Applications of a Mechanistic Physiology Applications of a Mechanistic Physiology Based Pharmacokinetic Model for Imaging Based Pharmacokinetic Model for Imaging Agent DevelopmentAgent Development

Mary SpilkerJohn GrafBrion Sarachan

2008 American Conference on PharmacometricsTucson, AZ

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Molecular Imaging

1913

MedicalX-Ray Tube

1976

ComputedTomography

1983 2005+2000

DigitalImaging

MagneticResonance

Imaging

GE has a long history in medical imaging

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Molecular and contrast-enhanced imaging

Visualize Anatomy

CT contrast agent for imaging vasculature1

Visualize Molecular Processes

SPECT agent that binds to dopamine transporters3

Normal Parkinson’s Disease

1) Leonard, Medical Imaging Magazine, 2005 (http://www.medicalimagingmag.com/issues/articles/2005-06_02.asp)

2) Roberts et al, AJNR 20003) http://www.epda.eu.com/medInfo/medInfo-MedicalDevices.shtm

• Imaging agents provide contrast and report on anatomy, function and molecular processes.

MRI contrast agent assessing vascular permeability in brain tumours2

Visualize Functional Processes

Pre Contrast Post Contrast

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Development process for imaging agents

Interested in the pharmacokinetics of an agent– Distribution (Delivery), Retention (Binding), Biotransformations, Clearance

Want specific localization to the target with low uptake and fast clearance from non-target tissues

Dosing – typically intravenous dose– dose is normally at tracer levels for molecular imaging applications

– Larger doses for anatomic and functional imaging

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Clinical TrialsFe

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Clinical Trials

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Mechanistic Physiology Based Pharmacokinetic Model

Whole Body Structure

Fine Tissue Structure

Cell & Organelle Structure

Input: • Agent & Target properties (Mol Wt, LogD, Kd, kon, koff , Location, Concentration)• Animal Model (mouse, rat, human)• Experiment parameters (dose, sampling time points)

Output: • Agent concentration in different compartments at different times post injection.

Arterial Blood

Lymph Vein

Muscle

Heart

glomerular

filtration

Bolus Injection

Urine

Venous Blood

Bile

CSF drainage

tubul ar reabsorpti on & secretion

Chyme

Skin

Adipose

Bone

Tumor

Spleen

GI Tract

Liver

Kidney

Brain

LungBlood

Feces

BBB

Pancreas

Arterial Blood

Lymph Vein

Muscle

Heart

glomerular

filtration

Bolus Injection

Urine

Venous Blood

Bile

CSF drainage

tubul ar reabsorpti on & secretion

Chyme

Skin

Adipose

Bone

Tumor

Spleen

GI Tract

Liver

Kidney

Brain

LungBlood

Feces

BBB

Pancreas

Arterial Blood

Lymph Vein

Muscle

Heart

glomerular

filtration

Bolus Injection

Urine

Venous Blood

Bile

CSF drainage

tubul ar reabsorpti on & secretion

Chyme

Skin

Adipose

Bone

Tumor

Spleen

GI Tract

Liver

Kidney

Brain

LungBlood

Feces

BBB

Pancreas

Arterial Blood

Lymph Vein

Muscle

Heart

glomerular

filtration

Bolus Injection

Urine

Venous Blood

Bile

CSF drainage

tubul ar reabsorpti on & secretion

Chyme

Skin

Adipose

Bone

Tumor

Spleen

GI Tract

Liver

Kidney

Brain

LungBlood

Feces

BBB

PancreasBlood Cells

Plasma

Interstitial

Intracellular

Vasculature

Endothelial Cells

Apical Compartment(urine, bile, chyme)

Tissue Cells

Blood Cells

Interstitial

Intracellular

Vasculature

Apical Compartment(urine, bile, chyme)

Blood Cells

Plasma

Interstitial

Intracellular

Vasculature

Endothelial Cells

Apical Compartment(urine, bile, chyme)

Tissue Cells

Blood Cells

Interstitial

Intracellular

Vasculature

Apical Compartment(urine, bile, chyme) Cell Cytosol,

Nucleus, Mitochondria, Peroxisome

Early Endosomes

Late Endosomes, Lyosomes,Golgi, Endoplasmic Reticulum (ER)

Interstitial

Intracellular

Cell Cytosol

Early Endosomes

Late Endosomes Lyosomes

Interstitial

Intracellular

Cell Cytosol, Nucleus, Mitochondria, Peroxisome

Early Endosomes

Late Endosomes, Lyosomes,Golgi, Endoplasmic Reticulum (ER)

Interstitial

Intracellular

Cell Cytosol

Early Endosomes

Late Endosomes Lyosomes

Interstitial

Intracellular

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Measured DataModel Prediction

Experiment:Bolus injection of 99mTc-DTPA in control miceTissues measured using a gamma counter%ID/g = percent of injected dose per gram tissue

Model Validation

NN

N

O

O

O

O

O

O O

O

O O

99mTc

0

1

2

3

4

5

0 30 60 90 1200

4

8

12

16

20

0 30 60 90 120

Blood Kidney

0

10

20

30

40

50

0 30 60 90 120

Muscle

Liver%

ID/g

Time (minutes) Time (minutes) Time (minutes)

0

2

4

6

8

10

0 30 60 90 1200

2

4

6

8

10

0 30 60 90 1200

20

40

60

80

100

%ID

0 30 60 90 120

UrineLung

%ID

/g%

ID/g

%ID

/g

%ID

/g

0

1

2

3

4

5

0 30 60 90 1200

1

2

3

4

5

0 30 60 90 1200 30 60 90 1200

4

8

12

16

20

0 30 60 90 1200

4

8

12

16

20

0 30 60 90 1200 30 60 90 120

Blood Kidney

0

10

20

30

40

50

0 30 60 90 1200

10

20

30

40

50

0 30 60 90 1200 30 60 90 120

Muscle

Liver%

ID/g

Time (minutes) Time (minutes) Time (minutes)

0

2

4

6

8

10

0 30 60 90 1200 30 60 90 1200

2

4

6

8

10

0 30 60 90 1200 30 60 90 1200

20

40

60

80

100

%ID

0 30 60 90 1200 30 60 90 120

UrineLung

%ID

/g%

ID/g

%ID

/g

%ID

/g

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Uses of the Model

• Predict agent distribution

• Consolidate data

• Explore Mechanism of Action

• Quantitatively evaluate feasibility of new ideas

• Address ‘what if’ questions

Understanding Mechanism of Action

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Oncology Application

• Mouse model with tumor.

• Intracellular target located in the cell cytosol.

• Target upregulated in tumors following a specific therapy.

• Agent: – high affinity peptide (Ki < 1 nM)– negative LogP – multiple charges

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Initial experimental results suggest localized uptake in target tissue

0

2

4

6

8

10

12

0 30 60 90 120

%ID

/ g

Time Post Injection (Minutes)

BloodTumorMuscle

%ID/g = percent of injected dose per gram tissue

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0

1

2

3

4

5

6

0 30 60 90 120

Initial model predictions unable to replicate dataMinimal amount of agent predicted to enter cell through passive processes.

Tum

or %

ID /

g

Time Post Injection (Minutes)

%ID/g = percent of injected dose per gram tissue

Measured Tumor Conc

Total Predicted Tumor Conc

Vascular Contribution

Interstital Contribution

Intracellular Contribution

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An alternative mechanism is proposedProposed Mechanism: endocytosis This mechanism could explain the tumour uptake, but would isolate the agent from the target present in the cytosol.

Knowledge used to develop/support this mechanismVesicle volumes, intracellular volume fraction Measured fluid influx (Besterman,1981), (Steinman 1983)Influence of molecular charge upon the endocytosis (Davies 1981)Increased pinocytosis in tumor cells (Allison 1974)

Measured Tumor Conc

Total Predicted Tumor Conc

Vascular Contribution

Interstital Contribution

Cell Cytosol Contribution

Early Endosome Contribution

Late Endosome Contribution0

1

2

3

4

5

6

0 30 60 90 120

Tum

or %

ID /

g

Time Post Injection (Minutes)

%ID/g = percent of injected dose per gram tissue

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Additional experiments show no difference between targeted agent and negative control

0

2

4

6

8

10

12

0 30 60 90 1200

2

4

6

8

10

12

0 30 60 90 120

BloodTumorMuscle

%ID

/ g

Time Post Injection (Minutes)

Negative Control: Scrambled peptide vectorKi >1uM

Target AgentKi < 1 nM

%ID/g = percent of injected dose per gram tissue

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When the model output and data don’t agree

Discrepancies can lead to – re-evaluation of hypotheses– additional experiments focused on mechanism of

action– further evaluation of the model

Simulation of Clinical Images

L R

Anterior

Posterior

SimulatedImages

Physics-Based Image SimulatorsWhole Body

3D Anatomical Maps*

Clinical Image Simulation

Mechanistic PBPK Model

Image Analysis Results

Relevant anatomy & physiology

Biomarker and Agent Properties

TissueConcentrationData

Imaging Feasibility

#1

#2

#3 #4 #5

#6

#7

lesion

*Zubal Phantom (http://noodle.med.yale.edu/phant.html#Zubal) Zubal, et al. (1994). Med Phys 21(2).

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Future Directions

• Continue to improve the model based on increased understanding of physiology

• Further validate the model

• Incorporate in vitro information (metabolism)

• Apply the model to agents beyond small molecules

• Incorporate parameter variability

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Acknowledgements

John Graf (graf@crd.ge.com)

Brion Sarachan (sarachanbd@crd.ge.com)

Colin Archer

Paul Bennett

Christoph Hergersberg

Michael Idelchik

Nadeem Ishaque

Marivi Mendizabal

Michael Montalto

Matthew Morrison

Sally-Ann Ricketts

Ian Wilson