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Applied ToxicologyNURS 735 - Module 1

DOSE RESPONSE CONCEPTS

The underlying principles of toxicology rely on an understanding of the causal

relationships between exposure and effect. In order to better comprehend how

exposure-related effects can be explained, the concept of dose-response is

important. Although this is covered in your textbook as well as the on-line

materials, it may be helpful, particularly for non-science majors, to spend

some time looking at how dose response relationships are graphed and

interpreted. In this PowerPoint presentation, we will focus on the concept

of dose response.

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“All substances are poisons: there is nonewhich is not a poison. The right dosedifferentiates a poison and a remedy.”

Paracelsus (1493-1541)

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And as a review of some of the “History of Toxicology” handout, Paracelus is

noted for stating the concept that “THE DOSE MAKES THE POISON.”

At extremely low doses, a given substance may be non-toxic and even beneficial

(a concept known as hormesis), while at intermediate doses, it may be toxic.

At high doses, it may be lethal. This again underscores the importance of

understanding dose response relationships.

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Let’s start by looking at some dose response data. To do this, we can begin by

graphing some datapoints. Experimental data is typically plotted with the x-axis

representing the different doses or exposure concentrations, often on a log scale.

The units may be mg/kg or ppm, for example. The concentrations go from low

to high (left to right). The y-axis shows the response data for each

experimentally-derived dose exposure. The units for the y-axis in

this graph is percent cumulative response over a given time period. For now,

let’s assume these these data represent mouse mortality responses taken from

preliminary trials with a new drug. Mortality data are often used to determine

what is known as the LD50 for a drug or compound. Determining the LD50

of a drug, compound or toxicant is an important first step in discerning its

relative toxicity.

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Here is the experimental data from 10 mice exposed to each of eight

drug doses (80 mice total in the experiment). Each datapoint represents

the percent mortality at each dose. In the first, low dose we see a 0%

response (in other words 0 out of 10 mice succumbed due to the

exposure). At higher and higher doses, there was an increase in the %

response.

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If we fit a line to these datapoints we see that there is a sigmoid shape to the data.

This is typical for dose response graphs. Note that the curve is a “best fit” line;

it does not necessarily go through each and every datapoint. What I’d like to

point out is that the mid-range experimental dose groups responded in

a linear fashion with respect to the dose (I am referring to the responses

between 16 and 84%).

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NOEL

The lowest experimental dose where there is no measurable effect is known

as the No Observable Effect Level (or NOEL). Your textbook refers to this

at the No Observable Adverse Effect Level (same thing). In our example, this is the

concentration that the experimenters failed to see any mortality. But it could

have just as well been an experiment looking at the highest dose that failed to

cause enzyme inhibition, or skin pathology or whatever the measured endpoint

of the study was. As we will learn later on, the NOEL is a useful measurement

for extrapolating risk and safe exposure concentrations. Keep in mind that

If the lowest dose tested elicits a response, it is possible to have a dataset

for a drug or toxicant where there no NOEL derived.

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Dose-response curves for 4 different chemicals

Potency = range of doses over which a chemical produces increasing responses. A > B; C > D.

Maximal efficacy = limit of dose-response relationship. A=B; C<D.

Let’s look at a comparison between the dose-response curves for four different

compounds and bring up the notions of POTENCY and MAXIMAL

EFFACACY. In these two graphs, the sigmoid shape of the dose-response curves

Is no longer evident because the response (y) axis is now on a probit scale. Plotting

concentration on a log scale and response on a probit scale mathematically

transforms the data to appear linear; at times this facilitates making

comparisons between toxicants, and generating LC50s (which we will get

to shortly). Looking at the left panel, we see the dose-response

lines for compounds A and B. Note that the slopes of the two response

lines are similar, but the line for compound A is to the left of the line for

compound B. In other words, it takes less of compound A to illicit a similar

response with compound B. Therefore, we can say that compound A is more

POTENT than compound B. And, since the definition of POTENCY is the range

of concentrations over which a chemical produces increasing responses, we

can similarly say that compound C is more POTENT than compound D.

If we now focus on the right panel, we can address the concept of MAXIMAL

EFFICACY. This concept reflects the limit of the dose-response relationship

on the response axis to a certain chemical. The MAXIMAL EFFICACY of

compound C is less than that of compound D. The MAX EFFICACY of

compounds A and B are equal.

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So, here is how transformed data on a log-probit scale can yield

What is known as an LD50. The LD50, or statistically-derived dose

that is lethal to 50% of a modeled population, is a helpful tool to

compare the toxicity of different compounds, or between different

population models. Since the data is linear here, we can extrapolate

a line across from the 50% response on the y-axis, to thecorresponding concentration on the X-axis. In this example, the LD50is approximately 100 mg/kg.

Note that LD50s are used to compare responses between drugs or

animal species. However, if we look at the toxicity of a compound

as it is exposed to fish in water, for example, we would not be looking

at an administered dose, but at the concentration that the animals

were exposed to. In this case we’d be deriving the LC50 (lethal

concentration). At the same time, we might be interested in

experimentally deriving an effective dose for a compound, and we

would similarly calculate the EC50, or the effective concentration.

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Comparison of effective, toxic and lethal dosages

Here is an example of data illustrating the EFFECTIVE DOSE RESPONSE,

the TOXIC DOSE RESPONSE, and the LETHAL DOSE RESPONSE for the

same drug in the same animal model. The order of these response lines

from left to right should be intuitive. Think of an example of a drug and

see if this makes sense to you.

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Let’s go back to our sigmoid-shaped dose response curve and take a closer

look at interpreting the concept of variability. Here we are looking at

percent response at each concentration or dose. The sigmoid curve between

16 and 84 percent is relatively linear. These limits on the response axis

represent +/- 1 standard deviation of the mean in a population with a normal

distribution.

To better understand the concept of SD and its importance in describing

responses, let’s look at the same data plotted such that the total number

of animals used at ALL doses or concentrations are shown. This is

known as a cumulative frequency response.

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This frequency response graph works well for QUANTAL data

(i.e., all or none responses, such as mortality, tumor production,

etc.). We can see that the data takes the shape of a BELL CURVE,

with the majority of the responders mostly in the middle. It

appears from this dataset that the mean (mathematical average)

response is approx 110 mg/kg. There are fewer responders at

the ends of the bell curve, and this expected in most data that is

normally distributed. The reason for this distribution is that there

are differences in inter-individual responses. This is known as

BIOLOGICICAL VARIABILITY. Responders at the far left of the mean

are typically HYPERSUSCEPTIBLE, whereas the those at the far right

are RESISTANT. In a normally distributed population, 1 SD of mean

represents 68% of the population; 2 SD of the mean represents

95% of the population; 3 SD represents 99% of the popopulation.

So, the SD, derived from some statistical mathematics, helps us

to identify the portion of the test population that tends to deviate

most from those who respond with a tendency around the mean.

From this estimate of deviation, we can estimate a % of the

population that would tend to not response similarly to the “average”

individual (i.e., an individual that has a response tendency around the mean).

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Comparison of dose-response relationship for twodifferent chemicals plotted on a log dose-probit scale

As we described, log-dose data provides a linear plot, easier for interpretation

and extrapolation. This graphs shows two curves on either side of the response

line or compound A. These curves are CONFIDENCE INTERVALS for compound A

data. CI’s are related to standard deviation and tell us how confident we can be

that the true population will respond similarly to the experimental (model) population,

based on the experimental variability encountered. Note that CI’s are tightest

closest to the data line nearest the 50% probit response, closest to the mean.

This makes sense since we know that response variability increases as we move

away from the mean response.

The dose-response line for Chemical B is steeper > Chemical A. Take a second look

at the data, and make a judgment call as to which chemical is safer to use?

Please answer this question and state your reasoning on the Discussion Board.

The next two slides are additions to this ppt presentation, and were not included

in the audio version.

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“The problem with toxicology is notthe practicing toxicologists, butchemists who can detect, precisely,toxicologically insignificant amountsof chemicals”

Rene Truhaut, University of Paris (1909-1994)

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Here is a final concept worth considering. Current technology allows us

to accurately measure trace quantities of chemicals. Yet, the biological

significance of the presence of these extremely small amounts of these

chemicals is sometimes not clear (e.g., carcinogens). Just because a

drug or environmental contaminant is measurable, does that mean

that it poses a threat?

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This slide is food for thought…

Many environmental contaminants (and even foodstuffs) are

Considered differently by different scientific groups and

agencies with regard to their safety. It is, therefore, up

to the “consumer” in many cases to make a judgment call

as to how much exposure is reasonable, based on available

information. This concept is related to RISK ASSESSMENT.