Development of a Sensitive Outcome for Economical Drug Screening for Progressive Multiple Sclerosis Treatment

Dynamic disability measures decrease the clinico-radiological gap in people with severely affected multiple sclerosis

BACKGROUND

Expanded Disability Status Scale (EDSS) is limited when utilized in highly disabled people with multiple sclerosis (pwMS).

OBJETIVE

To explore the relationship between disability measures and MRI outcomes in severely-affected pwMS.

METHODS

PwMS recruited from The Boston Home (TBH), a specialized residential facility for severly-affected pwMS and University at Buffalo (UB) MS Center were assessed using EDSS, MS Severity Scale, age-related MSS, Scripps Neurological Rating Scale (SNRS) and Combinatorial Weight-Adjusted Disability Score (CombiWISE). In all scores except SNRS, higher score indicates greater disability. MRI measures of T1, T2-lesion volume (LV), whole brain, gray matter, medulla oblongata and thalamic volumes (WBV, GMV, MOV, TV) and thalamic dysconnectivity were obtained.

RESULTS

Greatest disability differences between the TBH and UB pwMS were in SNRS (24.4 vs 71.9, p < 0.001, Cohen's d = 4.05) and CombiWISE (82.3 vs. 38.9, p < 0.001, Cohen's d = 4.02). In combined analysis of all pwMS, worse SNRS scores were correlated with worse MRI pathology in 8 out of 9 outcomes. EDSS only with 3 measures (GMV, MOV and TV). In severely-affected pwMS, SNRS was associated with T1-LV, T2-LV and WBV (not surviving false discovery rate (FDR) correction for multiple comparisons) whereas EDSS did not.

CONCLUSION

Granular and dynamic disability measures may bridge the clinico-radiologcal gap present in severely affected pwMS.

Clemastine fumarate accelerates accumulation of disability in progressive multiple sclerosis by enhancing pyroptosis

Multiple sclerosis (MS) is an immune-mediated demyelinating disease of the central nervous system (CNS). Clemastine fumarate, the over-the-counter antihistamine and muscarinic receptor blocker, has remyelinating potential in MS. A clemastine arm was added to an ongoing platform clinical trial TRAP-MS (NCT03109288) to identify a cerebrospinal fluid (CSF) remyelination signature and to collect safety data on clemastine in patients progressing independently of relapse activity (PIRA). The clemastine arm was stopped per protocol-defined criteria when 3/9 patients triggered individual safety stopping criteria (χ2 p=0.00015 compared to remaining TRAP-MS treatments). Clemastine treated patients had significantly higher treatment-induced disability progression slopes compared to remaining TRAP-MS participants (p=0.0075). Quantification of ~7000 proteins in CSF samples collected before and after clemastine treatment showed significant increase in purinergic/ATP signaling and pyroptosis cell death. Mechanistic studies showed that clemastine with sub-lytic doses of extracellular ATP activates inflammasome and induces pyroptotic cell death in macrophages. Clemastine with ATP also caused pyroptosis of induced pluripotent stem cell-derived human oligodendrocytes. Antagonist of the purinergic channel P2RX7 that is strongly expressed in oligodendrocytes and myeloid cells, blocked these toxic effects of clemastine. Finally, re-analyses of published snRNAseq studies revealed increased P2RX7 expression and pyroptosis transcriptional signature in microglia and oligodendrocytes in MS brain, especially in chronic active lesions. CSF proteomic pyroptosis score was increased in untreated MS patients, was higher in patients with progressive than relapsing-remitting disease and correlated significantly with rates of MS progression. Thus, pyroptosis is likely first well-characterized mechanism of CNS injury underlying PIRA even outside of clemastine toxicity.

A systematic review of the application of machine-learning algorithms in multiple sclerosis

INTRODUCTION

The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years.

OBJECTIVE

We present a systematic review of the application of ML algorithms in MS.

MATERIALS AND METHODS

We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected.

CONCLUSIONS

After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.

Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms

While autopsy studies identify many abnormalities in the central nervous system (CNS) of subjects dying with neurological diseases, without their quantification in living subjects across the lifespan, pathogenic processes cannot be differentiated from epiphenomena. Using machine learning (ML), we searched for likely pathogenic mechanisms of multiple sclerosis (MS). We aggregated cerebrospinal fluid (CSF) biomarkers from 1305 proteins, measured blindly in the training dataset of untreated MS patients (N = 129), into models that predict past and future speed of disability accumulation across all MS phenotypes. Healthy volunteers (N = 24) data differentiated natural aging and sex effects from MS-related mechanisms. Resulting models, validated (Rho 0.40-0.51, p < 0.0001) in an independent longitudinal cohort (N = 98), uncovered intra-individual molecular heterogeneity. While candidate pathogenic processes must be validated in successful clinical trials, measuring them in living people will enable screening drugs for desired pharmacodynamic effects. This will facilitate drug development making, it hopefully more efficient and successful.

Machine learning-optimized Combinatorial MRI scale (COMRISv2) correlates highly with cognitive and physical disability scales in Multiple Sclerosis patients

Composite MRI scales of central nervous system tissue destruction correlate stronger with clinical outcomes than their individual components in multiple sclerosis (MS) patients. Using machine learning (ML), we previously developed Combinatorial MRI scale (COMRISv1) solely from semi-quantitative (semi-qMRI) biomarkers. Here, we asked how much better COMRISv2 might become with the inclusion of quantitative (qMRI) volumetric features and employment of more powerful ML algorithm. The prospectively acquired MS patients, divided into training (n = 172) and validation (n = 83) cohorts underwent brain MRI imaging and clinical evaluation. Neurological examination was transcribed to NeurEx™ App that automatically computes disability scales. qMRI features were computed by lesion-TOADS algorithm. Modified random forest pipeline selected biomarkers for optimal model(s) in the training cohort. COMRISv2 models validated moderate correlation with cognitive disability [Spearman Rho = 0.674; Lin's concordance coefficient (CCC) = 0.458; p < 0.001] and strong correlations with physical disability (Spearman Rho = 0.830-0.852; CCC = 0.789-0.823; p < 0.001). The NeurEx led to the strongest COMRISv2 model. Addition of qMRI features enhanced performance only of cognitive disability model, likely because semi-qMRI biomarkers measure infratentorial injury with greater accuracy. COMRISv2 models predict most granular clinical scales in MS with remarkable criterion validity, expanding scientific utilization of cohorts with missing clinical data.

The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review

BACKGROUND

Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging.

METHODS

Systematic searches through eight databases were conducted for literature published in 2014-2020 on MS and specified ML algorithms.

RESULTS

Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms.

CONCLUSIONS

ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.

Enhancing the clinical value of serum neurofilament light chain measurement

BACKGROUNDSerum neurofilament light chain (sNFL) is becoming an important biomarker of neuro-axonal injury. Though sNFL correlates with CSF NFL (cNFL), 40% to 60% of variance remains unexplained. We aimed to mathematically adjust sNFL to strengthen its clinical value.METHODSWe measured NFL in a blinded fashion in 1138 matched CSF and serum samples from 571 patients. Multiple linear regression (MLR) models constructed in the training cohort were validated in an independent cohort.RESULTSAn MLR model that included age, blood urea nitrogen, alkaline phosphatase, creatinine, and weight improved correlations of cNFL with sNFL (from R2 = 0.57 to 0.67). Covariate adjustment significantly improved the correlation of sNFL with the number of contrast-enhancing lesions (from R2 = 0.18 to 0.28; 36% improvement) in the validation cohort of patients with multiple sclerosis (MS). Unexpectedly, only sNFL, but not cNFL, weakly but significantly correlated with cross-sectional MS severity outcomes. Investigating 2 nonoverlapping hypotheses, we showed that patients with proportionally higher sNFL to cNFL had higher clinical and radiological evidence of spinal cord (SC) injury and probably released NFL from peripheral axons into blood, bypassing the CSF.CONCLUSIONsNFL captures 2 sources of axonal injury, central and peripheral, the latter reflecting SC damage, which primarily drives disability progression in MS.TRIAL REGISTRATIONClinicalTrials.gov NCT00794352.FUNDINGDivision of Intramural Research, National Institute of Allergy and Infectious Diseases, NIH (AI001242 and AI001243).

Current Status and Future Opportunities in Modeling Clinical Characteristics of Multiple Sclerosis

Development of effective treatments requires understanding of disease mechanisms. For diseases of the central nervous system (CNS), such as multiple sclerosis (MS), human pathology studies and animal models tend to identify candidate disease mechanisms. However, these studies cannot easily link the identified processes to clinical outcomes, such as MS severity, required for causality assessment of candidate mechanisms. Technological advances now allow the generation of thousands of biomarkers in living human subjects, derived from genes, transcripts, medical images, and proteins or metabolites in biological fluids. These biomarkers can be assembled into computational models of clinical value, provided such models are generalizable. Reproducibility of models increases with the technical rigor of the study design, such as blinding, control implementation, the use of large cohorts that encompass the entire spectrum of disease phenotypes and, most importantly, model validation in independent cohort(s). To facilitate the growth of this important research area, we performed a meta-analysis of publications (n = 302) that model MS clinical outcomes extracting effect sizes, while also scoring the technical quality of the study design using predefined criteria. Finally, we generated a Shiny-App-based website that allows dynamic exploration of the data by selective filtering. On average, the published studies fulfilled only one of the seven criteria of study design rigor. Only 15.2% of the studies used any validation strategy, and only 8% used the gold standard of independent cohort validation. Many studies also used small cohorts, e.g., for magnetic resonance imaging (MRI) and blood biomarker predictors, the median sample size was <100 subjects. We observed inverse relationships between reported effect sizes and the number of study design criteria fulfilled, expanding analogous reports from non-MS fields, that studies that fail to limit bias overestimate effect sizes. In conclusion, the presented meta-analysis represents a useful tool for researchers, reviewers, and funders to improve the design of future modeling studies in MS and to easily compare new studies with the published literature. We expect that this will accelerate research in this important area, leading to the development of robust models with proven clinical value.

Assessment of Smartphone-Based Spiral Tracing in Multiple Sclerosis Reveals Intra-Individual Reproducibility as a Major Determinant of the Clinical Utility of the Digital Test

Technological advances, lack of medical professionals, high cost of face-to-face encounters, and disasters such as the COVID-19 pandemic fuel the telemedicine revolution. Numerous smartphone apps have been developed to measure neurological functions. However, their psychometric properties are seldom determined. It is unclear which designs underlie the eventual clinical utility of the smartphone tests. We have developed the smartphone Neurological Function Tests Suite (NeuFun-TS) and are systematically evaluating their psychometric properties against the gold standard of complete neurological examination digitalized into the NeurExTM app. This article examines the fifth and the most complex NeuFun-TS test, the "Spiral tracing." We generated 40 features in the training cohort (22 healthy donors [HD] and 89 patients with multiple sclerosis [MS]) and compared their intraclass correlation coefficient, fold change between HD and MS, and correlations with relevant clinical and imaging outcomes. We assembled the best features into machine-learning models and examined their performance in the independent validation cohort (45 patients with MS). We show that by involving multiple neurological functions, complex tests such as spiral tracing are susceptible to intra-individual variations, decreasing their reproducibility and clinical utility. Simple tests, reproducibly measuring single function(s) that can be aggregated to increase sensitivity, are preferable in app design.

Drug library screen identifies inhibitors of toxic astrogliosis

BACKGROUND

Multiple sclerosis (MS) is a chronic neuroinflammatory disorder, in which activated immune cells directly or indirectly induce demyelination and axonal degradation. Inflammatory stimuli also change the phenotype of astrocytes, making them neurotoxic. The resulting 'toxic astrocyte' phenotype has been observed in animal models of neuroinflammation and in MS lesions. Proteins secreted by toxic astrocytes are elevated in the cerebrospinal fluid (CSF) of MS patients and reproducibly correlate with the rates of accumulation of neurological disability and brain atrophy. This suggests a pathogenic role for neurotoxic astrocytes in MS.

METHODS

Here, we applied a commercially available library of small molecules that are either Food and Drug Administration-approved or in clinical development to an in vitro model of toxic astrogliosis to identify drugs and signaling pathways that inhibit inflammatory transformation of astrocytes to a neurotoxic phenotype.

RESULTS

Inhibitors of three pathways related to the endoplasmic reticulum stress: (1) proteasome, (2) heat shock protein 90 and (3) mammalian target of rapamycin reproducibly decreased inflammation-induced conversion of astrocytes to toxic phenotype. Dantrolene, an anti-spasticity drug that inhibits calcium release through ryanodine receptors expressed in the endoplasmic reticulum of central nervous system cells, also exerted inhibitory effect at in vivo achievable concentrations. Finally, we established CSF SERPINA3 as a relevant pharmacodynamic marker for inhibiting toxic astrocytes in clinical trials.

CONCLUSION

Drug library screening provides mechanistic insight into the generation of toxic astrocytes and identifies candidates for immediate proof-of-principle clinical trial(s).

Measuring Treatment Response in Progressive Multiple Sclerosis-Considerations for Adapting to an Era of Multiple Treatment Options

Disability in multiple sclerosis accrues predominantly in the progressive forms of the disease. While disease-modifying treatment of relapsing MS has drastically evolved over the last quarter-century, the development of efficient drugs for preventing or at least delaying disability in progressive MS has proven more challenging. In that way, many drugs (especially disease-modifying treatments) have been researched in the aspect of delaying disability progression in patients with a progressive course of the disease. While there are some disease-modifying treatments approved for progressive multiple sclerosis, their effect is moderate and limited mostly to patients with clinical and/or radiological signs of disease activity. Several phase III trials have used different primary outcomes with different time frames to define disease progression and to evaluate the efficacy of a disease-modifying treatment. The lack of sufficiently sensitive outcome measures could be a possible explanation for the negative clinical trials in progressive multiple sclerosis. On the other hand, even with a potential outcome measure that would be sensitive enough to determine disease progression and, thus, the efficacy or failure of a disease-modifying treatment, the question of clinical relevance remains unanswered. In this systematic review, we analyzed outcome measures and definitions of disease progression in phase III clinical trials in primary and secondary progressive multiple sclerosis. We discuss advantages and disadvantages of clinical and paraclinical outcome measures aiming for practical ways of combining them to detect disability progression more sensitively both in future clinical trials and current clinical routine.

Smartphone-based symbol-digit modalities test reliably captures brain damage in multiple sclerosis

As the burden of neurodegenerative diseases increases, time-limited clinic encounters do not allow quantification of complex neurological functions. Patient-collected digital biomarkers may remedy this, if they provide reliable information. However, psychometric properties of digital tools remain largely un-assessed. We developed a smartphone adaptation of the cognitive test, the Symbol-Digit Modalities Test (SDMT) by randomizing the test’s symbol-number codes and testing sequences. The smartphone SDMT showed comparable psychometric properties in 154 multiple sclerosis (MS) patients and 39 healthy volunteers (HV). E.g., smartphone SDMT achieved slightly higher correlations with cognitive subscores of neurological examinations and with brain injury measured by MRI (R² = 0.75, Rho = 0.83, p < 0.0001) than traditional SDMT. Mathematical adjustment for motoric disability of the dominant hand, measured by another smartphone test, compensates for the disadvantage of touch-based test. Averaging granular home measurements of the digital biomarker also increases accuracy of identifying true neurological decline.

Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.

A systematic review of the application of machine-learning algorithms in multiple sclerosis

INTRODUCTION

The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years.

OBJECTIVE

We present a systematic review of the application of ML algorithms in MS.

MATERIALS AND METHODS

We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected.

CONCLUSIONS

After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.

A multiple sclerosis disease progression measure based on cumulative disability

BACKGROUND

Existing severity measurements in multiple sclerosis (MS) are often cross-sectional, making longitudinal comparisons of disease course between individuals difficult.

OBJECTIVE

The objective of this study is to create a severity metric that can reliably summarize a patient's disease course.

METHODS

We developed the nARMSS - normalized ARMSS (age-related MS severity score) over follow-up, using the deviation of individual ARMSS scores from the expected value and integrated over the corresponding time period. The nARMSS scales from -5 to +5; a positive value indicates a more severe disease course for a patient when compared to other patients with similar disease timings.

RESULTS

Using Swedish MS registry data, the nARMSS was tested using data at 2 and 4 years of follow-up to predict the most severe quartile during the subsequent period up to 10 years total follow-up. The metric used was area under the curve of the receiver operating characteristic (AUC-ROC). This resulted in measurements of 0.929 and 0.941. In an external Canadian validation cohort, the equivalent AUC-ROCs were 0.901 and 0.908.

CONCLUSION

The nARMSS provides a reliable, generalizable and easily measurable metric which makes longitudinal comparison of disease course between individuals feasible.

Extensive Healthy Donor Age/Gender Adjustments and Propensity Score Matching Reveal Physiology of Multiple Sclerosis Through Immunophenotyping

Quantifying cell subpopulations in biological fluids aids in diagnosis and understanding of the mechanisms of injury. Although much has been learned from cerebrospinal fluid (CSF) flow cytometry in neuroimmunological disorders, such as multiple sclerosis (MS), previous studies did not contain enough healthy donors (HD) to derive age- and gender-related normative data and sufficient heterogeneity of other inflammatory neurological disease (OIND) controls to identify MS specific changes. The goals of this blinded training and validation study of MS patients and embedded controls, representing 1,240 prospectively acquired paired CSF/blood samples from 588 subjects was (1) to define physiological age-/gender-related changes in CSF cells, (2) to define/validate cellular abnormalities in blood and CSF of untreated MS through disease duration (DD) and determine which are MS-specific, and (3) to compare effect(s) of low-efficacy (i.e., interferon-beta [IFN-beta] and glatiramer acetate [GA]) and high-efficacy drugs (i.e., natalizumab, daclizumab, and ocrelizumab) on MS-related cellular abnormalities using propensity score matching. Physiological gender differences are less pronounced in the CSF compared to blood, and age-related changes suggest decreased immunosurveillance of CNS by activated HLA-DR+T cells associated with natural aging. Results from patient samples support the concept of MS being immunologically single disease evolving in time. Initially, peripherally activated innate and adaptive immune cells migrate into CSF to form MS lesions. With progression, T cells (CD8+ > CD4+), NK cells, and myeloid dendritic cells are depleted from blood as they continue to accumulate, together with B cells, in the CSF and migrate to CNS tissue, forming compartmentalized inflammation. All MS drugs inhibit non-physiological accumulation of immune cells in the CSF. Although low-efficacy drugs tend to normalize it, high-efficacy drugs overshoot some aspects of CSF physiology, suggesting impairment of CNS immunosurveillance. Comparable inhibition of MS-related CSF abnormalities advocates changes within CNS parenchyma responsible for differences in drug efficacy on MS disability progression. Video summarizing all results may become useful educational tool.

Idebenone does not inhibit disability progression in primary progressive MS

Background:

Multiple sclerosis (MS) is a chronic, immune-mediated neurodegenerative disorder of the central nervous system (CNS). While current MS therapies target the inflammatory processes, no treatment explicitly targets mitochondrial dysfunction and resulting axonal loss. Therefore, the aim of this study was to determine whether idebenone inhibits mitochondrial dysfunction and accumulation of disability in primary progressive MS (PPMS) and to enhance understanding of pathogenic mechanisms of PPMS progression using cerebrospinal fluid (CSF) biomarkers.

Methods:

The double-blind, placebo-controlled Phase I/II clinical trial of Idebenone in patients with Primary Progressive MS (IPPoMS; NCT00950248) was an adaptively designed, baseline-versus-treatment, placebo-controlled, CSF-biomarker-supported trial. Based on interim analysis of the 1-year pre-treatment data, change in the area under the curve of Combinatorial Weight-Adjusted Disability Score (CombiWISE) became the primary outcome, with >80% power to detect ≥40% efficacy with 28 patients/arm treated for 2 years in baseline versus treatment paradigm. Changes in traditional disability scales and in brain ventricular volume were secondary outcomes. Exploratory outcomes included CSF biomarkers of mitochondrial dysfunction (Growth/differentiation factor 15 [GDF15] and lactate), axonal damage (neurofilament light chain [NFL]), innate immunity (sCD14), blood brain barrier leakage (albumin quotient) and retinal nerve fiber layer thinning.

Results:

Idebenone was well tolerated but did not inhibit disability progression or CNS tissue destruction. Concentrations of GDF15, secreted predominantly by astrocytes and choroid plexus epithelium in vitro, increased after exposure to mitochondrial toxin rotenone, validating the ability of this biomarker to measure intrathecal mitochondrial damage. CSF GDF15 levels correlated strongly with age and MS patients had CSF levels of GDF15 significantly above age-adjusted healthy volunteers, with highest levels measured in PPMS. Idebenone did not change CSF GDF15 levels.

Conclusion:

Mitochondrial dysfunction exceeding normal aging reflected by age-adjusted CSF GDF15 is present in the majority of PPMS patients, but it is not inhibited by idebenone.

The proportion of myeloid-derived suppressor cells in the spleen is related to the severity of the clinical course and tissue damage extent in a murine model of multiple sclerosis

Multiple Sclerosis (MS) is the second cause of paraplegia among young adults, after all types of CNS traumatic lesions. In its most frequent relapsing-remitting form, the severity of the disease course is very heterogeneous, and its reliable evaluation remains a key issue for clinicians. Myeloid-Derived sSuppressor Cells (MDSCs) are immature myeloid cells that suppress the inflammatory response, a phenomenon related to the resolution or recovery of the clinical symptoms associated with experimental autoimmune encephalomyelitis (EAE), the most common model for MS. Here, we establish the severity index as a new parameter for the clinical assessment in EAE. It is derived from the relationship between the maximal clinical score and the time elapsed since disease onset. Moreover, we relate this new index with several histopathological hallmarks in EAE and with the peripheral content of MDSCs. Based on this new parameter, we show that the splenic MDSC content is related to the evolution of the clinical course of EAE, ranging from mild to severe. Indeed, when the severity index indicates a severe disease course, EAE mice display more intense lymphocyte infiltration, demyelination and axonal damage. A direct correlation was drawn between the MDSC population in the peripheral immune system, and the preservation of myelin and axons, which was also correlated with T cell apoptosis within the CNS (being these cells the main target for MDSC suppression). The data presented clearly indicated that the severity index is a suitable tool to analyze disease severity in EAE. Moreover, our data suggest a clear relationship between circulating MDSC enrichment and disease outcome, opening new perspectives for the future targeting of this population as an indicator of MS severity.

A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases

Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.

Genetic model of MS severity predicts future accumulation of disability

No genetic modifiers of multiple sclerosis (MS) severity have been independently validated, leading to a lack of insight into genetic determinants of the rate of disability progression. We investigated genetic modifiers of MS severity in prospectively acquired training (N = 205) and validation (N = 94) cohorts, using the following advances: (1) We focused on 113 genetic variants previously identified as related to MS severity; (2) We used a novel, sensitive outcome: MS Disease Severity Scale (MS-DSS); (3) Instead of validating individual alleles, we used a machine learning technique (random forest) that captures linear and complex nonlinear effects between alleles to derive a single Genetic Model of MS Severity (GeM-MSS). The GeM-MSS consists of 19 variants located in vicinity of 12 genes implicated in regulating cytotoxicity of immune cells, complement activation, neuronal functions, and fibrosis. GeM-MSS correlates with MS-DSS (r = 0.214; p = 0.043) in a validation cohort that was not used in the modeling steps. The recognized biology identifies novel therapeutic targets for inhibiting MS disability progression.

Quantifications of CSF Apoptotic Bodies Do Not Provide Clinical Value in Multiple Sclerosis

Multiple sclerosis (MS) is an inflammatory disease of the central nervous system (CNS) that leads to the death of neurons and oligodendrocytes, which cannot be measured in living subjects. Physiological cellular death, otherwise known as apoptosis, progresses through a series of stages which culminates in the discharge of cellular contents into vesicles known as apoptotic bodies (ABs) or apoptosomes. These ABs can be detected in bodily fluids as Annexin-V-positive vesicles of 0.5–4.0 μm in size. In addition, the origin of these ABs might be detected by staining for cell-specific surface markers. Thus, we investigated whether quantifications of the total and CNS cell-specific ABs in the cerebrospinal fluid (CSF) of patients provided any clinical value in MS. Extracellular vesicles, from CSF of 64 prospectively-acquired subjects, were collected in a blinded fashion using ultra-centrifugation. ABs were detected by flow cytometry using bead-enabled size-gating and Annexin-V-staining. The origin of these ABs was further classified by staining the vesicles for cell-specific surface markers. Upon unblinding, we evaluated the differences between diagnostic categories and correlations with clinical measures. There were no statistically significant differences in the numbers of total or any cell-specific ABs across different disease diagnostic subgroups and no significant correlations with any of the tested clinical measures of CNS tissue destruction, disability, MS activity, and severity (i.e., rates of disability accumulation). Overlap of cell surface markers suggests inability to reliably determine origin of ABs using antibody-based flow cytometry. These negative data suggest that CNS cells in MS either die by non-apoptotic mechanisms or die in frequencies indistinguishable by current assays from apoptosis of other cells, such as immune cells performing immunosurveillance in healthy conditions.

Evolution of Visual Outcomes in Clinical Trials for Multiple Sclerosis Disease-Modifying Therapies

: BACKGROUND:: The visual pathways are increasingly recognized as an ideal model to study neurodegeneration in multiple sclerosis (MS). Low-contrast letter acuity (LCLA) and optical coherence tomography (OCT) are validated measures of function and structure in MS. In fact, LCLA was the topic of a recent review by the Multiple Sclerosis Outcome Assessments Consortium (MSOAC) to qualify this visual measure as a primary or secondary clinical trial endpoint with the Food and Drug Administration (FDA) and other regulatory agencies. This review focuses on the use of LCLA and OCT measures as outcomes in clinical trials to date of MS disease-modifying therapies.

METHODS

A Pubmed search using the specific key words "optical coherence tomography," "low-contrast letter acuity," "multiple sclerosis," and "clinical trials" was performed. An additional search on the clinicaltrials.gov website with the same key words was used to find registered clinical trials of MS therapies that included these visual outcome measures.

RESULTS

As demonstrated by multiple clinical trials, LCLA and OCT measures are sensitive to treatment effects in MS. LCLA has been used in many clinical trials to date, and findings suggest that 7 letters of LCLA at the 2.5% contrast level are meaningful change. Few clinical trials using the benefits of OCT have been performed, although results of observational studies have solidified the ability of OCT to assess change in retinal structure. Continued accrual of clinical trial and observational data is needed to validate the use of OCT in clinical trials, but preliminary work suggests that an intereye difference in retinal nerve fiber layer thickness of 5-6 μm is a clinically meaningful threshold that identifies an optic nerve lesion in MS.

CONCLUSIONS

Visual impairment represents a significant component of overall disability in MS. LCLA and OCT enhance the detection of visual pathway injury and can be used as measures of axonal and neuronal integrity. Continued investigation is ongoing to further incorporate these vision-based assessments into clinical trials of MS therapies.

CSF inflammatory biomarkers responsive to treatment in progressive multiple sclerosis capture residual inflammation associated with axonal damage

BACKGROUND

Development of treatments for progressive multiple sclerosis (MS) is challenged by the lack of sensitive and treatment-responsive biomarkers of intrathecal inflammation.

OBJECTIVE

To validate the responsiveness of cerebrospinal fluid (CSF) inflammatory biomarkers to treatment with natalizumab and methylprednisolone in progressive MS and to examine the relationship between CSF inflammatory and tissue damage biomarkers.

METHODS

CSF samples from two open-label phase II trials of natalizumab and methylprednisolone in primary and secondary progressive MS. CSF concentrations of 20 inflammatory biomarkers and CSF biomarkers of axonal damage (neurofilament light chain (NFL)) and demyelination were analysed using electrochemiluminescent assay and enzyme-linked immunosorbent assay (ELISA).

RESULTS

In all, 17 natalizumab- and 23 methylprednisolone-treated patients had paired CSF samples. CSF sCD27 displayed superior standardised response means and highly significant decreases during both natalizumab and methylprednisolone treatment; however, post-treatment levels remained above healthy donor reference levels. Correlation analyses of CSF inflammatory biomarkers and NFL before, during and after treatment demonstrated that CSF sCD27 consistently correlates with NFL.

CONCLUSION

These findings validate CSF sCD27 as a responsive and sensitive biomarker of intrathecal inflammation in progressive MS, capturing residual inflammation after treatment. Importantly, CSF sCD27 correlates with NFL, consistent with residual inflammation after anti-inflammatory treatment being associated with axonal damage.

Smartphone Level Test Measures Disability in Several Neurological Domains for Patients With Multiple Sclerosis

Our long-term goal is to employ smartphone-embedded sensors to measure various neurological functions in a patient-autonomous manner. The interim goal is to develop simple smartphone tests (apps) and evaluate the clinical utility of these tests by selecting optimal outcomes that correlate well with clinician-measured disability in different neurological domains. In this paper, we used prospectively-acquired data from 112 multiple sclerosis (MS) patients and 15 healthy volunteers (HV) to assess the performance and optimize outcomes of a Level Test. The goal of the test is to tilt the smartphone so that a free-rolling ball travels to and remains in the center of the screen. An accelerometer detects tilting and records the coordinates of the ball at set time intervals. From this data, we derived five features: path length traveled, time spent in the center of the screen, average distance from the center, average speed while in the center, and number of direction changes underwent by the ball. Time in center proved to be the most sensitive feature to differentiate MS patients from HV and had the strongest correlations with clinician-derived scales. Its superiority was validated in an independent validation cohort of 29 MS patients. A linear combination of different Level features failed to outperform time in center in an independent validation cohort. Limited longitudinal data demonstrated that the Level features were relatively stable intra-individually within 4 months, without definitive evidence of learning. In contrast to previously developed smartphone tests that predominantly measure motoric functions, Level features correlated strongly with reaction time and moderately with cerebellar functions and proprioception, validating its complementary clinical value in the MS app suite. The Level Test measures neurological disability in several domains in two independent cross-sectional cohorts (original and validation). An ongoing longitudinal cohort further investigates whether patient-autonomous collection of granular functional data allows measurement of patient-specific trajectories of disability progression to better guide treatment decisions.

Cerebrospinal fluid biomarkers link toxic astrogliosis and microglial activation to multiple sclerosis severity

BACKGROUND

Once multiple sclerosis (MS) reaches the progressive stage, immunomodulatory treatments have limited efficacy. This suggests that processes other than activation of innate immunity may at least partially underlie disability progression during late stages of MS. Pathology identified these alternative processes as aberrant activation of astrocytes and microglia, and subsequent degeneration of oligodendrocytes and neurons. However, we mostly lack biomarkers that could measure central nervous system (CNS) cell-specific intrathecal processes in living subjects. This prevents differentiating pathogenic processes from an epiphenomenon. Therefore, we sought to develop biomarkers of CNS cell-specific processes and link them to disability progression in MS.

METHODS

In a blinded manner, we measured over 1000 proteins in the cerebrospinal fluid (CSF) of 431 patients with neuroimmunological diseases and healthy volunteers using modified DNA-aptamers (SOMAscan®). We defined CNS cell type-enriched clusters using variable cluster analysis, combined with in vitro modeling. Differences between diagnostic categories were identified in the training cohort (n = 217) and their correlation to disability measures were assessed; results were validated in an independent validation cohort (n = 214).

RESULTS

Astrocyte cluster 8 (MMP7, SERPINA3, GZMA and CLIC1) and microglial cluster 2 (DSG2 and TNFRSF25) were reproducibly elevated in MS and had a significant and reproducible correlation with MS severity suggesting their pathogenic role. In vitro studies demonstrated that proteins of astrocyte cluster 8 are noticeably released upon stimulation with proinflammatory stimuli and overlap with the phenotype of recently described neuro-toxic (A1) astrocytes.

CONCLUSION

Microglial activation and toxic astrogliosis are associated with MS disease process and may partake in CNS tissue destruction. This hypothesis should be tested in new clinical trials.

Impact of trial design and patient heterogeneity on the identification of clinically effective therapies for progressive MS

Clinically effective immunomodulatory therapies have been developed for relapsing-remitting multiple sclerosis (RRMS), but they have generally not translated to a corresponding slowing of disability accumulation in progressive forms of multiple sclerosis (MS). Since disability is multifaceted, progressive patients are heterogeneous, and the drivers of disease progression are still unclear, it has been difficult to identify the most informative outcome measures for progressive trials. Historically, secondary outcome measures have focused on inflammatory measures, which contributed to the recent identification of immunomodulatory therapies benefiting younger patients with more inflammatory progressive MS. Meanwhile, agents capable of treating late-stage disease have remained elusive. Consequently, measures of neurodegeneration are becoming common. Here, we review completed clinical trials testing immunomodulatory therapies in primary progressive multiple sclerosis (PPMS) or secondary progressive multiple sclerosis (SPMS) and discuss the features contributing to trial design variability in relation to trial outcomes, and how efforts toward better patient stratification and inclusion of reliable progression markers could improve outcomes.

NeurEx: digitalized neurological examination offers a novel high-resolution disability scale

Objective

To develop a sensitive neurological disability scale for broad utilization in clinical practice.

Methods

We employed advances of mobile computing to develop an iPad‐based App for convenient documentation of the neurological examination into a secure, cloud‐linked database. We included features present in four traditional neuroimmunological disability scales and codified their automatic computation. By combining spatial distribution of the neurological deficit with quantitative or semiquantitative rating of its severity we developed a new summary score (called NeurEx; ranging from 0 to 1349 with minimal measurable change of 0.25) and compared its performance with clinician‐ and App‐computed traditional clinical scales.

Results

In the cross‐sectional comparison of 906 neurological examinations, the variance between App‐computed and clinician‐scored disability scales was comparable to the variance between rating of the identical neurological examination by multiple sclerosis (MS)‐trained clinicians. By eliminating rating ambiguity, App‐computed scales achieved greater accuracy in measuring disability progression over time (n = 191 patients studied over 880.6 patient‐years). The NeurEx score had no apparent ceiling effect and more than 200‐fold higher sensitivity for detecting a measurable yearly disability progression (i.e., median progression slope of 8.13 relative to minimum detectable change of 0.25) than Expanded Disability Status Scale (EDSS) with a median yearly progression slope of 0.071 that is lower than the minimal measurable change on EDSS of 0.5.

Interpretation

NeurEx can be used as a highly sensitive outcome measure in neuroimmunology. The App can be easily modified for use in other areas of neurology and it can bridge private practice practitioners to academic centers in multicenter research studies.

Identifying and Quantifying Neurological Disability via Smartphone

Embedded sensors of the smartphones offer opportunities for granular, patient-autonomous measurements of neurological dysfunctions for disease identification, management, and for drug development. We hypothesized that aggregating data from two simple smartphone tests of fine finger movements with differing contribution of specific neurological domains (i.e., strength & cerebellar functions, vision, and reaction time) will allow establishment of secondary outcomes that reflect domain-specific deficit. This hypothesis was tested by assessing correlations of smartphone-derived outcomes with relevant parts of neurological examination in multiple sclerosis (MS) patients. We developed MS test suite on Android platform, consisting of several simple functional tests. This paper compares cross-sectional and longitudinal performance of Finger tapping and Balloon popping tests by 76 MS patients and 19 healthy volunteers (HV). The primary outcomes of smartphone tests, the average number of taps (per two 10-s intervals) and the average number of pops (per two 26-s intervals) differentiated MS from HV with similar power to traditional, investigator-administered test of fine finger movements, 9-hole peg test (9HPT). Additionally, the secondary outcomes identified patients with predominant cerebellar dysfunction, motor fatigue and poor eye-hand coordination and/or reaction time, as evidenced by significant correlations between these derived outcomes and relevant parts of neurological examination. The intra-individual variance in longitudinal sampling was low. In the time necessary for performing 9HPT, smartphone tests provide much richer and reliable measurements of several distinct neurological functions. These data suggest that combing more creatively-construed smartphone apps may one day recreate the entire neurological examination.

Intrathecal B Cells in MS Have Significantly Greater Lymphangiogenic Potential Compared to B Cells Derived From Non-MS Subjects

Although B cell depletion is an effective therapy of multiple sclerosis (MS), the pathogenic functions of B cells in MS remain incompletely understood. We asked whether cerebrospinal fluid (CSF) B cells in MS secrete different cytokines than control-subject B cells and whether cytokine secretion affects MS phenotype. We blindly studied CSF B cells after their immortalization by Epstein-Barr Virus (EBV) in prospectively-collected MS patients and control subjects with other inflammatory-(OIND) or non-inflammatory neurological diseases (NIND) and healthy volunteers (HV). The pilot cohort (n = 80) was analyzed using intracellular cytokine staining (n = 101 B cell lines [BCL] derived from 35 out of 80 subjects). We validated differences in cytokine production in newly-generated CSF BCL (n = 207 BCL derived from subsequent 112 prospectively-recruited subjects representing validation cohort), using ELISA enhanced by objective, flow-cytometry-based B cell counting. After unblinding the pilot cohort, the immortalization efficiency was almost 5 times higher in MS patients compared to controls (p < 0.001). MS subjects' BCLs produced significantly more vascular endothelial growth factor (VEGF) compared to control BCLs. Progressive MS patients BCLs produced significantly more tumor necrosis factor (TNF)-α and lymphotoxin (LT)-α than BCL from relapsing-remitting MS (RRMS) patients. In the validation cohort, we observed lower secretion of IL-1β in RRMS patients, compared to all other diagnostic categories. The validation cohort validated enhanced VEGF-C production by BCL from RRMS patients and higher TNF-α and LT-α secretion by BCL from progressive MS. No significant differences among diagnostic categories were observed in secretion of IL-6 or GM-CSF. However, B cell secretion of IL-1β, TNF-α, and GM-CSF correlated significantly with the rate of accumulation of disability measured by MS disease severity scale (MS-DSS). Finally, all three cytokines with increased secretion in different stages of MS (i.e., VEGF-C, TNF-α, and LT-α) enhance lymphangiogenesis, suggesting that intrathecal B cells directly facilitate the formation of tertiary lymphoid follicles, thus compartmentalizing inflammation to the central nervous system.

Clinical trial design for progressive MS trials

The design of clinical trials is a key aspect to maximizing the possibility to detect a treatment effect. This fact is particularly challenging in progressive multiple sclerosis (PMS) studies due to the uncertainty about the right target and/or outcome in phase-2 studies. The aim of this review is to evaluate the current challenges facing the design of clinical trials for PMS. The selection of patients, the instrumental and clinical outcomes that can be used in PMS trials, and issues in their design will be covered in this report.

Pharmacotherapy in Secondary Progressive Multiple Sclerosis: An Overview

Multiple sclerosis is an immune-mediated inflammatory disease of the central nervous system characterised by demyelination, neuroaxonal loss and a heterogeneous clinical course. Multiple sclerosis presents with different phenotypes, most commonly a relapsing-remitting course and, less frequently, a progressive accumulation of disability from disease onset (primary progressive multiple sclerosis). The majority of people with relapsing-remitting multiple sclerosis, after a variable time, switch to a stage characterised by gradual neurological worsening known as secondary progressive multiple sclerosis. We have a limited understanding of the mechanisms underlying multiple sclerosis, and it is believed that multiple genetic, environmental and endogenous factors are elements driving inflammation and ultimately neurodegeneration. Axonal loss and grey matter damage have been regarded as amongst the leading causes of irreversible neurological disability in the progressive stages. There are over a dozen disease-modifying therapies currently licenced for relapsing-remitting multiple sclerosis, but none of these has provided evidence of effectiveness in secondary progressive multiple sclerosis. Recently, there has been some early modest success with siponimod in secondary progressive multiple sclerosis and ocrelizumab in primary progressive multiple sclerosis. Finding treatments to delay or prevent the courses of secondary progressive multiple sclerosis is an unmet and essential goal of the research in multiple sclerosis. In this review, we discuss new findings regarding drugs with immunomodulatory, neuroprotective or regenerative properties and possible treatment strategies for secondary progressive multiple sclerosis. We examine the field broadly to include trials where participants have progressive or relapsing phenotypes. We summarise the most relevant results from newer investigations from phase II and III randomised controlled trials over the past decade, with particular attention to the last 5 years.

New Multiple Sclerosis Disease Severity Scale Predicts Future Accumulation of Disability

The search for the genetic foundation of multiple sclerosis (MS) severity remains elusive. It is, in fact, controversial whether MS severity is a stable feature that predicts future disability progression. If MS severity is not stable, it is unlikely that genotype decisively determines disability progression. An alternative explanation tested here is that the apparent instability of MS severity is caused by inaccuracies of its current measurement. We applied statistical learning techniques to a 902 patient-years longitudinal cohort of MS patients, divided into training (n = 133) and validation (n = 68) sub-cohorts, to test four hypotheses: (1) there is intra-individual stability in the rate of accumulation of MS-related disability, which is also influenced by extrinsic factors. (2) Previous results from observational studies are negatively affected by the insensitive nature of the Expanded Disability Status Scale (EDSS). The EDSS-based MS Severity Score (MSSS) is further disadvantaged by the inability to reliably measure MS onset and, consequently, disease duration (DD). (3) Replacing EDSS with a sensitive scale, i.e., Combinatorial Weight-Adjusted Disability Score (CombiWISE), and substituting age for DD will significantly improve predictions of future accumulation of disability. (4) Adjusting measured disability for the efficacy of administered therapies and other relevant external features will further strengthen predictions of future MS course. The result is a MS disease severity scale (MS-DSS) derived by conceptual advancements of MSSS and a statistical learning method called gradient boosting machines (GBM). MS-DSS greatly outperforms MSSS and the recently developed Age Related MS Severity Score in predicting future disability progression. In an independent validation cohort, MS-DSS measured at the first clinic visit correlated significantly with subsequent therapy-adjusted progression slopes (r = 0.5448, p = 1.56e−06) measured by CombiWISE. To facilitate widespread use of MS-DSS, we developed a free, interactive web application that calculates all aspects of MS-DSS and its contributing scales from user-provided raw data. MS-DSS represents a much-needed tool for genotype-phenotype correlations, for identifying biological processes that underlie MS progression, and for aiding therapeutic decisions.

Pharmacodynamic effects of daclizumab in the intrathecal compartment

Objective

It was previously demonstrated that daclizumab therapy normalizes cellular cerebrospinal fluid (CSF) abnormalities typical of multiple sclerosis (MS) in the majority of treated patients. However, CSF cells represent only the mobile portion of intrathecal immune responses. Therefore, we asked whether daclizumab also reverses compartmentalized inflammation and if not, whether residual inflammation correlates with clinical response to the drug.

Methods

Forty MS patients treated with an intravenous or subcutaneous injection of daclizumab were followed for up to 16 years in two open‐label clinical trials. MRI contrast‐enhancing lesions (CELs), clinical scales, and CSF biomarkers quantified residual disease.

Results

Rapid decreases in CELs, sustained throughout the observation period, were observed with daclizumab treatment. Daclizumab therapy induced modest but statistically significant (P < 0.0001) decreases in CSF levels of T‐cell activation marker CD27 and IgG index. Interleukin 2 (IL‐2) CSF levels increased from baseline levels during treatment, consistent with reduced IL‐2 consumption by T cells, as a consequence of daclizumab's saturation of high‐affinity IL‐2 receptors. CSF levels of IL‐12p40, chitinase‐3‐like protein‐1 (CHI3L1), chemokine C‐X‐C motif ligand 13, and neurofilament light chain (NFL) were also significantly reduced by daclizumab. Among them, inhibition of CHI3L1 correlated with inhibition of NFL and with lack of disease progression.

Interpretation

These observations confirm daclizumab's direct pharmacodynamics effects on immune cells within central nervous system tissues and identify inhibition of CSF biomarkers of myeloid lineage as a stronger determinant of reduction in clinical MS activity than inhibition of biomarkers of adaptive immunity.

Comparative utility of disability progression measures in PPMS: Analysis of the PROMiSe data set

OBJECTIVE

To assess the comparative utility of disability progression measures in primary progressive MS (PPMS) using the PROMiSe trial data set.

METHODS

Data for patients randomized to placebo (n = 316) in the PROMiSe trial were included in this analysis. Disability was assessed using change in single (Expanded Disability Status Scale [EDSS], timed 25-foot walk [T25FW], and 9-hole peg test [9HPT]) and composite disability measures (EDSS/T25FW, EDSS/9HPT, and EDSS/T25FW/9HPT). Cumulative and cross-sectional unconfirmed disability progression (UDP) and confirmed disability progression (CDP; sustained for 3 months) rates were assessed at 12 and 24 months.

RESULTS

CDP rates defined by a ≥20% increase in T25FW were higher than those defined by EDSS score at 12 and 24 months. CDP rates defined by T25FW or EDSS score were higher than those defined by 9HPT score. The 3-part composite measure was associated with more CDP events (41.4% and 63.9% of patients at 12 and 24 months, respectively) than the 2-part measure (EDSS/T25FW [38.5% and 59.5%, respectively]) and any single measure. Cumulative UDP and CDP rates were higher than cross-sectional rates.

CONCLUSIONS

The T25FW or composite measures of disability may be more sensitive to disability progression in patients with PPMS and should be considered as the primary endpoint for future studies of new therapies. CDP may be the preferred measure in classic randomized controlled trials in which cumulative disability progression rates are evaluated; UDP may be feasible for cross-sectional studies.

Finger and foot tapping as alternative outcomes of upper and lower extremity function in multiple sclerosis

Background

While magnetic resonance imaging contrast-enhancing lesions represent an excellent screening tool for disease-modifying treatments in relapsing–remitting multiple sclerosis (RRMS), this biomarker is insensitive for testing therapies against compartmentalized inflammation in progressive multiple sclerosis (MS). Therefore, alternative sensitive outcomes are needed. Using machine learning, clinician-acquired disability scales can be combined with timed measures of neurological functions such as walking speed (e.g. 25-foot walk; 25FW) or fine finger movements (e.g. 9-hole peg test; 9HPT) into sensitive composite clinical scales, such as the recently developed combinatorial, weight-adjusted disability scale (CombiWISE). Ideally, these complementary simplified measurements of certain neurological functions could be performed regularly at patients’ homes using smartphones.

Objectives

We asked whether tests amenable to adaptation to smartphone technology, such as finger and foot tapping have comparable sensitivity and specificity to current non-clinician-acquired disability measures.

Results

We observed that finger and foot tapping can differentiate RRMS and progressive MS in a cross-sectional study and can also measure yearly and two-year disease progression in the latter, with better power (based on z-scores) in comparison to currently utilized 9HPT and 25FW.

Conclusions

Replacing the 9HPT and 25FW with simplified tests broadly adaptable to smartphone technology may enhance the power of composite scales for progressive MS.