June 29, 2026 • 23 mins
A comprehensive manual for quantitative real-time PCR (qPCR), a primary technology used to measure nucleic acids. The editors, Roberto Biassoni and Alessandro Raso, present a detailed framework covering the method's evolution, technical mechanics, and diverse clinical applications in fields like oncology and microbiology. A significant focus is placed on the MIQE guidelines, which provide essential standards for experimental design and transparent reporting to ensure research is accurate and reproducible. The sources also emphasize the critical nature of quality control, specifically regarding the selection of stable reference genes for data normalization and the assessment of sample purity. Furthermore, the text introduces digital PCR as an emerging third-generation technique capable of providing high-sensitivity absolute quantification for complex diagnostic needs.
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You can listen and download our episodes for free on more than 10 different platforms:
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Produced by:
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
You know, when we talk about diagnostic technology, we tend
to imagine this like perfect infallible machine, right, like.
Speaker 2 (00:07):
A high end molecular photocopy.
Speaker 1 (00:09):
Exactly, you put a patient sample in, you press a button,
and out pops a perfectly accurate account of whatever virus
or mutated gene you're looking for.
Speaker 2 (00:18):
Because the expectation is just absolute precision. You know, you're like,
it is either there or it isn't.
Speaker 1 (00:24):
The ultimate clarity, right, But when you actually step into
a real laboratory, that pristine image kind of shatters because
suddenly that molecular photocopier is sensitive to I mean everything
from the temperature of the room to the exact chemical
brand you're using.
Speaker 2 (00:39):
Oh. Absolutely, You're really no longer in a world of
absolute clarity at that point.
Speaker 1 (00:42):
No, you are navigating diagnostic muddy waters. And that is
exactly what we are waiting into today on this deep dive.
For those of you listening, whether you are a pharmacy student,
a young professional, or just a lifelong learner, our mission
today is to give you a custom, tailored, really comprehensive
look into the twenty four teen textbook Quantitative Real Time
(01:02):
PCR Methods and protocols.
Speaker 2 (01:05):
Yeah, edited by Biasoni and Rosso. It's a fantastic resource.
Speaker 1 (01:08):
It really is. We are going to unpack the evolution
of qPCR, avoiding the unnecessary jargon, and really focus on
the practical clinical applications you're going to see in pharmacology.
Speaker 2 (01:19):
It's such a fascinating journey, honestly. I mean, since its
first report back in nineteen ninety three, QBCR has essentially
become the gold standard for quantifying nucleic acids, just a
massive leap forward from traditional endpoint PCR.
Speaker 1 (01:32):
Okay, so let's unpack this before we really get into
the weeds. What is the fundamental difference between simply amplifying
DNA with like legacy PCR and quantifying it in real time?
Speaker 2 (01:44):
Well, it really comes down to when you are looking
at the data in traditional endpoint PCR, you run the
entire amplification reaction first, okay, you heat and cool the
DNA to copy it over and over, and then only
at the very end of the whole process you run
that amplify DNA through a gel to see if your
target sequence is actually there.
Speaker 1 (02:03):
So it's qualitative, like it just tells you as simple
yes or no.
Speaker 2 (02:06):
Exactly. You get a visual band on a gel, it
proves you made copies, but it doesn't really tell you
how many copies you started with in the original sample.
Speaker 1 (02:13):
Right, But in real time PCR, you introduce fluorescent dyes
or specific fluorescent probes into the chemical mix itself.
Speaker 2 (02:23):
Yeah, and as the carget DNA is copied, that fluorescence increases.
So the machine actually reads this fluorescence during every single
cycle in real time.
Speaker 1 (02:32):
So it's measuring exactly when that glowing signal rises above
the background noise precisely.
Speaker 2 (02:38):
That's called the quantification cycle. And by finding that exact point,
you can calculate exactly how much target DNA was in
the sample to begin with.
Speaker 1 (02:45):
So it's not just did we make copies, it's how
fast did we make copies, which tells us our starting amount. Right,
And the leap in sensitivity there is staggering. I mean,
we're talking about a dynamic range of at least nine locks,
which is huge. Yeah, just to put that in perspective
for you guys, listening nine logs means we can detect
the difference between a single viral particle and a billion
of them using the exact same test.
Speaker 2 (03:07):
It is incredibly powerful, but that extreme sensitivity is exactly
what leads to its biggest historical.
Speaker 1 (03:14):
Flaw, right, which brings us to the whole reproducibility crisis.
Because if this technology is so precise, how were so
many people getting their results completely wrong?
Speaker 2 (03:25):
Well, because KPCR is so hypersensitive, it's uniquely vulnerable to
the slightest variations in how you set up the experiment,
and for a long time researchers just weren't standardizing or
even reporting those variables. And if you don't report exactly
how you handle the sample, well nobody can replicate your work.
Speaker 1 (03:42):
To me an example of that, like, how bad did
this actually get in practice? Oh?
Speaker 2 (03:46):
We got bad? Look at what happened in Washington, DC
in nineteen ninety seven. There was this notoriously flawed autism trial.
Speaker 1 (03:53):
Oh right, I've heard this.
Speaker 2 (03:54):
Yeah. Researchers published data claiming a direct link between the
measles virus gut pathology autism, but a public dissection of
their data later revealed just a massive catalog of inconsistencies.
Speaker 1 (04:06):
So what were they actually doing wrong in the lab
to get those results?
Speaker 2 (04:09):
Basically, they used completely inappropriate reverse transcription qPCR protocols But crucially,
and this is the big one, they ignored their negative controls.
Speaker 1 (04:20):
A negative control is a sample where you know for
a fact the virus isn't there right exactly, So.
Speaker 2 (04:26):
If your negative control starts glowing and showing a positive result,
it means your lab is contaminated or your assay design
is just pulling in random genetic noise. Wow, they were
getting massive false positives. But because the reporting standards across
the industry were so laxed back then, this totally flawed
data made it right into the public consciousness.
Speaker 1 (04:47):
That is just terrifying. I mean for you as a
future pharmacology professional, the lesson here is pretty profound. A
diagnostic test is really only as reliable as the rigor
of its experimental design one hundred percent. You can have
the most advanced machine in the world, but if your
pre analytical steps are flawed, your results are well essentially fiction.
Speaker 2 (05:04):
Precisely, And to combat this sort of wild West of reporting,
a group of researchers finally introduce the MIQE Guidelines IQE. Right, Yeah,
it stands for Minimum Information for the Publication of Quantitative
Real Time PCR Experiments. It breaks the entire process down
into nine sections and eighty five specific parameters categorized is
(05:26):
either essential or desirable.
Speaker 1 (05:28):
I mean, it sounds like a bureaucratic checklist, but it's
really just the scientific method demanding receipts.
Speaker 2 (05:33):
It really is. And to truly understand why MIQE is
so essential, we have to kind of zoom in on
those specific pre analytical steps.
Speaker 1 (05:41):
Let's do that. Let's look at where a qPCR essay
is most likely to fail or just be subtly skewed
before the PCR machine even turns on.
Speaker 2 (05:49):
Yeah. So, one of the biggest hurdles is RNA extraction
and reverse transcription, usually called the RT.
Speaker 1 (05:53):
Step, right, because you're taking fragile RNA from a virus
or cell and turning it into stable complementary DNA CBNA.
Because the standard PCR machine only reads.
Speaker 2 (06:03):
DNA exactly, and the variance at this specific RT step
is just astonishing. The process of reverse transcription isn't perfectly linear.
Simply using different commercially available RT enzymes can cause a
massive one hundred and twenty eight fold difference in the
quantification of your target.
Speaker 1 (06:19):
Wait, one hundred and twenty eightfold if the variance is
that wild, just from buying a different brand of enzyme.
How did any early PCR tests even pass FDA approval.
I mean, that's the difference between diagnosing a patient with
a mild infection versus a life threatening viral load, just
based on the brand of chemical you bought.
Speaker 2 (06:36):
Which is exactly why the MIQE guidelines demand you state
exactly which enzyme you used. But it gets even more
complicated when you choose your priming strategy primers right, Yeah,
to start reverse transcription, the enzyme needs a primer. It's
basically a short starting block of nucleic acid to latch onto.
Speaker 1 (06:54):
Kind of like giving the enzyme a landing.
Speaker 2 (06:56):
Pad, exactly a landing pad, and you have choices. You
can use random x which are random six letter sequences, okay.
You can use penned cameras which are fifteen letter sequences.
You can use oligo DT primers which specifically target the
tail end of messenger RNA, or you can use gene
specific primers.
Speaker 1 (07:12):
Okay, But does the specific landing pad really matter that
much in the end?
Speaker 2 (07:16):
Drastically, so, oligo dts might work beautifully for one type
of messenger RNA, but then completely fail to transcribe another
due to secondary folding structures in the.
Speaker 1 (07:26):
RNA secondary folding structures. Okay, So RNA isn't just like
a straight line of code.
Speaker 2 (07:31):
No, not at all. RNA is a single strand, and
it likes to fold in on itself, binding to itself
to create these complex three D shapes I see. Think
of it like a tangled headphone cord. If it's too tangled,
the oligo DT primer literally can't physically access the starting point.
The enzyme just cannot push through the not Oh.
Speaker 1 (07:50):
Man, here's where it gets really interesting. If we play
this out practically. Imagine you have four different highly respected
clinical laboratories, right, Okay, they're all fallow best practices, but
Lab one uses enzyme and random hexamers, LAP two uses
enzyme B and oligodts, and Legs three and four use
other entirely valid combinations. If you send them the exact
(08:11):
same patient blood sample, they're going to get four entirely different,
technically accurate, yet completely incomparable results.
Speaker 2 (08:18):
And that right there is the crux of the reproducibility crisis.
If they don't publish those exact parameters, nobody can cross
reference their diagnostic data.
Speaker 1 (08:26):
That is wild. Okay, so enzyme choice and primers are
huge variables. What else is skewing the data behind the scenes?
Speaker 2 (08:34):
The patient samples themselves. Honestly, biological samples are full of
PCR inhibitors. For example, f FPE tissues f FPE.
Speaker 1 (08:43):
That's formal and fixed paraffin embedded tissue. Right Like when
a surgeon removes a tumor, ethology preserves it in formal
in chemicals and blocks of wax so they can slice.
Speaker 2 (08:51):
It correct And those preservation chemicals are notoriously harsh on
nucleic acids, they actually act as direct inhibitors to the
PCR enzymes.
Speaker 1 (09:00):
It makes sense.
Speaker 2 (09:01):
Then you have things like EDTA, which is the chemical
inside those purple top blood collection tubes to stop clotting.
You also have leftover phenol or ethanol from the laboratory
extraction process itself.
Speaker 1 (09:11):
So all of these things can just suppress the PCR amplification.
Speaker 2 (09:15):
Yes, leading to an artificially low reading.
Speaker 1 (09:17):
But if an inhibitor is silently suppressing the reaction, how
do you even know what's happening? Like a suppressed positive
result just looks like a negative result to the technician.
Speaker 2 (09:27):
To solve this, researchers introduce the spud assay.
Speaker 1 (09:31):
The spud asset.
Speaker 2 (09:32):
Yeah, it's an ingenious internal control. You add a known
artificial sequence of DNA called the SPUD sequence, directly into
your patient's extracted sample. Then you add SPUD specific primers,
so you.
Speaker 1 (09:45):
Are basically spiking the sample with a fake gene exactly.
Speaker 2 (09:49):
And since you know exactly how much SPU you put
in and you know it worked perfectly, it should amplify
and cross the fluorescence threshold at a very specific cycle,
say cycle twenty Okay, I fall if it amplifies it
cycle twenty five. Instead, you know your patient sample contains
chemical inhibitors that are actively slowing down the reaction.
Speaker 1 (10:08):
That is brilliant, so it flags a false negative before
you mistakenly telepatient they are cancer free exactly. But knowing
about inhibitors like SPU only tells us the test is broken.
It doesn't actually fix the tests right. So if biological
samples are inherently full of these inhibitors and our RT
enzymes are this variable, how do we get around them
without ruining the sample entirely?
Speaker 2 (10:30):
That is where the math gets really complicated, specifically with normalization,
because traditional qPCR is measuring relative amounts of RNA. You
need a baseline. A baseline you have to compare your
target gene to a reference gene, often called housekeeping gene.
This is a gene that is theoretically expressed at a
constant stable level in all cells, no matter what kind of.
Speaker 1 (10:51):
Like comparing the speed of a passing car to the
speed limit sign like you need a constant to understand
the variable.
Speaker 2 (10:56):
Great analogy, yes, but the frustrating reality and biology is
that there is no single universal reference gene of course
not A housekeeping gene might be perfectly stable in liver
tissue but fluctuate wildly in brain tissue. So to solve
this you have to use complex algorithms like gen genorme.
Speaker 1 (11:14):
How does that work?
Speaker 2 (11:14):
It was developed by researchers Helmans and Van Deissmpel. Instead
of relying on one guess, genorme evaluates multiple reference gene
simultaneously in your specific tissue type. Well that's smart. Yeah.
It runs a statistical analysis to find the most stable
combination of reference genes, creating a highly accurate custom baseline
for normalization.
Speaker 1 (11:35):
You know, this raises an important question if traditional qPCR,
which is a relative quantification method has so many inherent variables,
from enzyme knots to chemical inhibitors to the headache of
finding reference genes. How can we ever achieve absolute certainty
in a clinical diagnosis.
Speaker 2 (11:55):
Well, that exact frustration drove the field to evolve into
its third generation pr or dPCR.
Speaker 1 (12:01):
Ah Okay.
Speaker 2 (12:02):
It was first described by Bert Vogelstein in nineteen ninety nine,
and it basically bypasses the relative nature and the normalization
headaches of standard qPCR entirely.
Speaker 1 (12:11):
I love the way to visualize this. Think about traditional
qPCR like trying to estimate the amount of blue dye
in a massive swimming pool just by looking at the
overall shade of the water, right, comparing it to a
color chart your standard curve. But digital PCR is like
scooping that entire swimming pool up into a million tiny,
individual little cups. Then you simply walk down the line
(12:32):
and definitively count this cup has dye, this cup doesn't one,
two three. It is absolute counting.
Speaker 2 (12:39):
That is a highly accurate analogy. In practice, the mechanics
of dPCR work by partitioning a standard twenty micro leader
PCR reaction into hundreds, thousands, or even millions of tiny
sub reactions.
Speaker 1 (12:52):
Wait, but how do you physically divide a single drop
of liquid into a million smaller.
Speaker 2 (12:57):
Drops Depending on the instrument, you use microflu chips with
microscopic physical chambers, or you create a water and oil emulsion. Okay,
in emulsion, yeah, the oil encapsulates the water, generating thousands
of microscopic droplets.
Speaker 1 (13:09):
So each droplet acts as its own individual PCR tests to.
Speaker 2 (13:13):
Exactly you dilute the sample so much that each droplet
ideally contains either zero, one, or maybe a few copies
of your target DNA.
Speaker 1 (13:20):
Got it.
Speaker 2 (13:20):
You run the heating and cooling PCR cycle exactly as normal.
Then a laser reads each droplet individually. If it lights up,
it contains the target. It's positive. If it stays dark,
it's negative.
Speaker 1 (13:31):
But wait, if you just randomly dilute the sample, how
do you know some droplets didn't accidentally scoop up like
two or three copies of the DNA instead of just one.
Wouldn't that ruin your absolute count?
Speaker 2 (13:42):
What's fascinating here is how the math solves that physical problem.
You apply poisson statistics.
Speaker 1 (13:48):
Okay, break that down for me.
Speaker 2 (13:49):
Fors Song statistics is a probability model. If we have
a million tiny cups and we count that exactly one
hundred thousand of them have die in them, poss On
Math calculates it's the exact statistical probability of how many
of those cups accidentally scooped up two drops of dye
instead of just one.
Speaker 1 (14:06):
Oh wow, based on the ratio of empty cups to
full cups.
Speaker 2 (14:10):
Precisely. It allows the software to correct the final count automatically.
Speaker 1 (14:13):
So we use statistical probability to achieve an absolute count
of the DNA concentration in the original sample. Yes, and
the beauty is because we are counting absolute numbers, we
don't need an external standard curve anymore. We don't need
a reference gam We are literally just counting the hits exactly.
Speaker 2 (14:30):
And if we connect this to the bigger picture, the
pharmacological implications are massive. Because DBCR is essentially binary at
the droplet level, it is much less dependent on amplification efficiency.
Speaker 1 (14:43):
Right, because if an inhibitor like EDTA slightly slows down
the reaction inside one specific droplet, it doesn't matter exactly.
You aren't measuring how fast it glows anymore. As long
as it glows just enough to cross the threshold. By
the end of the test it counts as a yes.
So it is highly tolerant of all those inhibitors we
discussed earlier.
Speaker 2 (15:01):
Which makes it an absolute game changer for precise clinical diagnostics.
Speaker 1 (15:06):
All right, let's slow down and really look at how
this translates into the real world. We've moved from the
muddy waters of qPCR variables to the absolute clarity of
digital PCR counting. Yeah, let's look at some clinical case
studies you will actually encounter in your career. Let's start
with oncology and rare mutations.
Speaker 2 (15:22):
This is where dPCR truly shines. In oncology, you are
often looking for a needle in a haystack, a tiny
fraction of mutated tumor DNA floating in a massive sea
of wild type DNA.
Speaker 1 (15:34):
Wild type meaning the normal, healthy version of the genere exactly.
Speaker 2 (15:38):
Traditional qPCR struggles here because the sheer volume of healthy
wild type DNA overwhelms the fluorescent signal of the rare mutation.
He just gets drowned out. That makes sense, But droplet
digital PCR partitions that sample. It physically isolates the rare
mutated strands into their own individual droplets, completely separating them
(15:59):
from the healthy DNA. Wow, the sensitivity is profound. Droplet
digital PCR can detect point mutations at frequencies as low
as one in one.
Speaker 1 (16:07):
Hundred thousand one mutated cell among one hundred thousand healthy ones.
So you're saying we could theoretically skip the surgical biopsy entirely.
Speaker 2 (16:15):
That is the goal with circulating cell free DNA or cfDNA.
When tumor cells die, they shod tiny fragments of their
mutated DNA directly into the blood plasma. Because dPCR is
so sensitive, we can draw a simple tube of blood,
find those rare cfDNA fragments, and monitor the genetic profile
of a patient's cancer completely noninvasively.
Speaker 1 (16:35):
That is incredible for patient comfort and continuous monitoring. Another
major application I know of is in leukemia, specifically chronic
myloid leukemia or CML.
Speaker 2 (16:46):
YES. CML is driven by the bcr abl one fusion gene.
This happens when parts of chromosomes nine to twenty two
breakoff in swap places, creating a mutant gene often called
the philadelphia chromosome, and this.
Speaker 1 (16:59):
Mutation produces a rogue protein that causes white blood cells
to multiply out of control, right.
Speaker 2 (17:04):
Exactly, And clinicians use RTqPCR to quantify the mRNA transcripts
of that BCR abl one fusion gene. They are looking
for minimal residual disease, so.
Speaker 1 (17:14):
They put the patient on targeted therapies like tyrosine kinase
inhibitors and watch the PCR numbers drop.
Speaker 2 (17:20):
Right. But if those transcript levels start to slowly rise
again over successive tests.
Speaker 1 (17:24):
It's an early warning sign of relapse. You can actually
see the cancer growing back at a molecular level, allowing
pharmacologists to adjust the drug regiment months before the patient
ever feels a physical symptom precisely.
Speaker 2 (17:35):
And this diagnostic power also extends deeply into maternal and
fetal medicine. There's a really fascinating clinical reality regarding transplcental metastasis.
Speaker 1 (17:46):
Oh, let's set the stage for this because it sounds
like a medical mystery. So an infant is born and
doctors discover the infant has a tumor, perhaps melanoma or leukemia.
The immediate question is did this cancer originate in the
baby or did it originate in the mother, and cross
the placenta during pregnancy.
Speaker 2 (18:04):
It is extremely rare, but it does happen. And to
choose the correct pharmacological treatment for the infant, you actually
must know the genetic origin of that tumor.
Speaker 1 (18:13):
So how do they figure that out?
Speaker 2 (18:15):
Diagnosticians use qPCR to analyze short tandem repeats or STRs.
Speaker 1 (18:20):
What exactly are STRs?
Speaker 2 (18:22):
They are repetitive sequences of DNA that vary wildly from
person to person. They're essentially genetic fingerprints.
Speaker 1 (18:29):
Okay.
Speaker 2 (18:29):
By extracting DNA from the infants tumor and running an
SPR analysis, you compare that genetic fingerprint to a swab
from the mother and a swab from the infant's.
Speaker 1 (18:37):
Healthy tissue, and the PCR amplification will quickly and definitively
prove who that cancer belongs to, which dictates the entire
treatment plan exactly.
Speaker 2 (18:47):
And speaking of the maternal feel connection, dPCR is also
revolutionizing non invasive prenail diagnosis or NiPd n IPA. It
uses the exact same logic as the cancer cfDNA we discussed.
Like a tumor sheds DNA into the blood, the placenta
sheds fetal DNA into the mother's blood strain.
Speaker 1 (19:05):
Right.
Speaker 2 (19:06):
Because dPCR allows us to partition the blood and count
absolute numbers with extreme sensitivity, we can detect single gene
disorders in the fetus just by drawing the mother's.
Speaker 1 (19:15):
Blood completely avoiding the physical risks of an invasive procedure
like amniocentisis. It is brilliant, it is, But.
Speaker 2 (19:23):
As a pharmacology professional, you also have to consider the
practicalities of a clinical lab, and that means addressing cost efficiency.
High volume testing is expensive, right.
Speaker 1 (19:32):
Let's look at routine clinical multiplexing. Say you are testing
a patient swab for ten different strains of HPV at
the same time. In traditional real time PCR, you have
to buy custom sorescent probes for every single viral strain.
You're paying a premium to bind a custom glowing molecule
to a custom DNA sequence.
Speaker 2 (19:51):
It becomes economically unviable very quickly. To solve this, bioengineers
developed mediator probe PCR.
Speaker 1 (19:58):
How does that bypass the cost?
Speaker 2 (20:00):
It uses a really clever chemical workaround. You design a cheap,
label free probe that recognizes your specific target, say HPV sixteen.
It doesn't blow, it just binds to the virus. Okay,
but attached to this probe is a standard mediator sequence.
Speaker 1 (20:15):
So it's like a non glowing probe with a tail.
Speaker 2 (20:17):
Exactly when the PCR polymerase enzyme moves down the DNA
strand to copy it, it physically crashes into this probe,
chews it up and cleaves off that mediator tail.
Speaker 1 (20:27):
And then what happens.
Speaker 2 (20:27):
That mediator tail floats away and binds to a universal.
Speaker 1 (20:30):
Reporter, a universal report.
Speaker 2 (20:32):
Yeah, it is a generic, mass produced fluorescent molecule that
you just dump into every single reaction. The cleaves mediator
binds to the universal reporter, causing it to unfold and
emit a fluorescent glow.
Speaker 1 (20:43):
Oh wow, So you only have to buy one expensive
fluorescent universal reporter in bulk, and then you can use cheap,
non glowing custom probes for all your specific viral targets.
Speaker 2 (20:54):
Exactly. It's a brilliant piece of bioengineering. It makes high
volume pharmacological testing economically viable without sacrificing the precision of qPCR.
Speaker 1 (21:03):
That is so smart. So what does this all mean
for us? We started this conversation talking about a pristine
molecular photocopier and quickly learned that diagnostic technology as well
fraud with hidden variables enzyme knots and chemical inhibitors.
Speaker 2 (21:18):
But by understanding those variables, by respecting the rigorous transparency
of the MIQE guidelines, and by leveraging the mathematical genius
of digital PCR, we can navigate those muddy waters.
Speaker 1 (21:30):
Exactly we are taking pharmacology from theoretical chemistry to life
saving personalized patient monitoring, and to succeed in this new era,
you have to understand exactly why we are shifting from
the relative standard curve quantification of qPCR to the absolute
cup counting certainty of digital PCR. We've covered a tremendous
amount of ground today. To help reinforce your learning from
(21:51):
this deep dive, our expert has a short review exercise
for you to think through.
Speaker 2 (21:55):
Yes, so, based on our deep dive into the mechanics
of these technologies, what is the fun mental mechanistic difference
in how digital PCR achieves absolute target quantification without using
a standard curve compared to traditional q PCR. As a hint,
recall the swimming pool analogy and the concept of partitioning
a sample into sub reactions.
Speaker 1 (22:16):
Take a moment to map that out in your head.
Understanding how those individual droplets act as binary test tubes
is really the core concept that makes modern molecular diagnostics possible.
Speaker 2 (22:26):
It really is.
Speaker 1 (22:27):
And as we wrap up, I want to leave you
with a final provocative thought to mull over. If digital
PCR allows us to detect a single mutated cancer gene
among one hundred thousand healthy ones from a simple routine
blood draw, how will this extreme early detection fundamentally change
the pharmaceutical industry's approach to treating diseases.
Speaker 2 (22:45):
That's a huge question, right.
Speaker 1 (22:47):
Historically we only fought diseases once symptoms actually appeared. Will
we soon start treating cancer before a physical tumor even exists.
Speaker 2 (22:56):
It pushes the boundaries of preventive pharmacology to a place.
Speaker 1 (22:59):
We've just never it really does. The precision is there now.
It is up to the next generation of professionals to
use it responsibly. Keep questioning, keep learning, and will catch
you on the next deep dive.
You know, when we talk about diagnostic technology, we tend
to imagine this like perfect infallible machine, right, like.
Speaker 2 (00:07):
A high end molecular photocopy.
Speaker 1 (00:09):
Exactly, you put a patient sample in, you press a button,
and out pops a perfectly accurate account of whatever virus
or mutated gene you're looking for.
Speaker 2 (00:18):
Because the expectation is just absolute precision. You know, you're like,
it is either there or it isn't.
Speaker 1 (00:24):
The ultimate clarity, right, But when you actually step into
a real laboratory, that pristine image kind of shatters because
suddenly that molecular photocopier is sensitive to I mean everything
from the temperature of the room to the exact chemical
brand you're using.
Speaker 2 (00:39):
Oh. Absolutely, You're really no longer in a world of
absolute clarity at that point.
Speaker 1 (00:42):
No, you are navigating diagnostic muddy waters. And that is
exactly what we are waiting into today on this deep dive.
For those of you listening, whether you are a pharmacy student,
a young professional, or just a lifelong learner, our mission
today is to give you a custom, tailored, really comprehensive
look into the twenty four teen textbook Quantitative Real Time
(01:02):
PCR Methods and protocols.
Speaker 2 (01:05):
Yeah, edited by Biasoni and Rosso. It's a fantastic resource.
Speaker 1 (01:08):
It really is. We are going to unpack the evolution
of qPCR, avoiding the unnecessary jargon, and really focus on
the practical clinical applications you're going to see in pharmacology.
Speaker 2 (01:19):
It's such a fascinating journey, honestly. I mean, since its
first report back in nineteen ninety three, QBCR has essentially
become the gold standard for quantifying nucleic acids, just a
massive leap forward from traditional endpoint PCR.
Speaker 1 (01:32):
Okay, so let's unpack this before we really get into
the weeds. What is the fundamental difference between simply amplifying
DNA with like legacy PCR and quantifying it in real time?
Speaker 2 (01:44):
Well, it really comes down to when you are looking
at the data in traditional endpoint PCR, you run the
entire amplification reaction first, okay, you heat and cool the
DNA to copy it over and over, and then only
at the very end of the whole process you run
that amplify DNA through a gel to see if your
target sequence is actually there.
Speaker 1 (02:03):
So it's qualitative, like it just tells you as simple
yes or no.
Speaker 2 (02:06):
Exactly. You get a visual band on a gel, it
proves you made copies, but it doesn't really tell you
how many copies you started with in the original sample.
Speaker 1 (02:13):
Right, But in real time PCR, you introduce fluorescent dyes
or specific fluorescent probes into the chemical mix itself.
Speaker 2 (02:23):
Yeah, and as the carget DNA is copied, that fluorescence increases.
So the machine actually reads this fluorescence during every single
cycle in real time.
Speaker 1 (02:32):
So it's measuring exactly when that glowing signal rises above
the background noise precisely.
Speaker 2 (02:38):
That's called the quantification cycle. And by finding that exact point,
you can calculate exactly how much target DNA was in
the sample to begin with.
Speaker 1 (02:45):
So it's not just did we make copies, it's how
fast did we make copies, which tells us our starting amount. Right,
And the leap in sensitivity there is staggering. I mean,
we're talking about a dynamic range of at least nine locks,
which is huge. Yeah, just to put that in perspective
for you guys, listening nine logs means we can detect
the difference between a single viral particle and a billion
of them using the exact same test.
Speaker 2 (03:07):
It is incredibly powerful, but that extreme sensitivity is exactly
what leads to its biggest historical.
Speaker 1 (03:14):
Flaw, right, which brings us to the whole reproducibility crisis.
Because if this technology is so precise, how were so
many people getting their results completely wrong?
Speaker 2 (03:25):
Well, because KPCR is so hypersensitive, it's uniquely vulnerable to
the slightest variations in how you set up the experiment,
and for a long time researchers just weren't standardizing or
even reporting those variables. And if you don't report exactly
how you handle the sample, well nobody can replicate your work.
Speaker 1 (03:42):
To me an example of that, like, how bad did
this actually get in practice? Oh?
Speaker 2 (03:46):
We got bad? Look at what happened in Washington, DC
in nineteen ninety seven. There was this notoriously flawed autism trial.
Speaker 1 (03:53):
Oh right, I've heard this.
Speaker 2 (03:54):
Yeah. Researchers published data claiming a direct link between the
measles virus gut pathology autism, but a public dissection of
their data later revealed just a massive catalog of inconsistencies.
Speaker 1 (04:06):
So what were they actually doing wrong in the lab
to get those results?
Speaker 2 (04:09):
Basically, they used completely inappropriate reverse transcription qPCR protocols But crucially,
and this is the big one, they ignored their negative controls.
Speaker 1 (04:20):
A negative control is a sample where you know for
a fact the virus isn't there right exactly, So.
Speaker 2 (04:26):
If your negative control starts glowing and showing a positive result,
it means your lab is contaminated or your assay design
is just pulling in random genetic noise. Wow, they were
getting massive false positives. But because the reporting standards across
the industry were so laxed back then, this totally flawed
data made it right into the public consciousness.
Speaker 1 (04:47):
That is just terrifying. I mean for you as a
future pharmacology professional, the lesson here is pretty profound. A
diagnostic test is really only as reliable as the rigor
of its experimental design one hundred percent. You can have
the most advanced machine in the world, but if your
pre analytical steps are flawed, your results are well essentially fiction.
Speaker 2 (05:04):
Precisely, And to combat this sort of wild West of reporting,
a group of researchers finally introduce the MIQE Guidelines IQE. Right, Yeah,
it stands for Minimum Information for the Publication of Quantitative
Real Time PCR Experiments. It breaks the entire process down
into nine sections and eighty five specific parameters categorized is
(05:26):
either essential or desirable.
Speaker 1 (05:28):
I mean, it sounds like a bureaucratic checklist, but it's
really just the scientific method demanding receipts.
Speaker 2 (05:33):
It really is. And to truly understand why MIQE is
so essential, we have to kind of zoom in on
those specific pre analytical steps.
Speaker 1 (05:41):
Let's do that. Let's look at where a qPCR essay
is most likely to fail or just be subtly skewed
before the PCR machine even turns on.
Speaker 2 (05:49):
Yeah. So, one of the biggest hurdles is RNA extraction
and reverse transcription, usually called the RT.
Speaker 1 (05:53):
Step, right, because you're taking fragile RNA from a virus
or cell and turning it into stable complementary DNA CBNA.
Because the standard PCR machine only reads.
Speaker 2 (06:03):
DNA exactly, and the variance at this specific RT step
is just astonishing. The process of reverse transcription isn't perfectly linear.
Simply using different commercially available RT enzymes can cause a
massive one hundred and twenty eight fold difference in the
quantification of your target.
Speaker 1 (06:19):
Wait, one hundred and twenty eightfold if the variance is
that wild, just from buying a different brand of enzyme.
How did any early PCR tests even pass FDA approval.
I mean, that's the difference between diagnosing a patient with
a mild infection versus a life threatening viral load, just
based on the brand of chemical you bought.
Speaker 2 (06:36):
Which is exactly why the MIQE guidelines demand you state
exactly which enzyme you used. But it gets even more
complicated when you choose your priming strategy primers right, Yeah,
to start reverse transcription, the enzyme needs a primer. It's
basically a short starting block of nucleic acid to latch onto.
Speaker 1 (06:54):
Kind of like giving the enzyme a landing.
Speaker 2 (06:56):
Pad, exactly a landing pad, and you have choices. You
can use random x which are random six letter sequences, okay.
You can use penned cameras which are fifteen letter sequences.
You can use oligo DT primers which specifically target the
tail end of messenger RNA, or you can use gene
specific primers.
Speaker 1 (07:12):
Okay, But does the specific landing pad really matter that
much in the end?
Speaker 2 (07:16):
Drastically, so, oligo dts might work beautifully for one type
of messenger RNA, but then completely fail to transcribe another
due to secondary folding structures in the.
Speaker 1 (07:26):
RNA secondary folding structures. Okay, So RNA isn't just like
a straight line of code.
Speaker 2 (07:31):
No, not at all. RNA is a single strand, and
it likes to fold in on itself, binding to itself
to create these complex three D shapes I see. Think
of it like a tangled headphone cord. If it's too tangled,
the oligo DT primer literally can't physically access the starting point.
The enzyme just cannot push through the not Oh.
Speaker 1 (07:50):
Man, here's where it gets really interesting. If we play
this out practically. Imagine you have four different highly respected
clinical laboratories, right, Okay, they're all fallow best practices, but
Lab one uses enzyme and random hexamers, LAP two uses
enzyme B and oligodts, and Legs three and four use
other entirely valid combinations. If you send them the exact
(08:11):
same patient blood sample, they're going to get four entirely different,
technically accurate, yet completely incomparable results.
Speaker 2 (08:18):
And that right there is the crux of the reproducibility crisis.
If they don't publish those exact parameters, nobody can cross
reference their diagnostic data.
Speaker 1 (08:26):
That is wild. Okay, so enzyme choice and primers are
huge variables. What else is skewing the data behind the scenes?
Speaker 2 (08:34):
The patient samples themselves. Honestly, biological samples are full of
PCR inhibitors. For example, f FPE tissues f FPE.
Speaker 1 (08:43):
That's formal and fixed paraffin embedded tissue. Right Like when
a surgeon removes a tumor, ethology preserves it in formal
in chemicals and blocks of wax so they can slice.
Speaker 2 (08:51):
It correct And those preservation chemicals are notoriously harsh on
nucleic acids, they actually act as direct inhibitors to the
PCR enzymes.
Speaker 1 (09:00):
It makes sense.
Speaker 2 (09:01):
Then you have things like EDTA, which is the chemical
inside those purple top blood collection tubes to stop clotting.
You also have leftover phenol or ethanol from the laboratory
extraction process itself.
Speaker 1 (09:11):
So all of these things can just suppress the PCR amplification.
Speaker 2 (09:15):
Yes, leading to an artificially low reading.
Speaker 1 (09:17):
But if an inhibitor is silently suppressing the reaction, how
do you even know what's happening? Like a suppressed positive
result just looks like a negative result to the technician.
Speaker 2 (09:27):
To solve this, researchers introduce the spud assay.
Speaker 1 (09:31):
The spud asset.
Speaker 2 (09:32):
Yeah, it's an ingenious internal control. You add a known
artificial sequence of DNA called the SPUD sequence, directly into
your patient's extracted sample. Then you add SPUD specific primers,
so you.
Speaker 1 (09:45):
Are basically spiking the sample with a fake gene exactly.
Speaker 2 (09:49):
And since you know exactly how much SPU you put
in and you know it worked perfectly, it should amplify
and cross the fluorescence threshold at a very specific cycle,
say cycle twenty Okay, I fall if it amplifies it
cycle twenty five. Instead, you know your patient sample contains
chemical inhibitors that are actively slowing down the reaction.
Speaker 1 (10:08):
That is brilliant, so it flags a false negative before
you mistakenly telepatient they are cancer free exactly. But knowing
about inhibitors like SPU only tells us the test is broken.
It doesn't actually fix the tests right. So if biological
samples are inherently full of these inhibitors and our RT
enzymes are this variable, how do we get around them
without ruining the sample entirely?
Speaker 2 (10:30):
That is where the math gets really complicated, specifically with normalization,
because traditional qPCR is measuring relative amounts of RNA. You
need a baseline. A baseline you have to compare your
target gene to a reference gene, often called housekeeping gene.
This is a gene that is theoretically expressed at a
constant stable level in all cells, no matter what kind of.
Speaker 1 (10:51):
Like comparing the speed of a passing car to the
speed limit sign like you need a constant to understand
the variable.
Speaker 2 (10:56):
Great analogy, yes, but the frustrating reality and biology is
that there is no single universal reference gene of course
not A housekeeping gene might be perfectly stable in liver
tissue but fluctuate wildly in brain tissue. So to solve
this you have to use complex algorithms like gen genorme.
Speaker 1 (11:14):
How does that work?
Speaker 2 (11:14):
It was developed by researchers Helmans and Van Deissmpel. Instead
of relying on one guess, genorme evaluates multiple reference gene
simultaneously in your specific tissue type. Well that's smart. Yeah.
It runs a statistical analysis to find the most stable
combination of reference genes, creating a highly accurate custom baseline
for normalization.
Speaker 1 (11:35):
You know, this raises an important question if traditional qPCR,
which is a relative quantification method has so many inherent variables,
from enzyme knots to chemical inhibitors to the headache of
finding reference genes. How can we ever achieve absolute certainty
in a clinical diagnosis.
Speaker 2 (11:55):
Well, that exact frustration drove the field to evolve into
its third generation pr or dPCR.
Speaker 1 (12:01):
Ah Okay.
Speaker 2 (12:02):
It was first described by Bert Vogelstein in nineteen ninety nine,
and it basically bypasses the relative nature and the normalization
headaches of standard qPCR entirely.
Speaker 1 (12:11):
I love the way to visualize this. Think about traditional
qPCR like trying to estimate the amount of blue dye
in a massive swimming pool just by looking at the
overall shade of the water, right, comparing it to a
color chart your standard curve. But digital PCR is like
scooping that entire swimming pool up into a million tiny,
individual little cups. Then you simply walk down the line
(12:32):
and definitively count this cup has dye, this cup doesn't one,
two three. It is absolute counting.
Speaker 2 (12:39):
That is a highly accurate analogy. In practice, the mechanics
of dPCR work by partitioning a standard twenty micro leader
PCR reaction into hundreds, thousands, or even millions of tiny
sub reactions.
Speaker 1 (12:52):
Wait, but how do you physically divide a single drop
of liquid into a million smaller.
Speaker 2 (12:57):
Drops Depending on the instrument, you use microflu chips with
microscopic physical chambers, or you create a water and oil emulsion. Okay,
in emulsion, yeah, the oil encapsulates the water, generating thousands
of microscopic droplets.
Speaker 1 (13:09):
So each droplet acts as its own individual PCR tests to.
Speaker 2 (13:13):
Exactly you dilute the sample so much that each droplet
ideally contains either zero, one, or maybe a few copies
of your target DNA.
Speaker 1 (13:20):
Got it.
Speaker 2 (13:20):
You run the heating and cooling PCR cycle exactly as normal.
Then a laser reads each droplet individually. If it lights up,
it contains the target. It's positive. If it stays dark,
it's negative.
Speaker 1 (13:31):
But wait, if you just randomly dilute the sample, how
do you know some droplets didn't accidentally scoop up like
two or three copies of the DNA instead of just one.
Wouldn't that ruin your absolute count?
Speaker 2 (13:42):
What's fascinating here is how the math solves that physical problem.
You apply poisson statistics.
Speaker 1 (13:48):
Okay, break that down for me.
Speaker 2 (13:49):
Fors Song statistics is a probability model. If we have
a million tiny cups and we count that exactly one
hundred thousand of them have die in them, poss On
Math calculates it's the exact statistical probability of how many
of those cups accidentally scooped up two drops of dye
instead of just one.
Speaker 1 (14:06):
Oh wow, based on the ratio of empty cups to
full cups.
Speaker 2 (14:10):
Precisely. It allows the software to correct the final count automatically.
Speaker 1 (14:13):
So we use statistical probability to achieve an absolute count
of the DNA concentration in the original sample. Yes, and
the beauty is because we are counting absolute numbers, we
don't need an external standard curve anymore. We don't need
a reference gam We are literally just counting the hits exactly.
Speaker 2 (14:30):
And if we connect this to the bigger picture, the
pharmacological implications are massive. Because DBCR is essentially binary at
the droplet level, it is much less dependent on amplification efficiency.
Speaker 1 (14:43):
Right, because if an inhibitor like EDTA slightly slows down
the reaction inside one specific droplet, it doesn't matter exactly.
You aren't measuring how fast it glows anymore. As long
as it glows just enough to cross the threshold. By
the end of the test it counts as a yes.
So it is highly tolerant of all those inhibitors we
discussed earlier.
Speaker 2 (15:01):
Which makes it an absolute game changer for precise clinical diagnostics.
Speaker 1 (15:06):
All right, let's slow down and really look at how
this translates into the real world. We've moved from the
muddy waters of qPCR variables to the absolute clarity of
digital PCR counting. Yeah, let's look at some clinical case
studies you will actually encounter in your career. Let's start
with oncology and rare mutations.
Speaker 2 (15:22):
This is where dPCR truly shines. In oncology, you are
often looking for a needle in a haystack, a tiny
fraction of mutated tumor DNA floating in a massive sea
of wild type DNA.
Speaker 1 (15:34):
Wild type meaning the normal, healthy version of the genere exactly.
Speaker 2 (15:38):
Traditional qPCR struggles here because the sheer volume of healthy
wild type DNA overwhelms the fluorescent signal of the rare mutation.
He just gets drowned out. That makes sense, But droplet
digital PCR partitions that sample. It physically isolates the rare
mutated strands into their own individual droplets, completely separating them
(15:59):
from the healthy DNA. Wow, the sensitivity is profound. Droplet
digital PCR can detect point mutations at frequencies as low
as one in one.
Speaker 1 (16:07):
Hundred thousand one mutated cell among one hundred thousand healthy ones.
So you're saying we could theoretically skip the surgical biopsy entirely.
Speaker 2 (16:15):
That is the goal with circulating cell free DNA or cfDNA.
When tumor cells die, they shod tiny fragments of their
mutated DNA directly into the blood plasma. Because dPCR is
so sensitive, we can draw a simple tube of blood,
find those rare cfDNA fragments, and monitor the genetic profile
of a patient's cancer completely noninvasively.
Speaker 1 (16:35):
That is incredible for patient comfort and continuous monitoring. Another
major application I know of is in leukemia, specifically chronic
myloid leukemia or CML.
Speaker 2 (16:46):
YES. CML is driven by the bcr abl one fusion gene.
This happens when parts of chromosomes nine to twenty two
breakoff in swap places, creating a mutant gene often called
the philadelphia chromosome, and this.
Speaker 1 (16:59):
Mutation produces a rogue protein that causes white blood cells
to multiply out of control, right.
Speaker 2 (17:04):
Exactly, And clinicians use RTqPCR to quantify the mRNA transcripts
of that BCR abl one fusion gene. They are looking
for minimal residual disease, so.
Speaker 1 (17:14):
They put the patient on targeted therapies like tyrosine kinase
inhibitors and watch the PCR numbers drop.
Speaker 2 (17:20):
Right. But if those transcript levels start to slowly rise
again over successive tests.
Speaker 1 (17:24):
It's an early warning sign of relapse. You can actually
see the cancer growing back at a molecular level, allowing
pharmacologists to adjust the drug regiment months before the patient
ever feels a physical symptom precisely.
Speaker 2 (17:35):
And this diagnostic power also extends deeply into maternal and
fetal medicine. There's a really fascinating clinical reality regarding transplcental metastasis.
Speaker 1 (17:46):
Oh, let's set the stage for this because it sounds
like a medical mystery. So an infant is born and
doctors discover the infant has a tumor, perhaps melanoma or leukemia.
The immediate question is did this cancer originate in the
baby or did it originate in the mother, and cross
the placenta during pregnancy.
Speaker 2 (18:04):
It is extremely rare, but it does happen. And to
choose the correct pharmacological treatment for the infant, you actually
must know the genetic origin of that tumor.
Speaker 1 (18:13):
So how do they figure that out?
Speaker 2 (18:15):
Diagnosticians use qPCR to analyze short tandem repeats or STRs.
Speaker 1 (18:20):
What exactly are STRs?
Speaker 2 (18:22):
They are repetitive sequences of DNA that vary wildly from
person to person. They're essentially genetic fingerprints.
Speaker 1 (18:29):
Okay.
Speaker 2 (18:29):
By extracting DNA from the infants tumor and running an
SPR analysis, you compare that genetic fingerprint to a swab
from the mother and a swab from the infant's.
Speaker 1 (18:37):
Healthy tissue, and the PCR amplification will quickly and definitively
prove who that cancer belongs to, which dictates the entire
treatment plan exactly.
Speaker 2 (18:47):
And speaking of the maternal feel connection, dPCR is also
revolutionizing non invasive prenail diagnosis or NiPd n IPA. It
uses the exact same logic as the cancer cfDNA we discussed.
Like a tumor sheds DNA into the blood, the placenta
sheds fetal DNA into the mother's blood strain.
Speaker 1 (19:05):
Right.
Speaker 2 (19:06):
Because dPCR allows us to partition the blood and count
absolute numbers with extreme sensitivity, we can detect single gene
disorders in the fetus just by drawing the mother's.
Speaker 1 (19:15):
Blood completely avoiding the physical risks of an invasive procedure
like amniocentisis. It is brilliant, it is, But.
Speaker 2 (19:23):
As a pharmacology professional, you also have to consider the
practicalities of a clinical lab, and that means addressing cost efficiency.
High volume testing is expensive, right.
Speaker 1 (19:32):
Let's look at routine clinical multiplexing. Say you are testing
a patient swab for ten different strains of HPV at
the same time. In traditional real time PCR, you have
to buy custom sorescent probes for every single viral strain.
You're paying a premium to bind a custom glowing molecule
to a custom DNA sequence.
Speaker 2 (19:51):
It becomes economically unviable very quickly. To solve this, bioengineers
developed mediator probe PCR.
Speaker 1 (19:58):
How does that bypass the cost?
Speaker 2 (20:00):
It uses a really clever chemical workaround. You design a cheap,
label free probe that recognizes your specific target, say HPV sixteen.
It doesn't blow, it just binds to the virus. Okay,
but attached to this probe is a standard mediator sequence.
Speaker 1 (20:15):
So it's like a non glowing probe with a tail.
Speaker 2 (20:17):
Exactly when the PCR polymerase enzyme moves down the DNA
strand to copy it, it physically crashes into this probe,
chews it up and cleaves off that mediator tail.
Speaker 1 (20:27):
And then what happens.
Speaker 2 (20:27):
That mediator tail floats away and binds to a universal.
Speaker 1 (20:30):
Reporter, a universal report.
Speaker 2 (20:32):
Yeah, it is a generic, mass produced fluorescent molecule that
you just dump into every single reaction. The cleaves mediator
binds to the universal reporter, causing it to unfold and
emit a fluorescent glow.
Speaker 1 (20:43):
Oh wow, So you only have to buy one expensive
fluorescent universal reporter in bulk, and then you can use cheap,
non glowing custom probes for all your specific viral targets.
Speaker 2 (20:54):
Exactly. It's a brilliant piece of bioengineering. It makes high
volume pharmacological testing economically viable without sacrificing the precision of qPCR.
Speaker 1 (21:03):
That is so smart. So what does this all mean
for us? We started this conversation talking about a pristine
molecular photocopier and quickly learned that diagnostic technology as well
fraud with hidden variables enzyme knots and chemical inhibitors.
Speaker 2 (21:18):
But by understanding those variables, by respecting the rigorous transparency
of the MIQE guidelines, and by leveraging the mathematical genius
of digital PCR, we can navigate those muddy waters.
Speaker 1 (21:30):
Exactly we are taking pharmacology from theoretical chemistry to life
saving personalized patient monitoring, and to succeed in this new era,
you have to understand exactly why we are shifting from
the relative standard curve quantification of qPCR to the absolute
cup counting certainty of digital PCR. We've covered a tremendous
amount of ground today. To help reinforce your learning from
(21:51):
this deep dive, our expert has a short review exercise
for you to think through.
Speaker 2 (21:55):
Yes, so, based on our deep dive into the mechanics
of these technologies, what is the fun mental mechanistic difference
in how digital PCR achieves absolute target quantification without using
a standard curve compared to traditional q PCR. As a hint,
recall the swimming pool analogy and the concept of partitioning
a sample into sub reactions.
Speaker 1 (22:16):
Take a moment to map that out in your head.
Understanding how those individual droplets act as binary test tubes
is really the core concept that makes modern molecular diagnostics possible.
Speaker 2 (22:26):
It really is.
Speaker 1 (22:27):
And as we wrap up, I want to leave you
with a final provocative thought to mull over. If digital
PCR allows us to detect a single mutated cancer gene
among one hundred thousand healthy ones from a simple routine
blood draw, how will this extreme early detection fundamentally change
the pharmaceutical industry's approach to treating diseases.
Speaker 2 (22:45):
That's a huge question, right.
Speaker 1 (22:47):
Historically we only fought diseases once symptoms actually appeared. Will
we soon start treating cancer before a physical tumor even exists.
Speaker 2 (22:56):
It pushes the boundaries of preventive pharmacology to a place.
Speaker 1 (22:59):
We've just never it really does. The precision is there now.
It is up to the next generation of professionals to
use it responsibly. Keep questioning, keep learning, and will catch
you on the next deep dive.
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