1. Assay and Error issues in Pop modeling and TDM
Irina Bondareva, Ph.D.
Institute for Physical-Chemical Medicine
11/3/2018
USC LAPK

2. InTER-Individual Variability
Usually a single number (SD, CV%) in parametric population models
But there may be specific sub- populations
eg, fast, slow metabolizers, etc.
How describe all this with one number?
A good question!
11/3/2018
USC LAPK

3. InTRA-Individual Variability
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?
Why describe interoccasional variability only with one number?
11/3/2018
USC LAPK

4. Determining the assay error pattern – how credible is it
Determining the assay error pattern – how credible is it? What do we mean by credibility? What is a good MEASURE of credibility?
11/3/2018
USC LAPK

5. Fisher Information Fisher Info = 1/Varx
So need to know, or have a good estimate, of the SD of every TDM serum level
Var = SD2
Weightx = 1/Varx
11/3/2018
USC LAPK

6. Determining the Assay SD polynomial
Measure blank, low, medium, high, and very high samples in at least quadruplicate.
Get mean + SD for each sample.
Fit a polynomial to the mean and SD data.
SD = A0C0 + A1C1 + A2C2 + A3C3
Then can weight each measurement by the reciprocal of its variance (Fisher Info)
No lower detectable limit for PK work!
11/3/2018
USC LAPK

7. 11/3/2018
USC LAPK

8. 11/3/2018
USC LAPK

9. Assay CV SD
Concentration
11/3/2018
USC LAPK

10. Assay CV% versus weight, Fisher Information
Assume, for example, 10% assay CV
If conc = 10, SD = 1, var = 1, weight = 1
If conc = 20, SD = 2, var = 4, weight = ¼
So a constant % error (the assay CV) is NOT the correct measure of the error!
As conc approaches zero, CV% approaches infinity
But assay SD, var, weight always finite. Fisher info is the correct measure of assay error.
Therefore, NO LOQ for PK work!
11/3/2018
USC LAPK

11. Other environmental errors
Gamma – a multiplier of the assay error polynomial
Overall error = Gamma*(assay error polynomial)
Lamda – an additive term
Overall error = Lamda + (assay error polynomial)
11/3/2018
USC LAPK