Therapeutic drug monitoring of biopharmaceuticals in inflammatory rheumatic and musculoskeletal disease: a systematic literature review informing EULAR points to consider

The objectives of this review were to collect and summarise evidence on therapeutic drug monitoring (TDM) of biopharmaceuticals in inflammatory rheumatic and musculoskeletal diseases and to inform the EULAR Task Force for the formulation of evidence-based points to consider. A systematic literature review (SLR) was performed, covering technical aspects and (clinical) utility of TDM, to answer 13 research questions. MEDLINE, Embase and Cochrane were searched until July 2020. American College of Rheumatology and EULAR abstracts were also considered for inclusion. Data were extracted in evidence tables and risk of bias assessment was performed. For the search on technical aspects, 678 records were identified, of which 22 papers were selected. For the clinical utility search, 3846 records were identified, of which 108 papers were included. Patient-related factors associated with biopharmaceutical blood concentrations included body weight, methotrexate comedication and disease activity. The identification of a target range was hampered by study variability, mainly disease activity measures and study type. Evidence was inconsistent for multiple clinical situations in which TDM is currently applied. However, for some particular scenarios, including prediction of future treatment response, non-response to treatment, tapering and hypersensitivity reactions, robust evidence was found. There is currently no evidence for routine use of proactive TDM, in part because published cost-effectiveness analyses do not incorporate the current landscape of biopharmaceutical costs and usage. This SLR yields evidence in favour of TDM of biopharmaceuticals in some clinical scenarios, but evidence is insufficient to support implementation of routine use of TDM

Predicting anti-TNF treatment response in rheumatoid arthritis: An artificial intelligence-driven model using cytokine profile and routine clinical practice parameters

Introduction: Rheumatoid arthritis (RA) is a heterogeneous disease in which therapeutic strategies used have evolved dramatically. Despite significant progress in treatment strategies such as the development of anti-TNF drugs, it is still not possible to differentiate those patients who will respond from who will not. This can lead to effective-treatment delays and unnecessary costs. The aim of this study was to utilize a profile of the patient's characteristics, clinical parameters, immune status (cytokine profile) and artificial intelligence to assess the feasibility of developing a tool that could allow us to predict which patients will respond to treatment with anti-TNF drugs. Methods: This study included 38 patients with RA from the RA-Paz cohort. Clinical activity was measured at baseline and after 6 months of treatment. The cytokines measured before the start of anti-TNF treatment were IL-1, IL-12, IL-10, IL-2, IL-4, IFNg, TNFa, and IL-6. Statistical analyses were performed using the Wilcoxon-Rank-Sum Test and the Benjamini-Hochberg method. The predictive model viability was explored using the 5-fold cross-validation scheme in order to train the logistic regression models. Results: Statistically significant differences were found in parameters such as IL-6, IL-2, CRP and DAS-ESR. The predictive model performed to an acceptable level in correctly classifying patients (ROC-AUC 0.804167 to 0.891667), suggesting that it would be possible to develop a clinical classification tool. Conclusions: Using a combination of parameters such as IL-6, IL-2, CRP and DAS-ESR, it was possible to develop a predictive model that can acceptably discriminate between remitters and non-remitters. However, this model needs to be replicated in a larger cohort to confirm these findings