1. Parametric and Nonparametric Population Modeling: a brief Summary
Roger Jelliffe, M.D.
USC Lab of Applied Pharmacokinetics
12/1/2018
USC LAPK

2. InTER-Individual Variability
The variability between subjects in a population.
Usually a single number (SD, CV%) in parametric population models
But there may be specific subpopulation groups
eg, fast, slow metabolizers, etc.
How describe all this with one number?
What will you DO with it?
12/1/2018
USC LAPK

3. InTRA-Individual Variability
The variability within an individual subject.
Assay error pattern, plus
Errors in Recording times of samples
Errors in Dosage Amounts given
Errors in Recording Dosage times
Structural Model Mis-specification
Unrecognized changes in parameter values during data analysis.
How describe all this with one number?
How describe interoccasional variability only with one number?
What will you DO with these numbers?
12/1/2018
USC LAPK

4. Bottom line for research or for TDM
Don’t wait for steady state
Don’t wait for distribution after the dose
Start TDM with 1st dose
Use D-opt principles
12/1/2018
USC LAPK

5. Parametric Population Models: IT2B, NONMEM, etc
Assume shape (normal, lognormal) of param distribs. (Describe params parametrically)
Get the param param values = param means, SD’s, covariances, etc.
Get F from intermixed IV+PO dosage
Get SEM’s, some confidence limits, signif tests.
Separate “Inter -” from “Intra -” Individual from assay Variability
But, usually get only summary single values for parameter distributions.
And also, they are not consistent.
12/1/2018
USC LAPK

6. Summary Parametric Population Models
Single point parameter estimates only.
Only one (1) model.
Cannot discover unsuspected subpopulations
Cannot predict precision of goal achievement
Most currently available ones not consistent.
Can estimate Parameter ranges
12/1/2018
USC LAPK

7. A Parametric Population Model Joint Density
12/1/2018
USC LAPK

8. 800 Normally distributed (K,V) points, correlation = +0.6
Non parametric methods prodcue discrete distrbutions. This plot is 800 biavariate normal distribution with a 0.6 positive correlation –
V is a volume of dist,, K an elimination rate constant, from a large series of simulations of a single compartment model.
12/1/2018
USC LAPK

9. A Population Model, made by Breugel

10. What is the IDEAL Pop Model?
The correct structural PK/PD Model.
The collection of each subject’s exactly known parameter values for that model.
Therefore, multiple individual models, one for each subject.
Usual statistical summaries can also be obtained, but usually will lose info.
How best approach this ideal? NP!
12/1/2018
USC LAPK

11. An NPML Population Model, made by Mallet
12/1/2018
USC LAPK

12. 800 normal points give 70 NPAG support points
Here the area of the red circles found by the NP method NPAG, our current production algorithm, are proportional to the probability of the support points at their centers
12/1/2018
USC LAPK

13. Nonparametric Population Models (1)
Get the entire ML distribution, a DiscreteJoint Density: one param set per subject, + its prob.
Shape of distribution not determined by some equation, only by the data itself.
Multiple indiv models, up to one per subject.
Can discover, locate, unsuspected subpopulations.
Get F from intermixed IV+PO dosage.
12/1/2018
USC LAPK

14. Nonparametric Population Models (2)
The multiple models permit multiple predictions.
Can predict precision of goal achievement by a dosage regimen.
Behavior is consistent.
Use IIV +/or assay SD, stated ranges.
12/1/2018
USC LAPK

15. 12/1/2018
USC LAPK