Monitoring multiple sclerosis: digital and fluid phase biomarkers.

Abstract

Purpose of review: Monitoring of disease activity and treatment response in multiple sclerosis (MS) currently relies on the integration of qualitative clinical and radiological data that is of limited predictive value. An array of quantitative digital and fluid biomarkers, many on the cusp of broad clinical translation, is expected to herald a new era of data-driven therapeutic strategy, particularly with respect to the sequencing of disease-modifying therapies (DMTs). Available highly-effective DMTs, which largely abolish acute inflammatory activity in early, relapsing MS, have a limited impact on progressive MS disease biology. However, robust digital and fluid biomarkers of progression independent of relapse activity (PIRA) have emerged as a major unmet need, fuelled by the imminent availability of treatments that target pathomechanisms such as chronic active or smouldering brain inflammation.

Recent findings: The criteria for MS diagnosis incorporate both imaging and cerebrospinal fluid (CSF) biomarkers of the disease, which lacks a single diagnostic 'test'. The recent validation of objective and quantitative CSF biomarkers, such as the k-FLC index, promises to improve diagnostic accuracy, particularly in patients with atypical or minor imaging changes. Precision monitoring of disease and is response to therapy is being transformed by the advent of clinically integrated, quantitative digital imaging tools; digital wearables and patient reported outcomes, including cognitive batteries delivered on personal devices; and an array of ultra-sensitive, readily-obtained serum fluid biomarkers that indicate the severity of tissue injury in MS. The promise of data-driven therapeutic strategy is being further explored in multimodal digital/fluid and digital twin biomarker studies that incorporate predictive artificial intelligence algorithms.

Summary: Here, we review the key near-term biomarkers that will guide individualised therapy for people with MS, targeting no evidence of disease activity (NEDA) in both early relapsing and established disease. In the medium term, composite digital and fluid biomarkers, integrated with clinical outcomes and underpinned by predictive artificial intelligence will have a transformative effect on the management of MS.