Biomarkers in amyotrophic lateral sclerosis: current status and future prospects

Disease heterogeneity in amyotrophic lateral sclerosis poses a substantial challenge in drug development. Categorization based on clinical features alone can help us predict the disease course and survival, but quantitative measures are also needed that can enhance the sensitivity of the clinical categorization. In this Review, we describe the emerging landscape of diagnostic, categorical and pharmacodynamic biomarkers in amyotrophic lateral sclerosis and their place in the rapidly evolving landscape of new therapeutics. Fluid-based markers from cerebrospinal fluid, blood and urine are emerging as useful diagnostic, pharmacodynamic and predictive biomarkers. Combinations of imaging measures have the potential to provide important diagnostic and prognostic information, and neurophysiological methods, including various electromyography-based measures and quantitative EEG–magnetoencephalography-evoked responses and corticomuscular coherence, are generating useful diagnostic, categorical and prognostic markers. Although none of these biomarker technologies has been fully incorporated into clinical practice or clinical trials as a primary outcome measure, strong evidence is accumulating to support their clinical utility. This Review describes the emerging landscape of diagnostic, categorical and pharmacodynamic biomarkers for amyotrophic lateral sclerosis and considers the role of these biomarkers in the rapidly evolving landscape of new therapeutics for this condition. Key points The heterogeneity in both the clinical presentation and underlying pathobiology of amyotrophic lateral sclerosis in humans has limited the translation from preclinical models to successful clinical trials. Disease categorization might be improved using ancillary and companion measures. Such biomarkers can serve diagnostic, categorical and prognostic functions. Biomarkers that are closest to use in the clinical domain include neurofilaments, which can be measured in serum and cerebrospinal fluid. Promising emerging biomarkers include those derived from neurophysiological assessment such as quantitative EEG and transcranial magnetic stimulation. No single biomarker modality will adequately cover all of the diagnostic, categorical prognostic and pharmacodynamic requirements to support the successful development of novel therapeutics. The future landscape is likely to both integrate different biomarker modalities and use deep learning and artificial intelligence to fully address the compl