Digital Disruption in Biopharma How digital transformation can … · 2019. 12. 24. · How Digital...

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Digital Disruption in Biopharma How digital transformation can reverse declining ROI in R&D

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How Digital Transformation can reverse declining ROI on R&D

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Contents

Introduction 3

The potential of transformative technologies: Big Data, AI, Blockchain, Cell-on-a-Chip, Advanced Statistical Modelling, Quantum Computing 7

Finding the right partner for the right digital expertise 12

Artificial Intelligence 13

Advanced Statistical Modelling 17

Organ-on-a-Chip, Blockchain and Quantum Computing 18

Clinical trial of the future 20

Conclusion: What’s needed to move forward 22

About the ICON Digital Disruption survey 24

Further reading 25

References 26


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What can be done to bring R&D costs under control and restore ROI to sustainable levels?

Many industry experts see a need to transform the way clinical trials are conceived, designed and conducted. This transformation will rely heavily on harnessing the power of digital technologies. Whilst earlier waves of digital disruption such as the advent of the web, social media and smartphones were highly disruptive in many industry sectors, they were much less so for pharma, with disruption largely confined to internal communication and external patient and market facing channels. However, the current wave of emerging digital technologies now offer real opportunity to significantly disrupt

pharma business operating models and improve R&D productivity & ROI in a variety of ways, including automating processes, making efficient use of massive new data sets, and supporting early decision-making with increasingly powerful predictive analytics and statistical models.

Robotic Process Automation (RPA) will streamline or eliminate many costly, time-consuming and error-prone manual steps. Big Data techniques will aggregate and scrub massive, disparate new data sets, making them available for efficient use. Artificial Intelligence (AI) will filter and process Big Data far faster than any human, generating insights supporting early decision-making with increasingly powerful predictive analytics and statistical models. (1), (3), (4), (5)

Introduction

In 2018, the mean projected return on new drug research and development (R&D) investments by a dozen large cap biopharma firms fell to 1.9 percent, from 10.1 percent in 2010, according to an ongoing analysis by Deloitte (1). That return is already well below the benchmark 10-year US Treasury bond, and on pace to plunge below zero by 2020, an unsustainable trend by any definition.

While declining projected sales contribute, R&D expense is the biggest factor.

$1.1 billion in 2010 Mean cost of bringing a new asset to market:

$2.1 billion in 2018 with clinical trials, especially late trials, making up a large and growing share (1), (2)


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This digital transformation is already underway and likely to accelerate, according to an ICON survey of almost 350 executives, managers and professionals in biopharma and medical device development firms. Nearly 80 percent of respondents said their firm plans to use, or is using, AI or Big Data approaches to improve R&D performance. Within five years, two-thirds of survey respondents said they will pilot or use these analytic technologies in select programmes, and another 20 percent plan to use them in all development programs. The umbrella category of AI and advanced analytics was seen as the digital technology with the most potential to improve R&D productivity. Close behind were identification of biomarkers and use of EHRs and clinical registries, which are likely to use AI tools to optimise efficiency and effectiveness in various ways. In addition, respondents ranked targeting biomarkers as the therapeutic approach most likely to benefit from digitally enabled technologies - perhaps reflecting the need to find actionable correlations in masses of data from disparate sources. Following closely were gene therapies, customised therapies targeting specific disease stages, comorbidities and other specific patient characteristics.

328 qualified responses

Notable responses:

We will make more use of AI and big data

analytics in select programs

We will pilot AI and big data analytics

We will use AI or big data analytics in all

development programs

We do not anticipate using AI or big data

analytics

We will make less use of AI and big data

analytics


Notable responses:

We plan to use AI and big data analytics, but

have not yet begun

We are piloting AI and big data analytics

We do not use AI and big data analytics and

have no plans to do so

We use AI and big data analytics in select

development programs

We have a comprehensive program in place

for incorporating AI and big data analytics

into our development programs

Which of the following best describes your organisation’s current use of AI or big data analytics?

Which of the following best describes where you believe your organisation’s use of AI or big data analytics will be in five years?

86

7469

66

31

149

69

66

3113


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But can AI and other digital technologies improve R&D productivity enough to restore ROI to sustainable levels? The evidence that it might is tantalising...

For example, trials that use biomarkers to select patients who have a high probability of responding are three times more likely to progress from Phase 1 clinical trials to approval, and AI is perfectly suited to identify such opportunities (6). Similarly, last year, the FDA quickly approved an AI-powered device for detecting diabetic retinopathy in primary care offices, signalling growing regulatory support for such technologies (7).

Our survey respondents were optimistic that such developments will significantly increase R&D returns. Two-thirds said they have the potential to increase productivity by 26 percent or more, with 22 percent expecting 51 to 99 percent and 5.5 percent expecting 100 percent or more. Less than one percent expects no improvement.

Industry optimistic that digital transformation may restore ROI on R&D to sustainable levels

These findings dovetail with our projections of the global need for R&D productivity improvement. Based on industry trends over the past decade, we project that an overall productivity increase of 20 to 25 percent is needed to restore R&D returns to sustainable levels by 2030.

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-5

-10

-152010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2029 2030

R&D ROI T-Bond R&D +10% R&D +20% R&D +25%

Effect of increasing clinical trial efficiency on R&D ROI


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Count Percent

Mid double-digit improvement (26%-50%) 127 39.0%

Low double-digit improvement (10%-25%) 87 26.7%

High double-digit improvement (51%-99%) 72 22.1%

Single-digit improvement (1%-9.9%) 19 5.8%

Double or more (100%+) 18 5.5%

No improvement 3 0.9%

Harnessing these new technologies will involve significant organisational change. Already, they have resulted in the breaking down of internal functional silos or formal reorganisation, or both, at 70 percent of respondents’ firms. Many respondents see a need to develop essential new skills and practices internally, and to partner with outside experts, including technology firms and CROs, to develop digital technology capabilities. As detailed in our previous white paper, Improving Pharma R&D Efficiency: The Case for a Holistic Approach to Transforming Clinical Trials (8), digital transformation requires a holistic organisational approach, using technology symbiotically and strategically, rather than just adopting a particular technology or disparate, bottom-up projects. Nonetheless, harnessing the benefits of these innovative technologies requires understanding how they work. This paper is a guide on the pathway to digital transformation. In it we discuss:

– The potential of specific transformative digital technologies

– The impact of these technologies and how they might transform trial operations and multiply ROI on R&D

– The resources, expertise and organisational changes required to harness these technologies

How much do you anticipate digital technologies improving R&D productivity?


Breakdown of responses:


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The potential of transformative technologies: Big Data, AI, Blockchain, Cell-on-a-Chip, Advanced Statistical Modelling, Quantum Computing

Big DataBig Data is the raw material of digital transformation - and the volume and complexity of health data collected by providers, insurers, government, researchers and industry is doubling every 12 to 14 months (9). According to a 2014 report by consulting firm IDC, 153 exabytes (one exabyte = one billion gigabytes) of data were produced in 2013, and an estimated 2,314 exabytes will be produced in 2020, representing an overall rate of increase at least 48 percent annually. This constituted about one-third of data generated from all sources in 2013, and healthcare’s share has grown since. If this year’s data were printed on paper, the stack would reach to the moon and back six times (10).

Needless to say, the hardware required to store and process this volume of data is also growing exponentially. According to a 2016 estimate by the Michigan Institute for Data Science at the University of Michigan, the number of transistors required to process the more than 20 petabytes of genomic data and 10 petabytes of neuroimaging data currently produced is about 1011, an increase of two orders of magnitude from 2014.

The bandwidth needed to move it is rising even faster. While the potential value of this data grows with its volume, its value declines quickly with time. The ability to expand processing and analytic infrastructure to keep up with this growth will be essential to maximising its value (11). Creating and maintaining this capability represents a significant challenge for pharma sponsors.

As new digital technologies continue to emerge, they converge to have a greater impact collectively, than any one technology can achieve individually.

For example, combining historical information from EHRs with imaging, genetic and molecular test data is driving the development of highly targeted oncology treatments, such as CAR-T and other cell therapies, giving hope to patients resistant to more conventional approaches. Similarly, data from mobile sensors and apps make possible new treatments for Parkinson’s and other neurological disorders. Moreover, they enable the creation of novel endpoints that matter to patients with chronic conditions, such as the ability to work, cook and participate in other daily life activities.

In a market that is increasingly driven by outcomes and personalised therapies, mastery of digital technologies will be essential to generating sales, and improving the efficiency and reducing the cost of clinical trial operations. Here we discuss the potential of emerging technologies to increase returns on pharmaceutical R&D, how they interrelate and a framework for successfully integrating them.


Data Type Major Sources Uses Potential Impact

Structured clinical data

– Current and past clinical trials

– Registries

– Peer-reviewed studies

– Spot safety, efficacy and performance issues quicker

– Inform clinical trial design

– Identify promising sites

– Synthetic control and platform trials

– Track real-world performance

– Real-time detection of safety and data quality issues reduce delays, data loss, study failure risk

– Earlier go/no go and adaptive change decisions shorten development

– Fewer protocol revisions cuts time and cost

– Fewer low-performing sites speeds recruitment

– Fewer patients for some trials cuts time and cost

– Guide label expansion, new products, portfolio assessment

– Evidence for approval and payment

Traditional clinical data

– Clinical EHRs

– Labs

– Imaging

– Pharmacies

– Insurance claims

– Identify patient needs, characteristics, locations

– Inform trial design, site selection, recruitment strategy

– Replace or supplement controls in some cases

– Real-world evidence of need and performance

– Realistic inclusion criteria reduce amendment cost and delays

– Identifying potential patients targets recruiting

– Patient-centric studies speed recruiting, reduce attrition

– Fewer low-performing sites speeds recruitment

– Real-world evidence of patient benefit supports approval and payment decisions

Emerging real-world data (RWD)

– Mobile clinical monitors

– Patient-reported outcome and self-assessment apps

– Internet of Things, including smartphone and commercial monitors

– Digitied imaging studies

– Genetic studies

– Proteomic and other molecular studies

– Continuous, real-time clinical trial monitoring

– Virtual visits

– Patient engagement

– Compliance reminders

– Virtual endpoints

– Identify patient needs, characteristics

– Biomarker development

– Real-world evidence of need and performance

– Identify new therapy targets

– Denser, real-time data detect efficacy and safety signals sooner

– Reduced patient burden aids recruitment and retention

– Engagement and compliance reminders reduce data loss

– Biomarkers increase approval chances

– Virtual endpoints more valuable to patients

– Real-world evidence of patient benefit supports approval and payment decisions

– New therapy targets expand portfolio, support targeted medicine

Supplemental data – Weather, environmental conditions, economic, education, demographic, language, location records

– Monitor conditions that might affect therapy performance

– Guide culturally appropriate protocol development

– Guide more-effective therapy design

– Filtering out environmental “noise” may increase study sensitivity, reducing time and sample size

– Culturally appropriate study design and therapies more improve recruitment and retention

– Real-world effectiveness increase supports approval and reimbursement decisions

Sources of Big Data; their potential and limitsUnquestionably, Big Data is diverse in its sources and quality, and massive in its volume. As a result, it takes considerable effort to evaluate, normalise and structure it so that it can be reliably used for analysis. In our industry survey, this was identified as a top challenge in adopting digital technologies. The nature, sources and quality of data also influence its value and how it can be used.

Below are some of the major data sources, with a brief analysis of their potential value and limits for improving clinical R&D performance.

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Different types of Big Data


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Structured clinical dataThese include data from current and past clinical trials, real-world evidence (RWE) from registries and peer-reviewed studies.

The quality and value of structured clinical data vary depending on collection method and rigor. Clinical trial data are most reliable and can be used for a wide range of purposes that can improve clinical study efficiency. For example, data captured automatically from currently active trials can quickly identify unanticipated safety issues, and flag anomalies that might indicate protocol deviations early enough to prevent costly study delays or failures. They can also help make go/ no-go, adaptive study changes earlier, and help close out studies faster, potentially cutting weeks or months off overall timelines.

Structured clinical data from previous trials can be very helpful in streamlining current trial protocols by predicting potentially high-performing study sites, which is invaluable for shortening study timelines and keeping trials on schedule. In some cases, historical trial data may be used as a synthetic control arm in an active trial, though this requires careful matching of trial populations and data collection processes to ensure data comparability.

Platform trials - in which a single control arm is used to test multiple treatment approaches, and is sometimes run by different sponsors - is a variation on the structured clinical data approach. Both reduce the number of patients to be recruited and increase the proportion of patients receiving treatment. Historical trial data may also be used to guide new research and suggest possible label indication expansion.

Registry data are increasingly required by regulators to evaluate real-world use of therapies as a condition of approval. While these data alone generally are not reliable enough to support approval, they help identify possible label extensions and make the case for reimbursement - particularly when combined with other real-world data (RWD) from EHRs, pharmacies and insurers. Similarly, broader registries operated by governments or speciality groups can help identify quality issues at a population level, such as failure rates for knee or hip implants. They can also study low-incidence complications, such as intraocular infections after cataract surgery. Finally, they can help identify unmet patient needs to guide future product development decisions, and may help establish evidence of efficacy for payment.

Traditional clinical data These include data from clinical EHRs as well as from labs, pharmacies and insurance claims.

Clinical EHRs are designed to support clinical practice rather than research, and there are wide and unpredictable variations in how and what data are captured. As a result, EHR data are not typically useful for establishing efficacy in clinical studies, except for some rare or serious conditions that preclude the use of controls. However, EHR data are increasingly valuable for guiding study design and exclusion criteria, as well as identifying promising study sites. They are also proving powerful for identifying patients at high risk of developing chronic diseases, particularly when merged with genetic data. Moreover, they are helpful in producing RWE for value-based payment models.

Lab, pharmacy and insurance information are similarly limited, but have similar uses.


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Emerging RWD sourcesThese include mobile clinical monitors, patient-reported outcome apps, internet of medical things such as motion detectors, as well as imaging, genomic and molecular studies.

Mobile monitors and apps range from commercial devices, such as Fitbits and cell phone accelerometers, to medical-grade heart, blood pressure and glucose monitors. For use in clinical studies, these devices must be rigorously validated. Moreover, they must address potential concerns such as placing a monitor on someone other than the patient, as well a device issues such as cybersecurity, battery life and usability, durability and even aesthetics in everyday life. For example, a trial patient may remove a monitoring device that is uncomfortable, clashes with clothing or attracts unwanted social attention.

Addressing these issues requires special expertise across several disciplines, including device design, patient engagement and digital endpoint validation, all of which are expensive. However, the payoff can be significant, as the volume and granularity of data from mobile devices can increase the statistical power of subject data, allowing shorter periods to establish efficacy.

Mobile data also can monitor therapy compliance and alert patients if they miss a dose, helping to protect trial data integrity and reducing the need for extra recruits to compensate for noncompliance. Mobile devices can further improve trial efficiency by reducing clinic visits and costs, and improving patient recruiting and retention by making studies more convenient. Like traditional clinical data, mobile monitoring and apps create a detailed picture of everyday life that is extremely useful in guiding development decisions and supporting value-based payment.

Similarly, genomic, proteomic and imaging studies provide detail on an unprecedented level that can be used for diagnosis, monitoring and therapy development. The potential power of analysis of mass imaging datasets can be illustrated by an algorithm developed by Google with the Aravind eye hospital network in India that not only screens for diabetic retinopathy with high reliability, but also accurately predicts cardiovascular disease risk based on retinal images.

Genomic and proteomic data are particularly valuable for finding biomarkers of diverse diseases including cancer that can dramatically increase response by specifically targeting markers. The potential for these technologies for improving the efficiency of new molecule development is difficult to overstate, and has the potential to dramatically increase approval rates, multiplying R&D efficiency.

Emerging supplemental, environmental, economic and social dataThese include everything from environmental data to insurance status, education and income markers that may influence therapy response and study success. Such data can be critical for properly interpreting mobile monitoring data.

For example, high pollen counts or pollution can affect asthma or COPD, possibly producing a blip in response that might otherwise be attributed to a trial medication. Supplemental information helps filter out this kind of noise in datasets, potentially reducing the time and size of trials.

Similarly, general educational level and health literacy can have a significant effect on therapy compliance. This can be useful during trials to interpret data and is important for designing therapies that are more likely to be successful in the real world.


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Top factors favouring digital technology adoption

Improve return on R&D investments 3.23

Improve product safety and efficacy 3.08

Reduce clinical trial costs 3.05

Post-market regulatory monitoring 2.87

Compete in targeted medicine markets 2.84

Payer demands for RWE 2.83

Recruiting patients for clinical trials 2.68

Get closer to patient communities 2.61

Get closer to prescriber communities 2.57

The increased data granularity, specificity and volume of Big Data have the potential to increase clinical R&D efficiency. Harnessing this potential requires significant infrastructure, expertise and judgment to determine when and how to best deploy it.

Survey results


Finding the right partner for the right digital expertise

Creating and maintaining this continually expanding data gathering and processing capacity represents a significant challenge for all types of healthcare enterprises, including pharma sponsors. Indeed, lack of internal resources and understanding of how to develop and apply digital technology were the leading barriers to adoption in our industry survey.

Healthcare currently accounts for almost 18 percent of the US economy and is in the double digits in much of the developed world. Thus, the size of the opportunity, in addition to its complexity, has attracted the attention of the world’s tech giants. According to Deloitte, six of the 10 largest technology companies are diversifying into healthcare (12).

Significant ventures include (13):

– Alphabet’s Google Ventures invests about one-third of its capital in approximately 60 healthcare and life sciences start-ups ranging from genetics to telemedicine, while Google DeepMind Health focuses on structuring data from various sources using machine learning. Verily, its life sciences division, is collaborating with major research institutions, including Duke University and Stanford Medicine, on Project Baseline, a genetic study to improve our understanding of chronic diseases.

– Apple is focusing on mobile data and patient interface technologies, and also collaborates with researchers on projects for detecting heart rhythm problems and Parkinson’s symptoms using Apple devices.

– Amazon recently launched Comprehend Medical to mine and decode unstructured data in medical records using machine learning.

Pharma sponsors are partnering with these and other large tech companies to leverage their core expertise in digital science. They are also looking to the burgeoning ecosystem of smaller tech companies to develop potentially disruptive

data innovation and new methods of obtaining and tracking relevant clinical and socioeconomic data, to improve patient interfaces. Analysing complex data sets and integrating data from disparate sources were among the top-three digital challenges sponsors identified in our survey, reflecting their tech-focused needs. More than 55 percent of survey respondents said they were partnering with tech companies, making it the number-one partner choice.

While technology giants are providing necessary infrastructure and support today, and will likely generate innovations that will transform clinical R&D tomorrow, data and expertise – which are more specific to current trial processes and needs – are also essential to transform clinical trial efficiency.

For example, data on how trial sites have performed in the past can help select sites that are more likely to successfully recruit patients. Reducing the under-recruitment challenge is critical to cut the cost of opening sites that never see a patient, and to keep studies on track (14). Specific data on past trial designs and how they performed also help streamline current trial protocols involving similar conditions or test therapies. Applying advanced statistical and trial design to specific study needs was the other top-three challenge sponsors identified that requires the skills and knowledge of CROs and other clinical trial experts.

In our experience, identifying and addressing these current study needs using Big Data not only improves trial efficiency significantly in the near term, but also builds competence and confidence in applying digital technology needed to tackle more complex, longer-term needs. 12


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Digital technologies with the most potential for improving R&D productivity

Advanced analytics and AI 3.26

Biomarkers 3.23

Clinical registries 3.15

EHRs 3.14

Demographic data 2.96

Patient self-assessment and PRO apps

2.86

Mobile sensors 2.86

Virtual trials 2.85

Cells or organs on chip 2.75

Internet of Medical Things 2.74

Quantum computing 2.72

Environmental sensing data 2.62

Social media data 2.53

Blockchain platform 2.51

If Big Data is the raw material of digital transformation, AI is the engine that sponsors rely on to make use of it.

AI-powered capabilities, including pattern recognition and evolutionary modelling, are essential to gather, normalise, analyse and harness the growing masses of data that fuel modern therapy development. Indeed, AI and advanced analytics were viewed as the digital technology with the most potential to improve clinical R&D productivity in our industry survey.

AI has many potential applications in clinical trials both near- and long-term. These range from automating routine study data entry functions, to analysing EHR data to find suitable candidates and sites for clinical studies, to monitoring and encouraging patient compliance with study protocols, to adaptive dose-finding, to discovering and modelling potential new molecules and therapies.

But what, exactly, is AI? And how can it be developed and used to transform clinical trials, while adhering to the rigorous scientific validity standards required to demonstrate drug safety and efficacy?

First coined in 1956 by researcher John McCarthy, the term “Artificial Intelligence” covers a wide range of hardware and software that exhibit behaviour that appears intelligent (15). Currently, all industrial applications of AI are considered ‘narrow’ (or ‘weak’) AI in that they typically focus on a particular task such as natural language processing, image processing, voice processing, machine learning and robotics. ‘General’ (or ‘strong’) AI is an anticipated (far) future state in which AI technology has broad-based and integrated cognitive abilities comparable to a human being. These differing terms, applications and levels of maturity can lead to confusion and a large amount of hype (16).

ICON survey results

Artificial Intelligence


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Here we outline different approaches to AI and the benefits of each.

Expert systems

Some of the earliest and most widely used AI applications are expert systems that use rules-based algorithms to mimic specific human expertise. One example is decision-support trees for routine diagnostic tasks, such as differentiating between bacterial and viral respiratory infections for prescribing antibiotics, which are built into virtually every EHR drug-ordering module. However, rules-based systems require humans to codify knowledge and write unambiguous rules, which limit their use to addressing relatively uncomplicated and well-defined problems. Robotic process automation

Variations on this approach have significant value in improving clinical trial efficiency in the realm of robotic process automation (RPA). Robotic process automation (RPA) are specialised computer programs that automate and standardise processes based on rules-based algorithms. Of itself , RPA has no ‘intelligence’ – however, increasingly it is typically integrated with other AI technologies to create faster automation, and it’s organizational impact is proving to be significant. In clinical trials this includes automatically:

– Capturing routine clinical data, such as patient vital signs

– Collecting operational data, such as drug administration dose and time

– Testing data to flag safety issues, such as an out-of-range lab result

– Assessing potential data entry errors, such as duplicated or missing data points

– Detecting potential protocol deviations, such as emergence of a non-random variation trend

– Forwarding clean data to the trial master file, and alerting trial monitors to anomalies

Immediate efficiency benefits of robotic process automation include:

– Reduced manpower - eliminating the need for manually transferring data from clinical sites to trial master files typically reduces clinical research assistant (CRA) and trial monitoring headcounts by two full-time equivalents or more

– Reduced errors and delays - automatic forwarding of validated data eliminates the possibility of data entry errors, as well as delays signing off on incoming data by human reviewers

– Reduced data loss – automatic data analysis detects anomalies much sooner and more reliably than manual review, and alerts human CRAs to do what they do best, which is to investigate site issues and get sites back on track

Without fundamentally changing existing trial processes, robotic automation can cut days or weeks off of trial timelines simply by reducing human error and delays due to business hours, weekends and time off. Redesigning trials to take full advantage of robotic processes can cut even more by refocusing CRAs and trial monitors on a consultative role supporting trial sites.

This entails a culture and skills change within the workforce, though it’s positive for workers since it relieves them of drudgery in favour of exercising and developing higher level, and higher value, skills. Not only does robotic process automation yield immediate efficiency benefits, but also, it lays the groundwork for incorporating massive data sets from EHRs, mobile devices, automated image scanning, and individual patient genomic and molecular data. As such, it is a fundamental stepping stone on the path to harnessing the transformational power of AI to make use of Big Data. Yet, as straightforward as all this may sound, accomplishing these things, while ensuring trial data and process integrity, requires a deep understanding of study processes and ends. Insights from CROs and others with extensive experience are critical to designing and testing process automation to avoid the classic computer programming problem – GIGO, or ‘Garbage In, Garbage Out’. In other words, automating inadequate processes only produces more mistakes faster.


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Linking trial stagesAnother way to leverage robotic process automation is linking processes across study stages. This involves considering the final outputs – which are data supporting regulatory approval and commercial payment – in the design of every study step and automatically adjusting those steps when a change occurs. For example, results midway through a Phase 2 study suggest that a new cancer therapy may be much more effective in a patient subgroup with a particular biomarker. Changes in the target population and how it is assessed will require not only amendments to the ongoing study phase, but also in Phase 3 design. Moreover, the endpoints and data needed for regulatory and payment approval will need to be reassessed. Automatically linking study requirements from end-to-end can significantly reduce the delays and manual effort required to fully implement a protocol amendment. It changes everything from the forms needed to collect new information, to the data analysis and charts required to present results to regulators and insurers. However, designing linked stage automation also requires a deep understanding of conventional trial processes, as well as regulatory and payer evidence requirements. Beyond the benefits for an individual development program, adopting a linked process automation approach facilitates portfolio management decisions by allowing developers to model how specific changes in study

Deep machine learning is essentially the same process, with the exception of the extent to which humans prime the machine. In traditional machine learning, a human may extract the features it wants the machine to process in great detail, and the optimisation algorithm the machine applies is limited.

The greater processing power of modern computers enables deep machine learning, in which the device itself extracts features from a raw data set and has multiple layers of optimisation processing modelled on how neurons process information. This allows the machine to discover patterns in the data that do not depend on the insight or expertise of a human programmer, making deep learning more powerful for assessing images and other extremely complex data sets (15).

For example, AI deep learning machine techniques have improved the formulae for predicting the power of intraocular lenses needed to get close to uncorrected 20/20 vision after cataract surgery.

protocols might affect development timelines, as well as prospects for approval and market prospects. Experienced partners, such as CROs, bring the needed expertise to the table to implement automated end-to-end study planning and integrate it with holistic portfolio management. Machine learning and deep machine learningMoving up a step on the AI complexity ladder are machine learning and deep machine learning. Machine learning is potentially more flexible than rules-based expert systems because it does not rely entirely on programmers to provide a fully worked out set of rules, but allows the computer to improve its performance, or its “learning,” based on training. Typically, the machine is trained using a large input data set, which it processes according to an initial algorithm that assigns weights to various factors and mathematically transforms them, using processes such as random forests, Bayesian networks and support vectors, to predict an outcome (15). The predicted outcome is then compared with the known outcome for the training data set, and the algorithm is altered and run again, with better predictive results guiding changes at each round. This iterative process proceeds until predictive power plateaus. The algorithm may then be tested against an unknown data set to determine its predictive accuracy, and the training set may be enlarged and diversified to further improve accuracy and extend its range.


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for diabetic retinopathy, which has the potential to dramatically improve screening for this potentially blinding condition by primary care physicians (7). Alphabet’s Deep Mind program has developed a similar capability and can even predict the risk of heart attack and stroke from retinal images alone. Image analysis is also used to assess oncology pathology and heart rhythm with accuracy that rivals or exceeds experienced clinicians. AI has many near- and long-term applications for improving clinical research returns (18), including:

– Patient identification - AI capabilities, including natural language processing and association rule mining, help extract data from unstructured medical records to find patients suitable for clinical studies, and can help identify those most likely to complete a trial

– Site selection - Helps identify sites with the right patients and capabilities to successfully recruit and retain patients

– Patient monitoring and support - AI enabled mobile devices help identify when patients deviate from protocols and send reminders

– Cohort composition - AI helps identify biomarkers to find patients most likely to show benefit from a particular dose or combination therapy

Ensuring that the right patients and sites are recruited for studies goes a long way toward preventing costly delays. Improved patient protocol compliance and denser data sets may reduce

patient attrition and the need to over-recruit to offset projected subject or data losses.

Newly discovered genetic and molecular biomarkers make possible identification of compounds more likely to reach market (6).

Limits of AI Still, AI has its limits and must be handled with care to ensure it is producing valid, reliable results. Its unstructured nature can lead to results that are not useful or defy causal logic. For example, one algorithm for diagnosing tuberculosis (TB) by reading chest x-rays considered not only the x-ray image but also film metadata, assigning greater weight to images taken by mobile machines used in hospitals than by stationary machines in clinics – apparently having “learned” that patients with lung complaints severe enough for hospitalisation were indeed more likely to have TB. Various ways of opening the machine learning “black box,” including flagging the data the machine reviews and the weights it assigns, have been proposed (19). However, given the conservative nature of clinical research, in addition to the cost and complexity of developing AI solutions, it is likely to be a long time before their full potential can be realised. As with Big Data, collaboration with outside tech experts and clinical study process experts will be critical for success.

Historically ophthalmic surgeons achieved an

However, a recently designed AI-based formula has pushed that to

The most common pre-surgery refractive error.

As the data set has grown, the formula has become more accurate for less common errors, says its inventor, Warren Hill (17). He notes that the neural network approach is more efficient than rules-based methods for modelling any system that is not completely understood – which includes just about everything in clinical medicine and research.

The power of AI for revolutionising clinical practice is already evident. For example, the FDA last year approved the first AI diagnostic device that requires no clinician intervention to detect and refer

90% for all patients

98%

for patients with near-sightedness

80%

success rate reaching one-half dioptre of target refraction.


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Advanced Statistical Modelling

Applying advanced statistical models to the greatly expanded range and granularity of data available through Big Data and AI technologies has the potential to significantly improve clinical R&D productivity in a variety of ways. These include modelling and simulations, and cumulative analysis using sequential, Bayesian and meta-analytic techniques. They are particularly useful for conducting smaller studies that are gaining importance as therapies increasingly target limited populations. Sequential analysis uses accumulating data, sometimes over long periods of time, with the objective of ending a trial as soon as sufficient data have accumulated. On average, this leads to a smaller overall sample size, and allows ongoing research into rare conditions such as sickle cell anaemia (20). Bayesian statistics, which refine analysis based on accumulating data, are well-suited for these kinds of studies. They’re also useful for designing paediatric studies that can reliably guide dosing across a wide range of age and weight variables (21). Meta-analysis may also be used to generate starting points for such research, and to support evidence for studies where randomising a control group is not possible for ethical reasons. In addition, computer models of pharmacodynamic activity and disease state progression may be useful for assessing how future populations impact patient needs, as in a 30-year predictive study commissioned by the American Diabetes Association (22). Similar approaches are useful for sponsor portfolio assessment. Perhaps the most mature and widely used statistical modelling application directly used in clinical trials is predicting optimal dosing ranges prior to Phase 3 trials - a critical step in avoiding late-stage product failures (22). Methods such as MCP-Mod (or Multiple Comparisons & Modelling) have been shown to do a better job

than traditional pair-wise, dose-finding studies in predicting what doses will succeed in pivotal studies. This two-step method first chooses candidate dose-response curves based on pre-clinical and other existing evidence, and then models early phase data to determine Phase 3 dosing (23). Considered an adaptive analytic tool, when combined with adaptive Phase 2/3 designs, it can significantly reduce study sample sizes and shorten timelines. Other potential uses of advanced statistical modelling include establishing synthetic control arms and shared control arms for platform trials, in which multiple therapies are evaluated against one control group, often by different sponsors. As with AI and Big Data, advanced statistical modelling requires a high degree of technical and clinical study-specific knowledge. Powerful integrated statistical packages, such as ADDPLAN neo, are also essential to design and execute such adaptive studies, as they require extensive modelling in advance to validate - and ongoing analysis to guide - adaptive changes, such as expanding samples, reallocating patients among study arms, or early closure for either success or futility. Partnering with a CRO, or other entity with extensive trial experience, can help ensure studies are designed to produce sound results that will be acceptable to regulators and insurers.


18

While Big Data, AI and advanced statistical models are all in active use in clinical R&D, several new digital technologies are on the horizon with potential to transform the industry. We outline a few of these below. Organ-on-a-Chip - A major contributor to low clinical R&D productivity is a lack of robust preclinical models for gauging the potential efficacy and toxicity of drug candidates. Animal models can be informative, but the results often do not translate to humans. Cultured human cells are of limited use because they generally lack physiological function and are removed from their circulatory support system, making it difficult to assess drug efficacy, toxicity and organ interaction.

Organ-on-a-chip and body-on-a-chip are in development to address these issues. The technology uses micromanufacturing techniques, such as photolithography, to create a microfluidics environment on a silicon chip that mimics in vivo conditions. These chips are then populated with differentiated human cells in physiologic arrangement.

Organ-on-a-Chip, Blockchain and Quantum Computing

For example, an artificial liver has been developed that features three-dimensional scaffolds in a cell culture chamber perfused at physiological oxygen levels and stress. This promotes growth of hepatocellular aggregates that structurally and functionally resemble hepatic acini that remain viable for up to two weeks. Such a system would be valuable for testing the way drug candidates affect the liver, which is the organ most often responsible for drug metabolisation.

Organs-on-chips have been developed for lungs, kidneys and gut tissues. Similarly, a body-on-a-chip, including several organs, has been developed to assess how drugs might interact across organ systems. While the technology may one day dramatically reduce the cost of pre-clinical development and reduce the risk of human trials, it requires additional development and validation before it can be practically use. This will require significant collaboration among engineers, biologists and clinicians (24).


19

Quantum computing - Led by several governments, and big technology companies such as IBM, Microsoft, Google, Alibaba and Intel, there has been significant investment into developing quantum computing technology over the last several years.

Quantum computers perform calculations using linear algebra to manipulate matrices of complex numbers (‘qubits’) - effectively connecting in multiple dimensions. This enables quantum computers to conduct vast numbers of computing calculations simultaneously, whereas conventional computers must work through calculations linearly, one at a time. This makes quantum computers much more capable of solving complex problems, involving multiple connections among multiple data points, much, much faster. For example, quantum computers can break in a few weeks encryption based on factoring very large numbers that would take conventional computers millions of years (26). Many problems in assessing enormous data sets can take advantage of this nearly inconceivable leap in computing power. Although some applications have begun to emerge (e.g. secure quantum communications networks), currently the hardware and software to support quantum computing will require years of development before it is widely available for application in clinical trials. However, given its potential to revolutionise computing, industry executives should monitor this technology.

Blockchain - Data integrity and transparency are essential to maintaining trust in clinical R&D and ensuring data are properly interpreted and analysed. At the same time, maintaining patient confidentiality is an ethical and legal requirement. Within clinical trials, patient data is the most notable item of transactional nature between networks such as healthcare institutions, patients, and regulators.

Blockchain technology which is essentially a decentralised ledger system that is fully transparent and immutable – has been shown to provide a web-based framework that allows patients and researchers access to their own data. It allows for user confidentiality, protecting patient privacy during exchange of data between parties.

Blockchain technology allows for complete transparency of data, which has immense potential within clinical trials. With blockchain, there is an audit trail built into transaction of data, which allows for verification of the original source of the information being transacted, as well as the ability to detect any attempts to tamper with it.

Blockchain allows for greater data availability. When all data is shared openly within a network, issues with data systems interoperability are reduced, and opportunities open up new possibilities for using that data. For example, availability and accessibility of patient information could be used for patient feasibility analysis and population studies. Moreover, blockchain allows researchers to submit queries for data that are stored off chain, protecting patient privacy. Despite the potential benefits, further functionality will need to be added (25).


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Quantum analysis of masses of genomic and proteomic data combined with years of medical records reveal biomarkers for five new heart failure subtypes. Quantum modelling quickly develops and assesses candidate molecules specifically targeting the identified pathways. Then, they determine the best candidates for synthesis and testing based on a virtual population using empirical physiology models developed with AI.

Organ-on-a-chip using cells with the target pathways are developed to test the activity of the compound in the heart, followed by body-on-a-chip to assess systemic risks. The top five candidates are validated for clinical studies, complete with preliminary information on likely dose response.

AI analysis of electronic records identifies study candidates in five countries, including those most likely to successfully complete a trial. Analysis of previous site performance helps recruit investigators. Electronic patient education tools, connected with live support speaking local languages, help recruit patients. Trial recruitment goals are reached on schedule.

Clinical trial of the future

Studies are planned using digital heart monitors and a custom patient app to monitor patients at home, greatly expanding the potential patient pool beyond the five percent currently involved in clinical studies. Study site costs are also cut by nearly half. Continuous monitoring backed by reminders to follow the protocol nearly eliminate protocol deviations. The higher data density and lower loss reduce the number of patients needed for the trial and send early efficacy and safety signals for go-no go decisions (27), (28).

Three of the five compounds advance in an adaptive study design that seamlessly rolls from Phase 2 to Phase 3. Automated data collection and analysis provide a robust dataset meeting newly established regulatory standards for digital studies that leads to approval for three new drugs, a success rate of 60 percent, or six times the current average. The entire process from discovery to approval takes less than five years – half the current average – yielding better treatments for more patients sooner, and better returns on research investment.

Future clinical trials that make full use of digital technologies will look very different at each stage of development – and may have a much higher chance of approval at lower cost. Just imagine:


21

“Machine learning and other technologies are expected to make the hunt for new pharmaceuticals quicker, cheaper and more effective. Its potential applications are numerous and potentially game-changing.”

ICON survey respondent


Conclusion: What’s needed to move forward

Harnessing digital technology to transform clinical trials will require sponsors to develop or acquire a range of capabilities.

Beyond that, it may fundamentally change the way sponsors are organised and integrate R&D into the overall enterprise. Critical steps for moving forward include:

1. Identifying and developing operational and IT expertise and capacity. Given the rapid growth of IT science and sheer computing capacity required, supplementing internal capacity with partnerships with firms specialising in IT, as well as those experienced in clinical trial automation, are likely the most productive choice.

2. Developing statistical expertise. Once again, the highly technical nature of statistical analysis – particularly paired with adaptive trials and data-driven techniques such as developing and validating virtual study endpoints – suggests a need for partnering with firms specialising in these activities.

3. Developing global reach. The growing need to target specific population needs and to comply with national regulations makes clinical development an increasingly international enterprise. Partnering with global research firms provides the expertise and resources needed to accomplish the task.

4. Managing change. Successfully harnessing digital technology requires training and often organisational change. Sponsors must be prepared to rethink and reorganise their businesses to make the change.

Weighted Average

Improve return on R&D investments 3.23

Recruiting patients for clinical trials 2.68

Reducing clinical trial costs 3.05

Accommodate payer demand for RWE of value 2.83

Compete in targeted medicine markets 2.84

Improve product safety and efficacy 3.08

Meet post-market monitoring regulatory requirements 2.87

Get closer to patient communities 2.61

Get closer to prescriber communities 2.57

How important are the following factors in driving your organisation’s adoption of digital technology?

323 Number of Qualified Responses*


100%

80%

60%

40%

20%

0%

0 1 2 3 4

Improve return on

R&

D investm

ents

Improve return on

R&

D investm

ents

Reducing clinical

trial costs

Accom

modate

payer demand for

RW

E of Value

Com

plete in targeted m

edicine m

arkets

Improve product

safety and efficacy

Meet post-m

arket m

onitoring regulatory requirem

ents

Get closer to patient

comm

unities

Get closer

to prescriber com

munities

22


23

Weighted Average

Lack of internal understanding of digital technology potential

3.23

Lack of internal resources to develop and apply digital technology

2.68

Internal resistance to change 3.05

Lack of payer understanding of digital technology potential 2.83

Lack of regulatory support for digital technology transformation

2.84

How would you rate the following as potential barriers to adopting digital technologies at your organisation?



How has your move to digital technology affected the way your organisation operates and is organised?

328 Number of Qualified Responses


Both breaking down functional silos and

reorganisation across functions

Breaking down internal functional silos,

but no reorganisation

No effect on operations or the organisation

Reorganisation but little effect on

functional roles

70

85

77

96

100%

80%

60%

40%

20%

0%Lack of internal understanding of digital technology potential

0 1 2 3 4

Lack of internal resources to develop and apply digital technology

Internal resistance to change

Lack of payer understanding of digital technology potential

Lack of regulatory support for digital technology transformation


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How important are the following challenges in developing digital technology capabilities?



Weighted Average

Data Storage and processing capability 2.82

Norming and integrating data from multiple sources 3.19

Creating a one-source interface 3.13

Creating Analysing complex data sets 3.25

Applying advanced statistical and trial design to specific study needs

3.18

Working with researchers and study sites 2.87

About the ICON Digital Disruption survey

In May and June 2019 ICON surveyed industry leaders across N America and the EU to share their insights on the application of AI in Pharmaceutical R&D. Of the 350 qualified responses, 97 respondents were C-Level or Executive (VP or Senior VP). Respondents provided responses to quantitative pre-defined survey questions as well as providing free-form qualitative written responses.

100%

80%

60%

40%

20%

0%Data Storage and processing capacity

0 1 2 3 4

Norming and integrating data from multiple sources

Creating a one source interface

Analysing complex data sets

Applying advanced statistical and trial design to specific study needs

Working with researchers and study sites


What strategies are you pursuing to implement digital technologies?



With proper implementation and expert help, digital technology has the potential to reverse the downward trend in clinical research R&D. The key is an end-to-end solution that integrates new technology with established research standards. For information on how digital technology can streamline trials today and multiply returns tomorrow, contact ICON today.

Further reading

Addressing the challenges of increasing complexity and declining ROI in drug developmentPerspectives from senior pharma executives on how a new approach to drug development could lead to increased efficiency in clinical trials.

ICONplc.com/efficiency

Improving Pharma R&D EfficiencyA survey of pharmaceutical executives and professionals by ICON and Informa Pharma Intelligence provides valuable insight into key clinical research challenges and potential solutions for clinical development.

ICONplc.com/pharma

AI and Clinical TrialsA collection of ICON thought leadership content on the potential of AI and Big Data to solve many key clinical trail challenges.

ICONplc.com/AI

Real World DataA collection of ICON thought leadership content on optimising the use of real world data to drive effective outcomes.

ICONplc.com/RWD

100%

80%

60%

40%

20%

0%

Develop InternalCapacity

Partner with technology companies

Partner with CROs or other clinical support companies

Partner with management or technology consultants

25

http://ICONplc.com/disruptive-innovation

http://ICONplc.com/pharma

http://ICONplc.com/AI

http://ICONplc.com/RWD


26

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7. Lee KJ. AI device for detecting diabetic retinopathy earns swift FDA approval. American Academy of Ophthalmology, April 12, 2018. https://www.aao.org/headline/first-ai-screen-diabetic-retinopathy-approved-by-f

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16. Olson P. Nearly half of all ‘AI startups’ are cashing in on hype. Forbes, March 4, 2019. https://www.forbes.com/sites/parmyolson/2019/03/04/nearly-half-of-all-ai-startups-are-cashing-in-on-hype/#2f44d81fd022

17. Hill W. ASCRS, San Diego, 2019.

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21. Stockmann C et al. Use of Modeling and Simulation in the Design and Conduct of Pediatric Clinical Trials and the Optimisation of Individualised Dosing Regimens. CPT Pharmacometrics Syst Pharmacol. 2015 Nov; 4(11): 630–640. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4716585/

22. (Jadhav S. Modelling and Simulation in Clinical Trials: Real Potential or Hype? Applied Clinical Trials, Feb 14, 2017. http://www.appliedclinicaltrialsonline.com/modeling-and-simulation-clinical-trials-real-potential-or-hype?pageID=2

23. Rekic D et al. Request for Qualification of MCP-Mod as an efficient statistical methodology for model-based design and analysis of Phase II dose-finding studies under model uncertainty. Office of Clinical Pharmacology, Division of Pharmacometrics, US FDA, April, 2015. Alosh M et al. Statistical Review and Evaluation Qualification of Statistical Approach for MCP-Mod. Lavage L, Zina I. FDA Determination letter 26 May 2016 https://www.fda.gov/Drugs/DevelopmentApprovalProcess/ucm505485.htm

24. Kimura H et al. Organ/body-on-a-chip based on microfluidic technology for drug discovery. Drug Metabolism and Pharmacokinetics, 2018; 33:43-48. https://www.sciencedirect.com/science/article/pii/S1347436717301957

25. Maslove D et al. Using blockchain technology to manage clinical trials data: A proof-of-concept study. JMIR Med Infom, 2018; 6(4): e11949. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320404/

26. Schtsky D and Puliyakodil R. From fantasy to reality: Quantum computing is coming to the marketplace. Deloitte University Press, 2017.

27. Coravas A. Software-enabled clinical trials. Andrea’s blog, Sept 4, 2017. https://blog.andreacoravos.com/software-enabled-clinical-trials-8da53f4cd271

28. Comstock J. Survey: 64 percent of clinical trial execs have used digital technology, 97 percent plan to. Mobihealth news, sept 22, 2016. https://www.mobihealthnews.com/content/survey-64-percent-clinical-trial-execs-have-used-digital-technology-97-percent-plan

https://www2.deloitte.com/content/dam/Deloitte/global/Documents/Life-Sciences-Health-Care/deloitte-u



https://www.scribd.com/document/330419323/Tufts-CSDD-Briefing-on-RD-Cost-Study-Nov-18-2014

https://www.scribd.com/document/330419323/Tufts-CSDD-Briefing-on-RD-Cost-Study-Nov-18-2014

https://www2.deloitte.com/global/en/pages/life-sciences-and-healthcare/articles/global-life-sciences



https://hbr.org/visual-library/2018/05/10-ai-applications-that-could-change-health-care%20class=



https://www.biospace.com/article/artificial-intelligence-is-ramping-up-in-drug-development-/

https://www.biospace.com/article/artificial-intelligence-is-ramping-up-in-drug-development-/

https://www.bio.org/sites/default/files/Clinical%20Development%20Success%20Rates%202006-2015%20-%20B



https://www.aao.org/headline/first-ai-screen-diabetic-retinopathy-approved-by-f



https://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm

https://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4795481/




https://healthcareweekly.com/how-the-big-4-tech-companies-are-leading-healthcare-innovation/



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https://www.mmcventures.com/wp-content/uploads/2019/02/The-State-of-AI-2019-Divergence.pdf

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https://www.mobihealthnews.com/content/survey-64-percent-clinical-trial-execs-have-used-digital-tech


27ICONplc.com/digital

Driving Digital Disruption

The impact of disruptive innovation is forcing pharmaceutical companies and their partners to reshape how they look at everything they do across the entire spectrum of drug development.

ICON is leading the way in embracing digital advances to transform trials and reduce costs.

http://ICONplc.com/digital

ICON plc Corporate Headquarters

South County Business ParkLeopardstown, Dublin 18Ireland T: (IRL) +353 1 291 2000T: (US) +1 215 616 3000F: +353 1 247 6260

ICONplc.com

About ICON

ICON plc is a global provider of outsourced drug and device development and commercialisation services to pharmaceutical, biotechnology, medical device and government and public health organisations. The company specialises in the strategic development, management and analysis of programs that support clinical development - from compound selection to Phase I-IV clinical studies. With headquarters in Dublin, Ireland, ICON currently, operates from 98 locations in 40 countries. Further information is available at ICONplc.com

© 2019 ICON plc. All rights reserved.

http://ICONplc.com

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