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If it were not for the great variability among individuals, medicine might be a science and not an art.
No, that's Sir William Osler, way back in 1892.
And it still rings so true, doesn't it?
We see it all the time.
Same drugs, same dose,
completely different results in two different people.
We know things like age, sex, other meds, they all play a part.
But okay, let's unpack this.
In this deep dive, we want to get deeper.
We're looking at the genetic blueprint itself,
pharmacogenomics.
This is really about laying that foundation for future clinicians like you advanced nursing students and PAs to really grasp how we can make medicine more precise, less art, more science maybe.
Exactly.
It's all about that blueprint.
And we often hear two terms, sometimes used interchangeably, but they're slightly different.
Pharmacogenetics that usually focuses on just one or maybe a few specific gene variations, polymorphisms we call them.
And then there's the bigger picture, pharmacogenomics, that looks at the whole genome's influence on how someone responds to a drug.
Both are absolutely essential tools now for tailoring therapy, for keeping patients safe.
Okay, so let's start with the basics, our DNA.
Three billion base pairs sounds massive.
It is.
But here's the wild part.
About 99 .9 % of that is identical between any two people.
Wow, only 0 .1 % different.
That tiny fraction, plus things like diet, environment, that's what makes each patient unique.
And critically, it dictates how their body handles drugs.
You know, the ADME process, absorption, distribution, metabolism, and excretion.
And the most common kind of genetic variation we talk about in this context is the SMP.
Correct.
SMP, S -N -P stands for single nucleotide polymorphism.
Okay.
It's exactly what it sounds like.
A single letter change in the DNA sequence.
So maybe the code should be A -A -G -C -T -A, but in one person, it's A -A -G -T -T -A.
That T instead of a C, that's a SMP.
And we all have lots of these.
Millions.
But most don't really do anything noticeable.
The ones we care about in pharmacogenomics are those that fall within a gene's coding region, or maybe in the control panel areas that regulate how much of a protein gets made.
That's where they can really impact drug metabolizing enzymes.
So to keep track of all these important variations, especially for drug metabolism genes, there's a special naming system.
Yes.
The star allele nomenclature.
You'll see it written like CYP3A5 -2.
And you'd pronounce that.
CYP3A5 -2.
That star followed by a number tells you precisely which known variation or allele we're talking about for that specific cytochrome P450 gene.
It's crucial for clear communication.
Imagine the confusion without it.
Definitely.
Okay, so these SMPs, these star alleles, they lead to different ways people process drugs.
You mentioned four main groups,
or phenotypes.
That's right.
Understanding these four is key to interpreting the genetic test results.
First you have the baseline, extensive metabolizers, EMs.
These folks have what we consider normal enzyme function.
They typically have two wild type alleles, often designated one actually.
They process the drug as expected.
Got it.
The standard group.
Then you have the ultra rapid metabolizers, or URMs.
These individuals might have multiple copies of an active gene or perhaps alleles that are just naturally more active, like 117.
So they process drugs faster.
Much faster.
They chew through the drugs so quickly that it might not reach effective levels in the body.
So risk of therapeutic failure.
The drug gets cleared out before it can really work.
Okay, that makes sense.
And on the other end.
You have the intermediate metabolizers, the IMs.
They might have one normal allele and one reduced function allele, maybe like a one half combination.
Their enzyme activity is, well, intermediate,
slower than normal.
Meaning the drug sticks around longer.
Exactly.
Which increases the risk of dose dependent side effects because the drug can accumulate.
And the last group.
The poor metabolizers, TMs.
These individuals usually have two alleles that have little or no function.
Think two or maybe three three.
Their enzyme activity is significantly reduced or even absent.
So the drug barely gets metabolized at all.
Right.
It can build up rapidly in their system, leading to a much higher risk of toxicity even at standard doses.
And it's really fascinating that cytochrome P450 enzyme system, mostly in the liver, it handles something like 75 % of all medications we use.
75%.
That's huge.
It is.
So you can see why even small genetic changes in those CYP enzymes can have such a massive impact on whether a drug works safely or doesn't work at all or causes serious harm.
Okay.
Let's make this really concrete.
Let's talk clinical applications.
Right.
Clopidogrel, that's Plavix, right?
A really common antiplatelet drug used after heart attacks or strokes for acute coronary syndrome.
Exactly.
And clopidogrel is a perfect pharmacogenomics case study because it's what we call an inactive prodrug.
You mean it doesn't work right out of the bottle.
Correct.
You take the pill, but it's biologically inert initially.
It needs to be activated primarily by the liver enzyme, CYP2C19.
And get this, only about 15 % of the dose actually gets converted to the active form.
Only 15%.
Yeah.
But that active form is what irreversibly binds to the platelet receptor P2RY12, stopping platelets from clumping together.
So if that activation step fails because of genetics,
the patient isn't actually getting the antiplatelet effect they need.
Precisely.
Imagine a patient who is a poor metabolizer, PM, for CYP2C19.
Maybe they have two copies of the two alleles, so they're two -two.
Their CYP2C19 enzyme just doesn't work properly.
So they can't make the active drug.
Barely any.
The clinical result, therapeutic failure.
Their platelets remain sticky and they have a significantly higher risk of major adverse cardiovascular events, like another heart attack or stent thrombosis.
It's dangerous.
Wow.
So what's the recommendation for these patients?
Well based on strong evidence, but the CPIC guidelines, that's the Clinical Pharmacogenomics Implementation Consortium, and the FDA actually recommend using alternative antiplatelet drugs for known CYP2C19 poor metabolizers.
And what about the intermediate metabolizers, the IMs?
They're also a concern.
People with genotypes like 1 .5 or 1 .3, they have reduced activation, higher platelet reactivity, and also face an increased risk of bad outcomes compared to extensive metabolizers.
They might also benefit from alternative therapy.
It's about proactively identifying who won't respond well.
That's a clear case of genetics preventing a drug from working.
Let's flip to the other side of the coin with warfarin or coumadin.
Here genetics can make the drug too effective, right?
Leading to bleeding.
Warfarin is the classic example of a drug with a very narrow therapeutic index.
It's used commonly for things like atrial fibrillation or blood clots, VTE.
Narrow therapeutic index meaning?
Meaning the dose needed for it to work effectively is very close to the dose that causes toxicity, in this case, serious bleeding.
There's very little wiggle room.
And it works by interfering with vitamin K.
Yes.
It's a vitamin K antagonist.
It stops the enzyme vitamin K epoxide reductase complex 1, or VKORC1, from recycling vitamin K.
And you need active vitamin K to produce functional clotting factors, specifically factors 2, 7, IX, and X.
Okay.
And the drug itself is a mix, but one part is more important.
Right.
Warfarin is given as a racemic mixture, meaning it has two mirror image forms.
R -warfarin and S -warfarin.
The S -warfarin isomer is much more potent, about three to five times more active as an anticoagulant.
And how does pharmacogenomics play into S -warfarin levels?
The key enzyme responsible for clearing or inactivating the potent S -warfarin is CYP2C9.
Ah, another CYP enzyme.
Yep.
And there are common variants, polymorphisms, in the CYP2C9 gene that significantly reduce its activity.
The main ones are the 2 and 3 alleles.
So if someone has, say, the 3 allele.
Their ability to break down S -warfarin can plummet.
The 3 allele can cause up to a 90 % decrease in enzyme function.
90%.
So the active drug just builds up.
Exactly.
It accumulates because it's not being cleared effectively.
This leads to over anticoagulation, a much higher INR than intended, and a dramatically increased risk of major, potentially fatal, bleeding.
That's terrifying.
But you mentioned warfarin's target enzyme also has relevant genetics, VKORC1.
Correct.
It's a double whammy sometimes.
The gene for the target enzyme, VKORC1, also has common polymorphisms.
One key one is the NAGTRA 1639GA SMP.
People who inherit the A variant, or A haplotype, naturally produce less VKORC1 enzyme.
So if they already have less of the target.
They need less warfarin to block it effectively.
They are inherently more sensitive to the drug.
So you can imagine, if you have someone who is both a poor metabolizer for CYP2C9 and has the low VKORRC1, a haplotype.
They need a tiny dose compared to average.
Significantly lower.
Maybe 50 -70 % less than someone with standard genetics.
This is why genetic testing for both CYP2C9 and VKORRC1 is so strongly suggested before starting warfarin.
It helps guide that initial dosing much more safely.
Okay, those two really highlight the CYP system.
But you mentioned pharmacogenomics isn't always about liver metabolism.
Let's touch on the amino glycoside example.
These are antibiotics, right?
Yes, amino glycosides like gentanesin or tobamycin, powerful antibiotics often used for serious gram -negative bacterial infections.
But they carry known risks.
Nephrotoxicity, so kidney damage,
and ototoxicity, hearing loss.
And the hearing loss can be permanent.
Yes, irreversible hearing loss.
And there's a specific genetic factor that dramatically increases this risk, and it's not related to CYT enzymes at all.
It's linked to our mitochondria.
The powerhouses of the cell, how does that work?
Well, there's a specific mutation in the DNA within the mitochondria, which, remember, we inherit solely from our mothers.
That increases how strongly amino glycosides bind to the ribosomes inside the mitochondria.
Wait, mitochondrial ribosomes?
I thought ribosomes were for making protein.
They are.
And mitochondrial ribosomes are actually structurally a bit similar to bacterial ribosomes, which is why amino glycosides work, they target bacterial ribosomes.
But this mutation makes the mitochondrial ribosomes in the hair cells of the inner ear extra sticky for the drug.
So the drug binds more tightly inside the ear cells.
Exactly.
This increased binding affinity means the drug hangs around much longer in the inner ear, essentially poisoning the mitochondria.
It disrupts ATP production, messes up ion gradients, and ultimately leads to the death of those critical hair cells.
Resulting in hearing loss.
Permanent irreversible hearing loss.
And think about the impact, especially on a child receiving these antibiotics.
The rehabilitation, the lifelong consequences, it's devastating.
So connecting this back,
given how severe and permanent this is, genetic screening before starting amino glycosides seems really crucial, especially if there are alternative antibiotics available.
Absolutely.
Identifying patients with this mitochondrial mutation before the first dose allows clinicians to choose a different drug or, if absolutely necessary, use extreme caution with dosing and monitoring.
It's a powerful example of preventing a catastrophic adverse drug reaction through genetics.
These examples really drive home the potential here.
Genetic testing to predict if a drug will work or if it might cause serious harm.
It seems like a game changer.
And you mentioned costs are coming down.
They are.
Testing is becoming much more affordable and we're seeing more and more studies showing it's actually cost effective in the long run by preventing hospitalizations from ADRs or avoiding ineffective treatments.
The opportunity is huge.
But it's not quite standard practice everywhere yet.
What are the roadblocks?
You mentioned ELSI's.
Right.
The ethical, legal and social issues or ELSI's.
These present real policy challenges.
Think about patient privacy who owns this genetic data.
How is it stored securely?
What about the potential for discrimination?
Doesn't the GenA law address discrimination?
Yes.
The Genetic Information Non -Discrimination Act of 2008 was a landmark law in the U .S.
It protects against discrimination based on genetic information in health insurance and employment.
That was a huge step.
But challenges remain.
Definitely.
GenA doesn't cover things like life insurance, disability insurance or long -term care insurance.
And then there are questions about direct consumer testing, data sharing, incidental findings.
It's complex terrain we're still navigating.
Plus, how do we ensure equitable access to testing and interpretation?
And another major hurdle, honestly, is education.
Research consistently shows a knowledge gap among many practicing clinicians when it comes to genetics and genomics.
So even if the tests are available, clinicians might not know when to order them or how to interpret the results effectively.
Exactly.
That's why integrating this into the training of future clinicians, like all of you listening, is so incredibly important.
You'll be the generation that really implements this routinely.
So for our listeners, the advanced practice students and future PAs, what are the go -to resources to stay on top of this rapidly changing field?
There are a few key places I'd recommend bookmarking.
First, the FDA maintains a table of pharmacogenomic biomarkers in drug labeling.
It lists drugs, I think, over 260 now, where the official label includes pharmacogenomic information.
It's a great starting point.
OK.
FDA table.
What else?
Second, DailyMed, run by the NIH.
It provides the full, up -to -date package inserts for drugs.
You can search any drug and find the official labeling, which increasingly contains pharmacogenomic details.
DailyMed.
Got it.
And for practical guidance?
The most critical resource for clinical decision -making is probably the Clinical Pharmacogenomics Implementation Consortium, or CPIC.
CPIC publishes peer -reviewed guidelines that translate genetic test results into actionable prescribing recommendations.
They tell you, based on the genotype, what to do, adjust the dose, choose a different drug, etc.
CPIC guidelines.
Excellent.
So, FDA table, DailyMed, and CPIC.
All right, let's wrap this up.
The big takeaway here seems clear.
Pharmacogenomics is shifting how we approach some really critical drugs.
It helps ensure drugs like clopidogrel actually work, helps us dose tricky drugs like warfarin safely, and can even prevent devastating side effects like hearing loss from aminoglycosides.
It's fundamentally about moving medicine toward that goal.
Osler hinted at making it more of a precise science.
It really is.
And looking forward, it raises some profound questions, doesn't it?
We have GINA for protection in the U .S., but think globally.
As whole genome sequencing gets cheaper and more common, maybe even routine at birth,
how do we handle that information responsibly?
Will it become part of everyone's standard medical record?
What are the implications for preventative care versus reacting after a problem occurs?
That's definitely something to think about, the shift from reactive to potentially proactive pharmacogenomic profiling for everyone.
A fascinating glimpse into the future.
Thank you so much for joining us for this deep dive into the essentials of pharmacogenomics.
We hope this helps build your foundation in this critical area of practice.