Ofatumumab for rheumatoid arthritis

  • Protocol
  • Intervention



This is the protocol for a review and there is no abstract. The objectives are as follows:

To assess the benefits and harms of ofatumumab in reducing disease activity, pain, and improving function in people with RA.


Description of the condition

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease with predominant joint involvement. RA affects the synovial lining of many joints and tendon sheaths resulting in irreversible joint damage and deformities (Lee 2001). It is associated with significant morbidity, mortality and impaired quality of life (Wong 2001; Health Canada 2003).

RA is a common auto-immune disorder with disease prevalence between 0.1% and 5% (Spector 1990; Peschken 1999). Peak age onset is between 50 and 75 years and women are affected 2 to 3 times more often than men (Spector 1990). The etiology of RA is multifactorial with role of environmental and genetic factors. The hallmark of disease is activation of immune cells and inflammation. There is involvement of T-cells, B-cells, macrophages and mast cells (Firestein 2003; Woolley 2003). These cells produce cytokines such as tumor necrosis factor (TNF)-alpha, Interferon-gamma (IFN- γ), Granulocyte macrophage-colony stimulating factor (GM-CSF), interleukin (IL)-1, 6, 13, 15 and 17, which have important role in inflammation and are therefore targets for therapy (McInnes 2007; McInnes 2011). There is also role of numerous other mediators of inflammation in disease pathogenesis.

The disease mainly affects joints and joint involvement is classically polyarticular and symmetric and may lead to destruction of joint structures leading to deformity. Other accompanying clinical symptoms are pain and morning stiffness. Since it is a systemic inflammatory condition, extraarticular manifestations are common and include anaemia, fatigue, cutaneous involvement (subcutaneous ("rheumatoid") nodules, ulcers and neutrophilic dermatosis), eye involvement (uveitis, scleritis, ulcerative keratitis), pleuropericarditis, myocarditis, lung parenchymal diseases, neuropathy, splenomegaly, Sjögren's syndrome, vasculitis, and renal disease (Akil 1995).

Description of the intervention

The general principles of treatment are directed towards the pain/symptomatic management, control of synovitis and prevention of further joint injury. The pharmacologic therapy includes non-biologic disease-modifying anti-rheumatic drugs (DMARDs), biologic agents and Nonsteroidal anti-inflammatory drugs (NSAIDs) and other anti-inflammatory agents as adjuncts (Saag 2008). The choice of treatment depends on level of disease activity, stage of therapy and patient preference. DMARDs improve disease activity and decrease the natural progression leading to significant clinical improvement. DMARDs are now the mainstay of therapy and include non-biologics and biologics. The non-biologic DMARDs including methotrexate (Suarez-Almazor 2000), hydroxychloroquine (Suarez-Almazor 2000a), sulfasalazine (Suarez-Almazor 2000b), and leflunomide (Osiri 2003), have shown to significantly improve all clinical outcomes and delay radiologic progression in well-conducted systematic reviews and meta-analyses. However, a significant proportion of RA patients do not respond to non-biologic DMARDs or are unable to tolerate them long term. Biologic DMARDs generally target cytokines or their receptors or cell surface molecules involved in RA pathogenesis and tend to work more quickly than non-biologic DMARDs (van Vollenhoven 2009). These include anti-cytokine therapies, such as the TNF-alpha inhibitors, etanercept (Lethaby 2013), infliximab (Blumenauer 2002), adalimumab (Navarro-Sarabia 2005), golimumab (Singh 2010), and certolizumab pegol (Ruiz 2011); the IL-1 receptor antagonist, anakinra (Mertens 2009); and the IL-6 receptor antagonist tocilizumab (Singh 2010a). Other biologics are T-cell co-stimulation blocker, abatacept (CTLA4-Ig) (Maxwell 2009), and the anti-CD20 B-cell depleting monoclonal antibodies, rituximab, ofatumumab and ocrelizumab. Newer therapies also include janus kinase inhibitors, tofacitinib (Feist 2013), which block the cytokine signalling involved in lymphocyte mediated immune responses in RA.

B-cell depleting antibodies were developed with the aim of depleting the B-cell clones responsible for producing the autoantibodies, thereby resulting in sustained remission (Edwards 2001). Anti-CD20 monoclonal antibodies have shown significant clinical improvement and improvement in disease activity alone and in combination with other DMARDs (Shaw 2003; Singh 2009). Rituximab, a B-cell depleting monoclonal anti-CD20 antibody, has also shown improvement in DMARDs and anti-TNF resistant RA (Cohen 2006; Jois 2007). Ofatumumab is another antibody of the same class which is approved for treatment of chronic lymphocytic leukemia (NCI 2013). Ofatumumab is a human Immunoglobulin G subclass 1 (IgG1) monoclonal antibody that specifically binds to the human CD20 antigen inducing potent B-cell lysis.

How the intervention might work

Ofatumumab is unique in its epitope which is more proximal and distinct from the epitope recognized by rituximab or by other anti-CD20 monoclonal antibodies (Teeling 2006). The proximity of this epitope probably accounts for the high efficiency of B-cell killing observed with ofatumumab in both in vitro and in vivo preclinical studies (Teeling 2004; Ostergaard 2010; Taylor 2011). The significant role of B-cells in RA and the uniqueness of ofatumumab's epitope resulting in more efficient killing than other B-cell deleting antibodies makes it ideal for use in RA (Ruuls 2008). It has demonstrated significant clinical efficacy compared to placebo in active RA patients (with inadequate response to methotrexate), in phase II and III clinical trials (Ostergaard 2010; Taylor 2011). A double-blind multi-center phase III trial also showed significant clinical improvement in ofatumumab treated patients compared to placebo (Taylor 2011). The common adverse effects reported were rash and urticaria. Other adverse effects include infusion reactions, pruritis, headache, nasopharyngitis, hypersensitivity and dyspnoea. The infections were more frequent in ofatumumab group however, serious infections were rare and comparable to placebo (Taylor 2011). A single-blind, phase I/II study comparing subcutaneous ofatumumab with placebo in RA showed that subcutaneous route also resulted in profound and prolonged B-cell depletion without significant adverse events (Kurrasch 2013).

Why it is important to do this review

The current drawbacks in use of biologics include the inconvenience of intravenous administration, the high costs and the adverse events, preventing their wide use as first-line therapy. However, if high quality evidence shows a significant improvement in symptoms and radiological progression without significantly increased risk of adverse events; it could be a new hope for patients with RA. So, there is a need for a critical appraisal of randomized controlled trials (RCTs) to assess ofatumumab in patients with RA. This Cochrane review including a rigorous assessment of the risk of bias, of the most up-to-date evidence will provide more definitive evidence regarding the efficacy and safety of ofatumumab in RA patients and will help clinicians make informed decisions on its use for treating patients with RA. This systematic review will also be helpful in generating an overview of biologics as treatment therapies in RA (Singh 2009; Singh 2011; Tugwell 2011).


To assess the benefits and harms of ofatumumab in reducing disease activity, pain, and improving function in people with RA.


Criteria for considering studies for this review

Types of studies

We will consider all RCTs comparing ofatumumab alone or in combination with any DMARD or biologic versus placebo or DMARDs or biologics alone or in combination with DMARDs, with minimum trial duration of three months. Corticosteroids will be accepted if patients were on stable doses and were randomly assigned to treatment with ofatumumab or to treatment without ofatumumab.

Types of participants

Adults 18 years or older, with RA meeting the 1987 American College of Rheumatology Classification (ACR) criteria for RA (Arnett 1988), or the 2010/2009 ACR criteria for classification of RA, whichever is used by the authors of the studies.

Types of interventions

Ofatumumab alone or in combination with any DMARD or biologic to placebo or DMARDs or biologics alone or in combination with DMARDs. There will be no restrictions with regard to the dosage.

Types of outcome measures

Major outcomes
  • ACR50, defined as 50% improvement in both tender and swollen joint counts and 50% improvement in three of the five following five variables: patient global assessment, physician global assessments, pain scores, Health Assessment Questionnaire (HAQ) score, and acute phase reactants (erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP)). We chose ACR50 as an outcome since clinical and statistical evidence shows that this is the preferred endpoint for contemporary RA clinical trials (Felson 1995)

  • Achieving a "good state"

  • Function measured by HAQ score or modified HAQ calculated as score changes (Fries 1980; Pincus 1983) and the proportion achieving minimally clinically important difference on HAQ ≤ 0.22 (Wells 1993)

  • Serious adverse effects

  • Quality of Life, measured by Short-Form-36 (SF-36)

  • Withdrawal due to adverse events used as a proxy measure of safety

Minor outcomes
  • ACR20 and ACR70 defined as 20% and 70% improvement in variables defined above under major outcome, respectively

  • Changes in either DAS, a composite index of tender and swollen joint counts, patient global assessment and ESR (van der Heijde 1993), or DAS28 score (when a 28 joint count is used as the index (Prevoo 1995))

  • Total number of withdrawals

  • Withdrawal due to lack of efficacy

  • Number and type of adverse effects

  • Death

Search methods for identification of studies

Electronic searches

We will search the following databases:

  1. Current issue of The Cochrane Library;

  2. Ovid MEDLINE(R) In-Process & Other Non-Indexed Citations and Ovid MEDLINE(R) 1946 to present;

  3. Ovid EMBASE Classic and EMBASE 1947 to present;

  4. Web of Science Conference Proceedings – 1996 to present;

  5. ClinicalTrials.gov (http://clinicaltrials.gov/);

  6. World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) search portal (http://apps.who.int/trialsearch/).

We will not apply language, year of publication or type of publication restrictions. The specific search strategy for MEDLINE is shown in Appendix 1 and will be adapted for other databases.

Searching other resources

We will search websites of the following regulatory agencies for reported adverse events using the terms 'rheumatoid arthritis', 'ofatumumab' and 'arzerra':

In addition, reference lists from comprehensive reviews and identified clinical trials will be searched for possible references not otherwise found. We will contact the pharmaceutical companies that manufacture ofatumumab (GlaxoSmithKline, Genmab, Lonza) for details of any unpublished data.

Data collection and analysis

Selection of studies

Two authors (VA, SK) will independently review the titles and abstracts of the studies identified from the literature search. We will obtain full-text articles for studies that clearly meet the selection criteria. If it is clear from the study title or abstract that the study does not meet the selection criteria, then it will be excluded. If it is unclear, then we will retrieve the full-text of the article and assess it against the selection criteria. We will consult a third author (MLO) in the case of disagreement during this process. Where there is insufficient published information in order to make a decision about inclusion, we will contact the authors of the trial for additional information. We will create a flow-chart of study selection as suggested by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (Liberati 2009).

Data extraction and management

Two authors (VA, SK) will independently extract data. Studies reported in non-English language journals will be translated before assessment. Where more than one publication of one study exists, we will group the reports together and use the publication with the most complete data in the analyses. Where relevant outcomes are only published in earlier versions this data will be used. Any discrepancy between published versions will be highlighted. The discrepancies will be resolved through discussion or, if required by consulting other review authors (MLO, JS). We will extract data from each included trial in a pre-designed data extraction form to document the following information: general study information: title, authors, study design, setting, follow-up, funding, number of patients randomized, number of patients analyzed, risk of bias; characteristics of participants: age, sex, disease duration, DMARD or biologic resistant disease, concurrent treatments; characteristics of intervention: dosages, route of administration, frequency, duration of treatment, withdrawals, drop-outs; characteristics of control: active or placebo; if active, then drug name, dosages, route of administration, frequency, duration of treatment, withdrawals, drop-outs and all outcomes assessed by the authors.

We will enter data into the Cochrane Collaboration's statistical software, Review Manager 2013, and check for accuracy. We will ensure accuracy by comparing data extracted independently by the two authors (VA, SK) and we will resolve any discrepancies by discussion or by consultation with the other review authors (MLO, JS). When information regarding any of the above is unclear, we will contact the authors of the trial.

Assessment of risk of bias in included studies

Two authors (VA, SK) will independently assess the risk of bias in included studies using the Cochrane Collaboration's tool for assessing risk of bias (Higgins 2011). Disagreements will be resolved by consensus or by discussion with a third author (MLO). We will contact study authors/investigators for further information when insufficient information is provided to determine the risk of bias. We will summarize the risk of bias assessment for every outcome included in the summary of findings tables within a study. We will assess studies for selection bias, performance bias, detection bias, attrition bias, reporting bias, and other biases as illustrated below:

  1. sequence generation;

  2. allocation sequence concealment;

  3. blinding of participants, personnel and outcome assessors for each outcome (for ACR50, DAS, radiographic progression);

  4. incomplete outcome data (for ACR50, withdrawals, adverse events);

  5. selective outcome reporting; and

  6. other potential sources of bias (i.e. baseline imbalance).

We will grade each important outcome as low, unclear, or high risk of bias, and will provide a quote from the study and justification for each judgement in the 'Risk of bias tables'. We will present findings of the final assessment for all included studies in 'Risk of bias' figures.

Measures of treatment effect

We will perform all analyses using Review Manager 2013. We will arrange data in two-by-two tables and synthesize data into a summary test statistic using a random-effects model as it provides more conservative estimates than a fixed-effect model. We will calculate risk ratios (RRs) and 95% confidence intervals (95% CI) for dichotomous outcomes. We will pool data using the mean difference (MD) if continuous data from two or more studies will be available for the same instrument of evaluation with the same units of measurement. Conversely, we will use the standardized mean difference (SMD) if the studies expressed the same variables through different instruments or different units of measurement with the same instrument.

Unit of analysis issues

We anticipate that the units of randomization and analysis in the included trials will be the individual patients. We will analyze treatment groups separately.

Dealing with missing data

We will attempt to obtain data that were measured but not reported from study authors. If there is a discrepancy in the number randomized and the number analyzed in each treatment group, the percentage lost to follow up will be calculated in each group and reported. We will make no assumptions about loss to follow up for continuous data and analyses will be based on those completing the trial. For dichotomous data, if drop-outs will exceed 10% for any trial, we will assign the worse outcome to those lost to follow-up. We will assess the impact of this assumption in a sensitivity analyses comparing worst outcome results with available case results (i.e. using only patients who completed the studies).

Assessment of heterogeneity

In the case of important clinical heterogeneity, we will not perform statistical analysis to pool the results. We will assess the extent of between-trial differences and the consistency of results of pooled-analysis by visual inspection of forest plots and by calculating the Chi2 statistic (Higgins 2003) (a significance level of less than 0.10 will be interpreted as evidence of heterogeneity). We will also use the I2 statistic to quantify inconsistency across studies. This statistic describes the percentage of the variability in effect estimates that is due to heterogeneity rather than sampling error or chance. We will interpret the I2 value as:

  • 0% to 25%: low level of heterogeneity;

  • 26-50: moderate heterogeneity;

  • 50% to 100%: substantial heterogeneity.

We will further explore reasons for statistical heterogeneity when I2 is > 50% (Higgins 2003).

Assessment of reporting biases

If a sufficient number of trials is available (i.e. more than 10), we will use funnel plots to assess potential publication bias. Asymmetry in the plot may be attributed to publication bias, poor methodology quality, or due to true heterogeneity. We will explore bias according to Egger's methods (Egger 1997).

Data synthesis

We will perform meta-analysis when it is reasonable to assume that studies are estimating the same underlying treatment effect (i.e. where trials are examining the same intervention, and the trials' populations and methods are judged to be sufficiently similar). We will use a random-effects model for meta-analysis.

Dichotomous data

If appropriate for pooled analysis, we will combine dichotomous data (i.e. the number of patients achieving more than 50% symptomatic improvement (ACR50), ACR20, ACR70, and those who withdraw due to adverse events and overall withdrawals) from the included studies and calculate the Mantel-Haenszel estimates on an intention-to-treat basis. We will calculate the proportion of patients who achieved response and report as a pooled RR and 95% CIs.

Continuous data

For continuous variables (i.e. changes in either DAS or DAS28, quality of life), we will calculate MDs or SMDs along with 95% CIs. When two or more studies present data derived from the same instrument of evaluation, and with the same units of measurement, we will present results using the MD. Conversely, when primary studies express the same variables through different instruments, and different units of measurement, we will present data as the SMD.

Subgroup analysis and investigation of heterogeneity

We will perform the following subgroup analyses in order to explore possible effect size differences in the main efficacy outcome, ACR50:

  1. intervention - different dosage (300 mg, 700 mg, or 1,000 mg or any other doses used in the trial), duration of treatment (intermediate duration (> 6 to 12 months) or long-duration (> one year));

  2. characteristics of participants (e.g. severity of baseline disease, age, sex; disease duration (categorized as early RA defined as duration of less than two years vs. established RA, duration 2 to 10 years vs. late RA defined as duration of greater than 10 years (Barlow 1999; Boers 2001)));

  3. previous DMARD vs. DMARD naive;

  4. concomitant methotrexate vs. no methotrexate;

  5. use in patients who have traditional-DMARD failure (most commonly methotrexate) vs. biologic-DMARD failure vs. none;

  6. ofatumumab alone vs. combination biologic therapy.

Sensitivity analysis

With adequate number of studies, we will perform sensitivity analyses to explore heterogeneity and the robustness of the results. We will consider the following factors for sensitivity analysis: random sequence generation and allocation concealment quality (low risk versus unclear risk or high risk); blinding of participants and personnel as well as outcome assessment (low risk versus unclear risk or high risk or unblinded studies); rates of participant withdrawal; studies with particularly large or small sample populations; and length of follow-up. Since data from abstracts are commonly inconsistent with data in published articles, we will also perform a sensitivity analysis excluding abstract publications.

'Summary of findings' table

The overall quality of evidence supporting the primary outcomes will be assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach and reported in a 'Summary of findings' table using the GRADE profiler software (GRADEPro 2008; Guyatt 2011a). The GRADE approach appraises the quality of a body of evidence based on the extent to which one can be confident that an estimate of effect or association reflects the item being assessed. Randomized trials start as high quality evidence, but may be downgraded due to: risk of bias (methodological quality), indirectness of evidence, unexplained heterogeneity, imprecision (sparse data) and publication bias. The overall quality of the evidence for each outcome will be determined after considering each of these factors and graded as:

  • high: further research is very unlikely to change confidence in the estimate of effect;

  • moderate: further research is likely to have an important impact on confidence in the estimate of effect and may change the estimate;

  • low: further research is very likely to have an important impact on confidence in the estimate of effect and is likely to change the estimate; and

  • very low: any estimate of effect is very uncertain.

We will use the principles of the GRADE system to assess the quality of the body of evidence associated with all major outcomes (ACR50, DAS (low or remission), HAQ for function, X-ray or appropriate imaging changes, serious adverse effects, health-related quality of life, withdrawals due to adverse events).


We thank the editors and editorial staff of the Cochrane Musculoskeletal Group for their comments and support; and external reviewers for their comments.


Appendix 1. MEDLINE search strategy

Database: Ovid MEDLINE(R) In-Process & Other Non-Indexed Citations and Ovid MEDLINE(R) <1946 to Present>

Search Strategy:


1 exp arthritis, rheumatoid/ (101620)

2 (arthritis adj2 rheumat$).tw. (80195)

3 (felty$ adj2 syndrome).tw. (671)

4 (caplan$ adj2 syndrome).tw. (112)

5 rheumatoid nodule.tw. (238)

6 (sjogren$ adj2 syndrome).tw. (11389)

7 still$ disease.tw. (1619)

8 or/1-7 (124768)

9 randomized controlled trial.pt. (387867)

10 controlled clinical trial.pt. (89725)

11 randomized.ab. (302919)

12 placebo.ab. (162931)

13 clinical trials as topic.sh. (174936)

14 randomly.ab. (214413)

15 trial.ti. (130602)

16 or/9-15 (928201)

17 exp animals/ not humans.sh. (4048630)

18 16 not 17 (858052)

19 Antibodies, Monoclonal/tu [Therapeutic Use] (43314)

20 (anti-CD20 adj3 B-cell adj3 depleting).tw. (24)

21 (anti CD20 adj3 B cell adj3 depleting).tw. (24)

22 ofatumumab.mp. (202)

23 Arzerra.tw. (5)

24 HuMax-CD20.mp. (10)

25 Antibodies, Monoclonal, Humanized/tu (4769)

26 or/19-25 (47432)

27 8 and 18 and 26 (948)

Contributions of authors

  • Draft the protocol: VA, SKG, MLO, JS

  • Study selection: VA, SKG

  • Extract data from studies: VA, SKG

  • Enter data into RevMan: VA, SKG

  • Carry out the analysis: VA, SKG

  • Interpret the analysis: VA, SKG, MLO, JS

  • Draft the final review: VA, SKG, MLO, JS

  • Disagreement resolution: MLO, JS

  • Update the review: VA, SKG, MLO, JS

Declarations of interest

VA, SKG, MLO: None Known.

JAS: Research grants from Takeda and Savient, and consultant fees from Savient, Takeda, Allergan and Regeneron.

Sources of support

Internal sources

  • None, Not specified.

External sources

  • None, Not specified.