#41 Cracking the genetic code of complex chronic illness with Steve Gardner, PrecisionLife

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Steve Gardner: We have suddenly
built out this landscape from
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stumbling around in the dark. We
switch the light on. We can see
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all of the major points on the
map that we should go and
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investigate and explore. We've
got a really detailed
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understanding of the disease. We
know which patients have
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problems with particular
mechanisms. We may be able to
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solve by finding a drug that's
been studied in another disease
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that is acting on the same
genetic target.
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Emily Kate Stephens: Welcome to
Make Visible, the podcast
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shining a light on complex
chronic illness. I am your host,
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Emily Kate Stephens. Welcome
back to our latest episode of
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Make Visible. We have had a
slight hiatus. Welcome to Gez.
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Hi, Gez.
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Gez Medinger: Hello, Emily. How
are you?
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Emily Kate Stephens: I'm good. I
was away last week at the
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International Society of Long
COVID and Post-Acute Infection
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Syndrome conference in
Amsterdam, and I was there
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working with them. I got to meet
such a wealth of incredible
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people in this space. I think
there were 300 people there,
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over 100 presentations given,
and it was absolutely
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remarkable.
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Gez Medinger: What was the
emotion that it left you with?
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Did it lead you with the "Oh
God, we've got so far to go"
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feeling, or did it leave you
feeling hopeful and excited?
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Emily Kate Stephens: Look,
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there is amazing work being done
in this
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space, and within the community,
there is huge support, and we
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are starting to see some of
those clinical trials coming
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through that have the potential
to make a difference on a large
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scale. But actually, I hosted a
two and a half hour live stream
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at the end, which was a patient
recap of the whole conference,
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and I ran polls in that, and the
patients that were watching
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basically said that none of that
is still translating to them.
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And I think that was really
great for the panelists that I
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had there to actually hear in
real time, because what we need
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to do now is disseminate at all
levels. So moving it from the
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lab to the clinic to scalability
and moving all of that
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information then back down
through the hospital chains
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right to your primary care
provider, absolutely vital. But
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yeah, it's really exciting to see
the work that's going on.
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Amazing to see the passion from
the people that are involved and
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actually be there in person with
them. But we've we've still got
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far to go in terms of actually
getting the information where
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it's needed.
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Gez Medinger: I think being a
patient with a complex chronic
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illness is a little bit like
sitting in your house, being
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really hungry, and hearing about
all these people who are doing
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loads of farming and planting
loads of potatoes and making
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cooking shows. And you're like,
yeah, but there's nothing in my
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fridge. And when's that going to
turn up in my fridge? I'm really
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hungry.
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Emily Kate Stephens: Gez, I love
your analogies. Yeah,
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absolutely, it really is. And
you could really sense that
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frustration in this patient
recap-the difference between the
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science that is starting to come
and what the patients are even
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hearing about.
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Gez Medinger: Yeah, but you've
got to plant the potatoes before
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you can eat them, so at least
we're planting them, haven't
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you? Yeah.
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Emily Kate Stephens: How are
you?
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Gez Medinger: Looking forward to
bedtime. In all honesty, I feel
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like someone's taken out my eyes
and pickled them and put them
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back into my head, and I'm
really excited about getting to
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8 o'clock, which is the
psychological time where I feel
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like it's just about acceptable
to close the curtains and put my
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head down. I've just got to get
through to something that
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approximates bedtime.
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Emily Kate Stephens: Okay. Well,
this week we actually have an
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interview with someone that I
met, Steve Gardner. He really is
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doing the high level science in
this space. He is the CEO of
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PrecisionLife, which is an AI
health and life sciences
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company, and they do work into
genomics. We had a short clip of
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him in the episode, which was
the overview of where we are in
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terms of ME/CFS. And here is the
rest of the conversation with
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Steve Gardner. You were part of
the Human Genome Project. Good
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entry point to explain the work
that you now do in genomics.
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Steve Gardner: Sure. So back in
about 1992, 93, in a company
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that I was working in, we had
Jim Watson on our scientific
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advisory board, and Jim's thesis
was very much that if we could
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sequence the whole human genome,
we would be able to find where
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the genes were, and we would be
able to spot mutations in those
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genes that were causing disease.
And you you have to remember at
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the time that our conception of
a genetic disease was in terms
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of sickle cell anemia or cystic
fibrosis or Huntington's
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disease, things where one gene
goes wrong and it automatically
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causes this disease. And they
were very successful in getting
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the human genome funded, and we
have seen the findings from that
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study absolutely transform the way
that we think about
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oncology. Back in 1993, if you
had breast cancer, you had a
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tumor in the breast, and that
was it. That was what we knew
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about it. It was anatomically
defined. You fast forward to
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today, and we have hundreds of
therapies, a palette of
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potential therapies, diagnostic
tools that get to exactly what
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are the mutations that are
involved in your particular
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tumor, which inform the
clinicians about which therapies
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are most likely to work for you,
and we've seen this result in an
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ever increasing improvement of
survivability across most
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cancers over the last 25 years,
so that's clearly inspirational.
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But it's also incredibly
frustrating that we haven't seen
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the same level of impact on
complex chronic diseases. And
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just to put this in context,
oncology and rare genetic
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disorders represent about 10%
actually a bit less than 10% of
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healthcare spending. 85% of
healthcare spending is in
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complex chronic conditions, and
I'm talking about neurological,
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inflammatory disorders, women's
health, respiratory,
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cardiovascular, metabolic, those
kinds of things, which affect
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literally billions of patients
around the world. They cost
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trillions of dollars. Many of
those diseases have significant
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genetic components, but the clue
is in the name:
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I call them complex chronic
disorders. They are not driven
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by one gene at a time. They're
driven by lots of different
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genes interacting with one
another. You know, we have
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feedback loops in biology. Some
of them inhibit each other. Some
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of them accelerate each other.
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Emily Kate Stephens: That's very
eye-opening. Bringing it back to
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that level in terms of the
complexity, because I think a
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lot of the time people view the
complexity as being the outward
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symptoms in terms of the
layperson viewers of the
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symptoms. But you are saying
that that genetically they are
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complex and they are
multifaceted.
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Steve Gardner: Yeah, and and in
many cases, the clinical label
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that we use doesn't actually
help us very much. So you can
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think about ME and long COVID,
and each of those diseases has,
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you know, a couple of hundred
symptoms that are recognized and
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that occur across all manner of
organs in the body, you know,
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you you have brain fog, you have
fatigue, you have GI issues. You
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know, there's a variety of
different impacts. Actually, in
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reality, these are not really
one disease. There are lots of
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molecular causes which will lead
you to this symptom or that
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symptom, and a patient may have
more than one of them. So what
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you see is this spectrum of
presentation of different
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symptoms, and and in many ways
calling it one disease is
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responsible for a lot of the
enduring complexity that is
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inhibiting the space. You know,
is inhibiting pharma companies
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from coming in and saying no,
it's this target for this type
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of drug, and really
understanding the the complexity
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of that biology, breaking it
down so that we can see what is
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driving disease within
individual patients, linking
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that to their symptoms, and then
finding therapies that are going
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to be most effective for that
patient is the real hope in this
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space.
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Emily Kate Stephens: And that is
your mission at PrecisionLife.
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To put it simply, and I don't
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imagine there's anything simple about
looking at those genes, what you are
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intending to do is take this
overarching data and use this
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genomic information to actually
filter through to understand
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what treatments can help people,
but very very specifically, and
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this is one of the huge things
that I am told by everyone that
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I interview is you are hoping to
be able to identify which people
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can respond to which treatments.
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Steve Gardner: Absolutely, yeah.
So this is this is precision
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medicine. We are aiming to get
the right drug in the right
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patient, and in fact, at the
right time when it can have an
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impact on on their disease. And
these kind of pictures behind me
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are different diseases. But the
principle is that we can
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identify genetic signatures. If
you take the yellow group above
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my shoulder here. That is a
distinct group of patients
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within this disease who share a
common mechanism that is driving
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their disease. And because it's
that thing that's going wrong,
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if we fix that thing with a
yellow drug, that patient
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population should respond much
more strongly than perhaps red
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group, who nominally have the
same clinical diagnosis, but the
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cause of their disease is
different. The yellow drug
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doesn't help the red patients,
and vice versa would also be
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true. Understanding that,
thinking about the disease in
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those terms as a collection of
causes that actually we can
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potentially do something about.
It is a really important driver
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of how to think about research
in this space. There's a really
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important point here, which is
if you have a great yellow drug
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that would help those yellow
patients, but those yellow
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patients only make up 20% of
your patient population, and you
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go to a clinical trial just with
the ME/CFS label, and you've got
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everybody in your trial. You're
only going to get at best a 20%
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drug response rate, and that's
not enough to put a new drug or
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a repurposed drug onto the
market. So choosing yellow
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patients to go into that trial
means that many more of them
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will respond to the drug in the
first place, and therefore you
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have a much stronger chance of
actually getting a successful
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trial outcome. This is what
holds the field back because
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there are so many groups here.
We've never been able to
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stratify them and get to that
level of resolution of who is
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actually going to benefit from
the therapy that we think will
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help some patients.
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Emily Kate Stephens: This is
potentially what has been so
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detrimental in studies and
trials that have been done to
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date, because you have not had
those subgroups. So you have
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been putting entire cohorts with
multi different, essentially
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different diseases into the same
section and expecting the same
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response across them, but as you
are actually, that's a really
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great visual. If you've only got
the 20% yellow people, that
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yellow drug is not going to be
sufficiently statistically
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significant in your trial data.
This, I believe, is one of your
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key things that you're aiming to
do with the Mello study. Is that
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right?
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Steve Gardner: Yeah, that's
absolutely right. What we did in
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the Mello study, which was a
study based out of Salt Lake
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City with the Metrodora
Institute at the time, it was
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funded by the Complex Disorders
Alliance, was really look at
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whether we could see the same
signals that we had seen in a
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scientific research population
inside patients who were
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presenting either with a formal
diagnosis or indeed just
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self-reported that they felt
that they had ME-like symptoms
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or long COVID-like symptoms, and
we demonstrated that we could
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the the signals that we were
seeing coming out of the
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research studies from UK Biobank,
All of Us, the Sano Genetics GOLD
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study and DecodeME showed up in
that participant population,
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and that yes, we could distinguish
different
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mechanisms that we believed
would be suitable for, in
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particular drug repurposing
studies, where we take existing
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medications that are safe and
well tolerated, and which we
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believe will give a clinical
benefit, they will help the
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symptoms of some of those
patient subgroups and connect
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the dots between them. So we did
the first part of the analysis
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in the in the Mello study, and we
have a new paper coming out
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on on that, so that that study
is going. It has also led us to
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designing with groups around the
world drug repurposing studies,
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which are targeted. So, if we
have an existing drug that we
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think will help those yellow
patients, we can go and we can
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use the genetic reporting to
find patients who would
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potentially benefit the most
from a yellow drug. Design a
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trial specifically around them
and use one of these repurposed
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medications to see if we
actually see that alleviation of
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their symptoms.
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Emily Kate Stephens: That's
fascinating. Let's take it back
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to the top line of how you do
this, because there are multiple
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strands of PrecisionLife that
involve the genetic testing. And
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interestingly, you are hoping to
develop this in in some of these
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complex diseases to be actually
completely non-invasive testing.
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Is that am I right in thinking?
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Steve Gardner: We're designing
genetic reports, which can be
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used by consumers to become
informed more about their
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health, their wellness, and the
impact that they could
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potentially have on their
symptoms.
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Emily Kate Stephens: So that is
just one component. Explain to
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me the multiple arms of the way
that you work, the way that you
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gather data, the way that you
analyze it, and the other side
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that also involves you analyzing
pharma and biotech their
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pipelines to establish then the
drugs that are either already on
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the market or in production that
might might help because that
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kind of umbrella is actually
what has the potential to enable
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us to move forward for the
patient, not just in our
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understanding.
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Steve Gardner: Yeah, absolutely.
It stops it being a science
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exercise and turns it into
things that will benefit
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patients. And it's the quickest
and cheapest way I know of
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getting effective therapies into
the hands of patients, which is
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why we've pursued this. But
yeah, to back up, so I mentioned
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the frustration that we'd had
with not being able to get the
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same level of results as we've
seen with oncology in complex
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diseases. ME and long COVID are
complex, really complex
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diseases. So, unfortunately, to
date, there have been very few,
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and in fact, no really
reproduced genetic associations
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published in either long COVID
or ME. There are hints of this
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through the DecodeME study, the
eight loci that were
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highlighted, but one gene, eight
genes-they don't explain the
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entirety of this disease. So
what we wanted to do first off
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was to map the landscape of all
of the different genes that we
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could see in ME, all the
different genes that we could
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see associated with long COVID.
That's two separate studies, and
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then see whether there was an
overlap between those two, and
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then within those, come through
and evaluate which ones were
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most what we call druggable-the
ones where we had a really good
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sense that we would be able to
intervene in that process and
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fix it. So we started with
different datasets. We started
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with UK Biobank in ME, and then
we ultimately the findings of
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that study we replicated in
DecodeME. On the long COVID side,
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we partnered with Sano Genetics,
got access to their Gold dataset
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long COVID patients, and Sano we
we replicated in the All of Us
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database,
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Gez Medinger: Which is a U.S.
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based database.
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Steve Gardner: It is U.S. based. A
very very diverse ancestry, very
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diverse socio demographics. This
is a series of four major
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studies over three years or so.
What we found were several genes
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that were associated with long
COVID in the first instance. We
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had something like 80 genes
within the highest confidence of
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long COVID because some patients
in that study it was a very
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early study people didn't really
know what long COVID was when
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the data were collected so it
was a little bit noisy but we
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ended up with about 80 genes in
the long COVID side of things
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and then from the first study in
UK Biobank we had 14 genes that
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we found in ME. Ultimately, we
expanded that to 259 using the
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DecodeME database. So now we've
got 260 genes over here. We've
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got 70 odd genes over here, and
we wanted to look at the overlap
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between them. And the long story
short is, we did this three
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times using three completely
different methods, and it
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appears that about 40% of the
genes that confer risk of ME/CFS
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also confer risk of long COVID.
So there is a significant
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overlap. We see genes like the
insulin receptor, which deals
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with energy production. We see
genes like the clock gene, which
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deals with circadian rhythm,
showing up in in both cohorts.
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But also, 60% of the genes are
different, so these aren't the
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same disease, but they share
quite a bit of biology. So then
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the second piece is, well, what
can we do about this? That's
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great science, as you noted
earlier. Lots of papers that
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we've put out in this, but what
can we do about it? And the
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first thing that we can do,
because everything that we're
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finding in ME and long COVID is
a new finding, it hasn't been
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studied by a pharmaceutical
company in the context of ME or
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long COVID. Now that doesn't
mean it hasn't been studied in
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the context of other diseases,
maybe diabetes, maybe obesity,
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maybe neurological disease,
maybe something fibromyalgia,
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for example, pain-related
disorder. And what your
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listeners may not appreciate is that
there are literally tens of thousands
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of compounds that have been
trialed by the
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pharmaceutical industry, or in
the process of development, that
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are different from the drugs
that are actually on market. At
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the same time, there are also
some drugs that were on market
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have now their patents have
expired, and so they become what
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we call generic. Yeah,
ibuprofen, you know,
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paracetamol, aspirin. These are
all great examples of generic
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drugs. They become cheaper and
much more widely available. But
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we also know, because they've
been prescribed for decades,
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that they're safe, they're well
tolerated, and we understand the
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doses that we can give to have
an effect on the body. So now
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we've got a really detailed
understanding of the disease. We
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know which patients have
problems with particular
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mechanisms that we may be able
to solve by finding a drug
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that's been studied in another
disease that is acting on the
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same genetic target and either
amplifying its effect or
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inhibiting its effect as required.
If we can find a
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match, it's a bit like playing
Snap, I suppose. If we can find
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a match between those two
things, we have a really strong
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scientific hypothesis and the
clinical development tools, the
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biomarkers that we can use to
select the patient population
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that says these people should
respond to this medication, and
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that's phenomenal. That in
itself allows us to perform a
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series of clinical trials, which
are much smaller than they would
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otherwise have to be, and have a
much higher chance of reading
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out with success.
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Emily Kate Stephens: Are we set
up for this yet in terms of the
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way that clinical trials are
designed, either UK, US, within
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Europe. I don't know which
specific authorities you might
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be able to talk on, but in terms
of at the moment, you tend to
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have to have this the diagnosis
for you to register. So you you
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put it in as ME/CFS. If you are
then saying we're taking a
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subset of ME/CFS patients to
treat, not a blanket. Are we set
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up yet in terms of the approvals
for the actual level of science
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or that level of detail that
you're able to offer?
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Steve Gardner: This is where all
I think of the major governments
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and major regulators want to go.
They've been encouraging the
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industry to to go down this
path. But if you're talking
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about the pharma industry, it
tends to see an opportunity of a
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whole disease as being a bigger
market.
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Emily Kate Stephens: Almost more
is better.
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Steve Gardner: Yeah, yeah,
exactly. So take up on that
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capability has has not
necessarily been great when
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you're thinking about new drug
discovery, but here we're
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talking about repurposing, and
so in the UK, NIHR has made
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money available for a couple of
centers in the UK to do
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precisely this kind of
genetically targeted trial
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design. In Australia, we're
working with groups in Melbourne
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who are wanting to use precisely
this, actually across multiple
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different mechanisms, an
adaptive trial design that says
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you'll go on initially onto the
drug that we think will benefit
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you the most, but we're going to
have three drugs in the trial,
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and we'll then switch you onto a
combination therapy to see
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whether you get additional
benefit from one or other of the
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other medications, Germany is
setting itself up to do this.
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They've got huge amount of
federal funding. The Netherlands
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is doing it, and various centers
in the U.S. particularly those
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affiliated with the Open
Medicine Foundation, are also
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looking to use these kinds of
tools. So, yeah, I think it is
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really it's so essential for the
space because again we don't
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have patient populations of 90%
of ME patients who will all
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respond to one thing. The
disease just isn't like that. So
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in order to be successful at any
level, we need to bring this
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stratification into that design.
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Emily Kate Stephens: Now, from
those studies, from the LOCOME
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and that cross that crossover of
the genes, I believe that you
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found nine safe generic drugs
that could potentially help
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across both.
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Steve Gardner: Yeah,
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Emily Kate Stephens: are those
drugs that require prescription.
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Steve Gardner: Some of them are.
Some of them are not.
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Emily Kate Stephens: Okay, but
they are widely available and
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safety tested.
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Steve Gardner: Yes, yes. I want
to be very careful. However,
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when we published the paper, we
published all of the all of the
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SNPs, all the mutations, all of
the major findings of the
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results. What I do not want
people to go and do is suddenly
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start googling these things, and
you know seeing which of the
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drugs they can get hold of. They
will not work,
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Emily Kate Stephens: you know,
because this is the foundation
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of the work that you're doing
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Steve Gardner: exactly, and that
could do a lot more harm than
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good, both at an individual
level and also in terms of the
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way that we're approaching this.
The community needs to get
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behind responsible science and
build the evidence to say yes
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for these people. These drugs
will work. So that's what I want
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to do. So yes, on LOCOME, we found
nine repurposing
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candidates that we felt would
work across both disorders. In
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the latest publication, the DecodeME
analysis, we actually identified 42
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repurposing candidates. That's
obviously many more than we have
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clinical centers. One of the
things that we said in the
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paper, we say very openly, is
any bona fide research group
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that's out there, we will be
delighted to help them design
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ethically sound trials that
protect the patients don't raise
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false expectations, but which
will go and test the efficacy of
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these medications in those
patient subgroups.
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Emily Kate Stephens: And that is
the most important thing: that
402
00:25:15.570 --> 00:25:19.950
your work is based on the
subgroups and identifying which
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patients will be suitable for
which treatments?
404
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Steve Gardner: Absolutely.
405
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Emily Kate Stephens: And yes,
there is this genetically shared
406
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pathophysiology between ME and
long COVID. You're not saying
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they're the same conditions. I had
the conversation with Amy Rochlin
408
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, and this very complicated
situation of we have
409
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these diseases that are being
used together, and we almost
410
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need to break out the subsets of
those diseases, and then look at
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the components of those subsets,
and then potentially group them
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back together where the ME and
long COVID overlaps.
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Steve Gardner: Yeah, if you're a
pharmaceutical company, at the
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end of the day, people are
entitled to have their views
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about commercial research in
this. But I think it's pretty
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undeniable that we would all
benefit from having the interest
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and the R&D budgets of major
pharma companies focused on
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these diseases. So what we've
tried to do is to show that
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there are a series of
mechanisms, there are a series
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of drugs that can affect both
populations at the same time.
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And let's be honest, that's at
least 65 million patients around
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the world, quite possibly more,
where there is no competition,
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and we don't have existing drugs
in the same way as we have with
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the GLP-1s, for example, where
there are hundreds of companies
425
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trying to play in that space,
but these would, for this
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patient population, this would
be a very attractive marketplace
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if we can get through that
complexity of the disease,
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demonstrate that we can select
patients who would benefit from
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drugs targeting particular
mechanisms or genes. If you have
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that clarity at a mechanism and
gene level of what what should I
431
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be building a drug against, and
if you can also demonstrate that
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you can select the patients who
will benefit from that drug,
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that is a level of confidence that
I think biopharma has been
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missing, and it's one of the
huge reasons why they've ignored
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this space for so long.
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Emily Kate Stephens: There are a
lot of people, individuals,
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spending huge amounts of money
on unlicensed supplements and
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unproven techniques. People are
desperate, so people are
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spending their own money on it.
There is definitely the
440
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appetite, but I absolutely
understand what you're saying,
441
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and I'm not necessarily
condoning them because obviously
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we would all like to move more
quickly. But I understand why
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there's not the appetite for
them to, without that genetic
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reassurance, to invest money.
I've read in one of your papers
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specifically that you have
identified predictive biomarkers
446
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and treatment response, but can
we say that you have identified
447
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biomarkers for these conditions
that could be replicable in
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00:28:17.940 --> 00:28:21.060
terms of diagnosis and therefore
future treatment?
449
00:28:21.180 --> 00:28:24.710
Steve Gardner: So this is where
it becomes challenging, and
450
00:28:24.740 --> 00:28:27.620
we've got to be careful about
the language that we use because
451
00:28:28.280 --> 00:28:31.940
the word diagnostic has a
regulated meaning, and the
452
00:28:31.940 --> 00:28:35.330
fundamental problem is that
there is no agreement on what ME
453
00:28:35.330 --> 00:28:39.500
is or what long COVID is, and
certainly not a regulated
454
00:28:39.590 --> 00:28:42.430
diagnostic that can say yes,
this is that, and this is the
455
00:28:42.580 --> 00:28:45.460
other. By the same token,
there's no clinical care pathway
456
00:28:45.760 --> 00:28:49.030
that we can guide people to,
even if we did have such a
457
00:28:49.090 --> 00:28:53.500
diagnostic. So the disease is at
a very early stage. From a
458
00:28:53.740 --> 00:28:57.940
purely science perspective, what
we can say is that we have very
459
00:28:58.330 --> 00:29:03.150
strong association between the
subgroups that we're seeing and
460
00:29:03.180 --> 00:29:06.420
the genes and mechanisms that
are involved in those, and the
461
00:29:06.480 --> 00:29:10.230
symptoms that we see for those
patients. Now, I'm being very
462
00:29:10.410 --> 00:29:14.160
careful about language here.
This is at a science, a research
463
00:29:14.370 --> 00:29:16.740
level. We can make those
connections. They're
464
00:29:16.860 --> 00:29:19.560
statistically significant.
They're reproducible across
465
00:29:19.770 --> 00:29:23.540
multiple patient populations.
They hit all the standards of
466
00:29:23.600 --> 00:29:28.040
evidence that are required. But
the key experiment to run is to
467
00:29:28.130 --> 00:29:33.080
find out whether drugs that have
an impact on those targets in a
468
00:29:33.110 --> 00:29:37.370
particular direction are going
to benefit patients that we can
469
00:29:37.460 --> 00:29:41.590
see. So it is these targeted
drug repurposing trials that I
470
00:29:41.680 --> 00:29:45.100
think are absolutely critical. I
think they will take us further
471
00:29:45.370 --> 00:29:49.990
and faster than anything else in
terms of getting the diseases
472
00:29:50.080 --> 00:29:52.720
recognized as having a
biological basis, getting
473
00:29:52.840 --> 00:29:56.170
clinicians to understand that
they can do something for
474
00:29:56.230 --> 00:29:59.470
patients, which I think is one
of the reasons why there's so
475
00:29:59.620 --> 00:30:03.810
much gaslighting out there
because clinicians feel helpless
476
00:30:03.960 --> 00:30:06.570
in in the face of a complex
disease that they don't even
477
00:30:06.660 --> 00:30:09.270
know where to start with. I
think that'll bring pharma
478
00:30:09.630 --> 00:30:13.500
dollars in because they can see
something is working in 20% of a
479
00:30:13.530 --> 00:30:17.700
400 million patient market.
That's a huge new marketplace
480
00:30:17.820 --> 00:30:21.560
for them to work in. And the
analogy I draw is if you
481
00:30:21.590 --> 00:30:25.130
remember all the way back to the
COVID pandemic in the UK, we
482
00:30:25.400 --> 00:30:28.970
instituted very very quickly the
RECOVERY trial, and this was
483
00:30:29.090 --> 00:30:33.110
using the NHS patient population
essentially as a big open label,
484
00:30:33.410 --> 00:30:37.700
randomized but still open label
clinical study to test the
485
00:30:37.970 --> 00:30:41.530
impact of drug repurposing
candidates, and within six
486
00:30:41.650 --> 00:30:45.700
weeks, for less than £2 million,
we found dexamethasone.
487
00:30:45.880 --> 00:30:50.470
Dexamethasone has saved way more
than a million lives now. It
488
00:30:50.620 --> 00:30:54.250
became almost overnight the
standard of care for severe,
489
00:30:55.000 --> 00:30:59.590
yeah, for severe acute COVID-19
patients. There is absolutely no
490
00:30:59.740 --> 00:31:02.910
reason why we shouldn't be able
to do the same thing in exactly
491
00:31:02.940 --> 00:31:07.740
the same way with more targeting
in long COVID and ME/CFS, and in
492
00:31:07.800 --> 00:31:09.930
fact a whole bunch of other
diseases as well.
493
00:31:10.410 --> 00:31:11.910
Emily Kate Stephens: That
description with the
494
00:31:12.090 --> 00:31:18.240
dexamethasone definitely gives
hope. But you said there that we
495
00:31:18.960 --> 00:31:23.180
are in an early stage of the
disease. When we're talking
496
00:31:23.210 --> 00:31:28.730
about ME, people will say we are
30 years down the line, really
497
00:31:28.820 --> 00:31:32.600
in terms of our scientific
trying to get some understanding
498
00:31:32.750 --> 00:31:37.100
on it. That if we think back
also to the Human Genome Project
499
00:31:37.190 --> 00:31:41.800
in the early 90s, how far do you
feel that we have come as a
500
00:31:41.920 --> 00:31:47.170
scientific field in terms of our
understanding and what we have
501
00:31:47.230 --> 00:31:50.710
the potential to do for ME/CFS
patients.
502
00:31:51.010 --> 00:31:54.490
Steve Gardner: I think we hit a
transformational point with the
503
00:31:54.640 --> 00:31:58.360
publication of our last paper.
It was a collaboration between
504
00:31:58.720 --> 00:32:04.290
ourselves, DecodeME and Action for
ME. Innovate UK funded the LOCOME
505
00:32:04.380 --> 00:32:09.150
project. It's not that we can't
find the genes associated with
506
00:32:09.150 --> 00:32:13.110
the disease. It's that there are
too many genes associated with
507
00:32:13.110 --> 00:32:16.710
the disease. This is not one
disease. It's technically what
508
00:32:16.710 --> 00:32:19.470
we call polygenic, and it's
technically what we call
509
00:32:19.620 --> 00:32:23.840
heterogeneous. We have now
proved that there are hundreds
510
00:32:24.110 --> 00:32:27.920
of genes involved in this
disease, and therefore our
511
00:32:28.040 --> 00:32:32.180
understanding of how we should
go about studying this disease,
512
00:32:32.570 --> 00:32:36.260
how we should go about designing
clinical trials, how we should
513
00:32:36.350 --> 00:32:40.040
think about how to impact the
disease within patients is
514
00:32:40.400 --> 00:32:43.960
fundamentally different from
where it was, let's say three,
515
00:32:44.050 --> 00:32:47.320
four years ago. Now we
understand we have to do
516
00:32:47.800 --> 00:32:52.570
stratification, just like we do
with cancers these days. We have
517
00:32:52.690 --> 00:32:56.290
to do the the genetics. We have
to understand the mechanisms at
518
00:32:56.380 --> 00:32:59.980
work in individual patients, and
we have to design therapies for
519
00:33:00.100 --> 00:33:04.620
them at an individual level, out
of a combination of medications
520
00:33:04.680 --> 00:33:08.490
that are most likely to work for
them individually. That is a
521
00:33:08.700 --> 00:33:13.260
fundamentally different starting
point. We have 260 genes now
522
00:33:13.470 --> 00:33:17.250
that we can go after. We have 42
repurposing candidates that we
523
00:33:17.430 --> 00:33:21.320
can trial. We have suddenly
built out this landscape from
524
00:33:21.410 --> 00:33:24.620
stumbling around in the dark, we
switch the light on, and now we
525
00:33:24.800 --> 00:33:28.820
can see it. We haven't solved it
yet, but we can see all of the
526
00:33:28.850 --> 00:33:32.270
the major points on the map that
we should go and investigate and
527
00:33:32.330 --> 00:33:36.080
explore. And I think that is a
profound shift in where we are
528
00:33:36.170 --> 00:33:38.840
with the disease, and it gives
me great hope that we're going
529
00:33:38.840 --> 00:33:42.880
to be able to find solutions for
patients much quicker.
530
00:33:43.120 --> 00:33:46.120
Emily Kate Stephens: That is
incredible. You wrote that the
531
00:33:46.360 --> 00:33:51.730
study reinforces that ME is a
complex, multi-systemic
532
00:33:51.820 --> 00:33:56.170
condition with a clear genetic
basis, and I think it is that
533
00:33:56.590 --> 00:34:01.110
clear genetic basis that, for
patients, takes it from being
534
00:34:01.380 --> 00:34:06.360
something that has been so
stigmatized and ignored and
535
00:34:07.020 --> 00:34:10.620
blamed on the patient because
you have that scientific
536
00:34:10.800 --> 00:34:14.790
evidence that there's this
genetic basis for it, which is
537
00:34:14.790 --> 00:34:15.390
remarkable,
538
00:34:16.500 --> 00:34:19.410
Steve Gardner: and it's
unarguable now. While we were
539
00:34:19.500 --> 00:34:23.030
trying to find one SNP or one
gene, and nobody was really
540
00:34:23.090 --> 00:34:25.670
agreeing with each other.
Everybody has their own theory
541
00:34:25.820 --> 00:34:29.510
about which things are involved.
They're probably all right, but
542
00:34:29.570 --> 00:34:32.750
not for the same patient. So
different subgroups of patients.
543
00:34:33.290 --> 00:34:37.820
Now we have unequivocal,
peer-reviewed, multiple times,
544
00:34:38.330 --> 00:34:42.220
multiply replicated results that
are out there that demonstrate
545
00:34:42.310 --> 00:34:45.370
there is really strong
biological basis for both
546
00:34:45.580 --> 00:34:46.600
diseases.
547
00:34:46.990 --> 00:34:50.980
Emily Kate Stephens: Actually, how can
our audience, who is interested in
548
00:34:51.190 --> 00:34:54.370
involvement in the future with
PrecisionLife and your data
549
00:34:54.430 --> 00:34:59.200
gathering, can you tell us how
we can get involved?
550
00:34:59.500 --> 00:35:02.370
Steve Gardner: Yes, on that side
of things, we're designing with
551
00:35:02.790 --> 00:35:05.010
key opinion leaders, with
research groups around the
552
00:35:05.100 --> 00:35:10.110
world, studies that will use a
genetic selection as the front
553
00:35:10.230 --> 00:35:14.370
entry to the trial. So, if we
have three different drugs that
554
00:35:14.430 --> 00:35:18.510
we're aiming to trial, we'll
test you once, and if you are
555
00:35:18.690 --> 00:35:22.070
positive for one of those, you
will be recruited into that arm
556
00:35:22.160 --> 00:35:26.390
of the trial. We also want to
use these with existing trial
557
00:35:26.480 --> 00:35:30.140
designs. GLP-1s is one example of
that, but there are others
558
00:35:30.530 --> 00:35:34.640
where we believe that we will be
able to enrich the population
559
00:35:34.730 --> 00:35:38.330
who will benefit and therefore
increase the probability of
560
00:35:38.390 --> 00:35:42.160
success of that trial by
pre-selecting patients who have
561
00:35:42.400 --> 00:35:45.460
those mechanisms that will
respond to those forms of drugs.
562
00:35:45.790 --> 00:35:48.730
That's what we're doing in the
short term on the research side
563
00:35:48.820 --> 00:35:54.130
of things. This is also science
that we are in a more
564
00:35:54.220 --> 00:35:59.110
consumer-focused sense also
aiming to bring forward genetic
565
00:35:59.200 --> 00:36:03.300
reports, which can help patients
understand their disease, their
566
00:36:03.390 --> 00:36:07.170
genetic profile a little bit
better, and help them with
567
00:36:07.380 --> 00:36:11.280
wellness insights that can
support awareness, can support
568
00:36:11.340 --> 00:36:15.480
informed conversations about
their health as well. All of
569
00:36:15.540 --> 00:36:19.110
this is obviously based off
similar kinds of underpinning
570
00:36:19.230 --> 00:36:19.680
research.
571
00:36:19.950 --> 00:36:22.880
Emily Kate Stephens: In terms of
what you are moving towards in
572
00:36:23.690 --> 00:36:27.530
the future in PrecisionLife,
you're also hoping to use some
573
00:36:27.590 --> 00:36:31.400
of this understanding. As you
said, what's the protective
574
00:36:31.460 --> 00:36:35.090
biology in some of these people?
Why do some people remain
575
00:36:35.240 --> 00:36:38.570
healthy? And you are looking
potentially. I know that this
576
00:36:38.600 --> 00:36:41.590
doesn't help with the people who
are already infected, but you
577
00:36:41.800 --> 00:36:46.030
are in the future looking. Maybe
there are protective vaccines
578
00:36:46.120 --> 00:36:47.140
that could be developed.
579
00:36:47.920 --> 00:36:51.730
Steve Gardner: Yeah, this is
really exciting science. We've
580
00:36:51.760 --> 00:36:56.530
been able for the first time to
almost turn the analysis on its
581
00:36:56.620 --> 00:37:00.580
head. Instead of saying which
genes show up more in patients
582
00:37:00.940 --> 00:37:03.660
than in healthy people. We're
asking the question the other
583
00:37:03.690 --> 00:37:07.140
way round. We're saying who are
the healthy people who have lots
584
00:37:07.260 --> 00:37:11.640
of risk genes in their makeup
but don't get the disease, even
585
00:37:11.730 --> 00:37:15.630
though they may have had
repeated viral infections and
586
00:37:15.870 --> 00:37:20.480
other disease risk factors. The
hypothesis is that they're not
587
00:37:20.540 --> 00:37:24.410
getting sick because the
background processes in the body
588
00:37:25.580 --> 00:37:28.400
are preventing them from from
doing that. They're resisting
589
00:37:28.550 --> 00:37:33.470
disease pressure. We have found
in other diseases such as ALS or
590
00:37:33.530 --> 00:37:37.070
motor neurone disease, and in
endometriosis that these
591
00:37:37.190 --> 00:37:42.430
processes exist and they do
indeed actively work to resist
592
00:37:42.430 --> 00:37:46.360
the development of the onset of
symptoms and the severity of
593
00:37:46.420 --> 00:37:49.750
those symptoms. So this is
great. Now you could think, what
594
00:37:49.780 --> 00:37:53.920
does the COVID mRNA vaccine do,
for example? Well, basically,
595
00:37:53.980 --> 00:37:58.030
what it does is it turns your
muscle cells into a little
596
00:37:58.330 --> 00:38:02.370
production factory for certain
proteins. In the COVID vaccine,
597
00:38:02.640 --> 00:38:06.270
they produced viral spike
protein, and we can talk about
598
00:38:06.450 --> 00:38:10.110
the pros and cons of having lots
of viral spike protein kicking
599
00:38:10.260 --> 00:38:14.340
around in the context of a
therapeutic vaccine. What they
600
00:38:14.460 --> 00:38:19.290
would be producing are more
copies of your existing proteins
601
00:38:19.530 --> 00:38:21.830
that are the ones that are
resisting these disease
602
00:38:21.920 --> 00:38:26.810
processes, So you're turning up
the volume on this
603
00:38:26.930 --> 00:38:30.500
protective signal, and in
principle, that benefits pretty
604
00:38:30.560 --> 00:38:33.200
much everybody. It's not like a
drug that's just fixing one
605
00:38:33.260 --> 00:38:36.290
thing that's gone wrong in in a
certain set of patients.
606
00:38:36.530 --> 00:38:39.530
Everybody would benefit some,
some a lot, some less from
607
00:38:39.860 --> 00:38:43.240
having more of this protection.
Of course, you can also use drug
608
00:38:43.330 --> 00:38:46.990
repurposing. So we're also
actively looking for compounds
609
00:38:47.080 --> 00:38:49.960
that stimulate those same
processes, and there are
610
00:38:50.320 --> 00:38:53.350
examples of those. And in fact,
I referenced one of the studies
611
00:38:53.410 --> 00:38:58.360
where we've got three arms. Two
of them are for risk genes. One
612
00:38:58.360 --> 00:39:01.200
of them is for a protective
gene. So I'm really excited
613
00:39:01.260 --> 00:39:05.970
about that as a as a way of
giving us a better toolkit with
614
00:39:06.000 --> 00:39:08.070
which to to deal with the
disease.
615
00:39:08.310 --> 00:39:09.750
Emily Kate Stephens: This is
very exciting. It's one of the
616
00:39:09.750 --> 00:39:12.030
things that I speak to so many
people about is that our
617
00:39:12.120 --> 00:39:14.640
healthcare system is not
actually a healthcare system.
618
00:39:14.640 --> 00:39:17.760
It's not set up for health. It's
set up for sickness. So you're
619
00:39:18.270 --> 00:39:21.350
suggesting that you might have
in this, and I guess to a
620
00:39:21.410 --> 00:39:25.010
degree, when they give us
vaccines as babies, we do have
621
00:39:25.250 --> 00:39:27.950
to a degree the healthcare
because we are trying to prevent
622
00:39:28.040 --> 00:39:31.880
certain things. But the idea
here is that you are helping to
623
00:39:32.060 --> 00:39:36.350
prevent people with that known
susceptibility from succumbing
624
00:39:36.560 --> 00:39:38.510
to the disease, which is
remarkable.
625
00:39:38.900 --> 00:39:41.440
Steve Gardner: And I think this
gets really down to how, in the
626
00:39:41.440 --> 00:39:44.680
future, we would want to manage
the disease. I think if we know
627
00:39:44.920 --> 00:39:47.920
that there are people who are
particularly at risk, we would
628
00:39:48.040 --> 00:39:51.910
want to work harder to ensure
that they're vaccinated. We'd
629
00:39:52.000 --> 00:39:55.180
want to work harder to make sure
that they weren't exposed to
630
00:39:56.020 --> 00:39:59.320
disease triggers and particular
infections, and we'd want to
631
00:39:59.380 --> 00:40:04.170
work harder in terms of
augmenting protective processes
632
00:40:04.260 --> 00:40:07.320
that they have going on in their
body, and all of those measures
633
00:40:07.470 --> 00:40:12.210
together, I think would give us
a way of preventing many people
634
00:40:12.300 --> 00:40:16.440
from experiencing the most
debilitating forms of disease.
635
00:40:16.650 --> 00:40:20.040
And I think that everything I've
said is true in ME. It's true in
636
00:40:20.100 --> 00:40:23.600
long COVID, and I think it's
true across all complex chronic
637
00:40:23.720 --> 00:40:27.200
diseases. There are shared
attributes of these diseases
638
00:40:27.740 --> 00:40:30.860
that have made them more
difficult to study, have made
639
00:40:30.950 --> 00:40:34.310
them more difficult to get
effective drugs or better
640
00:40:34.400 --> 00:40:37.880
diagnostics, and they would all
benefit from from similar types
641
00:40:37.970 --> 00:40:38.570
of approaches.
642
00:40:38.810 --> 00:40:40.930
Emily Kate Stephens: Can I ask a
question that, for you who works
643
00:40:41.080 --> 00:40:44.920
in genetics, might seem very
obvious, but if we are talking
644
00:40:45.070 --> 00:40:50.320
about some people having the
genetic code to actually suggest
645
00:40:50.350 --> 00:40:53.020
that they could develop these
conditions, does that give a
646
00:40:53.080 --> 00:40:57.370
suggestion that there is a
potential hereditary inherited
647
00:40:57.580 --> 00:41:00.340
element of that genetic code?
648
00:41:00.700 --> 00:41:03.210
Steve Gardner: Family history
remains one of the strongest
649
00:41:03.240 --> 00:41:07.410
predictors for many complex
diseases. That is also true to
650
00:41:07.500 --> 00:41:11.880
some degree in long COVID and ME.
Most of the studies that you
651
00:41:12.090 --> 00:41:15.870
see, the level of genetics that
we can say definitively are
652
00:41:15.900 --> 00:41:19.530
associated with the disease
actually don't explain all of
653
00:41:19.590 --> 00:41:23.120
that heritability. I remember
the figure for endometriosis, so
654
00:41:23.270 --> 00:41:27.380
forgive me for using a different
one. But endometriosis is about
655
00:41:27.470 --> 00:41:32.630
50% heritable, but the genetics
that we found using those tools
656
00:41:32.720 --> 00:41:36.590
that were developed originally
for cancer only explain about 5%
657
00:41:37.160 --> 00:41:41.530
of the disease. 90% of the
genetic signal we're just not
658
00:41:41.680 --> 00:41:45.370
seeing, so we've developed a
different methodology that
659
00:41:45.490 --> 00:41:48.730
allows us to find more of that
signal in the first place. But
660
00:41:48.730 --> 00:41:52.240
the second piece of that is it
goes back to this clinical label
661
00:41:52.330 --> 00:41:55.570
question: Did those people
actually have the disease? Were
662
00:41:55.720 --> 00:41:58.990
they diagnosed correctly? And
were people who were dismissed
663
00:41:59.170 --> 00:41:59.860
out of the clinic
664
00:42:00.220 --> 00:42:00.970
Emily Kate Stephens: not
diagnosed?
665
00:42:01.210 --> 00:42:03.420
Steve Gardner: Probably, yeah,
not diagnosed. They were missed
666
00:42:03.840 --> 00:42:07.350
as opposed to misdiagnosed, and
I think there's a there's a lot
667
00:42:07.410 --> 00:42:11.490
of that going on inside these
diseases. As we get better at
668
00:42:11.790 --> 00:42:15.000
understanding the genetics of
these disorders, that will give
669
00:42:15.000 --> 00:42:18.750
us better diagnostic tools, and
those better diagnostic tools
670
00:42:18.840 --> 00:42:22.610
will in turn allow us to define
much more specifically the
671
00:42:22.640 --> 00:42:25.160
patient populations that we're
dealing with, and that has
672
00:42:25.280 --> 00:42:28.580
absolutely been the the
experience in oncology. We now
673
00:42:28.820 --> 00:42:33.440
know HER2 positive breast
cancer, we know hormone
674
00:42:33.530 --> 00:42:36.500
responsive breast cancer, we
know triple negative breast
675
00:42:36.530 --> 00:42:40.570
cancer, and they're defined much
more narrowly and much more
676
00:42:40.660 --> 00:42:44.470
accurately than previously, and
that it has led, as I said, to
677
00:42:44.800 --> 00:42:48.190
sustained improvement in
survivability of those those
678
00:42:48.280 --> 00:42:48.820
tumor types.
679
00:42:49.060 --> 00:42:50.740
Emily Kate Stephens: It's
amazing. I think it's very
680
00:42:50.980 --> 00:42:56.050
hopeful, but I also like your
caveating. The research is
681
00:42:56.140 --> 00:43:00.160
there, but warning our audience
that because we have this
682
00:43:00.220 --> 00:43:01.410
information doesn't mean that
they can.
683
00:43:01.740 --> 00:43:03.450
Steve Gardner: It doesn't mean
that it will work for them.
684
00:43:04.260 --> 00:43:07.800
Unfortunately, we still need to
do the science. We still need to
685
00:43:07.860 --> 00:43:11.610
do the clinical trials. I would
say two things. Number one, if
686
00:43:11.610 --> 00:43:15.030
if your listeners want to read
up about this, they're very
687
00:43:15.840 --> 00:43:19.320
welcome to go to the PrecisionLife
website. We have a link
688
00:43:19.440 --> 00:43:22.310
through precisionlife.com/mecfs.
689
00:43:23.090 --> 00:43:24.560
Emily Kate Stephens: I'll
include that in the show notes.
690
00:43:24.710 --> 00:43:27.230
Steve Gardner: Thank you. All of
those studies are referenced
691
00:43:27.350 --> 00:43:31.460
there, plus some talks,
podcasts, etc. The second thing
692
00:43:31.610 --> 00:43:35.330
I would ask, if your listeners
are are interested, we are
693
00:43:35.390 --> 00:43:40.070
trying to understand the
appetite of the community for a
694
00:43:40.190 --> 00:43:44.800
consumer-facing genetic report
and trying to understand how
695
00:43:45.280 --> 00:43:49.750
best to structure that and how
best to bring it to market. So
696
00:43:49.840 --> 00:43:53.410
on that page as well, there is a
button to take a very short
697
00:43:53.530 --> 00:43:57.790
survey, a two-three minute
survey that we would like to
698
00:43:58.690 --> 00:44:02.220
hear from the community whether
this would be valuable to them,
699
00:44:02.370 --> 00:44:05.730
how it should be reported, and
how best we can essentially
700
00:44:05.850 --> 00:44:10.500
bring this to patients with
these diseases. So the help
701
00:44:10.770 --> 00:44:12.990
would be very gratefully
received on that.
702
00:44:13.170 --> 00:44:14.700
Emily Kate Stephens: I've signed
up. It's really, really very
703
00:44:14.880 --> 00:44:17.760
simple, very straightforward. So
I encourage people to go and do
704
00:44:17.820 --> 00:44:20.840
it because if it enhances your
understanding of what the
705
00:44:20.930 --> 00:44:24.140
community needs when you have
access or you have the ability
706
00:44:24.290 --> 00:44:27.980
to do this very very wide
ranging research, then I really
707
00:44:28.010 --> 00:44:29.660
really would encourage people to
take part.
708
00:44:30.320 --> 00:44:32.390
Steve Gardner: I'm being very
careful what I say. This is a
709
00:44:32.450 --> 00:44:37.070
consumer facing wellness test.
However, in the fullness of
710
00:44:37.160 --> 00:44:40.360
time, what we would like to do
to address the question that you
711
00:44:40.420 --> 00:44:44.050
raised earlier, we would like to
be able to bring this into a
712
00:44:44.170 --> 00:44:48.190
more diagnostic test. We're not
there yet. This isn't. This test
713
00:44:48.220 --> 00:44:51.700
is not that test. There's more
science to be done. There's more
714
00:44:52.030 --> 00:44:55.720
research and validation work to
be done. But these are stepping
715
00:44:55.840 --> 00:44:58.900
stones that we need to make with
the disease in order to be able
716
00:44:58.960 --> 00:45:02.280
to understand it better and to
have those tools that we could
717
00:45:02.460 --> 00:45:05.970
then use in the clinic once we
do have some medicines that work
718
00:45:06.330 --> 00:45:08.760
to guide people towards things
that are actually going to be
719
00:45:08.820 --> 00:45:09.510
effective for them.
720
00:45:09.780 --> 00:45:12.240
Emily Kate Stephens: For them,
and that is the key to what
721
00:45:12.270 --> 00:45:15.990
you're trying to do. Thank you
so so much for your time today.
722
00:45:16.230 --> 00:45:18.960
It has been an absolutely
brilliant conversation. I've so
723
00:45:19.080 --> 00:45:19.830
much enjoyed it.
724
00:45:20.070 --> 00:45:22.550
Steve Gardner: It's been a
pleasure, and thank you for your
725
00:45:22.610 --> 00:45:26.900
advocacy and the awareness that
you bring, and the information
726
00:45:26.990 --> 00:45:29.450
that you bring for the patient
community. It really is
727
00:45:29.510 --> 00:45:30.170
appreciated.
728
00:45:35.760 --> 00:45:37.920
Emily Kate Stephens: So, Gez,
tell me your thoughts.
729
00:45:38.090 --> 00:45:40.010
Gez Medinger: Well, first of
all, just so pleased to hear
730
00:45:40.100 --> 00:45:42.490
someone banging the same drum
that I've just been banging,
731
00:45:42.640 --> 00:45:46.150
feeling like I've just been
waving my fist at clouds. I
732
00:45:46.330 --> 00:45:49.690
loved the way that Steve put it,
which is: let's say we've got
733
00:45:49.870 --> 00:45:52.210
ME, or let's say we've got long
COVID, or any complex chronic
734
00:45:52.240 --> 00:45:55.720
condition. Inside those, we've
got a rainbow of colors, which
735
00:45:55.840 --> 00:45:58.720
are essentially the phenotypes
of people inside those
736
00:45:58.840 --> 00:46:02.850
conditions. And what we need to
do is to test the drugs that
737
00:46:02.910 --> 00:46:05.880
might work on the yellow people
with the yellow drugs, right?
738
00:46:05.970 --> 00:46:08.370
Put those people into a trial
together, and don't put the
739
00:46:08.460 --> 00:46:10.710
yellow drugs into a trial with
the whole rainbow, because
740
00:46:10.770 --> 00:46:12.960
you're only going to get a small
amount of people responding to
741
00:46:13.080 --> 00:46:14.850
it, and then it won't be
statistically significant, and
742
00:46:14.850 --> 00:46:17.640
you've got a null result. This
might well have been, or this is
743
00:46:17.790 --> 00:46:20.450
what rumor has it was the
problem with trials like the
744
00:46:20.720 --> 00:46:24.170
BC007 one, and again, there's some
word on the street that
745
00:46:24.170 --> 00:46:26.570
some of the antiviral trials
that are happening at the moment
746
00:46:26.570 --> 00:46:30.320
are having similar responses.
That is to say, some people
747
00:46:30.320 --> 00:46:33.500
respond incredibly well and are
marvelously better than they
748
00:46:33.650 --> 00:46:36.470
were, and other people don't
respond at all. But when that's
749
00:46:36.590 --> 00:46:40.720
15% of your group, that's not
enough for that treatment to go
750
00:46:40.870 --> 00:46:44.020
on and reach the next phase of a
trial. So having the right tests
751
00:46:44.470 --> 00:46:47.920
that we don't have to argue
about. This is the dream, right?
752
00:46:47.920 --> 00:46:50.710
I think there is still a little
bit of argument about exactly
753
00:46:50.800 --> 00:46:53.350
what these biomarkers should be
and just how much we can read
754
00:46:53.410 --> 00:46:56.950
into these genetic results. But
fundamentally, if we have a set
755
00:46:57.070 --> 00:46:59.740
of things that we can just do a
blood test and go, great. These
756
00:46:59.770 --> 00:47:01.560
are your genes. These are the
ones that are at high risk.
757
00:47:01.860 --> 00:47:04.650
These are the drugs that you
should consider taking because
758
00:47:04.740 --> 00:47:07.350
they've all been through RCTs
and they have X percent
759
00:47:07.440 --> 00:47:11.040
efficacy. Now that we can see
you're in the green group, here
760
00:47:11.070 --> 00:47:14.250
are the green green drugs. And
what's what's really interesting
761
00:47:14.970 --> 00:47:18.960
is how what we might find is
that whilst long COVID and
762
00:47:18.960 --> 00:47:22.640
ME/CFS have different rainbows
or different shades in all of
763
00:47:22.700 --> 00:47:26.510
them, we might find that the
yellow group in long COVID is
764
00:47:26.630 --> 00:47:29.720
actually the same as the yellow
group in ME/CFS or the green
765
00:47:29.810 --> 00:47:32.600
group in ME/CFS. However, you
want to use the metaphor, but
766
00:47:32.630 --> 00:47:36.950
fundamentally, if we've got this
40% crossover with the genetic
767
00:47:36.980 --> 00:47:40.490
results they're seeing between
ME/CFS and long COVID, what's
768
00:47:40.520 --> 00:47:43.420
really interesting to me is that
that's about the proportion of
769
00:47:43.450 --> 00:47:47.560
people 40 to 50% of people who
have long COVID who fit an
770
00:47:47.620 --> 00:47:51.700
ME/CFS diagnosis, who fit the
criteria. I'm one of those
771
00:47:51.820 --> 00:47:54.940
people. It's about labeling is
so important as well, right? And
772
00:47:55.000 --> 00:47:58.660
are we going to go on to a place
where we have eight different
773
00:47:58.690 --> 00:48:02.970
labels for subtypes of long
COVID, and are they going to be
774
00:48:03.360 --> 00:48:06.870
eight for ME/CFS or 20 or or who
knows? And as Steve said,
775
00:48:06.900 --> 00:48:08.400
there's so many genes. The
problem they've got at the
776
00:48:08.460 --> 00:48:11.490
moment is actually too many
results rather than not enough
777
00:48:11.610 --> 00:48:13.170
results. And how do you make
sense of those?
778
00:48:13.590 --> 00:48:15.030
Emily Kate Stephens: We've
spoken to people before about
779
00:48:15.060 --> 00:48:17.820
this comparison with autoimmune.
Initially, it was kind of
780
00:48:17.880 --> 00:48:22.010
classified as one umbrella term,
and now it's been broken out. If
781
00:48:22.130 --> 00:48:25.130
you have the ability to break it
out in terms of the genetics and
782
00:48:25.250 --> 00:48:29.690
operate on this can fix the
yellow, this can fix the green
783
00:48:29.840 --> 00:48:33.110
genetics. Then you have a far
greater chance of people
784
00:48:33.470 --> 00:48:37.820
actually yielding the results.
But I think that is fascinating
785
00:48:37.880 --> 00:48:40.660
if we reflect back on those
trials that have proved
786
00:48:40.750 --> 00:48:44.560
inconclusive, or that haven't
worked, despite very positive
787
00:48:44.650 --> 00:48:46.630
indications before entering the
trial phase.
788
00:48:47.050 --> 00:48:48.940
Gez Medinger: Very positive
indications in the trial phase,
789
00:48:49.060 --> 00:48:52.690
but just not on enough scale. You
know, like with BC007,
790
00:48:52.690 --> 00:48:55.000
we had a bunch of people coming
out saying, "I'm better," and
791
00:48:55.120 --> 00:48:58.030
still, it wasn't enough to make
the whole trial work. But the
792
00:48:58.150 --> 00:49:01.710
sheer mechanics of making trials
work is half the battle, and
793
00:49:01.770 --> 00:49:04.320
we've just got to get the
groundwork right. And it sounds
794
00:49:04.350 --> 00:49:06.600
like we're finally in the
position to be able to do that.
795
00:49:06.660 --> 00:49:09.600
And that's what's really
exciting about this. But again,
796
00:49:10.050 --> 00:49:12.360
this is planting the seeds in
the fields. It's going to be a
797
00:49:12.450 --> 00:49:14.880
while before the potatoes turn up
in our fridge, sadly.
798
00:49:15.330 --> 00:49:17.310
Emily Kate Stephens: Absolutely.
The other exciting thing about
799
00:49:17.610 --> 00:49:21.860
this is though that PrecisionLife
are working with a huge
800
00:49:21.950 --> 00:49:25.790
number of the other groups in
this space, so the the way in
801
00:49:25.820 --> 00:49:28.850
which different groups are
collaborating, and he had a call
802
00:49:28.910 --> 00:49:32.180
to action for researchers who
want to actually start doing
803
00:49:32.300 --> 00:49:35.600
some of the trials with them.
The other thing that has come
804
00:49:35.690 --> 00:49:39.050
out in the past week in terms of
PrecisionLife is that they have
805
00:49:39.230 --> 00:49:44.050
announced that they now have a
US partner to launch and deliver
806
00:49:44.140 --> 00:49:49.030
that DNA wellness report that he
spoke about right at the end,
807
00:49:49.360 --> 00:49:54.310
which is going to include DNA
home testing and the idea of
808
00:49:54.700 --> 00:49:58.090
being able to do that kind of
testing to understand your
809
00:49:58.180 --> 00:50:02.460
genetic makeup without having to
go into a lab is going to make
810
00:50:02.550 --> 00:50:06.330
such difference for all of those
people who have not been able to
811
00:50:06.360 --> 00:50:09.810
be involved in certain trials or
not been able to establish
812
00:50:09.900 --> 00:50:13.740
certain things about their
bodies because they're unable to
813
00:50:13.830 --> 00:50:18.030
travel. So the way in which they
are designing really, really is
814
00:50:18.270 --> 00:50:20.930
with the limitations of the
community in mind.
815
00:50:21.200 --> 00:50:23.300
Gez Medinger: Yeah, absolutely.
There was just one other point
816
00:50:23.330 --> 00:50:26.810
that I, I guess, I would just like
to point out, or clarify,
817
00:50:27.470 --> 00:50:29.810
and also just point out my own
confusion on, and that is the
818
00:50:29.840 --> 00:50:33.170
what is genetics, right? It's an
incredibly confusing subject,
819
00:50:33.470 --> 00:50:36.050
and we all know that we have
genes. That's why you look a bit
820
00:50:36.080 --> 00:50:38.540
like your brothers and sisters
and your parents and the rest of
821
00:50:38.810 --> 00:50:41.860
it. But the genes I think that
are being talked about here are
822
00:50:41.980 --> 00:50:44.920
the myriad number of genes that
you have in your body that have
823
00:50:44.980 --> 00:50:48.460
a function, and they can switch
on and they can switch off. And
824
00:50:48.580 --> 00:50:51.100
what we're talking about when we
talk about therapeutics here is
825
00:50:51.460 --> 00:50:54.400
about modifying the function of
that gene. We're not turning you
826
00:50:54.430 --> 00:50:57.610
into some sort of mutant
superhero by changing what your
827
00:50:57.670 --> 00:51:00.780
fundamental genetics are. We are
simply affecting, but at a very
828
00:51:01.260 --> 00:51:04.170
fundamental level, what switches
on or switches off certain
829
00:51:04.230 --> 00:51:07.650
processes in your body, and by
modulating those, we can change
830
00:51:07.740 --> 00:51:10.530
everything that's downstream of
those. So it has the potential
831
00:51:10.830 --> 00:51:14.730
to be really very effective, and
potentially far more effective
832
00:51:15.240 --> 00:51:17.640
than trying to treat symptoms,
which is treating from the
833
00:51:17.640 --> 00:51:20.040
bottom up. This is trying to
look at the top to try and treat
834
00:51:20.070 --> 00:51:20.720
from the top down,
835
00:51:21.140 --> 00:51:22.100
Emily Kate Stephens: Yeah, I think
he used the word
836
00:51:22.280 --> 00:51:23.840
augmenting, didn't he?
837
00:51:24.020 --> 00:51:24.080
Gez Medinger: Yeah.
838
00:51:24.800 --> 00:51:27.312
Emily Kate Stephens: You're trying
to augment the behavior,
839
00:51:27.378 --> 00:51:31.477
I think, of those genes. So it's
an exciting space. And if you
840
00:51:31.543 --> 00:51:35.774
want to know more about the work
into genetics and genomics that
841
00:51:35.840 --> 00:51:39.807
PrecisionLife are doing, Steve and
his co-founder Rowan
842
00:51:39.873 --> 00:51:43.642
have a fantastic podcast that
delves deeper, and I highly
843
00:51:43.708 --> 00:51:47.807
recommend checking it out. Gez,
this has been fantastic. Thank
844
00:51:47.873 --> 00:51:50.650
you so much for gracing me with
your time.
845
00:51:50.650 --> 00:52:00.560
Gez Medinger: You too.
846
00:52:00.610 --> 00:52:02.730
Emily Kate Stephens: Thank you
for listening to Make Visible.
847
00:52:03.030 --> 00:52:06.510
Please do like, follow, or
subscribe to listen to our next
848
00:52:06.570 --> 00:52:09.390
episode, where we'll be
uncovering more insights into
849
00:52:09.450 --> 00:52:13.800
complex chronic illness. This
was brought to you by the team
850
00:52:13.980 --> 00:52:16.680
at Visible, a group of
scientists and engineers whose
851
00:52:16.770 --> 00:52:19.140
lives have been affected by
energy-limiting health
852
00:52:19.230 --> 00:52:22.760
conditions. We're building
wearable technology that's
853
00:52:22.820 --> 00:52:26.330
helping 100,000 people measure
and manage their complex chronic
854
00:52:26.420 --> 00:52:31.220
illness. To find out more about
what we're working on and how
855
00:52:31.370 --> 00:52:35.120
Visible could help you, visit
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