Prodromal signs and symptoms of serious infections with tocilizumab treatment for rheumatoid arthritis: Text mining of the Japanese postmarketing adverse event-reporting database

Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives

Autoimmune rheumatic diseases (ARD) present a significant global health challenge characterized by a rising prevalence. These highly heterogeneous diseases involve complex pathophysiological mechanisms, leading to variable treatment efficacies across individuals. This variability underscores the need for personalized and precise treatment strategies. Traditionally, clinical practices have depended on empirical treatment selection, which often results in delays in effective disease management and can cause irreversible damage to multiple organs. Such delays significantly affect patient quality of life and prognosis. Artificial intelligence (AI) has recently emerged as a transformative tool in rheumatology, offering new insights and methodologies. Current research explores AI's capabilities in diagnosing diseases, stratifying risks, assessing prognoses, and predicting treatment responses in ARD. These developments in AI offer the potential for more precise and targeted treatment strategies, fostering optimism for enhanced patient outcomes. This paper critically reviews the latest AI advancements for predicting treatment responses in ARD, highlights the current state of the art, identifies ongoing challenges, and proposes directions for future research. By capitalizing on AI's capabilities, researchers and clinicians are poised to develop more personalized and effective interventions, improving care and outcomes for patients with ARD.

Incidence of serious infections in the working-age Japanese adult population with rheumatoid arthritis treated with tumor necrosis factor-α inhibitors and interleukin-6 inhibitors: A nationwide retrospective cohort study

AIM

This retrospective cohort study aimed to compare the risk of serious infections in patients with rheumatoid arthritis (RA) treated with tumor necrosis factor-α inhibitors (TNFαi) and interleukin-6 inhibitors (IL-6i), with no prior use of biological disease-modifying antirheumatic drugs (bDMARDs).

METHODS

We employed the nationwide insurance claims database encompassing the years 2005 to 2018 in Japan. The inclusion criteria specified patients who were prescribed any type of bDMARDs, including TNFαi and IL-6i. The following exclusion criteria were applied: missing prescription dates, RA not diagnosed, below 16 years of age, bDMARDs prescribed within 6 months of registration, RA diagnosed post-bDMARDs prescription, and incidence of serious infections within 2 weeks before bDMARDs therapy. We applied stabilized inverse probability weights and utilized a Cox regression model to estimate the risk of serious infections associated with TNFαi and IL-6i.

RESULTS

The cohort of 2493 patients with RA was categorized into a TNFαi group and an IL-6i group of 2018 and 475 participants, respectively. The median follow-up duration (interquartile range) was 347 (147-820) days in the TNFαi group and 369 (149-838) days in the IL-6i group. In the inverse probability-weighted cohort, the incidence rates (95% confidence interval) of serious infections were 2.13 (1.65-2.71) and 3.25 (2.15-4.69) per 100 person-years for the TNFαi and IL-6i groups, respectively. The hazard ratio (95% confidence interval) comparing the TNFαi group to the IL-6i group was 0.66 (0.36-1.20, p = 0.168).

DISCUSSION

The results underscore the lack of evidence to preferentially favor either TNFαi or IL-6i as later-line therapy in the management of bDMARDs-naive RA to mitigate the risk of serious infections.

Understanding the role and adoption of artificial intelligence techniques in rheumatology research: An in-depth review of the literature

The major and upward trend in the number of published research related to rheumatic and musculoskeletal diseases, in which artificial intelligence plays a key role, has exhibited the interest of rheumatology researchers in using these techniques to answer their research questions. In this review, we analyse the original research articles that combine both worlds in a five- year period (2017-2021). In contrast to other published papers on the same topic, we first studied the review and recommendation articles that were published during that period, including up to October 2022, as well as the publication trends. Secondly, we review the published research articles and classify them into one of the following categories: disease identification and prediction, disease classification, patient stratification and disease subtype identification, disease progression and activity, treatment response, and predictors of outcomes. Thirdly, we provide a table with illustrative studies in which artificial intelligence techniques have played a central role in more than twenty rheumatic and musculoskeletal diseases. Finally, the findings of the research articles, in terms of disease and/or data science techniques employed, are highlighted in a discussion. Therefore, the present review aims to characterise how researchers are applying data science techniques in the rheumatology medical field. The most immediate conclusions that can be drawn from this work are: multiple and novel data science techniques have been used in a wide range of rheumatic and musculoskeletal diseases including rare diseases; the sample size and the data type used are heterogeneous, and new technical approaches are expected to arrive in the short-middle term.

The Effect of Tocilizumab on Inflammatory Markers in Patients Hospitalized with Serious Infections. Case Series and Review of Literature

Background: The human anti-IL-6 receptor antibody tocilizumab (TCZ) has been approved for the treatment of rheumatoid arthritis (RA) and giant cell arteritis (GCA). It is observed that CRP levels drop quickly after starting TCZ treatment. This may lead to misinterpretation of laboratory results when accessing the patient with infectious disease while on TCZ. We conducted this study to report cases treated with tocilizumab who developed serious infections with special reference to levels of CRP and to review the literature on the effect of tocilizumab on acute phase response (APR) during infections. Methods: The files of RA and GCA patients hospitalized in the Tel Aviv medical center between 2009–2019 were reviewed. Cases of patients with RA and GCA treated with tocilizumab who were hospitalized due to severe infections were reviewed with special emphasis on the duration of treatment, type of infection, and APR. Results: We identified nine admissions. Seven patients were treated with tocilizumab for RA, two for GCA. The diagnosis was pneumonia in three cases, osteomyelitis in one, cellulitis in one, endocarditis due to Whipple disease in one, abscess of cervix uteri in one, meningitis in one, and perforated diverticulitis in one. The mean CRP levels on admission were 4.75 mg/L (normal range, up to 5 mg/L). All cases were diagnosed correctly on admission. Conclusions: CRP levels may not correctly reflect the severity of infectious diseases during tocilizumab treatment. Increased awareness of the masking effect of tocilizumab on the APR during infection is needed in order to avoid a delay in the diagnosis.

Pharmacovigilance of Biopharmaceuticals in Rheumatic Diseases, Adverse Events, Evolution, and Perspective: An Overview

Since we have gained an understanding of the immunological pathophysiology of rheumatic diseases such as rheumatoid arthritis and systemic lupus erythematosus, treatment based on biological drugs has become a fundamental axis. These therapies are oriented towards the regulation of cytokines such as tumour necrosis factor-alpha (TNF-α), interleukin (IL)-6, IL-1, and the modulation of cell-mediated immunity (B cells and T cells) by anti CD20 or anti CTAL-4 agents, and can increase the risk of associated infections or adverse events (AE). In this context, the entry of biotherapeutics represented a challenge for pharmacovigilance, risk management and approval by the main global regulatory agencies regarding biosimilars, where efficacy and safety are based on comparability exercises without being an exact copy in terms of molecular structure. The objective of this review is divided into three fundamental aspects: (i) to illustrate the evolution and focus of pharmacovigilance at the biopharmaceutical level, (ii) to describe the different approved recommendations of biopharmaceuticals (biological and biosimilars) and their use in rheumatic diseases (RDs) such as rheumatoid arthritis (RA), juvenile idiopathic arthritis (JIA), psoriatic arthritis (PsA), ankylosing spondylitis (AS), systemic lupus erythematosus (SLE) and other less frequent RD like cryopyrin-associated autoinflammatory syndromes (CAPS), and (iii) to identify the main AE reported in the post-marketing phase of RD biopharmaceuticals.

Influence of prior antibiotic use on risk of rheumatoid arthritis: case control study in general practice

Objectives

To test the hypothesis that prior antibiotics influences the risk of developing RA.

Methods

A case–control study was conducted over 15 years using the UK’s Royal College of General Practitioners Research and Surveillance Centre database. The frequency and type of antibiotic prescription for patients who subsequently developed RA were compared with antibiotic prescriptions in a control group of patients who remained free of RA. Cases, defined as patients with a new diagnosis of RA made between 2006 and 2018, were matched with up to four RA-free controls on practice, age, gender and date of diagnosis. Exposure was measured by the number and type of prescriptions for antibiotics prior to the RA diagnosis or to the index date in controls.

Results

A total of 8482 patients with a new diagnosis of RA between 2006 and 2018 were compared with 22 661 controls. There was a higher likelihood of an RA diagnosis after antibiotic prescriptions within 1 year, 5 years and ever with a strong dose–response. Patients receiving >10 antibiotics in a 5 year period were more than twice as likely to receive an RA diagnosis as controls [adjusted odds ratio 2.65 (CI 2.40, 2.93)].

Conclusion

Exposure to antibiotics prior to the diagnosis was a significant risk factor for RA. This could reflect an immunological response to a compromised microbiome. Alternatively, patients with pre-symptomatic or early undiagnosed RA may have been more likely to present to their general practitioner with infections due to an unrecognized effect of RA.

IL-6 inhibitor for the treatment of rheumatoid arthritis: A comprehensive review

Tocilizumab (TCZ) is an interleukin-6 (IL-6) inhibitor used for the treatment of rheumatoid arthritis (RA). It was developed in 2008, and its effectiveness is supported by evidence from all over the world based on its first decade of use. Although the overall efficacy and safety profiles of TCZ are similar to those of tumor necrosis factor (TNF) inhibitors, TCZ displays certain differences. The most notable advantage of TCZ is its usefulness as a monotherapy. Additionally, TCZ is favorable in the improvement of systemic inflammatory symptoms such as anemia and fatigue. The low immunogenicity of TCZ contributes favorably to long-term drug retention. Due to frequent relapse after TCZ cessation, TCZ use should be tapered beyond remission. During TCZ therapy, C-reactive protein (CRP) is unable to recognize disease activity and the severity of infection. The most common adverse events (AEs) are infection and abnormalities in laboratory findings including dyslipidemia, neutropenia, thrombocytopenia, and abnormality of liver enzymes. TCZ obscures the symptoms of infection. Therefore, stealth infections without obvious CRP elevation can sometimes cause severe damage to patients. Lower intestinal perforation is an uncommon but serious AE in TCZ therapy. Further clinical investigations will continue to refine the IL-6 inhibitory strategy.