116 – Real-World Evidence Impact (S8E11)

All right, welcome back to the deep dive. Today,
we're going to be tackling something that I think
is super important. And that is we talk a lot
about how drugs get approved, but we don't always
talk about what happens after that approval when
the drug actually gets out there into the real
world. Yeah. And for all of you out there who
like to cut to the chase, basically, This deep
dive is your shortcut to understanding how we
continue to learn about how safe and effective
medications are long after they've been approved.
I love that cut to the chase. That's what this
show is all about. So we've got a lot to cover
today. We're looking at studies, the science
behind how drugs are regulated, and the systems
that, like you said, are in place to monitor
them after they're out there. Our mission is
really to give you all a clear understanding
of how real world evidence RWE, is used to ensure
that these drugs continue to be safe and effective,
and how they actually inform the decisions that
are made about them over time. OK, so let's get
right to it. First, can you just break down what
exactly real -world evidence is? It sounds pretty
straightforward, but I feel like there's nuance
to it. Yeah, it's a really good question to start
with because it's important to understand how
it's different from the data that's used to initially
approve a drug. That process relies very heavily
on clinical trials, which are very controlled
environments. Real -world evidence comes from,
well, the real world, from observing how medications
are used and what happens in everyday medical
practice. So it's less about those controlled
settings and more about the actual experience
of using these medications in a complex healthcare
environment. Okay, so that makes a lot of sense.
But if it's not from clinical trials, where does
this real -world information come from? I mean,
are we just like asking people on the street?
No, not exactly. There are actually a few key
sources that provide really valuable information.
One of them is electronic health records, or
EHRs. So, you know, these are the digital records
that your doctor uses to track your health information.
Right, yeah. Those records contain a ton of detail
about your health journey. That includes the
medications you've taken, dosages, how they've
been combined with other treatments, and of course
the outcomes. So when you look at this kind of
data for a lot of patients, it can reveal some
pretty significant insights into how a drug performs
in different populations and under various real
-world conditions. So it's like a massive database
of everyone's experiences with a certain treatment.
Exactly, yeah. And another big source of data
comes from insurance claims. They're mainly for
billing purposes, but when you look at that information
across large numbers of people, it can show patterns
in how frequently medications are prescribed,
the costs associated with different treatment
approaches, and even broader trends in patient
outcomes. You know, kind of give you a big picture
view of medication use in the real world. It's
like seeing who's getting which drugs and what
the overall impact is on their health and health
care costs. That makes a lot of sense. So we've
got EHRs and insurance claims. What else? We
also have patient registries. These are a bit
more focused. They're designed to collect very
specific and consistent information on groups
of individuals who all share a particular condition
or disease. They often track the treatments patients
receive and their progress over time in a structured
way. which allows for in -depth analysis within
those specific patient populations. OK, so it's
a way to dive deep into how a drug works for
people with a certain disease, right? Now, I
remember reading about something that I found
really interesting. It said that AI, artificial
intelligence, and even social media could be
used for real world evidence. And I'm thinking,
how is that even possible? Oh, yeah, that's a
super interesting area. You know how much information
patients share online these days in forums and
on social media and in disease specific communities
like patients like me? Right. I mean people talk
about everything online, but well the thing is
AI tools can actually be used to analyze all
of that unstructured text data and identify recurring
themes like What side effects are people reporting
or how effective do they perceive a drug to be?
It's kind of like tapping into the collective
lived experience of patients Wow It's like turning
online chatter into actual data points that you
can analyze. I actually remember reading about
23andMe partnering on Parkinson's research. And
then there's Diabetes Connect, which offers something
called information therapy. Are those examples
of that kind of thing? Exactly. They're really
good examples of how we can use these platforms
to learn more about disease progression and how
people are actually responding to treatments
in their daily lives. And then, of course, you
have services like iGuard, which directly collect
data on medication side effects and drug drug
interactions as they occur. So we're pulling
in data from so many different places, electronic
records, insurance claims, patient conversations,
and even specialized monitoring services. It's
a lot to manage. How do we even begin to make
sense of it all? That's where the analytic platforms
and methods come in. And honestly, some of these
tools are pretty sophisticated. Like one of the
key applications is what we call AI -driven signal
detection. I'm intrigued. What is that exactly?
Well, Imagine you have these huge data sets,
you know, EHRs, claims data, registries, and
you need to sift through them all to find those
needles in the haystack. So we use algorithms
to look for potential safety issues, unexpected
patterns in how well a drug is working, or any
other kind of red flag. These signals might be
really subtle and wouldn't necessarily have been
obvious in the initial smaller clinical trials.
So the AI is like a detective looking for clues
that something might be off. Yes, precisely.
And then there are machine learning models. Those
can take all that real world data and actually
build predictive models. So for example, they
could help us understand which patients are most
likely to benefit from a particular drug or who
might be at a higher risk of experiencing side
effects based on those real world patterns. That's
amazing. So it's like personalized medicine,
but based on real data and not just a guess.
Yeah, exactly. You also mentioned post market
surveillance programs. How does all this real
world evidence we've been talking about actually
get used in those programs? Well, post market
surveillance is basically the process of continuously
monitoring the safety and effectiveness of a
drug after it's been approved and is out there
being used by a lot of people. Makes sense. Keep
an eye on things, right? Yeah. And RWE is really
the driving force behind those programs. By constantly
analyzing all that data from EHRs, claims, patient
reports, and those monitoring services, we can
get a much clearer picture of the long -term
effects and the safety profiles of these medications,
especially in diverse groups of patients. Patients
who might have multiple health conditions or
be taking other medications, things like that.
Those factors might not have been fully captured
in the initial clinical trial. Right. So it's
like a constant feedback loop, learning more.
and more as the drug is used more widely. But
all this information has to go somewhere, right?
I mean, how does it impact the decisions made
by regulatory agencies like the FDA? That's where
the real impact of real -world evidence comes
in. Agencies like the FDA are increasingly using
RWE to make decisions about approved drugs. They're
not just giving a drug the green light and then
walking away. Their oversight continues throughout
the entire life cycle of the medication, including
this crucial post -market surveillance phase.
It's all about managing risk and making sure
that drugs remain safe and effective for the
people who need them. And the FDA has that Office
of Regulatory Affairs, right? I remember reading
that they're involved in monitoring clinical
trials. So how does their work connect to the
real world evidence that's collected later? Yeah,
that's the ORA. Their primary focus is on making
sure clinical trials are done properly and reviewing
those initial results before a drug even gets
approved. So it's like they're laying the groundwork.
But sometimes the things we learn from real world
evidence after a drug is already out there can
actually prompt the FDA to go back and take another
look at the original clinical trial data. They
might want to see if anything in those early
trials could explain a signal that popped up
later in the real world. It all kind of works
together. OK, so it's not just separate processes.
It's all interconnected. So what happens when
the FDA does find something concerning in the
real world data? I mean, what are the tangible
outcomes of this RWE analysis? Well, the findings
from real world evidence can actually lead to
changes in how a drug is used. Like, one outcome
is that it can lead to updates in the drug's
labeling. So, for example, if the data reveals
a side effect that wasn't previously known or
if it identifies a particular risk for a specific
group of patients, the FDA might require the
drug manufacturer to update the official prescribing
information, you know, so it reflects that new
information. Or if they find that a drug is particularly
effective for a certain group of patients that
wasn't initially identified in the clinical trials,
the label could be updated to reflect that new
understanding. It's like making sure that the
information about a drug is as accurate as possible
and reflects what's actually happening out there
in the real world. Now what about when those
safety concerns do pop up? How does RWE help
us develop strategies to minimize the risks?
RWE plays a big role in that too. It can be really
helpful in figuring out whether we need risk
mitigation strategies and what those strategies
should be. For instance, if the real -world data
suggests that there's a higher risk of a particular
adverse event than we initially expected, the
FDA might work with a drug company to put certain
measures in place. That could be anything from
changing the recommended dosage to implementing
more intensive monitoring guidelines for patients
taking the drug. Sometimes it even means launching
public awareness campaigns to make sure health
care providers and patients are aware of the
potential risk and how to minimize it. So it's
a way to be proactive and manage potential safety
issues that might not have been obvious during
those initial trials. It's kind of amazing, isn't
it? It's not just about looking back, it's also
about shaping how we use drugs in the future
to make them safer and more effective. Absolutely.
And the connections go even further, like what
we learn from RWE can actually help shape the
design of future clinical trials. For example,
if real -world data shows that we need more information
about a drug's effectiveness in a certain group
of patients, a future trial might be designed
specifically to answer that question. Or if RWE
unexpectedly reveals a potential benefit for
a condition that the drug wasn't originally intended
for, that could lead to a whole new area of research.
It's really a cycle of learning and refining
our understanding. It really is. What happens
in the real world informs our understanding,
which then helps us improve how we do research
and how we regulate medications. It all comes
full circle. Exactly. Real world evidence is
like this essential feedback loop constantly
giving us more information about the safety and
effectiveness of medications as they're used
by a much broader and more diverse patient population
than we typically see in those initial carefully
controlled clinical trials. So to kind of sum
things up for everyone listening, the big takeaway
is that this continuous monitoring fueled by
all these different data sources and some really
powerful analytical tools like AI is absolutely
vital. It's what shapes the decisions made by
regulatory agencies, keeps drug labels updated,
and guides the development of risk mitigation
strategies. It's all about ensuring that medications
are used as safely and effectively as possible.
And when you consider how much real -world data
is becoming available all the time and how quickly
AI technology is advancing, it's really mind
-boggling to think about how our understanding
of drug safety and effectiveness will continue
to evolve in the years to come. It's pretty exciting
to think about the potential of learning from
the real -world use of medications and how that
might shape the future of healthcare. I agree.
It's definitely something for all of us to keep
in mind as we move forward. All right, that's
all the time we have for today, folks. Thanks
for tuning in to the Deep Dive, and we'll see
you next time. See you all next time.