Use of Magnetic Resonance Imaging In Multiple Sclerosis Trials

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Use of Magnetic Resonance Imaging In Multiple
Sclerosis Trials

• Richard A. Rudick, M.D.
• Mellen Center for Multiple Sclerosis,
  Neurological Institute, Cleveland Clinic
• March 7, 2009

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Conflict of Interest Statement

• Cleveland Clinic has accepted research funding
  from NIH (NINDS, NCRR), NMSS, Biogen Idec, and
  Nancy Davis Center Without Walls for my research
• I have accepted honoraria or consulting fees (lt
  10,000 per annum) from Biogen Idec, Millenium,
  Genzyme, Teva, and Novartis

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Grant Support For Work Presented Today
NIH PO1 NS38667 Project 3 and MRI/Pathology Core Pathogenesis of Multiple Sclerosis (Brain atrophy in multiple sclerosis)
NIH RO1 NS38667 NMSS RG 3099 MSCRG Trial of IM IFN?-1a for RR-MS
NMSS RG 3099 Brain atrophy in CIS
NMSS PP0540 MS image analysis methodology
Biogen Idec (BGEN 801) F/U of IFN?-1a study
Nancy Davis Center Without Walls Clinical trial methodology
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Outline of Talk

• MRI / Clinical Correlations
• Relapses / Disability
• Use of MRI in MS Trials
• Hot topics
• MRI Defined Rx Response
• Gray Matter Pathology

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Multiple Sclerosis Clinical Course
Preclinical
Relapsing-Remitting
Secondary Progressive
Disability ?
Time ?
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MRI in Multiple Sclerosis

• Useful in diagnosis, management
• Used to test new treatments
• Provides insight into pathogenesis

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Evolution of MS MRI lesions

Richert N. unpublished (1998)
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MRI in MS
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FLAIR/T2 all lesions (edema,
inflammation, demyelination, gliosis, axonal
loss) Gad-enhancing breakdown of blood-brain
barrier, active
inflammation T1 hypointense axonal loss, severe
tissue destruction MTR hypointense demyelination,
axonal loss
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Low Correlation Between MRI Lesions and Relapses
(SLC data)

• MRI lesions did not predict relapses in a
  multivariate model (n 306) Held et al,
  Neurology 2005
• The project to validate MRI lesions as a relapse
  surrogate was stopped since the pre-requisite of
  correlation between MRI lesions and relapses was
  not met (n 208) Petkau et al, Mult Scler 2008
  
• Retrospectice analysis of 31 placebo arms of RCT
  no correlation between Gad lesions and relapses
  (n 409) Daumer et al, Neurology 2009

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Low Correlation Between MRI Lesions and Relapses

• Most lesions are clinically silent
• Kinetics (particularly as relates to disability)

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Low Correlation Between MRI Lesions and Relapses

• Most lesions are clinically silent
• Kinetics (particularly as relates to disability)

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Disease Activity Over 6.5 Years
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If 90 Of New Lesions Are Subclinical, The
Correlation Between New Lesions And Relapses
Would Be 0.34
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Annals of Neurology, 2009, in press

• 23 RCTs with MRI and relapse data
• Does the treatment effect on MRI lesions
  correlate with the treatment effect on relapses?
• A weighted linear regression was run

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log(REL effect) -0.01 0.57 log (MRI effect)
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Validation with separate studies
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In groups, the effect of a treatment on new
lesions explains gt 80 of the variance of the
relapse treatment effect
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Low Correlation Between MRI Lesions and Relapses

• Most lesions are clinically silent
• Kinetics (particularly as relates to disability)

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Does MRI Predict Future Clinical Status?
Preclinical
Relapsing-Remitting
Secondary Progressive
Disability ?
Time ?
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Do MRI Lesions Predict Future Status?
MS patients F/U (yrs) Reference
CIS 5, 10,14 Miller, Fillipi
CIS 14 Chard
RRMS 8, 13 Rudick
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Baseline T2 Lesion Load Predicts Disability at 5
Years
EDSS gt 3.0
Filippi et al. Neurology 1994 44635-641
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T2 Lesion Growth In 5 Years Predicts Disability
at 14 Years
30
25
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T2 Lesion Volume
15
10
5
0
0
5
10
14
BL
5 years
10 years
14 years
Years from presentation
Brex et al. NEJM 2002346158-164
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T2 Lesion Growth In 5 Years Predicts Brain
Atrophy at 14 Years
T2 Lesion Load SRCC p Value
Baseline -0.262 0.178
? BL to 5 Years -0.528 0.004
? 5 Years to 10 Years -0.326 0.090
? 10 Years to 14 Years -0.016 0.936
Chard et al. JNNP 2003 741551-1554
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T2 Lesions In RRMS Predict Atrophy and Clinical
Disability after 13 Years
RRMS from Phase III trial (IFNB-1a)
MRI Brain Atrophy MSFC PASAT
T2 LL Baseline -0.66 (lt0.0001) -0.44 (0.02)
? T2LL over 2 yr -0.40 (0.031) -0.50 (0.005) -0.54 (0.003)
No correlation between T2 LL and future EDSS
Rudick et al. Ann. Neurol.200660236-242
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Brain Atrophy in the IM IFNß-1a Clinical Trial
(RRMS)
Healthy Controls (n16) Mean /- 2 SD
p lt 0.0001
BPF
p 0.0001
p 0.0001
Placebo Patients (n72) Mean /-SEM
BPF 0.830 z - 5.2
BPF 0.824 z - 6.0
BPF 0.820 z - 6.5
Baseline
Year 1
Year 2
Rudick, et al. Neurology 1999 531698-1704
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Brain Atrophy During The Trial Correlates With
Disability At 8 Year F/U
gt EDSS 6.0 At F/U
Quartiles of BPF ? During 2 Year Clinical Trial
Fisher, et al. Neurology 2002
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MRI Predicts EDSS gt 6
Small
Medium
Large
Estimated Effect Size
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MRI / Clinical Correlations (in RRMS)

• Lesions correlate with relapses in early MS
• Effect of intervention on lesions correlates
  strongly with relapse effect
• Lesions correlate with future disability and
  brain atrophy
• Brain atrophy during RR-MS correlates with future
  neurological disability

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Natural History Of Multiple Sclerosis
Typical MS SP-MS 10-20 Years After Onset
Severe MS SP-MS After 5 Years
Progressive Disability Threshold
Amount Of CNS Injury
BenignMS Never Progresses
0
5
10
15
20
25
30
Years After MS Onset
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Outline of Talk

• UseMRI / Clinical Correlations
• Relapses / Disability
• MRI in MS Trials
• Hot topics
• MRI Defined Rx Response
• Gray Matter Pathology

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MRI Parameters in MS Trials

• Gadolinium lesions are the primary outcome
  measure used to screen treatments
• Secondary outcome measures for registration
  trials
• Gad lesions / T2 lesions
• T1 holes / brain atrophy

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Disease Modifying Drugs Clinical and MRI Outcomes Disease Modifying Drugs Clinical and MRI Outcomes Disease Modifying Drugs Clinical and MRI Outcomes Disease Modifying Drugs Clinical and MRI Outcomes Disease Modifying Drugs Clinical and MRI Outcomes
IM IFNb-1a SC IFNb-1a (44-µg) IFNb-1b (8-MIU) Glatiramer Acetate
Relapse rate reduction 32 p0.002 32 plt0.005 34 p0.0001 29 p0.007
Disability progression reduction 37 p0.02 31 plt0.05 29 pNS 12 pNS
Number of Gd lesions reduction 52 p0.05 84 plt0.001 NR 29 p0.00331
N/E T2 lesion number reduction 33 p0.002 78 plt0.0001 83 p0.009 31 plt0.003
at 9 months
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Disease Modifying Drugs Clinical and MRI Outcomes Disease Modifying Drugs Clinical and MRI Outcomes Disease Modifying Drugs Clinical and MRI Outcomes Disease Modifying Drugs Clinical and MRI Outcomes Disease Modifying Drugs Clinical and MRI Outcomes
IM IFNb-1a SC IFNb-1a (44-µg) IFNb-1b (8-MIU) Glatiramer Acetate
Relapse rate reduction 32 p0.002 32 plt0.005 34 p0.0001 29 p0.007
Disability progression reduction 37 p0.02 31 plt0.05 29 pNS 12 pNS
Number of Gd lesions reduction 52 p0.05 84 plt0.001 NR 29 p0.00331
N/E T2 lesion number reduction 33 p0.002 78 plt0.0001 83 p0.009 31 plt0.003
at 9 months
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Brain Atrophy in MS Trials

• Measured in nearly all trials (usually brain
  volume normalized to brain size)
• Issues
• Slow rate of atrophy, short trial durations
• Kinetic issues - fluid shifts and time course of
  neurodegeneration
• Multiple studies show reduced atrophy with
  anti-inflammatory therapies in RRMS
• No study has shown an atrophy effect in SPMS or
  PPMS

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MRI Parameters in MS Trials

• Newer measures
• Diffusion Tensor Imaging
• Magnetic Resonance Spectroscopy
• Magnetization Transfer Imaging
• T1, T2 Relaxation Times
• Functional MRI
• NYRFPT

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Outline of Talk

• Use of MRI in MS Trials
• MRI / Clinical Correlations
• Relapses / Disability
• Hot Topics
• MRI Defined Rx Response
• GM Pathology

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• Original Avonex Study
• The 2-year cohort
• 167 patients
• Classification of responders
• Relapses in 2yr cutoff 2 or more
• Gd lesions (yr 1 yr2) cutoff 2 or more
• New T2 at yr 2 (versus baseline)- cutoff 3 or
  more
• Outcome EDSS, MSFC, BPF

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EDSS Change In Responder Groups Based on T2
Lesions
n62
n20
n46
n39
Rudick et al, Annals of Neurology 2004
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Atrophy (BPF) Change In Responder Groups Based on
T2 Lesions
n36
n47
n18
n33
Rudick et al, Annals of Neurology 2004
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• Do Lesions During Initial 2 Years Predict
  Outcome (EDSS 6) At 15 Years?

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Predictors of gt EDSS 6.0 at 15 Years in Avonex
Group
p 0.03
n30
n36
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Predictors of gt EDSS 6.0 at 15 Years in Placebo
Group
p 0.59
n30
n36
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Predictors of gt EDSS 6.0 at 15 Years in Avonex
Group
p 0.04
n32
n35
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Predictors of gt EDSS 6.0 at 15 Years in Placebo
Group
p 1.0
n32
n35
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Author Responder Classification Results
Rudick Number of N/E T2 lesions at 2 yrs in PLC-controlled trial of IM IFNß-1a Gad lesions at Yr 1 and 2 172 pts studied for 2 years. gt 3 new lesions predicted worse disease progression over 2 years f/u at 15 years confirmed findings
Pozzilli Gad lesions or new T2 lesions 1 year after beginning IFNß 101 of 242 pts had MRI activity as defined. Higher likelihood of relapses in the 4-year observation (OR 3.6)
Tomassini Gad lesions 1 year after beginning IFNß NAb while on IFN 68 patients followed for 6 years. Gad lesions predicted relapse or disability (OR 7.9) NAb associated with poor outcome (OR 7.3)
Rio N/E T2 lesions or Gad lesions 1 year after starting IFNß 152 pts studied for 2 years. gt2 active lesions at 1 year was the primary factor predicting sustained EDSS progression (OR 8.3)
Durelli Gad or T2 lesions 6 months after starging IFNß and IFNß NAb during study 147 pts studied for 2 years. MRI lesions and/or NAb predicted relapse or sustained EDSS increase in the next 18 months
Kinkel Gad or new T2 lesions 6 months after starting IFNß in CIS 383 pts with CIS studied up to 2 years. Active MRI lesions predicted CDMS in IFNß-1a but not placebo patients
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Longitudinal studies consistently indicate that
MRI activity while using IFNß indicates patients
with a relatively poor response to therapy and
poor prognosis
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Outline of Talk

• Use of MRI in MS Trials
• MRI / Clinical Correlations
• Relapses / Disability
• Hot Topics
• MRI Defined Rx Response
• GM Pathology

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Cortical Pathology in MSPost-Mortem Studies

• gt 90 of MS patients have cortical lesions
  (Lumsden 1970)
• Percentage of demyelinated area significantly
  higher in cortex than WM - 26.5 vs. 6.5
  (Bo, et al. 2003)
• Most cortical lesions are not detectable on
  conventional MRI (Geurts, et al. 2005)

SPMS
Kutzelnigg et al Brain 2005
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Gray Matter Atrophy

• Segmentation of gray matter based on intensity
  and anatomic probability
• Gray matter fraction GMF GM volume / brain
  volume
• Provides estimate of overall amount of GM tissue
  damage

Nakamura and Fisher. NeuroImage 2009
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Gray Matter Atrophy in Multiple Sclerosis NIH
Longitudinal Study

• 87 subjects followed over 4 years
• 17 controls
• 7 CIS
• 37 RRMS
• 26 SPMS
• Measured EDSS, MSFC, change in fractional brain
  volumes, lesion volumes, MTR

Fisher, et al. Ann Neurol 2008
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Atrophy Rates Relative to Healthy Controls
Fisher, et al. Ann Neurol 2008
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Gray Matter Atrophy Correlates with MS Disability
Progression

• 87 subjects followed over 6.5 years
• 17 healthy controls
• 7 CIS
• 36 RRMS
• 27 SPMS
• Measured EDSS, MSFC, changes in fractional brain
  volumes
• Categorized patients as progressed or stable
  based on MSFC change over 6.5 years

Rudick, et al. (JNNP 2009)
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Gray Matter Atrophy Correlates with MS Disability
Progression
Rudick, et al. (JNNP 2008)
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Gray Matter Pathology in MS

• Gray matter pathology increasingly dominates the
  pathology
• Gray matter atrophy correlates with disability
  progression
• Is MS a gray matter disease?

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Outline of Talk

• MRI / Clinical Correlations
• Relapses / Disability
• Use of MRI in MS Trials
• Hot topics
• MRI Defined Rx Response
• Gray Matter Pathology

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Future MRI Directions

• More specific MRI measures of MS pathology
• Imaging markers for GM pathology
• Use of MRI in neuroprotection trials
• Use of MRI to monitor / manage Rx

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Collaborators

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Acknowledgements

• Biomedical Engineering
• Elizabeth Fisher, Ph.D.
• Raghavan Gopalakrishnan, M.S.
• Patricia Jagodnik, B.S.
• Kunio Nakamura, B.S.
• Smitha Thomas, M.D.
• Bhaskar Thoomukuntla, M.S.
• Neurosciences
• Angela Chang, M.D.
• Richard Ransohoff, M.D.
• Susan Staugaitis, M.D., Ph.D.
• Bruce Trapp, Ph.D.

• Radiology
• John Cowan, R.T.
• Mark Lowe, Ph.D.
• Mike Phillips, M.D.
• Derek Tew, R.T.
• Mellen Center
• Jeff Cohen, M.D.
• Bob Fox, M.D.
• Claire Hara-Cleaver, RN, NNP
• Dee Ivancic
• Lael Stone, M.D.
• Biostatistics
• Jar-Chi Lee, M.S.

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Thank You For Your Attention