linked to the industrys growing pursuit of more advanced treatments,such as cell and gene therapies,which r

43、equire more intricate handling and resource-intensive manufacturing processes.Figure 3:Annual change in biopharmaceutical manufacturing TFP,1988202122 -15%-10%-5%0%5%10%198819921996200020042008201220162020INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 7 Figure 4:Annual change in bio

44、pharmaceutical manufacturing labor productivity,1988202123 THE ROLE OF AI IN DRUG DEVELOPMENT Amid declining productivity in drug development,emerging technologies offer the potential to boost biopharmaceutical productivity.Advancements such as AI,quantum computing,CRISPR(clustered regularly intersp

45、aced short palindromic repeats),3D bioprinting,organ-on-a-chip,and nanotechnology could significantly reduce the time and cost of bringing new therapies to market,making life-saving therapies more widely accessible to patients.Next-generation sequencing has enabled the production of large biomedical

46、 datasets,particularly genomics(the study of an organisms genes)and transcriptomics(the study of RNA transcripts),among others.24 Along with data from experimental research and pharmaceutical studies,these vast datasets provide fertile ground for AI,which can analyze these datasets to uncover patter

47、ns and make predictions that serve as valuable inputs across different phases of drug developmentfrom target identification and clinical trials to regulatory processes,manufacturing,and supply chain optimization.Quantum computing,while still in the early stages,can complement AI by solving problems

48、beyond the capabilities of classical computerssuch as molecular simulations and protein foldingboth critical for modeling biological systems and identifying new drug candidates.Together,these technologies can accelerate biopharmaceutical innovation,offering more efficient pathways to novel therapies

49、.25 In Prediction Machines:The Simple Economics of Artificial Intelligence,Ajay Agrawal,Joshua Gans,and Avi Goldfarb described AI as a tool that transforms data into predictions,offering valuable insights to inform decision-making.In the biopharmaceutical industry,AI can analyze vast biological data

50、sets to predict which therapeutic targets are linked to diseases,identify promising drug candidates,and forecast drug responses.26 Using advanced machine learning-15%-10%-5%0%5%10%198819921996200020042008201220162020INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 8 techniques,particu

51、larly deep learning based on neural networks trained on large datasets,AI excels at recognizing patterns and generating predictions.Importantly,AI complements rather than replaces human scientists.A key AI limitation lies in performing advanced causal inference an essential task that requires human

52、judgment.While AI is adept at identifying correlations,such as linking a target to a disease,it struggles to grasp the complex underlying biological mechanisms that explain why these links exista crucial aspect of effective drug development.The vast amount of data provides fertile ground for AI,whic

53、h can analyze datasets to uncover patterns that support each phase of drug development,from target identification and clinical trials to regulatory processes,manufacturing,and supply chain optimization.Still,AI provides a powerful tool that can significantly advance different phases of drug developm

54、ent,from discovery and preclinical testing to clinical trials,regulatory review,and manufacturing.In drug discovery,AI accelerates the analysis of large datasets to identify promising compounds,reducing the time and cost of finding new drug candidates.During preclinical testing,AI models simulate bi

55、ological processes to predict how a drug will behave in humans,reducing reliance on animal testing.In clinical trials,AI can optimize patient selection,improve trial design,and speed up data analysis,leading to faster,more accurate outcomes.It can also streamline the regulatory process by assisting

56、in the preparation of complex documentation required by agencies such as Americas FDA and Europes European Medicines Agency(EMA).Finally,in manufacturing,AI can enhance production processes,improving efficiency and ensuring consistent quality.By integrating AI throughout these stages,the process of

57、bringing a new drug to market can become more efficient,possibly bringing much-needed therapies to market quicker.Facilitating Drug Discovery AI is often hailed as the future of drug discovery,with the ability to reduce the time and cost of identifying therapeutic targets and promising drug compound

58、s,optimize drug chemical structures for greater efficacy,and enhance the molecular diversity of potential targets.27 Traditional experimental methods for target discovery can be slow and limited in scope,but AI offers a more efficient approach by predicting a set of targets from a large dataset base

59、d on properties that suggest their involvement in disease.28 Scientists can then validate these predictions and develop therapeutic hypotheses about why those targets are linked to disease in order to develop safe and effective therapies.29 Illustrative Example:Identifying Therapeutic Targets in Lun

60、g Cancer AI is transforming drug discovery by helping scientists identify potential therapeutic targets in cancer by analyzing vast genomic and clinical data.Goal:Suppose a lung cancer research team aims to identify new therapeutic targets to guide drug development.The team plans to use AI to predic

61、t genes associated with lung cancer that could serve as therapeutic targets.If a gene consistently shows higher expression levels in cancer patients,it could represent a promising target for developing a novel drug.Data:A dataset of 10,000 individuals,including 5,000 with lung cancer(labeled as 1)an

62、d 5,000 without(labeled as 0).Contains data on each individuals gene expression levels for 20,000 genes,obtained through whole-genome sequencing.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 9 The Role of Human Scientists:Before training the AI model,researchers apply their deep kn

63、owledge of biological processes and causal inference to narrow the list of genes from 20,000 to a more relevant subset,say 500 genes.This selection process often involves formulating hypotheses regarding the genes causal role in cancer development,employing causal inference methods and reviewing exi

64、sting scientific literature to identify genes linked to lung cancer progression,drug resistance,and treatment outcomes.By focusing on biologically relevant genes,scientists enhance the likelihood of identifying meaningful drug targets while managing the complexities of high-dimensional genomic data.

65、This approach improves the predictive power of AI models and ensures that the selected genes have a solid biological foundation.The Role of AI:The AI model employs predictive modeling through statistical modelssuch as logistic regression,probabilistic regression,bagging(e.g.,random forest),boosting,

66、and others which predict whether a patient has lung cancer based on gene expression levels.The models can identify patterns in the data,assign weights to each gene,and test the accuracy of its predictions on partitions of the dataset.30 How It Works:Training the AI model involves analyzing gene expr

67、ession levels for the subset of 500 genes across the 10,000 individuals and looking for correlations with lung cancer status.AI models assign a weight to each gene,indicating the extent to which each gene contributes to predicting the likelihood of lung cancer.Genes with large positive weightsor hig

68、her importanceare flagged as potential drug development targets.In July 2021,Google DeepMinds AlphaFold2 system solved a pivotal aspect of the long-standing“protein-folding problem,”a 50-year-old biology challenge.By predicting the 3D structures of nearly all known proteins from their amino acid seq

69、uences,AlphaFold2 has transformed a critical field of biological research.The system,using a vast database of known protein structures,reduced the time needed to predict protein structure from months to minutes.In collaboration with the European Molecular Biology Laboratory(EMBL),Google DeepMind mad

70、e this data freely available to the scientific community.31 This breakthrough has profound implications for understanding protein function,which is key for designing more effective drugs.32 This achievement earned Demis Hassabis and John M.Jumper of Google DeepMind the 2024 Nobel Prize in Chemistry“

71、for protein structure prediction.”The prize was also shared with David Baker“for computational protein design.”33 AI is often hailed as the future of drug discovery,with the ability to reduce the time and cost of identifying therapeutic targets and promising drug compounds,optimize drug chemical str

72、uctures for greater efficacy,and enhance the molecular diversity of potential targets.Additionally,AI can assist with bioactivity predictionevaluating how effectively a drug interacts with its intended targetby identifying promising compounds from large candidate pools,thereby minimizing the need fo

73、r costly,time-consuming experiments.In 2024,a research team developed ActFound,an AI model that employs pairwise and meta-learning techniques,trained on millions of data points from ChEMBL,a public bioactivity database maintained by EMBLs European Bioinformatics Institute(EMBL-EBI).ActFound has been

74、 shown to be more accurate and less costly than traditional computational methods.34 INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 10 Genentechs Lab-in-the-Loop Genentech,a biotechnology pioneer founded in 1976 and now part of the Roche Group,is dedicated to combating serious,life-

75、threatening diseases.Its notable achievements include the development of the first targeted antibody for cancer and the first drug for primary progressive multiple sclerosis.In 2023,Genentech partnered with NVIDIA to launch an AI platform named“lab-in-the-loop,”designed to accelerate the use of gene

76、rative AI in drug discovery and development.Created by Genentechs Prescient Design accelerator unit,the platform utilizes Genentechs granular data to train algorithms for designing new drug compounds.These compounds are tested in the lab,and the resulting data is fed back to refine the AI algorithms

77、.This interdisciplinary approach creates a continuous loop between wet lab work and computational methods,evolving experimental and clinical data into predictive models for potential drug candidates,thus accelerating the development of life-saving therapies.35 Dr.Aviv Regev,executive vice president

78、and head of Genentech Research and Early Development,described the lab-in-the-loop approach as:the mechanism by which you bring generative AI to drug discovery and development.When we try to discover drugs,were only as good as our data.We take the data and we use it to train algorithms;use these alg

79、orithms that weve trained to generate new kinds of molecules that we havent tested before,which we will take back to the lab,and generate experimental data for them again.And those test results will be sent to the AI,to improve itself,to get a better algorithm,and we repeat this process again and ag

80、ain and again until we reach a molecule that has all the right properties that we need for it to be a real medicine for patients.36 In January 2024,Genentech began recruiting patients for a clinical trial to test the effectiveness of its experimental drug vixarelimab in treating ulcerative colitis,a

81、n inflammatory bowel disease.The drug had previously been tested only in lung and skin disorders.Traditionally,determining a drugs potential for different indications can take years of laboratory work,but Genentechs AI platform expedited this process,helping scientists determine in just nine months

82、that vixarelimab could be a promising drug candidate for treating diseases affecting colon cells.Genentech leverages AI in drug discovery to deepen scientists understanding of health and disease,using these insights to develop more effective therapies.By integrating knowledge from fields such as bio

83、logy,computation,genomics,and machine learning,Genentech aims to drive biopharmaceutical innovation.While AI is still in its early stages,several indicators suggest that it is already streamlining drug discovery.A study by the Boston Consulting Group(BCG)examines the research pipelines of 20 AI-focu

84、sed pharmaceutical companies and finds that 5 out of 15 AI-assisted drug candidates that advanced to clinical trials did so in under four years,compared with the historical average of five to six years.37 A 2023 report by BCG and the Wellcome Trust projects that AI-enabled efforts could reduce the t

85、ime and cost of the drug discovery and preclinical stages by 25 to 50 percent.38 And a 2019 report by the U.S.Government Accountability Office and the National Academy of Medicine notes that one company estimated AI-accelerated drug discovery could save between$300 million and$400 million per drugst

86、emming from improved R&D productivity,as AI increases the efficiency of capital investment,enabling better drugs to be identified earlier and more quickly.39 INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 11 AI can significantly advance the main phases of drug developmentfrom discov

87、ery and preclinical testing to clinical trials,regulatory review,and manufacturing.Several AI-enabled drug discovery companies have begun reporting significantly accelerated drug discovery timelines.In September 2019,Canadian biotechnology firm Deep Genomics announced its first AI-discovered therape

88、utic drug candidate aimed at treating Wilsons disease,a genetic disorder that leads to excess copper in the blood,causing liver and neurological issues.40 This drug candidate was proposed just 18 months after target discovery efforts began,with the companys AI platform analyzing over 2,400 diseases

89、and 100,000 pathogenic mutations.41 In January 2020,British pharmaceutical company Exscientia,in collaboration with Japans Sumitomo Dainippon Pharma,reported that its AI-developed compound for obsessive-compulsive disorder(OCD)had reached clinical trials in just 12 months,compared with the typical f

90、ive-to six-year timeline.This compound was also reportedly the first AI-designed drug to enter clinical trials.42 In June 2023,Hong Kong-and New York-based Insilico Medicine announced that it had advanced its AI-developed drug for idiopathic pulmonary fibrosis,a chronic lung disease,to clinical tria

91、ls in under 30 months.43 While this early evidence suggests that AI has the potential to significantly reduce the speed of drug discovery,it will take longer to determine how AI-developed drugs perform in clinical trials compared with their non-AI counterparts.44 The BCG study reveals that AI-enable

92、d drug discovery companies tend to concentrate their pipelines on well-established therapeutic target classes.45 Over 60 percent of their disclosed targets come from familiar classes,such as enzymes(e.g.,kinases)and G-protein-coupled receptors.This focus on well-known targets likely reflects a strat

93、egy to mitigate the risks associated with drug development and to demonstrate the viability of their AI platforms.In INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 12 contrast,large pharmaceutical companies typically maintain more diverse pipelines,balancing both new and established

94、 targets.46 Moreover,a recent report from Deep Pharma Intelligence shows that in the geographical landscape of AI adoption in drug discovery,the United States is a global leader.As of 2023,more than 50 percent of all AI-enabled drug discovery biotechnology companies were based in the United States,f

95、ollowed by 17 percent in Europe and close to 4 percent in China.(See figure 5.)47 Figure 5:Share of companies using AI for drug discovery,202348 Moreover,the number of partnerships between large pharmaceutical companies and AI companies has surged,from 21 such new partnerships in 2017 to 66 in 2022,

96、a more than threefold increase.(See figure 6,reproduced from the report.)49 According to a recent study by S&P Global Ratings,examples of such partnerships in drug discovery include AstraZeneca and BenevolentAI,which have partnered for target identification optimization;GSK and Insilico Medicine,whi

97、ch are working together on the identification of novel biological targets and pathways;and Pfizer and Genetic Leap,which are pursuing the development of RNA genetic drug candidates.50 United States53.3%European Union17.1%United Kingdom10.0%Rest of Asia7.7%Canada4.6%China3.6%900 AI CompaniesINFORMATI

98、ON TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 13 Figure 6:Number of AI-focused partnerships for large pharma companies,2017202251 Streamlining Clinical Trials Clinical trials,used to assess the safety and efficacy of proposed new therapies,represent a crucial step in the drug development pr

99、ocess.However,conducting them is expensive,time consuming,and risky.52 As of 2020,the global clinical trial market was valued at$44.3 billion.53 One study finds that the average clinical development time for a typical drugfrom first-in-human trials to regulatory approvalis 9.1 years,though this can

100、vary by therapeutic area,indication,trial design,and patient availability.54 A recent BIO report details that,on average,Phase I trials take 2.3 years,Phase II 3.6 years,Phase III 3.3 years,and regulatory review 1.3 years.55 Furthermore,fewer than 8 percent of drug candidates that enter Phase I tria

101、ls succeed.56 Given these challenges,AI has the potential to streamline and improve clinical trials in several ways,making them faster and more effective and cost-efficient.57 Enhancing Patient Eligibility and Recruitment One of the most time-consuming aspects of clinical trials is identifying and r

102、ecruiting eligible patients,a process that can take up to one-third of the total trial duration.Moreover,20 percent of trials fail to recruit the required number of participants.To address this challenge,researchers have explored relaxing overly strict eligibility criteria while maintaining patient

103、safety.58 Trial Pathfinder,an AI system developed at Stanford University,analyzes completed clinical trials to assess how modifying criteria,such as blood pressure thresholds,impacts adverse events such as serious illness or death among participants.59 In a study of non-small cell lung cancer trials

104、,Trial Pathfinder showed that loosening certain criteriasome of which,like lab test results,had little impact on the trials outcomecould double the number of eligible patients without increasing the risk of adverse events.This approach proved effective for other cancers,including melanoma and follic

105、ular lymphoma,and in some cases,even reduced negative outcomes by including sicker patients who stood to benefit more from the treatment in the trial.60 These findings suggest that relaxing restrictive eligibility criteria can expand patient access to promising new therapies.050100150200250201720182

106、019202020212022New partnershipsPrevious totalINFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 14 After determining eligibility criteria,the next major challenge is recruiting patients,as failure to do so can produce delays and trial terminations.61 Typically,patient recruitment involv

107、es manual prescreening due to the complexity of clinical criteria text,which often includes confusing abbreviations and terminology.62 Criteria2Query is an AI system that can streamline this process by parsing eligibility criteria from natural language and converting it into structured,searchable da

108、ta.Researchers can input inclusion and exclusion criteria in plain language that Criteria2Query translates into formal database queries that sift through electronic health records(EHRs)to find matching participants.63 This system aims to enhance humanAI collaboration,optimizing cohort generation by

109、combining machine efficiency with human expertise to simplify complex concepts and train algorithms.Criteria2Query can accelerate recruitment and help include populations such as children and the elderly,who are often unnecessarily excluded from trials,thereby speeding up and diversifying clinical t

110、rials.Several AI-enabled drug discovery companies have begun reporting significantly accelerated drug discovery timelines.Beyond systems such as Criteria2Query,which match trials to patients,AI also improves patient-to-trial matching.TrialGPT,for example,helps patients find suitable trials by predic

111、ting their eligibility for specific studies.Patients provide a description of their condition in plain language,and TrialGPT generates a score reflecting their fit for a given trial,along with explanations for each eligibility criterion.In testing,has TrialGPT achieved nearly human-level accuracy an

112、d reduced screening time for trial matching by over 40 percent,highlighting the potential of AI to enhance clinical trial efficiency and accelerate patient recruitment.64 In October 2023,the Dana-Farber Cancer Institute,supported by a Meta grant,began developing a novel open source AI platform to“co

113、mputationally match patients with cancer to clinical trials.”65 This initiative is leveraging Metas large language model,Llama 3,to analyze unstructured clinical notes and trial eligibility criteria,enabling quicker and more accurate matching of patients to suitable clinical trials.66 AI can also pl

114、ay a critical role in identifying biomarkers that predict therapy outcomes and disease progression,which is crucial for matching patients to the most effective clinical trials and therapies.A research team led by Dan Theodorescu,director of Cedars-Sinai Cancer in Los Angeles,developed the Molecular

115、Twin Precision Oncology Platform(MT-POP)to discover biomarkers that predict disease progression in pancreatic cancer,one of the most lethal cancers.Their findings revealed that relying solely on CA 19-9the only FDA-approved biomarker for pancreatic cancerwas suboptimal for predicting therapy outcome

116、s.67 Instead,a set of multiple biomarkers provided far more accurate predictions.This multi-omics approach demonstrates the potential to optimize patient selection for clinical trialsas well as approved therapiesby identifying those most likely to benefit from specific treatments.68 Such advancement

117、s could help refine trial designs and improve the likelihood of success in developing new therapies for pancreatic ductal adenocarcinoma and other challenging diseases.Optimizing Clinical Trial Design AI can also enhance clinical trial design by optimizing aspects such as drug dosages,patient enroll

118、ment numbers,and data collection strategies.At the University of Illinois,researchers developed the HINT(Hierarchical Interaction Network)algorithm to predict trial success based INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 15 on factors such as the drug molecule,target disease,an

119、d patient criteria.Trained on pharmacokinetic and historical trial data,HINT has demonstrated high accuracy across different clinical trial phases and diseases.69 Building on this foundation,the team created SPOT(Sequential Predictive Modeling of Clinical Trial Outcome),which tracks trial progress i

120、n real time and refines predictions as more data becomes available,allowing for timely adjustments during the trial to improve outcomes.70 Moreover,the Illinois-based company Intelligent Medical Objects has developed SEETrials,which utilizes OpenAIs GPT-4 to extract safety and efficacy data from cli

121、nical trial abstracts,helping clinical researchers evaluate different trial designs.71 Further,AI-powered clinical trial design start-up QuantHealth has developed Katina,an AI platform that simulates hundreds of thousands of potential trial protocol combinationssuch as various patient groups,treatme

122、nt parameters(e.g.,treatment dose,administration route,duration),and trial endpoints(e.g.,tumor shrinkage)to enhance the likelihood of trial success.Trained on extensive biomedical,clinical,and pharmacological data,the AI-guided workflow aims to enhance trial design and execution.72 In August 2024,Q

123、uantHealth reported that Katina had simulated over 100 trials with 85 percent accuracy.It could predict Phase II trial outcomes with 88 percent accuracy,significantly higher than current success rates of 28.9 percent,and Phase III trial outcomes with 83.2 percent accuracycompared with 57.8 percent.T

124、he platform was also able to reduce trial costs and durations.QuantHealth reported that its collaboration with a pharmaceutical companys respiratory disease team led to a significant$215 million reduction in clinical trial costs,achieved by shortening trial duration by an average of 11 months,requir

125、ing 251 fewer trial participants,and using 1.5 fewer full-time employees to conduct the trial.73 Streamlining Clinical Trial Protocols AI can also streamline the analysis of clinical trial protocols(CTPs),which are typically lengthy,complex documents exceeding 200 pages.CTPs cover everything from tr

126、ial objectives and design to participant eligibility criteria and statistical methods,serving as a blueprint to conduct trials in a structured,compliant way.By using AI to extract valuable insights from unstructured documents,researchers can enhance participant diversity and reduce dropout rates,ult

127、imately accelerating drug development by improving the efficiency of clinical trials.74 For example,Genentech employed Snorkel AI to extract key information from over 340,000 CTP eligibility criteria.This effort led to better study designs and more accurate participant inclusion/exclusion criteria.T

128、he process helped visualize how different eligibility criteria could impact the demographics of trial participants,allowing for more informed decisions about trials.Snorkel AI identified patterns in clinically relevant characteristics and performed demographic trade-off analyses,enabling Genentech t

129、o adjust criteria to enhance trial diversity and success.75 Improving diversity in clinical trials is essential for addressing health inequities.According to the FDA,concerningly,75 percent of clinical trial participants for drugs approved by the FDA in 2020 were white,while only 11 percent were His

130、panic,8 percent were Black,and 6 percent were Asian.76 This lack of diversity is problematic not only because certain diseases are more prevalent in specific underrepresented groups,but also because different populations may respond differently to therapies.For instance,Albuterol,the most-prescribed

131、 bronchodilator inhaler in the world,is less effective for Black children compared with their white counterparts.Since 95 percent of lung disease studies were conducted on individuals of European descent,this genetic difference went undetected for years.77 Scientists later linked specific genetic IN

132、FORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 16 variants involved in lung capacity and immune response to the reduced efficacy of Albuterol in Black children.78 In response to a lack of diversity in clinical trials,the FDA issued draft guidance in 2022 encouraging more inclusive rep

133、resentation.79 Pharmaceutical companies are increasingly leveraging AI,including tools such as TrialPathfinder and Criteria2Query,to safely recruit more diverse,representative participants,helping to improve trial outcomes for underrepresented populations.80 Johnson&Johnson Leveraging AI to Diversif

134、y Clinical Trials and Transform Precision Medicine Data science and AI are transforming how J&J discovers,develops,and delivers new therapies to bring transformative medicines to people around the world.J&J is leveraging AI tools to build and scale capabilities that enhance the design,execution,and

135、diversification of its clinical trials to facilitate drug development.For clinical trial design,J&J has developed an interactive,AI-enabled platform,Clinical Studio,which uses real-world data(RWD)including EHRs and patient registriesand internal operational data to enable digitization of protocols,p

136、roviding transparency into clinical cost,protocol complexity,and patient burden and helping researchers develop fit-for-purpose protocols for clinical trials,without impacting the scientific rigor of the study.For example,the platform can be used to define and shape inclusion/exclusion criteria and

137、associated outcomes,which has a direct impact on the cohort of eligible patients,including diversifying the patient pool.J&Js internal platform,Trials360.ai,is helping to guide clinical trial site selection as well as engagement and patient recruitment efforts.The tool,coupled with clinical and oper

138、ational expertise,aims to accelerate clinical development and trial recruitment by meeting patients where they are.With AI algorithms applied to real-world and clinical data,J&J can more accurately predict trial locations with the highest probability of enrolling patients,enabling it to place trial

139、sites in locations where patients are,rather than establishing sites where trials have historically taken place.Findings show that sites ranked in the top 50 percent by its AI models enroll up to three times more patients than do those ranked in the bottom 50 percent.J&J has leveraged AI to review c

140、linical data to identify potential data anomalies and query sites and resolve data discrepancies in an ongoing manner.Further,across therapeutic areas,J&J is leveraging a data-driven,AI approach to support the advancement of diversity,equity,and inclusion(DEI)in clinical trialsfrom trial planning to

141、 executionaiming to ensure trial results are generalizable to diverse,real-world populations impacted by the diseases J&J is tackling.In immunology,J&J has set ambitious goals for several trials,and as of late 2023,it had surpassed its year-end diversity goals for those trials,five months ahead of s

142、chedulesupported by robust recruitment tactics and AI-supported site selection.In oncology,J&J is leveraging data science,coupled with clinical and operational expertise and community engagement,to help advance DEI in its multiple myeloma(MM)studies through clinical trial site selection and patient

143、recruitment.A recent meta-analysis of 431 MM studies conducted between 2012 and 2022 across the industry shows that only 4 percent of enrolled patients were Black.81 Early enrollment data for five ongoing J&J MM trials indicate that a combination of human expertise and AI-driven insights has led twi

144、ce as many Black patients to consent and enroll in clinical trials in the United States as compared with prior trials.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 17 Optimizing Regulatory Submissions The regulatory submission phase of clinical trials is both resource intensive and

145、 time consuming.Regulatory affairs teams serve as the critical link between pharmaceutical companies and regulatory bodies,working to secure approvals in accordance with current guidelines.These teams compile the necessary data and documents for submission while ensuring that applications meet all r

146、egulatory requirements.AI can support this process by reducing the time needed to gather and standardize data,as well as by generating drafts based on templates and guidelines.Furthermore,AI can enhance workflows by providing timely insights,automating tasks such as data monitoring,document manageme

147、nt,and adverse event detection as well as ensuring compliance with evolving regulations.82 For example,Medidatas Clinical Data Studio uses AI to monitor clinical trial data in real time,streamlining clinical data management,operations,and safety.The platform automates data reviews,identifies pattern

148、s through visualizations,monitors trial site performance and compliance,and mitigates data quality risks by detecting anomalies such as inconsistencies or outliers in patient data.This proactive approach enables clinical researchers to address potential issues early,helping to ensure data integrity

149、throughout the trial and reducing the risk of compromising regulatory compliance.Ultimately,this helps improve trial efficiency and reliability,expediting the regulatory review process.83 Another notable example is Veeva Vault RIM(Regulatory Information Management),an AI-powered platform developed b

150、y Veeva Systems.The platform offers a comprehensive suite of services designed to manage submissions,ensure compliance,and streamline regulatory processes across regions.With its compliance tracking features,Veeva Vault RIM continuously monitors and updates evolving regulatory guidelines worldwide,e

151、nhancing the efficiency of the regulatory submission process.84 When new guidelines are issued by regulatory bodies such as the FDA or EMA,the platform alerts clinical trial teams,analyzes the changes,and recommends adjustments to trial procedures or documentation in real time to ensure ongoing comp

152、liance.Additionally,the platform automates the regulatory submission process by aggregating data from various sources,formatting it according to the latest guidelines,and generating submission-ready documents,which reduces manual effort and the risk of errors.85 Enhancing Manufacturing and Supply Ch

153、ains AI can also enhance drug manufacturing and strengthen supply chains by optimizing production processes,reducing downtime through predictive maintenance,improving demand forecasting,and streamlining inventory management,ultimately leading to greater efficiency.AI in Manufacturing In addition to

154、its role in drug discovery,clinical trials,and regulatory submissions,AI can enhance manufacturing and help strengthen supply chains.It can optimize drug manufacturing by streamlining production processes,improving quality control,and reducing costs.Predictive analytics can identify efficient produc

155、tion pathways,forecast equipment failures,anticipate necessary maintenance,and improve resource allocation.AI can also support the development of continuous manufacturing systems,enabling more flexible,responsive drug production,and AI-driven demand forecasting can help reduce both stockouts and ove

156、rproduction.86 INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 18 AsimovInnovating Gene Therapy Design and Manufacturing With AI Asimov,a Boston-based pioneering bioengineering company,uses AI and synthetic biology to transform the design and production of therapies.Genetic engineeri

157、ng,the process of modifying DNA,holds immense potential for drug development by targeting diseases at their genetic roots.The process is vital for developing biologics,cell and gene therapies such as CAR-T,and precision medicine to tackle complex conditions such as cancer and inherited genetic disor

158、ders.Asimovs AI-driven molecular simulations and computational biology accelerate the traditionally slow,labor-intensive,and trial-and-error genetic engineering process,making drug development faster,cheaper,and more reliable.Asimovs AI-driven bioengineering tools apply to both drug design and manuf

159、acturing.In drug design,AI generates novel DNA sequences and simulates complex biological processes,expediting the identification of promising drug candidates.In manufacturing,these tools can increase the precision and efficiency of therapeutics production,supporting the creation of complex biologic

160、s such as antibodies and viral vectors.This is critical for improving the scalability,reliability,and cost effectiveness of drug manufacturing,especially for targeted cell and gene therapies that require customized production methods.Asimovs AAV Edge platform is an example of the companys innovation

161、 in gene therapy manufacturing.The platform optimizes the production of adeno-associated viruses(AAV),a type of virus used in gene therapies.Its AI-designed tissue-specific promoters restrict gene expression to intended tissues,ensuring,for example,that a therapy targeting heart disease is“on”in hea

162、rt tissue but“off”in liver tissue to reduce toxicity.This is critical because while gene therapies hold significant potential,a key challenge is that they can cause liver toxicity.AAV Edge helps increase the precision,effectiveness,and safety of gene therapies while reducing side effects.87 Currentl

163、y,Asimov focuses on drug manufacturing,particularly drugs enabled by genetic engineering,including antibodies,protein biologics,cell and gene therapies,and RNA vaccines.By employing AI tools,Asimovs suite of services seeks to improve drug efficiency and accelerate manufacturing timelines,yielding hi

164、gher,faster,cheaper,and better therapeutic production.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 19 Asimovs future vision is to extend its AI-driven bioengineering solutions to the entire biotech space,including agriculture and food,industrial biotechnology,and environmental bio

165、technology,to accelerate innovation in these areas by automating traditionally slow,manual processes,increasing precision,and enabling customized solutions.AI in Supply Chains Drug shortages are a growing public health concern,with significant financial impacts on health-care systems.In the United S

166、tates alone,these shortages cost an estimated$230 million a year due to health-care expenses,productivity losses,and adverse patient outcomes.The root causes of such shortages include supply chain management challenges,inadequate business continuity planning,and market dynamics such as fluctuating d

167、emand.88 The global pharmaceutical supply chain constitutes an elaborate network of manufacturers,suppliers,and distributors spanning multiple countries,rendering it vulnerable to disruptions from natural disasters,transportation delays,and regulatory hurdles.AI can help mitigate these risks by opti

168、mizing supply chain processes.89 It can analyze transportation costs,lead times,and supplier performance,enabling better route planning,cost saving,and improved delivery times.For example,the United States Pharmacopeias(USPs)Medicine Supply Map employs AI to analyze millions of data points worldwide

169、 to predict disruptions,allowing timely interventions to prevent shortages.90 Overall,AI can drive improvements in manufacturing efficiency,supply chain management,and the scalability of advanced therapies,contributing to faster,more cost-effective drug production and supply chain resilience.Merck e

170、mploys an AI system developed by Aera to optimize its supplier network through data analysis and proactive recommendations.This system enables Merck to anticipate supply chain fluctuations,adjust production schedules,and reroute shipments,thereby enhancing efficiency and improving supply chain resil

171、ience.91 Overall,AI can drive improvements in manufacturing efficiency,supply chain management,and the scalability of advanced therapies,contributing to faster,more cost-effective drug production and supply chain resilience.CHALLENGES FOR AI ADOPTION AI adoption in drug development faces several sig

172、nificant challenges.Data access and privacy issues present substantial hurdles,as stakeholdersincluding health-care providers,pharmaceutical companies,researchers,and federal agenciesmust collect and share vast amounts of data to develop effective AI models.Improved coordination,both within and acro

173、ss organizations,is crucial to achieve this goal.Additionally,validating AI algorithms is essential to address concerns about the“black box”nature of how AI systems arrive at decisions and to mitigate possible biases,thereby building trust in these technologies.Integrating AI with existing workflows

174、 can also be challenging,especially in the face of unclear regulatory pathways.Misaligned incentives,including fears of job displacement,may further slow adoption.92 However,with proactive strategies and enhanced collaboration between public and private INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|N

175、OVEMBER 2024 PAGE 20 sectors,these challenges can be effectively addressed,paving the way for the successful integration of AI in drug development.Data Access,Quality,and Privacy AI can play a crucial role in supporting drug development,but its effectiveness hinges on the availability and quality of

176、 training data.Several types of data are essential for AI applications in this area.First,clinical data,including EHRs,provides information on patient medical histories,diagnoses,lab results,imaging data,and treatment plans.Second,genomic data,obtained through sequencing techniques,helps identify ge

177、netic variants and provides insights into gene function and regulation,which are vital for understanding disease mechanisms.Third,pharmaceutical data includes information on drug efficacy,adverse reactions,pharmacokinetics,and pharmacodynamics.Fourth,chemical and structural data is important for ide

178、ntifying and optimizing drug candidates from extensive compound libraries.Finally,data from scientific literaturesuch as research papers,clinical guidelines,clinical trial outcomes,and patentshelps synthesize existing knowledge and identify gaps for new drug development.93 Together,these different t

179、ypes of data create a comprehensive picture that enhances the ability to identify promising therapeutic targets and drug candidates,target treatments to individual patients,and streamline the drug development process.Importantly,linking genotypes(genetic information found in genomic records)to pheno

180、types(observable traits found in clinical records)enables researchers to better understand how genetic variations influence disease risk and treatment responses and disease processes.This connection is particularly important for developing targeted therapies and advancing precision medicine.Yet,chal

181、lenges remain in collecting,sharing,and linking this data to support AI applications.Two decades ago,the Human Genome Project(HGP)successfully decoded the human genetic code.A core principle that contributed to the success of this international scientific effort was data sharing.94 HGP leadership es

182、tablished the Bermuda Principles,which committed all project participants to electronically share data and make human genome sequences publicly available in order to advance scientific progress.95 Since then,advances in sequencing techniques have generated vast amounts of genomic data from millions

183、of individuals,now stored in repositories around the world.The Bermuda Principles,adopted by journals and funding agencies,aim to ensure that published genome study data remains accessible to all,fostering further scientific discoveries.96 However,the vast influx of diverse and sensitive data has pr

184、ompted governments,funding agencies,and research consortia working with them to develop custom databases for managing this information.The existence of incompatible and non-shareable datasets further complicates efforts to link genomic and phenotypic data,which is key for breakthroughs.97 Uncovering

185、 the genetic causes of complex diseases such as cancer and cardiovascular disorders requires pinpointing multiple genetic risk factors across the genome.This is achieved through genome-wide association studies(GWAS),which analyze the genotypes of hundreds of thousands of individuals,both with and wi

186、thout the disease,to identify relevant genetic variations.98 These studies integrate both genomic and phenotypic data,which often come from EHRs and medical cohort studiessuch as the Framingham Heart Study,which tracked multiple generations to investigate cardiovascular risk factorsproviding insight

187、s into how genetic variations contribute to disease.Yet,data integration problems present challenges for GWAS,as the success of these studies relies on the integration of large-scale genomic and phenotypic datasets.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 21 Since 2005,over 10

188、,700 GWAS have been conducted,generating vast datasets largely stored in controlled-access databases to protect personal information for legal and ethical reasons.Researchers must navigate strict vetting processes to access such data.For example,National Institutes of Health(NIH)grant recipients are

189、 required to deposit their GWAS data into an official repository,the Database for Genotypes and Phenotypes(dbGaP),while European researchers are encouraged to use the European Genome-Phenome Archive(EGA)housed at EMBL-EBI.Yet,despite such efforts,the process of accessing such publicly available data

190、 remains cumbersome.99 Moreover,a growing number of countries around the world have initiated large-scale genomic sequencing efforts on their own populations,including Americas All of Us Research Program,Genomas Brasil,the Qatar Genome Program,the Turkish Genome Project,and the Korean Genome Project

191、,among others.Such global genomic sequencing initiatives are crucial for capturing genetic diversity across populations,which can lead to more inclusive and effective biopharmaceutical research.For now,though,how the data from these initiatives will be shared and integrated into existing workstreams

192、 and consortia remains unclear,diluting the potential of such large-scale datasets.Initiatives such as the Global Alliance for Genomics and Health(GA4GH)work to create technical standards to link disparate genomic databases globally.100 Additionally,the GWAS Catalog,an open-access collaboration betw

193、een EMBL-EBI and the National Human Genome Research Institute(NHGRI),is working to standardize and centralize GWAS data to support both a deeper understanding of disease mechanisms and the discovery of new therapeutic targets and causal variants.101 Other efforts,such as the Human Cell Atlas(HCA),a

194、global consortium creating publicly accessible,detailed reference maps of human cells,further seek to support AI-enabled drug development and advance our understanding of health and disease.102 Electronic Health Records In addition to genomic data,clinical dataoften stored in EHRsprovides crucial de

195、tails about patients medical histories and plays a vital role in deepening our understanding of disease mechanisms.Together,these two types of data are essential for uncovering how genetic variations(genotypes)influence observable traits(phenotypes)and disease outcomes,forming the foundation for bui

196、lding effective AI models for drug development.The push for electronic health information exchange began in 2009 with the signing of the American Recovery and Reinvestment Act(ARRA),which established the Health Information Technology for Economic and Clinical Health(HITECH)Act.This provision promote

197、d the digitization of patient records to improve health-care delivery,allocating over$35 billion to support the adoption of EHRs by hospitals and clinics.103 Today,EHR adoption is widespread,with 96 percent of U.S.hospitals and almost 80 percent of office-based physicians using them.104 Some of the

198、largest EHR systems include Epic Systems,Oracle Cerner,and MEDITECH.Epic holds the largest market share at 37.7 percent,followed by Oracle Cerner at 21.7 percent,and MEDITECH at 13.2 percent.105 Epic,the leading provider in the United States,is used by many prominent hospital systems and academic me

199、dical centers,including the Cleveland Clinic,the Mayo Clinic,and Johns Hopkins Medicine.Beyond the issues posed by privacy concerns,the presence of hundreds of EHR systems across the United States,each with distinct clinical terminologies and technical standards,complicates interoperability.True int

200、eroperability requires not just the exchange but also the use of INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 22 standardized data,a long-standing issue in the U.S.health-care system that continues to limit electronic data sharing.Moreover,achieving interoperability depends on col

201、laboration between many stakeholders,including patients,providers,software vendors,legislators,and health information technology(IT)professionals.Yet,the health-care system remains fragmented,with data often treated as a commodity for competitive advantage rather than a shared resource for improving

202、 patient care.106 Recently,the FDA pushed for an increased use of RWDincluding EHRs,billing data,and administrative claimsin drug development.This data,rich in patient details including disease status,treatments,procedures,and outcomes,could enhance drug development.With the growing EHR adoption acr

203、oss the United States,the FDA has issued guidance on using EHR data in clinical research and regulatory submissions.107 However,integrating RWD with AI presents several challenges.EHRs are often inconsistently documented by clinicians,making it difficult to extract data,such as treatment outcomes,un

204、iformly.Issues including missing data and selection bias further complicate the analysis.Additionally,the lack of standardization and harmonization across different data sources hinders both replication and reproducibility.108 Despite these challenges,the creation of large research networks such as

205、the national Patient-Centered Clinical Research Network(PCORnet),the Observational Health Data Sciences and Information(OHDSI)consortium,and the Clinical and Translational Science Award Accrual to Clinical Trials(CTSA ACT)network have improved data sharing.These networks span multiple sites around t

206、he world,employing standardized data infrastructure,and provide access to a diverse patient population,enabling large-scale studies to explore factors influencing health and disease.109 EHR-driven genomic research is valuable,as an integrated approach can augment GWAS by replicating the studies and

207、extending conventional GWAS findings to underrepresented populations.In 2013,the National Science Foundation(NSF)convened a workshop to identify challenges and set a research agenda to achieve a national-scale Learning Health System(LHS).An LHS is an infrastructure that enables rapid data sharing an

208、d knowledge generation to inform health-care decisions,ultimately improving health outcomes.The agenda emphasized the need for systematic integration of data,evidence,and practice,highlighting the importance of standardizing data stored in EHRs.By facilitating access to comprehensive patient data,EH

209、Rs enable health-care providers to engage in continuous learning.The agenda noted that,to maximize the potential of EHRs in fostering a collaborative,well-functioning LHS,establishing standardized data-sharing protocols and ensuring interoperability are essential for adaptation and innovation in res

210、ponse to emerging health-care challenges.110 Linking EHRs with other data sources,including genomic data,can further enable the study of drug-phenotype and drug-gene interactions.Researchers from the Vanderbilt Electronic Systems for Pharmacogenomic Assessment(VESPA)project have shown that EHR-based

211、 biobanksrepositories of human biological materialsprovide cost-effective tools for biomedical discoveries,as they allow for the reuse of biological samples across multiple studies and enhance research efficiency.111 Such EHR-driven genomic research(EDGR)is valuable,as an integrated approach can aug

212、ment GWAS by replicating the studies and extending conventional GWAS INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 23 findings to underrepresented populations.This is because EDGR cohorts reflect the populations cared for in clinical settings,which are much more diverse than those

213、typically represented in genomic research.112 EHR-linked genomic studies can also enable phenome-wide association studies(PheWAS),which analyze the effect of genetic variants across multiple diseases and traits captured in EHRs.A major obstacle,however,remains the need for the development and adopti

214、on of international standardized consent models from patients for the use of their clinical data in biomedical research,as consent regimens vary significantly across regions.113 Lucia Savage,chief privacy and regulatory officer at Omada Health and former chief privacy officer at the U.S.Department o

215、f Health and Human Services(HHS)Office of the National Coordinator for Health IT,has highlighted that regulations governing the use of data collected through traditional health-care processes vary widely across countries.In the United States,there is more leeway for using patient data in tertiary re

216、search applications,such as developing AI models for drug development,as the Health Insurance Portability and Accountability Act(HIPAA)allows the use of patients health data for research without explicit consent when that data is de-identified.In contrast,many other countries,including those governe

217、d by the EUs General Data Protection Regulation(GDPR),impose stricter restrictions on such uses and the international transfers of personal data.These differences in legal frameworks make cross-border data sharing and access more challenging.Therefore,it is crucial to consider the regulatory environ

218、ment in which the data was collected and explore possibilities for interoperability to navigate such challenges.114 Gilead Using AI to Identify Underdiagnosed Hepatitis C Individuals115 Gilead is leveraging AI to identify underdiagnosed individuals,particularly for diseases such as HIV and hepatitis

219、 C virus(HCV),where traditional screening methods are expensive and burdensome for patients.In diagnosis,there is a trade-off between privacy and data usability,as often,the higher the privacy standard,the less usable the data.Hence,striking a balance between privacy and usability is key.Too much pr

220、ivacy can make it difficult to identify patients,particularly from underserved communities.Greater data access,sharing,and interoperability,supported by privacy-enhancing techniques,could help with more accurate diagnoses.Gileads machine learning algorithm,trained on ambulatory electronic medical re

221、cords(EMRs),aims to predict initial HCV diagnoses and identify undiagnosed HCV patients,prioritizing them for screening.The EMRs used to train the algorithm include data on age,gender,HCV-related predictors such as birth cohort,opioid usage,laboratory test results,diagnosis codes,treatments,data on

222、social determinants of health,chronic health conditions,and other variables.HCV,one of the most common blood-borne viruses and a leading cause of liver-related illness in the United States,is the target of a World Health Organization(WHO)initiative to eradicate it as a public health threat by 2030.T

223、he National Academies of Science,Engineering,and Medicine(NASEM)have highlighted improved detection of undiagnosed HCV cases as central to eliminating the virus.Universal one-time screening is recommended in the United States,but it is difficult to implement in practice,and screening rates remain lo

224、w.Gileads AI approach,trained on a large EMR dataset,requires fewer patients to be screened compared with traditional methods while improving precision.The AI model can prioritize patients for HCV screening,and has the potential to make resource allocation more efficient,reduce clinician workload,pr

225、event disease progression,and lower health-care costs.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 24 Integrating AI into EMR systems and clinical workflows shows significant potential for accelerating HCV elimination efforts.Effective targeting could improve diagnosis rates,reduc

226、e morbidity and mortality through earlier detection,and identify patients often overlooked by risk-based screening.The AI algorithm generates continuous risk scores,allowing for a more nuanced triage process and targeted screeningpatients with higher scores calculated from their EMR data can be prio

227、ritized for screening,diagnosis,and linkage to care.This data-based approach can help identify harder-to-find patients in a way that does not stigmatize individuals.It could also improve the allocation of finite health-care resources and the return on investment of screening programs,as well as the

228、rates of HCV diagnoses,treatment,and transmission.Gileads AI use case demonstrates how machine learning and EMRs,when combined,present new opportunities to improve population health management and achieve better clinical outcomes.Moreover,it supports public policies that promote EMR adoption and int

229、eroperability,which could play a critical role in identifying underdiagnosed individuals,particularly within underserved communities.By enhancing data sharing and integration,these policies could not only streamline clinical workflows,but also help reduce health disparities,supporting efforts to imp

230、rove health equity and ensure timely,effective care for all patients.Beyond RWD,there is growing interest in synthetic dataartificially generated by computer algorithms to simulate the statistical properties of RWDas a scalable,cost-effective,and privacy-preserving alternative for training AI models

231、.However,challenges remain:Synthetic data may not fully capture the complexity and variability of real clinical populations,such as diverse demographics and the intricate biological and clinical interactions that influence treatment responses and side effects.There are also questions about how well

232、synthetic data reflects the evolving nature of diseases,treatment protocols,and patient populations.The use of synthetic data in drug development remains an emerging area of research.116 Pharmaceutical Data Pharmaceutical data,alongside genomic and clinical data,plays a key role in biopharmaceutical

233、 innovation.While biopharmaceutical companies often prefer to keep their data private for competitive reasons,collaboration between companies and research institutions could significantly speed up drug development.Public-private partnerships(PPPs)that bring together academia,industry,and government

234、offer a path forward,particularly in precompetitive research,where the risks are lower.One notable example is the Innovative Health Initiative(IHI),the largest biomedical PPP in the world,launched in 2008 by the European Union and the European Federation of Pharmaceutical Industries and Associations

235、(EFPIA)to accelerate the development of next-generation therapies.By encouraging the sharing of data among academia,industry,and government,particularly in precompetitive research,PPPs are producing valuable training data for AI-enabled drug development.Other examples include the Alzheimers Disease

236、Neuroimaging Initiative(ADNI),launched by the NIHs National Institute on Aging(NIA)to advance Alzheimers research;Project Data Sphere,launched by the CEO Roundtable on Cancer to accelerate drug development by facilitating access to de-identified oncology clinical trial data;Open Targets,which uses b

237、iological data to identify and validate therapeutic targets;and the Structural Genomics Consortium,focused on advancing knowledge of human protein structures.117 By encouraging the sharing of data among INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 25 academia,industry,and governme

238、nt,particularly in precompetitive research,PPPs are producing valuable training data for AI-enabled drug development.Beyond PPPs,alternative payment models can provide incentives for pharmaceutical companies to share data.Data monetization allows companies to profit from their data by providing acce

239、ss to anonymized datasets on a subscription basis.For example,Flatiron Health,acquired by Roche,operates under this model by aggregating and analyzing de-identified oncology RWD to offer insights that enhance cancer care and accelerate drug development.This model treats data as a product,creating a

240、continuous revenue stream for companies.118 Role of Privacy-Enhancing Technologies When sharing data,a key consideration is ensuring that it can be done in a secure,privacy-enhancing manner,given the confidential and sensitive nature of the data involved in drug development.Traditional methods of sh

241、aring data between different entities involve sending data to third parties,such as by creating copies of data for each entity or aggregating data in a single repository that all entities can access.Entities can maintain privacy of shared data through access controls,oversight,and legal mechanisms.H

242、owever,newer decentralized methods for data sharing allow multiple entities to collaborate on AI model training without sharing raw internal data.Decentralized data sharing keeps data distributed across multiple locations,eliminating the need to transfer or aggregate it in a central repository.This

243、method is particularly valuable in privacy-sensitive settings,as it enables collaboration while safeguarding sensitive information.In drug development,decentralized approaches enable different research institutions and companies to share data,supporting the development of safer,more effective therap

244、ies.119 PETs can support the training of AI algorithms on vast biological,chemical,and clinical datasets,accelerating AI-enabled drug development.Key aspects of decentralized data sharing include a strong emphasis on data privacy,collaboration,and security.In this approach,each party retains control

245、 over its data,lowering the risk of leaks from third parties and minimizing the threat of a single point of failure that could compromise the entire dataset.Decentralized approaches are often supported by privacy-enhancing technologies(PETs),such as secure multiparty computation(SPMC),federated lear

246、ning(FL),fully homomorphic encryption(FHE),and differential privacy(DP),which enable privacy-enhancing access to and analysis of diverse data sources.120 PETs can support the training of AI algorithms on vast biological,chemical,and clinical datasets,accelerating AI-enabled drug development.121 Secu

247、re Multiparty Computation SPMC is a cryptographic method that enables multiple participants to collaborate on computations using private data without revealing it to each other,allowing teams to work together with internal data while maintaining its confidentiality.SPMC ensures that only the final r

248、esults of a computation are revealed to participants without disclosing any intermediate information from a joint analysis,thereby providing a higher level of security.122 In drug discovery,SPMC has several important applications.For instance,it can be used to predict interactions between therapeuti

249、c targets and drugs based on genomic and chemical data.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 26 This is a critical step in developing promising drugs,and large datasets are required to train AI models to generate accurate predictions.Public datasets,including ChEMBLa databa

250、se of bioactivity data for drug-like compoundsoften rely on contributions from academic institutions,pharmaceutical companies,and collaborative projects and help train such models on vast data.But they are only one piece of the puzzle.The integration of other sensitive data,including EHRs,could furt

251、her enhance algorithms.Methods such as SMPC can help,and feasible SMPC protocols have been deployed for GWAS and drug-target interaction prediction problems.However,efforts to develop comprehensive SMPC protocols for AI-enabled drug discovery are still a work in progress.123 Consider how SMPC can be

252、 useful in drug discovery.Pharmaceutical companies,even competitors,benefit from collaboration but must protect their proprietary data.For example,Company A,which specializes in high-throughput screening of small molecules,and Company B,which has comprehensive datasets of biological targets,can use

253、SMPC to securely combine their datasets to discover compounds that interact with certain biological targets.In this manner,they can jointly identify potential drug candidates for further exploration,all while safeguarding each companys proprietary data and enhancing their research.124 Federated Lear

254、ning FL emphasizes collaborative training of AI models while keeping the training data decentralized and local to each participant.In this approach,each participant trains the AI model on their own data and shares only the model parametersrather than raw datawith a central server,which aggregates th

255、ese parameters to enhance the global AI model.The refined model is then returned to the participants for further local training,enabling collaborative model development without exchanging sensitive data.Unlike SMPC,which allows multiple parties to jointly perform computations on private data,FL cent

256、ers on the collaborative building of models.For instance,hospitals could use FL to collaboratively train a model that predicts patient outcomes based on their EHRs without exchanging any actual patient data.125 In drug discovery,FL is particularly dynamic and offers numerous applications.A notable e

257、xample is IHIs MELLODDY(MachinE Learning Ledger Orchestration for Drug DiscoverY)project,which embodies a blend of cooperation and competition.This initiative emerged from a joint call by 10 of the worlds largest pharmaceutical companies aimed at developing predictive AI models for drug discovery wh

258、ile safeguarding data privacy.The collective dataset for this project will encompass over 10 million small molecules and more than 1 billion activity labels measured in biological assays,making it one of the largest FL-based efforts in the field.126 Fully Homomorphic Encryption FHE is another powerf

259、ul technique that enables computations on encrypted data without requiring decryption,ensuring that data remains secure.127 In 2017,experts from industry,government,and academia established the Homomorphic Encryption Standardization Consortium,which developed a standard in 2018 outlining security re

260、quirements for FHE applications.128 In drug discovery,FHE presents a promising solution to privacy concerns by allowing scientists to perform computations on encrypted data,thus protecting sensitive information such as the structures of new drug compounds and genomic data.129 While algorithmic impro

261、vements have already enhanced the efficiency of FHE,widespread implementation still faces challenges,INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 27 including high computational demands and integration with existing data workflows.Advances in hardware acceleration and optimized al

262、gorithms could,however,enhance its use in privacy-enhancing decentralized AI model building.130 Differential Privacy DP,introduced by Cynthia Dwork in 2006,is a mathematical concept that enables data sharing while preserving individual privacy.The core principle of DP is that the outcome of a comput

263、ation should remain nearly the same whether a single data point is included or excluded.This is achieved by adding calibrated noise to the results,effectively hiding individual contributions while maintaining the overall accuracy of the analysis.Although DP results are approximate and may vary with

264、repeated analyses,sensibly calibrated noise enables AI models to balance privacy and utility.Still,DP requires careful tuning of noise levels to ensure that data utility is not compromised.131 DP has proven particularly useful in applications such as drug sensitivity prediction.For example,a 2022 st

265、udy shows that combining DP with deep learning,a technique known as differentially private deep learning,can effectively predict breast cancer status,cancer type,and drug sensitivity using genomic data,all while preserving individual privacy.132 Beyond the importance of advancing the development of

266、PETsincluding SPMC,FL,FHE,and DPto enable secure data sharing in AI-enabled drug development,it is crucial to make these tools accessible and user friendly for a wide range of scientists working in biopharmaceutical innovation.Organizations at the forefront of PET development include U.S.-based Open

267、DP,Duality Technologies,and Actuate,as well as the United Kingdoms OpenMined.133 For example,Duality Technologies has collaborated with leading research institutions such as the Dana-Farber Cancer Institute to enhance oncology outcomes.134 Algorithm Validation and Bias Mitigation As AI becomes incre

268、asingly integral to drug development,attributes such as model accuracy,reliability,transparency,and interpretability are important for creating effective therapies.However,the complexity of neural networks underlying AI often causes these models to function as black boxes.To tackle this challenge,eX

269、plainable AI(XAI)is emerging as a branch of AI focused on developing models that are clearer and more interpretable.135 This enables researchers to identify and correct potential errors,such as inaccurate predictions due to flaws in the training data.136 Furthermore,drug development is an increasing

270、ly multidisciplinary process involving chemists,biologists,clinicians,and data scientists,and XAI helps make AI-driven insights more accessible to non-AI experts,fostering collaboration.The effective integration of human expertise with AI insights is essential for accelerating the development of saf

271、e and effective therapies.137 The National Institute of Standards and Technology(NIST)defines trustworthy AI as“valid and reliable,safe,secure and resilient,accountable and transparent,explainable and interpretable,privacy-enhanced,and fair with harmful bias managed.”138 Organizations such as NSFs I

272、nstitute for Trustworthy AI in Law&Society(TRAILS)at the University of Maryland and the AI Now Institute are working to promote these principles for the use of AI across different industries,including biopharmaceuticals.139 INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 28 An increa

273、singly important reason to validate AI is to identify and mitigate potential biases in the models.The FDA defines algorithmic bias as“the systematic deviation in model predictions or outcomes for certain data points or groups compared to others.”140 Bias can emerge at different stages of the drug de

274、velopment processfrom biases in clinical research data used to identify new therapeutic targets and drug candidates,which may disadvantage underrepresented populations,to biases in manufacturing data that can undermine model performance and limit generalizability in the production of therapies.Witho

275、ut human oversight,AI systems can unintentionally learn and perpetuate biases present in their training data.In drug development,such biases may lead to inequitable outcomes,such as underrepresenting certain patient groups,which can skew predictions about drug efficacy or safety.This could result in

276、 therapies that are less effective,or even harmful,for these populations.Validating AI models to detect and correct biases ensures that these technologies support fairness and inclusivity in drug development,producing safer,more effective therapies for all patients.To promote equity in AI-supported

277、drug development,it is important to use diverse,representative datasets.Yet,a 2009 study revealed that 96 percent of GWAS participants were of European descent,a figure that decreased to 78 percent by 2018.141 The lack of diversity in genomics research could introduce bias in AI algorithms and exace

278、rbate health-care disparities if left unaddressed by both failing to account for differences in drug response among diverse groups and neglecting diseases prevalent in underrepresented populations.142 For example,as previously mentioned,Albuterol,the most-prescribed bronchodilator inhaler,is less ef

279、fective in Black children due to genetic differences that had gone unnoticed for years,as 95 percent of lung disease clinical trial participants were of European descent.Identifying the specific genetic variants that contribute to differences in Albuterol response has both enhanced scientists unders

280、tanding of drug efficacy and highlighted the value of inclusive research.143 To address such biases,it is essential to use more diverse datasets that represent various demographic and clinical attributes and employ techniques such as data reweighting to balance underrepresented groups,cross-populati

281、on model validation,and regular audits.Moreover,expanding genetic sequencing efforts and integrating clinical data from underrepresented groups through EHRs could help,because EHR data better reflects the diverse populations seen in real-world clinical settings.144 Beyond the potential consequences

282、of algorithmic bias in clinical research,data biases can also arise in drug manufacturing,harming model performance and generalizability.For example,variability between production batches(e.g.,differences in raw materials or slight equipment changes)can introduce biases if AI models are trained disp

283、roportionately on data from a particular batch,leading to inaccurate predictions and process adjustments that do not generalize across batches.Mitigating algorithmic bias throughout drug development,from clinical research to manufacturing,is therefore essential.Efforts to Reduce Algorithmic Bias Sev

284、eral efforts are underway to improve the representativeness of data used in biomedical research.One notable example is the NIHs All of Us research program,launched in 2018.This large-scale genetic sequencing initiative aims to advance health equity by gathering data from a diverse population.With ov

285、er$3.1 billion in funding,it seeks to create the worlds most diverse INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 29 genetic dataset for biomedical research.Currently,80 percent of All of Us participants come from groups historically underrepresented in biomedical research,spannin

286、g different races,ethnicities,ages,geographies,access to health care,and disability status.The data includes survey responses,EHRs,genome sequences,and activity data from devices such as Fitbit.Researchers can access this information through a centralized cloud-based workbench.Since its first data r

287、elease in 2020,over 200 peer-reviewed articles have been published based on this data.145 All of Us fills a critical gap in genomics research by providing the enhanced diversity that is largely absent from most major biobank resources.For example,the UK Biobank,the worlds largest whole-genome datase

288、t,has released about half a million genomes,with approximately 88 percent coming from white individuals.146 A study on type 2 diabetes illustrates the strength of having a diverse dataset.With data from over 2.5 million individualsalso drawing from All of Usincluding nearly 40 percent from non-Europ

289、ean ancestries,the researchers identified 611 genetic markers that could influence the development and progression of diabetes,145 of which were previously unknown.These discoveries hold the potential to guide more precise,genetically informed diabetes care.Furthermore,population-specific difference

290、s in genetic variants linked to drug metabolism underscore that certain drugs may be safer or more effective for specific groups.Such findings are crucial in ensuring that AI-enabled drug development is equitable and reflective of diverse populations.147 All of Us fills a critical gap in genomics re

291、search by providing the enhanced diversity that is largely absent from most major biobank resources.Integration With Workflows AI has the potential to reshape production processes in biopharmaceutical innovation,quickly scanning vast datasets to uncover correlations and make predictions.But AI shoul

292、d be viewed as a complement to,rather than a substitute for,human scientists.While AI excels at detecting patterns,it cannot discern causal relationshipssuch as whether a protein implicated in a disease actually causes the disease or is merely associated with it.Human expertise remains crucial for d

293、esigning effective drugs,as scientists uncover and map such critical cause-and-effect relationships,in which AI models can then ground their predictions.148 This can also help alleviate potential concerns about job displacement.149 By repackaging a jobs tasks into distinct categoriessome suited for

294、humans,and others for AIwe can clarify their respective,complementary roles in drug development.150 Effective integration of AI into drug development workflows is crucial for leveraging both AIs predictive power and human judgment to accelerate the development of safe and effective therapies.151 Thi

295、s integration involves substantial up-front costs for staff training and workflow system adjustments.Implementing these changes requires coordinated efforts,complementary assets,financial resources,management support,and long-return investment perspective.152 POLICY RECOMMENDATIONS Supporting the ad

296、option of AI in drug development requires a comprehensive approach that combines public policies to address a number of technical,operational,and regulatory INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 30 challenges while fostering innovation.The following are several recommendati

297、ons for policies that could promote effective AI integration in drug development.Support for Privacy-Enhancing Data Sharing and Access A 2019 Information Technology and Innovation Foundation(ITIF)report argues in support of the development of a National Health Research Data Exchange in the United St

298、ates,which could increase the availability of data to support data-driven drug development.153 One prominent initiative in this direction,which seeks to facilitate the secure,effective exchange of health data and advance interoperability on a national level,is the Trusted Exchange Framework and Comm

299、on Agreement(TEFCA).TEFCA was introduced as part of the 21st Century Cures Act of 2016 and implemented by HHS.Formally launched in early 2022,TEFCA went live at the end of 2023.154 It aims to create a nationwide network for the secure exchange of health data,enabling interoperability and sharing of

300、EHRs among health-care providers,patients,and public health agencies.155 Seven organizations are currently designated as Qualified Health Information Networks(QHINs)under TEFCA,facilitating data sharing across various stakeholders,including Epic Nexus,eHealth Exchange,and Health Gorilla.156 Addition

301、al public policies supporting EHR adoption,such as proposed legislation aimed at enhancing the use of EHRs in behavioral health services,could further help.157 Policies that promote the standardization of data formats across the biopharmaceutical ecosystem,making them less fragmented and disparate,w

302、ould also enable AI models to share and access diverse datasets,enhancing robustness and effectiveness.158 Moreover,EHR systems require a deeper conceptual framework that can systematically capture the complexity of clinical mental models and integrate additional data,such as patient-reported outcom

303、es,to improve AI model training.PETs could further support and encourage the sharing of different sources of data.While significant advances have been made in PETs,more progress is needed in their development,implementation,and adoption.Recent policy efforts aim to bolster these advancements.In Octo

304、ber 2023,the White House issued the Executive Order Advancing a Vision for Privacy-Enhancing Technologies spearheaded by the Office of Science and Technology Policy(OSTP).159 This order calls for the design,development,and deployment of PETs.Subsequently,in April 2024,the Privacy Enhancing Technolog

305、y Research Act(H.R.4755)passed the House and as of this writing is under consideration in the Senate.160 This legislation would mandate NSF to support research into PETs and directs OSTP to coordinate efforts with other federal agencies to accelerate their development,deployment,and adoption.Key org

306、anizations driving PET innovation include U.S.-based OpenDP,Duality Technologies,Actuate,and U.K.-based OpenMined.161 Policies that support PET advances can foster increased use of sensitive data in biomedical research and thus accelerate AI-enabled drug development.162 Further,governments could sup

307、port the establishment of data bankslarge-scale repositories that collect clinical,genetic,and pharmaceutical datato enhance the training of AI models.A notable example is the NIHs All of Us program,which,by creating a comprehensive and diverse data resource,can serve as training data for AI-enabled

308、 biopharmaceutical research.Another example is the Cancer Moonshot Initiative,which fosters collaboration and data sharing across the health care and pharmaceutical sectors to advance cancer treatment.163 In addition to privacy-enhancing data sharing approaches,other strategies could further encoura

309、ge cooperation across the biopharmaceutical ecosystem.One promising approach is the INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 31 establishment of PPPs to foster collaboration among industry,academia,and government stakeholders.Such PPPs can align the interests of public institu

310、tions and private companies by addressing key concerns such as IP rights and competitive advantages.While biopharmaceutical companies often keep their datasets confidential to maintain a competitive advantage,fostering collaboration between companies and research institutions could accelerate AI-ena

311、bled drug development.This collaborative approach could lead to safer,more transparent,more reproducible innovation processes,while also reducing redundancy and associated risks.164 A number of PPPs have emerged to focus on precompetitive research,a phase in which data sharing does not compromise a

312、companys competitive edge.In this setting,companies can benefit from shared research costs,access to larger and more diverse datasets,and streamlined processes.This model has been successfully implemented in different regions.For example,IHI,the worlds largest biomedical PPP,aims to enhance the EUs

313、competitive position in pharmaceutical research and accelerate the development of next-generation therapies through initiatives such as its aforementioned federated learning platform MELLODDY.Policies that support PET advances can foster increased use of sensitive data in biomedical research and thu

314、s accelerate AI-enabled drug development.Another example is the ADNI,launched in 2004 the NIHs National Institute on Aging(NIA).It remains the largest PPP in Alzheimers research,convening leading research centers across the United States,NIA,pharmaceutical companies,and foundations to advance Alzhei

315、mers disease research.ADNIs publicly available database,hosted by the Laboratory of Neuroimaging,has also made significant contributions to enhancing the understanding of complex diseases beyond Alzheimers,fostering broader scientific discoveries.As mentioned,another prominent PPP is Open Targets,a

316、collaboration between research institutions such as the EMBL-EBI and the Wellcome Sanger Institute,as well as pharmaceutical companies such as Pfizer,Genentech,and GSK.Its goal is to use biological data to identify and validate therapeutic targets for drug discovery,making data publicly available to

317、 accelerate progress.165 The Structural Genomics Consortium(SGC)is a PPP that unites academic researchers and pharmaceutical companies such as Johnson&Johnson,Pfizer,and Genentech.It focuses on open-access research aimed at understanding the structure and function of human proteins and generates pub

318、licly available data such as protein structures and chemical probes.166 Building on SGCs open-science model,a new initiative called Conscience was launched in 2023,in partnership with SGC and supported by a$49 million investment from the government of Canada.Conscience brings together academics,rese

319、archers,industry,and community leaders to address market failures in drug development,particularly in areas such as neurodegenerative diseases,pandemic preparedness,rare childhood diseases,and antimicrobial-resistant bacteria.Consciences approach relies on two key pillars:AI and open science.By fost

320、ering collaboration among companies and researchers,Conscience aims to advance best practices in AI,build tools to accelerate drug discovery,and create benchmarks for shared learning.Its open-science model encourages the exchange of data and discoveries between organizations,reducing duplication,ine

321、fficiencies,and costs.Together,these initiatives fuel an entire ecosystem approach to INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 32 innovation that bridges the gap between industry,academia,government,and nonprofit organizations to accelerate AI-enabled drug development.167 Thes

322、e initiatives highlight how PPPs can encourage data sharing in precompetitive research,support the training of AI models,and therefore accelerate AI-enabled drug development when structured effectively.168 Collaborations between academia,industry,and government can establish secure,privacy-enhancing

323、 frameworks for data sharing and analysis,benefiting multiple stakeholders across the biopharmaceutical ecosystem and driving innovation while promoting principles such as reproducibility,transparency,and explainability and reducing risks and redundancies.Public Funding Governments could significant

324、ly advance research in AI-enabled drug development by increasing funding for key initiatives.For example,NIHs Bridge2AI Program seeks to accelerate AI adoption in biomedical research through interdisciplinary collaborations and large-scale data creation.169 Further,the NIH Small Business Innovation

325、Research(SBIR)and Small Business Technology Transfer(STTR)programs provide seed funding to small businesses,including those developing AI solutions for drug development,while NSFs Directorate for Technology,Innovation,and Partnerships has supported AI-enabled platforms to speed up drug discovery.170

326、 The Advanced Research Projects Agency for Health(ARPA-H)also leads several initiatives to enhance AI-enabled drug discovery.171 Public fundingas a precursor and complement to biopharmaceutical company investmentsis essential for several reasons.It supports foundational research,such as AI studies o

327、n new drug development algorithms and basic science applications of AI that may not yield immediate commercial returns but can pave the way to major breakthroughs and further incentivize private sector investment.By funding research without direct commercial application or with high uncertainty,publ

328、ic funding can foster innovation that might otherwise be missed.Moreover,public funding can help increase efficiencies,de-risk early-stage innovation,and ensure that AI tools address public health needs,enhancing health equity.This is crucial for tackling unmet medical needs in areas such as antimic

329、robial/antibacterial research,where private investment may be insufficient.By funding research without direct commercial application or with high uncertainty,public funding can foster innovation that might otherwise be missed.For example,ARPA-Hs mission to address large-scale health challenges mirro

330、rs past efforts such as the Defense Advanced Research Projects Agencys(DARPAs)work to develop the early infrastructure for the Internet and Global Positioning System(GPS)technology.These past successes show how public funding can drive transformative advances by supporting high-risk innovations that

331、 can later be commercialized.Public funding also encourages open science and collaboration,resulting in the creation of public goods such as large datasets,tools,and infrastructure that are available to all agents in the drug development ecosystem.Finally,public funding can address ethical and priva

332、cy concerns by ensuring that AI development aligns with guidelines such as fairness,transparency,and the mitigation of bias to support equitable AI-enabled drug development.INFORMATION TECHNOLOGY&INNOVATION FOUNDATION|NOVEMBER 2024 PAGE 33 Governments can also promote collaboration between academia

333、and the private sector to advance AI research and applications in drug development.For example,the Accelerating Medicines Partnership program,a PPP launched between NIH,the FDA,biopharmaceutical companies,and nonprofit organizations in 2014,could be expanded to support AI-enabled drug discovery.172 Finally,offering incentives for companies investing in AI-based drug development could lower financi