Chancen und Limitationen von PK/PD Modellen - PEG-Symposien

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Chancen und Limitationen von PK/PD Modellen - PEG-Symposien

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


Time scale

in order of

increasing

magnitude

Observation Levels in

Long-term

clinical outcome

Clinical endpoints

Cell/tissue response

Molecular level

Adapted from: Post et al. (2005) Pharm Res 22:1038-1049.

Lesko and Schmidt (2012) Clin Pharmacol Ther 92: 458-466.

Pharmacology

Disease processes

in order of increasing

complexity

Pharmacometric

Models

Mechanism-Based

Models

System Pharmacology

Models

2


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

• http://team.inria.fr/popix/files/2011/11/Populati

onApproach.swf

3


Population vs. Traditional Approaches

Population PK(/PD)

• Sparse sampling

• Single large study or data

pooled from different

studies

• Heterogeneous population

• Allows studying several

factors

• Complex data analysis

• Exploratory

for PK(/PD) Data

Traditional PK(/PD)

• Extensive sampling

• Single small study

• Homogeneous population

• Single factor per study

• Non-compartmental data

analysis

• Confirmatory

4


Cp [ng/mL]

400

300

200

100

0

Data Requirements

0 2 4 6 8 10 12

t [h]

5


Drug

Dose

Mechanism-Based Models

Target

exposure

Physiologicallybased

pharmacokinetic

modeling

Target

binding

&

activation

Receptor

theory

Modified from: Danhof et al. (2007) Annu. Rev. Pharmacol. Toxicol. 47:357-400.

Homeostatic

feedback

Transduction

Dynamical

systems

analysis

EFFECT

6


Intrinsic/extrinsic Factors

Huang and Temple, 2008

Individual or combined effects

on human physiology

Zhao P, et al Clin Pharmacol Ther 2011

Physiology-Based Modeling

Blood

System component

(drug-independent)

Lung

Rapidly perfused

organs

Slowly perfused

organs

Dosing

Kidney

Liver

Intestines

Elimination

Blood

Predict, Learn, Confirm, Apply

PBPK Model components

PBPK Model

Drug-dependent

component

ADME, PK, PD and

MOA

Metabolism

Active transport

Passive diffusion

Protein binding

Drug-drug interactions

Receptor binding

7


Extrapolation (Scaling) of PK/PD

by Function Rather Than Size

? ?

8


Mechanism-Based PK/PD Models

Bulitta et al. (2011) Curr Pharm Biotechnol. 12:2044-61.

9


Genes

Systems Pharmacology Models:

Effect Effect

A E B 1

Target

1

Network Analysis

Target

2

Effect

C

Target

3

Modified from: Kohl et al. (2010) Clin Pharmacol Ther. 88: 25-33.

Effect

D

Biological networks

Target

4

Effect Effect

E F E 6

Target

5

-

Target

6

Environment

10


Drug

(hours)

Peterson and Riggs (2010) Bone 46:49-63.

What Are the Challenges?

Bone turnover

markers

(weeks)

Bone mineral

density

(months)

Fracture probability

(years)

11


System Pharmacology Models

Bone formation markers (i.e. BSAP)

Bone resorption markers (i.e. NTX)

Post et al. currently under submission with J Pharmacokinet Pharmacodyn

Bone mineral density (TH, LS)

12


Bone Removal

uNTX

(pmol/μmol Cr)

Bone Formation

BSAP

(U/L)

Ca + Placebo Ca + 0.625mg Ca + 2.5mg

Time since study start (days)

uNTX

BSAP

13


Bone Mineral Density (Lumbar Spine)

BMD

(mg/cm2 )

Bone Mineral Density (Total Hip)

BMD

(mg/cm2 )

Ca + Placebo Ca + 0.625mg Ca + 2.5mg

Time since study start (days)

14


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


Opportunities for Evaluating

On/Off-Target Effects

16


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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