Digital Disruption in Biopharma How digital transformation can reverse declining ROI in R&D - ICONplc.com/digital
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Digital Disruption in Biopharma
How digital transformation can
reverse declining ROI in R&D
ICONplc.com/digital
1
How Digital Transformation can reverse declining ROI on R&D 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 2
How Digital Transformation can reverse declining ROI on R&D
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 What can be done to bring pharma business operating models
R&D costs under control and improve R&D productivity & ROI in
a variety of ways, including automating
and restore ROI to
billion in 2010 sustainable levels?
processes, making efficient use of
massive new data sets, and supporting
Mean cost of bringing early decision-making with increasingly
a new asset to market: Many industry experts see a need to powerful predictive analytics and
transform the way clinical trials are statistical models.
conceived, designed and conducted.
This transformation will rely heavily Robotic Process Automation (RPA)
on harnessing the power of digital will streamline or eliminate many costly,
technologies. Whilst earlier waves time-consuming and error-prone
of digital disruption such as the manual steps. Big Data techniques
advent of the web, social media will aggregate and scrub massive,
and smartphones were highly
$2.1
disparate new data sets, making them
disruptive in many industry sectors, available for efficient use. Artificial
they were much less so for pharma, Intelligence (AI) will filter and process
with disruption largely confined to Big Data far faster than any human,
billion in 2018 internal communication and external
patient and market facing channels.
generating insights supporting early
decision-making with increasingly
with clinical trials, especially late trials, However, the current wave of emerging powerful predictive analytics and
making up a large and growing digital technologies now offer real statistical models. (1), (3), (4), (5)
share (1), (2) opportunity to significantly disrupt
3
How Digital Transformation can reverse declining ROI on R&D
This digital transformation is already Which of the following best describes your organisation’s
underway and likely to accelerate, current use of AI or big data analytics?
according to an ICON survey of
almost 350 executives, managers 326 qualified responses
and professionals in biopharma and
Notable responses: 31
medical device development firms.
Nearly 80 percent of respondents W
e plan to use AI and big data analytics, but
said their firm plans to use, or is using, have not yet begun 86
AI or Big Data approaches to improve W
e are piloting AI and big data analytics 66
R&D performance. Within five years,
W
e do not use AI and big data analytics and
two-thirds of survey respondents said
have no plans to do so
they will pilot or use these analytic
technologies in select programmes, W
e use AI and big data analytics in select
and another 20 percent plan to use development programs
them in all development programs. W
e have a comprehensive program in place
for incorporating AI and big data analytics
74
The umbrella category of AI and into our development programs 69
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 Which of the following best describes where you believe
clinical registries, which are likely to your organisation’s use of AI or big data analytics will be
use AI tools to optimise efficiency in five years?
and effectiveness in various ways. 13
328 qualified responses 31
In addition, respondents ranked Notable responses:
targeting biomarkers as the therapeutic
We
will make more use of AI and big data
approach most likely to benefit from
analytics in select programs
digitally enabled technologies -
perhaps reflecting the need to find We
will pilot AI and big data analytics
66 149
actionable correlations in masses We
will use AI or big data analytics in all
of data from disparate sources. development programs
Following closely were gene therapies, We
do not anticipate using AI or big data
customised therapies targeting specific analytics
disease stages, comorbidities and
We
will make less use of AI and big data
other specific patient characteristics.
analytics 69
4
How Digital Transformation can reverse declining ROI on R&D
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 These findings dovetail with our
probability of responding are three times more likely to progress from Phase 1 projections of the global need for R&D
clinical trials to approval, and AI is perfectly suited to identify such opportunities (6). productivity improvement. Based on
Similarly, last year, the FDA quickly approved an AI-powered device for detecting industry trends over the past decade,
diabetic retinopathy in primary care offices, signalling growing regulatory support we project that an overall productivity
for such technologies (7). increase of 20 to 25 percent is needed
to restore R&D returns to sustainable
Our survey respondents were optimistic that such developments will significantly levels by 2030.
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
Effect of increasing clinical trial efficiency on R&D ROI
15
10
5
0
-5
-10
-15
2010 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%
5
How Digital Transformation can reverse declining ROI on R&D
How much do you anticipate digital technologies improving Harnessing these new technologies
R&D productivity? will involve significant organisational
change. Already, they have resulted in
326 qualified responses the breaking down of internal functional
Breakdown of responses: silos or formal reorganisation, or
Count Percent both, at 70 percent of respondents’
firms. Many respondents see a need
Mid double-digit improvement (26%-50%) 127 39.0% to develop essential new skills and
practices internally, and to partner with
Low double-digit improvement (10%-25%) 87 26.7% outside experts, including technology
High double-digit improvement (51%-99%) 72 22.1% firms and CROs, to develop digital
technology capabilities.
Single-digit improvement (1%-9.9%) 19 5.8%
As detailed in our previous white
Double or more (100%+) 18 5.5% paper, Improving Pharma R&D
No improvement 3 0.9% 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
6
How Digital Transformation can reverse declining ROI on R&D
The potential of transformative technologies: Big Data, AI,
Blockchain, Cell-on-a-Chip, Advanced Statistical Modelling,
Quantum Computing
Big Data
Big 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
As new digital technologies continue to emerge, share has grown since. If this year’s
they converge to have a greater impact collectively, data were printed on paper, the stack
than any one technology can achieve individually. would reach to the moon and back
six times (10).
For example, combining historical information from EHRs with imaging, genetic
and molecular test data is driving the development of highly targeted oncology Needless to say, the hardware required
treatments, such as CAR-T and other cell therapies, giving hope to patients to store and process this volume of
resistant to more conventional approaches. Similarly, data from mobile sensors data is also growing exponentially.
and apps make possible new treatments for Parkinson’s and other neurological According to a 2016 estimate by the
disorders. Moreover, they enable the creation of novel endpoints that matter to Michigan Institute for Data Science at
patients with chronic conditions, such as the ability to work, cook and participate the University of Michigan, the number
in other daily life activities. of transistors required to process the
more than 20 petabytes of genomic
In a market that is increasingly driven by outcomes and personalised therapies, data and 10 petabytes of neuroimaging
mastery of digital technologies will be essential to generating sales, and improving data currently produced is about 1011,
the efficiency and reducing the cost of clinical trial operations. Here we discuss the an increase of two orders of magnitude
potential of emerging technologies to increase returns on pharmaceutical R&D, from 2014.
how they interrelate and a framework for successfully integrating them.
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.
7
How Digital Transformation can reverse declining ROI on R&D
Sources of Big Data; their potential and limits
Unquestionably, Big Data is diverse in its sources and quality, and massive in Below are some of the major data
its volume. As a result, it takes considerable effort to evaluate, normalise and sources, with a brief analysis of their
structure it so that it can be reliably used for analysis. In our industry survey, potential value and limits for improving
this was identified as a top challenge in adopting digital technologies. The nature, clinical R&D performance.
sources and quality of data also influence its value and how it can be used.
Different types of Big Data
Data Type Major Sources Uses Potential Impact
Structured –– Current and past –– Spot safety, efficacy and performance –– Real-time detection of safety and data quality
clinical data clinical trials issues quicker issues reduce delays, data loss, study failure risk
–– Registries –– Inform clinical trial design –– Earlier go/no go and adaptive change decisions
–– Peer-reviewed studies –– Identify promising sites shorten development
–– Synthetic control and platform trials –– Fewer protocol revisions cuts time and cost
–– Track real-world performance –– 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 –– Clinical EHRs –– Identify patient needs, characteristics, –– Realistic inclusion criteria reduce amendment
data –– Labs locations cost and delays
–– Imaging –– Inform trial design, site selection, –– Identifying potential patients targets recruiting
recruitment strategy –– Patient-centric studies speed recruiting,
–– Pharmacies
–– Replace or supplement controls reduce attrition
–– Insurance claims
in some cases –– Fewer low-performing sites speeds recruitment
–– Real-world evidence of need –– Real-world evidence of patient benefit supports
and performance approval and payment decisions
Emerging real- –– Mobile clinical monitors –– Continuous, real-time clinical –– Denser, real-time data detect efficacy and safety
world data (RWD) –– Patient-reported trial monitoring signals sooner
outcome and self- –– Virtual visits –– Reduced patient burden aids recruitment
assessment apps –– Patient engagement and retention
–– Internet of Things, –– Compliance reminders –– Engagement and compliance reminders
including smartphone reduce data loss
–– Virtual endpoints
and commercial –– Biomarkers increase approval chances
monitors –– Identify patient needs, characteristics
–– Virtual endpoints more valuable to patients
–– Digitied imaging studies –– Biomarker development
–– Real-world evidence of patient benefit supports
–– Genetic studies –– Real-world evidence of need
approval and payment decisions
and performance
–– Proteomic and other –– New therapy targets expand portfolio,
molecular studies –– Identify new therapy targets
support targeted medicine
Supplemental data –– Weather, environmental –– Monitor conditions that might affect –– Filtering out environmental “noise” may increase
conditions, therapy performance study sensitivity, reducing time and sample size
economic, education, –– Guide culturally appropriate protocol –– Culturally appropriate study design and therapies
demographic, language, development more improve recruitment and retention
location records
–– Guide more-effective therapy design –– Real-world effectiveness increase supports
8 approval and reimbursement decisions
How Digital Transformation can reverse declining ROI on R&D
Structured clinical data Traditional clinical data
These include data from current and past clinical trials, real-world evidence These include data from clinical EHRs
(RWE) from registries and peer-reviewed studies. as well as from labs, pharmacies and
insurance claims.
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 Clinical EHRs are designed to support
range of purposes that can improve clinical study efficiency. For example, data clinical practice rather than research,
captured automatically from currently active trials can quickly identify unanticipated and there are wide and unpredictable
safety issues, and flag anomalies that might indicate protocol deviations early variations in how and what data are
enough to prevent costly study delays or failures. They can also help make go/ captured. As a result, EHR data are not
no-go, adaptive study changes earlier, and help close out studies faster, typically useful for establishing efficacy
potentially cutting weeks or months off overall timelines. in clinical studies, except for some rare
or serious conditions that preclude the
Structured clinical data from previous trials can be very helpful in streamlining use of controls. However, EHR data are
current trial protocols by predicting potentially high-performing study sites, which increasingly valuable for guiding study
is invaluable for shortening study timelines and keeping trials on schedule. In some design and exclusion criteria, as well as
cases, historical trial data may be used as a synthetic control arm in an active identifying promising study sites. They
trial, though this requires careful matching of trial populations and data collection are also proving powerful for identifying
processes to ensure data comparability. patients at high risk of developing
chronic diseases, particularly when
Platform trials - in which a single control arm is used to test multiple treatment merged with genetic data. Moreover,
approaches, and is sometimes run by different sponsors - is a variation on the they are helpful in producing RWE
structured clinical data approach. Both reduce the number of patients to be for value-based payment models.
recruited and increase the proportion of patients receiving treatment. Historical
trial data may also be used to guide new research and suggest possible label Lab, pharmacy and insurance
indication expansion. information are similarly limited,
but have similar uses.
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.
9
How Digital Transformation can reverse declining ROI on R&D
Emerging RWD sources Mobile data also can monitor therapy Emerging supplemental,
These include mobile clinical monitors, compliance and alert patients if they environmental, economic
patient-reported outcome apps, miss a dose, helping to protect trial and social data
internet of medical things such as data integrity and reducing the need These include everything from
motion detectors, as well as imaging, for extra recruits to compensate environmental data to insurance status,
genomic and molecular studies. for noncompliance. Mobile devices education and income markers that
can further improve trial efficiency may influence therapy response and
Mobile monitors and apps range by reducing clinic visits and costs, study success. Such data can be
from commercial devices, such as and improving patient recruiting and critical for properly interpreting
Fitbits and cell phone accelerometers, retention by making studies more mobile monitoring data.
to medical-grade heart, blood pressure convenient. Like traditional clinical
and glucose monitors. For use in data, mobile monitoring and apps For example, high pollen counts or
clinical studies, these devices must create a detailed picture of everyday pollution can affect asthma or COPD,
be rigorously validated. Moreover, life that is extremely useful in guiding possibly producing a blip in response
they must address potential concerns development decisions and supporting that might otherwise be attributed
such as placing a monitor on someone value-based payment. to a trial medication. Supplemental
other than the patient, as well a information helps filter out this kind
device issues such as cybersecurity, Similarly, genomic, proteomic and of noise in datasets, potentially
battery life and usability, durability imaging studies provide detail on an reducing the time and size of trials.
and even aesthetics in everyday unprecedented level that can be used
life. For example, a trial patient may for diagnosis, monitoring and therapy Similarly, general educational level and
remove a monitoring device that is development. The potential power of health literacy can have a significant
uncomfortable, clashes with clothing analysis of mass imaging datasets can effect on therapy compliance. This
or attracts unwanted social attention. be illustrated by an algorithm developed can be useful during trials to interpret
by Google with the Aravind eye hospital data and is important for designing
Addressing these issues requires network in India that not only screens therapies that are more likely to be
special expertise across several for diabetic retinopathy with high successful in the real world.
disciplines, including device design, reliability, but also accurately predicts
patient engagement and digital cardiovascular disease risk based
endpoint validation, all of which are on retinal images.
expensive. However, the payoff can
be significant, as the volume and Genomic and proteomic data are
granularity of data from mobile devices particularly valuable for finding
can increase the statistical power of biomarkers of diverse diseases
subject data, allowing shorter periods including cancer that can dramatically
to establish efficacy. 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.
10How Digital Transformation can reverse declining ROI on R&D
Survey results
Top factors favouring digital technology adoption
Improve return on R&D investments 3.23
The increased data granularity,
Improve product safety and efficacy 3.08 specificity and volume of Big Data have
the potential to increase clinical R&D
Reduce clinical trial costs 3.05 efficiency. Harnessing this potential
requires significant infrastructure,
Post-market regulatory monitoring 2.87 expertise and judgment to determine
when and how to best deploy it.
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
11How Digital Transformation can reverse declining ROI on R&D
Finding the right partner for the right digital expertise
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
Creating and maintaining this continually expanding data
that will transform clinical R&D tomorrow,
gathering and processing capacity represents a significant data and expertise – which are more
challenge for all types of healthcare enterprises, including specific to current trial processes and
pharma sponsors. Indeed, lack of internal resources and needs – are also essential to transform
understanding of how to develop and apply digital technology clinical trial efficiency.
were the leading barriers to adoption in our industry survey.
For example, data on how trial sites
Healthcare currently accounts for almost 18 percent of the US economy and is in have performed in the past can help
the double digits in much of the developed world. Thus, the size of the opportunity, select sites that are more likely to
in addition to its complexity, has attracted the attention of the world’s tech giants. successfully recruit patients. Reducing
According to Deloitte, six of the 10 largest technology companies are diversifying the under-recruitment challenge is
into healthcare (12). critical to cut the cost of opening
sites that never see a patient, and
Significant ventures include (13): to keep studies on track (14). Specific
––Alphabet’s Google Ventures invests about one-third of its capital in data on past trial designs and how
approximately 60 healthcare and life sciences start-ups ranging from genetics they performed also help streamline
to telemedicine, while Google DeepMind Health focuses on structuring data current trial protocols involving similar
from various sources using machine learning. Verily, its life sciences division, conditions or test therapies. Applying
is collaborating with major research institutions, including Duke University advanced statistical and trial design
and Stanford Medicine, on Project Baseline, a genetic study to improve our to specific study needs was the other
understanding of chronic diseases. top-three challenge sponsors identified
that requires the skills and knowledge
––Apple is focusing on mobile data and patient interface technologies, and also of CROs and other clinical trial experts.
collaborates with researchers on projects for detecting heart rhythm problems
and Parkinson’s symptoms using Apple devices. In our experience, identifying and
addressing these current study needs
––Amazon recently launched Comprehend Medical to mine and decode
using Big Data not only improves
unstructured data in medical records using machine learning.
trial efficiency significantly in the near
Pharma sponsors are partnering with these and other large tech companies term, but also builds competence
to leverage their core expertise in digital science. They are also looking to the and confidence in applying digital
burgeoning ecosystem of smaller tech companies to develop potentially disruptive technology needed to tackle more
12 complex, longer-term needs.How Digital Transformation can reverse declining ROI on R&D
Artificial Intelligence
ICON survey results
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
2.86
and PRO apps
If Big Data is the raw material of digital transformation,
AI is the engine that sponsors rely on to make use of it. Mobile sensors 2.86
AI-powered capabilities, including pattern recognition and evolutionary modelling, Virtual trials 2.85
are essential to gather, normalise, analyse and harness the growing masses of
data that fuel modern therapy development. Indeed, AI and advanced analytics Cells or organs on chip 2.75
were viewed as the digital technology with the most potential to improve clinical
Internet of Medical Things 2.74
R&D productivity in our industry survey.
Quantum computing 2.72
AI has many potential applications in clinical trials both near- and long-term.
These range from automating routine study data entry functions, to analysing Environmental sensing data 2.62
EHR data to find suitable candidates and sites for clinical studies, to monitoring
and encouraging patient compliance with study protocols, to adaptive dose- Social media data 2.53
finding, to discovering and modelling potential new molecules and therapies.
Blockchain platform 2.51
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).
13How Digital Transformation can reverse declining ROI on R&D
Here we outline different ––Assessing potential data entry errors, This entails a culture and skills
approaches to AI and the such as duplicated or missing change within the workforce,
data points though it’s positive for workers
benefits of each.
since it relieves them of drudgery in
––Detecting potential protocol
favour of exercising and developing
deviations, such as emergence
Expert systems higher level, and higher value, skills.
of a non-random variation trend
Some of the earliest and most widely
used AI applications are expert ––Forwarding clean data to the trial Not only does robotic process
systems that use rules-based master file, and alerting trial monitors automation yield immediate
algorithms to mimic specific human to anomalies efficiency benefits, but also, it lays
expertise. One example is decision- the groundwork for incorporating
support trees for routine diagnostic Immediate efficiency benefits of robotic massive data sets from EHRs, mobile
tasks, such as differentiating between process automation include: devices, automated image scanning,
bacterial and viral respiratory infections and individual patient genomic and
––Reduced manpower - eliminating the
for prescribing antibiotics, which are molecular data. As such, it is a
need for manually transferring data
built into virtually every EHR drug- fundamental stepping stone on the
from clinical sites to trial master files
ordering module. However, rules-based path to harnessing the transformational
typically reduces clinical research
systems require humans to codify power of AI to make use of Big Data.
assistant (CRA) and trial monitoring
knowledge and write unambiguous headcounts by two full-time
rules, which limit their use to addressing Yet, as straightforward as all this
equivalents or more
relatively uncomplicated and well- may sound, accomplishing these
defined problems. ––Reduced errors and delays - things, while ensuring trial data and
automatic forwarding of validated process integrity, requires a deep
data eliminates the possibility of understanding of study processes
Robotic process automation
data entry errors, as well as delays and ends. Insights from CROs and
Variations on this approach have signing off on incoming data by others with extensive experience
significant value in improving clinical human reviewers are critical to designing and testing
trial efficiency in the realm of robotic process automation to avoid the
process automation (RPA). Robotic ––Reduced data loss – automatic classic computer programming
process automation (RPA) are data analysis detects anomalies problem – GIGO, or ‘Garbage In,
specialised computer programs that much sooner and more reliably than Garbage Out’. In other words,
automate and standardise processes manual review, and alerts human automating inadequate processes
based on rules-based algorithms. CRAs to do what they do best, only produces more mistakes faster.
Of itself , RPA has no ‘intelligence’ which is to investigate site issues
– however, increasingly it is typically and get sites back on track
integrated with other AI technologies
to create faster automation, and it’s Without fundamentally changing
organizational impact is proving to be existing trial processes, robotic
significant. In clinical trials this includes automation can cut days or weeks
automatically: off of trial timelines simply by reducing
human error and delays due to
––Capturing routine clinical data,
business hours, weekends and time
such as patient vital signs
off. Redesigning trials to take full
––Collecting operational data, such as advantage of robotic processes can
drug administration dose and time cut even more by refocusing CRAs
and trial monitors on a consultative
––Testing data to flag safety issues,
role supporting trial sites.
such as an out-of-range lab result
14How Digital Transformation can reverse declining ROI on R&D
Linking trial stages protocols might affect development Deep machine learning is essentially
Another way to leverage robotic timelines, as well as prospects for the same process, with the exception
process automation is linking approval and market prospects. of the extent to which humans prime
processes across study stages. Experienced partners, such as CROs, the machine. In traditional machine
This involves considering the final bring the needed expertise to the table learning, a human may extract the
outputs – which are data supporting to implement automated end-to-end features it wants the machine to
regulatory approval and commercial study planning and integrate it with process in great detail, and the
payment – in the design of every study holistic portfolio management. optimisation algorithm the
step and automatically adjusting those machine applies is limited.
steps when a change occurs. Machine learning and
deep machine learning The greater processing power of
For example, results midway through Moving up a step on the AI complexity modern computers enables deep
a Phase 2 study suggest that a new ladder are machine learning and deep machine learning, in which the device
cancer therapy may be much more machine learning. Machine learning itself extracts features from a raw
effective in a patient subgroup with is potentially more flexible than rules- data set and has multiple layers of
a particular biomarker. Changes based expert systems because it does optimisation processing modelled
in the target population and how not rely entirely on programmers to on how neurons process information.
it is assessed will require not only provide a fully worked out set of rules, This allows the machine to discover
amendments to the ongoing study but allows the computer to improve patterns in the data that do not depend
phase, but also in Phase 3 design. its performance, or its “learning,” on the insight or expertise of a human
Moreover, the endpoints and data based on training. programmer, making deep learning
needed for regulatory and payment more powerful for assessing images
approval will need to be reassessed. Typically, the machine is trained using a and other extremely complex
large input data set, which it processes data sets (15).
Automatically linking study according to an initial algorithm that
requirements from end-to-end assigns weights to various factors and
can significantly reduce the delays mathematically transforms them, using
and manual effort required to fully processes such as random forests,
For example, AI deep
implement a protocol amendment. Bayesian networks and support learning machine techniques
It changes everything from the forms vectors, to predict an outcome (15). have improved the formulae
needed to collect new information, to for predicting the power of
the data analysis and charts required The predicted outcome is then intraocular lenses needed
to present results to regulators and compared with the known outcome for to get close to uncorrected
insurers. However, designing linked the training data set, and the algorithm
stage automation also requires a deep is altered and run again, with better
20/20 vision after cataract
understanding of conventional trial predictive results guiding changes surgery.
processes, as well as regulatory at each round. This iterative process
and payer evidence requirements. proceeds until predictive power
plateaus. The algorithm may then be
Beyond the benefits for an individual tested against an unknown data set to
development program, adopting a determine its predictive accuracy, and
linked process automation approach the training set may be enlarged and
facilitates portfolio management diversified to further improve accuracy
decisions by allowing developers to and extend its range.
model how specific changes in study
15How Digital Transformation can reverse declining ROI on R&D
Historically ophthalmic for diabetic retinopathy, which has patient attrition and the need to
surgeons achieved an the potential to dramatically improve over-recruit to offset projected
screening for this potentially blinding subject or data losses.
80% condition by primary care physicians (7).
Alphabet’s Deep Mind program has
success rate Newly discovered genetic
developed a similar capability and can
reaching one-half
dioptre of target even predict the risk of heart attack and molecular biomarkers
refraction. and stroke from retinal images alone. make possible identification
Image analysis is also used to assess of compounds more likely
However, a recently designed AI-based oncology pathology and heart rhythm
formula has pushed that to
to reach market (6).
with accuracy that rivals or exceeds
experienced clinicians.
AI has many near- and long-term Limits of AI
applications for improving clinical
Still, AI has its limits and must be
research returns (18), including:
handled with care to ensure it is
––Patient identification - producing valid, reliable results. Its
90% for all patients AI capabilities, including natural unstructured nature can lead to results
language processing and association that are not useful or defy causal
rule mining, help extract data from logic. For example, one algorithm for
98% unstructured medical records to find
patients suitable for clinical studies,
diagnosing tuberculosis (TB) by reading
chest x-rays considered not only the
for patients with
near-sightedness and can help identify those most x-ray image but also film metadata,
likely to complete a trial assigning greater weight to images
taken by mobile machines used in
The most common pre-surgery ––Site selection - Helps identify
hospitals than by stationary machines
refractive error. sites with the right patients and
in clinics – apparently having “learned”
capabilities to successfully recruit
that patients with lung complaints
As the data set has grown, the formula and retain patients
severe enough for hospitalisation
has become more accurate for less ––Patient monitoring and support were indeed more likely to have TB.
common errors, says its inventor, - AI enabled mobile devices help
Warren Hill (17). He notes that the identify when patients deviate from Various ways of opening the machine
neural network approach is more protocols and send reminders learning “black box,” including flagging
efficient than rules-based methods the data the machine reviews and
for modelling any system that is ––Cohort composition - AI helps the weights it assigns, have been
not completely understood – which identify biomarkers to find patients proposed (19). However, given the
includes just about everything most likely to show benefit from conservative nature of clinical research,
in clinical medicine and research. a particular dose or combination in addition to the cost and complexity
therapy of developing AI solutions, it is likely
The power of AI for revolutionising to be a long time before their full
clinical practice is already evident. Ensuring that the right patients and sites potential can be realised. As with Big
For example, the FDA last year are recruited for studies goes a long Data, collaboration with outside tech
approved the first AI diagnostic way toward preventing costly delays. experts and clinical study process
device that requires no clinician Improved patient protocol compliance experts will be critical for success.
intervention to detect and refer and denser data sets may reduce
16How Digital Transformation can reverse declining ROI on R&D
Advanced Statistical Modelling
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
Applying advanced statistical models to the greatly expanded establishing synthetic control arms
and shared control arms for platform
range and granularity of data available through Big Data and trials, in which multiple therapies are
AI technologies has the potential to significantly improve evaluated against one control group,
clinical R&D productivity in a variety of ways. These include often by different sponsors.
modelling and simulations, and cumulative analysis using
sequential, Bayesian and meta-analytic techniques. As with AI and Big Data, advanced
They are particularly useful for conducting smaller statistical modelling requires a high
degree of technical and clinical
studies that are gaining importance as therapies study-specific knowledge. Powerful
increasingly target limited populations. integrated statistical packages, such
as ADDPLAN neo, are also essential
Sequential analysis uses accumulating data, sometimes over long periods of time, to design and execute such adaptive
with the objective of ending a trial as soon as sufficient data have accumulated. studies, as they require extensive
On average, this leads to a smaller overall sample size, and allows ongoing modelling in advance to validate - and
research into rare conditions such as sickle cell anaemia (20). Bayesian statistics, ongoing analysis to guide - adaptive
which refine analysis based on accumulating data, are well-suited for these kinds changes, such as expanding samples,
of studies. They’re also useful for designing paediatric studies that can reliably reallocating patients among study
guide dosing across a wide range of age and weight variables (21). arms, or early closure for either
success or futility. Partnering with a
Meta-analysis may also be used to generate starting points for such research, CRO, or other entity with extensive trial
and to support evidence for studies where randomising a control group is not experience, can help ensure studies
possible for ethical reasons. In addition, computer models of pharmacodynamic are designed to produce sound results
activity and disease state progression may be useful for assessing how future that will be acceptable to regulators
populations impact patient needs, as in a 30-year predictive study commissioned and insurers.
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
17How Digital Transformation can reverse declining ROI on R&D
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
While Big Data, AI and advanced statistical models are all in several organs, has been developed
active use in clinical R&D, several new digital technologies to assess how drugs might interact
are on the horizon with potential to transform the industry. across organ systems. While the
We outline a few of these below. technology may one day dramatically
reduce the cost of pre-clinical
Organ-on-a-Chip - A major contributor to low clinical R&D productivity is a lack development and reduce the risk
of robust preclinical models for gauging the potential efficacy and toxicity of drug of human trials, it requires additional
candidates. Animal models can be informative, but the results often do not translate development and validation before
to humans. Cultured human cells are of limited use because they generally lack it can be practically use. This will
physiological function and are removed from their circulatory support system, require significant collaboration among
making it difficult to assess drug efficacy, toxicity and organ interaction. engineers, biologists and clinicians (24).
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.
18How Digital Transformation can reverse declining ROI on R&D
Blockchain - Data integrity Blockchain technology allows for Quantum computing - Led by
and transparency are essential to complete transparency of data, several governments, and big
maintaining trust in clinical R&D and which has immense potential technology companies such as IBM,
ensuring data are properly interpreted within clinical trials. With blockchain, Microsoft, Google, Alibaba and Intel,
and analysed. At the same time, there is an audit trail built into there has been significant investment
maintaining patient confidentiality is transaction of data, which allows into developing quantum computing
an ethical and legal requirement. Within for verification of the original source technology over the last several years.
clinical trials, patient data is the most of the information being transacted,
notable item of transactional nature as well as the ability to detect any Quantum computers perform
between networks such as healthcare attempts to tamper with it. calculations using linear algebra
institutions, patients, and regulators. to manipulate matrices of complex
Blockchain allows for greater data numbers (‘qubits’) - effectively
Blockchain technology which is availability. When all data is shared connecting in multiple dimensions.
essentially a decentralised ledger openly within a network, issues with This enables quantum computers to
system that is fully transparent and data systems interoperability are conduct vast numbers of computing
immutable – has been shown to reduced, and opportunities open calculations simultaneously, whereas
provide a web-based framework up new possibilities for using that conventional computers must work
that allows patients and researchers data. For example, availability and through calculations linearly, one
access to their own data. It allows for accessibility of patient information at a time. This makes quantum
user confidentiality, protecting patient could be used for patient feasibility computers much more capable of
privacy during exchange of data analysis and population studies. solving complex problems, involving
between parties. Moreover, blockchain allows multiple connections among multiple
researchers to submit queries data points, much, much faster. For
for data that are stored off chain, example, quantum computers can
protecting patient privacy. Despite break in a few weeks encryption based
the potential benefits, further on factoring very large numbers that
functionality will need to be added (25). 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.
19How Digital Transformation can reverse declining ROI on R&D
Clinical trial of the future
Future clinical trials that Quantum analysis of masses Studies are planned using digital heart
make full use of digital of genomic and proteomic data monitors and a custom patient app
technologies will look very combined with years of medical to monitor patients at home, greatly
records reveal biomarkers for five expanding the potential patient pool
different at each stage of new heart failure subtypes. Quantum beyond the five percent currently
development – and may modelling quickly develops and involved in clinical studies. Study
have a much higher chance assesses candidate molecules site costs are also cut by nearly half.
of approval at lower cost. specifically targeting the identified Continuous monitoring backed by
Just imagine: pathways. Then, they determine the reminders to follow the protocol
best candidates for synthesis and nearly eliminate protocol deviations.
testing based on a virtual population The higher data density and lower loss
using empirical physiology models reduce the number of patients needed
developed with AI. for the trial and send early efficacy and
safety signals for go-no go decisions
Organ-on-a-chip using cells with the (27), (28)
.
target pathways are developed to test
the activity of the compound in the Three of the five compounds advance
heart, followed by body-on-a-chip to in an adaptive study design that
assess systemic risks. The top five seamlessly rolls from Phase 2 to
candidates are validated for clinical Phase 3. Automated data collection
studies, complete with preliminary and analysis provide a robust dataset
information on likely dose response. meeting newly established regulatory
standards for digital studies that leads
AI analysis of electronic records
to approval for three new drugs, a
identifies study candidates in five
success rate of 60 percent, or six times
countries, including those most
the current average. The entire process
likely to successfully complete a trial.
from discovery to approval takes
Analysis of previous site performance
less than five years – half the current
helps recruit investigators. Electronic
average – yielding better treatments
patient education tools, connected
for more patients sooner, and better
with live support speaking local
returns on research investment.
languages, help recruit patients.
Trial recruitment goals are reached
on schedule.
20How Digital Transformation can reverse declining ROI on R&D
“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
21How Digital Transformation can reverse declining ROI on R&D
Conclusion: What’s needed to move forward
Harnessing digital technology How important are the following factors in driving
to transform clinical trials will your organisation’s adoption of digital technology?
require sponsors to develop or
323 Number of Qualified Responses*
acquire a range of capabilities.
Breakdown of responses:
Beyond that, it may fundamentally
change the way sponsors are
organised and integrate R&D into 100%
0 1 2 3 4
the overall enterprise. Critical steps 80%
for moving forward include:
60%
1. Identifying and developing 40%
operational and IT expertise and
20%
capacity. Given the rapid growth
of IT science and sheer computing 0%
capacity required, supplementing
R&D investments
Improve return on
R&D investments
Improve return on
trial costs
Reducing clinical
RWE of Value
payer demand for
Accommodate
markets
targeted medicine
Complete in
safety and efficacy
Improve product
requirements
monitoring regulatory
Meet post-market
communities
Get closer to patient
communities
to prescriber
Get closer
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. Weighted Average
Once again, the highly technical
nature of statistical analysis – Improve return on R&D investments 3.23
particularly paired with adaptive trials
Recruiting patients for clinical trials 2.68
and data-driven techniques such
as developing and validating virtual Reducing clinical trial costs 3.05
study endpoints – suggests a need
for partnering with firms specialising Accommodate payer demand for RWE of value 2.83
in these activities.
Compete in targeted medicine markets 2.84
3. Developing global reach.
Improve product safety and efficacy 3.08
The growing need to target specific
population needs and to comply Meet post-market monitoring regulatory requirements 2.87
with national regulations makes
clinical development an increasingly Get closer to patient communities 2.61
international enterprise. Partnering
Get closer to prescriber communities 2.57
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.
22How Digital Transformation can reverse declining ROI on R&D
How has your move to digital technology affected the
way your organisation operates and is organised?
328 Number of Qualified Responses
Breakdown of responses:
77 70
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 96 85
functional roles
How would you rate the following as potential barriers
to adopting digital technologies at your organisation?
322 Number of Qualified Responses*
Breakdown of responses:
100%
0 1 2 3 4
80%
60%
40%
20%
0%
Lack of internal Lack of internal Internal resistance Lack of payer Lack of regulatory
understanding of resources to to change understanding of support for digital
digital technology develop and apply digital technology technology
potential digital technology potential transformation
Weighted Average
Lack of internal understanding of digital
3.23
technology potential
Lack of internal resources to develop and apply
2.68
digital technology
Internal resistance to change 3.05
Lack of payer understanding of digital technology potential 2.83
Lack of regulatory support for digital technology
2.84
transformation
23How Digital Transformation can reverse declining ROI on R&D
How important are the following challenges in developing
digital technology capabilities? About the
323 Number of Qualified Responses* ICON Digital
Breakdown of responses: Disruption survey
100%
0 1 2 3 4 In May and June 2019 ICON
80% surveyed industry leaders across
N America and the EU to share
60% their insights on the application
of AI in Pharmaceutical R&D.
40% Of the 350 qualified responses,
20% 97 respondents were C-Level
or Executive (VP or Senior VP).
0% Respondents provided responses
Data Storage and Norming and Creating a one Analysing complex Applying advanced Working with to quantitative pre-defined survey
processing capacity integrating data source interface data sets statistical and trial researchers and
from multiple design to specific study sites questions as well as providing free-
sources study needs form qualitative written responses.
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
3.18
study needs
Working with researchers and study sites 2.87
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