Female Speaker: And so, for working group three we have Simone
Pinero, I’m not doing good with any last names today [laughs] who’s joining us from
the U.S. Food and Drug Administration presenting on behalf of the working group that was led
by her and Bob Davis from the University of Tennessee Health Science Center.
Simone Pinero:
So, good evening.
I’ll try -- we had very interesting discussions last evening and I -- Bob and I sort of wrote
down what we thought be the key points or the key take home messages from these discussions.
But, if I am in any way not representing them well or if I’m not capturing everything
that was discussed, I’ll ask that the group members feel free to chime in.
And these were the group members.
So we’re tasked with several questions and one of them were to identify key gaps in pharmacosurveillance.
And, by far, the main challenge that we discussed were difficult in case as ascertainment for
identification of the phenotype.
We thought that we needed in order to achieve that, we would need standard case definitions.
It’d be helpful to have a minimum set of variables that can differentiate well cases
from non-cases.
And, it’d be helpful to have that to be generalizable to commentate our models so
we can use large datas.
And, at the end of the day, we need the ability to dig deeper.
We need to be able to validate these cases.
We need to be able to figure out timing of the exposure to try and attempt to establish
causality between the exposure of -- the potential exposure of causative exposure and the keys.
So some next steps that we discussed in regards to that particular topic were to evaluate
different case definitions that are currently being used by other groups.
Some examples are listed there.
EuroSCAR, regiSCAR, and other SJS projects.
And, at the end of the day, this is going to be an iterative process among researchers
and clinicians to arrive at a common definition.
But, that -- if we can do that, that would be extremely helpful.
The bottom line, is that active surveillance with real-time data collection is very different
from retrospective collected data with [unintelligible] validation and a set of items that could satisfy
both collection efforts would be very helpful.
We also talked about capabilities of developing active monitoring in the United States, specifically.
And, there are several challenges.
One of them is the fact that our health care system is very fragmented.
But, at the end of the day, we may need to -- multiple strategies that include use of
both perspective and retrospective data collection.
For perspective data collection efforts would need to --allow us to have more compete case
ascertainment for pharmacogenomics studies.
And we discussed a lot -- perhaps the use of burn units.
And it seems to be a promising approach since that’s where many of these patients end
up in care for.
And perhaps, focusing on a few large areas.
Cities in the United States may be helpful, given the difficulties of doing this nationally
of course.
That’s the ideal
Use of existing large databases for active surveillance can be promising because of the
large numbers, however, there’s still several things that need to be accomplished in order
for us to get there.
Some capabilities are being developed in some databases for pharmacogenomics studies, but
we need the ability to identify cases reliably.
And the ability to conduct case validation blinded of course.
The exposure status.
We need standardized processes for collecting genetic data across desperate sides, and so
on.
And eMERGE, as we heard yesterday has had success in suffer aging this process.
We also talked about challenges in estimating rates of SJS/TEN and that’s also initial
-- particularly important for the U.S.
But, a lot of the discussions here and I’m curious to hear also from others in this room.
One of the issues we discussed is whether this is truly our priority.
Whether we need to understand rates.
And, of course, for our -- events as rare as SJS and TEN, trans may not -- over time
may not be helpful but, this is probably very relevant for cost effectiveness studies, to
estimate burden of disease, and to calculate products specific rates.
And if we need to do so of course near a complete capture of cases and ability to identify them
and enumerate a population is needed.
And if we can develop this capability, we can consider assessing productive products
specific rates.
Perhaps, raise specific rates which may be very helpful and informative to us.
And, as we consider that it would be important of course to restrict this type of study to
new users of a drug.
Since -- if you include prevalent users of a drug, these people may no longer be at risk
for SJS and TEN if you’ve been using a drug for years.
We also talked about gaps in knowledge in terms of understanding disease progression
and also gaps in knowledge in terms of understanding long term outcomes of SJS and TEN.
Some strategies that we discussed was to consider use of score ten, which is used in Europe
-- mostly seven point score used for prognosis calculated on day three of the disease.
But some of the mutations of the data are not always collected on day three such as
for example lab values and some characteristics such as lung involvement, may not be -- are
not considered in score ten.
And now the next steps to consider -- studies should address the range and extent of outcomes
and disabilities in these patients.
And there’s also another of loose items that we discussed yesterday that we thought
that would be important to bring up.
Large-scale collaborations in U.S. might be simulated by concerted efforts to deposit
data into public databases.
Some of them discussed was database such as ClinVar and others.
And we also talked about the challenges with current screening recommendations because
of the low PPV of these meaning not everyone that’s screens positive would actually go
on to develop the disease.
So, if we could come up with ways to improve that.
Perhaps, talking about if you have a haplotype plus a series of risk factors in order to
increase the predicted value of these screen tests may be helpful as well.
So this is all I have and I’ll open for the group members in case we forgot anything
important -- was not accurate enough of course.
Lois La Grenade: We also discussed a network such as Dillon
[spelled phonetically].
Remember?
That was at the end.
And I don’t think you mentioned that.
It probably mentioned networks in general.
But at --we thought, the group thought it would be useful.
And some members didn’t know about Dillon or how it was formed.
So they were looking forward this morning to [unintelligible] talk.
So I think we are more informed now.
Simone Pinero: Thank you.
Female Speaker: Yeah I just wanted to mention when we talked
about the clinical classification, there was not an issue that the clinical had classifications
that are no good.
They are very good for prospective cases.
The difficulty that we identify was when we thought there’s retrospective setting.
How could you first have inclusion criteria to identify potential cases and how could
you validate them?
Because if you are as strict as with cases that you have.
Everything available, prospectively from photographs to biopsy to everything.
It’s not such a difficulty.
I mean sometimes it’s challenging in individual cases, but in principle the setting is there.
But for retrospective cases it can be quite complicated.
And that was one thing.
And the other thing, was trying to make a link between retrospective data assessment
and potential genetic investigation or genomics.
You know, how could you have access to the blood of patients if you have the data retrospectively?
Are there any means or not?
Certainly not for the cells because you need them in the acute stage.
But, perhaps, for DNA.
And we also thought maybe there can be a question whether the patient associations could help
to donate blood.
We have done that in France and we had about 70 cases, but we went back to the case charts.
Reviewed everything and then could include these cases for genetic studies because the
patients donated samples for a delayed analysis.
So, that maybe just in addition was some of the talk.
Female Speaker: Great.
Mark.
Mark Avigan: Yeah, I just to add on to that.
Since our charge was surveillance, we took that in a very broad sense and this relates
to the comments I made at work group one is that it was clear that the criteria that would
be used for an active surveillance, an early warning system would look different than a
back-end where you want really -- so it’s really, you want high sensitivity.
And you can sacrifice some sensitivity -- some specificity if you’re doing an active surveillance
program.
But, for that retrospective, you know, research to learn more about it than you really want
to emphasize the specificity.
And so, one of the keys of defining sort of the minimum data elements for case definition
would be to reflect with those elements may be different depending on whether you’re
in an active surveillance mode versus more of a study research mode.
And so, the ability to reconcile both of those perspectives and develop tools that would
support both of those perspectives is important.
Simone Pinero: And I just wanted to quickly add to what Maja
said in terms of getting -- having the ability of obtaining additional sample from patients
is -- we talked about yesterday about, in some of these databases there are patients
with bio specimens available.
When we’re talking about SJS and TEN, even if you can identify these cases reliably,
retrospectively in these databases, they may not be the ones that have the bio specimens
available.
So, there’s a lot of patients without these specimens available.
So, the ability to go back and be able to obtain these additional samples in these patients
becomes very important in these severe diseases.
Female Speaker: Any questions or feedback from people who
weren’t in that particular working group?
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