140 – Digital Twins in Process Development (S10E5)

Welcome to the deep dive. Today, we're tackling
something pretty transformative in manufacturing,
digital twins. Right. And maybe forget the sci
-fi image of robot doubles. This is about virtual
copies of actual processes. Exactly. Think of
it as creating a really detailed virtual replica
of a manufacturing process. And it's got two
main jobs, really. OK. First. helping figure
out the best way to run the process optimization
and second, making it way easier to fix things
when they go wrong troubleshooting. Got it, so
it's like a virtual window into the real thing.
That's a good way to put it. It shows you things
you just can't see by looking at the physical
machines alone. And that's what we're digging
into today, how this tech is shaking up process
development. We're especially keen on the pharmaceutical
side of things. Yeah, that's a really interesting
area for this. We've got a mix of sources, some
general intros, but also some pretty specific
stuff from journals like Organic Process Research
and Development. And honestly, even in the technical
bits, there are some real aha moments. Oh, definitely.
What's really striking is how digital twins change
the whole approach. Instead of just relying on
physical trial and error, which, you know, takes
ages and costs a lot. Right, like a lab work.
Exactly. Now you can build this virtual world
first and simulate the entire production line.
Okay, let's unpack that. Simulating before you
actually build anything physical. Yeah. So imagine
you're setting up a new line for, I don't know,
a new drug. Normally you build it, run tests.
Hit some snags. That's expected, right? Pretty
much standard procedure, but with the digital
twin, you first... build that virtual model of
the whole line, then you can stress test it.
How so? Well, you can play around with all sorts
of parameters virtually, like what's the absolute
optimal temperature inside this reactor, or what's
the perfect mixing speed for these ingredients.
Ah, OK. Things that are critical for, say, crystallization
in making medicines. Precisely. You can tweak
these things in the simulation, change the temperature,
adjust the flow rate, modify the mixing, and
see the predicted outcome immediately. without
actually using any raw materials or time on the
real equipment. That's the beauty of it. You
get to see how changing one little thing might
affect everything else down the line, virtually.
Which must massively cut down the risk when you
finally do go to full production scale up. Huge
difference. Because scale up, as a lot of our
sources highlight, that's a massive hurdle in
pharma. Making something work in a lab flask
versus a giant industrial tank, it's often not
straightforward. Right, things don't always scale
linearly. Not at all. So this virtual testing
de -risks that transition significantly. And
thinking about our listeners trying to get up
to speed efficiently, this simulation approach...
It offers a clearer path to understanding complex
processes, doesn't it? I think so. Instead of
drowning in data from endless physical tests,
you can visualize the whole thing, see the connections.
It's more intuitive. Focused learning, maybe.
Less noise. Yeah, less noise, better signal.
And ultimately, think about the impact potentially
lower costs, getting crucial medicines out faster.
Refining things virtually helps efficiency in
the real world. OK, that makes a lot of sense.
Something else that kept popping up was predictive
maintenance. Sounds a bit futuristic. It does
sound a bit like Minority Report for Machines,
maybe. But it's very real. The idea is that these
digital twins aren't just static blueprints.
They can be connected to the actual physical
equipment using sensors. So the twin is constantly
getting live data feeds about how the real machinery
is performing. So it's not just a plan. It's
like a live virtual mirror of the factory floor.
Exactly that. A dynamic reflection. And then
by combining that live data with historical performance
information. Past breakdowns, maintenance logs,
that kind of thing. Right. The digital twin uses
analytics, often AI or machine learning, to spot
patterns and predict when a piece of equipment
might be heading towards a failure. Whoa! So,
hang on, instead of waiting for, say, a critical
pump on the line to just stop... Which could
shut down the whole line. Right. The twin sees
warning signs in the sensor data before it happens.
That's the goal. Proactive maintenance. And this
is incredibly valuable, especially in pharma,
where consistency and quality are non -negotiable.
You know, FDA regulations and all that. Yeah,
unexpected downtime. There isn't just lost production.
It could compromise a whole batch of medicine.
It absolutely could. big financial hit, potential
safety concerns. OK, walk me through an example.
Make it concrete. Sure. Think about continuous
manufacturing. That's a big trend. Lots of talk
about it in places like OPR &D. You have a line
running constantly. Right, not batch by batch.
Exactly. And on that line, you've got, let's
say, pumps moving fluids around. Critical components.
The digital twin linked to sensors on a specific
pump, monitors its vibration levels, maybe its
temperature, its energy use, the flow rate it's
delivering. All sorts of operational data. Yeah.
Now, if the data starts showing a subtle drift,
maybe tiny increases in vibration over time,
or slightly higher energy use for the same output,
things a human might not even notice day to day.
The quen flags it. The twins' algorithms flag
it as a potential precursor to failure. It alerts
the maintenance team before the pump actually
breaks down. So no sudden shutdown, no potentially
ruined product batch. They can schedule maintenance
during a planned stop. Exactly. Minimal disruption,
consistent production, better quality control.
It's a huge operational advantage. You mentioned
Organic Process Research and Development, OPR
&D again. It sounds like that journal is a gold
mine for seeing this stuff in action in pharma
labs and plants. It really is. You find loads
of case studies where companies describe how
they solved specific development problems. Now,
they might not always explicitly label it digital
twin, especially in older papers. There's a terminology
of all. Yeah, but the core idea using advanced
modeling and simulation to optimize processes
that's been building for years, and it's all
over OPRND. So they were kind of building the
foundations for what we now call digital twins.
Absolutely. You might see, for instance, a paper
detailing how they used, say, computational fluid
dynamics. CFD. OK, fancy simulation software.
Right. To model exactly how reactants are mixing
inside a vessel. They'd simulate different mixer
blade designs or speeds to see how it impacts
the drug yield or purity. Doing it on the computer
first. Doing on the computer first to find the
best conditions before they even run a physical
experiment. Saves a ton of lab work. Makes sense.
Trial and error, but virtual. Pretty much. And
think about another tricky area. Solid forms
of drugs, getting the right crystal structure,
ensuring it's stable, that's crucial. Polymorphism,
right. Where the same drug can crystallize in
different ways. Exactly. And those different
forms, polymorphs, can have really different
properties. Solubility, stability, how easily
they process into tablets. It's a big deal. So
how can a digital twin help there? Well, you
can use it to model the crystallization process
itself, simulate how factors like temperature
changes or the type of solvent used might influence
which polymorph forms. Ah, so you could predict
if you're likely to get the stable desirable
form under certain conditions. That's the idea.
Predict and control it. You can simulate different
scenarios to find the processing window that
consistently gives you the polymorph you want
and avoids the problematic ones. Okay. Virtually
designing the crystallization to avoid downstream
surprises. Precisely. And this ties directly
into continuous crystallization to another hot
topic in OPR &D. Imagine a system where the drug
crystallizes as it flows continuously. More complex
to control, I bet. Definitely. So a digital twin
could simulate that flow, the temperature profile
along the way, how adding seed crystals affects
growth. all to optimize the final crystal size
and shape and prevent things like secondary nucleation.
Where unwanted tiny crystals suddenly appear
and mess things up. Exactly. It allows for much
finer control over these really quite complex
continuous processes. It really sounds like these
twins are becoming indispensable for process
chemists and engineers. They really are. And
it all feeds into this bigger philosophy in pharma
called quality by design, or QBD. Right, we've
talked about QBD before. Building quality in
from the start. Yes. And digital twins are a
perfect tool for QBD. They let you define the
design space virtually. The safe operating window
for the process. Exactly. You can simulate how
critical process parameters like that temperature
or mixing speed affect the critical quality attributes
of the drug, like its purity or dissolution rate,
all done virtually before making large batches.
So you understand the process deeply and build
in robustness from day one, virtually. That's
the power of it. Proactive quality assurance,
driven by simulation. OK, so that covers optimization
and prediction. What about when things just go
wrong? Despite all the planning, sometimes a
batch doesn't meet spec or there's an unexpected
issue on the line. Can the twin help, then? Oh,
absolutely. That's the troubleshooting angle,
and it's incredibly valuable. when something
unexpected happens in the real world. Instead
of scratching your head and running frantic tests.
Right. Instead of just guessing possible causes
and launching maybe weeks of physical investigation,
you can use the digital twin as a diagnostic
tool. How does that work? You take the data from
the real world problem, say, a batch of tablets
isn't dissolving correctly. Which affects how
the drug works in the body, right? Absolutely.
So you feed the problematic data into the twin.
Then you can rapidly test hypotheses. Maybe you
simulate changing the binder amount in the granulation
step that happened earlier, or maybe you simulate
a slight temperature fluctuation during drying.
And see if the virtual simulation replicates
the real -world bad result. Exactly. If tweaking
a specific parameter in the twin makes the virtual
tablet show the same poor dissolution, you've
likely found your root cause, or at least narrowed
it down significantly. Wow, that sounds way faster
than trying as changes one by one on the actual
production line. Orders of magnitude faster,
potentially. Yeah. It's like having that virtual
sandbox, as you said. Test fixes without stopping
production or wasting materials. Find the problem,
implement the fix, much quicker resolution. OK,
so wrapping this up a bit. The big advantages
seem to be better optimization through virtual
testing, proactive troubleshooting when things
go sideways, and this predictive capability for
maintenance. Is that a fair summary? I think
that nails it. Enhanced optimization, smarter
troubleshooting, and predictive power. It's a
toolkit for understanding these complex manufacturing
systems much more deeply and efficiently. Leading
to better quality, less risk, more efficiency,
especially in critical areas like pharma. Definitely.
It's a powerful shortcut to process understanding
and improvement. Which brings us to a final thought
for everyone listening. As manufacturing gets
even more complex, think about making biologics,
these huge protein drugs, or nanomedicines with
tiny engineered particles. Yeah, the complexity
is only increasing. How sophisticated will digital
twins need to become to keep up? And what totally
new insights might these even more advanced virtual
replicas unlock for developing the next wave
of treatments? Something to chew on. A very interesting
question for the future. Indeed. Thanks for diving
deep with us today.