Transcriber: Stevie Preater Reviewer: Leonardo Silva
Every year,
700,000 people are killed by superbugs
To put that in perspective,
that's more than the combined populations of the cities of Bristol and Bath.
What's more is that in about thirty years
that number is expected to rise to 10 million people a year,
which would mean that superbugs would have killed more people than cancer.
Now, many of us may have heard the term 'superbug'
thrown around at some point in our lives.
But lets take some time to think about what it is we're really referring to,
how we got here,
and most importantly,
how do we get ourselves out of this crisis?
So what is a superbug?
'Superbug' is a name given to a particular group of bacteria,
otherwise known as a strain,
that cannot be treated with most of the antibiotics
available or in use today.
They're a formidable enemy when you consider the fact
that many people would not have lived to see their sixtieth birthday
prior to the discovery of penicillin and other classes of antibiotics.
Raise your hand if you've ever had an antibiotic prescribed to you.
OK.
Well, you'll be familiar with the process I'm about to describe next.
It probably all started when you weren't feeling very well:
a persistent cough, a fever,
or perhaps that burning sensation when you'd go for a wee.
(Laughter)
Having put up with it all weekend,
you finally mustered up that courage to go out and speak to your doctor.
After reviewing your symptoms,
you're given a prescription for antibiotics and told:
'Come back after the third dose if your symptoms persist'.
Now, I'm an engineer, so I love my flowcharts.
And they really help me get my head around what's going on.
So I took the liberty of putting this little schematic together.
So step one: bacterial infection starts.
Step two: signs and symptoms develop.
Step three: person goes out to get medical attention.
Step four: antibiotic prescription is given.
And step five: after three doses,
check whether symptoms are getting better.
If yes, carry on.
If no, order a diagnostic test to determine what is making you sick,
and then return to step three.
Now, I've got the utmost respect for our friends in the medical profession,
but this system is fundamentally flawed, for a couple of reasons.
Firstly, it encourages your doctor to start treatment
before having all the information.
Secondly,
each time we go around that loop,
what we're actually doing is killing off all the bacteria
that cannot defend themselves against a given antibiotic,
leaving behind a monolithic group of bacteria that can.
In effect, we're helping the enemy sift out unfit soldiers.
Now, we're in this predicament because, still today,
one of the most commonly used methods for identifying bacteria
involves taking a sample of urine, of mucus, of blood,
and growing it under varying conditions to help us piece together the identity.
Think of it like a process of elimination carried out in the lab.
Once the bacteria is identified,
we then use another process
to determine which antibiotic most likely will work.
The problem is that that takes time,
two days or more,
and in some extreme cases,
people have actually died before their doctors got the answers.
Nevertheless, this is our system; treatment first, diagnostic second.
So how do we get ourselves out of this?
Actually, it would be a lot better
if we could first work out what's making you sick,
and then selecting the most appropriate antibiotic.
Not only would you get better days sooner, but we'd also curb the rise of superbugs.
But to do that, we'd need a test that's fast.
I mean, like, really, really fast;
fast enough to meet the 20 minutes or less
that you have with your doctor or your pharmacist.
But also, it'd also need to be affordable;
affordable enough that developing countries
could make that transition.
And finally, it would need to be easy enough to use
that it's as effortless as checking your temperature.
And that is exactly what my team and I have been working towards
over the last two years.
We've been developing a technique based on receptor-mediated sensing.
We've quoted it GMS for short, and it's a lot simpler than it sounds.
For many bacteria, the first step of infecting you
involves sticking themselves to the cells in your body
using a hook and loop system
that's quite similar to what we've come to love in Velcro.
(Laughter)
The hooks in this case would be specific proteins, or receptors,
on the surface of the bacteria,
and the loops would be specific molecules on the surface of the cells in your body.
What we've done is created a low-cost, light-emitting material
and coated it in loops;
let's call them probes.
When we mix our probes with a sample - for example, urine -
they stick to bacteria like Velcro.
And by measuring the light coming off of them,
we can then calculate the number of bacteria present in that sample.
The final process itself will be quite simple.
Take a bit of urine, add it to a special cartridge.
Then place that cartridge in our very own bug detector.
The cartridge would mix the urine with our probes
before separating out the bacteria and measuring the light coming off.
That final step will allow us to determine whether bacteria is present,
which one, and how many,
all within 15 minutes.
From two days, to 15 minutes.
Now, as with most scientific developments,
the journey is fraught with challenges and sometimes disappointments, frankly.
And I'll share a couple of them with you.
One of our first challenges was: how do you develop a probe
that effectively mimics the cells in your body?
It took a team of researchers at the University of Bristol
months of experimenting
to get the recipe just right
to have the right type and balance of loops to mimic the cell.
I mean, this is not like bashing out bad pancakes on a Sunday morning.
This is the kind of effort you put into winning Masterchef.
(Chuckling)
The second challenge for us was actually finding a detection method
that was low-cost, yet powerful enough to detect the probes
once attached to the bacteria.
For that, we turned to a well-established technique based on fluorescence.
Fluorescence is a phenomenon
where a material stimulated with light absorbs some of it,
and re-emits a different colour.
As a detection technique,
we'd effectively take a specific colour of light,
shine it at that material, and observe the colour coming back,
and that helps us tell what's present.
So, we've got probes, they stick to the right bacteria,
and we can detect them.
Job done, right?
Actually, not quite.
Most of the foods that we eat actually get broken down
into light-emitting materials that end up in your wee.
And so, these are things like energy drinks, vitamin supplements,
pregnancy supplements,
and all of these together could lead to false positives.
In other words, they make it really hard for us to determine the difference
between friend or foe.
And so, differentiating our probes from the remnants of your last energy drink
is absolutely important
for ensuring that we do not report that bacteria is present
even when there aren't any.
This is where the research took a really interesting turn,
because getting around this meant getting familiar with our probes,
but also getting really familiar with urine.
We carried out several experiments observing how our probes behaved
when stimulated with different colours of light.
Particularly, we're interested in how much green light is emitted
for every colour of light that we stimulated them with.
This is the profile of our probes,
and it does not change.
So any deviation from that would then tell us
that there's other stuff mixed in with the sample,
and it would look a little bit like when it's mixed with urine, for example.
By measuring that change, we can actively correct for interference
from anything else that's present in the urine.
So pulling it all together, the probes help us find the bacteria
and fluorescence helps us find the probes.
We're still at the early stages of our journey,
but we were able to take our first prototype into a hospital lab
where we tested for E. coli in human urine.
GMS was able to detect the presence of urine without any false positives,
and it correctly reported the absence of E. coli in the urine in 63% of cases.
Finally,
by placing three measurements in parallel,
we were able to achieve an average time per test of just four minutes.
This is certainly not the end of the story for us.
We're now developing other forms of loops to address other harmful bacteria,
and we aim to test and improve the system a few more times
before it gets to your GP.
But once we do this,
combining our faster, accurate diagnostics
with better antibiotic usage and continued public engagement,
we could finally tip this war back in our favour
and overcome superbugs once and for all.
Thank you.
(Applause)