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