Science Webinar Series · Image based disease end points ... Imaging probes: drug research concept...

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DETERMINING THE IMPACT OF NEW THERAPEUTICAPPROACHES: ADVANCING IMAGING IN ANIMALS

DETERMINING THE IMPACT OF NEW THERAPEUTICAPPROACHES: ADVANCING IMAGING IN ANIMALS

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THERAPEUTIC APPROACHES:THERAPEUTIC APPROACHES:Advancing Imaging in Animals Advancing Imaging in Animals

7 December, 20117 December, 2011

Participating Experts:


Patrick McConville, Ph.D.Molecular Imaging, Inc.Ann Arbor, MI

Matthias Nahrendorf, M.D., Ph.D.Harvard Medical SchoolBoston, MABoston, MA

Sponsored by:

IN VIVO IMAGING OF DRUG RESPONSE AND FMT: RESPONSE AND FMT: A CRO PERSPECTIVE

Patrick McConville, Ph.D. CSO/COO - Molecular Imaging, Inc.

Presentation Outline

Background: imaging in drug research Brief history/evolution

f Rationale for use

Image based disease end points Anatomical, functional and molecular imaging end points

Image based biomarker: definition Image based biomarker: definition

Surrogate marker

Probe facilitated imaging of efficacy

RA model/optical probe example

Imaging probe based biomarkers and FMT Rationale for FMT in drug discovery and development

Example 1: protease and bone imaging in RA

Example 2: acute inflammation in sponge granuloma

Example 3: tumor burden

Future applications

Preclinical imaging: a brief history

70s: Major clinical imaging developments

80s: Widespread use of clinical imaging for diagnosis

90s: Translation of imaging technology for rodent work

Last decade: introduction of dedicated rodent imaging systems

substantial development of new imaging probes MR, CT, PET, optical, SPECT, ultrasound, MR, CT, PET, optical, SPECT, ultrasound,

pharmaceutical industry invests and relies on imaging technology

Last 5-10 years: anatomical functional/molecular

d ffdiagnostic efficacy

academia industry

Next 5-10 years: efficacy safety/toxicologyy y y/ gy

Preclinical imaging: evolution

WAS IS

Modality centric Modality agnostic

Decentralized Centralized

A complex technology for physicists and engineers

A black box technology for multi-disciplinary scientists

A fA tool for disease progression and therapeutic

A tool for disease diagnosisA tool for disease progression and therapeutic

response

Expensive Expensive

Not a standard in drug development A standard in drug development

In academic institutions In industry

in vivo Imaging in Drug Research

IND NDA

Single Compound

Discovery Development Introduction/Registration

High throughput screeningscreening

Lead CandidateSelection

PreclinicalEarly-PhaseClinical

Phase II-III Phase IV


Imaging in Drug Discovery and DevelopmentDiscovery and Development


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Imaging in Drug Discovery and DevelopmentDiscovery and Development


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Imaging in Drug Discovery and DevelopmentPharmacology, Toxicology

and PathologyDiscovery and Developmentand Pathology

in vivo Imaging in Drug Research: Why ?

Faster ? Early prediction

Eg imaging inflammatory cell recruitment Eg. imaging inflammatory cell recruitment

Better resolved ? Tissue, sub-tissue, cellularPET , ,

Eg. imaging of tissue heterogeneity

More relevant ?

PETMRI

Access to unique, mechanistic endpoints Eg. imaging of cathepsin activity

Translational ? Translational ? Discovery/development continuum

Eg. MRI and PET image based biomarkers

CT Optical/FMT Cost effective ?

Yes, if one or more of the above is true



f Rationale for use



Surrogate marker







Future applications

In Vivo Imaging in Drug Research

ANATOMICAL FUNCTIONAL MOLECULAR

Microscopy

Morphology

Structure

Hypoxia

Proliferation

Inflammation

Necrosis

Apoptosis

Receptor occupancyStructure

Bone resorption/loss

Implanted devices

Inflammation

Blood flow

Perfusion

Receptor occupancy

Metabolite levels

Biodistribution

Imaging of plaques

Vascular mapping

Metabolism

Ejection fraction

H d i

Protease activity

Immune cell tracking

C ll i tiMyocardial infarction

Medical devices

Hydration Cell migration

Target modulation

In vivo imaging in safety and toxicology

In Vivo Imaging in Drug ResearchCorrelation with Disease Progression

Image-based Quantifiable Image-based

BiomarkerProgression (validation I)Parameter Biomarker

Correlation with Clinical End Point Clinical End Point

(validation II)

Surrogate Surrogate Marker

Imaging: the Core Facility Challenge

OpticalU/S

IntegratedMultiple Modality

Imaging

Imaging: the Core Facility ChallengeM l i l i i d li i• Multiple imaging modalities

IntegratedMultiple Modality

• Multiple protocols per modality• Multiple therapeutic areas (TAs)

Imaging

• M lti l d l TA• Multiple models per TA• Safety• Medical devices



f Rationale for use



Surrogate marker







Future applications

Biomarker access through imaging probes

Imaging probe Molecule or nanoparticle designed to modulate imaging contrast

Degree of signal or contrast modulation is generally dependent on: probe concentration (voxel based)

Tissue access

PK/PD Degree of uptake (for captured probes)

Degree of activation (for conditional probes)

Probe Type Examples

Targeted Receptors (eg. integrins, estrogen R, VEGF R)Bone (eg. hydroxyapetite)Proteins (eg. VEGF)

Conditionally ‘captured’

Metabolic cycle (eg. FDG)Cell cycle (eg. FLT)Hypoxic cells (eg. MISO)

d llConditionally activated probes

pHProteases (eg. caspases, cathepsins, MMPs)

Imaging probes: drug research concept

Drug Target or Bi l f i t tBiology of interest

PROBE FACILITATED IMAGING OF MECHANISM

Model choice

Imaging probe choice

Why FMT ? - the CRO perspective

Quantitative, three-dimensional imaging High throughput, efficient imagingg g p , g g

probe multiplexing

10-20min per subject for image runs of up to 50+ animals possible

Available biomarkers bounded only by probe developers rapidly increasing commercial availability

access to prior large probe patent estates access to prior large probe patent estates

technology is now emerging from major probe discovery labs

True molecular imaging disease mechanism

drug response mechanism

Applications readily cross disease states and into safety Applications readily cross disease states and into safety Clinical translation potential

Validation: the CRO perspective

‘Pharma-ready’ validation Biomarker readout correlates with:

disease progression response to therapy

Correlation t diti l li i l t

Understood for:• EACH probe or

traditional clinical measurements traditional biomarkers histopath

Study design and limitations understood

biomarkerin• EACH model Study design and limitations understood

image timing probe clearance uncoupling or interference

EACH modelwith • EACH

study powering Advantage/value vs traditional

uniqueness; more predictive time saving

treatment

time saving clinically translatable

V lid i ‘Ph d ’

Example: Mouse RAValidation: ‘Pharma-ready’

Histopath CorrelationTraditional in vivo vs image based end pointsTraditional in vivo vs image based end points

Clinical Score2.5

Traditional250

Control

Image Based

heps

in

al S

core

1 0

1.5

2.0

100

150

200Vehicle

Prednisolone

p38 MAPKi

Celecoxib

Cat

h

ControlCAIAPrednisoloneP38 MAPKi

Clin

ica

0.0

0.5

1.0

0 1 2 3 40

50

100

0 1 2 3 4P

P38 MAPKiCelecoxib

ing

Paw Swelling

0 60.70.80.91.0

50

60

70

80

MM

P

Paw

sw

ell

0 10.20.30.40.50.6

10

20

30

40

50

Histopath Score

0.00.1

0 1 2 3 40

0 1 2 3 4

RA: Inflammation imaging with ProSense

Naïve Quantification of ProSense 680 Uptake in Right (blue) & Left

DAY 7

29.624.9

25 0

30.0

p g ( )(red) Paws

∆ = 5-6 x DAY 7

20.621.1

15 0

20.0

25.0

scen

ce (p

mol

s)

DAY 11 5.8

9.2

8.95 0

10.0

15.0

Am

t. of

Flu

ores

R

DAY 21

3.5

0.0

5.0

Naïve Day 7 Day 11 Day 21

L ft ( d) & Ri ht (bl ) P

Response correlates with known onset of

paw inflammation

Confidential© 2008 VisEn Medical, Inc.25

Left (red) & Right (blue) Paws

RA: Bone imaging with OsteoSense

NaïveQuantification of OsteoSense

750 Uptake in Right (blue) & Left (red) Paws

DAY 720.8

29.6

23.8

19 725.6

25.0

30.0

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19.720.9

19.7

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Response correlates

DAY 11

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DAY 210.0

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Naïve Day 7 Day 11 Day 21

L ft ( d) & Ri ht (bl ) P

© 2008 VisEn Medical, Inc.26

Left (red) & Right (blue) Paws

M l d l

Example: Acute inflammationMouse sponge granuloma model

19F MRI: Imaging of Macrophages

PBS Sponge CFA SPBS Sponge CFA Sponge

Low 19F signal High 19F signal

V-Sense in Sponge Granuloma Model of Acute Inflammation

Cathepsin Imaging: Acute Inflammation

CFA SpongePBS Sponge ImplantImplant

Fluorescence SignalFluorescence Signal

CFA

PBS

Pre-injection6.5 Hours Post-injection24 Hours Post-injection

Pre-Injection 6.5 Hours 24 HoursPre-Injection 6.5 Hours 24 Hours

Sponge Granuloma Model of Acute Inflammation

Activated probes in acute inflammation

MC MCSponge Granuloma Model of Acute Inflammation

pre

C: Cathepsinactivation (Prosense)

urs

M: MMP activation (MMP Sense)

Greater cathepsin and

6 ho

u Greater cathepsin and MMP activity was detected in CFA sponges (diseased animals), compared with PBS sponges (controls) 6h

4 ho

urs

PBS sponges (controls) 6h and 24h after probe injection.

24

PBS (control) CFA (diseased)

Feasibility of tumor burden measurement across

Example: Tumor imaging• Feasibility of tumor burden measurement across

a broad panel of models• Testing imaging throughput and workflow (104 g g g g p (

scans in a 24h period)

FMT imaging in tumor models

Study # Study Name Description Mouse

Strain# of

Mice

# FMT In-vivo Scans

Agent 1 Required

Agent 1: Doses

Required

Agent 2 Required

Agent 2: Doses

Required1 CAIA In-vivo imaging & Quantification of Collagen Antibody-Induced Arthritis (CAIA) Model in mice

OsteoSense Control Mouse #1 Nu/Nu 2 2 ProSense 680 1 OsteoSense 800 1

Experimental Mice #2-#8 Nu/Nu 6 14 ProSense 680 7 OsteoSense 800 7

2 Bone Metastasis In-vivo imaging & Quantification of Bone metastasis from Prostate Adenocarcinoma (PC-3M-luc) in murine models

Exp Mice #1-#3 Nu/Nu 1 2 ProSense 680 1 OsteoSense 1Exp. Mice #1-#3 Nu/Nu 1 2 ProSense 680 1 800 1

4 Breast Adenocarcinoma In-vivo Imaging & Quantification of Sub-Q tumours of Breast Adenocarcinoma (MX-1) in murine models

Exp. Mice #1-#3 Nu/Nu 1 2 MMPSense 680 1 ProSense 750 1

5 Breast Adenocarcinoma In-vivo Imaging & Quantification of Sub-Q tumours of Breast Adenocarcinoma (A-375) in murine models


6 Large Cell Lung Carcinoma In-vivo Imaging & Quantification of Sub-Q tumours of Lung Carcinoma (H-460) in murine models


7 Colorectal Carcinoma In-vivo Imaging & Quantification of Sub-Q tumours of Colorectal Carcinoma (HT-29) in murine models

Exp. Mice #1-#3 Nu/Nu 1 2 IntegriSense 680 1 ProSense 750 1

8 Colorectal Carcinoma In-vivo Imaging & Quantification of Sub-Q tumours of Colorectal Carcinoma (HCT-116) in murine models


Colorectal

© 2008 VisEn Medical, Inc.32

9 Colorectal Carcinoma In-vivo Imaging & Quantification of Sub-Q tumours of Colorectal Carcinoma (Colo-205) in murine models


FMT imaging in tumor models

Study # Study Name Description Mouse

Strain# of

Mice

# FMT In-vivo Scans

Agent 1 Required

Agent 1: Doses

Required

Agent 2 Required

Agent 2: Doses

Required10 Gastric Carcinoma In-vivo Imaging & Quantification of Sub-Q tumours of Gastric Carcinoma (N-87) in murine models


11 Colorectal Adenocarcinoma In-vivo Imaging & Quantification of Sub-Q tumours of Colorrectal Adenocarcinoma (DLD-1) in murine models


12 Pancreatic Carcinoma In-vivo Imaging & Quantification of Sub-Q tumours of Pancreatic Carcinoma (Panc-1) in murine modelsCarcinoma


13 Pancreatic Carcinoma In-vivo Imaging & Quantification of Sub-Q tumours of Pancreatic Carcinoma (MiaPaca-2) in murine models

Exp. Mouse #1-#2 Nu/Nu 1 2 MMPSense 680 1 ProSense 750 1

14 Pancreatic C i In-vivo Imaging & Quantification of Sub-Q tumours of Pancreatic Carcinoma (MiaPaCa-2) in murine models14 Carcinoma In vivo Imaging & Quantification of Sub Q tumours of Pancreatic Carcinoma (MiaPaCa 2) in murine models

Exp. Mice #1-#3 Nu/Nu 1 2 IntegriSense680 1 AngioSense

750 1

15 Bone Metastasis In-vivo Imaging & Quantification of Bone Metastasis from Prostate Adenocarcinoma (PC-3M-luc) in murine models

Exp. Mice #1-#3 Nu/Nu 1 2 IntegriSense 680 1 AngioSense

750 1680 750

16 Pancreatic Carcinoma

In-vivo Imaging & Quantification of Sub-Q tumours of Pancreatic Epithelioid Carcinoma (Panc-1) in murine models

Exp. Mice #1-#3 Nu/Nu 1 2 IntegriSense 680 1 AngioSense

750 1

17 Ovarian Adenocarcinoma In-vivo Imaging & Quantification of Sub-Q tumours of Ovarian Adenocarcinoma (A2780) in murine models

33


Totals 52 104 N/A 52 N/A 52

se68

04000

6000

MX-1

A-375

DLD 1

FMT technology in d l

MM

PSen

s

0

2000

DLD-1

Panc-1

MiaPaca-2

N-87

tumor models

CONCLUSIONS• Can be used to locate tumors

se75

0

150

200

250MX-1

A-375

HT-29

Colo-205

0

mol

)

• Can be used to track tumor growth

• Best probe for each model should be determined

ProS

en

0

50

100HCT-116

DLD-1

Panc-1

MiaPaca-2

N-87resc

ence

(p • Imaging throughput not limiting in running large, powered industry drug response studies

nse

680

0 A-2780

4500

6000

7500

HT-29

Colo-205

HCT-116

Flou

r

ADVANTAGES• Does not require transfected

line (eg. luciferase)

• Facilitates rapid use of new

Inte

griS

en

0

1500

3000

HCT 116

Panc-1

MiaPaCa-2

A-2780

ppatient derived models

• Deep tissue models not limiting

34

0

Tumor Size

Future expanded applications ?

To come … Biodistribution

Huge current emphasis on targeted biologics

Cell tracking

Obesity and metabolic disease Lack of mechanistic end points

New biomarkers

N d d Increasing focus on cell based therapies

Pulmonary disease COPD, Pulmonary fibrosis

Lack of non-invasive biomarkers for preclinical study

Neuro-degenerative diseases MS: eg. EAE model

Alzheimer’s

Parkinson’s

Inflammatory Bowel Disease Lack of robust preclinical end points

Atherosclerosis Lack of non invasive end points

Systemic tumor models Leukemia

Lymphoma

Metastasis Lack of non-invasive end points

Preclinical imaging remains deficient in athero models

Hybrid athero/metabolic models

Imaging burden AND mechanism in tumors

primary, patient derived models

genetically well characterized models

target modulation

Imaging outlook including FMT• Proof of principle has been established for most of these examples

• Further widespread adoption and application needed• Further widespread adoption and application needed

• More comprehensive validation – qualified biomarkers

Expanded use and benefit in pharmaceutical research





Participating Experts:


Patrick McConville, Ph.D.Molecular Imaging, Inc.Ann Arbor, MI

Matthias Nahrendorf, M.D., Ph.D.Harvard Medical SchoolBoston, MABoston, MA

Sponsored by:





k f bi i h i


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