Download presentation

Presentation is loading. Please wait.

Published byLuis Stanfield Modified over 2 years ago

1
An introduction to population kinetics Didier Concordet NATIONAL VETERINARY SCHOOL Toulouse

2
Preliminaries Definitions : Random variable Fixed variable Distribution

3
Random or fixed ? Definitions : A random variable is a variable whose value changes when the experiment is begun again. The value it takes is drawn from a distribution. A fixed variable is a variable whose value does not change when the experiment is begun again. The value it takes is chosen (directly or indirectly) by experimenter.

4
Example in kinetics A kinetics experiment is performed on two groups of 10 dogs. The first group of 10 dogs receives the formulation A of an active principle, the other group receives the formulation B. The two formulations are given by IV route at time t=0. The dose is the same for the two formulations D = 10mg/kg. For both formulations, the sampling times are t 1 = 2 mn, t 2 = 10mn, t 3 = 30 mn, t 4 = 1h, t 5 =2 h, t 6 = 4 h.

5
Random or fixed ? The formulation Dose The sampling times The concentrations The dogs Fixed Random Fixed Random Analytical error Departure to kinetic model Population kinetics Classical kinetics

6
Distribution ? The distribution of a random variable is defined by the probability of occurrence of the all the values it takes. Clearance 00.10.20.30.4 8.07.88.28.4 Concentrations at t=2 mn

7
An example 30 horses Time Concentration

8
Step 1 : Write a PK (PK/PD) model A statistical model Mean model : functional relationship Variance model : Assumptions on the residuals

9
Step 1 : Write a deterministic (mean) model to describe the individual kinetics

11
residual

12
Step 1 : Write a model (variance) to describe the magnitude of departure to the kinetics Time Residual

13
Step 1 : Write a model (variance) to describe the magnitude of departure to the kinetics Time Residual

14
Step 1 : Describe the shape of departure to the kinetics Time Residual

15
Step 1 :Write an "individual" model j th concentration measured on the i th animal j th sample time of the i th animal residual CV Gaussian residual with unit variance

16
Step 2 : Describe variation between individual parameters Distribution of clearances Population of horses Clearance 00.10.20.30.4

17
Step 2 : Our view through a sample of animals Sample of horsesSample of clearances

18
Step 2 : Two main approaches Sample of clearances Semi-parametric approach

19
Step 2 : Two main approaches Sample of clearances Semi-parametric approach (e.g. kernel estimate)

20
Step 2 : Semi-parametric approach Does require a large sample size to provide results Difficult to implement Is implemented on confidential pop PK softwares Does not lead to bias

21
Step 2 : Two main approaches Sample of clearances 00.10.20.30.4 Parametric approach

22
Step 2 : Parametric approach Easier to understand Does not require a large sample size to provide (good or poor) results Easy to implement Is implemented on the most popular pop PK softwares (NONMEM, S+, SAS,…) Can lead to severe bias when the pop PK is used as a simulation tool

23
Step 2 : Parametric approach A simple model :

24
ln Cl ln V Step 2 : Population parameters

25
Mean parameters Variance parameters : measure inter-individual variability

26
Step 2 : Parametric approach A model including covariables

27
BW Age Age i BW i Step 2 : A model including covariables

28
Step 3 :Estimate the parameters of the current model Several methods with different properties Naive pooled data Two-stages Likelihood approximations Laplacian expansion based methods Gaussian quadratures Simulations methods

29
Naive pooled data : a single animal Does not allow to estimate inter-individual variation. Time Concentration

30
Two stages method: stage 1 Concentration Time

31
Two stages method : stage 2 Does not require a specific software Does not use information about the distribution Leads to an overestimation of which tends to zero when the number of observations per animal increases Cannot be used with sparse data

32
The Maximum Likelihood Estimator Let

33
The Maximum Likelihood Estimator is the best estimator that can be obtained among the consistent estimators It is efficient (it has the smallest variance) Unfortunately, l(y, ) cannot be computed exactly Several approximations of l(y, )

34
Laplacian expansion based methods First Order (FO) (Beal, Sheiner 1982) NONMEM Linearisation about 0

35
Laplacian expansion based methods First Order Conditional Estimation (FOCE) (Beal, Sheiner) NONMEM Non Linear Mixed Effects models (NLME) (Pinheiro, Bates)S+, SAS (Wolfinger) Linearisation about the current prediction of the individual parameter

36
Laplacian expansion based methods First Order Conditional Estimation (FOCE) (Beal, Sheiner) NONMEM Non Linear Mixed Effects models (NLME) (Pinheiro, Bates)S+, SAS (Wolfinger) Linearisation about the current prediction of the individual parameter

37
Gaussian quadratures Approximation of the integrals by discrete sums

38
Simulations methods Simulated Pseudo Maximum Likelihood (SPML) simulated variance Minimize

39
Properties Naive pooled dataNeverEasy to useDoes not provide consistent estimate Two stagesRich data/ Does not require Overestimation of initial estimatesa specific softwarevariance components FOInitial estimate quick computation Gives quickly a result Does not provide consistent estimate FOCE/NLMERich data/ smallGive quickly a result. Biased estimates when intra individual available on specific sparse data and/or variance softwares large intra Gaussian Alwaysconsistent andThe computation is long quadratureefficient estimates when P is large provided P is large SMPLAlwaysconsistent estimates The computation is long when K is large Criterion When Advantages Drawbacks

40
Step 4 : Graphical analysis Observed concentrations Predicted concentrations Variance reduction

41
Step 4 : Graphical analysis Time The PK model seems good The PK model is inappropriate

42
Step 4 : Graphical analysis BW Age BW Age Variance model seems good Variance model not appropriate

43
Step 4 : Graphical analysis Normality acceptable under gaussian assumption Normality should be questioned add other covariables or try semi-parametric model

44
To Summarise Write a first model for individual parameters without any covariable Write the PK model Are there variations between individuals parameters ? (inspection of ) No Simplify the model Yes Check (at least) graphically the model Is the model correct ? No Yes Add covariables Interpret results

45
What you should no longer believe Messy data can provide good results Population PK/PD is made to analyze sparse data Population PK/PD is too difficult for me No stringent assumption about the data is required

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google