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Jeffrey Curtis, MD MS MPH University of Alabama at Birmingham Director, Arthritis Clinical Intervention Program (ACIP) Co-Director, UAB Center for Education.

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Presentation on theme: "Jeffrey Curtis, MD MS MPH University of Alabama at Birmingham Director, Arthritis Clinical Intervention Program (ACIP) Co-Director, UAB Center for Education."— Presentation transcript:

1 Jeffrey Curtis, MD MS MPH University 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

2 Acknowledgements & Disclosures Funding AHRQ R01-HS018517 AHRQ U18-HS016956-01 NIH AR053351 Doris Duke Charitable Foundation

3 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

4 Which Biomarkers Might be Important in RA? InterleukinsReceptorsHormonesSkeletalOthers IL1AAGERFollicle stimulating hormone Aggrecan Adiponectin IL1B*EGFRGastric inhibitory polypeptide C2C Adrenomedullin IL1RA *IL2RAghrelin CS846-epitope Amyloid P component, serum IL2IL4RGLP-1COMPBone morphogenetic protein 6 IL3IL6R*Growth hormone 1 ICTP*c5a IL4IL-1 receptor, type Iinsulin Keratan sulphatec5b-9 IL5IL-1 receptor, type IILeptin*OsteocalcinCALCB IL6*KITNT-proBNPOsteonectinCalprotectin* IL7sFLT4Pancreatic polypeptideOsteopontinCD40 ligand IL8*sKDRPOMC PIIANP CRP* IL9TNFRI*Prolactin PYD* Cystatin C IL10 PTHrP DKK IL12 PYYFibrinogen IL12B Resistin *FLT3 ligand IL13 Growth FactorsTNF SuperfamilyTNFR SuperfamilyOther Cytokines Glial cell derived neurotrophic factor IL15FGF2APRILCD30EPOgp130 IL17EGF* BAFF* FASGCSFHaptoglobin IL18* HGFLIGHTOsteoprotegerinGMCSF HSP90AA1 IL23 NGFLTATNFRSF1AIFNA1IGFBP1 PDGF-AARANKLTNFRSF1BIFNA2Neurotrophin 4 PDGF-ABTNF-alphaTNFRSF9IFNGPentraxin 3 PlGFTNFSF18 LIF S100A12 TGFATWEAK MCSFSAA1* VEGFA* CCL22*sclerostin SelectinsAdhesion MoleculesEnzymesApolipoproteinsMatrix Metalloproteinases SERPINE1 Selectin EICAM1*Alkaline phosphataseAPOA1*MMP1*sFLT1 Selectin LICAM3LysozymeAPOA2MMP10SLPI Selectin PVCAM1*MyeloperoxidaseAPOBMMP2Thrombomodulin Thyroid peroxidaseAPOC2*MMP3* YKL40* APOC3MMP9 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.

5 Vectra™ DA: Development Studies Adapted from: Bakker et al. Presented at: ACR 2010; Poster #1753. Curtis et. al. Manuscript under review. SCREENING FEASIBILITYDEVELOPMENT Select biomarkers Build prototypes > 500 patients > 700 samples Finalize algorithm ~800 patients > 800 samples 25 Candidate Biomarkers Validated Vectra DA 137 Candidate Biomarkers 12 Final Biomarkers VALIDATION >300 patients >300 samples Biomarker Screening Identify candidate biomarkers Feasibility I Feasibility 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 396 Candidate Biomarkers

6 Cohorts Used in Vectra™ DA Development 6 BRASS (n=637)Oklahoma (n=288) InFoRM (n=685) Leiden EAC (n=77) CAMERA (n=74) DescriptionBrigham 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) TypeObservational Inception CohortRandomized Open Label (Tight control) Inclusion criteria Patients with RA > 18 yrs 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 arthritis299 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 TherapiesDMARDs, biologics DMARDS, analgesics MTX +/- cyclosporine Timeline2003 - ongoing2007-ongoing2009-20101993-ongoing1999-2003 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.

7 RA: A Disease with a Diverse Biology

8 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-6 SAA YKL-40 EGF TNF-RI Leptin VEGF-A VCAM-1 MMP-1 MMP-3 Resistin TJC28 SJC28 Patient Global CRP Biomarkers Used To Estimate Each DAS Component DAS28CRP=0.56√TJC + 0.28√SJC + 0.14PG + 0.36log(CRP+1) + 0.96 TJC=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 +1 PT JC=predicted TJC, PSJC=predicted SJC, PPG =predicted PG Bakker et al. Presented at: ACR 2010; Poster #1753. Curtis et. al. Manuscript under review.

9 Vectra™ DA Validation and Performance *low versus moderate/high disease activity using DAS28CRP = 2.67 as the threshold Curtis 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* True Positives False Positives True Positives False Positives 9

10 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;

11 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 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;

12 Vectra™ DA algorithm score was not affected by common comorbidities in a study of 512 patients Subgroupn (%)CRPCDAIDAS28CRP Vectra DA Algorithm Score Hypertension 223 (44)0.981.32*1.14*1.05 Osteoarthritis 172 (34)0.881.171.131.05 Osteoporotic bone fractures 131 (26)0.911.051.021.05 Degenerative joint disease 113 (22)1.201.181.11*1.07 Diabetes 73 (14)1.011.091.041.07* Current smoker 67 (13)1.461.45*1.17*0.91 Asthma 50 (10)1.281.111.05 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

13 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)Ratio INDICES Median Vectra DA algorithm Score 47421.1 Median DAS28CRP 4.33.31.3 Median CDAI 18111.6 COMPONENTS Mean swollen joint count 4.74.31.1 Mean tender joint count 9.15.21.8 Mean patient global 50331.5 Median CRP (mg/L) 7.04.21.7 Measures of Disease Activity in RA Patients With and Without Fibromyalgia Shadick NA, et al. EULAR Annual Meeting 2011; Presentation FRI0305

14 In the BeSt study, the Vectra DA algorithm score had greater observed correlations with 12 month change in total Sharp-van der Heijde score (  TSS) than measures available in routine clinical practice* (n=89) Vectra™ DA significantly associated with radiographic progression in the BeSt study 14 Allaart CF, et al. EULAR Annual Meeting 2011; Presentation THU0319 Spearman Correlation Relative performance of variables measured at Year 1 that predict TSS change from Year 1 to Year 2

15 Vectra™ DA algorithm score associated with radiographic progression in Leiden EAC 15 EAC = Early Arthritis Cohort van der Helm-van-Mil AH et al. As presented at EULAR 2011. FRI0013 Spearman correlation n = 271 visits Correlation to Radiographic Progression Over 12 months

16 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=( 44) Van der Helm-van Mil, ACR Annual Meeting 2011 Presentation SUN323 RR=1.5* RR=2.3* RR=3.1* *p<0.05

17 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 17 Weinblatt 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)

18 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

19 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

20 Overview More on Measurement –Biomarker-Based Assessment of RA Disease Activity –Technology-based approaches Safety –Infections –GI Perforations –CV Events Putting It All Together

21 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…

22

23 Physician Collected Data

24 Final Scoring Page

25 Longitudinal Trends In Disease Activity RAPID3 Score

26 Predicting Response with Clinical Data Collected Early Curtis JR. Ann Rheum Disease 2011; epub ahead of print

27 ACR Rationale for Evidence-Based Clinical Practice Recommendations Summarize efficacy and safety considerations for busy clinicians Are advisory, NOT proscriptive May help to improve health care –Quality –Appropriateness –Cost effectiveness Should be based on evidence and created using a formal process (e.g. RAND method)

28 ACR RA Recommendations 2011 Update Revision of the ACR RA Recommendations published in 2008* using the same methodology Added new drugs: TCZ, GOL, CZP Advisory, not proscriptive “Positive” recommendations only –No statements on when not to start a therapy, based on efficacy considerations –No statements on when it is “okay” to use an agent based on safety considerations Adding and switching therapies now included Focus on common patients, NOT exceptional cases ; cost not considered but alternate choices are * Saag K et al. Arthritis Rheum. 2008;59:762

29 Task Force Panel (TFP) C Bombardier MD MSc Maxime Dougados MD AF Kavanaugh MD D Khanna MD MSc JM Kremer MD Charles King MD Karen Kolba MD Amye Leong MBA Eric Matteson MD MPH Eileen Moynihan MD JT Schousboe MD MS

30 Core Expert Panel (CEP) K Saag MD MSc J Singh MD MPH D Furst MD J Curtis MD MPH H Paulus MD Tim Beukelman MD Maria Almazor MD MPH S Louis Bridges MD PhD W Winn Chatham MD L Moreland MD Ted Mikuls MD MSPH James O’dell MD

31 Submit to ACR Working group and CEP Systematic Literature Review Develop Evidence Report Create Clinical Scenarios Review report Rate clinical scenarios Analyze 1 st ratings Iterative Group Meetings and Internet Teleconferences Review rating, discuss evidence, second rating Analyze 2 nd ratings Draft recommendations Finalize recommendations TFP RAND-UCLA Method Grade recommendations ACR RA RECOMMENDATION DEVELOPMENT PROCESS Saag K et al. Arthritis Rheum. 2008;59:762

32 Systematic Literature Review- Article Selection: Update 2010 BIOLOGICSNON-BIOLOGICS 1765 Started 253 articles after titles and abstracts review 663 Started 160 articles after titles and abstracts review 69 Started 27 articles after titles and abstracts review Newer-BIOLOGICS Data from 149 articles included and abstracted after full text review

33 Sample Evidence Table: Update 2011

34 DMARD failure definition With features of poor prognosis Low Disease Activity Moderate Disease Activity High Disease Activity 1Optimal MTX/SSZ/HCQ a. 3 months 123456789123456789123456789 21131111321115 b. 6 months 123456789123456789123456789 11312111517 2Optimal MTX alone a. 3 months 123456789123456789123456789 22121111111211213 b. 6 months 123456789123456789123456789 1221112213134 3Optimal MTX and another DMARD in combination a. 3 months 123456789123456789123456789 21221111321124 b. 6 months 123456789123456789123456789 1131211617 Clinical Scenario Example- When To Use a Biologic RED = Median score with TFP agreement; GREEN = NO median score due to disagreement

35 Interpretation of Group Median Values Median score 7 or greater: “Appropriate” Median score 3 or less: “Inappropriate” Median score 3.5 to 6.5: “Uncertain” 123456789 “Inappropriate” “Uncertain” “Appropriate”

36 Key Assumptions for 2011 ACR RA Clinical Scenarios Focus on common patients, NOT exceptional cases Cost not considered Alternate therapeutic choices taken into account When a particular drug not recommended; does NOT mean it’s contraindication Scenarios for which agreement not achieved noted explicitly Optimal dose of medication given for 3 mos before therapy escalation or switching Disease activity and prognosis assessments mostly between 3-6 mos, although can be reassessed as early as 3 mos Assume a clinical indication (based upon disease activity) for use of each therapeutic choice Contraindications related to safety present at the time of making treatment decision and NOT based solely on history of condition

37 Examples of Features of Poor RA Prognosis Functional limitation –Defined using standard scales (e.g., health assessment questionnaire-DI) Extra-articular disease –Includes presence of rheumatoid nodules, Felty’s syndrome, RA lung disease, etc. Positive Rheumatoid Factor (RF) Positive anti-cyclic citrullinated peptide (CCP) Bony erosions by radiograph

38 Overview Methods for ACR 2011 Guideline Development Highlights from 2011 ACR Guidelines Update Safety –Infections –GI Perforations –CV Events Biomarker-Based Assessment of RA Disease Activity

39 Methods: Defining Disease Duration and Markers or Poor RA Prognosis Disease Duration Intervals <6 months (early disease) 6–24 months (intermediate disease) >24 months (longer disease duration) Clinical Markers of Poor RA Prognosis Extra-articular disease (eg, vasculitis, Sjögren’s syndrome, RA lung disease, etc) Bony erosions by radiography Functional limitation (eg, HAQ Disability Index) RF positivity and/or positive anti-CCP antibodies

40 Measuring and Goals of RA Therapy The ACR 2011 Task Force panel recommends –Measuring RA disease activity in a quantitative fashion at least every 3-6 mos –Setting the target for disease activity as either remission, or low disease activity

41 Example Methods For Assessing RA Disease Activity Instrument Score Range Thresholds of Disease Activity LowModerateHigh Disease Activity Score (DAS) in 28 joints 0 – 9.4≤ 3.2> 3.2 – ≤ 5.1> 5.1 Simplified Disease Activity Index (SDAI) 0.1– 86.0≤ 11> 11 – ≤ 26> 26 Clinical Disease Activity 0 – 76.0≤ 10> 10 – ≤ 22> 22 Rheumatoid Arthritis Disease Activity Index (RADAI) 0 – 10< 2.2≥ 2.2 – ≤ 4.9> 4.9 † Routine Assessment Patient Index Data (RAPID) 0 – 30< 6≥ 6 – ≤ 12> 12 †Median

42 2011 ACR Recommendations for Early RA (< 6 months)

43 Established RA* Indications for DMARD therapy and Switching between DMARDs If a patient has low disease activity after 3-6 months of DMARD monotherapy (hydroxychloroquine, methotrexate, minocycline or sulfasalazine), add methotrexate or leflunomide. If a patient has at least moderate disease activity after 3 months of methotrexate- 1. Add another non-methotrexate DMARD to methotrexate OR 2.Switch to a different non-methotrexate DMARD If a patient has at least moderate disease activity after 3 months of DMARD monotherapy, methotrexate or leflunomide should be added. *disease duration ≥ 6 months

44 2011 ACR Recommendations for Established RA

45 Established RA on DMARDs, When to Add a Biologic Low disease activity and poor prognosis after 3-6 months of methotrexate monotherapy or methotrexate-based DMARD combination: Add or switch to a anti-TNF biologic At least moderate disease activity after 3 months of methotrexate monotherapy or methotrexate- based DMARD combination-therapy: Add or switch to an anti-TNF biologic OR Add or switch to abatacept or rituximab in anti-TNF- naïve patients OR Add or switch DMARDs

46 Established RA, When to Switch between Biologics At least moderate disease activity 3 months after anti-TNF biologic has failed: Switch to another anti-TNF biologic or a non-TNF biologic OR Add or switch DMARDs At least moderate disease activity 6 months after failing a non-TNF biologic (abatacept, rituximab or tocilizumab): Switch biologics (no specific recommendation on which one)

47 Established RA Switching between Biologics Due to Harms/Adverse Event At least moderate disease activity after failing anti- TNF biologic because of non-serious adverse events* - 1.Switch to another anti-TNF biologic or non-TNF biologic. At least moderate disease activity after failing a non-TNF biologic because of an adverse event (serious or non-serious)- 1.Switch to anti-TNF biologic. As defined by FDA:

48 Established RA Switching to Non TNF biologics DISEASE ACTIVITY Failed anti-TNF trial Failed MTX or MTX+DMARD trial Lack/Loss of benefit afterAdverse EventDuration 3 months6 monthsNon seriousSerious3 months6 months Low Disease Activity Moderate Disease Activity in anti-TNF- naïve patients* High Disease Activity in anti-TNF- naïve patients* *Abatacept and Rituximab are recommended

49 Established RA Switching to anti-TNF Biologics DISEAS E ACTIVIT Y Failed anti-TNF trialFailed non-TNF trial Failed MTX or MTX+DMARD Lack/Loss of benefit after Adverse Event Lack/Loss of benefit after Adverse EventDuration 3 months 6 months Non serious Serious 3 months 6 months Non serious Serious3 months 6 months Low Disease Activity With poor prognosis Moderat e Disease Activity High Disease Activity

50 Use of biologics in RA patients with past treated malignancy MalignancyRecommended Level of Evidence Any treated solid malignancy or treated non-melanoma skin cancer > 5 years ago Any biologicC Treated solid malignancy or treated non-melanoma skin cancer 5 years ago RituximabC Treated lymphoproliferative malignancy (ever)RituximabC Recommendations supported by evidence level C are largely based upon expert opinion

51 Use of biologics in RA patients with hepatitis HepatitisRecommended Not Recommended Level of Evidence Past history of acute hepatitis B with positive hepatitis B core antibody AbataceptC Hepatitis C (treated or not)EtanerceptC Untreated chronic Hepatitis B, or treated chronic Hepatitis B, with Child-Pugh Class B and higher Any biologicC Recommendations supported by evidence level C are largely based upon expert opinion

52 2011 ACR RA Recommendations for TB screening with biologics

53 Tuberculosis (TB) Screening for Biologics Other recommendations 1.Annual testing in patients who live, travel, or work in situations where TB exposure is likely while they continue treatment with biologics. 2.Patients who test positive for TST or IGRA at baseline are expected to remain positive for these tests even after successful treatment of TB. 3.These patients need monitoring for clinical signs and symptoms of recurrent TB, since repeating tests will not help in diagnosis of recurrent TB

54 Recommended vaccination in patients* starting or currently receiving DMARDs or biologics Before starting a DMARD or a biologic: 1.All killed vaccines (Pneumococcal, Influenza and Hepatitis B) 2.Recombinant vaccine (Human Papilloma Virus vaccine for cervical cancer) 3.Live attenuated vaccine (Herpes Zoster) Patients already taking a DMARD or a biologic 1.All above vaccinations, EXCEPT 2.Herpes Zoster Vaccine is recommended ONLY during DMARD therapy, contraindicated in biologic users (± 30d exposure) * assuming age appropriate

55 Overview More on Measurement –Biomarker-Based Assessment of RA Disease Activity –Technology-based approaches Safety –Infections –GI Perforations –CV Events

56 Biologics Revolutionary for Many Diseases Ankylosing spondylitis Inflammatory bowel disease Juvenile Idiopathic arthritis (JIA) Psoriasis Psoriatic arthritis Rheumatoid arthritis (RA) Multiple sclerosis Systemic lupus Gout In large randomized controlled trials, shown to yield –Improved disease related outcomes –Improved function –Improved quality of life –Improved work and home productivity Typically very expensive (e.g. ~$30,000/year)

57 Biologics: Lots of Choices, Lots of Risks? ChoicesRisks? Anti-TNF Adalimumab (Humira) Certolizumab (Cimzia) Etanercept (Enbrel) Golimumab (Simponi) Infliximab (Remicaide) B-cell depleting therapies Rituximab (Rituxan) IL-1Ra Anakinra (Kineret) IL-6Ra Tocilizumab (Actemra) IL-12/23 Ustekinemab (Stelara) T-cell costimulatory inhibitors Abatacept (Orencia) Alefacept (Amevive) Autoimmune syndromes (e.g. SLE) Cancer Heart Failure Demyelinating Events (e.g. MS) Gastrointestinal Perforation Ischemic events (e.g. MI) Hypersensitivity Leukopenia Liver Failure Psoriasis (new onset) Serious Infections TB, Histo, PML, Cocci, … Death

58 IgG1 Fc Fab Monoclonal antibody Recombinant receptor/Fc fusion protein Fab ′ PEG PEGylated Fab′ Anti-TNF Structure of the Anti-TNFs Etanercept Infliximab Adalimumab Golimumab Certolizumab pegol p75 soluble TNF receptor n All three reagents are bivalent and have an active isotype Fc n CZP is structurally different as it is PEGylated, univalent and does not have an Fc

59 Case Study Ms. Jones, 38 year old AA woman Seropositive rheumatoid arthritis, diagnosed 1 year ago Treated with methotrexate 20mg/week, prednisone 10mg/day, celecoxib 200mg/day Somewhat improved, still with active disease –Tender joint count = 12 –Swollen joint count = 8 Rheumatologist is recommending addition of a biologic (anti-TNF therapy) In preparation for asking your opinion as her PCP, she’s done some research…

60 Safety Profile of 2 Biologics for RA

61 Case Study, continued After further searching the internet, she learns that anti-TNF therapy –Doubles the risk of serious infections* –Increases risk for TB 12-fold** –Is associated with PML***, a severe, sometimes fatal disease for which few if any effective therapies exist –Is associated with death She declines biologic therapy * Curtis JR, et al., Arthritis Rheum 2007; 56(4):1125-33; Listing et al., Arthritis Rheum 2005;52:3403-12 ** Tubach F et. al., Arthritis Rheum 2009 Jul;60(7):1884-94. *** Kumar et. al. Arthritis Rheum. 2010 Nov;62(11):3191-5

62 Challenges in Studying Biologic Safety Biologic use associated with disease severity Biologics frequently used with prednisone or other immunosupressive agents Clinical trials not powered to detect adverse events High rate of co-morbidities in real-world patients Methodologic issues in analysis –Duration of exposure of some biologics unclear –Drug switching common

63 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

64 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) Pneumonia0.88 (0.37-2.12) Any bacterial infection1.34 (0.85-2.13) 6-9 mg (n = 1.510) Pneumonia2.01 (0.87-4.66) Any bacterial infection1.53 (0.95-2.48) 10-19 mg (n = 4,435) Pneumonia2.97 (1.41-6.23) Any bacterial infection2.86 (1.80-4.56) ≥20 mg (n = 2,891) Pneumonia6.69 (2.83-15.8) Any bacterial infection5.48 (3.29-9.11) Schneeweiss, S. et al., Arthritis Rheum 2007;56:1754-64. Schneeweiss S. Arthritis Rheum. 2007 Jun;56(6):1754-64

65 Dixon et. al. Arthitis Rheum 2007; 56(9): 2896-2904. New Users, early period of treatment Ongoing Users Relative Rate of Infection: Overall: 1.0 (0.7–1.6) First 90 days: 4.6 (1.8– 11.9)

66 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)

67 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;52:3403-126.3 BSRBR: Dixon et al., Arthritis Rheum 2006;54(8):2368-765.3 ARTIS: Askling et al., Ann Rheum Dis 2007;66:1339-44 5.4* 5.4* Curtis JR, et al., Arthritis Rheum 2007; 56(4):1125-33 2.9** 2.9** Schneeweiss S, et al., Arthritis Rheum 2007; 56(6):1754-642.2 Adj. relative rates of serious infections on anti-TNF therapy compared to RA patients on non-biologic DMARDS RABBIT: Listing et al., Arthritis Rheum 2005;52:3403-122.2 BSRBR: Dixon et al., Arthritis Rheum 2006;54(8):2368-76 1.0 (4.6 early) ARTIS: Askling et al., Ann Rheum Dis 2007;66:1339-44 1.4* 1.4* Curtis JR, et al., Arthritis Rheum 2007; 56(4):1125-331.9 Schneeweiss S, et al., Arthritis Rheum 2007; 56(6):1754-641.0 *only prior hospitalized patient, first year ** in the first six months after biologic use

68 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;52:3403-126.3 BSRBR: Dixon et al., Arthritis Rheum 2006;54(8):2368-765.3 ARTIS: Askling et al., Ann Rheum Dis 2007;66:1339-44 5.4* 5.4* Curtis JR, et al., Arthritis Rheum 2007; 56(4):1125-33 2.9** 2.9** Schneeweiss S, et al., Arthritis Rheum 2007; 56(6):1754-642.2 *only prior hospitalized patient, first year ** in the first six months after biologic use

69 Resolving Discordant Results across Biologic Safety Analyses: Who is in the Control Group? Example: Comorbidities BSRBR: significant higher frequency of COPD and current smokers in the control group

70 Resolving Discordant Results across Biologic Safety Analyses: Who is in the Control Group? Example: Comorbidities BSRBR: significant higher frequency of COPD and current smokers in the control group Crude Incidence Rate Ratio in RABBIT registry: 2.5 / 0.7 = 3.6 Crude Incidence Rate Ratio in BSRBR registry: 2.1 / 2.7 = 0.8

71 Rates of Serious Infections Largely Driven by Disease, Comorbidities and Patient Factors, Not Biologics OPTION study MTX + TCZ 8mg/kg n = 205 RADIATE study MTX + TCZ 8mg/kg n = xx Age51 +- 1254 +- 13 RA disease duration7 +- 713 +- 9 Number of prior DMARDs 1.5 +- 1.41.9 +- 1.7 HAQ1.6 +- 0.61.7 +- 0.6 Steroid users, %55%52% Diabetes8% 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 + DMARD3.8 Combination MTX + TCZ, Overall5.2 RR= 5.2 / 3.8 = 1.4

72 Rates of Serious Infections Largely Driven by Disease, Comorbidities and Patient Factors, Not Biologics OPTION study MTX + TCZ 8mg/kg n = 205 RADIATE study MTX + TCZ 8mg/kg n = xx Age51 +- 1254 +- 13 RA disease duration7 +- 713 +- 9 Number of prior DMARDs 1.5 +- 1.41.9 +- 1.7 HAQ1.6 +- 0.61.7 +- 0.6 Steroid users, %55%52% Diabetes8% 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 + DMARD3.8 Combination MTX + TCZ, Overall5.2 TOWARD (DMARD failure, biologic naive) (MTX +TCZ 8mg/kg) vs. (MTX + PBO)5.9 vs. 4.7

73 Rates of Serious Infections Largely Driven by Disease, Comorbidities and Patient Factors, Not Biologics OPTION study MTX + TCZ 8mg/kg n = 205 RADIATE study MTX + TCZ 8mg/kg n = xx Age51 +- 1254 +- 13 RA disease duration7 +- 713 +- 9 Number of prior DMARDs 1.5 +- 1.41.9 +- 1.7 HAQ1.6 +- 0.61.7 +- 0.6 Steroid users, %55%52% Diabetes8% 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 + DMARD3.8 Combination MTX + TCZ, Overall5.2 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

74 Applying Research Results to Clinical Care How much should a ~1.5 to 2-fold increased risk of infection matter to my patients?

75 Example Patient Hypothetical Baseline Serious Infection Rate Hypothetical RR of Infection Associated with Biologic Use Resulting Infection Rate #1: 42 yo, severe RA MTX, HCQ no other medical problems 1% / yr2.0 2% / yr Putting Relative Risks into Context: Two Examples

76 Example Patient Hypothetical Baseline Serious Infection Rate Hypothetical RR of Infection Associated with Biologic Use Resulting Infection Rate #1: 42 yo, severe RA MTX, HCQ no other medical problems 1% / yr2.0 2% / yr #2: 65 yo, moderate RA MTX, prednisone 7.5 mg/day Diabetes, COPD, hosp. for pneumonia last year 10% / yr2.0 20% / yr

77 Safety Assessment of Anti-TNF Agents Used in Autoimmune Disease (SABER) Sponsored by FDA / AHRQ THE UNIVERSITY OF ALABAMA AT BIRMINGHAM CCEB

78 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

79 Research Plan: The Overall Structure Data Coordinating Center at KPNC Jeffrey R. Curtis, MD, MPH Marie R. Griffin, MD, MPH Lisa J. Herrinton, PhD James D. Lewis, MD, MSCE Liyan Liu, MD Parivash Nourjah, PhD Rita Ouellet-Hellstrom, PhD, MPH Kenneth G. Saag, MD, MSc Daniel H. Solomon, MD, MPH Executive Committee Seven Working Groups (defined by outcomes)

80 Centers, Working Groups, & Datasets CenterWorking Group (Outcomes Lead) Datasets used for Each Outcome UABInfections (including Opportunistic, TB) Medicare Standard Analytic Files & MAX, 1999-2006 HMORNDeath, Pulmonary Fibrosis KPNC, 1998-2007 Univ PennMalignancies- VanderbiltCongenital anomalies & pregnancy outcomes Fractures TennCare, 1998- 2007 Brigham and Women’s DEcIDE Center CardiovascularPACE, PAAD,’ 98- ’06 BCLHD, ’96-’06 Horizon BCBSNJ, ’96-’07

81 New Paradigms to Pool Data to Study Rare Adverse Events Rassen J. Med Care. 2010 Jun;48(6 Suppl):S83-9.

82 SABER Results for Serious Bacterial Infections

83

84 Figure 3. Incidence Rates and hazard Ratios for Specific TNF-a Antagonists and Serious Infections Among Patients with Rheumatoid Arthritis

85 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 RA MTX, HCQ no other medical problems 1% / yr2.0 2% / yr #2: 65 yo, moderate RA MTX, prednisone 7.5 mg/day Diabetes, COPD, hosp. for pneumonia last year 10% / yr2.0 20% / yr Number needed to treat (NNT) with a biologic: To achieve a good response (ACR50): ~ 3 to 8 To result in 1 “extra” serious infection: Example patient #1: 100 Example patient #2: 10

86 Is Serious Infection Risk Additive, or Multiplicative, for anti-TNF Users? Assumptions for this hypothetical scenario DMARD 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

87 Is Serious Infection Risk Additive, or Multiplicative, for anti-TNF Users? Assumptions for this hypothetical scenario: multiplicative risk doubles the rate of infection, additive risk increases it by 3 per 100 patient years

88 Is Serious Infection Risk Additive, or Multiplicative, for anti-TNF Users? Assumptions for this hypothetical scenario: multiplicative risk doubles the rate of infection, additive risk increases it by 3 per 100 patient years

89 Infection Risk Constant for High Risk and Low Risk Patients Curtis JR, ACR 2011 annual meeting, manuscript under review

90 TB Risk for those on Anti-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

91 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

92 Winthrop KL, et al. Rates of Tuberculosis and Nontuberculous Mycobacterial Disease among Rheumatoid Arthritis Patients Who Use Anti-Tumor Necrosis Factor-alpha Therapy; from the Safety of Biologic ThERapy (SABER) Study, ACR scientific meeting 2010, abstract #404. TNF Inhibitors and the Risk of Mycobacterial Infections 29,500 new users of TNFi therapy for RA (Kaiser No. Cal, U.S. Medicare + Medicaid, Tenncare) 24 NTM and 11 TB cases, IR 66.7/100,000 (95% CI 40-93) and 45.9/100,000 (14-78) patient years, respectively Rate of TB in the general population was 5.1/100,000py In the US, rate of TB among RA patients who start TNFi therapy is –9-fold higher than the general population –2-3 fold higher than among RA patients starting non-biologic RA medications

93 J Baddley EID, in press IR = Incidence Rates of Histoplasmosis in patients ≥65 from U.S. Medicare data 1999-2008 (5% sample) 3.3/100,000 p-yrs Do I Need to Worry About “Endemic” Fungal Infections Outside of Endemic Regions? Rates shown per 100,000 patient years

94 Progressive Multifocal Leukoencephalopathy (PML) Demyelinating disease of CNS. Caused by the polyoma “JC” virus Pathology first described in 1958; virus identified by microscopy in 1965, by culture in 1971 Initials “JC” are from patient whose brain was used to first culture the organism in 1971 Occurs in those with depressed cell-mediated immunity 80% of population is seropositive by adulthood Disease occurs from reactivation of latent virus Clinical: mental slowness, disorientation, behavioral changes, motor dysfunction: lack of coordination, gait disturbances, ataxia, hemiparesis Imaging: multifocal areas of white matter demyelination that do not conform to vascular territories and do not exhibit mass effect or enhancement Diagnosis: imaging, biopsy, CSF PCR for JCV Death often occurs from weeks to months after diagnosis

95 PML Clinical: mental slowness, disorientation, behavioral changes Motor dysfunction: lack of coordination, gait disturbances, ataxia, hemiparesis Headaches are uncommon Imaging: multifocal areas of white matter demyelination that do not conform to vascular territories and do not exhibit mass effect or enhancement Diagnosis: -imaging, biopsy -CSF PCR for JCV (>90% sensitivity?) Death occurs from weeks to months after diagnosis

96 PML and Biologics PML increasingly associated with treatment of rheumatologic diseases (>45 cases); but rarely in patients using TNF-α antagonists Rates per 100,000 hospital discharges: SLE: 4; RA: 0.4; Other CTDs 2 Appeared in 0.1% of patients treated with natalizumab for MS or Crohn’s (31 cases 2005- 2009). Estimated incidence of 1 per 1000 patients. Four cases (1 in 500) with efalizumab use for plaque psoriasis Fifty-seven patients who developed PML after rituximab (2 SLE, 1 RA) Molloy and Calabrese, Arthritis Rheumatism 2009;60:3761-5.

97 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 100,000 Bharat A, Curtis JR. Arthritis Care & Research, in press

98 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.

99 Hypotheses Zoster vaccine is safe and effective in patients with selected rheumatic and autoimmune diseases –Zoster vaccine not associated with increased HZ risk within 42 days after vaccination for patients currently using anti-TNF or other biologics –Zoster vaccine reduces long-term HZ risk

100 Eligible Participants 100% of Medicare beneficiaries, 2006-2009 –Age ≥ 60 years –With fee for service coverage, enrolled in a Part D drug plan –≥ 2 physician diagnoses of rheumatoid arthritis, psoriatic arthritis, psoriasis, ankylosing spondylitis, or inflammatory bowel diseases occurring between 7 and 365 days apart –A baseline period of at least 6 months of continuous coverage No evidence for HZ infection during baseline period

101 Study Design Retrospective cohort study using 100% sample of Medicare data –age >= 60 –RA, psoriasis, PsA, AS, or IBD based upon >= 2 MD diagnoses Vaccination Unvaccinated Person-time Effectiveness analysis: > 42 days after vaccination Safety analysis: ≤ 42 days after vaccination End of Follow-up Start of Follow-up

102 Exposure and Outcome Zoster vaccination Current Procedural Terminology (CPT) code, National Drug Code (NDC), and G code Medication exposure NDC code, CPT code Biologics, disease modifying anti-rheumatic drugs (DMARDs), and oral glucocorticoids Incident HZ infection ICD-9 diagnosis code + antiviral drug use (acyclovir, famcyclovir, valacyclovir) within 7 days ICD-9 diagnosis code only

103 Statistical analysis Safety endpoint –HZ incidence rate ≤ 42 days after vaccination Effectiveness endpoints –HZ incidence rate > 42 days after vaccination –Hazard ratio Vaccinated vs. unvaccinated regardless of timing Proportional hazard regression, controlling for age, gender, race, comorbidities, concurrent medications, and health care utilization

104 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

105 Herpes Zoster Incidence Rates, Unvaccinated Not Exposed to Oral Glucocorticoids Medications (exclusive groups)# HZ cases*HZ IR ‡ 95% CI Any anti-TNF (regardless of non-biologic DMARDs use) 73812.611.7-13.5 Adalimumab9811.8 Etanercept15011.5 Infliximab49013.2 Other anti-TNFs<1115.6 Any non-TNF biologics (regardless of non- biologic DMARDs use) 10914.311.8-17.3 Abatacept5312.1 Rituximab4817.5 Non-biologic DMARDs without biologics1,07411.011.0-11.7 Methotrexate (regardless of other non- biologic DMARDs use) 62510.4 All other non-Methotrexate DMARDs alone or in combination 44911.9 * HZ, Herpes Zoster; IR, Incidence Rate per 1,000 Person-Years; 95% CI, Confidence Interval

106 Herpes Zoster Incidence Rates, Unvaccinated Not Exposed to Oral Glucocorticoids Medications (exclusive groups)# HZ cases*HZ IR ‡ 95% CI Any anti-TNF (regardless of non-biologic DMARDs use) 73812.611.7-13.5 Adalimumab9811.8 Etanercept15011.5 Infliximab49013.2 Other anti-TNFs<1115.6 Any non-TNF biologics (regardless of non- biologic DMARDs use) 10914.311.8-17.3 Abatacept5312.1 Rituximab4817.5 Non-biologic DMARDs without biologics1,07411.011.0-11.7 Methotrexate (regardless of other non- biologic DMARDs use) 62510.4 All other non-Methotrexate DMARDs alone or in combination 44911.9 * HZ, Herpes Zoster; IR, Incidence Rate per 1,000 Person-Years; 95% CI, Confidence Interval

107 Herpes Zoster Incidence Rates, Unvaccinated, by Steroid Exposure Exposure to Glucocorticoids NoYes Medications (exclusive groups)HZ IR ‡ IR Ratio95% CI Any anti-TNF (regardless of non-biologic DMARDs use) 12.622.41.81.6-2.0 Adalimumab11.821.7 Etanercept11.520.7 Infliximab13.223.2 Other anti-TNFs15.626.2 Any non-TNF biologics (regardless of non- biologic DMARDs use) 14.318.61.31.0-1.7 Abatacept12.117.1 Rituximab17.520.4 Non-biologic DMARDs without biologics11.018.61.71.6-1.7 Methotrexate (regardless of other non- biologic DMARDs use) 10.418.2 All other non-Methotrexate DMARDs alone or in combination 11.919.3 * HZ, Herpes Zoster; IR, Incidence Rate per 1,000 Person-Years; 95% CI, Confidence Interval

108 Herpes Zoster Incidence Rates, Unvaccinated, by Steroid Exposure Exposure to Glucocorticoids NoYes Medications (exclusive groups)HZ IR ‡ IR Ratio95% CI Any anti-TNF (regardless of non-biologic DMARDs use) 12.622.41.81.6-2.0 Adalimumab11.821.7 Etanercept11.520.7 Infliximab13.223.2 Other anti-TNFs15.626.2 Any non-TNF biologics (regardless of non- biologic DMARDs use) 14.318.61.31.0-1.7 Abatacept12.117.1 Rituximab17.520.4 Non-biologic DMARDs without biologics11.018.61.71.6-1.7 Methotrexate (regardless of other non- biologic DMARDs use) 10.418.2 All other non-Methotrexate DMARDs alone or in combination 11.919.3 * HZ, Herpes Zoster; IR, Incidence Rate per 1,000 Person-Years; 95% CI, Confidence Interval

109 Herpes Zoster Incidence Rates by Vaccination Status and Medication Exposure Safety Endpoint: ≤ 42 Days Following Vaccination Unvaccinated # HZ# ParticipantsIR* Overall<117,7817.811.6 Drug Exposure Biologics (regardless of concomitant DMARDs or oral glucocorticoids) 0636-15.8 Anti-TNF therapies 0556-15.7 DMARDs (without biologics but regardless of oral glucocorticoids) <111,81714.613.8 Oral glucocorticoids alone <111,21521.217.1 *HZ, Herpes zoster; IR, HZ incidence rate per 1,000 person-Years

110 Herpes Zoster Incidence Rates by Vaccination Status and Medication Exposure Safety Endpoint: ≤ 42 Days Following Vaccination Unvaccinated # HZ# ParticipantsIR* Overall<117,7817.811.6 Drug Exposure Biologics (regardless of concomitant DMARDs or oral glucocorticoids) 0636-15.8 Anti-TNF therapies 0556-15.7 DMARDs (without biologics but regardless of oral glucocorticoids) <111,81714.613.8 Oral glucocorticoids alone <111,21521.217.1 *HZ, Herpes zoster; IR, HZ incidence rate per 1,000 person-Years

111 Herpes Zoster Incidence Rates by Vaccination Status and Medication Exposure Safety Endpoint: ≤ 42 Days Following Vaccination Unvaccinated Infections, n Vaccinated, n IR* Overall<117,7817.811.6 Drug Exposure Biologics (regardless of concomitant DMARDs or oral glucocorticoids) 0636-15.8 Anti-TNF therapies 0556-15.7 DMARDs (without biologics but regardless of oral glucocorticoids) <111,81714.613.8 Oral glucocorticoids alone <111,21521.217.1 *HZ, Herpes zoster; IR, incidence rate per 1,000 person-Years

112 Herpes Zoster Incidence Rates by Vaccination Status and Medication Exposure Effectiveness Endpoint: > 42 Days Following Vaccination Unvaccinated # HZ# ParticipantsIR* Overall 7899647.8 11.6 Drug Exposure Biologics (regardless of concomitant DMARDs or oral glucocorticoids) <118389.5 15.8 Anti-TNF therapies <1172511.0 15.7 DMARDs (without biologics but regardless of oral glucocorticoids) <1114194.9 13.8 Oral glucocorticoids alone 15105914.2 17.1 *HZ, Herpes zoster; IR, incidence rate per 1,000 person-years

113 Herpes Zoster Incidence Rates by Vaccination Status and Medication Exposure Effectiveness Endpoint: > 42 Days Following Vaccination Unvaccinated # HZ# ParticipantsHZ IR* Overall 7899647.8 11.6 Drug Exposure Biologics (regardless of concomitant DMARDs or oral glucocorticoids) <118389.5 15.8 Ant-TNF therapies <1172511.0 15.7 DMARDs (without biologics but regardless of oral glucocorticoids) <1114194.9 13.8 Oral glucocorticoids alone 15105914.2 17.1 *HZ, Herpes Zoster; IR, Incidence Rate per 1,000 Person-Years

114 Reduced Risk of Zoster Associated With Vaccination, Varying Case Definitions Outcome Definition Hazard Ratio* 95% CI Diagnosis code + anti-viral medications 0.690.56-0.86 Diagnosis code only0.720.71-0.84 *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)

115 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. SPOI and Influence of Stop Time 2 1.0 0.6 0.4 0.2 Adjusted OR (95% Cl) "On 28" "Off" 1.15 0.38 "Off 28" On/Off at Surgery "On" On/Off 28 Days Before Surgery

116 Treatment of PML Immune reconstitution: -ART for HIV (prolongs survival) -discontinuation of biologics -decreasing steroids, tacrolimus, etc. Cidofovir- No benefit over ART. Toxic. Mirtazipine- improvement in case reports Cytarabine- has helped 2 patients with dermatomyositis Mefloquine- has in vitro anti-JCV activity

117 Cancers Askling J et al. Ann Rheum Dis 2009;68:648–653. Askling J et al. Ann Rheum Dis. 2005 October; 64(10): 1421–1426. Wolfe F et al. Arthritis Rheum. 2007 Sep;56(9):2886-95 Relative risks Anti-TNF vs general population Anti-TNF vs anti- TNF-naive RA Lymphomas 2.721.35 Standardized Incidence Ratio vs. general population All RAAnti-TNF users All solid carcinomas1.050.9 Risk of Skin Cancer in TNF Users vs. Comparator RA Non-melanomaMelanoma Odds ratio (95% CI) for skin cancer1.5 (1.2-1.8)2.3 (0.9-5.4)

118 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)

119 Serious infections under anti-TNF treatment – Reasons for “conflicting” results Comparing results of the various data sources is difficult Differences in absolute & relative rates may result from: –Differences in patient populations (e.g. inclusion / exclusions, comorbidities) –Sites & seriousness of infection –Definition of an “infection” –Timing since starting / stopping anti-TNF –Concomitant therapies –Special situations (e.g. surgery)

120 Understanding Infection Risks: Site of Infection 9000 patients, followed Dec 2001-Sept 2005 Physician documented infection *Adjusted for age, sex, RA severity, extraarticular manifestations, steroids, diabetes, COPD/asthma, smoking Anti- TNF (n=7,664) Non-biologic (n=1,354) RR serious Infection * 1.03 (0.7-1.6) Ref. RR skin/soft tissue infection * 4.3 (1.1-17.2) Ref. Dixon WG et al. Arthritis and Rheum 2006; 54(8):2368-2376.

121 Understanding Infection Risks: Time since starting anti-TNF & DMARDs Propensity score adjusted rate ratio (95% CI) Drug exposure Short-term effects (1-90 days after treatment initiation) Longer-term effects (>90 days after treatment initiation) TNF α antagonists1.39 (0.40-4.83)0.90 (0.52-1.56) Glucocorticoids2.99 (1.60-5.60)1.20 (0.79-1.84) Cytotoxic DMARDs2.99 (1.18-7.52)0.95 (0.51-1.79) Non-cytotoxic DMARDs1.11 (0.50-2.50)1.51 (0.96-2.36) Schneeweiss, S. et al., Arthritis Rheum 2007;56:1754-64.

122 n=OperationsGroup A (n=1023)Group B (n=104)Group C (n=92) Number of Infections4568 Cumulative Incidence of Infection 4%5.8%8.7% Group A: Had not received anti-TNF Group B: Stopped anti-TNF at least 4 half lives preop: (adalimumab 64 days, etanercept 12 days, infliximab 36 days Group C: Continued anti-TNF preoperatively Authors conclusions:  Factors associated with postop infections are similar to other studies in patients not using anti-TNFs  Continued perioperative blockade of TNF does not seem to be a strong risk factor for postoperative infection Special Situations: What to do with Biologics around Time of Surgery? Den Broeder et. al. J Rheum 2007; 34: 689-95 Perioperative use of anti-TNF was not significantly associated with an increase in surgical site infection rates (OR 1.5, 95% CI 0.43–5.2)

123 Characteristics and Recommendations for Vaccination for Autoimmune Disease Patients Inactivated VirusLive Virus* PneumovaxX- InfluenzaXX H1N1 (swine flu)XX Zoster vaccine-X Hepatitis BX- HPV vaccine**X- Saag, et al. ACR Recommendations for the Use of Non-biologic and Biologic Therapies in RA. ACR 2008; 59(6):762-784. *** Curtis JR. Arthritis Res Ther. 2010;12(4):R144. * Live virus vaccines not recommended for patients on biologics ** recommended for women up to age 26 Pneumovax, yearly influenza given to 65***

124 124 Rates of GI Perforations for Patients on Biologics and DMARDs Drug Exposure GroupRate/1000 PYs (95% CI) Biologics with glucocorticoids1.87 (1.46–2.35) Biologics w/o glucocorticoids1.02 (0.80–1.29) Methotrexate with glucocorticoids2.24 (1.82–2.74) Methotrexate w/o glucocorticoids1.08 (0.86–1.35) Other DMARDs* with glucocorticoids3.03 (2.34–3.85) Other DMARDs* w/o glucocorticoids1.71 (1.34–2.16) Glucocorticoids w/o any DMARD or biologic2.86 (2.27–3.56) No DMARDs, biologics, or glucocorticoids1.68 (1.44–1.96) Total1.70 (1.58–1.83) 124 DMARD=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

125 125 Relative Risk of GI Perforation During Follow-up–Adjusted Results 125 Reference 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. 012341113151719 Urban Female Age 40-64 Age 65+ Baseline CCI NSAID No DMARD or glucocorticoid Other DMARDs w/o glucocorticoids Biologics w/o glucocorticoids Biologics w/ glucocorticoids Methotrexate w/ glucocorticoids Glucocorticoids w/o any DMARD Other DMARDs w/ glucocorticoids Diverticulosis w/o diverticulitis Diverticulitis Hazard Ratios With 95% Confidence Intervals Exposure On or After Index 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

126 RA Is an Independent Risk Factor for MI, Stroke Solomon DH et al. Ann Rheum Dis. 2006;65:1608-1612. 18-4950-64 65-74 75+ Incidence Rate (per 1000 person-years) Age Range (y) Patients With RA (n=25,385) Patients Without RA (n=252,976) 0 10 20 30 40 50 60 70

127 127 Changes in Lipids Associated with Tocilizumab (IL-6Ra) ACT 8 (n = 288) ACT 8 + DMARD (n = 1582) ACT 4 + MTX (n = 774) HDL (mg/dL)LDL (mg/dL) Mean Change From Baseline in 6-Month Controlled Period * From tocilizumab prescribing information (PI)

128 Increase in Total Cholesterol associated with Anti-TNF therapy n = 80 n = 45 n = 97 n = 10 n = 52 n = 32 n = 55 n = 19 n = 56 n = 69 n = 33 n = 50 n = 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

129 Greenberg JD. Ann Rheum Dis. 2011 Apr;70(4):576-82. CV Events HR TNF MTX 0 0.5 1.0 1.5 2.0 0.6 0.3 TNF Inhibitor Therapy in RA 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

130 * Both TNF and DMARD users had ‘failed’ MTX Solomon et. al., ACR 2011 annual meeting (unpublished data) Risk of MI in TNF-treated patients compared to new DMARD users*

131 Solomon et. al., ACR 2011 annual meeting (unpublished data) Subgroup Analyses for Risk of MI in new TNF- treated patients compared to new DMARD users

132 Putting It All Together: Applying Research Results to Clinical Care Communicating Risk

133 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 pain1347 (34.7%)1340 (34.6%) Pain in extremity453 (11.7%)430 (11.1%) Musculoskeletal pain297 (7.6%)291 (7.5%) Hypercholesterolemia280 (7.2%)236 (6.1%) Cystitis228 (5.9%)225 (5.8%) * As observed in pivotal 3 year trial

134 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

135 100 patients, Active Disease, on MTX           102030405060708090100

136 Likelihood of Achieving an Good Clinical Response, Remaining on MTX☻☻  ☻☻  ☻  ☻  ☻  ☻  ☻  ☻  ☻  ☻  102030405060708090100

137 Likelihood of Achieving a Good Clinical Response, Adding a Biologic☻☻☻☻☻  ☻☻☻☻☻  ☻☻☻☻☻  ☻☻☻☻☻  ☻☻☻☻☻  ☻☻☻☻☻  ☻☻☻☻☻  ☻☻☻☻☻  ☻☻☻☻☻  ☻☻☻☻  102030405060708090100

138 Likelihood of a Serious Bacterial Infection, Remaining on MTX           102030405060708090100

139 Likelihood of a Serious Bacterial Infection, After Adding a Biologic           102030405060708090100

140 Risk:Benefit Curve of Aggressive Therapy Severity of Comorbidities Need for Aggressive Rx increased toxicity +/- benefit limited toxicity + benefit (control of inflammation lowers risk) Risk of Therapy ……older age, disability,steroids, etc Serious adverse event Death

141 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

142 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

143 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

144 Issues Affecting Interpretation of Safety Data How data is expressed –Proportion of patients vs events/ patient-yrs Clinical trial vs registry vs administrative data Adjustment for disease severity –DAS, number DMARD failures, disability Adjustment for co-medication and co-morbidities –e.g. steroids, non-biologic DMARDs, diabetes, prior infection

145 German Biologic Registry Patients enrolled 5/2001-9/2003. Follow-up 9/2004 Physician-documented infections Listing et al., Arthritis Rheum 2005; 52(11):3403-3412 *Adjusted for probability of receiving biologic agent (# of DMARD failures, RA duration and severity, RF positivity), steroids, age, sex, diabetes, lung disease Etanercept (n=512) Infliximab (n=346) Non-biologic (n=601) RR Infection total * 2.3 (1.4-3.9) * 3.0 (1.8-5.1) Ref. RR Severe Infection 2.8 (1.4-5.9) * 2.2 (0.9-5.4) 2.7 (1.3-5.9) * 2.1 (0.8-5.5) Ref.

146 Least Specific Definition Most Specific Definition ICD-9 infection claims alone Physician- Assessed ‘Definite’ or ‘Empirically treated’ infection Physician Assessed ‘Definite’ infection Met criteria of pre-specified evidence based infection definition Persons classified as having an infection*, % (sensitivity) 100856635 Unadjusted HR of Infection in anti TNF-α users 1.1 (0.9-1.2) 1.1 (0.8 – 1.5) 1.4 (1.0 – 2.0) 1.6 (1.0 – 2.6) Adjusted HR of Infection in anti TNF-α users 1.6 (1.1- 2.4) 1.7 (1.2 – 2.6) 1.9 (1.3 – 2.8) 2.3 (1.2 – 4.3) * n = 217 suspected infections HR = hazard ratio Understanding Infection Risks: Defining an “Infection” Curtis J, et al. Arthritis Rheum. 2007;56:1125-1133

147 Understanding Infection Risks: Time since Starting anti-TNF Curtis J, et al. Arthritis Rheum. 2007;56:1125-1133.; Curtis JR, et. al. Arthritis Rheum 2007; 56: 4226-7. 0 1 2 3 4 5 6 ETN <6 Months ETN >6 Months INF <6 Months INF >6 Months Conclusion: Adjusted relative rate of infection associated with TNF inhibitor use = 1.9

148 Dixon et. al. Arthitis Rheum 2007; 56(9): 2896-2904. Relative Rate of Infection: Overall: 1.0 (0.7–1.6)

149 Serious Infection Rates EtanerceptInfliximabAdalimumab Pt Exposure (pt-yrs)833624584870 - TNF Antagonist - Placebo 4.0 * 4.0 3.0 4.0 2.0 * Incidence per 100 pt years Clinical Trials FDA AAC March 2003

150 BSRBR: Adj. IRR for serious infections compared to controls using different at-risk periods DMARDAll anti-TNF On drug (until 1st missed dose)IRR Rate per 100 PYs Referent3.91.25.6 On drug + 90 daysIRR Rate per 100 PYs Referent3.91.36.1 Ever treatedIRR Rate per 100 PYs Referent3.91.46.3 Understanding Infection Risks: Time since Stopping anti-TNF Dixon WG et al., Arthritis Rheum 2007;56:2896-2904

151 Infection Due To RA & Prednisone Risk factors for serious infection –RA severity, extra-articular RA, Diabetes, chronic lung disease, older age No increased risk with disease modifying anti-rheumatic drugs (DMARDS) Corticosteroids HR=1.56 (95% CI 1.22-2.01) Doran et al. Arthritis Rheum 2002

152 Prednisone & Infection in RA Patients 16,788 RA patients over 3.5 years (NDB for Rheumatic Diseases) Risk of hospitalization for pneumonia –Any prednisone use: Hazard Ratio (HR) 1.7 (1.5-2.0) –Dose effect noted < 5mg/day: HR 1.4 (1.1-1.6) 5-10mg/day: HR 2.1 (1.7-2.7) 10-15mg/day: HR 2.3 (1.6-3.2) Controlled for RA severity and length, other co-morbidities No increase with DMARD or anti-TNF therapy Wolfe et al., Arthritis Rheum 2006; 54(2):628-634.

153 Infectious Complications in Elective Surgery Post Anti-TNF Den Broeder AA, et al. EULAR 2005, Abstract OP0014. Design Retrospective cohort study - RA patients undergoing elective orthopedic surgery 696 operations in 474 pts Infections criteria (pus or positive culture PLUS symptoms) Group A had not received anti-TNF Group B stopped anti-TNF at least 4 half- lives preoperatively (adalimumab 64 days, etanercept 12 days, infliximab 36 days) Group C continued anti-TNF preoperatively

154 Anti-TNF Therapy And Risk Of Serious Post-operative Infection: Results From BSR Biologics Register (BSRBR) Methods: Pts who had received anti-TNF treatment @ any time prior to surgery were categorised in the following manner: 1.Group A: exposed to drug @ time of surgery or Group B: not exposed to drug @ the time of surgery 2.Group 1: Exposed to drug < 28 days preceding surgery or 3.Group 2: Exposed to drug > 28 days preceding surgery OR calculated (adjusted using logistic regression) Dixon, W G. et al. Abstract # OP0215 EULAR 2007 British Biologic Registry

155 Conclusion: Exposure to anti-TNF therapy at the time of surgery increases risk of SPOI compared to DMARD cohort Risk of infection nearly two-fold lower in pts not exposed to anti-TNF therapy < 28 days prior to surgery than those who were Anti-TNF Therapy And Risk Of Serious Post-operative Infection: Results From BSR Biologics Register (BSRBR) Gp 1: On drug during 28 days pre- surgery Gp 2: Off drug during 28 days pre-surgery Gp A: On drug at time of surgery Gp B: Off drug at time of surgery DMARD Group # of operations14212731357337179 SPOI n (%)103 (7.3)13 (4.8)96 (7.1)20 (5.9)7.3% Odds Ratio (95%CI)* Referent 0.63 (0.35, 1.14) Referent 0.81 (0.49, 1.35) 1.07 (0.58,1.96) Odds Ratio (95%CI)** Referent 0.56 (0.30, 1.04) Referent 0.75 (0.44, 1.28) 1.71 (0.60, 4.89) Dixon, W G. et al. Abstract # OP0215 EULAR 2007 British Biologic Registry *Sex & age adjusted; ** Fully adjusted Group A: Exposed to drug @ time of surgery Group B: Not exposed to drug @ the time of surgery Group 1: Exposed to drug < 28 days preceding surgery or Group 2: Exposed to drug > 28 days preceding surgery

156 Are Serious Bacterial Infections Increased in RA Patients Receiving TNFi? Complicated issue Appears to be increased early after starting anti-TNF therapy Data controversial Differences in populations, comorbidities, and methodologies likely account for seemingly different results For responders, may be offset by reduction in disease activity and tapering glucocorticoids Importance of a drug-related increased relative rate of infection largely related to patient factors No greater risk than with moderate dose glucocorticoid use Overall risk-benefit profile of anti-TNF therapy likely to be favourable for almost all patients Communicating Risk to Patients Remains Challenging


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