The University of California San Diego, Skaggs School of Pharmacy and Pharmaceutical Sciences Drug Development course brings you lectures from both faculty and industry experts. With this course, recorded on campus at UCSD, we seek to share our access to top people in the field who bring an unprecedented range of expertise on drug development. In this course you will learn the different stages of clinical development as well as the regulatory including but not limited to, an Investigational New Drug Application (IND), New Drug Application (NDA), and product labeling. Additionally you will learn how to Incorporate study design methods for consideration in the design of clinical protocols to assess safety, tolerability, and efficacy in multiple therapeutic areas. In this course you will learn the different phases of clinical development: * Phase 1 or early stage clinical trial are conducted primarily to determine how the new drug works in humans, its safety profile and to predict its dosage range. It typically involves between 30 and 100 healthy volunteers. * Phase 2 or Proof of Concept POC studies test for efficacy as well as safety and side effects in a group of between 30 to 200 hundred patients with the disease for which the new drug is being developed. * Phase 3 or late stage clinical development involve much larger group of patients, between a few hundred to thousands, depending on the indication, which will help determine if the new drug can be considered both safe and effective. It will involve control groups using placebo and/or current treatment as a comparison. * Product registration and approval process after a drug is considered safe and effective from Phase 3 trials, it must be authorized in each individual country before it can be marketed. All data generated about the small molecule or biologic is collected and submitted to the regulatory authorities in the US at the FDA, Food and Drug Administration FDA, in Europe the EMA or European Medicines Agency, Japan Ministry of Health and other countries which may require their own national approvals. This course is intended as part 2 of a series: Drug Discovery (https://www.coursera.org/learn/drug-discovery), Drug Development and Drug Commercialization (https://www.coursera.org/learn/drug-commercialization). We would highly recommend that you take the courses in order since it will give you a better understanding on how a drug is discovered in the lab before being tested in clinical trials and then launched in the market place.
Clinical Trials: Phase 2 Part IV
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From the course by University of California San Diego
Drug Development
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From the lesson
Clinical Trials: Phase 2, Kourosh Parivar, M.Pharm.
Next we hear from Kourosh Parivar, M. Pharm, Vice President & Head, Clinical Pharmacology at Pfizer
Meet the Instructors
Williams S. Ettouati, Pharm.D.Director, Industrial Relations & Development; Health Sciences Associate Clinical Professor, N.S.
Skaggs School of Pharmacy and Pharmaceutical Sciences
Joseph D. MaAssociate Professor of Clinical Pharmacy
Skaggs School of Pharmacy and Pharmaceutical Sciences
I got to give you quicker view about the difference between different TA's,
different therapeutic areas. In some therapeutic areas you use
surrogate end, points earlier end points if you can do that instead of waiting for
the actual clinical effect. Yeah, you take diabetes, in diabetes for
instance you look at fasting blood glucose as a surrogate marker of, of
looking at the effect eh, effect of your product on, on, on, on a.
Here in treatment of diabetes instead of going to looking at HIV.
So we will take longer time. So here and for glucose you just wait two
weeks or three weeks instead of waiting yeah a number of weeks up to two months.
Yeah, viral load for instance is a good biomarker for HIV.
Products which have an effect on HIV, it's effects can be very very effectively
be followed by viral load. So, instead of designing the study to
look at the mortality of HIV, you actually look at viral load and design
your phase two study based on that. Yeah, in some other therapeutic areas.
You don't have reliable biomarkers and maybe oncology could be a good example of
that one for majority of tumor types. The standard role and the standard for
effect would be overall survival which, which, which is on the effect end point
which happens depending on the tumor type.
Forward in time you may, you may need to go yeah, in the, in the case of breast
cancer years or for some more aggressive cancers earlier.
But, but the yeah, that's, that's what the yeah, what the regulatory agencies
want to see would be the actual clinical, clinical end point.
Yeah, and then in some cases if you're lucky enough you have a surrogate marker,
is marker is a biomarker, which has been in the virtue.
By virtue of large, large studies it has been shown and proven that it could be
replacing the actual all clinical end point and then blood pressure could be
good example of that. Yeah, just two quick charts of showing
you the differences in outcomes in phase two crossed the therapeutic areas.
Far to left you see eh, column saying for all, all phase two studies the success
rate is about 30%. Eh, ema, eh, this chart is being updated
on an annual basis because that, that success rate is changing quite rapidly.
Yeah, and then you have a difference of course depending on different therapeutic
areas. Some therapeutic areas are having less
success survival rate in, in, in phase two setting.
Meaning that nine out of ten drugs, for instance if you look at the GI it has
pretty low success rate. Majority of the drugs tested there in
phase two die for instance. let me then go and show you phase three.
The outcome of that one for all phase three studies is you have about 75%
success rate. And again in variabilioty across
different therapeutic areas. Sometimes you can try to figure out a
correlation between your success rate between phase two and phase three.
And it has to do again with the, with the discussion we kept earlier on, that if
you keep the hurdle too high phase two, the few products which go to phase three
actually has a lot lot higher potential for success.
And then you may be able to see some of that in here, I would look at it for
respiratory instance. You see, here in phase three, the
respiratory rate is 95% in outcome success in phase three.
And if you go to phase two, you see the respiratory success is about 15%.
Yeah, and that gives me an idea that, that they had the pretty tough criteria
in phase two for respiratory drugs for them to pass the hurdle of phase two.
But the products which passed the hurdle of phase two and then to phase three were
[INAUDIBLE] were 95% successful. So again, this is a balance between phase
three and phase two, how you play it out. With the risk you take, you can balance
it out where you want to take it. I'm going to give you about a couple of
examples and finish up my presentation. First example is status and the effect of
two different products. One of them is a gemcabene up and the
second one is ezetimibe on and the affect of them on coronary artery disease, when
you combine them with different statins. Yeah, so we did actually apply the
modelling simulation which I told you earlier is one of the tools that we have
in trying to find out. You figure out the potential outcome in
phase three without having the data in phase two.
And that's the beauty of it that if you have the other type of models you
actually don't need to do the phase three study you can actually predict the
outcome of phase three studies. So here I've I'm showing five different
curves, and these are simple. Correlation between dose and the LDL
yeah, change from from the baseline for different products, for five different
status. A well known fact that these are all well
published, you know it. If we can take this data point and we can
model for them and we can predict yeah, yeah, or, or estimate the parameters of
this, this, this this, this relationship, mathematically.
and then, you look at the two other products, the new products that you
want to test it. You have ezetimibe on top, and you have
the the new product on bottom. And you do the same thing.
You do again, you develop a exposure response for each of them.
Yeah, and, and then on the right hand side you see I've two curves.
In one of them I've taken statin and I've added ezetimible to it far right hand
side. And other box here I've taken same
product statin and added in, in gemcabene on it.
And I look at the additive function, if the top curve would be, after the status
was given stain and the bottom curve would be that is given in combination.
So if you look at the for right hand side, you can see that when the product
is ezetimibe combination, is it a? You have a steady effect and additive
effect of that additional combination therapy which, which is sustained across
different doses and it goes from zero to 80.
You see the difference between two kilos of sustain meaning if you add up a new
product in top of the previous one, you have a benefit, added benefit.
If then you look at the the new test product, you can see that the effect is
not really there, I mean there is in the beginning.
But when you increase the dose of statin itself, the added bebefit you got from
the combination therapy is becoming less and less.
So when your, are you in the range of 60 milligram or 80 milligram.
The additional effect added benefit of, of, of of the product is fairly small.
Just based on this information the the product was discontinued or the, or the
concept was discontinued. So it was not real big need to go to
phase three setting, and run hundreds of patients to find this out.
The second example here would be just the example of a single arm study.
In phase two study and with drug X, a potent inhibitor of VEGFR's in thyroid
cancer, we have primary objective would be
You look at the activity of the drug in advanced thyroid measured by, by response
rates overall. And then there's a number of secondary
objectives listed we look at the few clinical objectives, we look at
population PK analysis and so forth. This, this it's like if you view about
the statistical expectations, where you get to know all hypothesis and
alternative hypothesis, that was acceptable or not.
Yeah, and the number of patients they recruited and then in this slide, I'm
giving you a view about what the actual results end up to be.
Yeah, it's a [UNKNOWN] charts. Yeah, everything going downwards, meaning
a decrease in the tumor size. And then, this, and the information from
this study could be interpreted as, Kaplan-Meier charts.
this chart shows you, you have the survival, PFS progression free survival
of 18.6 months. Okay, what does it really say?
Its a single arm study and that's where the issue comes as we were talking
before. In single arm study, we don't have any
reference points, so in order for me to interpret this result, if this drug is
better or not, I need to go again and start searching literature.
For other products which are being used for Thyroid cancer.
How many months, in fact, they have. Are they less or more than 18.6 months?
Yeah, but by the virtue of that form I'm bringing a bias, because I don't know
what patient population they use, what type of equipment they use in order to
look at the the size of tumor and so forth.
And as you know, medical technology is increasing, too.
Going to become better and better. So if the study that I'm looking as
reference was done ten years ago, the CT Scanner ten years ago were not as good as
CT Scanners today. So you look at the size of tumor a lot
lot better today than you were doing it ten 15 years ago.
So again, it's difficult for me to find a good reference point when you have single
arm studies and that was what I wanted to depict with this example.
You have just one arm, you don't really know the reference point.
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