jeffrey curtis, md ms mph university of alabama at birmingham director, arthritis clinical...
TRANSCRIPT
Jeffrey Curtis, MD MS MPHUniversity of Alabama at Birmingham
Director, Arthritis Clinical Intervention Program (ACIP)Co-Director, UAB Center for Education and Research on Therapeutics (CERTS)
of Musculoskeletal Diseases
Measurement Considerations In Rheumatology: Integrating Biomarkers, Technology, Safety, and Comorbidities
to Assess Risks and Benefits of Treatment
Acknowledgements & Disclosures
Research / Consulting Centocor, Amgen, Abbott, UCB, CORRONA, Crescendo, BMS, Roche/Genentech, Pfizer
Funding• AHRQ R01-HS018517• AHRQ U18-HS016956-01 • NIH AR053351• Doris Duke Charitable Foundation
Overview
• More on Measurement–Biomarker-Based Assessment of RA
Disease Activity–Technology-based approaches
• Safety & Relationship with Comorbidities– Infections– GI Perforations– CV Events
• Putting It All Together
Which Biomarkers Might be Important in RA?Interleukins Receptors Hormones Skeletal Others
IL1A AGER Follicle stimulating hormone Aggrecan Adiponectin IL1B* EGFR Gastric inhibitory polypeptide C2C Adrenomedullin
IL1RA * IL2RA ghrelin CS846-epitope Amyloid P component, serumIL2 IL4R GLP-1 COMP Bone morphogenetic protein 6IL3 IL6R* Growth hormone 1 ICTP* c5aIL4 IL-1 receptor, type I insulin Keratan sulphate c5b-9IL5 IL-1 receptor, type II Leptin* Osteocalcin CALCBIL6* KIT NT-proBNP Osteonectin Calprotectin*IL7 sFLT4 Pancreatic polypeptide Osteopontin CD40 ligand IL8* sKDR POMC PIIANP CRP*IL9 TNFRI* Prolactin PYD* Cystatin C
IL10 PTHrP DKKIL12 PYY Fibrinogen
IL12B Resistin * FLT3 ligand IL13 Growth Factors TNF Superfamily TNFR Superfamily Other Cytokines Glial cell derived neurotrophic factorIL15 FGF2 APRIL CD30 EPO gp130IL17 EGF* BAFF* FAS GCSF Haptoglobin
IL18* HGF LIGHT Osteoprotegerin GMCSF HSP90AA1IL23 NGF LTA TNFRSF1A IFNA1 IGFBP1
PDGF-AA RANKL TNFRSF1B IFNA2 Neurotrophin 4PDGF-AB TNF-alpha TNFRSF9 IFNG Pentraxin 3
PlGF TNFSF18 LIF S100A12TGFA TWEAK MCSF SAA1*
VEGFA* CCL22* sclerostin Selectins Adhesion Molecules Enzymes Apolipoproteins Matrix Metalloproteinases SERPINE1Selectin E ICAM1* Alkaline phosphatase APOA1* MMP1* sFLT1Selectin L ICAM3 Lysozyme APOA2 MMP10 SLPISelectin P VCAM1* Myeloperoxidase APOB MMP2 Thrombomodulin
Thyroid peroxidase APOC2* MMP3* YKL40* APOC3 MMP9 APOE
*Indicates biomarkers selected for development; 25 total were selected Bakker et al. Presented at ACR 2010; Poster #1753.Curtis et. al. Manuscript under review.
Vectra™ DA: Development Studies
Adapted from: Bakker et al. Presented at: ACR 2010; Poster #1753.Curtis et. al. Manuscript under review.
SC
RE
EN
ING FEASIBILITY DEVELOPMENT
• Select biomarkers• Build prototypes• > 500 patients• > 700 samples
• Finalize algorithm• ~800 patients• > 800 samples
25Candidate
Biomarkers
ValidatedVectra DA
137Candidate
Biomarkers
12Final
Biomarkers
VA
LID
AT
ION
>30
0 p
atie
nts
>30
0 sa
mp
les
Biomarker Screening
• Identify candidate biomarkers
Feasibility IFeasibility
II
• Qualify assays
Feasibility
III
• Select top candidates
Feasibility IV
• Build prototypes
Assay Optimization
• Optimize analytical performance of individual assays
Training
• Develop algorithm
Verification
• Refine algorithm and validate analytically
Validation
• Evaluate in independent cohort
• Prepare for development
396Candidate
Biomarkers
6
Cohorts Used in Vectra™ DA Development
BRASS (n=637) Oklahoma (n=288)
InFoRM (n=685)
Leiden EAC (n=77)
CAMERA (n=74)
Description Brigham and Women’s RA Sequential Study (Massachusetts)
Oklahoma City Community Cohort (Oklahoma)
Index For RA Measurement -Crescendo Bioscience study (N Amer)
Leiden Early Arthritis Cohort (Netherlands)
Computer Assisted Management in Early RA (Netherlands)
Type Observational Observational Observational Inception Cohort Randomized Open Label (Tight control)
Inclusion criteria
Patients with RA > 18 yrs
Patients age 18-90 with RA
Patients age 18-90 with RA
Patients with early arthritis (all arthritis; <2yrs)
Patients age >16 with early RA (<1 yr)
Patients >1100 >800 >1300 >1800 all arthritis 299
Sample and clinical exam schedule
Annual clinical exam and samples
One clinical exam and sample per patient
3 visits/patient, ~3 months apart, with clinical exam and samples
Baseline and 3 months then yearly sample and clinical exam
Clinical exam and sample at every visit: Conventional group every 3 months, intensive group every 4 wks
Therapies DMARDs, biologics DMARDs, biologics
DMARDs, biologics
DMARDS, analgesics
MTX +/- cyclosporine
Timeline 2003 - ongoing 2007-ongoing 2009-2010 1993-ongoing 1999-2003
InFoRM Fleischmann et al. Presented at EULAR 2010. Poster #SAT0518. BRASS Iannaccone et al. Rheumatology (Oxford). 2010 Sep 16. [Epub ahead of print] Leiden van Aken et al. Clin Exp Rheumatol. 2003;21(5 suppl 31):S100-S105. van der Linden et al. Arthritis Rheum. 2010;62:3537–46. CAMERA Verstappen et al. Ann Rheum Dis. 2007:1443-49.
peripheral
monocytes, macrophages, dendritic cells
endothelial cells
fibroblast-like
synoviocytes
T cells
B, plasmacells
osteoclasts
osteoblasts
chondrocytes
neutrophils
cartilage degradation
bone erosion
IL-6
IL-6
IL-6
IL-6
IL-6
IL-6
IL-6
IL-6
IL-6
VCAM1VCAM1
VCAM1
VEGF
VEGF
EGF
EGF
MMP1
MMP3
MMP1
SAACRP
IL-6
lep
lep
lep
VEGFIL-6
res
VCAM1EGF
IL-6
IL-6
TNFRITNFRI
TNFRI
TNFRITNFRI
TNFRI
TNFRI
leukocyte recruitment & angiogenesis
VEGF
hyperplasia
adaptiveimmunity
systemicinflammatory
response
synovial tissue
bone
cartilage
synovial fluidperipheral bloodand organs
IL-6
IL-6
TNFRI
lep
res
res
resres
res
res
res
res
VCAM1
VEGFMMP1
VCAM1
EGF
SAA
innateimmunity
MMP1
MMP3VCAM1
EGF
EGF
EGF
VEGF
TNFRI
SAA
YKL40
YKL40
YKL40
YKL40
YKL40
YKL40
IL-6
leptin, resistin
YKL-40
MMP-1, MMP-3
EGF, VEGF
VCAM-1
IL-6, TNF-RI
SAA, CRP
RA: A Disease with a Diverse Biology
Vectra™ DA Algorithm• Includes 12 biomarkers and uses a formula similar to DAS28CRP • Different subsets and/or weightings of biomarkers are used to
estimate SJC28, TJC28, and PG
CRP
IL-6SAA
YKL-40
EGFTNF-RI
LeptinVEGF-AVCAM-1
MMP-1MMP-3
Resistin
TJC28SJC28
PatientGlobal CRP
Biomarkers Used To Estimate Each DAS
Component
DAS28CRP=0.56√TJC + 0.28√SJC + 0.14PG + 0.36log(CRP+1) + 0.96TJC=tender joint count; SJC=swollen joint count; PG =patient global health
Vectra DA Score =(0.56√PTJC + 0.28√PSJC + 0.14PPG + 0.36log(CRP+1) + 0.96) * 10.53 +1PT JC=predicted TJC, PSJC=predicted SJC, PPG =predicted PG
Bakker et al. Presented at: ACR 2010; Poster #1753.Curtis et. al. Manuscript under review.
Vectra™ DA Validation and Performance
*low versus moderate/high disease activity using DAS28CRP = 2.67 as the thresholdCurtis et al. Presented at ACR 2010; Poster #1782
• The Vectra DA score was significantly associated with disease activity categories compared to the gold standard of the DAS28CRP* (p<0.001)
RF+ and/or Anti-CCP+• AUROC = 0.77*
RF- and Anti-CCP- • AUROC = 0.70*
Tru
e P
osit
ives
False Positives
Tru
e P
osit
ives
False Positives
9
Vectra™ DA algorithm score tracks disease activity over time
• Studies demonstrate that change in Vectra DA algorithm score is significantly correlated with change in DAS28 (p<0.001)
.
.
• In the BeSt Study:– Vectra DA algorithm score
significantly correlated with change in DAS28 (0.54, p < 0.0001)
Hirata S,et al. Ann Rheum Dis 2011;70(Suppl3):593;
• Vectra DA algorithm score was significantly associated with remission by ACR/EULAR Boolean criteria (by AUROC, p<0.001)
• Similar AUROCs were seen for CDAI, SDAI, DAS28CRP and DAS28ESR remission (p≤0.001)
Vectra™ DA algorithm score discriminates low disease activity from remission
11
1.0 0.8 0.6 0.4 0.2 0.0
0.0
0.2
0.4
0.6
0.8
1.0
Specificity
Se
nsiti
vity
AUROC = 0.7495% CI = [0.60,0.85]
p<0.001
ROC curve for Vectra DA algorithm score classification of Boolean-defined remission vs. non-remission.
Ma MH, et al. EULAR Annual Meeting 2011; Presentation SAT0047;
Vectra™ DA algorithm score was not affected by common comorbidities in a study
of 512 patients
Subgroup n (%) CRP CDAI DAS28CRPVectra DA Algorithm
Score
Hypertension 223 (44) 0.98 1.32* 1.14* 1.05
Osteoarthritis 172 (34) 0.88 1.17 1.13 1.05Osteoporotic bone fractures 131 (26) 0.91 1.05 1.02 1.05
Degenerative joint disease 113 (22) 1.20 1.18 1.11* 1.07
Diabetes 73 (14) 1.01 1.09 1.04 1.07*
Current
smoker67 (13) 1.46 1.45* 1.17* 0.91
Asthma 50 (10) 1.28 1.11 1.05 1.05
12
Ratio of Disease Activity Measure’s Median Value Between RA Patients With and Without Common† Comorbidities
Shadick NA, et al. EULAR Annual Meeting 2011; Presentation FRI0305
† Present in ≥10% of the study population* Nominal p < 0.05; adjusted for age and gender. When adjusted for multiple comparisons, none were statistically significant
Exploratory Analysis: Fibromyalgia had smaller observed effects on the Vectra™ DA algorithm score
than on other disease activity measures
• The slight elevation of the Vectra DA algorithm score was of similar magnitude to the elevation in the swollen joint count
13
FM (n=33) Non-FM (n=475) RatioINDICES
Median Vectra DA algorithm Score 47 42 1.1Median DAS28CRP 4.3 3.3 1.3Median CDAI 18 11 1.6
COMPONENTSMean swollen joint count 4.7 4.3 1.1Mean tender joint count 9.1 5.2 1.8Mean patient global 50 33 1.5Median CRP (mg/L) 7.0 4.2 1.7
Measures of Disease Activity in RA Patients With and Without Fibromyalgia
Shadick NA, et al. EULAR Annual Meeting 2011; Presentation FRI0305
• In the BeSt study, the Vectra DA algorithm score had greater observed correlations with 12 month change in total Sharp-van der Heijde score (DTSS) than measures available in routine clinical practice* (n=89)
Vectra™ DA significantly associated with radiographic progression in the BeSt study
14Allaart CF, et al. EULAR Annual Meeting 2011; Presentation THU0319
Spe
arm
an C
orre
lati
on
Vectra
DA a
lgorit
hm s
core
SJC28
CRP
DAS28CRP
DAS28
SJC44
DAS
Patient G
lobal
TJC28
ESRRAI
0
0.1
0.2
0.3
0.40.34
0.310.25 0.23
0.200.15
0.120.10 0.10 0.09
0.05
Relative performance of variables measured at Year 1 that predict TSS change from Year 1 to Year 2
• Patients in DAS28CRP remission had a significantly higher risk of progression if they also had a high Vectra DA algorithm score
High Vectra™ DA algorithm score in DAS28CRP remission indicates increased joint damage risk
16
EAC = Early Arthritis Cohort; TSS = total van der Heijde sharp score; DAS CRP remission=(< 2,32); High Vectra DA algorithm score= (> 44)Van der Helm-van Mil, ACR Annual Meeting 2011 Presentation SUN323
>0 >3 >5 0%
20%
40%
60%
80%
100%
58%
20%11%
87%
47%
33%
Risk of radiographic progression in a subset of the Leiden EAC. All patients in DAS28CRP Remission (<2.32)
DAS28CRP Remission (n=83)
DAS28CRP Remission and High Vectra DA algorithm score (n=15)
Δ TSS Threshold for Progression
Ris
k o
f P
rog
res
sio
n RR=1.5*
RR=2.3*
RR=3.1*
*p<0.05
Significant change in the mean Vectra™ DA algorithm score occurred as early as 2 weeks
after initiation of therapy
• The majority of the decrease in the Vectra DA Algorithm Score occurred during the first 2 weeks
Δ BL to: n Mean Δ
(95% CI) p value
Wk 2 43 -8.0 (-12 to -4.1) <0.001
Wk 6 43 -7.9 (-11 to -4.6) <0.001
Wk 12 29 -8.4 (-13 to -3.7) 0.001
17Weinblatt M, et al. EULAR Annual Meeting 2011; Presentation THU0339. BL, baseline
Bold Line indicates Median and Boxes Indicate the IQR
Change in Vectra DA algorithm score (in both responders and non responders)
• The change in Vectra DA algorithm score at the last study visit was significantly associated with ACR50 (AUROC=0.69, p=0.03)
• The %change in CRP was not significantly associated with ACR50 (AUROC=0.60, p=0.30)
Change in Vectra™ DA Score significantly discriminates between ACR50 responders vs.
non-responders; Change in CRP does not
Weinblatt M, et al. EULAR Annual Meeting 2011; Presentation THU0339 18
• Assist in clinical management when more information is needed
• Allow for more rapid switching of therapies in Phase 2/3 studies & clinical practice
• Impact patient-physician communication• Predict
– Successful therapy withdrawal– Flare– Radiographic progression
• Proxy for synovitis on MSK US & MRI
Potential Uses of Measuring Biomarkers in RA
19
Overview
• More on Measurement–Biomarker-Based Assessment of RA
Disease Activity–Technology-based approaches
• Safety– Infections– GI Perforations– CV Events
• Putting It All Together
Electronically Collected PROs: One Example at UAB
Also and optionally collects MDHAQ, RAPID3, Patient Acceptable Symptom State (PASS), EQ5D, SF-12, SF-6D, RADAI, patient preferences…
Physician Collected Data
Final Scoring Page
Longitudinal Trends In Disease ActivityR
AP
ID3
Sco
re
Predicting Response with Clinical Data Collected Early
Curtis JR. Ann Rheum Disease 2011; epub ahead of print
Overview
• More on Measurement–Biomarker-Based Assessment of RA
Disease Activity–Technology-based approaches
• Safety– Infections– GI Perforations– CV Events
Increased Infection Due To RA Itself and Active Disease
• 609 RA patients and 609 controls matched on age, residence, sex* residing around Rochester, Minnesota– Greater than 12 years of follow-up, Pre-biologic era– Risk for hospitalized infection associated with RA:
hazard ratio = 1.83 (1.52-2.21)• CORRONA registry**
– More than 25,000 RA patients– More active RA higher rate of infection
* adjusted for smoking, diabetes, chronic lung disease, steroid use, and leukopenia
* Doran et al. Arthritis Rheum 2002; 46(9):227-2293
** Au et. al. Ann Rheum Disease May 2011;70(5):785-91
Potentially Confounding Factors:Concomitant Glucocorticoid Use
Mean daily dose of glucocorticoids (no. of treatment episodes), outcome
Propensity score adjusted rate ratio (95% CI)
≤5 mg (n = 1,781)
Pneumonia 0.88 (0.37-2.12)
Any bacterial infection 1.34 (0.85-2.13)
6-9 mg (n = 1.510)
Pneumonia 2.01 (0.87-4.66)
Any bacterial infection 1.53 (0.95-2.48)
10-19 mg (n = 4,435)
Pneumonia 2.97 (1.41-6.23)
Any bacterial infection 2.86 (1.80-4.56)
≥20 mg (n = 2,891)
Pneumonia 6.69 (2.83-15.8)
Any bacterial infection 5.48 (3.29-9.11)
Schneeweiss, S. et al., Arthritis Rheum 2007;56:1754-64.Schneeweiss S. Arthritis Rheum. 2007 Jun;56(6):1754-64
Effect of Anti-TNF Therapy on the Incidence of Serious Infections in RA Patients:
Results from Clinical Trials
Bongartz T et al, JAMA, May 17 2006, Vol 295: No. 19, 2275-2285
Summary Relative Risk of Infection = 2.0 (1.3 – 3.1)
Results from Observational Studies: Serious infections under anti-TNF treatment
Incidence of serious infections in anti-TNF treated patients (per 100 patient years)
RABBIT: Listing et al., Arthritis Rheum 2005;52:3403-12 6.3
BSRBR: Dixon et al., Arthritis Rheum 2006;54(8):2368-76 5.3
ARTIS: Askling et al., Ann Rheum Dis 2007;66:1339-44 5.4*
Curtis JR, et al., Arthritis Rheum 2007; 56(4):1125-33 2.9**
Schneeweiss S, et al., Arthritis Rheum 2007; 56(6):1754-64 2.2
*only prior hospitalized patient, first year** in the first six months after biologic use
Rates of Serious Infections Largely Driven by Disease, Comorbidities and
Patient Factors, Not Biologics
PBO = placebo; TCZ = tocilizumab Kremer et. al. ACR 2008, abstract 1668; Smolen et. al., ACR 2008, abstract 1669;Genovese 2008 (TOWARD); Emery 2008 (RADIATE)
Rate of Serious Infections per 100 person-years
PBO + DMARD 3.8
Combination MTX + TCZ, Overall 5.2 RR= 5.2 / 3.8 = 1.4
Rates of Serious Infections Largely Driven by Disease, Comorbidities and
Patient Factors, Not Biologics
PBO = placebo; TCZ = tocilizumabKremer et. al. ACR 2008, abstract 1668; Smolen et. al., ACR 2008, abstract 1669;Genovese 2008 (TOWARD); Emery 2008 (RADIATE)
Rate of Serious Infections per 100 person-years
PBO + DMARD 3.8
Combination MTX + TCZ, Overall 5.2
TOWARD (DMARD failure, biologic naive) (MTX +TCZ 8mg/kg) vs. (MTX + PBO) 5.9 vs. 4.7
Rates of Serious Infections Largely Driven by Disease, Comorbidities and Patient
Factors, Not Biologics
PBO = placebo; TCZ = tocilizumabKremer et. al. ACR 2008, abstract 1668; Smolen et. al., ACR 2008, abstract 1669;Genovese 2008 (TOWARD); Emery 2008 (RADIATE)
Rate of Serious Infections per 100 person-years
PBO + DMARD 3.8
Combination MTX + TCZ, Overall 5.2
TOWARD (DMARD failure, biologic naive) (MTX +TCZ 8mg/kg) vs. (MTX + PBO) 5.9 vs. 4.7
RADIATE (TNF Failures, refractory RA) (MTX +TCZ 8mg/kg) vs. (MTX + PBO) 9.9 vs. 9.6
Risk difference for patients on MTZ + TCZ who have diabetes compared to those who don’t is ~ 4 / 100py
Applying Research Results to Clinical Care
How much should a ~1.5 to 2-fold increased risk of infection
matter to my patients?
Example Patient
Hypothetical Baseline
Serious Infection Rate
Hypothetical RR of Infection
Associated with Biologic Use
Resulting Infection
Rate
#1: 42 yo, severe RAMTX, HCQ no other medical problems
1% / yr 2.0 2% / yr
Putting Relative Risks into Context:
Two Examples
Putting Relative Risks into Context:
Two Examples
Example Patient
Hypothetical Baseline
Serious Infection Rate
Hypothetical RR of Infection
Associated with Biologic Use
Resulting Infection
Rate
#1: 42 yo, severe RAMTX, HCQ no other medical problems
1% / yr 2.0 2% / yr
#2: 65 yo, moderate RA MTX, prednisone 7.5 mg/dayDiabetes, COPD, hosp. for pneumonia last year
10% / yr 2.0 20% / yr
Safety Assessment of Anti-TNF Agents Used in Autoimmune
Disease (SABER)Sponsored by FDA / AHRQ
THE UNIVERSITY OFALABAMA AT BIRMINGHAM
CCEB
Specific Aims
• Aim #1: To estimate incidence rate ratio (RR) of SAEs associated with each biologic agents among users and comparable nonusers– To estimate the RR of SAEs after considering time since first use,
duration of use, concomitant drug use and relevant comorbidities
• Aim #2: To estimate the RR of SAEs in vulnerable populations including
(1) low income groups;
(2) minority groups;
(3) women (especially pregnant women);
(4) children;
(5) the elderly;
(6) individuals classified as disabled;
(7) patients with co-morbidities;
(8) patients living in rural or inner city areas who may have reduced access to health care.
SAE = serious adverse events
Centers, Working Groups, & Datasets Center Working Group
(Outcomes Lead)Datasets used for Each Outcome
UAB Infections (including Opportunistic, TB)
Medicare Standard Analytic Files & MAX, 1999-2006
HMORN Death, Pulmonary Fibrosis
KPNC, 1998-2007
Univ Penn Malignancies -
Vanderbilt Congenital anomalies & pregnancy outcomesFractures
TennCare, 1998- 2007
Brigham and Women’s DEcIDE Center
Cardiovascular PACE, PAAD ,’ 98- ’06BCLHD, ’96-’06Horizon BCBSNJ, ’96-’07
New Paradigms to Pool Data to Study Rare Adverse Events
Rassen J. Med Care. 2010 Jun;48(6 Suppl):S83-9.
SABER Results for Serious Bacterial Infections
Figure 3. Incidence Rates and hazard Ratios for Specific TNF-a Antagonists and Serious Infections Among
Patients with Rheumatoid Arthritis
Is Serious Infection Risk Additive, or Multiplicative, for anti-TNF Users?
Low Risk Medium Risk High Risk0
2
4
6
8
10
12
14
16
18
20
DMARD OnlyTNF UsersTNF Users2
Assumptions for this hypothetical scenarioDMARD rate of infection is 3 per 100 patient years; TNF user rate is 6 per 100 patient years. Rate ratio = 6 / 3 = 2.0; Rate difference is 6 - 3 = 3.0 per 100 py
Is Serious Infection Risk Additive, or Multiplicative, for anti-TNF Users?
Low Risk Medium Risk High Risk0
2
4
6
8
10
12
14
16
18
20
DMARD OnlyMultiplicativeAdditive
Assumptions for this hypothetical scenario: multiplicative risk doubles the rate of infection, additive risk increases it by 3 per 100 patient years
Infection Risk Constant for High Risk and Low Risk Patients
Curtis JR, ACR 2011 annual meeting, manuscript under review
TB Risk for those onAnti-TNF Therapy
Dixon WG et al. Ann Rheum Dis 2010:69:522-528
UK Biologic Registry
Cochrane: TB rate 200/100,000 persons receiving drug
Drug-Specific Risks of Other Opportunistic Infections from French RATIO registry
• 45 cases of opportunistic infections• Most common infections were zoster, PCP, listeria,
nocardia, non-tuberculosis mycobacteria• Overall absolute event rates 1.5 / 1000 py
Adjusted Odds Ratio (95% CI)
Most recent TNF Etanercept Adalimumab Infliximab
1.0 (referent)10.0 (2.3 – 44.4)17.6 (4.3 – 72.9)
Prednisone > 10mg/day or bursts No Yes
1.0 (referent)6.3 (2.0 – 20.0)
Salmon-Ceron et. al. Ann Rheum Dis 2011; 70:616–623
Incidence of PML in SABER
• Among 712,708 unique individuals with RA, PsA, PsO, JIA, IBD, or AS, a total of 55 hospitalizations with PML diagnoses identified
• 55 suspected cases– 29 had insurance coverage for > 6 months prior to the PML
case date and > 1 physician diagnoses of a rheumatic disease that occurred before PML case date
– 82% with HIV; 10% with malignancy
• Overall case rate = 7.7 per 100,000 individuals• Among biologic users, 1 cases among inflixumab
users, 2 among rituximab users• Case rate among patients with autoimmune diseases
on biologics w/o HIV or cancer ~0.2 per 100,000Bharat A, Curtis JR. Arthritis Care & Research, in press
What About Infections for Which We Can Vaccinate?
• Patients with rheumatic and autoimmune diseases are at increased risk of herpes zoster (HZ), also known as shingles
• A live zoster vaccine reduces risk by 51%– Treatment-related contraindication – Safety concern: vaccine might trigger HZ in
these patients within 4-6 weeks– Safety and efficacy not clear
Strangfeld et al., JAMA. 2009;301(7):737-744.Oxman et al., N Engl J Med. 2005;352(22):2271-2284.Harpaz et al., MMWR Recomm Rep. 2008;57(RR-5):1-30; quiz CE32-34.
Study DesignRetrospective cohort study using 100% sample of Medicare data
– age >= 60 – RA, psoriasis, PsA, AS, or IBD based upon >= 2 MD
diagnoses
Vac
cina
tion
Unvaccinated Person-timeEffectiveness analysis: > 42 days after vaccination
Safety analysis:≤ 42 days after vaccination E
nd o
f Fo
llow
-up
Star
t of
Foll
ow-u
p
Results
• 463,104 eligible patients with at least one of the 5 autoimmune diseases of interest– Mean age 74 years– 72% women– 86% Caucasian– 20,570 (4.4%) received zoster vaccine– 10,032 developed HZ during follow-up– Patients with RA contributed over half (65.3%)
of the total person-years during follow-up
Herpes Zoster Incidence Rates, Unvaccinated, by Steroid Exposure
Exposure to Glucocorticoids
No Yes Medications (exclusive groups) HZ IR‡ HZ IR‡ IR Ratio 95% CI
Any anti-TNF (regardless of non-biologic DMARDs use)
12.6 22.4 1.8 1.6-2.0
Adalimumab 11.8 21.7 Etanercept 11.5 20.7 Infliximab 13.2 23.2 Other anti-TNFs 15.6 26.2 Any non-TNF biologics (regardless of non-biologic DMARDs use)
14.3 18.6 1.3 1.0-1.7
Abatacept 12.1 17.1 Rituximab 17.5 20.4 Non-biologic DMARDs without biologics 11.0 18.6 1.7 1.6-1.7
Methotrexate (regardless of other non-biologic DMARDs use)
10.4 18.2
All other non-Methotrexate DMARDs alone or in combination
11.9 19.3
*HZ, Herpes Zoster; IR, Incidence Rate per 1,000 Person-Years; 95% CI, Confidence Interval
Herpes Zoster Incidence Rates by Vaccination Status and Medication Exposure
Safety Endpoint:≤ 42 Days Following Vaccination Unvaccinated
Infections, n
Vaccinated,n
IR* IR*
Overall <11 7,781 7.8 11.6Drug Exposure
Biologics (regardless of concomitant DMARDs or oral glucocorticoids)
0 636 - 15.8
Anti-TNF therapies 0 556 - 15.7DMARDs (without biologics but regardless of oral glucocorticoids)
<11 1,817 14.6 13.8
Oral glucocorticoids alone <11 1,215 21.2 17.1
*HZ, Herpes zoster; IR, incidence rate per 1,000 person-Years
Reduced Risk of Zoster Associated With Vaccination,
Varying Case Definitions
Outcome Definition Hazard Ratio* 95% CI
Diagnosis code + anti-viral medications 0.69 0.56-0.86
Diagnosis code only 0.72 0.71-0.84*Controlling for age, gender, race, concurrent medications (anti-TNF, non-TNF biologics, non-biologic DMARDs, oral glucocorticoids), and health care utilization (hospitalization and physician visits)
TNF Inhibitors and Risk of Post-Op Infections: Impact of Stop Time
Conclusions• Patients off TNF inhibitor >28 days before surgery had ~60% reduction in
infections
• Data support discontinuing TNF inhibitor at least 4 weeks prior to surgery
Dixon W, et al. Presented at: 2007 EULAR Annual Meeting. Barcelona, Spain. Abstract OP0215.
On/Off at Timeof Surgery
On/Off 28 Days Before Surgery
On OffOn 28 Days
Off 28 Days
Infections, N (%)
49 (3.0)
15 (3.5)
59 (3.4) 5 (1.4)
Adjusted OR (95% CI)
Ref.1.15 (0.62-2.12)
Ref.0.38 (0.18-0.93)
SPOI and Influence of Stop Time
2
1.0
0.6
0.4
0.2
Ad
just
ed O
R (
95%
Cl)
"On 28"
"Off"
1.15
0.38
"Off 28"
On/Off at Surgery
"On"
On/Off 28 DaysBefore Surgery
Are Anti-TNF Users at Higher Risk for Recurrent Malignancies?
Dixon WG et al. Arthritis Care Res (Hoboken). 2010 June; 62(6): 755–763
DMARD (n =117)
Anti-TNF (n =177)
Person-years of followup 235 515 Median (IQR) follow-up time, yrs 1.9 (1.3–2.7) 3.1 (2.0–3.9)
Incident malignancies, no. 9 13
Rate per 1,000 person-years 38.3 (17.5–72.7) 25.3 (13.4–43.2)
IRR (95% CI) 1.0 (referent) 0.56 (0.23–1.35)
124
Rates of GI Perforations for Patients on Biologics and DMARDs
Drug Exposure Group Rate/1000 PYs (95% CI)
Biologics with glucocorticoids 1.87 (1.46–2.35)
Biologics w/o glucocorticoids 1.02 (0.80–1.29)
Methotrexate with glucocorticoids 2.24 (1.82–2.74)
Methotrexate w/o glucocorticoids 1.08 (0.86–1.35)
Other DMARDs* with glucocorticoids 3.03 (2.34–3.85)
Other DMARDs* w/o glucocorticoids 1.71 (1.34–2.16)
Glucocorticoids w/o any DMARD or biologic 2.86 (2.27–3.56)
No DMARDs, biologics, or glucocorticoids 1.68 (1.44–1.96)
Total 1.70 (1.58–1.83)
124DMARD=disease modifying antirheumatic drug; PYs=person years.*Azathioprine, chloroquine, hydroxychloroquine, cyclosporine, D-penicillamine, leflunomide, sulfasalazine, gold compounds. Curtis JR et. al. presented at EULAR 2011, London
125
Relative Risk of GI Perforation During Follow-up–Adjusted Results
125Reference groups are as follows: for all drug groups except NSAIDs = methotrexate without steroids; for NSAIDs = the absence of NSAIDs; for all binary variables = the absence of the condition or status. CCI=Charlson Comorbidity Index; DMARD=disease-modifying antirheumatic drug; NSAIDS=Non-Steroidal Anti-Inflammatory Drug.
0 1 2 3 4 11 13 15 17 19
UrbanFemale
Age 40-64Age 65+
Baseline CCINSAID
No DMARD or glucocorticoid
Other DMARDs w/o glucocorticoidsBiologics w/o glucocorticoids
Biologics w/ glucocorticoidsMethotrexate w/ glucocorticoids
Glucocorticoids w/o any DMARDOther DMARDs w/ glucocorticoids
Diverticulosis w/o diverticulitisDiverticulitis
Hazard Ratios With 95% Confidence Intervals
Exp
osur
e O
n or
Aft
er I
ndex
Results of Sensitivity Analysis that Varied Definition of GI Perforation• Exclusion of diverticulitis/diverticulosis + GI surgery decreased incidence rate to
1.25 (95% CI, 1.12–1.34) per 1000 PYs• Hazard ratio for diverticulitis ranged from 3.6 to 14.5
RA Is an Independent Risk Factor for MI, Stroke
Solomon DH et al. Ann Rheum Dis. 2006;65:1608-1612.
18-49 50-64 65-74 75+
Inci
den
ce R
ate
(per
100
0 p
erso
n-y
ears
)
Age Range (y)
Patients With RA (n=25,385)
Patients Without RA (n=252,976)
0102030
40506070
127
Changes in Lipids Associatedwith Tocilizumab (IL-6Ra)
0
5
10
15
20
25
30
5
20
4
25
3
13
ACT 8 (n = 288)
ACT 8 + DMARD (n = 1582)
ACT 4 + MTX (n = 774)
HDL (mg/dL) LDL (mg/dL)
Me
an
Ch
an
ge
Fro
m B
as
eli
ne
in
6-M
on
th C
on
tro
lle
d P
eri
od
* From tocilizumab prescribing information (PI)
Increase in Total Cholesterol associated with Anti-TNF therapy
1 2 3 4 5 6 7 8 9 10 11 12 130.0
5.0
10.0
15.0
20.0
25.0
30.0 28.0
1.4
7.25.8
0.7 0.4
9.0
13.0
20.0
6.7 6
2.5 3.6
Infliximab* Adalimumab
Ch
an
ge
fro
m B
as
elin
e (
mg
/dL
)
n = 80n = 45n = 97
n = 10n = 52
n = 32
n = 55
n = 19
n = 56
n = 69n = 33
n = 50n = 8
Pollono EN. Clin Rheumatol. 2010; 29(9):947-55.
*Two additional studies with total n of 35 had a mean change in total cholesterol of -5.4 (Popa, et al. Ann rheum Dis 64(2):303-305) and -2.3 (Perez-Galan, et al. Med Clin (Barc) 126(19): 757) mg/dL.
Study
Greenberg JD. Ann Rheum Dis. 2011 Apr;70(4):576-82.
CV Events
HR
TNFMTX0
0.5
1.0
1.5
2.0
0.6
0.3
TNF Inhibitor Therapy inRA and CV Outcomes
• Examined 10,870 patients with RA from CORRONA registry– Median RA duration: 7 years– Median follow-up: 2 years
• Conclusions– Compared with non-biologic therapies
excluding methotrexate (MTX)• Substantial reduction in CVD risk
for patients treated with TNF inhibitors (RR 0.3)
• Intermediate reduction in CVD risk for patients treated with MTX (RR 0.6)
– Prednisone an independent risk factor for CVD
Putting It All Together: Applying Research Results
to Clinical Care
Communicating Risk
Know What Your Patients are Reading about Safety
• “The most common side effects of Prolia® are back pain, pain in your arms and legs, high cholesterol, muscle pain, and bladder infection.” (manufacturer website at www.prolia.com)
Denosumab* (n = 3886) Placebo* (n = 3876)
Back pain 1347 (34.7%) 1340 (34.6%)
Pain in extremity 453 (11.7%) 430 (11.1%)
Musculoskeletal pain 297 (7.6%) 291 (7.5%)
Hypercholesterolemia 280 (7.2%) 236 (6.1%)
Cystitis 228 (5.9%) 225 (5.8%)
* As observed in pivotal 3 year trial
Communicating Benefits and Risks of Biologics to Patients
• “Ms. Jones, there’s a good chance that you will respond to this medication, but…
• “It may increase your risk of infection by 50 to 100%”
OR
“There is an extra 2 out of 100 chance over the next year of having a serious infection
OR
100 patients, Active Disease, on MTX
10 20 30 40 50 60 70 80 90 100
Likelihood of Achieving an Good Clinical Response, Remaining on MTX
☻ ☻
☻ ☻
☻
☻
☻
☻
☻
☻
☻
☻ 10 20 30 40 50 60 70 80 90 100
Likelihood of Achieving a Good Clinical Response, Adding a Biologic
☻ ☻ ☻ ☻ ☻
☻ ☻ ☻ ☻ ☻
☻ ☻ ☻ ☻ ☻
☻ ☻ ☻ ☻ ☻
☻ ☻ ☻ ☻ ☻
☻ ☻ ☻ ☻ ☻
☻ ☻ ☻ ☻ ☻
☻ ☻ ☻ ☻ ☻
☻ ☻ ☻ ☻ ☻
☻ ☻ ☻ ☻ 10 20 30 40 50 60 70 80 90 100
Likelihood of a Serious Bacterial Infection, Remaining on MTX
10 20 30 40 50 60 70 80 90 100
Likelihood of a Serious Bacterial Infection, After Adding a Biologic
10 20 30 40 50 60 70 80 90 100
Risk:Benefit Curve of Aggressive Therapy
Severity of Comorbidities
Nee
d fo
r A
ggre
ssiv
e R
xincreased toxicity
+/- benefitlimited toxicity+ benefit(control of inflammation lowers risk)
Risk of Therapy
……older age,disability,steroids, etc
Serious adverse event
Death
Summary & Conclusions
• Biomarkers appear useful to assess disease activity in an objective manner and may predict future outcomes (e.g. structural damage, CV risk, future response to tx)
• Clinical data, perhaps in conjunction with biomarkers, may be maximally useful; technology may assist in collecting this data
• Infections• Increased risk of infections, largely early after starting• Risk difference compared to non-biologic therapies low
(~1-4 / 100py)• Appears similar for low vs. high risk patients• No greater than risk for moderate dose glucocorticoid use• Risk for zoster does not appear to be increased with vaccination,
even for biologic users• No apparent increase in primary or recurrent malignancy except
possibly non-melanoma skin cancer
Summary & Conclusions
• Increases in lipids but neutral or even reduced CV risk• Low absolute rates of other SAEs (e.g. gastrointestinal
perforation)• Lots of data, new methods needed to study rare SAE• Overall risk-benefit profile of biologic therapy likely to be
favourable for almost all patients who need it• Communicating Risk to Patients Challenging, Better
Tools Needed• Absolute risk (not relative risk) likely to be most
informative
Acknowledgements & Collaborators
• UAB– John Baddley, MD MPH– Tim Beukelman, MD MSCE– Aseem Bharat, MPH– Lang Chen, PhD– Elizabeth Delzell, ScD– Mary Melton– Paul Muntner, PhD– Ryan Outman, MS– Nivedita Patkar, MD MPH– Kenneth Saag, MD MSc– Monika Safford, MD– Jas Singh, MD MPH– Fenglong Xie, MS– Shuo Yang, MS– Jie Zhang, PhD
• OHSU– Kevin Winthrop, MD
• U Nebraska– Ted Mikuls, MD MSPH
• U Utah– Grant Cannon, MD– Scott Duvall, PhD
• Vanderbilt University– Carlos Grijalva, MD– Marie Griffin, MD
• Brigham and Women’s Hospital– Dan Solomon, MD MPH– Jeremy Rassen, ScD– Sebastian Schneeweiss, ScD