Response following a single dose

18 important questions on Response following a single dose

What are reasons for the time delay (the gap between the plasma concentration and the response)?

- tissue distribution: delayed distribution to the site of action.
-  pharmacodynamics:
- systems in flux: when the site of meassurements is in another compartment than the site of action.

What 2 reasons can be a cause for the delay of the response after the concentration?

Both pharmacokinetics and pharmacodynamics.  The shorter the turnover time the more rapid the change in the meassured response for a change in drug concentration.

By what kind of factors is the time of onset of response govered?

- route of administration
- absorption
- distribution to target site
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What are the PK variables?

F, Ka, Vd, Vd, Clren, Clmet, Cl12
So, there has to be 1 equation with 7  unknown variables.

What is target AUC dosing?

This requires frequent blood sampling and concentration measurements to estimate the AUC.
This is not feasible in daily clinical practice because of patient discomfort and costs. So this is not efficient.

What kind of methods can be used to determine optimal sampling?

- experimental (this requires rich data sets for derivation of equation and separate data sets for validation are also necessary.
- Monte Carlo simulations: This requires pharmacokinetic population models.

So what is the step plan in the experimental approach?

1) collect "rich" data sets from a group of patients. e.g. time points 0, 1, 2, 3, 4, 6, ,8, 10. 12.
2) divide patients into 2 groups: calculate the AUC from the limited samples of the study group and use the other group as validation group for the selecting of optimal sampling times.

What is the classical approach for the study group?

- From the rich data sets for the study groups the "true" AUC is calculated using all data points.
- Calculate the relation between C and AUC by multiple regression analysis. e.g. for 2 data points. repeat this for any combination of data points.

What is the classical approach for the validation group?

The rich data sets are of course similar to the study group.
-Calculate for each subject the true AUC using all data points
-Calculate for each subject the estimated AUC using the equation for each combination of data points.
- calculate the root mean squared error (RMSE) (measure of precision)

How goes the evaluation according to the classical approach?

- select the best predictive equation (that means that one with the lowest RMSE and that one with the most optimal sampling times)
- select minimal number of samples needed (so that acceptable precision and minimal sampling times are obtained)

What are limitations and drawbacks of the classical approach?

- the number of subjects are limited so a limited precision.
- The number of data points are limited so a limited accuracy.
- A measurement at exact time point is required what is difficult in practice.
- it is sensitive to outliers so a limited precision
- The true AUC is not exact so a limited accuracy
- there are many calculation so it really takes time.

What are the improvements since the classical approach?

- a replace use of measurement in subjects, by data generated by Monte carlo simulation. (in this simulation there is an unlimited number of subjects and an unlimited  number of data points)
- A replace multiple regression analysis by Bayesian estimation of PK parameters. This means that you have a best estimate of CL, F and AUC.
- Automatic calculation

How is the first step of the Monte Carlo simulation performed? (generation of data)

a) population PK parameters (means, sd)
in between: random noise (interindividual variability)
b) individual PK parameters (for each subject)
in between: PK model, dose, time schedule
c) true plasma concentrations (at time points of measurement)
in between: random noise due to assay error
d)This results into a measured plasma concentration

How is the second step of the monte carlo analysis (analysis of dat) performed?

a) you have the measured plasma concentrations
in between: you perform analysis
b) you have an estimated AUC
in between: you compare the estimated AUC with the true AUC
c) evaluation

Of course you also have a mean error and a root mean squared error which are a measure of bias and a measure of precision, respectively during the evaluation.

How are accuracy and precision defined?

accuracy: All datapoints are close together, but it does not necessarily mean that all those point are located on the right place. It is represented by the mean error
precision: All datapoints are may be wide scattered but the mean value is near to the expected value.  It is represented by the RMSE (root mean squared error)

What is the formal definition of accuracy?

Accuracy is the degree of conformity of a measured or calculated quantity to its actual (true) value.
bias = lack of accuracy

What is the formal defiinition of precision?

Precision (reproducibility or repeatability) is the degree to which further measurements or calculations show the same or similar results.

What are the advantages of the Monte Carlo simulation?

- unlimited number of subjects
- unlimited number of measurements
- exact AUC known
- data of all patients used (for PK population model)
- study design can be chosen freely

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