Biological therapy for rheumatoid arthritis: is personalised medicine possible?

Callaghan, C A; Boyter, Anne; Mullen, Alexander B.; McRorie, Euan

doi:10.1136/ejhpharm-2013-000386

Cited by 8 publications

(14 citation statements)

References 45 publications

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“…Most of the factors included in the matrix model are validated by a number of studies that have analysed individual predictors of RA treatment outcomes in studies of other biologics [ 1 , 2 ], thereby increasing the face-validity of the model. Some characteristics, such as smoking, were not predictive in this dataset, but may be predictors in the overall RA population.…”

Section: Discussionmentioning

confidence: 99%

“…To make best use of resources for biologic treatment in patients with RA, it would be useful to identify a set of predictors that enable selection of patients who will benefit most from such treatment and avoid treatment of patients who are unlikely to respond. Several studies have evaluated predictors of outcomes during anti-TNF treatment (for a review, see [ 1 , 2 ]). One limitation of some of these studies [ 3 ] is that the predictive capacity of single predictors is low ( vs combinations of predictors), and they are less useful when making practice decisions for individual patients.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of remission and low disease activity in disease-modifying anti-rheumatic drug-refractory patients with rheumatoid arthritis treated with golimumab

et al. 2016

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Objective. To create a tool to predict probability of remission and low disease activity (LDA) in patients with RA being considered for anti-TNF treatment in clinical practice.Methods. We analysed data from GO-MORE, an open-label, multinational, prospective study in biologic-naïve patients with active RA (DAS28-ESR ⩾3.2) despite DMARD therapy. Patients received 50 mg s.c. golimumab (GLM) once monthly for 6 months. In secondary analyses, regression models were used to determine the best set of baseline factors to predict remission (DAS28-ESR <2.6) at month 6 and LDA (DAS28-ESR ⩽3.2) at month 1.Results. In 3280 efficacy-evaluable patients, of 12 factors included in initial regression models predicting remission or LDA, six were retained in final multivariable models. Greater likelihood of LDA and remission was associated with being male; younger age; lower HAQ, ESR (or CRP) and tender joint count (or swollen joint count) scores; and absence of comorbidities. In models predicting 1-, 3- and 6-month LDA or remission, area under the receiver operating curve was 0.648–0.809 (R2 = 0.0397–0.1078). The models also predicted 6-month HAQ and EuroQoL-5-dimension scores. A series of matrices were developed to easily show predicted rates of remission and LDA.Conclusion. A matrix tool was developed to show predicted GLM treatment outcomes in patients with RA, based on a combination of six baseline characteristics. The tool could help provide practical guidance in selection of candidates for anti-TNF therapy.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of remission and low disease activity in disease-modifying anti-rheumatic drug-refractory patients with rheumatoid arthritis treated with golimumab

et al. 2016

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“…ADA is the first fully human therapeutic anti-TNF monoclonal antibody, while ETN is a recombinant human TNF receptor (p75)-Fc fusion protein that competitively inhibits TNF (5). Although these TNFi have revolutionized the treatment of RA, ~30% of patients do not respond well to their initial anti-TNF therapy (4). Treatment failure elevates the risk of adverse events such as infections and puts additional socioeconomic burden on the patients (3,6).…”

Section: Introductionmentioning

confidence: 99%

“…Upon failure or loss of efficacy of csDMARDs, patients are switched to biologic DMARDs (bDMARDs), such as tumor necrosis factor inhibitors (TNFi) (3). Currently, there are different biologic TNFi, including adalimumab (ADA) and etanercept (ETN), available for clinical use (4). ADA is the first fully human therapeutic anti‐TNF monoclonal antibody, while ETN is a recombinant human TNF receptor (p75)–Fc fusion protein that competitively inhibits TNF (5).…”

Section: Introductionmentioning

confidence: 99%

“…To predict treatment response, it is crucial to identify reliable predictors. Katchamart et al (1) and Callaghan et al (4) reviewed 18 and 154 studies, respectively, and identified several potential predictors of RA remission and response to biologic therapy, including age, sex, disease duration, disease activity, smoking status, and concomitant methotrexate therapy, among others (1,4). Vastesaeger et al (7) developed a matrix tool based on 6 predictors (sex, Health Assessment Questionnaire, presence of comorbidities, age, tender joint count, and erythrocyte sedimentation rate) to predict remission and low disease activity in RA patients treated with golimumab (anti‐TNF) therapy.…”

Section: Introductionmentioning

confidence: 99%

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Multiomics and Machine Learning Accurately Predict Clinical Response to Adalimumab and Etanercept Therapy in Patients With Rheumatoid Arthritis

Tao

Concepcion

Vianen

et al. 2020

Arthritis & Rheumatology

106

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Objective To predict response to anti–tumor necrosis factor (anti‐TNF) prior to treatment in patients with rheumatoid arthritis (RA), and to comprehensively understand the mechanism of how different RA patients respond differently to anti‐TNF treatment. Methods Gene expression and/or DNA methylation profiling on peripheral blood mononuclear cells (PBMCs), monocytes, and CD4+ T cells obtained from 80 RA patients before they began either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6 months, treatment response was evaluated according to the European League Against Rheumatism criteria for disease response. Differential expression and methylation analyses were performed to identify the response‐associated transcription and epigenetic signatures. Using these signatures, machine learning models were built by random forest algorithm to predict response prior to anti‐TNF treatment, and were further validated by a follow‐up study. Results Transcription signatures in ADA and ETN responders were divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes up‐regulated in CD4+ T cells from ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differentially methylated positions (DMPs) were strongly hypermethylated in responders to ETN but not to ADA. The machine learning models for the prediction of response to ADA and ETN using differential genes reached an overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached an overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow‐up study validated the high performance of these models. Conclusion Our findings indicate that machine learning models based on molecular signatures accurately predict response before ADA and ETN treatment, paving the path toward personalized anti‐TNF treatment.

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A Machine Learning Approach for Prediction of CDAI Remission with TNF Inhibitors: A Concept of Precision Medicine from the FIRST Registry

Sonomoto,

Fujino,

Tanaka

et al. 2024

Rheumatol Ther

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Introduction This study aimed to develop low-cost models using machine learning approaches predicting the achievement of Clinical Disease Activity Index (CDAI) remission 6 months after initiation of tumor necrosis factor inhibitors (TNFi) as primary biologic/targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) for rheumatoid arthritis (RA). Methods Data of patients with RA initiating TNFi as first b/tsDMARD after unsuccessful methotrexate treatment were collected from the FIRST registry (August 2003 to October 2022). Baseline characteristics and 6-month CDAI were collected. The analysis used various machine learning approaches including logistic regression with stepwise variable selection, decision tree, support vector machine, and lasso logistic regression (Lasso), with 48 factors accessible in routine clinical practice for the prediction model. Robustness was ensured by k-fold cross validation. Results Among the approaches tested, Lasso showed the advantages in predicting CDAI remission: with a mean area under the curve 0.704, sensitivity 61.7%, and specificity 69.9%. Predicted TNFi responders achieved CDAI remission at an average rate of 53.2%, while only 26.4% of predicted TNFi non-responders achieved remission. Encouragingly, the models generated relied solely on patient-reported outcomes and quantitative parameters, excluding subjective physician input. Conclusions While external cohort validation is warranted for broader applicability, this study highlights the potential for a low-cost predictive model to predict CDAI remission following TNFi treatment. The approach of the study using only baseline data and 6-month CDAI measures, suggests the feasibility of establishing regional cohorts to generate low-cost models tailored to specific regions or institutions. This may facilitate the application of regional/in-house precision medicine strategies in RA management. Supplementary Information The online version contains supplementary material available at 10.1007/s40744-024-00668-z.

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Biological therapy for rheumatoid arthritis: is personalised medicine possible?

Cited by 8 publications

References 45 publications

Prediction of remission and low disease activity in disease-modifying anti-rheumatic drug-refractory patients with rheumatoid arthritis treated with golimumab

Prediction of remission and low disease activity in disease-modifying anti-rheumatic drug-refractory patients with rheumatoid arthritis treated with golimumab

Multiomics and Machine Learning Accurately Predict Clinical Response to Adalimumab and Etanercept Therapy in Patients With Rheumatoid Arthritis

A Machine Learning Approach for Prediction of CDAI Remission with TNF Inhibitors: A Concept of Precision Medicine from the FIRST Registry

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