make you more understanding about ppk and ppd

1. Interpreting population
pharmacokinetic-pharmacodynamic
analyses – a clinical viewpoint

2. Introduction
• The primary purpose of PK and PKPD analysis
– To individualize drug choice and dosing regimen
• The population approach
– Arose from 1972 to 1977
• An exponential growth in publication
– Since 1985

3. Population analysis software

4. What is a population analysis?
• It is the application of a model to describe
data that arise from more than one individual.
• Allows the use of sparse sampling study
designs
• Quantify the influence of patient
characteristics and any remaining unexplained
variability between patients.

5. Simulation dataset
Gentamicin-like drug
A volume of distribution: 20 l
Clearance: 41/h
IV bolus
Simulate data for 30 pts who received
1. 420 mg single IV bolus
2. Seven blood samples at 0.25, 0.5, 1,
2, 4, 8 and 12 h

6. Three elements of a population model
• A model for the typical response
– This is the response for a typical (average) patient
• A model for heterogeneity
• A model for uncertainty

7. A model for the typical response
A structural model

8. For pharmacokinetics
• A compartmental model
– Plasma drug concentration over time

9. A model for heterogeneity

10. A model for heterogeneity
• Describe the variability between individuals
• Also called
– Between subject variability (BSV)
– Interindividual variability (IIV)

11. Two distinct model
• One model
– Describe predictable reasons why individuals are
different
• Second
– Quantify the remaining source of random
variability
• A statistical model for random variability

12. The range of model predictions
Not only the typical response of the population, but
also to predict the likely range of responses that may
occur

13. Creatinine clearanceNon-renal clearance
Fraction of unchanged drug eliminated by the kidney
Remaining (residual) variability

14. c
Normal distribution Log-normal distribution
Negative or zero are not allowed

15. Predicted concentrations from this PK
model
Dashed lines
PK model includes the covariate CLcr
as a covariate on CL

16. A model for uncertainty
• Also called residual error
• Uncertainty arises from
– Process error
• Where the dose or timing of dose or blood samplings are
not conducted at the times that they are recorded
– Measurement error
• Where the response (concentration) is not measured exactly
– Model misspecification
• Too simple equation in reality
– Moment to moment variability within a patient

17. For error
The error for jth observation for the ith individual
This error represents the (residual) difference of the
model prediction from the data.
Usual to consider e to be normally distributed

18. Why are population PKPD analyses
performed?
• Descriptive population analyses
– The best model to describe the study data
• Predictive population analyses
– What dose or interval
– Max effects and min side effects
• Designing clinical trials
• Identification of covariates
– Phenotypic or genotypic

19. Interpretation of population analyses
• Two important questions
– Was the design appropriate to identify a covariate
relationship?
– Was the covariate relationship significant?

20. Design of covariate population
analyses
• Covariates can be assessed based on the
distribution of covariates in the study
population and the number of subjects in the
study
• A minimum of 50 to 100 pts are required to
provide accurate estimates of covariate effects
in a population analysis setting

21. Significance of covariate relationships
• Biological plausibility
– CL increases rather than decreases with increasing weight
• Clinical significance
– 20 % of drug is eliminated renally, but CL as a statistically
significant covariate
• Statistical significance
– Global test
• NONMEN: objective function values (OFV)
• > 3.84 units: p < 0.05
– Local test
• A reduction in unexplained between subject variability

22. Conclusions
• Population analysis is a powerful technique
that can be used to understand the time
course of drug effects.