KEVIN JULIAN: Hello, and welcome to our session
covering breakthrough insights across the life sciences value
chain.
I'm Kevin Julian, senior managing director
in Accenture's life sciences practice.
And later I'll be joined by my colleague, Mike Stapleton,
who's the head of strategy for Accenture's life sciences
INTIENT platform.
During this session, we'll be talking
about why creating breakthrough insights in life sciences
is more important than ever.
And we'll share how INTIENT, Accenture's life sciences
platform powered by Google, is able to generate insights using
leading-edge Google technology.
During this session, we'll spend most of the time illustrating
examples of how insights are being generated by INTIENT
across the life sciences value chain, from earlier
research through pivotal clinical trials
all the way through to delivering care to patients.
Our industry is at a turning point,
shifting from small molecule, large population
strategic approaches to a more targeted scientific approach.
This shift is powered by what we Accenture call New Science.
New Science is a novel chemistry or biology
in previously unsolved areas of need,
like cell and gene therapy, often enabled by technology
to deliver the therapy or to support
the patient, or sometimes maybe technology
itself, an app or other technology solution
that intervenes in an effective way
to improve the patient [INAUDIBLE]..
Why the shift?
Something had to change.
The old model simply was not supporting
the needs of the market.
New Science is attracting significantly more attention
and investment, and for good reason.
Accenture Research is determined that New Science therapies are
almost 50% more likely to be approved
and greater than 50% of sales over the next several years
are expected from New Science.
This shift to New Science brings the industry's dependence
on technology to the fore.
Increasingly, industry depends on connecting data
across the enterprise to generate insights
into how therapies should be developed and brought
to patients.
While this ambition is clear, it's also clear
that industry has yet to take the necessary actions needed
to achieve their goal.
According to this IDC study, more than 50% of respondents
did not have a clear path to connect, share, and analyze
information across their organization.
This is where INTIENT comes in.
INTIENT, Accenture's platform for the life sciences industry,
powered by Google Cloud, enables insights and collaboration
across the life sciences enterprise.
INTIENT helps the industry generate breakthrough insights
in three powerful ways--
through functionally oriented product suites
that have been built in key areas of insight need--
drug research, clinical trials, pharmacol vigilance,
and patient services--
with other use cases enabled by the INTIENT core platform,
and through additional use cases enabled by the INTIENT network,
our open ecosystem access solution.
Through access to innovative specialty software, services,
hardware content, and other business partners,
the INTIENT network offers industry-standard APIs
for pretty competitive functions that
easily and quickly integrate into existing systems.
The ability to rapidly and flexibly deploy
solutions and lower barrier to entry
for new, small, and/or niche software players--
all of this is made possible by the INTIENT core platform
powered by Google Cloud.
The core platform is a flexible and powerful
data-centric solution that connects
a variety of data sources and makes
insight generation possible.
How does it do that?
We start by using Google's Healthcare
API to accelerate solution development
and access third-party solutions with fully
managed enterprise scale, FHIR, HL7, and DICOM API.
We use Google's BigQuery to analyze
virtually unlimited amounts of data
using standard SQL at scale with zero operational overhead.
And AutoML provides non-expert users with access
to pre-configured, high-quality machine learning models,
such as image detection, translation,
and natural language processing.
Let's illustrate the power of INTIENT with a few examples.
To start, my colleague, Mike Stapleton,
will share some of the exciting insights
our clients are generating with INTIENT Research.
Mike?
MICHAEL STAPLETON: Thank you, Kevin.
In the next section of our presentation,
we're going to bring the power of INTIENT
to life, looking at three critical parts
of the pharmaceutical value chain.
I'll start with research, as Kevin mentions.
I'll then go through clinical trials,
and then hand back to Kevin, who will take it home,
connecting the value of research and development to the patient
and the impact we can have on us and society in general.
So let's start with research and the fundamental challenges that
face drug discovery scientists.
Whether they're starting with a disease
or starting with a target or starting with a biomarker
discovery program, or, indeed, repurposing existing research
efforts, in every single case, drug
discovery scientists are challenged,
bringing together a wide range of external data, lots
of historic, hard-to-retrieve internal data,
assembling that data, bringing it together in a format
that can be easily managed, easily visualized,
and decisions taken from that aggregated data.
Consistently across the whole of drug discovery research,
that integration of data, that thinking
through a single query, how easy it is to make a decision,
and how we can embed advanced tools--
artificial intelligence, machine learning--
so that more informed decisions, more informed predictions
can be made to advance faster and in a more focused way.
To bring this to life, I'm going to dig
into one of these in a bit more detail
and take a look at target discovery
as a particular example to show the power
of the INTIENT platform.
The key challenge here, quite easy to state,
is that as an industry, as a society,
we know more and more about human disease biology
than we ever have done.
And we continue to learn more and more on a daily basis.
Through the advances in genomics to the connectivity
and measurements of mobile devices that we all wear today,
we understand more and more about us
as patients and how that information
is connected to a therapeutic or a particular condition
or a lifestyle that we have as individuals.
But even with that, yet roughly 90% of candidate drugs
fail in the clinic.
And a large number of those fail because in early-stage drug
discovery, we still choose poor targets.
This has the obvious consequences to the industry--
long development times, increased cost,
and, quite significantly, negative patient outcomes.
So if we have the opportunity, as we do with a platform
strategy like INTIENT, to address this key bottleneck,
this key area of pharmaceutical R&D,
we would, quite clearly, have huge impact.
So why is it so difficult?
Let's have a look at the operational consequences
of drug discovery today.
Very diverse skill sets are required
to manually bring together that external and internal data.
Integration of that data is extremely time consuming
and often error-prone.
Key patterns, key indications, key predictions
can only be made through machine learning
and artificial intelligence capabilities,
not by the human mind.
And so that's where the INTIENT solution comes in.
We can use the INTIENT solution to bring in those broad array
of data sources.
We can aggregate those data sources, filter, pre-process,
and present the data in a much more meaningful and accessible
way to a scientist, through dashboards, alerts, predictions
that allow scientists to better prioritize, better guide,
and allow a portfolio activity of project teams
to better manage assets and to move forward through the drug
discovery process, ultimately identifying novel targets
and prioritizing targets which have
a much higher probability of success
further into pharmaceutical development.
Let's take a look at a screenshot of how
that would actually look.
And we see here a simple screen shot of a modern user interface
from INTIENT, where we bring together
that drill-down of both therapy, genome,
and target, and that important interplay
for a drug discovery scientist to make
all of those connections through a simple interface,
to click, to understand, and to predict.
But obviously, underneath all of this,
what is powering all of this, what makes all of this possible
is the INTIENT data fabric--
how we actually ingest those broad sources of data,
how we present those analytic viewpoints
so that they are useful and guide in an intelligent way,
how we support, as Kevin mentioned,
the broad range of both chemical and biological
research that now is just part of the armory
for our pharmaceutical research scientists.
And how, ultimately, we action--
how do we actually inform partner companies,
other scientists, colleagues across the globe
to do something as a consequence of our insight through a very
integrated request management capability?
So you can see at all levels here in drug discovery,
in pharmaceutical research, how INTIENT
and how the platform that Kevin described
addresses the key bottlenecks-- the bringing together the data,
aggregating the data, and presenting it
in an actionable and predictable fashion.
Success in research then takes us forward
into pharmaceutical development.
So let's take a similar view of clinical trials.
And we start in a very similar way.
We start with the complexity of managing very large, very
diverse sets of data, both internal and external
to the pharmaceutical company, hundreds
of patients across dozens of sites globally being monitored
and tested and the data from all of those patients in minute
detail being brought together for future analysis,
prediction, and regulatory filing--
hugely complex logistics that require a scale of computation
to cleanse, process, and integrate
that data across broad ranges of genetics, genomics,
mobile data, real-world data, so that ultimately, we
can create insights, presenting that aggregated data
in real time so the prediction can be made
and analysis made to actually guide future trial design,
to inform conduct and commercial viability,
and to ultimately facilitate the filing of a new drug
to the regulatory authorities.
Let's take a look at clinical operations as one example
here of how all this comes together.
The challenge being, as I just stated, bringing together
those very large, diverse formats and sources of data
from numerous internal systems across a pharmaceutical company
and significant sources of information from partner CROs.
And this leads, this challenge leads to inconsistency,
leads to delays in actually changing
trial guidelines, and ultimately,
increases costs and time.
So what makes operations in clinical different,
as we asked the question in research a few moments ago?
Again, we still might see similar patterns here.
Integration of those disparate data sources
is fundamental, extremely time consuming,
and extremely expensive.
Effective monitoring requires access
to comprehensive and timely data.
And trends in clinical trials are complex
and require incredibly complex interactive analyses.
So again, we see how the INTIENT solution, the platform
capabilities addressing these complexities and challenges
facing clinical operations--
robust, flexible data ingestion, transformation
of that data in a way that makes the data usable
and actionable, actions to actually remediate
issues in a much more timely fashion, and visualizations
through interactive dashboards, predictive tools,
predictive simulation, and analytics methods.
So again, if we go to look at a screenshot here,
we can see how all this comes together--
a single viewpoint, a clinical control tower, if you like,
a visualization of the state of a particular trial
or a set of trials, how we monitor these
through that dashboard, how we see red, amber,
and yellow indicators to easily flag activities that
are on track or are not on track and require action,
how we can drill down into problematic areas, regions,
countries, sites, and particular patient demographics,
and how those reports can easily be accessed
by all leadership and scientists and medical practitioners
collaborating globally on a particular program.
So with both research and clinical,
we've seen how the common themes of data integration,
aggregation and presentation about data,
our analysis and visualization and prediction of that data
comes together through INTIENT.
A success, then, means we move to the patient.
And Kevin will take it from here to explain how the value of R&D
is then accelerated through greater patient engagement.
KEVIN JULIAN: Thanks, Mike.
And indeed, even if one were to identify the best research
target and successfully take it through clinical trials
with all the capabilities that Mike just described,
it wouldn't achieve its ultimate potential
if we didn't solve for the needs of the patients we're
targeting here.
INTIENT helps life sciences organizations
get closer to the patient through our INTIENT Patient
solution.
Capabilities include integrating patient care, which
supports better treatment outcomes, real-world outcomes
research, creating greater understanding
of clinical and economic value, patient engagement
support, which improves initiation and compliance
to prescribe treatments, and precision medicine
delivery, which provides needle-to-needle support
for new treatment programs.
All of those are important.
But let's focus on integrated patient care.
With INTIENT Patient, we're enabled
to allow physicians to prescribe digital support programs,
allowing patients to use devices and apps,
and all connected by INTIENT Patient to manage their care.
We allow their care providers to monitor that care,
analyze, and make recommendations
to improve the care program.
All of this, importantly, enables
us to lower costs, improve patient adherence,
and ultimately improve patient outcomes.
Let's take a look at an example of our digital patient study
solution, which provides a tool for principal investigators
to cohesively manage virtual study and performance.
With INTIENT Patient, we can manage and track
patient enrollment across sites, easily configure and provision
the devices that patients use, track adherence to protocols,
and make early interventions that prevent patient dropout
from the clinical trial, and leverage insights
learned through the trial to ultimately create
an even better patient experience when that therapy is
brought to market.
All of these capabilities together
and the insights that are derived from them
help our life sciences customers develop
therapies that are built and delivered
with the patient in mind.
Those are the examples of insights
that we've generated across the life sciences value chain using
INTIENT, life sciences--
Accenture's life science platform powered by Google.
If you have any questions, our contact information
is on this slide.
And we'd be happy to answer them.
Now, it's for me to thank you for joining
us to learn more about generating breakthrough
insights across the life sciences value chain.
Thank you.