Original subtitles

Hello everyone and welcome to our module on dose response.

So before we talk about how to dose medications, let's talk about two

important terms. And the first term is efficacy.

And the efficacy of a drug is the maximal effect that the drug can

produce. And we can compare drugs in terms of efficacy.

For example, you can imagine that morphine is more efficacious than

aspirin for pain control. And that's because morphine is able to achieve

greater pain control than aspirin can for most patients.

The second important concept is called potency, and this is the amount of drug

needed for a given effect.

So suppose, for example, that drug A produces a given effect with 5

milligrams, and drug B can produce the same effect, but you have to give 50

milligrams. We would then say that drug A is 10 times more potent than drug B.

This is just an indication of the amount of milligrams of drug required to

achieve a given effect.

And it's important to know that more potent isn't necessarily superior.

It depends on what you're after.

And low potency really only becomes bad if the dose required to achieve an

effect is so high it's hard to administer. For example, if you had to

give 50,000 milligrams of drug B to get the same effect as 5 milligrams of drug

A, then obviously this would render drug B inferior to drug A because the dose

has just become so high it's cumbersome to deal with clinically.

Let's get back to our example of morphine versus aspirin for pain

control. So here's a graph.

showing the relative analgesia or pain control achieved by the two drugs as a

function of their dose on the x-axis.

And you can see that the peak effect of morphine is very very high compared to

the peak effect of aspirin. This means that morphine is more efficacious than

aspirin. You can also see that morphine achieves its effects at a much lower

dose on the x-axis compared to aspirin. To get the max effect of aspirin you

have to go all the way up to this dose here.

To get the max effect of morphine, you only have to go up to this dose here. So

this means that morphine is more potent as well. And you should get comfortable

identifying potency and efficacy from charts showing you the relative effect

at a given dose of two drugs.

Now we're going to talk about a very important concept in pharmacology, and

that's the relationship between the dose of medication and the response you see

clinically in the patient.

So for many drugs, we can measure the response as we increase the dose. And

this means that we can plot the dose on the x-axis versus the response on the y

-axis, and this is called a dose-response curve.

There are two types of responses that we measure and chart on dose-response

curves, and those are called graded or quantal. So a graded response is

something like blood pressure. It's a number, it's a measurable effect, and we

can monitor how the blood pressure changes as we give more and more of the

drug. A quantal response is a yes-no response, so quantal means divided into

discrete units.

So a quantal response is for drugs that either achieve their effect or do not.

So for example, if we wanted to chart the number of patients achieving a blood

pressure under 140, this is a yes-no thing. You either are or are not under

140. So we could measure the quantal effect of the drug by charting the

percent of patients who achieve that dose. And quantal effects are sometimes

the only effects you can monitor for certain drugs. For example, if the drug

eliminated headache, that would either be a yes or no function, and you would

have to create a quantal dose response curve.

because there is no number you can apply to headache yes or no.

Let's talk about the graded dose response curves first. So for most

drugs, as you increase the dose, you see a steady increase in the effect.

However, at some point, the curve starts to flatten out and you reach an effect

that you can't get beyond no matter how high you make the dose. And we call that

effect the Emax.

We also like to identify a point that is 50% up the y-axis to that Emax. We call

that the 50% effect.

And if you identify the dose associated with that 50% effect, we call that the

EC50 for effective concentration 50, or sometimes it's the ED50 for effective

dose 50.

But whatever you call it, it's the dose required to achieve 50% of the maximum

effect. And that's one of the characteristics for most drugs.

Now, sometimes in the pharmacology literature, they plot the log of the

dose on the x-axis. And why do they do this?

Well, when you plot the log dose, it spreads the data out. It converts it

into an S-shaped curve like what I've shown on the screen right here. And this

sometimes makes the data easier to visualize. But it's the same data and it

means the same things. The maximum effect that you can achieve is the Emax.

And if you go halfway up the Y-axis to the Emax, you can find the E50 point.

You identify that point on the curve and go down to the bottom. And that is the

dose at which you are achieving 50% of your maximum result.

And you should know that the lower the EC50 is, the higher the potency.

So drugs that can achieve 50% of the max effect way down here on the x-axis are

very potent. It doesn't take many milligrams to get halfway to the maximum

effect. On the other hand, drugs which achieve 50% of the maximum effect at a

dose way over here are much less potent. It takes many more milligrams of those

drugs to get the same effect.

I've illustrated that point here on this slide by drawing three dose response

curves for three different drugs. So drug A is in blue.

Drug B is in green and drug C is in red. And you can see that all three drugs are

able to achieve the same Emax. They have the same maximum effect that they're

able to achieve.

This means that the E50 point on the y-axis is the same for all three drugs.

But don't let that confuse you. The effective dose required to achieve this

50% is very different for the three drugs.

For drug A, it's right here at this point, very far down on the x-axis. Drug

A is very potent. It only takes a small number of milligrams here on the x-axis.

to achieve half the max effect.

Drug B is somewhere in the middle on the x-axis. And for drug C, you need a lot

of milligrams in order to achieve 50% of that max effect. Drug C is much less

potent than drug A. So the closer the curve is to the zero point on the dose

curve here, the more potent it is.

And I've written that up here. A is more potent than B, and B is more potent than

C. So the dose required to achieve this 50% mark is a marker of how potent a

drug is.

So you should also be able to compare efficacy for two different drugs from a

dose-response curve. And efficacy is easier. Whichever drug has the higher

Emax is more efficacious.

So here I've drawn drug A in blue, and you can see that its Emax here is

somewhat low relative to drug B, which achieves a higher Emax.

So essentially, the higher up the y-axis the curve reaches, the more efficacious

the drug is, because that means it's able to achieve a higher clinical

effect.

You're sometimes asked to interpret dose response curves for agonists and

antagonists together, so let's talk about how to do that.

So let's imagine that we could monitor the effect of stimulation of beta

receptors. Imagine we had some tool and we could determine how much beta

receptors in the human body are being stimulated. And let's imagine that we

administered steadily increasing dosages of norepinephrine, which, as you know,

is an agonist to beta receptors.

we would generate a curve like this blue curve right here which shows that at

steadily increasing dosages of norepinephrine we get an increased

effect on the beta receptors and that should make sense to you.

Now let's imagine that we repeat the experiment and again we administer

increasing dosages of norepinephrine but there is a competitive antagonist

present in the system. Maybe it's a beta blocker.

So what's going to happen when there's a beta blocker present is you're going to

shift from the blue curve to the green curve and let's talk about why that

would happen.

Well, if the beta blocker was a competitive antagonist, that means it

competes for the same binding sites on the receptor as norepinephrine. So once

you get the norepinephrine dose up high enough, you can overcome the effect of

that competitive antagonist and generate your same normal shaped curve, which is

what I've drawn in green.

So the curve has the same shape and it reaches the same maximum effect, but it

does all those things at higher dosages. Higher dosages are now required to

overcome the effect of the competitive antagonist.

And key points here are that the Emax is not going to change. The Emax is related

to how much effect norepinephrine can achieve on the beta receptors, and

that's not altered by the competitive antagonist. However, the E50 dose is

changed. So in the initial system with no antagonist, this was the E50 dose

right down here on the x-axis at a relatively low point. On the green

curve, the E50 dose is here, much higher point on the x-axis, and that should

make sense to you.

you need more dose of norepinephrine in order to achieve 50 of the max effect

because you've got to add extra dose to overcome that competitive antagonist of

the beta blocker now let's talk about what would happen if you added a non

-competitive antagonist so recall that a non-competitive antagonist is going to

bind to the beta receptor at a different site from norepinephrine it's going to

change the shape of the beta receptor so that norepinephrine doesn't work any

longer And increasing the dose of norepinephrine cannot overcome this

effect. That's an important point to understand.

So once again, we're going to plot the effect of the beta receptors on the y

-axis here. And we're going to plot the log dose of norepinephrine on the x-axis

here. So our baseline curve is the blue one right here. It is a normal shape and

a normal Emax.

But when we add a non-competitive antagonist, what happens is we reduce

the max effect. We lower the Emax. And that's because the non-competitive

antagonist is permanently changing the shape of those beta receptors.

It's the same thing as removing beta receptors from the system. So it lowers

the max effect that you can achieve.

So very important to remember that the Emax falls when you add a non

-competitive antagonist. Now notice that the point on the y-axis where you

achieve the E50 falls, okay, it's going to be lower for the green curve than the

blue curve. But don't let that confuse you. The dose associated with the 50%

effect on both curves is the same. This is the point right here.

for the blue curve and this is the point right here for the green curve and if

you follow them down to the x-axis they are unchanged so adding a non

-competitive antagonist does not change the effective dose to achieve 50 but it

does lower the maximum effect now let me talk about a concept called spare

receptors so there are certain systems in the body which have spare receptors

and these are receptors that can activate when others get blocked This

means that there are some systems where you can achieve a maximal response even

though you've permanently blocked some of the receptors.

We don't completely understand how these receptors work, but we know they exist

from experiments with irreversible or non-competitive antagonists. As you

know, these antagonists prevent binding of an agonist to a portion of receptors,

and they should lower the max response. But there are some systems where high

concentrations can still produce the max response.

So this is what the data look like from an experiment on a system with spare

receptors. So let's imagine that the dose here is the dose of norepinephrine.

I'm just going to make up a hypothetical example here.

And the effect is the effect on beta receptors.

Well, the blue curve is a normal system. So as we increase the dose of

norepinephrine, we see an increase in the effect on the beta receptors until

we reach a max result.

Now let's suppose we add a non-competitive antagonist to this

system. Well, as you know, a non-competitive antagonist should reduce

the Emax.

But instead, what we see is that the curve shifts right, like a competitive

antagonist. And the reason this happens, we think, is because there are spare

receptors which get activated in the presence of the non-competitive

antagonist, and they take over the job that those permanently blocked receptors

cannot do. So the system behaves normally. It's just shifted to the

right. It takes a higher dose of norepinephrine to achieve the normal

curve. Now, if we continue to add more of the non-competitive antagonist,

eventually, if we add a high dose, we will get a curve like this red one here.

And this is more like what you would expect.

The Emax has eventually fallen, but it takes a significant dose of the non

-competitive antagonist to do that because we have to not only antagonize

the regular receptors, we have to also antagonize the spare receptors.

So this is just an experimental concept that's been demonstrated in some

systems, and you should be aware of it, and you should know these three curves

on the screen because that's what the data look like.

Another special concept in dose response relationships is that of partial

agonists. So these are drugs which have a similar structure to agonists, but

they produce less than the full effect.

So let's use our example again of beta receptors.

So if we give a full agonist like norepinephrine, we'll get a curve like

this blue one right here.

However, if we give a partial agonist to the beta receptor, we will get a curve

like this green one right here.

It's a similar curve to the blue one but there is a decreased max effect because

like the name implies it is a partial and not complete agonist.

Now there are two confusing curves that are often shown in association with

partial agonists and let me go over them both now so that you've seen them and

you know what they represent.

The first curve is this one right here which represents a single dose of the

agonist given with increasing dosages of the partial agonist. So let's look down

at the graph. On the x-axis As we go higher up the x-axis, we're showing

increasing dosages of the partial agonist.

On the y-axis, what we're showing is the percent of receptors that are filled by

either the agonist or the partial agonist. So let's start by imagining

that we give zero for a dose of the partial agonist. So we give no partial

agonist whatsoever.

However, we still give that single dose of the agonist. So what we would see

then is that the agonist would bind 100% of the receptors, and that should make

sense to you.

However, as we steadily increase the dose of the partial agonist, what we see

is that fewer and fewer of the receptors are bound by the agonist and more and

more of the receptors begin to be bound by the partial agonist. And eventually,

if we get up to a very, very high dose of the partial agonist, what we see is

that 100% of the receptors are going to be bound by the partial agonist and 0%

of the receptors are going to be bound by the agonist itself.

So the key point of this graph is that in an environment where there is agonist

hanging around the receptors, if you give a high enough dose of the partial

agonist, you can eventually bump all that agonist out of the way and have the

receptors entirely bound by the partial agonist alone.

Now let's look at the second confusing graph that's often shown in association

with partial agonists. And once again, this graph describes a single dose of

the agonist with increasing dosages of the partial agonist.

So once again we've got increasing dosages of the partial agonist along the

x-axis but this time on the y-axis instead of the percent of the receptors

bound we have the clinical response.

Also different on this graph is that we have three different lines. So the first

line is the blue one here and this is the amount of the clinical response that

is created by the partial agonist. We also have the green curve which is the

amount of the clinical response created by the agonist.

And then we have a purple curve, which represents the combination of the two or

the total response.

It's going to be a mixture of the response from the agonist on the

receptors and the partial agonist on the receptors.

So once again, let's start by imagining we give a dose of zero for the partial

agonist, but we still give that single dose of the agonist. So what we're going

to see is a very large clinical response of about 100% of the clinical response

that's achievable.

Because remember, the agonist has a very powerful clinical response. And in this

case, there's no partial agonist around.

So the total response is all coming from the agonist, and that's a very large,

significant 100% clinical response.

As we start to increase the dose of the partial agonist, however, what we see is

that the amount of clinical response that comes from the partial agonist

starts to rise. So this blue line starts to climb.

The amount of clinical response we see from the agonist starts to fall.

And because the amount of clinical response that the partial agonist is

capable of generating is weaker than the agonist response, our total response

also starts to fall. Now once we get out here to a very high dose of partial

agonist, all the receptors are going to be bound by the partial agonist like

what I showed you on the last slide.

This means that the total response is going to be entirely dictated by the

partial agonist. And since the partial agonist is a weaker actor on the

receptors, this means the clinical response is only going to be about 50%

or some lower number than 100%. It won't be as high as you would see when all the

receptors were bound by the agonist because the partial agonist is a weaker

actor than the agonist itself.

So just like with the last curve, this curve is designed to show you that as

you raise the dose of partial agonist, eventually all the receptors will be

bound by the partial agonist. What's different about this curve is it's also

showing you that once you reach that very high point where the receptors are

all bound by the partial agonist, your clinical response is lower. It's about

50% or something less than 100% because the partial agonist is a weaker

stimulator of those receptors.

There are a couple of drugs which are partial agonists, and that's why these

are discussed in pharmacology. The first two are old beta blockers called

Pindolol and Acebutolol. These are old antihypertensives that nobody really

uses anymore.

They would activate beta receptors, but to a lesser degree than norepinephrine,

and this would lower the blood pressure in some patients who were highly

symptomatic from high blood pressure.

These drugs are described in the literature as having something called

intrinsic sympathomimetic activity, or IMA.

This just means that they activate the sympathetic nervous system, but to a

lesser degree than norepinephrine.

And the key thing you need to know about these drugs for your boards is that they

can cause angina by causing vasoconstriction in the coronary

vasculature. Now, this isn't really a clinical problem in the modern era

because we have better drugs and nobody uses it anymore, but you definitely

should know about this potential side effect for your boards.

Another partial agonist is the drug buprenorphine. It's a partial opioid

agonist, and this can be used in the treatment of opioid dependence because

it will somewhat stimulate the opioid receptors, but not as much as narcotics

do. And then another example of a partial agonist is clomiphene, which is

a partial agonist of estrogen receptors in the hypothalamus. So this is going to

activate those receptors, but to a lesser degree than the hormones do. This

is going to block the negative feedback from LH and FSH.

And this drug is sometimes used to treat infertility or polycystic ovarian

syndrome. Now let's talk about those other dose response curves, the quantal

dose response curves. Remember, these are for drug effects that are yes, no.

Remember, quantal means in discrete units.

So once again, we're going to plot the log dose on the x-axis here, but this

time on the y-axis, we're going to plot the percent of patients achieving the

therapeutic response.

And once again, we get an S-shaped curve like what I've shown here.

And we also have a 50% mark where 50% of the patients are achieving the

therapeutic response.

And we can follow that over to the point on the curve and go down to the x-axis

and mark the ED50 or the dose required for 50% of patients to respond.

And one thing you can do with quantal dose response curves is you can plot the

therapeutic response, but you can also plot adverse responses as well.

So for example, let's suppose the drug we were dealing with was warfarin. So

the therapeutic response could be an INR value.

that is greater than 2.0. So this S-shaped curve right here shows the

percent of patients achieving an INR greater than 2 as we increase the dose.

The adverse response could be patients who have a bleeding event.

And what we would see is that as we go up in the dose, we increase the number

of patients who are achieving a therapeutic response. But at some point,

we also start to increase the number of patients achieving the adverse response

or bleeding.

And just like we can identify the dose required for 50% of the patients to have

a therapeutic response, we can identify the dose required for 50% of the

patients to have an adverse response.

If the adverse response is death, then it's called the LD50 for the lethal

dose. If it's something toxic, then it's called the toxic dose 50 or the TD50.

Whichever one you call it, it's at this point right here on the curve where 50%

of the patients are having this adverse response.

So one measurement of drug safety that's often reported is something called the

therapeutic index, and this is the ratio of the LD50 to the ED50. So the higher

this number, the further apart those two points are, and the more room you have

to work with in terms of dose.

The lower this number, the tighter range you have to deal with, and it means that

you can very easily cross from the therapeutic dose range into the toxic

dose range for a given drug.

We can also define something called the therapeutic window.

So if we say that this point right here on the y-axis is the minimum effective

dose, then once we get above that, we are now in the therapeutic window.

And then if we identify a point on the toxic dose curve, and we say that above

that you are in the toxic dose range, then we can define the therapeutic

window as the range between those two points. So we might say the therapeutic

window for a drug is 50 to 100 milligrams, for example.

And this is just the range of dosages between the effective dose and the toxic

dose range.

And you should know that there are many drugs that have a low therapeutic index

or a narrow therapeutic window. And these are often the drugs where we

measure their levels to avoid toxicity.

So drugs like warfarin and digoxin and lithium and theophylline, these all have

a narrow therapeutic window or a low therapeutic index. And therefore, we

have to check their levels and make sure that patients aren't on too high of a

dose. And that concludes our module on dosing.