Personalized biological treatment for rheumatoid arthritis: a systematic review with a focus on clinical applicability

RHEUMATOID ARTHRITIS: THE PROBLEMS OF REMISSION AND THERAPY RESISTANCE

Rheumatoid arthritis (RA) is an immunoinflammatory (autoimmune) rheumatic disease of unknown etiology, which is characterized by chronic erosive arthritis and systemic visceral organ damage that results in early disability and shorter patient survival. Despite RA treatment advances associated with the design of novel drugs and the improvement of treatment strategies to achieve remission in many patients, there are still many theoretical and clinical problems concerning both the definition of the concept of remission, its characteristics and types and approaches to the optimum policy of symptomatic and pathogenetic drug therapy at different stages of the disease, the use of which will be able to rapidly induce and maintain remission in the long-term. Further investigations are needed to study the nature of heterogeneity of pathogenetic mechanisms of RA and approaches to early diagnosis, to improve methods for monitoring disease activity and biomarkers for the efficiency of and resistance to therapy and, finally, to develop differentiation therapy, including those related to a search for new therapeutic targets.

Prealbumin, platelet factor 4 and S100A12 combination at baseline predicts good response to TNF alpha inhibitors in rheumatoid arthritis

OBJECTIVES

Tumour necrosis factor-alpha inhibitors (TNFi) are effective treatments for Rheumatoid Arthritis (RA). Responses to treatment are barely predictable. As these treatments are costly and may induce a number of side effects, we aimed at identifying a panel of protein biomarkers that could be used to predict clinical response to TNFi for RA patients.

METHODS

Baseline blood levels of C-reactive protein, platelet factor 4, apolipoprotein A1, prealbumin, α1-antitrypsin, haptoglobin, S100A8/A9 and S100A12 proteins in bDMARD naive patients at the time of TNFi treatment initiation were assessed in a multicentric prospective French cohort. Patients fulfilling good EULAR response at 6 months were considered as responders. Logistic regression was used to determine best biomarker set that could predict good clinical response to TNFi.

RESULTS

A combination of biomarkers (prealbumin, platelet factor 4 and S100A12) was identified and could predict response to TNFi in RA with sensitivity of 78%, specificity of 77%, positive predictive values (PPV) of 72%, negative predictive values (NPV) of 82%, positive likelihood ratio (LR+) of 3.35 and negative likelihood ratio (LR-) of 0.28. Lower levels of prealbumin and S100A12 and higher level of platelet factor 4 than the determined cutoff at baseline in RA patients are good predictors for response to TNFi treatment globally as well as to Infliximab, Etanercept and Adalimumab individually.

CONCLUSION

A multivariate model combining 3 biomarkers (prealbumin, platelet factor 4 and S100A12) accurately predicted response of RA patients to TNFi and has potential in a daily practice personalized treatment.

RNA sequencing to predict response to TNF-α inhibitors reveals possible mechanism for nonresponse in smokers

BACKGROUND

Several studies have employed microarray-based profiling to predict response to tumor necrosis factor-alpha inhibitors (TNFi) in rheumatoid arthritis (RA); yet efforts to validate these targets have failed to show predictive abilities acceptable for clinical practice.

METHODS

The eighty most extreme responders and nonresponders to TNFi therapy were selected from the observational BiOCURA cohort. RNA sequencing was performed on mRNA from peripheral blood mononuclear cells (PBMCs) collected before initiation of treatment. The expression of pathways as well as individual gene transcripts between responders and nonresponders was investigated. Promising targets were technically replicated and validated in n = 40 new patients using qPCR assays.

RESULTS

Before therapy initiation, nonresponders had lower expression of pathways related to interferon and cytokine signaling, while also showing higher levels of two genes, GPR15 and SEMA6B (p = 0.02). The two targets could be validated, however, additional analyses revealed that GPR15 and SEMA6B did not independently predict response, but were rather dose-dependent markers of smoking (p < 0.0001).

CONCLUSIONS

The study did not identify new transcripts ready to use in clinical practice, yet GPR15 and SEMA6B were recognized as candidate explanatory markers for the reduced treatment success in RA smokers.

Validation study of genetic biomarkers of response to TNF inhibitors in rheumatoid arthritis

Genetic biomarkers are sought to personalize treatment of patients with rheumatoid arthritis (RA), given their variable response to TNF inhibitors (TNFi). However, no genetic biomaker is yet sufficiently validated. Here, we report a validation study of 18 previously reported genetic biomarkers, including 11 from GWAS of response to TNFi. The validation was attempted in 581 patients with RA that had not been treated with biologic antirheumatic drugs previously. Their response to TNFi was evaluated at 3, 6 and 12 months in two ways: change in the DAS28 measure of disease activity, and according to the EULAR criteria for response to antirheumatic drugs. Association of these parameters with the genotypes, obtained by PCR amplification followed by single-base extension, was tested with regression analysis. These analyses were adjusted for baseline DAS28, sex, and the specific TNFi. However, none of the proposed biomarkers was validated, as none showed association with response to TNFi in our study, even at the time of assessment and with the outcome that showed the most significant result in previous studies. These negative results are notable because this was the first independent validation study for 12 of the biomarkers, and because they indicate that prudence is needed in the interpretation of the proposed biomarkers of response to TNFi even when they are supported by very low p values. The results also emphasize the requirement of independent replication for validation, and the need to search protocols that could increase reproducibility of the biomarkers of response to TNFi.

Predictive value of serum calprotectin (S100A8/A9) for clinical response after starting or tapering anti-TNF treatment in patients with rheumatoid arthritis

Objectives

Calprotectin (S100A8/A9) has been correlated with disease activity in rheumatoid arthritis (RA). The aim of this study was to investigate the predictive value of serum calprotectin for clinical response after starting and tapering anti-tumour necrosis factor treatment in RA.

Methods

Serum samples and clinical outcomes were derived from two longitudinal RA studies.

At baseline (starting or tapering of adalimumab or etanercept), calprotectin levels were determined by ELISA. In the Biologic Individual Optimised Treatment Outcome Prediction (BIO-TOP) study, treatment effect was assessed after 6 months using the European League Against Rheumatism (EULAR) response criteria. In the Dose Reduction Strategies of Subcutaneous TNF Inhibitors (DRESS) study, patients were classified at 18 months as being successfully dose reduced, discontinued or not able to reduce the dose. Area under the receiver operating characteristic curves (AUC) were generated to evaluate the predictive value of calprotectin and logistic prediction models were created to assess its added value.

Results

In the BIO-TOP study, calprotectin levels were higher in responders (n=50: 985 ng/mL (p25–p75: 558–1417)) compared with non-responders (n=75: 645 ng/mL (p25–p75: 415–973), p=0.04).

AUC for predicting EULAR good response was 0.61 (95% CI 0.50 to 0.71). The prediction model with calprotectin (AUC 0.77, 95% CI 0.68 to 0.85) performed similarly to the baseline model (AUC 0.74, 95% CI 0.65 to 0.82, p=0.29). In the DRESS study, calprotectin levels were similar between the three groups (n=47; n=19; n=36) and calprotectin was not predictive for clinical response after tapering.

Conclusions

Serum calprotectin has some predictive value for clinical response after starting anti-TNF treatment, although it has no added value to other clinical factors. In patients with low disease activity, serum calprotectin is not predictive for clinical response after tapering anti-TNF treatment.

Trial registration number

NTR4647 (BIO-TOP study) and NTR3216 (DRESS study); Pre-results.

Immunohistochemical Analysis of Inflammatory Rheumatoid Synovial Tissues Using Anti-Human Podoplanin Monoclonal Antibody Panel

Podoplanin (PDPN) is a transmembrane sialoglycoprotein, which is expressed in several normal tissues and malignant tumors. Although PDPN expression in rheumatoid arthritis (RA) has been reported, the role of PDPN in RA and other arthritic conditions has not been fully elucidated. In this study, we examined PDPN expression in inflammatory synovial tissues using an anti-human PDPN (hPDPN) monoclonal antibody (mAb) panel to select the most useful one for evaluation of synovitis. Synovial tissue samples were obtained from 11 RA patients and 9 osteoarthritis (OA) patients undergoing joint surgery. PDPN-positive cells were immunostained by a panel of PDPN mAbs (NZ-1, LpMab-3, LpMab-7, LpMab-10, LpMab-12, LpMab-13, and LpMab-17), followed by cell grading of inflammation and cell counting of PDPN-positivity by a quantitative analyzer. Immunohistochemistry showed that PDPN was markedly expressed in both macrophage-like type A and fibroblast-like type B lining cells of the hyperplastic synovial lining cell layer, and macrophages and fibroblasts in the stroma of RA. Among anti-PDPN mAbs, LpMab-12 showed the highest score. In inflammatory OA synovium, PDPN expression was also detectable. Although LpMab-12 also showed the highest score in OA, the difference was not statistically significant. The inflammatory synovitis score of RA was significantly higher than that of OA. PDPN was expressed in inflammatory lining cells and sublining stroma of RA and OA synovium. In the seven anti-hPDPN antibodies examined, LpMab-12 was the most stainable antibody for PDPN in RA synovitis. Thus, LpMab-12 for PDPN has a possible and promising specific biomarker for evaluating synovitis in RA and inflammatory OA.

Predictive and prognostic factors influencing outcomes of rituximab therapy in systemic lupus erythematosus (SLE): A systematic review

BACKGROUND

The clinical outcomes following rituximab (RTX) treatment in patients with systemic lupus erythematosus (SLE) is highly variable. We aimed to identify predictive and prognostic factors associated with RTX therapy outcomes in patients with SLE.

METHODS

Studies in adults and paediatric patients with SLE were included. We included randomized clinical trials (RCTs) for predictors of differential treatment effect and cohort studies for potential prognostic factors in patients treated with RTX (global clinical, cutaneous and renal either response or relapse, and side effects). Methodological quality was assessed using Cochrane Collaboration Risk of Bias tool and the Quality In Prognosis Studies Tool (QUIPS) for RCTs and cohort studies, respectively. The quality of subgroup analyses testing predictors of differential treatment response was also evaluated. A best evidence synthesis was performed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework.

RESULTS

Sixteen articles were included (3 from 2 RCTs and 13 from 6 cohort studies). The overall quality of evidence (QoE) was low to very low (GRADE framework). QoE for predictive factors based on RCTs analysing sociodemographic variables, was rated very low due to the lack of interaction tests, limited power of subgroup analyses, study limitations, and imprecisions. Disease-related factors including clinical phenotype and severity, baseline anti-ENA antibodies and anti-Ro antibodies, interleukin (IL) 2/21 single nucleotide polymorphism (SNP), as well as post-RTX complete B-cell depletion and earlier B-cell repopulation showed some evidence for prognostic value, but were rated low to very low QoE because of early phase of investigation (exploratory analysis), insufficient adjustment for confounding in most studies, high risk of bias, inconsistency, and imprecisions.

CONCLUSIONS

To date, studies addressing prognostic factors are hypothesis generating and cannot be used to make any specific recommendations for routine clinical practice. A number of potential predictors/prognostic factors were identified, which require to be validated as being specific for response to RTX therapy and to enable more personalised use of this agent.

Little Evidence for Usefulness of Biomarkers for Predicting Successful Dose Reduction or Discontinuation of a Biologic Agent in Rheumatoid Arthritis: A Systematic Review

OBJECTIVE

To systematically review studies addressing prediction of successful dose reduction or discontinuation of a biologic agent in rheumatoid arthritis (RA).

METHODS

PubMed, Embase, and Cochrane Library databases were searched for studies that examined the predictive value of biomarkers for successful dose reduction or discontinuation of a biologic agent in RA. Two reviewers independently selected studies, and extracted data and assessed the risk of bias. A biomarker was classified as a "potential predictor" if the univariate association was either strong (odds ratio or hazard ratio >2.0 or <0.5) or statistically significant. For biomarkers that were studied multiple times, qualitative best-evidence synthesis was performed separately for the prediction of successful dose reduction and discontinuation. Biomarkers that were defined in ≥75% of the studies as potential predictors were regarded as "predictor" for the purposes of our study.

RESULTS

Of 3,029 nonduplicate articles initially searched, 16 articles regarding 15 cohorts were included in the present study. Overall, 17 biomarkers were studied multiple times for the prediction of successful dose reduction, and 33 for the prediction of successful discontinuation of a biologic agent. Three predictors were identified: higher adalimumab trough level for successful dose reduction and lower Sharp/van der Heijde erosion score and shorter symptom duration at the start of a biologic agent for successful discontinuation.

CONCLUSION

The predictive value of a wide variety of biomarkers for successful dose reduction or discontinuation of biologic treatment in RA has been investigated. We identified only 3 biomarkers as predictors, in just 2 studies. The strength of the evidence is limited by the low quality of the included studies and the likelihood of reporting bias and multiple testing.

Clinical utility of therapeutic drug monitoring in biological disease modifying anti-rheumatic drug treatment of rheumatic disorders: a systematic narrative review

INTRODUCTION

Biological Disease Modifying Anti-Rheumatic Drugs (bDMARDs) have improved the treatment outcomes of inflammatory rheumatic diseases including Rheumatoid Arthritis and spondyloarthropathies. Inter-individual variation exists in (maintenance of) response to bDMARDs. Therapeutic Drug Monitoring (TDM) of bDMARDs could potentially help in optimizing treatment for the individual patient. Areas covered: Evidence of clinical utility of TDM in bDMARD treatment is reviewed. Different clinical scenarios will be discussed, including: prediction of response after start of treatment, prediction of response to a next bDMARD in case of treatment failure of the first, prediction of successful dose reduction or discontinuation in case of low disease activity, prediction of response to dose-escalation in case of active disease and prediction of response to bDMARD in case of flare in disease activity. Expert opinion: The limited available evidence does often not report important outcomes for diagnostic studies, such as sensitivity and specificity. In most clinical relevant scenarios, predictive value of serum (anti-) drug levels is absent, therefore the use of TDM of bDMARDs cannot be advocated. Well-designed prospective studies should be done to further investigate the promising scenarios to determine the place of TDM in clinical practice.

Proteomics to predict the response to tumour necrosis factor-α inhibitors in rheumatoid arthritis using a supervised cluster-analysis based protein score

OBJECTIVE

In rheumatoid arthritis (RA), it is of major importance to identify non-responders to tumour necrosis factor-α inhibitors (TNFi) before starting treatment, to prevent a delay in effective treatment. We developed a protein score for the response to TNFi treatment in RA and investigated its predictive value.

METHOD

In RA patients eligible for biological treatment included in the BiOCURA registry, 53 inflammatory proteins were measured using xMAP® technology. A supervised cluster analysis method, partial least squares (PLS), was used to select the best combination of proteins. Using logistic regression, a predictive model containing readily available clinical parameters was developed and the potential of this model with and without the protein score to predict European League Against Rheumatism (EULAR) response was assessed using the area under the receiving operating characteristics curve (AUC-ROC) and the net reclassification index (NRI).

RESULTS

For the development step (n = 65 patient), PLS revealed 12 important proteins: CCL3 (macrophage inflammatory protein, MIP1a), CCL17 (thymus and activation-regulated chemokine), CCL19 (MIP3b), CCL22 (macrophage-derived chemokine), interleukin-4 (IL-4), IL-6, IL-7, IL-15, soluble cluster of differentiation 14 (sCD14), sCD74 (macrophage migration inhibitory factor), soluble IL-1 receptor I, and soluble tumour necrosis factor receptor II. The protein score scarcely improved the AUC-ROC (0.72 to 0.77) and the ability to improve classification and reclassification (NRI = 0.05). In validation (n = 185), the model including protein score did not improve the AUC-ROC (0.71 to 0.67) or the reclassification (NRI = -0.11).

CONCLUSION

No proteomic predictors were identified that were more suitable than clinical parameters in distinguishing TNFi non-responders from responders before the start of treatment. As the results of previous studies and this study are disparate, we currently have no proteomic predictors for the response to TNFi.

Polymorphisms in STAT4, PTPN2, PSORS1C1 and TRAF3IP2 Genes Are Associated with the Response to TNF Inhibitors in Patients with Rheumatoid Arthritis

Objective

Rheumatoid Arthritis (RA) is a progressive autoimmune disease characterized by chronic joint inflammation and structural damage. Remission or at least low disease activity (LDA) represent potentially desirable goals of RA treatment. Single nucleotide polymorphisms (SNPs) in several genes might be useful for prediction of response to therapy. We aimed at exploring 4 SNPs in candidate genes (STAT4, PTPN2, PSORS1C1 and TRAF3IP2) in order to investigate their potential role in the response to therapy with tumor necrosis factor inhibitors (TNF-i) in RA patients.

Methods

In 171 RA patients we investigated the following SNPs: rs7574865 (STAT4), rs2233945 (PSORS1C1), rs7234029 (PTPN2) and rs33980500 (TRAF3IP2). Remission, LDA, and EULAR response were registered at 6 months and 2 years after initiation of first line TNF-i [Adalimumab (ADA) and Etanercept (ETN)].

Results

STAT4 variant allele was associated with the absence of a good/moderate EULAR response at 2 years of treatment in the whole RA group and in ETN treated patients. The PTPN2 SNP was associated with no good/moderate EULAR response at 6 months in ADA treated patients. Patients carrying PSORS1C1 variant allele did not reach LDA at 6 months in both the whole RA group and ETN treated patients. TRAF3IP2 variant allele was associated with the lack of LDA and remission achievement at 6 months in all RA cohort while an association with no EULAR response at 2 years of treatment occurred only in ETN treated patients.

Conclusions

For the first time, we reported that SNPs in STAT4, PTPN2, PSORS1C1, and TRAF3IP2 are associated with response to TNF-i treatment in RA patients; however, these findings should be validated in a larger population.

BIOSIMILARS IN RHEUMATOLOGY

One of the most striking achievements in the pharmacotherapy of rheumatoid arthritis (RA) and other human immunoinflammatory diseases (IIDs) late in the 20th century is associated with the design of a fundamentally new group of medications that received the name biological agents (BAs). However, the introduction of innovative BAs into clinical practice not only could enhance the efficiency of therapy and improve prognosis in patients with the most severe forms of IIDs, but has also led to a dramatic increase in the cost of treatment. Progress in the design of biosimilars is related to many factors, including expiry of the term of patents for many brand-name BAs, such as infliximab, adalimumab, etanercept, and rituximab. Over the last 5 years, a large number of biosimilars have been designed and their design is being continued. The European Medicines Agency, the World Health Organization, and the U.S. Food and Drug Administration have developed a regulatory framework and general requirements for the manufacture and characteristics of biosimilars. Federal Law No. 429-FZ «On Amendments to the Federal Law "On Circulation of Medicines"» dated December 22, 2014 defines a biosimilar drug as a biological medicine product that is similar to the parameters of quality, efficiency, and safety with the reference biologic drug in the same formulation, and the same route of administration. The review presents an update on the requirements for biosimilars used to treat IIDs, their interchangeability and promises in further clinical application. Particular emphasis is laid on the chimeric monoclonal antibodies to tumor necrosis factor-α infliximab biosimilar Flammegis (Egis Pharmaceuticals, Hungary), that has been registered in Russia for the treatment of RA, ankylosing spondylitis and other IIDs, as well as the chimeric monoclonal antibodies to B-lymphocytes rituximab biosimilar Acellbia in a phase III clinical trial that is in the closing stage. Preliminary provisions and recommendations of the All-Russian public organization «Association of Rheumatologists of Russia» concerning the place of biosimilars in rheumatology are formulated.

Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis

In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA) respond insufficiently to TNF-α inhibitors (TNFis). The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients’ response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124). The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI). The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28), erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP). Out of 139 metabolites, the best-performing predictors were sn1-LPC(18:3-ω3/ω6), sn1-LPC(15:0), ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01). The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23) and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05). Our study established an accurate prediction model for response to TNFi therapy, containing metabolites and clinical parameters. Associations between metabolites and disease activity may help elucidate additional pathologic mechanisms behind RA.

Can baseline serum microRNAs predict response to TNF-alpha inhibitors in rheumatoid arthritis?

BACKGROUND

In rheumatoid arthritis, prediction of response to TNF-alpha inhibitor (TNFi) treatment would be of clinical value. This study aims to discover miRNAs that predict response and aims to replicate results of two previous studies addressing this topic.

METHODS

From the observational BiOCURA cohort, 40 adalimumab- (ADA) and 40 etanercept- (ETN) treated patients were selected to enter the discovery cohort and baseline serum profiling on 758 miRNAs was performed. The added value of univariately selected miRNAs (p < 0.05) over clinical parameters in prediction of response was determined by means of the area under the receiver operating characteristic curve (AUC-ROC). Validation was performed by TaqMan single qPCR assays in 40 new patients.

RESULTS

Expression of miR-99a and miR-143 predicted response to ADA, and miR-23a and miR-197 predicted response to ETN. The addition of miRNAs increased the AUC-ROC of a model containing only clinical parameters for ADA (0.75 to 0.97) and ETN (0.68 to 0.78). In validation, none of the selected miRNAs significantly predicted response. miR-23a was the only overlapping miRNA compared to the two previous studies, however inversely related with response in one of these studies. The reasons for the inability to replicate previously proposed miRNAs predicting response to TNFi and replicate those from the discovery cohort were investigated and discussed.

CONCLUSIONS

To date, no miRNA consistently predicting response to TNFi therapy in RA has been identified. Future studies on this topic should meet a minimum of standards in design that are addressed in this study, in order to increase the reproducibility.

Schistosoma japonicum cystatin attenuates murine collagen-induced arthritis

Recombinant SjCystatin (rSjCystatin), a recombinant protein of Schistosoma japonicum cystatin, has been reported to have an effect on immunoregulation mediated by IL-10 induction. Rheumatoid arthritis (RA) is a common autoimmune inflammatory arthropathy, and recombinant immune-modulating drugs for RA treatment are under development. We aimed to study the putative immune regulation of rSjCystatin and its prophylactic/therapeutic effects on murine collagen-induced arthritis (CIA). CIA was induced in DBA/1 mice by inoculation with bovine collagen II (CII). rSjCystatin was administered prior or post development of CIA. The severity of CIA was assessed using established clinical and histopathological scoring systems. The incidence was also determined. The CII-specific antibodies in sera and cytokines in splenocyte culture supernatants were measured by ELISA. Th1/Th2/Th17 cells and Tregs development in splenocytes were monitored by flow cytometry. The inflammatory mediators in the diseased joint were semiquantitated by qPCR. Prophylactic injection of rSjCystatin attenuated paw clinical scores, incidence, and histopathology scores of joints in CIA mice. The arthritis-alleviative effects were closely associated with the augmentation of IL-4, IL-10, and collagen-specific IgG1, and with the distinct reduction of IFN-γ, collagen-specific IgG2a, and the marked decrease of proinflammatory cytokines IL-6, IL-17, and TNF-α and RANKL. The data indicate that rSjCystatin may prevent cartilage destruction and inflammation of joints in CIA mice. The effects are related to the inhibitory modulation of Th1 and Th17 and upregulation of Tregs and Th2 via a shift of cytokines profiling from Th1 to Th2 response.