Data Analysis-Related Tools Used in PK/PD

Transcription

1 Data Analysis-Related Tools Used in PK/PD Emilie HÉNIN, PhD Meng EMR3738, Ciblage Thérapeutique en Oncologie Faculté de Médecine Lyon-sud Charles Mérieux

2 Foreword Wide variety of tools are available: Catalogue can be found at Some are dedicated to pharmacology some are not! Some are still developed and upgraded some are not! In this talk: Non-exhaustive presentation! Specific focus on softwares currently used for PK/PD model development There exist tools dedicated to PK/PD model applications: Clinical trial simulations, optimal design, TDM, 2

3 Drug discovery Drug approval Pre-clinical development Clinical Development Learn Cycle I Cycle II Confirm Phase I Phase II Phase III Pre-clinical PK/PD Translational PK/PD Clinical PK/PD Post-approval PK/PD Mecanistic models In vivo evaluation of potency and interactions Biomarker identification Model extrapolation from animal to Human Allometric scaling Physiologically-based modeling PK/PD Analyses Dose-concentraion-effect relationship (efficacy and toxicity) PK/PD Predictions Dosing regimen optimisation Clinical trial simulations Pharmacovigilance Exploration to other target populations Compliance Animal models for efficacy /toxicity Dose selection for dose escalation studies Evaluation of formulation and route of administration (bioavailability) Optimal design Optimisation of formulation Therapeutic index Integration of all information for go/no-go decisions Intrinsic/Extrinsic factors: food, drug-drug interactions, covariates Target populations Active metabolites Population PK/PD Adapted from Meinbohm et al, J Pharm Sci

4 Drug discovery Drug approval Pre-clinical development Clinical Development Learn Cycle I Cycle II Confirm Phase I Phase II Phase III Pre-clinical PK/PD Translational PK/PD Clinical PK/PD Post-approval PK/PD Mecanistic models In vivo evaluation of potency and interactions Biomarker identification Model extrapolation from animal to Human Allometric scaling Physiologically-based modeling PK/PD Analyses Dose-concentraion-effect relationship (efficacy and toxicity) PK/PD Predictions Dosing regimen optimisation Clinical trial simulations Pharmacovigilance Exploration to other target populations Compliance Animal models for efficacy /toxicity Dose selection for dose escalation studies Evaluation of formulation and route of administration (bioavailability) Optimal design Optimisation of formulation Therapeutic index Integration of all information for go/no-go decisions Intrinsic/Extrinsic factors: food, drug-drug interactions, covariates Target populations Active metabolites Population PK/PD Adapted from Meinbohm et al, J Pharm Sci

5 PK/PD models at different scales Population Individual Organ Cell Molecules MECHANISM/PHYSIOLOGY-DRIVEN DATA-DRIVEN 5

6 DATA-DRIVEN MODEL DEVELOPMENT METHODS 6

7 Data-driven model development From OBSERVATIONS to MODEL Mainly at clinical stage Data handling Graphical exploration Parameter estimation Diagnostics/model evaluation 7

8 What is Parameter Estimation? Find the set of parameters that describes data the best given a model function Model function Data Prediction Model parameters 8

9 Yobs From the least squares... Statistical test that is: Small when the model represents the data closely Large when the model represents the data poorly Least square methods aim to find the parameters that minimise the average distance between data and model predictions or L L m i 1 m i 1 n i j 1 n i j 1 y y ij ij pred pred ij ij 2 2 var ij

10 ... to the maximum likelihood Consideration is made about the distribution of the data about the model prediction Normal distribution of residuals (data pred) Likelihood: probability of the data given the model l y ij param, 2 1 exp y ij pred 2 ij 2 Maximum likelihood method seeks to find the set of parameters for which prediction is the mean of data 10

11 Population Approach : Mixed-effect modeling Expansion of the likelihood to account for the intersubject variability Individual parameters are not observed, defined by Distribution (e.g. normal or log-normal) Population parameters: mean and variance matrix probability density of individual parameters For each subject, individual likelihood: ind,j ind,j ind. ind. param param ind,j ind,j ind. ind. param ind,j ind. param ind,j ind. param Data ind,j ind. param ind,j ind. param param pop pop param param pop pop param pop param pop param param pop pop param pop param param pop pop param pop param pop param ind. param param pop pop param param 11

12 Likelihood does not have an analytical solution Maximising an approximation of likelihood: FO FOCE LAPLACE Maximising the exact likelihood by sampling param. distributions (Expectation-Maximisation algorithm): IMP SAEM 12

13 3-stage hierarchical methods: Bayesian methods Consider the uncertainty of the knowledge of the population parameters ind,j ind,j ind. ind. param param ind,j ind,j ind. ind. param Data ind,j ind. param ind,j ind. param param pop pop param param pop pop param pop param ind. param pop param param pop param prior prior prior prior pop. param prior prior prior Particularly appropriate if one has empirical knowledge of parameters based on a previous analysis 13

14 Non-parametric methods NP methods allow greater flexibility in inter-individual parameters No assumption is made on parameter distributions Estimate a joint density (discrete) for ind. param 14

15 DATA-DRIVEN MODEL DEVELOPMENT SOFTWARES 15

16 NONMEM (UCSF - Icon): NONlinear Mixed Effects Modeling Historical software among parametric ML softwares NM I released in 1980, Lewis Sheiner and Stuart Beal Current version NM (released in 2011) The NONMEM program itself is a very general (non-interactive) model analysis program that can be used to fit models to many different types of data. PREDPP is a powerful package of subroutines that can be used by NONMEM to compute predictions for population pharmacokinetic and pharmacodynamic data. Use of PREDPP obviates the need for the user to code kinetic-type equations and it also allows complicated patient-type data to be easily used. NM-TRAN is (non-interactive) preprocessor, allowing control and other needed inputs to NONMEM/PREDPP to be specified in a user friendly manner, with quick and comprehensive detection of a variety of errors that the user may have made in so doing. 16

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21 NONMEM Key Features: Population and Individual modeling Estimation + Simulation Several estimation methods are available: FO, FOCE, LAPLACE SAEM, IMP, EM BAYES (PRIOR) (non-parametric) Model libraries + user-defined (NM-TRAN) Integrated or ordinary differential equations NONMEM is (more or less) the reference software in population PK/PD modeling! 21

22 NONMEM User-unfriendly software for inputs and outputs! Several tools are continuously developed around NONMEM to help in pre- and post-processing PDx-POP (ICON) Wings for NONMEM + RFN (University of Auckland, New Zealand) PsN + Xpose (University of Uppsala, Sweden) Pirana (Pirana Software & Consulting BV) 22

23 Monolix (INRIA - Lixoft) The challenger in population PK/PD modeling!! First release in 2004 Current version: 4.2 (2012) NONMEM data entry format Graphic User Interface Model libraries + user-defined (MLXTRAN) Built-in model diagnostics Estimation Method: SAEM+Simulated Annealing (global optimizer) 23

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25 PK-BUGS in WinBUGS (Uni. of Cambridge) Bayesian estimation (prior distribution on pop. param. ) NONMEM data entry format Model library (PK-BUGS) + user-defined (WinBUGS) 25

26 Pmetrics (University of Southern California) Non-parametric Maximum Likelihood method Mostly used in TDM/routine data less in drug development No assumption on individual parameter distributions Non-parametric Adaptive Grid Script-based function package using R User-defined + model libraries (recently featured) Set of Functions for: Data import Parameter Estimation Graphics for model evaluation Simulations 26

27 Generic softwares/packages for parameter estimation nlme() proc nlmixed nlmefit 27

28 WinNonLin (Pharsight) Now, several tools integrated in Phoenix: WinNonLin, WinNonMix, Trial Simulator WinNonLin: Individual PK/PD model Non-Compartmental Analysis (NCA) Model Libraries + user-defined 28

29 DATA-DRIVEN MODEL DEVELOPMENT SOFTWARE COMPARISON 29

30 Several works aimed at comparing estimation methods/softwares Dartois et al, J. Pharmacokinet Pharmacodyn 2007 (NONMEM) (NONMEM) (Monolix) 30

31 E. HENIN Dartois Model-based et al, Medicine J. Pharmacokinet Development, December Pharmacodyn 11 th

32 MECHANISM-DRIVEN MODEL DEVELOPMENT 32

33 Mechanism-driven model development From knowledge to model: Gather all available knowledge Discrimination between system- and drug-dependent parameters Identification of known parameter values: Litterature data Experimental data Tables of physiological parameter values Numerical integration of the model equations Sensitivity analysis on unknown parameter values Model predictions (simulations) 33

34 PK-Sim (Bayer Technology Services) Whole-body physiologically based PK modelling (WB-PBPK) Different model structures available: Passive or active processes Minor structural model modifications allowed Databases of physiological parameters for human, mouse, rat, In vitro-in vivo extrapolation (IVIVE) Formulas for the prediction of drug-specific parameters in vivo Integrated tool for predicting absorption of oral drugs Population simulator (NHANES database) Mobi-Sim: allowing more flexible model structures Generic numerical integrator, compatible with PK-sim 34

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36 SimCyp Population-based simulator: Databases containing human physiological, genetic and epidemiological information Pediatric simulator allows pharmacokinetic behaviour to be modelled in infants, neonates and children Extensive libraries on demographics, developmental physiology and the ontogeny of drug elimination pathways Animal simulator PBPK modelling platform for rat, dog and knock-out mouse 36

37 GastroPlus (SimulationPlus Inc.) Mechanistically-based simulation software for complex absorption processes: Gastro-intestinal, buccal, intra-nasal, pulmonary, Linked to PBPK-PD model Huang et al, AAPS Journal, 2009 E. HENIN Model-based Agoram et al, Medicine Adv. Drug Development, Deliv. Rev., December th

38 Generic softwares for mechanism-based model development Differential equation solvers: Berkeley-Madonna Matlab/Simulink System's biology Mark-up Language (SBML) unified modeling language suitable for representing models for cell signaling pathways, metabolic pathways, biochemical reactions, gene regulation, 38

39 TAKE-HOME MESSAGE 39

40 Drug discovery Drug approval Pre-clinical development Clinical Development Learn Cycle I Cycle II Confirm Phase I Phase II Phase III Pre-clinical PK/PD Translational PK/PD Clinical PK/PD Post-approval PK/PD Mecanistic models In vivo evaluation of potency and interactions Biomarker identification Model extrapolation from animal to Human Allometric scaling Physiologically-based modeling PK/PD Analyses Dose-concentraion-effect relationship (efficacy and toxicity) PK/PD Predictions Dosing regimen optimisation Clinical trial simulations Pharmacovigilance Exploration to other target populations Compliance Animal models for efficacy /toxicity Dose selection for dose escalation studies Evaluation of formulation and route of administration (bioavailability) Optimal design Optimisation of formulation Therapeutic index Integration of all information for go/no-go decisions Intrinsic/Extrinsic factors: food, drug-drug interactions, covariates Target populations Active metabolites Population PK/PD Adapted from Meinbohm et al, J Pharm Sci

41 Numerous tools are available for PK-PD! PK/PD modeling can be implemented all along drug development Adequate tool should be chosen with respect to modeling purposes! 41