130 - Automation in Analytical Labs (S9E10)

All right. Welcome, everyone. Today we're diving
deep into something that's totally changing the
game in analytical labs. You got it. Automation.
We're going to break down how robotics, AI, you
name it, are changing how labs do those. Everyday
analyses, but also those really complex ones.
Yeah, and it's happening now not just some sci
-fi future thing We're talking about things like
prepping samples automatically figuring out the
best way to schedule those expensive lab instruments
So they're not sitting around and how all that
data is being analyzed automatically in some
really cool ways And you know for you the listener,
it's not just the technology that's cool. It's
the real -world impact We're gonna be looking
at you know the challenges of actually making
these systems work in a lab and the big question
Is it really worth it the return on investment
exactly? We want to look beyond just the idea
and see how labs are using this stuff To get
through more samples, you know reduce those little
human mistakes that can happen and even do a
ton of testing Without needing someone watching
it all the time And this whole shift, it's happening
for a reason. Things are getting super complex,
like in pharmaceutical research, and there are
more and more rules to follow. So automation
is becoming super important for labs across a
bunch of fields. Not just nice to have, but gotta
have it. OK, let's break this down, starting
at the beginning. Getting samples ready for analysis.
I mean, that can be a lot of work. How is automation
changing that? Yeah, and what's interesting is
how much it streamlines those very first steps.
I mean, think about it, all that careful measuring,
mixing, transferring. Automation really cuts
down on all that, and when you do that, less
risk of human error. Makes sense, especially
when you think about high throughput screening
in drug discovery, right? Yeah. You're talking
tons of compounds. Oh, yeah, it's huge. So to
find new drugs, they test, you know, thousands
of compounds, and automated sample prep is key,
like take human liver microsomes, HLMs, it's
basically using these tiny parts of the liver
in a test tube. Like a mini liver to see how
it breaks down a drug. Yeah, exactly. And the
research says these HLM assays are great for
high throughput screening and the gold standard
to predict how a drug is metabolized by the liver.
This means finding promising drug candidates
way faster. Wow. So these systems can run tests
on tons of compounds at once. Yeah. They measure
how fast the drug disappears in the microzones,
rank them based on stability, and then bam, the
research team knows which candidates to focus
on. Makes sense. The research mentioned other
systems like using hepatocytes or even liver
slices. How did those fit into this whole automated
sample prep thing? That's a good question. You
know, microsomes are great for a first look,
but they don't always show everything, especially
with phase two metabolism. Right. So that's when
the body attaches other molecules to the drug,
changing how it works and how fast it's cleared.
Isolated hepatocytes, which are liver cells that
have been separated, they give a better picture
because they have a wider variety of enzymes,
and some of those can even be frozen cryopreserved.
Then there are liver slices, keeping more of
the liver structure. Yeah. And the cool thing
is both of those, the hepatocytes and liver slices,
can be used in automated setups. So you get a
more complete metabolic profile of a drug. So
you could have different automated platforms
for different levels of analysis, depending on
where the drug is in development. Exactly. As
things move along, you need more specific info.
The research also talks about CYP reaction phenotyping.
So these are enzymes in the liver that break
down lots of drugs. OK. Knowing which CYPs are
involved helps predict drug interactions and
how genetic differences might affect things.
And guess what? Automation helps get that data,
too. Wow. So it's not just speed. It's getting
a more in -depth analysis earlier on. Really
cool. OK, let's move from sample prep to the
actual analysis, those high -tech instruments.
How does automation help with instrument scheduling?
Yeah, so analytical instruments, you know those
mass spectrometers or NMR machines, they're not
cheap. And scheduling systems can help make the
most of them, so they're running as much as possible,
minimizing downtime. So instead of someone manually
scheduling, hoping everything fits, the software
does it all. Right, takes into account what's
available, the analysis needed, can even prioritize
urgent stuff. Huge help for labs that have tons
of samples or strict deadlines, all about maximizing
output and getting those results back fast. Really
streamlines things. Now, with all these instruments
going, you get a ton of data, right? What about
processing all that? How does automation help
there? Oh, it's essential. Modern instruments
produce massive data sets. Analyzing all that
by hand would take forever and be prone to errors.
This is where specialized software and AI are
stepping in. The research even talked about AI
and radiology. helping analyze medical images.
Interesting comparison. How does that connect
to what's happening in analytical labs? It's
a good parallel. Like, AI is used to understand
images, create reports, even offer insights to
doctors. In labs, AI algorithms can be trained
to analyze complex outputs from things like mass
spectrometers or NMRs. Think of it as a super
smart assistant who can spot things we might
miss. Wow. Yeah, they can identify substances,
measure amounts, flag anything weird in the data.
So it's not just crunching numbers. It's about
finding the important stuff in all that data.
Absolutely. And AI can find those subtle patterns
or anomalies we might miss. Huge for quality
control or research. The research also talked
about specific applications like nanoelectrospray,
mass spectrometry, and high -throughput mass
spectrometry. So AI is moving beyond just images.
It's becoming a powerful tool to analyze complex
data in all sorts of fields. But implementing
all this can't be easy. What are some of the
challenges labs face? Well, the cost of the equipment
and software can be pretty steep, especially
for smaller labs. And it's not just buying it,
right? You need people who know how to use this
stuff. Exactly. Training is key. Scientists and
technicians need to know how to run it, fix problems
and understand the data. That might mean more
training or even hiring people with expertise.
And what about making all these new systems work
with what a lab already has? That seems tricky.
Oh, yeah. Integration can be tough. Labs often
have a mix of old and new equipment. Getting
them to communicate smoothly can be complex.
It might involve upgrading IT systems or even
creating custom software. So you've got cost,
training, and integration challenges. Labs really
have to think about all that. And of course,
the big question is, what's the return on investment?
The ROI. So the ROI comes in a few ways. Increased
throughput is a big one. Automation means processing
more samples faster, which means quicker results.
For businesses, that's more revenue. And in research,
it speeds up discoveries. And less human error
that's got to factor in. Definitely. Automating
steps reduces those manual errors in handling
and data entry. That means better data quality,
and you don't have to waste time and money reanalyzing
things. Also seems like you could free up scientists
and technicians from those repetitive tasks.
Right. It frees them up to focus on the complex
stuff, the thinking, the designing, the interpreting.
That can boost productivity and innovation in
a lab. Like, you know, the research talked about
how important fast and reliable data is for high
throughput screening and drug discovery. Automation
makes that happen and ultimately impacts drug
development. OK, so it's not just saving money.
It's about using your people in the best way
possible. Yeah. Now, can we look at some examples
of how automation is actually benefiting different
labs? Sure. Imagine a pharmaceutical lab using
automated liquid handling with mass spectrometry.
Like we talked about with HLM and hepatocyte
assays, this setup would let them screen way
more potential drugs for stability way faster.
They get good data quickly, and it speeds up
the whole discovery process. It's a big speed
boost. What about an example of reducing human
error? Take a quality control lab in a factory.
They could use robots to automatically take product
samples and load them into instruments for testing.
Less manual handling, less chance of mix -ups
or contamination. So the results are more reliable.
Exactly, which is crucial for safety and regulations.
Now, how about an example with AI and complex
data? Think of a research lab studying how a
molecule's structure affects its activity, you
know, for drug development. They often use NMR
spectroscopy, and AI software could analyze that
complex data automatically. The software could
even pinpoint structural features linked to biological
activity. That'd really speed up drug development,
finding what works best. Right. And lastly, high
volume testing with minimal human oversight.
Imagine a clinical lab processing thousands of
patient samples daily, automating everything
from receiving samples to analysis and reports
ensures fast, accurate results, even with tons
of samples. Those examples really show how powerful
automation can be. So as we wrap up, what are
the key takeaways? Automation offers a big increase
in how much a lab can do, a big reduction in
errors, and the ability to handle huge amounts
of testing without needing as much direct supervision.
And while there are those initial challenges
like cost and training, it seems like the ROI
is definitely there. Yeah, increased efficiency,
better data, and it lets those skilled analysts
focus on the important stuff. So here's something
to think about. As automation keeps growing in
labs, how will the role of the scientists change?
What new skills will be needed? What will this
mean for scientific discoveries and quality control
down the road? It's a really exciting time for
labs.