Webinar & Video Transcripts
Risk Assessment Series - Risk Controls
Hello, and welcome to the fourth
in the series of risk assessment webinars.
Throughout these webinars, we will walk you
through the protocol risk assessment
process in bite sized chunks.
My name is Macarena Sahores
and I am an RBQM operations
consultant at TRI.
I work alongside our customers,
pretty much focusing on RBQM
implementation in clinical trials.
I moved into clinical research
after holding a postdoctoral fellow role.
I started as a central monitoring associate
and then worked as a central monitor lead
manager of CMAs, risk manager
and also an associate director
focusing on RBQM implementation
across studies and partnerships.
This has been a rewarding journey
seeing how the industry
started really to embrace
all things RBQM.
Today I will be talking
about risk control as part of the
risk quality management process.
After this session, you will be confident
in continuing with the quality management process.
If you haven't seen the first webinars
on critical variable identification,
risk identification and risk evaluation,
I recommend you do so.
Today, we focus on controlling risks.
Here you can see the topics
we will discuss again
the intent of quality management,
risk control as part of the
Common misconceptions and best practices.
So what is required to implement
an effective, risk based
quality management process?
This process, it starts early.
You need to build quality into the trial.
Use risk assessment
to inform a well-designed
and clearly articulated protocol.
Conduct early and ongoing
risk assessments of the protocol
and use protocol risk assessment
to create an inform the monitoring plans.
If you have followed
the previous webinars,
you would notice that we have covered
E6(R2) and E8(R1) in more detail there,
So I will be just briefly commenting
E8 and E6
ICH E8(R1), or general
considerations of clinical studies
is pretty much focused on
clinical trial design principles.
So which are the key messages of E8?
Identify risks and focus designs
and processes to mitigate those risks.
Build quality into the trial from the beginning.
Involve external stakeholders
in trial design,
such as patients and sites.
the quality by design and critical
to quality factors ideas.
Together with the concept
of a risk proportionate approach.
It consolidates the principles
introduced in ICH GCP E6(R2).
Finalization of E8(R1) has been delayed.
Now, moving on to E6(R2)
which introduced the principles of RBQM,
mainly the risk assessment process
and risk based approach to monitoring.
This slide shows E6(R2) section
five, quality management,
which is a seven step
process whereby proactive
identification and prioritization of risk
to critical data and processes
will improve the quality
of the clinical trial.
As we have discussed
in previous webinars, this is an
For example, following
a protocol amendment, new critical variables
might be identified on your risks,
which means you need
to follow the same process
over and over again.
E6(R3) is currently being developed
to acknowledge the diversity
of trial science, data sources
and a different context
in which clinical trials
can be conducted,
and it is expected to be more
aligned with E8..
So we can say that E6(R3) is coming soon.
Now we moved into the
quality management process
and actually the focus on this webinar,
which is the fourth step on the risk
assessment process - how to control risks.
So you should not start
the process by deciding
which controls you will be implementing.
First, you will need to identify
your critical data and processes,
then identify your risks
and evaluate them.
Only then you'll be able to move on
to risk control.
As always, you need to ensure
during the process, as well as
sponsors and CROs working
as part of a partnership.
And if you want to examine E6(R2),
what does it tell us about risk control?
And it tells us that
the sponsor should decide
which risks to reduce
and or which risks to accept.
The approach used to reduce
risk to an acceptable level
should be proportionate
to the significance of the risk.
And in this slide
is about common misconceptions.
things we have experienced.
And the first one is,
for example, I've heard
I know which controls I need.
I do not have to identify
critical variables or risks
for this protocol.
And as we know, the intent is to control,
either minimize and or avoid the risk
So if you implement a formal risk
then you'll be able to determine
which are the critical variables
and the risks to patient
safety and data integrity
specific to your protocol.
And only then you can determine
the most appropriate controls.
Another common misconception is around
we will be implementing 100 % SDV
So we do not need
any of the functions involved.
to accomplish monitoring.
And actually, you have to think
which functional roles will be owning
the mitigations or controls
you will be putting in place.
Yes, you have to think
but you also need to consider
data management, medical management,
safety, center monitoring and so forth.
Of course, it would depend
on your organizational processes
and functional roles,
but also the requirements
of this specific protocol you're
Another example of a common
nothing has changed in the study,
so what do I need to review the controls
we agreed during startup?
And as you may have experience,
controls, agreed early on
may not be enough once
a study is ongoing.
You may realize you need
the risk levels may have changed.
You have to remember E6(R2)
calls for an approach
used to reduce risks to an acceptable
level should be proportionate
to the significance of the risk.
You need to avoid again, that sort of
have one same model across
all the studies.
You have to tailor your
your monitoring strategy
to your protocol, right?
You need to make sure you have controls
and mitigations in place
which are fit for your protocol.
So next, we are going to have
a few slides of what
which which are the items
we need to consider when determining
which controls to implement.
And you have to ask yourself
What actions are we going
to take to control the risk?
To what extent can we or do
we want to control each risk?
How much risk are we willing to tolerate?
What level of control is appropriate
Who will be responsible
for this control?
What risks can we manage
before the trial starts?
Identified, risks can be reduced
prior to or during the trial or both.
You may have to put different
controls in place.
Where possible, we should aim
to eliminate as much risk
through redesigns of your protocol.
And for those risks we have to accept
or those we can reduce,
we need ongoing management.
And as we discussed already,
you need to make sure
you review those risks
which have been eliminated to make sure
that nothing has changed since.
And when discussing which controls
to implement, often only
likelihood and impact are considered,
but however, detection
may be the easier way to reduce a risk
because better detectability
gives us the opportunity
to reduce impact, right?
Why does detectability matter?
If we can detect the cause
and stop it leading
to an event, the impact, it can be small
or even be zero right?
And also, we need to detect
with enough time to take action.
So either we will try to stop the cause early on,
you know, the cause
leading to the event
and negative consequences.
Or to put a contingency plan in place
to reduce the impact.
So yes, detectability
has to be considered
when you are trying
to implement controls.
And and now this slide
is just to have a word
about centralized monitoring
and centralized monitoring
being used as a risk control?
As as you should be aware,
right here is where E6(R2)
addresses the extent and nature
of monitoring to be implemented.
It acknowledges that onsite
monitoring is performed at the sites
where the clinical trial
is being conducted.
And it also mentions
that centralized monitoring
is a remote evaluation
of accumulating data.
And it is clear from there that we need
to broaden our approach
to monitoring study
conduct and forget the one
size fits all approach, right.
And continuing with E6(R2),
and the concepts within E6(R2)
centralized monitoring processes provide
additional monitoring capabilities
that can complement and reduce the extent
and or frequency of onsite monitoring,
and also help distinguish
between reliable data
and potentially unreliable data.
So this leads to the use
of statistical data monitoring,
trend analysis, key risk
indicators and performance metrics.
And all of these
these are all forms of detection.
So when you are discussing
which controls to implement,
you should be considering
central monitoring as well
And now what we're going to do
is, as we've done the previous webinars
we'll use some examples to illustrate
how to identify appropriate controls,
and we will be using the same mock protocol,.
So it's the same Phase II
Oncology study looking at dose
and efficacy of IMP compared
with standard treatments.
And when we started
our first webinar
when we were identifying
the critical variables
we identified setting
your secondary objective,
which is, you know, safety
our critical variable was toxicity.
identification and management.
And then doing the
second webinar we established
that one of the risks was
if sites are not fully aware
of how to reduce or delay drug doses
in response to toxicity events
due to inadequate
understanding of the protocol,
these could lead to patient
safety being compromised.
And then on our third webinar,
we assessed a risk
and it came with that risk score of 18.
Which is the second highest
possible risk score.
How likely was it
for this risk to occur?
We determined it was quite possible.
20 to 60 % chance of occurring.
We also determined
that the impact was significant
because you would have
an impact on patient safety.
And also that it would
be difficult to detect.
So this gave us a score of 18
and this is the second highest, highest
possible risk score.
And this means we need to put
mitigation in place
for this particular risk.
This risk needs specific management.
Risk is of sufficient significance
to safety of trial subjects
to warrant specific
mitigation and management activities.
So which controls do
we need to implement?
And we here to have
an example of a pre-study
risk mitigation, such as ensure site
staff are trained in dose
delay or reduction process
to understand what is required
for this protocol.
An example of a risk mitigation during
study conducted is,
appropriate source data review
of patient medical notes
conducted by monitors to identify
any dose delays or escalations
should have occurred
in response to patient events
And now we will have a second example
Again, at the time
the primary objective
was linked to efficacy progression,
free survival PFS.
We determined that
one of the critical variables
linked to this primary objective
was a PET-CT scan.
And on the second webinar,
we determined a risk was the following:
If imaging assessments are not completed
or of good enough quality,
then there may be not enough data
to assess primary
and secondary end points.
And on the third webinar
which was a previous webinar,
we also determined
why was the risk score
for this particular risk
related to PET-CT scan.
So we determined that
it was very unlikely, right?
Less than 20% chance of occurring.
The impact was quite significant
because you will have
an impact on data integrity
and reliability of primary
and secondary endpoints analysis
as well as subjects safety related to
And regarding detectability,
we determined it was a moderate detection.
Assessments would be going
to a third party vendor.
So this score actually
has a score of six,
which is low to moderate risk score.
However, we just did
have a mitigation in place
due to the high impact on both data
integrity and patient safety.
So as we have done
for the other control, and for the other risk
this risk needs specific management.
There is the risk of sufficient
significance to reliability
and integrity of trial results
and patient safety to warrant specific
mitigation and management activities.
So again, we need to think
which controls do we need to implement?
And here we have a few examples
of a pre-study risk mitigation.
Such as, ensure site staff
are trained in imaging requirements
and transmission process
to understand what is required
for this protocol.
Another example is ensure sites
have the correct equipment,
and that equipment will
be available to perform
imaging as per protocol,
and that an imaging
manual is available addressing
all accepted equipment.
A third example is
confirm with the vendor being used,
what reporting they have, for example,
will they report
if an image was expected
but not received?
And here we have
a few examples of risk mitigations
I will read this, but
I think the idea is here,
you may have one control,
you may have more than one control.
That would depend on your protocol.
So these are some examples of risk
mitigations that are in the study conduct.
one is related to the checks
to be programed into the EDC
to auto-query missing
Another example would be
remote data review
of vendor protal reports.
Another one is quality
control of first images before
allowing the second subject
to be enrolled.
And our last example here
is implemented key risk indicators
for missing and missed assessments.
And as you can see here,
these risk mitigations
could be assigned
to functions which are not,
or may not be clinical operations.
It could be data management,
it could be central
monitoring and so forth.
OK, now we're going to discuss
what are the best practices.
You have to make sure
the controls you
put in place are informed
by a formal risk assessment process.
You must engage
the cross-functional team.
may be standard or specific to the study.
You should discourage the use of
the one size fits
Has to be customized
to the protocol.
Once your controls are in place
with the study ongoing,
you must review and re-evaluate the risks
and confirm that controls
Also, you should be documenting
and communicating agreed
controls and responsibilities.
You can use your risk
assessments, document functional plans
and also as part of the overarching
Integrated Strategic Monitoring Plan.
If you haven't heard
of this strategy before,
we'll be discussing
the overarching Integrated
Strategic Monitoring Plan
or ISMP on the next webinar,
so make sure you register for it.
So going back to the quality
as we discussed,
only after you have identified
the critical variables
and risks and evaluated this
as well as identified your controls,
then you are ready to move on
to risk communication.
Remember to document
the critical variables and risks
you have identified and evaluated
which along with the justification
and their corresponding controls.
We will be covering
and beyond on our next webinar.
As always, you have to ensure
during the process.
And sponsors and CROs must work
as part of a partnership.
So what we covered today.
We discussed the intent
of quality management.
We reviewed risk controls
as part of the RBQM process,
as well as discussed
common misconceptions and best practices.
As I already mentioned,
we will be covering risk communication
and beyond on our next webinar
on the 26th of August
so please make sure you enrol.
And now I would like
to thank you for watching.
Enjoy the rest of your day.
ICH E6 & ICH E8 - How to make data quality a virtuous circle
Well, good afternoon or good morning, everybody.
Wherever you are joining us from in the world, it's
just after three o'clock
in the UK in the afternoon.
So let's get started.
So welcome to today's webinar,
on ICH E6 and E8 and how to make data
quality a virtuous circle.
Thank you very much for taking the time
to join us today.
My name is Ben Brummitt,
and I'm delighted to be able to co-present
today's webinar with my colleague,
who is the CEO and founder
of TRI, Duncan Hall. Hi Duncan.
Good afternoon, Ben.
Good to be here. Thanks.
Absolutely, we're we're glad
everybody could join us today
and we're sure
you'll find the next forty five
minutes is very, very valuable.
It's certainly a hot topic
around the industry at the minute,
all of the regulators reedited.
So just before we get into it,
we've got a little bit of housekeeping
just before we start.
No need to make notes
or take screenshots.
The slides will be available
at the end of the webinar today.
We are happy to take questions.
So if you're able to put your questions
in the chat box, then we can answer them
at the end of the webinar.
We do also have a
unique offer for anybody
that's attended today with regards
to some supports around
what we're going to be going through.
But we'll come to that
at the end of the webinar today.
So who we? Who are TRI?
Well, we've been running in this industry
for approximately eight years now.
We're a complete solution
provider for Risk-Based
or RBQM for short.
ICH E6 compliance, and Central Monitoring.
So we help CROs
and sponsors implement
a risk-based approach
to running clinical trials
more efficiently, to achieve
better data quality
and to comply with regulatory guidance.
So we do this through
a range of training,
consulting and technology
and all of our solutions are developed
through experience of
customer feedback, and importantly,
with the regulatory authorities.
So we know that every
whether you're just starting
to implement a risk- based approach
or you already further
advance down that journey.
We have solutions to enable you
to achieve your goals.
So that's who we are.
So why are we running today's webinar?
Well, let's start off with a statement,
if you're running a clinical trial
you're going to be doing
that under ICH E6(R2)
otherwise known as GCP.
But at the moment,
the ICH are in the process
of updating E8,
which will become the new E8(R1)
as well as updating E6(R2)
which will soon be E6(R3).
So we're still speaking
with companies that are
dealing with (R2) adoption.
Now from a regulatory view
It's only going to get more complex.
But on the upside,
there is a great opportunity to cater
for both today's guidance
and the upcoming guidance in one go. .
And today's webinar is going to help
you really navigate through
all of this while
trying to understand the relationship
between all of these different sets
of regulatory guidance.
So from a regulatory standpoint, then,
this is our current
understanding of the guidance.
So, currently, we should all be working under
E6(R2) which started
its adoption in 2017.
You can see in the EMA in 2017
and adopted in the FDA in 2018.
E8, which is the overarching
for clinical trials,
is under revision currently,
which has been delayed
by the pandemic, amongst other things.
The latest revision of the work
plan on the website is showing it
to have been done by May 2020.
But we're obviously expecting that to be finalized
towards the end of this year
or into next year.
We then have the third revision
So I’ve pulled these
dates from the ICH
which again can be found online.
any of you to go and have a look at that
if you are interested in
learning a little bit more.
But with all of these updates,
there's certainly some confusion
as to how you can apply
the principles from all of the guidance into
day-to-day working practices.
So in order to help
with understanding the relationship
between all of these sets of guidance,
we want to start off today
by really breaking down some key points
for each of the bits
of guidance, in our opinion.
So, Duncan, I'm going to
hand over to you at this point.
Thank you, Ben.
with E8 and the upcoming revision
one of E8, we are pretty much there.
We've got full draft guidance on that.
So we've actually got
some quite good content
as to what is expected
to be coming up with that revision
with E6, the third revision of E6.
We are very much
just down to sort of early
the working group outputs
the sort of early discussion papers
and the general principles.
So we're not actually into the draft
final guidance yet,
but there's definitely enough
information to go on so far,
I think, to give us a good feel for what
the intent of of that third
revision of GCP is going to be.
And so obviously,
we can only go with what we know
some of the discussions that we've had
around the industry as well.
To summarize some of the key messages
from E8(R1) then.
So let's let's focus on E8 first.
Really, what it boils down to is it's
it's about quality by design.
It's about building quality
into clinical trials to ensure
that we get the absolute highest quality
outcomes from from any given trial.
So starting by just
looking up at the top left here,
the objective is the study, making sure
that the objectives
of the study are clear.
That the endpoints of the study
are clearly defined
and therefore, there's
a very clear relationship
between those endpoints that we’re gathering
during the course of the trial.
And the question that we're
trying to answer with that trial
and I think of technical trials
is as being a scientific experiment.
We start with an endpoint to the question
we try to answer
we’re then gathering
the information to help us
answer that during
the course of the study.
And I think that fits
very well with the quality
by design principles
that if we understand
what we're trying to achieve,
that we understand what's required
in terms of data, and we can see a clear
relationship between the two,
we're going to get a much better outcome.
The second point
then is around what we already know,
and I think this is
something that is poorly served
in the current GCP guidance
and definitely something
that the new E8
update really, really tries to stress
is that we shouldn't
be running studies in isolation.
We should really be thinking about what
what we've run previously.
And, you know, what
what were the critical success
factors for those studies?
Do we know what sort of risks
occurred during those previous studies
which were identified
which actually were triggered?
Do we know what controls
that we put in place,
were those controls effective,
which controls what we put in place
that would just never have any value?
Where is the data going to come from?
How are we going to track
that quality management process?
And how do we access
that data, that historic data?
We're generating a huge amount of data,
not just the clinical data now,
but we're also generating
quality management data.
that was really just
sort of high level monitoring data.
What we were trying to do was to prove
that we were monitoring
and overseeing the study.
Now we're looking much more
at the quality management process
and what are we doing to manage quality
during the course of the clinical study.
But a lot of that information
gets lost at the end of the study.
So E8 is pushing us
to really start to think
about that information and how do we
how do we capture that information
in a format that allows us to look at it,
build up some history and
factor that in as we as
we move to new study designs.
We also want to be thinking about
what we know about the drug
and making sure that we understand,
you know, what
information is out there
in the public domain about the drug,
what information is out there
that we know within, you know,
within the sponsor organizations
that have run earlier phase
studies in this particular area.
And how can that be factored
into the protection of the patients
in the clinical trial?
So there are lots of public
data sources that we should be
we should be factoring in as well.
And then, but
not only are we talking about the data
that we know at the start of the study,
but we're also, there's also emerging data
apparent and available as
we go through the study.
And that's where as we start
to get on in this presentation,
you'll see that we're trying
to get much more cyclical
about our approach and our use of data,
because as this data emerges
during the course of a clinical trial,
we should be looking at that
and making ongoing considerations
as to whether we need to be adapting
the trial design
in the way that we're conducting
that clinical trial based
on that emergent data,
as well as the data that's available
internally to any organization,
as well as publicly before
we start the study.
Patient centricity is also very much
a commonly heard term at the moment.
It was especially prevalent
But patient centricity
is not just about using the patients
as a source of data
and sort of using the patients
as a data gathering mechanism
rather than relying more on
And, you know, I think there was a
that was a real fast move
towards the use of
of wearables and patient diaries
and other mechanisms to
to sort of collect data to enable
that data collection to continue,
but without obviously the need
for quite so much human to human contact.
But patient centricity
is more than that.
It's not just about
using them as a mechanism.
It's about putting them front and center
in trial design.
It's really about understanding
their views on areas
such as the treatment schedules,
the assessment procedures,
informed consent forms,
all those sorts of things.
Getting that early consideration
from patients during
the study design is going to give us
a much better feel for
whether or not that we're going
to get successful engagement
with those patients
during the clinical trial.
And overall, improved
engagement is going to improve
our recruitment ability
in the first place.
It's definitely going to improve
our patient retention
during the course of the study.
And, of course, the overall
patient experience. And again,
I think part of patient centricity
is much more about
thinking of patients as human beings
rather than subjects.
And we, I certainly in
when I'm talking about this stuff,
I've tried to move away
from the term subject.
I think that's a, it's a
it makes it very cold
and very scientific.
I think we are trying much more
to think about patients as human beings
and thinking about their experience
and what's it going to be .
What's it like already with whatever the
you know, whatever the issue is, whatever
the disease or whatever
the problem is that patient
is suffering from?
What does that
what's that going to be like?
What's the experience going to be like?
And then what's
what's putting them
through a clinical trial
going to really be like
for that person on top of whatever it is
that they're already
already suffering from.
So that's really,
again, is made very clear in E8
is something we should really be
taking a lot more into account
during the study design process.
Also, thinking about the
the current standard of care.
So, again, obviously, depending on
what therapy area is and
the disease that we're thinking about
or the condition
that we're thinking about.
There's almost certainly
going to be some sort of current
standard of care
and starting to think about
how far is this study
that we're about to conduct
and therefore the process
that that patient is going to go through,
how far different is that
from the current standard of care?
Obviously, the further that
that standard, the bigger the gap,
I guess, between
the current standard of care
and the trial that we're designing,
the risk, potentially the bigger the
the impact on the patient.
And so, again, taking
that into consideration
is really important. Now obviously,
if we're if we're trying
to break new ground with
with clinical research,
and we're trying new novel
approaches, there is going to be a void.
But we need to be very cognizant
of what that void is and how do we
how do we manage the risks
around that void?
And, of course,
how do we make the process
of being part of that clinical
trial is as comfortable
as we possibly can for the
for the patients.
So when we look at all
those things together in
E6(R2) the big new phrase
that was really introduced in
E6(R2) was quality management,
that whole new quality management
section five that appeared
I really think the buzz word
is quality by design.
It's a phrase that's used over
and over again in E8(R1).
Or certainly the draft guidance,
as we've currently
we currently have access to.
And all of those pieces
that I've just talked
about are all forward in that
that banner of quality by design.
We've covered them to some of the high
they're with E8(R1).
Let’s now start to think about
E6(R3), which is
which is really some of the more
that's been made available.
As I said earlier, the details on
GCP Revision 3 are much lighter.
They really are just
a set of guiding
principles at this stage.
And, but there's been
a lot of documentation
on the reasons for the need
for a revision to GCP.
And when you think about
how long it took for the last revision
so that the elapsed time between
E6(R2) was nearly 20 years.
But the time that
that's looking like
it's going to be between
(R2) and (R3)
is going to be closer to five years.
So I think that's a
really good indication
of the rate of change
in clinical research within the industry.
The fact that we're having
another revision of GCP
relatively, I say so soon after (R2)
It has been five years,
but, you know, as Ben said
right at the beginning,
for a lot of companies,
they're still in the process
of implementing GCP (R2).
And so, of course,
(R2) and E8(R1) are going to create
further change burden
on those organizations,
which is really what we're what
we're trying to highlight today.
I think a lot of the change
that we've seen in the industry was
was already in motion
a couple of years ago, but definitely
our experience has been
that the COVID 19 pandemic
and the adaptations
we've had to make
to clinical research during that time
has really accelerated
the rate of change.
And again, that could be partly
the reason that we're trying to
to move to this revision
sooner rather than later.
So I'm talking about
sort of four principles
now that are very prevalent
in the documentation
that's out there on
And the first principle, again, talks
directly about quality by design.
And it's a very clear and tangible link
between E6 and E8.
And it's really trying
to make it clear that quality by design
was always part of the intent of GCP.
And I think in E6(R2)
many of the changes
that came in in R2
were really just clarifications of things
that were assumed to be clear in (R1)
but were in many cases either
misinterpreted or just ignored.
And what it's really saying
is that as both technology evolves,
as well as trial designs
evolve, that quality by design
is as relevant as ever.
So it really is just a restating of that
need for quality by design.
The principles do
make specific references
to the impact of the pandemic
and obviously the challenges around
human to human contact
during that period.
But also it does
make it very clear reference
to the fact that (R2) really didn't
cover the scope of some of the advances
in the emerging practices
that are now prevalent today,
and certainly in terms
technologies that have become available
in the last few years.
Now, a new phrase was always interesting
when you sort of read
something that stands out
a little bit in regulatory guidance,
but a new phrase that
has been introduced in that update
that we're talking about
Now thoughtful is a
very broad based term.
But again, I think really
what it's harking back to
is both those quality
by design principles
that we've talked about and
and that patient centricity
that I've just talked about.
You know, it's
not just about collecting data
now, we're really being asked to consider
how we collect that data
and what that means to the stakeholders
for that particular trial.
And when I talk about stakeholders,
of course, first and foremost,
it is the patients.
They are the ones
that are absolutely vital
for the trial.
But also, you know,
that are involved in the study
conduct, in the assessments
of those patients,
the people, but also the people
that are analyzing the data and
and monitoring that data.
And I think
as we build
more and more studies
in our RBQM platform,
what we're seeing is
that we're getting more and more involved
earlier in the study design,
because we're starting to think about
not just “is the data
that we're collecting
during the course of this study
going to help us answer the question?”
as I talked about previously. You know,
is it the right data to be collecting
and is it going to actually answer
the exam question
or the scientific content of that study?
But we're also starts to think about
how we're going to analyze this data.
What is good data look like?
What does an inlier look like?
What does an outlier look like?
How are we going to segment this data?
How are we going to slice and dice it
to answer some of the ongoing questions
that we're going to be
posing of that data?
And as I said earlier,
talking about that, that emergent data
that we should be using
as we go through a study to actually
to actually determine
the path. Is the path
that we've set through
this trial the right path,
or is that emerging data
telling us something else?
Now, if you start to think
in that way,
that sort of frame of mind.
If you can think about that
at the start of the study is
we're starting to set things up
and think about, you know,
our CRF designs and the sorts of data
that we're going to be collecting
and start to think about
the analysis of that data
as the study goes on,
we can really give ourselves a big
a big head start
in actually getting the data right
so that we can perform analysis
and that we can get early indications
as to where the study's going well,
but also where the risks are
and is our quality management
You know, sometimes just catching
one more piece of demographic data
may allow us to completely segment
our data in a much more meaningful way
when we start to think about
that ongoing process of risk
based quality management.
So that's what we mean by that
and thoughtful study design.
It’s not just about the patients,
which is really important,
but is about what we’re going to do
with the data as well.
The third principle we can see here
is where we see this new phrase,
it was the heading for the webinar today,
It is starting to be spelt out
in the new guidance documentation.
And it's actually
one of the first areas that’s covered,
it really is right up front and central.
And again, I think it sets
a very clear expectation
that quality by design, and in fact,
this is actually a diagram in the in
some of the discussion papers
that we've seen, some of the slide decks
that have been produced by the group
that are analyzing the feedback on
on (R2) at the moment
is that there is a very clear expectation
that quality by design
will flow into quality management.
But that's also a backward cycle as well.
So there is there is a true cycle
between these two.
And it's not just an end-to-end process.
We see in that same section
the use of the term proportionality.
Now, again, this is not a new concept.
The concepts of proportionality was covered
in the first revision,
sorry, in the second revision (R2),
but again, I think
that it is going to be further stated.
I think it's one of those things
where the intent
was there in (R2).
It probably wasn't made clear enough
and that we are expected
to be proportionate
in the way that we manage
our clinical research.
And that really is the basis of risk
based quality management.
And I've said this on
just about every webinar
that I've been part of
in the last four
or five years, that the risk-based
isn't about taking risks.
It's about the identification of areas
of likely risk and managing
those areas in a manner
that is proportionate to the risk level,
or the perceived risk level.
The higher the risk
level or perceived risk level,
the greater our focus
should be on that area
and the more controls
we should be putting in place.
And of course, conversely,
the quid pro quo here is that for areas
where we see very low risk,
we don't need to be plowing valuable
R&D resources into
monitoring those areas.
They don't want to see lots of effort
being spent on areas
of very low risk
and areas that are not closely related
to critical to quality factors.
And then finally,
we start to see some references
to technology as well.
And of course, as we know, more
and more data is being collected
in clinical research from more and more
And the use of technology
is absolutely essential
for the collection
and analysis and decision
making in evidencing of that risk
based quality management approach.
And of course, now as we
start to think about this concept
of a quality continuum,
there's an even greater burden to show
that you are following a process
and that that process is cyclical,
that you are using information
and learnings from from
from the study itself
and, of course, from previous
studies and public information
as part of that process.
And of course, that presents
both a challenge and an opportunity.
We need technology to be
to make information available in
in a format that's usable.
But we also need to demonstrate
that we're following
that quality continuum.
And our technology
should be a key part
in demonstrating that we are
that we have a clear and coherent story
about that quality by design
and quality management process
that we followed.
Now, we also see for the first time
a real reference to information security
now as a software company,
is always on our minds .
It's something that we take
incredibly seriously for
for obvious reasons.
But it's really the first time
I've seen much of a reference to it in
in regulatory guidance.
Again, I think that just shows
how the regulatory authorities
are sort of catching up
with the importance of
of not only just technology,
but the security around technology.
And I'm sure that's, you know,
this is just to say
a principle at this stage.
I'm sure that (R3) is going
to go into a lot more specificity
around the processes
of data transfer, collection, processing
and the security levels
that needs to sit around that.
And I do wonder and I certainly do hope
that we may finally see the final nail
in the coffin for the non-validated
unsecured desktop solutions
like Word and Excel
through this process.
And certainly let me let me ask you
a question to the audience.
How would you feel if your bank
was managing all your personal
finance data in Excel
and emailing copies
of those spreadsheets back and forth
between different branches?
I'm sure you you wouldn't
be too excited about that,
but you'd be amazed the amount of
of important clinical data
that still gets managed
in spreadsheets and word documents
and emailed around
in a relatively unsecure manner.
So I really do hope that
that (R3) will
put more pressure
on companies actually using properly
validated and secured systems
for clinical research.
With all that said,
hopefully that's giving you
a bit of a feel for some of the
the sort of key principles
of E8(R1) and E6(R3).
Ben, I'm going to hand back to you
and perhaps you could just tell us
a little bit more
about what you're seeing,
some of the advances that we have seen
in quality management
and a bit more about what
sort of today's world looks like.
Thank you, Duncan
Certainly from what
we're seeing at the
minute out there,
and certainly some of the conversations
I'm having with with
customers is that from
from (R2) it's very clear
if we look visually
at how quality management section
5. of (R2) is laid out,
it's laid out in this linear
And, you know, from
what we're seeing is that quite
a lot of companies are treating quality as a
as a linear process, as being presented.
And you can understand why that is,
because it's in the guidance.
And you can see that.
And people are asking
people are asking themselves, you know,
what is the path that is running through
all of the guidance and more importantly,
what is the most efficient
pathway through it?
You know, maybe the reason why
people haven't got this down pat just yet
is that there are a
lot of challenges along the way.
You know, on a path you often divert
because of different opportunities
or different pitfalls that you encounter.
You could equate it to being like
a game of snakes and ladders.
Now, snakes and ladders,
I'm pretty sure everybody here,
everybody remembers, and it's certainly
certainly a game
we play in our household.
I'm sure everybody knows
the concept of it.
You start off at the bottom.
If you jump on
a ladder you advance more quickly.
If you jump on a snake, you go back,
you find yourself further back.
But we can we can really relate this idea
to this linear idea,
to our clinical trials.
So I'm going to go through
some of the things that we're seeing.
That are potentially the ladders
and potentially are going to be snakes.
So if we start with a prerequisite
for jumping on the board,
the fundamental key prerequisite,
excuse me, has to be leadership buy-in.
Now, you can't even start the game
without leadership buy-in if you know
that you're going to be fighting
an uphill battle.
You've really got to get that
get that buy-in to start with.
But that’s something
we're going to speak
about a little bit later on
in the webinar as we go.
So moving on to some of the ladders then.
Sfome of them include
defining critical variables,
defining your risk controls,
relating risks to variables,
defining risk review, your risk review schedule.
And as Duncan said before,
minimizing exploratory variables.
And certainly review and update
risks and controls.
That's a big one.
So all of these are laid out in our tool
which gives you a really good advantage
you along the game, so to speak.
However, there are obviously some snakes
in there as well.
So some of these snakes
and we see the often
the pitfalls we often see
risk controls not being proportionate
to risk levels, ignoring historic data
improper use of quality
a lack of cross-functional engagement,
certain parties working in isolation
and so on and so forth.
And a lack of standardized
scoring can be a big one as well.
And as we said before,
the lack of leadership engagement again
comes in. At the end.
You can do all of this great work,
but if you haven't got that leadership
engagement, you're right. at the end
you're going to end up right
back, right back to the start.
So those are some of
the snakes that we see,
you know, from that linear model. though.
Are we missing something?
So we can understand
why people have adopted
this more linear attitude.
However, you know what
that concept really misses
is a key point.
And as Duncan stressed, looking back
really has to be a key factor
in improvement for the future.
So instead of that traditional games
of snakes and ladders
quality, really is as Duncan said
is far more of a cyclical process.
So what we need is a better visual
to showcase the nature,
the cyclical nature of quality.
Which obviously leads us on
to our interpretation of what
the quality continuum looks like.
So Duncan, back over to you.
Thanks a lot, Ben.
So I’ve tried to be pretty ambitious here
and obviously helping.
I'm very much a visual person
and a visual learner myself.
I like infographics.
I like visual guidance.
And so what we try to do here,
really for the benefit
of everyone on the webinar,
and I will say that we will provide
this infographics to anybody,
the ones who wants it after the webinar
absolutely free of charge.
Ben and my colleague
Jo will be reaching out to you
after the webinar to offer
you copies of the slides
and this infographics specifically.
So, again, please
don't worry about taking screenshots
or making notes.
What I tried to do here
is just to kind of tie
these things together,
tie the principles from E8(R1)
and E6(R3) and (R2) together
to show what I think
what my interpretation
or our interpretation as a company of what
that quality continuum could look like.
And I can certainly guarantee
you'll get nothing like this
in the regulatory guidance.
You rarey get anything
as specific as this.
So it's really just to help
give a feel for what
that could look like.
And I'm trying to sort of
be a little bit predictive,
look into the future a little bit here
and give everyone a model
that they can start to think
about as they start to cater
for their sort of change management
plans over the next few years
as we really start to gear up for
in clinical clinical trials
so I think at the core of the process,
we've got this sort
of four step process going from design
through to final reporting.
And you can see again,
in each of these steps, quality is the
is the key word.
The quality of the design piece
then we've talked about quite a bit.
And this is really about the protocol.
It's about getting
the protocol fit for purpose
that we're building quality in
right from the get go,
that we've got, you know,
We understand what the endpoints are.
And, but again, that we're
we're building this
this previous experience,
industry knowledge into the protocol.
And that's where really
the first part of our continuum comes in,
because as we're conducting
more and more studies,
we should be collecting
data along the way.
And you can see I'll
come to this in a minute,
but you can see that
I've got data falling
out of the quality management process
into our knowledge base.
That is something
that we should be looking at
as we go through this
this protocol design process.
We shouldn't just be thinking about these
the overall objectives
of the study, but what else do we know?
You know, what risks
have we seen in these areas before?
How have we controlled those risks,
which those triggers, which are
those risks were triggered
what QTLs were used
and which of those were breached
and all of these areas.
And if we can start to think about those
if we can start to think
about these quality management assets
and build them into our design,
we will start to really drive
a process of continual improvement.
And that gets us away
from Ben's kind of snakes
and ladders model, which wesee
being so prevalent
in the industry right now
into more of this cyclical
process of continuing
improvements or a quality continuum.
But once we've
optimized our actual design itself,
we then move into the planning stage
here, we're really starting to think
about the protocol risk factors.
And again, this is areas around,
you know, we're seeing with
we're trying to encourage companies
to come up with sort of
protocol ranking models
where they can ask
sort of challenging
questions of the protocol
to get an overall sort of risk score
for the protocol
that really starts to think about,
you know, areas like
how close is this protocol design
to the current standard of care?
is this cutting edge or is it know,
is it really just incremental gain?
And what do we know?
What do we know about the patients?
What do we know about the
Really thinking about those critical
to quality factors.
Now, that, again, is a big component
E8(R1) this concept
of critical to quality.
So what are the things that are going to,
you know, are going to diminish our ability
to answer the exam question
or to compromise data quality
or to compromise patient security?
Those are the things
that we're talking about when we mean
critical to quality factors.
And what are the risks to those?
So if we identify what those factors are,
what are the risks
and what are those risks
going to look like?
And again, we talked about one
of the one of these snakes
that Ben talked about in his slides
Just now was about not using standardized
scoring mechanisms. OK,
if we don't use standardized
scoring mechanisms across
all of our studies,
how can we possibly get any idea
of whether any one risk or any one
protocol is high,
medium , low or indifferent?
If we don't have that view,
how can we possibly be expected
to create proportionate controls
if we just got if
every time we run a study,
we're coming up with
a new scoring mechanism
or it's a different team doing it,
and they've got a
of a scoring mechanism.
So we would need to think
about that proportionality.
And then, of course, thinking about,
OK, now that we understand
what's critical, what the risks are,
how are we going to control those,
how we're going to approach them,
who's going to monitor those controls?
How often are they going to monitor them?
To what level?
Where are we going to use
Where are we going to use
Where are we going to use
onsite, onsite monitoring?
Where does source
data review come into this,
where does source data
verification come into this?
Hopefully not very much,
but that's what we're talking about here.
You know, talking about our quality
continuum, talking about this process
of continual improvement,
we should have access as we start
to put our quality
management plans together.
We should have access to previous
relevant risk assessments.
So what are the studies
that we run in this area
or that were similar to this in design?
You know, what were the quality plans
that we put together with those plans,
effective or not?
Did we catch issues?
Did our controls work?
What risks did become issues?
What QTLs were breached? Again,
what were the common risks?
That information is all information
that should be fed into that process.
So we're not just looking
at the protocol at this point.
We're looking at the protocol
pulling further information into that.
Now, as we start to manage quality,
the quality management cycle itself
is a cyclical process.
So we are looking at our quality plan,
which we've created here,
where that plan will inform us as to what
we need to be looking at,
what data we're looking at,
who needs to be looking at
that data and when, what controls
we should be looking at.
And those controls
could be all sorts of things
from a simple phone
call to an investigator or to a patient
or whatever, or something mechanical,
or it could be something like
a key risk indicator or a key
performance indicator, or a QTL,
some sort of more analytical model
or a stat's monitoring approach,
which is actually
looking at data
and telling us where we think
there might be concerns.
Once we've looked at that data, that's
going to inform our monitoring approach.
So, again, this piece back here,
the monitoring approach
and monitoring levels,
once we've looked at our controls
and that will tell us
what's our approach
going to be at this point in time.
This is a great example
of that data emergence
coming out and informing the study.
We're not just
sleepwalking our way
through a routine monitoring plan.
We're using the data,
we're using the review
of those controls to inform us
what type of monitoring
we should be doing
and where we should be monitoring
and how we should be
focusing our attention
in that proportionate manner.
Once we've gone through that process
and we've got the feedback
from that process,
we can then update our quality management
plan if we need to.
might be just updating
the fact that we've done that review
and that we've observed these things.
Or it might be that we're saying, hey,
we've identified a bunch of new risks
that we hadn't really thought of.
Or this control
doesn't seem to be working,
we need to adjust it
or whatever that might be.
So that the next time
we come to our review,
we're already learning.
We're already building that knowledge
into the next review cycle.
So we get these virtuous circles
starting to build up.
Now, all the way through this process,
not only are we feeding
back into this particular
study that we're working on,
but data is starting to drop out
of the process into our knowledge base.
When I talk about the knowledge base,
that can be one of many things. You know,
from our perspective with our customers,
it is the OPRA 5 technology platform.
That's where we're storing
all this information, its where
we're performing this study conduct.
But it's also where we're collecting
a lot of information
that can then be analyzed
during these stages of the process.
So we're starting to look at the risk
statements, the scores, the activities.
You know, what are we doing?
As you know, if we trigger an activity
during this cycle here,
was that activity effective?
Did it result in the outputs,
or the piece of information
that we expected?
What were our
SDV and SDR levels going into the study
and how did those change
during the course of study?
That's all incredibly
to help us determine how we proceed.
And then as we move down to the
to the quality reporting side of things,
of course, this is now
where we need to report on all of this.
We need to show evidence of having
taken quality by design seriously up
front, of having fed all this information
into the process
of having put together
a solid quality management plan
and having followed that plan
and adjusted that plan,
there is absolutely no expectation
whatsoever in any regulatory guidance
that we create the perfect plan
and we execute that plan
to the letter perfectly.
is that we are constantly
challenging that plan and we document
All that information needs to be
pulled out into our quality reports.
And all of that information
is absolutely vital
for the Final CSR.
That final study report.
And if we do all those things,
if we follow this process,
we will absolutely end up
with a high quality output
and a high quality clinical trial.
All that information
then will be stored
in our knowledge place,
all that historic information,
and every time we start
a clinical trial,
we should be mining that information
and building those back in.
So that is what we think.
That's my interpretation at this stage
of what that quality continuum
should look like.
Now, none of this is stuff
that we can't do today.
This is exactly what we are doing
with our customers.
This is the, you know,
what the OPRA 5 platform
is all about.
But that is our quality continuum
and hopefully this infographic
will be really helpful
as you start to plan your clinical trials
and think about what
this looks like for you.
Just to show sort
of a bit of coverage then, so ICH E8
really is all about this up front.
It's all about that
the generals are considerations
and there's a lot of focus on
on quality by design
and the planning of a quality
The E6 is much more
about the actual conduct
of clinical research.
There's definitely some overlaps
I also think there are some gaps.
I don't think that E6(R3)
or E6(R2) certainly
really talked about
how do we get data out of our systems,
how do we feed that
back into that knowledge base?
And I do think that a big gap,
and I hope that with the
ER(R3) revisions, as they start
to become better defined,
that we will see
more of that information being pulled out
and more of a requirement for companies
to really be following this true
OK, well, I'm going to hand
back over to Ben now and
pick back up from from there.
Thank you Duncan.
I think the infographic is
will be really useful
and yeah, very happy
to send that out afterwards.
And I think, you know,
what are we doing to help
I think is the question?
what are we doing as a business?
So we've been you know,
we've been saying this for years, but,
you know, change management
is the greatest challenge
for RBQM being accepted.
You know, it's recently
highlighted in the ICH presentation.
It is the biggest challenge to adoption.
So what does that really mean?
You know, what does change
management really mean?
We break it down into three parts.
So we go people,
process and technology.
So on the people side then,
So we found that
really a sensible
route for change management,
which I know it's really tempting
to jump straight into technology,
but it's not the ideal approach.
If you throw technology at the issue
without the proper backup,
then things only get
a little more tricky.
What we found is
education is the first step.
So we offer a range
of training options to build knowledge
and understanding of ICH E6
and risk-based quality management
through free webinars like this one.
We also run workshops
and instructor led training.
your people really is essential.
So following on from that,
if we move on to the process part.
Embedding the concepts of a quality
continuum really relies on
you having robust processes
and also holding people accountable
to those processes.
So having supported dozens of companies
through this change management,
we've actually developed
a sense of standardized,
RBQM SOPs that we normally use
in conjunction with our gap analysis.
So what that allows companies to do
is to really be compliant with both E6
and then to prepare for the upcoming E8(R1).
So we're also working with companies
to put a number of change
management processes in place,
and we've developed a set of specific
programs which allow companies
to adopt RBQM.
So once a company has laid
the foundation of education
and compliant processes,
that's a good place
to introduce technology.
So, as Duncan's mentioned,
we've developed our OPRA technology
to enable that full
end-to-end continuum for any clinical trial.
We just released our version 5,
which is really exciting.
We've had some fantastic
But just to break it down, OPRA
is comprised of two modules.
So we have OPRA RAM
which is our risk assessment
and management piece.
And then OPRA CM
which is our central monitoring piece,
and it provides that single environment
that seems to collaborate a shared view
and obviously enables
that company to have a full end-to-end
So our business is all about
helping people through this process.
You know, we know that
it can be a very big investment.
And so we come up with a couple of offers
to make things as easy
as possible for you.
So for anybody that's
been on the webinar today
that's attended today, those of you
that are happy to
to contract for this before September.
We will give you these packages.
So we have people kickstarter pack.
So there's our e-learning.
So we've got risk assessment.
And also GCP at a 25 percent discount
along with our SOP and job descriptions
pack for free.
And then we have a technology
kick starter pack as well.
So that is your first risk assessment
study license fee for free
on OPRA RAM for 12 months.
And also, the SOP
and job descriptions pack
for free as well. So that
as I say applies
before the end of September.
So that is a unique offer for everybody
who is attended today.
So, as we said at the start,
we're happy to field questions
from the audience.
I'm just looking here, I can see three
that have come through here.
So if I start with the first one.
So why do you think change
management is the biggest issue?
Duncan, do you want to take that one?
I guess it's a big, big question.
Probably not why I'm not going to answer
in the next couple of minutes.
But I think the thing
with change management
here is, and certainly with RBQM
as you just said
in the last couple of slides,
it really is, it impacts
all areas of the business.
It impacts a lot of roles
within the business.
It does drive process
change and process improvements.
It certainly mandates the need for
for new and improved technology.
And I think for a lot of companies
who have been used to more iterative
change over time.
So, for example,
if you take the move from paper
based trials to electronic data capture,
you know that was a kind of automation
of an existing process.
So it wasn't that we weren't
capturing data previously
and now we're starting
it was that we were
the processes were already in place.
We were just implementing new technology.
But with RBQM, in many cases,
it is a wholesale change.
And I think for a lot of companies,
that's just a lot to take on.
And knowing where to start
can be really challenging.
And, you know, again, hence
some of the offers
that we've just made and,
you know, and our desire
to help people with that
change management piece.
Fantastic. Second question.
So quality management is perceived
as being a dry
and boring, boring topic.
Have you got any tips or recommendations
for getting engagement from people,
especially senior management?
Yeah, I think
so quality management.
Is it a dry and boring piece?
I mean, I think at the
end of the day, it's absolutely
the heart of everything we do
in this industry.
I think, I think you know
often people's experience
and what have you is
it's a lot of reading, a lot
a lot to deal with.
I think in terms of engagement,
to me, it's looking at the upside.
You know, whenever you're
trying to engage
someone in change management
and getting someone to change
their views on something
or to change
working practices, it's
about looking at the upside.
And I think, I think
the upside in
RBQM and this quality
continuum approach is
is it's in a number of areas.
of course, it's going to benefit
the patients themselves.
If if we're focusing on the patient,
we're being patient centric,
we're being considerate.
It's going to have
a very positive influence
on the patients.
You know, if we are thinking
about data quality
right from the beginning,
we're building it into the
to the protocol design .
And we're constantly thinking
about managing quality.
You know, we are going to get
a much more accurate
outcome from the study.
You know, we will know
whether that study is being successful
or not much sooner.
And if it is being successful,
we will have much
better evidence to prove
that it's is being successful
and hopefully get,
you know, get regulatory approval.
Much more, much more easily.
And that has huge
both for the for the sufferers
of whatever that that's,
you know, disease or therapy area
of course, for the company running
the clinical trial themselves.
And thirdly, efficiency.
You know, whilst
this seems like a lot whilst
it seems like there's
a lot to take on here,
what this is all about
is focusing our resources
on what matters.
And by focusing our resources
on what matters, it means that we are
saving huge amounts of human resource
and time and money,
which is currently , in many cases,
being wasted on stuff
that just doesn't matter.
And so there's no reason
why any of this should be considered
a cost increase in clinical research.
It absolutely should,
we've got plenty of examples
where it does have a huge positive
increase on both the timeliness
and the cost of a clinical trial.
And my advice to anyone trying to engage
their organization is
focus on those things
and then suddenly this stuff
seems a bit less boring
because it is enabling huge improvements.
I'm just looking at
it, we've got quite a few questions.
If you're just to say, if we don't
get around to answering today,
we will come back to you
in due course with the answers.
So we will answer all the questions
that are coming through.
I'm just looking at the time.
We'll go for one more
if that's OK.
So what's the biggest blocker
to making the virtual circle work?
I think factoring in the concept
of the virtuous circle
right from the beginning,
so that you're thinking about
how am I going to pull
data out of this process
and how am I going to store
it and learn from it is really important.
And being prepared
to do that thinking when,
you know, certainly
at the beginning of a study,
it's pretty hectic.
You've got you've got lots going on
and you've got recruitment going
on, you've got sides initiation going on,
and everyone's excited
and fired up and wants to get on with it.
But taking a bit of a step back
and thinking about that is important.
And then I think the other thing
is having the right technology, you know,
when when a lot of companies
are still doing quality management in
in BI tools and spreadsheets
and what have you,
which are very much
about the here and now,
it's very difficult
to go back and look at historic data
and actually get any
real value from that.
And so I think, again,
we're looking for real change
in mindset around
around the technologies
that are used that actually
not only allow us to conduct that quality
management cycle, but also to
to provide that historical story
and evidence and be able to go back
and look at that data
in a sensible manner,
you know, at an enterprise level,
and make sure that we're really factoring
those learnings in
as we as we start new studies.
And for a lot of companies, that's
that's just technology
they're not using yet.
Like I say, that's
that's all we've got time
for in terms of questions,
but we will come back to any questions
that we haven't answered today.
I can see there is quite a few
that have come through,
which is which is great,
but just to round off then
just at the end of the
at the end of the hour,
what we're going to do
following on from from this webinar
today, the copies of the slides
and the infographics will be available
which myself and my colleague Jo
will be following up
with everybody that's attended today.
We'll obviously follow up
with regards to the offer as well.
And if there's any interest there,
then it would be great
to have some engagements
If you would like any RBQM
historic RBQM guidance,
then I highly recommend that you go in to
look at our website,
which you can see on the screen
If anybody is interested
in a personal demo of our OPRA
then that's something we're very happy
to provide as well.
And any links to any additional
webinars will be coming up
from here as well.
it's been great to
speak to all of you.
Thank you Duncan for your time today as well.
Thank you, Ben.
And thanks to everyone for giving up
their valuable time.
And I really hope
people have got good value.
It's always great when we see everyone
staying to the end of a webinar.
We still got most of the people
that have joined today right on now.
So thanks, everyone.
And I wish you a great end to the week
and look forward to engaging
with you further.
Thank you, everybody.
Big Industry Challenges that OPRA 5 Addresses
There's a big industry drive
certainly at the minute around quality.
Now RBQM, risk based quality
management is a foundation
for quality in clinical trials.
And to quote from E8(R1)
quality is a primary
consideration for the design, planning,
conduct and analysis of clinical studies.
And to summarize
some of the key messages around E8(R1)
its about building quality
into a trial design.
And that's the best way
of ensuring quality outcomes.
We need to kind of break away
from the standardized checks.
What is important
today may not be as important
as it was yesterday
or on a monthly basis.
And we need to take time
to really identify
the critical factors in our trials.
So what we're seeing is that
sponsors are driving
more of a move to a balanced,
more balanced sort of central
sites, remote monitoring mix,
rather than just
a general monitoring plan.
So how does this
how does this relate to OPRA?
So OPRA covers Section
five, the quality management
section of ICH E6(R2).
So right away from risk assessment,
you know, in order to use risk assessment
to inform the well designed,
clearly articulated protocol,
to use the protocol risk assessment,
to create an informed
that monitoring functional plan.
That's where OPRA comes in, really.
OPRA follows that step-by-step basis,
specified in R2.
And it provides a single
environment for teams to collaborate,
to have a shared view and to manage
trial quality efficiently.
So it creates an ongoing record
of all quality management
activities, decisions, which is really
what the regulatory authorities
want to see as well.
Duncan Hall - Why OPRA 5 for RBQM
Good afternoon. My name is Duncan Hall. I'm the CEO and founder of tear II. And I'm just recording this short video clip in order to talk about opera five. Opera five is the latest release of our RB qm platform that has just gone live in the last month. Now, we spent more than 12 months developing opera five. And when you spend that amount of time and you invest that amount of money and resource into developing a software platform, you got to ask yourself the question why? And this video is all about answering that question. So why did we develop opera five? Well, fundamentally, it boils down to three reasons. And those three reasons have come from extensive conversations with our existing customers, people that were using the previous version of opera, our prospects, companies that were engaged in sales cycles with already or in earlier sales discussions with who often give us great feedback as to what they're looking for, from a software platform for our b QN. And, of course, the regulatory authorities as well, the ultimate customer for Rb QM. And as you all know, there are upcoming regulatory changes with IC h, e eight, with the first revision of that coming out this year, an IC h g six revision three, which is expected sometime next year. So we've been watching very closely what's going on there in the process of updating that guidance, and how that's going to impact the product and the need for technology support for risk based quality management going forward. So I talked about those three things, what are the three drivers for that have really caused us to want to spend the time and effort developing our okpara five platform? Well, the first thing is quality by design. Now, quality by design is becoming more and more prevalent in clinical research. It is talked about extensively in that revision eight, the first revision EHR one, and the concept of building quality into a clinical trial, and then managing that quality throughout the lifecycle of a clinical trial, which is really the foundation of risk based quality management. But it's becoming more and more prevalent. And what we're starting to see in some of the early guidance and drafts and webinars that have been released recently about e6 r three is this concept of a quality continuum, which again really means that end to end quality management right from the initial protocol design, all the way through to final study reporting. opera is a platform that enables that initial quality by design, the support of the analysis of a protocol, right through to the risk assessment, risk management, central monitoring, adaptive monitoring, changing the operational management of a clinical trial based on the quality and the risks, the quality all the way through that interim process. So that was the first driver that that quality continuum and support for quality by design. The second piece is around data and data standardisation. Now, as you know, when you're looking at risk based quality management, you're looking at data on long game basis centrally and you're using that data to make decisions about how you're going to monitor the study going forward. you're consuming a huge amount of data and that can take some setting up and management. Nobody wants to be spending time and money on on study setups that he builds they want to be getting on with execution. And so what we have created is a platform that allows standardisation, where standardisation is appropriate. And we know that all clinical trials are different. And you cannot standardise a clinical trial. You cannot standardise the data you look at a clinical trial. You cannot standardise the data visualisations but there are elements within that which you can standardise where that's possible we have but we also and the real challenge from a technical perspective is how do you standardise where appropriate, but create a platform that allows configuration and customization where needed as well, so that you can factor in those nuances of that particular study. The study design the way that you're going to execute that study the assessments, what data is being collected, the patient visit schedules, all those components that are specific to any given study. So we've created a platform that truly allows us to standardise where possible that to be bespoke and flexible where we need to be.
And then finally, one of our mantra all the way all the way through the opera lifecycle going back five years now when the product first was first released, is that it's got to be operational. It has always been our view that opera is an operational platform. It's going to be used by operational users to make operational decisions and we've Really continued that through. So with all of that, that quality by design, that that that that continuum of quality. Our mantra has always been, how do we make the information that's being gathered and assessed and the outputs in that process of operationally valuable? How do we really drive operational decisions based on that data? How do the operators of the system know what they're looking for? Know how to easily read the signals that they're getting from the platform and know what to do next, when they see a signal? How do we combine all of that into one platform? So all of that information, all that decision making all those resulting activities are captured in one place, and can be exported as a series of reports to be included? In the final CSR that show that true quality continuum? You know, what was my protocol design? What was the initial risk assessment? What did I just about the design? What were the the critical to quality factors? What were the risks to those factors? How did I manage them? And what the decisions and observations that I made along the way and what was the end result? That is operational management's in an RB qm environment? That's the third driver for the opera five design. Thank you very much. It's been a pleasure talking to you today. I hope this has really helped you understand why we've done what we've done. And what makes opera five such an exciting and valuable software platform. Thank you. Bye bye.