defining goals giovannoni berlin 4 5 may 2013
TRANSCRIPT
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GAVIN GIOVANNONI BARTS AND THE LONDON
Defining Goals and Patient
Expectations: The Importance of
Early Planning
1
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Consequences of Increasing EDSS Scores:
Loss of Employment
2
The proportion of patients employed or on long-term sick leave is calculated as a percentage of patients aged 65 or younger.
Kobelt G et al. J Neurol Neurosurg Psychiatry. 2006;77:918-926.
Austria
Belgium
Germany
Italy
Netherlands
Spain
Sweden
Switzerland
United Kingdom
Work Capacity by Disability Level P
rop
ort
ion
of
Pa
tie
nts
≤6
5 Y
ea
rs O
ld
Wo
rkin
g (
%)
0
10
20
30
40
50
60
70
80
90
0.0/1.0 2.0 3.0
EDSS Score
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MS patient’s QoL decreases tremendously
dependent on the EDSS score
Mean utility
Utilities at early
disease
Utility at severe disease
Austria 0.55 0.90 0.05
Belgium 0.51 0.85 0.06
Germany 0.62 0.86 0.10
Italy 0.53 0.80 0.06
Netherlands 0.61 0.85 0.05
Spain 0.55 0.87 0.08
Sweden 0.546 0.825 0.047
Switzerland 0.59 0.89 0.1
UK 0.51 0.92 0.18
EQ-5D was used to calculate utilities: Utility is a measure of people's well-being or preferences for outcomes.
Mean utilities and EDSS in Germany 1= perfect health; 0 = worst health/dead
Source: based on G. Kobelt et al.: The European Journal of Health Economics, Volume 7; suppl. 2006
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0,45
0,55
0,60
0,75
0,80
0,65
0,70
0,50
0,72 - mean utility of patients with rheumatoid arthritis
at stage 1 (Kobelt G. et al. 1999)
0,00 - Worst possible status
1,00 - Best possible health status
0,55 - mean utility of patients with multiple sclerosis (Kobelt G. et al. 2001)
0,48 - mean utility of severe haemophilia patients with inhibitors (Ekert H. et al. 2001)
0,82 - mean utility of aging patients with osteoporosis,
no fracture (Oleksik A et al. 2000)
0,58 - mean utility of patients with Parkinson’s Disease (Siderowf A. et al. 2002)
Utilities make diseases comparable
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MS is a disabling disease
Cognitive Dysfunction • Prevalence: 43% to 82%1,74
• Affects employment, activities of daily living, and social functioning2
Life Shortening • 5- to 7-year decrease in life
expectancy3-5
• 2- to 7-fold increase in suicide risk6
• 50% MS patients die of disease-related causes6
1Rao SM, et al. Neurology. 1991;41:685-691 2Rao SM, et al. Neurology. 1991;41:692-696 3Sadovnick AD, et al. Neurology. 1992;42:991-994 4Ebers GC. J Neurol Neurosurg Psychiatry. 2001;71:16-19 5Kingwell E, et al. J Neurol Neurosurg Psychiatry. 2012;83:61-6. 6Sadovnick AD, et al. Neurology. 1991;41:1193-1196. 7Giogkaraki et al., J Neurol Sci. 2008
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survival analysis
“hit hard and early ”
MS is an autoimmune disease hypothesis
15-20 year experiment
What is your treatment philosophy?
maintenance-escalation vs. induction
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7
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MS Treatment Decisions Are Complex:
Some factors to consider…….
Patient Preferences
Therapy Attributes
Efficacy Safety Tolerability Administration (route/frequency) Monitoring requirements Biomarkers (e.g. NAb) Adherence
Socio-demographic profile (lifestyle, work, family) Risk tolerance Likelihood of adherence
Geographic/Economic Factors
8
Patient and Disease Profile
Age, gender Disease activity (MRI/clinical) Impairment/disability Treatment history Comorbidities Biomarkers (e.g., Anti-JCV Ab)
Label Reimbursement / access to drug
Shared Treatment Decision
Shared treatment decision that optimally weighs these
considerations to arrive at the therapy that best meets the
patient’s needs
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Right Therapy, Right Patient, Right Time
Key Treatment Decision Steps
9
Monitoring:
Choosing therapy
X Y Z
Define the Individual’s Multiple Sclerosis
no
Treatment failure? yes
• Patient • MS prognosis • Life style and goals • Your goals for therapy
• Patient’s preferences? • Your choice?
Individual measures: • Evidence of disease activity? • Tolerability/safety? • Adherence? • Drug or inhibitory markers?
Monitoring
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Right Therapy, Right Patient, Right Time
Key Treatment Decision Steps
10
Monitoring:
Choosing therapy
X Y Z
Define the Individual’s Multiple Sclerosis
no
Treatment failure? yes
• Patient • MS prognosis • Life style and goals • Your goals for therapy
• Patient’s preferences? • Your choice?
Individual measures: • Evidence of disease activity? • Tolerability/safety? • Adherence? • Drug or inhibitory markers?
Monitoring
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vs.
1
2
3
Clinical
MRI
NABs
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100 MSers Who are the responders?
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~20% responders
~40% sub-optimal responders
~40% non-responders
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Emerging concepts in MS
NEDA; no evidence of disease activity
TTT; treat-to-target
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Treat-2-target
No Evidence of Disease Activity
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Discussing Risk of therapies with Patients: How Willing
Are Patients to Accept Risk ?
Adapted from Heesen et al. Mult. Scler. 2010, 16:1507-1512
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Success of PML Risk Stratification
• Anti-JCV antibody-based PML risk stratification has enabled
physicians and MS patients to make informed treatment decisions
• Anti-JCV antibody status is the number one factor of PML risk
stratification.
• Anti-JCV antibody testing is widely utilized in clinical practice
• Unilabs in 2011: 32,108 tests
• Unilabs in 2012: 45,709 (31,812 tests in new patients)
Biogen Idec, data on file.
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Negative* Anti-JCV
Antibody Status ≤0.07/1000 95% CI: 0–0.48
FACTOR 1
Treatment exposure time or prior IS use do not impact the risk estimates as long as the patient remains JCV Ab negative
Data beyond 4 years of treatment are limited. *Based on natalizumab exposure and 285 confirmed PML cases as of September 5, 2012. Prior IS data in overall natalizumab-treated patients based
on proportion of patients with IS use prior to natalizumab therapy in TYGRIS as of May 2011; and prior IS data in PML patients as of September 5,
2012. The analysis assumes that 55% of natalizumab-treated MS patients were anti-JCV antibody positive and that all PML patients test positive for
anti-JCV antibodies prior to the onset and diagnosis of PML. The estimate of PML incidence in anti-JCV antibody negative patients is based on the
assumption that all patients received at least 1 dose of natalizumab . Assuming that all patients received at least 18 doses of natalizumab , the estimate
of PML incidence in anti-JCV antibody negative patients was generally consistent (0.1/1000; 95% CI 0.00–0.62).
Biogen Idec, data on file.
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Factors Predicting the Ultimate Goal of Disease
Activity Silencing
Havrdova E. et al., ECTRIMS Meeting poster 905, Gothenburg, Sweden 2010 Havrdova E. et al. Neurology 2010 ;74:S3-7.
Variables Associated with Overall Freedom from Disease Activity (No Clinical or MRI Activity) over 2 Years in a Multivariate Logistic Regression Analysis
AFFIRM
Natalizumab-treated patients with fewer relapses, fewer MRI lesions, and lower EDSS scores at therapy initiation and who did not develop persistent anti-natalizumab antibodies were more likely to achieve freedom from disease activity over the course of the 2-year AFFIRM study.
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Can Stabilize the Disease, Not Revert it
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0 6 12 18 24 30 36 42 48
Med
ian
ED
SS
Sco
re
Time (months)
Kappos L et al. Presented at ENS; June 9–12, 2012; Prague, Czech Republic. O261.
22
Baseline EDSS Score ≤3.0 (n=1591)
Baseline EDSS Score >3.0 (n=1840)
Natalizumab should be used according to the SmPC
TOP: Overall Stabilization of EDSS Scores in Patients with Either a High or Low Starting EDSS
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Conclusions
• NEDA, T2T and DAF have entered the neurology lexicon
• We need an acceptable working definition of an MS cure
• DAF x 15 years?
• Should the definition be disease-stage specific?
• How do we deal with maintenance and induction therapies?
• Maintenance - absence of NEDA status indicates non-response
• Induction – absence of NEDA status indicates a time to retreat
• Improve risk mitigation tool
• Who should make the decision re early aggressive treatment?
• Regulators
• Payers
• Neurologists
• MSers
• Is it fair to make MSers wait 20 years for the outcome of an experiment?
• For example, alemtuzumab extension study
• What do we do about post-inflammatory neurodegeneration?
• We need better outcomes, e.g. regional brain atrophy and CSF neurofilament levels
• Neuroprotective treatments