David Mrazek: Well, thank you very much for the invitation
to come talk with you.
I think one of the reasons that I was invited was to tell you the story of what we’ve
actually been doing at the Mayo Clinic related to pharmacogenomic testing in the Department
of Psychiatry.
I have some relationships you should know about.
I’m primarily a full-time employee of the Mayo Clinic.
However, the Mayo Clinic has a small equity interest in a company that is named AssureRx
Health.
And AssureRx Health has license to intellectual property that’s owned by the Mayo Clinic.
I’m one of the inventors of this intellectual property.
And what AssureRx Health has done is built a web-based electronic interface to support
pharmacogenomic decision making in clinical practice of psychiatry.
My objectives today are pretty straightforward.
I want to tell you about what has been happening at the Mayo Clinic and what we are doing now.
And I want to talk a little bit about this question of clinical utility and put forward
my own thoughts about how to move forward.
Well, the story of psychiatric pharmacogenomic testing really begins in about 2001.
We started a series of clinical studies.
And at that time we focused on cytochrome P450 2D6.
That was our only informative gene.
And we found many associations between psychiatric medication response and metabolic capacity.
So in February of 2003, the clinical laboratory of Mayo Clinic decided to make this test available.
And we worked for a year to see how it settled in our practice.
And, by that time, there was a sense that other cytochrome P450 genes were also important.
So, 2C19 arrived and 2C9 arrived.
And in April 2003, the Mayo Medical Laboratories made these tests available to clinicians everywhere,
anywhere who had really contracted with Mayo Medical Laboratories to be a reference lab.
So that was a very important change.
Now, as you’ve heard from many different perspectives, implementation is a slow process.
And it took a couple of years before the faculty in the department, before the residents in
the department became comfortable with this new approach to adding information into the
process of making a decision.
But by 2006, the practice of ordering individual genes had really begun.
And I’ll show you a little bit of information about that.
Now there’s another shift which occurred in 2009, and that was the point where we stopped
ordering individual genotypes and started ordering an algorithmic profile of genes.
And I’ll tell you more about that.
Now I’m grateful to the organizers to have done some polling ahead of this meeting.
And, again, you’ll remember this particular scenario.
Consider CYP450 profile, 43-year-old Caucasian woman is being considered for an SSRI.
Should this variant be routinely used?
Well, this did catch me a little off guard, I must admit.
First off, I was surprised that more people didn’t know.
Only 31 percent claimed not to know, as a very definitive, intelligent, well-prepared
group.
But there were 16 percent who had a strong opinion it would not be routinely used.
So what does that mean?
Well, let’s deconstruct the question.
Related questions -- what is a CYP450 profile?
I’d argue that there really is none today.
You could create one, but that’s not what we do.
Important question, why is the woman receiving an SSRI?
Is she depressed?
Does she have hot flashes?
If we consider depression, does she have a treatment-resistant depression or a new depression
that has just begun?
And then the question, what variance, singular, is being considered?
Well, we’ll see about that.
And then what does “routinely” mean?
Does that mean that every single patient has the test, or does it only mean that patients
where it’s clinically indicated would have the test?
Well, again, that’s a little ambiguous.
Well, this is what we do now.
We have a algorithmic profile.
And we don’t do 2D6 testing.
We do these seven genes.
And, of course, we don’t do one variant.
We do more than 30 variants.
And that’s an important concept, because if you’re back in the mode of, well, one
gene variant influenced clinical practice, you’re not thinking about what we have available
to us today to guide treatment.
But let’s make it a little more interesting.
What about just 2D6?
Let’s go turn the clock back in time.
It’s a different question than is a profile useful, but is 2D6 information actionable?
Well, Dr. Weinshilboum in 2003 put forward some information in the New England Journal
that would argue at least that there was strong pharmacogenetic relationships.
And this famous pharmacogenetic profile, looking at different levels of metabolic capacity,
which was really compiled 18 years ago, shows a lot of difference in the area under the
curve of nortriptyline depending on how many active 2D6 drugs one has.
Again, there are also adverse effects.
A patient died taking doxepin.
Again, unknown metabolic capacity.
Originally, it was thought to be a cardiac death.
It was only at autopsy that it was a toxicology problem.
You can argue doxepin’s a pretty dangerous drug.
What about a safe drug like fluoxetine?
Well, again, it’s a rare event, but in the case of this trial, it was a needless death,
poor metabolizer.
It was not recognized.
And what about actionability?
Would you give a patient who had very poor 2D6 metabolic capacity Prozac, Paxil or Effexor?
Well, I would not.
Would you give them norpramin or tofranil?
No, I don’t think so.
Would you give this patient haldol or risperdal?
Nope.
Would you give this patient Strattera?
I don’t think so.
Would you give this patient codeine?
Well, I hope not.
Those are actions.
What about the reverse?
Would you give this patient Celexa, Lexapro or Luvox?
Well, yes, unless there was another contraindication.
Would you give this patient Cimbalta or Pristiq?
Yes, unless there was another indication.
Would you give this patient Zyprexa or Geodon?
Yes.
Would you give this patient Concerta?
So there are options to choose.
Now, I have given all of these drugs to patients with poor 2D6 metabolic capacity because I
didn’t know any better before 2000, but I try not to these days.
Now, what is this?
This is a component of the algorithmic pharmacogenomic report.
I’m just going to show you two.
I’m going to take a second to try to explain to you that in addition to stating the genotype,
in addition to stating the phenotype that we would impute, there’s guidance to the
clinician.
And medication in that first group are antidepressants.
And you see there are many.
There are 18 in that first set of rectangles.
And this is patient that has a very common genotype, not a poor metabolizer, but someone
with intermediate metabolism of 2D6 and 2Z19.
And, again, the three categories are use as directed, use with caution, and use with caution
and with more frequent monitoring.
So these medications can be used regardless of the genotype, but the focus of the clinician
should differ based on the genotype.
And, again, there are many choices for this individual, some probably better than others.
But now the picture changes when you have a patient -- there’s another patient that
has impairment, but this impairment is really inactivity of both of the key enzymes.
And there you see there are many more medications that are likely to be associated with side
effects in this patient.
And once you have this information, it’s quite unlikely to start prescribing a medication
that would be in that red rectangle.
Well, again, the Pharmacogenomic Research Network Group has been thinking about the
issue, how do we facilitate clinical implementation of pharmacogenomics.
And you heard quite a bit about that from Howard.
But one aspect of that is we tried to think through what is the method by which one can
establish the right amount of evidence.
So this is a commentary that Dr. Lerman and I wrote this summer.
And it argues for considering a broadening of the base of evidence.
So, yes, randomized control trials are a wonderful well-established gold standard for some things,
but given the nature of questions that are being addressed related to pharmacogenomic
testing, related to specific drugs and a wide range of different gene variants, we would
argue that the development of pragmatic clinical trials that are actually embedded within clinical
treatment settings is a reasonable alternative to provide additional information that is
more feasibly obtainable.
Now, again, there’s also other methods of finding out information that will help guide
a decision about adoption.
And well said that if there’s something related to safety, that really forecloses
the process.
So, when there was a focus on a risk for Stevens-Johnson with Carbamazepine, there were no randomized
control trials.
It was much more an issue of let’s be sure we know which patients are at risk.
Now we’ve tried to do retrospective studies.
And during the era before the algorithm, we reviewed 2,390 patients who were seen on just
the consultation service.
And, again, these are patients referred for all sorts of reasons.
And 19 percent were tested with at least one genotype, 58 percent of those with serious
treatment resistance to antidepressant treatment were tested.
We have two pragmatic clinical trials that have been conducted, one at the Hamm Clinic,
a proof of principle trial.
And there, to our surprise, despite the underpowered nature of this preliminary trial, there was
a difference in actual decrease in depressive symptoms in patients whose clinicians had
the information about their genotype from the algorithm before starting treatment versus
those who did not.
And then in a somewhat larger sample with a very similar design, again, in La Crosse,
Wisconsin, and not a particularly sophisticated but a very lovely place -- and again, the
finding was demonstrated that not only did clinicians and patients find this to be helpful,
but there was actually an outcome difference.
Again, these are early days and these, you know studies need to be replicated.
They need to be replicated in much larger samples.
But I think the direction is becoming clear.
And my conclusion is that for us to really move forward with implementation, we have
to make the reports more understandable and friendly and useful to clinicians.
Again, we had experience with reports that were genotypes and phenotypes.
They weren’t perceived to be particularly helpful.
What I think now we need to do is to make information available to clinicians that they
can actually use.
So I think my time just ended, so I’ll stop.
[applause]
Female Speaker: Thank you.
I think we have time just for a question or two.
Yeah.
Howard: Hello.
It struck me that most of the decisions you’re making were already being made for the exact
same pathway in the context of drug interactions.
So how do you look at the same pathway from those two lenses?
David Mrazek: Howard, that’s a terrific point, and I think
it actually does provide some insight into the rate of adoption that psychiatrists were
able to demonstrate here.
Because you’re entirely correct.
We were thinking about, you know, the impairment of pathways due to drug-drug interactions.
And, of course, the correct response is to put both of those features together.
You need to know the genotype, and you need to know the drug interaction.
And you can create a phenocopy of a poor metabolizer by simply inhibiting that particular enzyme.
So, my response is that it’s additive and perhaps synergistic as you have a more complete
perspective of what are the factors affecting drug response.
Female Speaker: Jonathan, did you have a question?
Jonathan: Yeah, just -- and this sort of gets at a topic
that was brought up in the last session about should genetic professionals, you know, genetic
counselors and medical geneticists be involved in these discussions.
And my feeling, and I was sort of chatting with Howard about this, is, you know, thinking,
say a few years from now, whenever the sort of genetic information is already there and
resident in some place in the medical record, these types of things almost don’t need
any genetic counselor involvement.
This is decision support matrices.
This is embedded in the medical record that is just-in-time information for physicians
that are doing prescribing with information there.
So, I think this is a good example of how, you know, once the genetic information is
already in their medical record, it becomes available when needed at times in the future
when drugs are being prescribed.
David Mrazek: I think that’s right.
Female Speaker: Okay, one more.
Male Speaker: So, you have mentioned standards of evidence
for the use of these tests.
And looking at it from the clinical pathology perspective, therapeutic drug monitoring is
available.
It’s not as readily available for these drugs as it is -- as functional testing, for
example, for warfarin.
But I’m wondering whether you’ve considered incorporating measurement of drug levels,
which is the hypothesized or posited method of a relationship between the genetic metabolic
-- the change in -- the genetic change in metabolic enzymes and the outcome, that fundamental
link of drug levels in the same patients in whom your performing studies could be done.
And I’m wondering whether you’ve considered doing it.
Thanks.
David Mrazek: Yeah, it’s a good question.
Basically you focused on what is the potential synergistic role between therapeutic drug
monitoring the level -- monitoring the level of medication in the patient versus genetic
testing.
And I do think that we would benefit in this country by doing more therapeutic drug monitoring.
And there are many reasons why.
Certainly, it’s reassuring to see a correlation between genotype and blood level.
But that, again, may not add much value, but there are other circumstances.
For example, the patient has a good metabolic profile and has a bad response to the medication,
you raise some hypotheses.
It may be a target gene variant, or it may be something as simple as not taking the medication.
And therapeutic drug monitoring is a very good way to identify that.
And we see this fairly regularly due to serum levels.
Female Speaker: Okay.
One more quick question.
Male Speaker: In relation to your trials, which I understand
are pragmatic, have you had problems persuading clinicians to randomize at the point of care
and problems with IRB approval of such work?
David Mrazek: Oh, okay, the question is related to our pragmatic
clinical trials and what are the issue related to the IRB and the issues related to potential
for randomization.
What we chose to do -- well, first, the IRB has been overseeing hundreds of genetic studies
at the Mayo Clinic.
So, the IRB is very knowledgeable about the risk related to genetic testing.
So we did not have a problem with a genetic testing component.
The way the studies were both designed, there are two phases.
So there is no randomization.
Phase one is a period of treatment in the standard practice, so without genetic testing,
and monitoring the outcome of those patients.
Phase two, the guided phases, clinicians are given the profile that you saw at the onset
of treatment and can use that information in any way they choose to care for the patient.
One interesting observation that I did not anticipate, and it relates to other topics
that were discussed at this meeting, is how valuable is genetic testing to the patient.
And we have had some NIH trials where we have worked very hard to recruit 700 patients and
have to approach two for every one we get and in some studies three for every one that
signs up.
In these two studies, because everyone eventually got a genomic profile, we had one person that
we approached who did not -- one patient, one subject who did not decide to participate
in this particular trial.
Female Speaker: Great, thank you.
David Mrazek: Good, thank you.