Dataset Information

Biologicals in rheumatoid arthritis: current and future.

ABSTRACT: The aim of the review is to highlight the current knowledge about established and new biologicals and to summarise recent advances by focusing on comparative efficacy, safety and possible discontinuation of treatment in patients with rheumatoid arthritis (RA). Up to now, comparative analyses showed only minor differences with respect to efficacy and safety among the established biologicals. Studies confirmed the excellent drug retention rate as well as efficacy and safety of approved biologicals including their use in monotherapy. Tapering and in some instances discontinuation of biologicals is possible in disease remission. In case of relapse, patients usually show full response after reintroduction of the same compound. The development of biologicals continues fast with several new biologicals targeting different or established cytokines or cellular subsets of the immune system. With several new biologicals in the pipeline and different formulations for established compounds, treatment options for RA will become even more versatile and sophisticated. Although we get closer to the aim of decreasing the proportion of refractory patients, many questions have to be addressed in the near future regarding emerging biosimilars and biologicals with new modes of action.

SUBMITTER: Avci AB

PROVIDER: S-EPMC4613149 | biostudies-other | 2015

REPOSITORIES: biostudies-other

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Biologicals in rheumatoid arthritis: current and future.

Avci Ali Berkant AB Feist Eugen E Burmester Gerd-R GR

RMD open 20150805 1

The aim of the review is to highlight the current knowledge about established and new biologicals and to summarise recent advances by focusing on comparative efficacy, safety and possible discontinuation of treatment in patients with rheumatoid arthritis (RA). Up to now, comparative analyses showed only minor differences with respect to efficacy and safety among the established biologicals. Studies confirmed the excellent drug retention rate as well as efficacy and safety of approved biologicals ...[more]

PMID: 26535144

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Project description:Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.

Dataset Information

Biologicals in rheumatoid arthritis: current and future.

Publications

Biologicals in rheumatoid arthritis: current and future.

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