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Interpreting population pharmacokinetic pharmacodynamic analyses – a clinical viewpoint

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Interpreting population pharmacokinetic pharmacodynamic analyses – a clinical viewpoint

  1. 1. Interpreting population pharmacokinetic-pharmacodynamic analyses – a clinical viewpoint
  2. 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. 3. Population analysis software
  4. 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. 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. 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. 7. A model for the typical response A structural model
  8. 8. For pharmacokinetics • A compartmental model – Plasma drug concentration over time
  9. 9. A model for heterogeneity
  10. 10. A model for heterogeneity • Describe the variability between individuals • Also called – Between subject variability (BSV) – Interindividual variability (IIV)
  11. 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. 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. 13. Creatinine clearanceNon-renal clearance Fraction of unchanged drug eliminated by the kidney Remaining (residual) variability
  14. 14. c Normal distribution Log-normal distribution Negative or zero are not allowed
  15. 15. Predicted concentrations from this PK model Dashed lines PK model includes the covariate CLcr as a covariate on CL
  16. 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. 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. 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. 19. Interpretation of population analyses • Two important questions – Was the design appropriate to identify a covariate relationship? – Was the covariate relationship significant?
  20. 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. 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. 22. Conclusions • Population analysis is a powerful technique that can be used to understand the time course of drug effects.

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