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