Chancen und Limitationen von PK/PD Modellen

Transcription

1 Chancen und Limitationen von PK/PD Modellen Stephan Schmidt, Ph.D. Center for Pharmacometrics and Systems Pharmacology (CPSP), University of Florida, Orlando, USA 23. Jahrestagung der Pau-Ehrlich-Gesellschaft fuer Chemotherapie e.v., Dresden, 12. Oktober 2012

2 Observation Levels in Pharmacology Long-term clinical outcome Pharmacometric Models Clinical endpoints Time scale in order of increasing magnitude Cell/tissue response Disease processes in order of increasing complexity Mechanism-Based Models Molecular level System Pharmacology Models Adapted from: Post et al. (2005) Pharm Res 22: Lesko and Schmidt (2012) Clin Pharmacol Ther 92:

3 Population PK(/PD) Analysis Determine PK model structure for the population Estimate typical (mean) population PK parameters and inter-individual variability Estimate individual PK parameters Estimate residual variability Identify measurable sources of variability in PK and describe their relationship to PK parameters onapproach.swf 3

4 Population vs. Traditional Approaches for PK(/PD) Data Population PK(/PD) Sparse sampling Single large study or data pooled from different studies Heterogeneous population Allows studying several factors Complex data analysis Exploratory Traditional PK(/PD) Extensive sampling Single small study Homogeneous population Single factor per study Non-compartmental data analysis Confirmatory 4

5 Data Requirements Cp [ng/ml] t [h] 5

6 Mechanism-Based Models Homeostatic feedback Drug Dose Target exposure Target binding & activation Transduction EFFECT Physiologicallybased pharmacokinetic modeling Receptor theory Dynamical systems analysis Modified from: Danhof et al. (2007) Annu. Rev. Pharmacol. Toxicol. 47:

7 Physiology-Based Modeling Intrinsic/extrinsic Factors PBPK Model components System component (drug-independent) Drug-dependent component Lung Rapidly perfused organs ADME, PK, PD and MOA Blood Slowly perfused organs Kidney Liver Blood Metabolism Active transport Passive diffusion Protein binding Drug-drug interactions Receptor binding Huang and Temple, 2008 Individual or combined effects on human physiology Dosing Intestines Elimination PBPK Model Zhao P, et al Clin Pharmacol Ther 2011 Predict, Learn, Confirm, Apply 7

8 Extrapolation (Scaling) of PK/PD by Function Rather Than Size?? 8

9 Mechanism-Based PK/PD Models Bulitta et al. (2011) Curr Pharm Biotechnol. 12:

10 Genes Systems Pharmacology Models: Network Analysis Effect Effect A E B 1 Effect C Effect D Effect Effect E F E 6 Target 1 Target 2 Biological networks Target 3 Target 4 Target 5 - Target 6 Environment Modified from: Kohl et al. (2010) Clin Pharmacol Ther. 88:

11 What Are the Challenges? Drug (hours) Bone turnover markers (weeks) Bone mineral density (months) Fracture probability (years) Peterson and Riggs (2010) Bone 46:

12 System Pharmacology Models Bone formation markers (i.e. BSAP) Bone resorption markers (i.e. NTX) Bone mineral density (TH, LS) Post et al. currently under submission with J Pharmacokinet Pharmacodyn 12

13 BSAP (U/L) untx (pmol/μmol Cr) Bone Removal Ca + Placebo Ca mg Ca + 2.5mg untx Bone Formation BSAP Time since study start (days) 13

14 BMD (mg/cm 2 ) BMD (mg/cm 2 ) Bone Mineral Density (Lumbar Spine) Ca + Placebo Ca mg Ca + 2.5mg Bone Mineral Density (Total Hip) Time since study start (days) 14

15 Impact on Study Design Left panel: change of BSAP and NTX over time due to disease (dotted), placebo (dashed) and tibolone (solid) treatment. Right panel: change in bone mineral density in lumbar spine (BMD LS ) due to disease (dotted), placebo (dashed) and tibolone (solid) treatment. 15

16 Opportunities for Evaluating On/Off-Target Effects 16

17 Challenges Availability of freely-accessible data Availability of easy-to-use software for computing and graphing Genetic and non-genetic data (covariates) to explain interindividual differences in treatment response Training of students and working professionals in multidisciplinary teams Crosstalk between disciplines 17

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