Serum Glial Fibrillary Acidic Protein and Neurofilament Light Chain Levels Reflect Different Mechanisms of Disease Progression under B-Cell Depleting Treatment in Multiple Sclerosis.

OBJECTIVE: To investigate the longitudinal dynamics of serum glial fibrillary acidic protein (sGFAP) and serum neurofilament light chain (sNfL) levels in people with multiple sclerosis (pwMS) under B-cell depleting therapy (BCDT) and their capacity to prognosticate future progression independent of relapse activity (PIRA) events. METHODS: A total of 362 pwMS (1,480 samples) starting BCDT in the Swiss Multiple Sclerosis (MS) Cohort were included. sGFAP levels in 2,861 control persons (4,943 samples) provided normative data to calculate adjusted Z scores. RESULTS: Elevated sGFAP levels (Z score >1) at 1 year were associated with a higher hazard for PIRA (hazard ratio [HR]: 1.80 [95% CI: 1.17-2.78]; p = 0.0079) than elevated sNfL levels (HR, 1.45 [0.95-2.24], p = 0.0886) in a combined model. Independent of PIRA events, sGFAP levels longitudinally increased by 0.49 Z score units per 10 years follow-up (estimate, 0.49 [0.29, 0.69], p < 0.0001). In patients experiencing PIRA, sGFAP Z scores were 0.52 Z score units higher versus stable patients (0.52 [0.22, 0.83], p = 0.0009). Different sNfL Z score trajectories were found in pwMS with versus without PIRA (interaction p = 0.0028), with an average decrease of 0.92 Z score units per 10 years observed without PIRA (-0.92 [-1.23, -0.60], p < 0.0001), whereas levels in patients with PIRA remained high. INTERPRETATION: Elevated sGFAP and lack of drop in sNfL after BCDT start are associated with increased risk of future PIRA. These findings provide a rationale for combined monitoring of sNfL and sGFAP in pwMS starting BCDT to predict the risk of PIRA, and to use sGFAP as an outcome in clinical trials aiming to impact on MS progressive disease biology. ANN NEUROL 2024.

Development of a risk-tailored approach and dashboard for efficient management and monitoring of investigator-initiated trials.

BACKGROUND: Most randomized controlled trials (RCTs) in the academic setting have limited resources for clinical trial management and monitoring. Inefficient conduct of trials was identified as an important source of waste even in well-designed studies. Thoroughly identifying trial-specific risks to enable focussing of monitoring and management efforts on these critical areas during trial conduct may allow for the timely initiation of corrective action and to improve the efficiency of trial conduct. We developed a risk-tailored approach with an initial risk assessment of an individual trial that informs the compilation of monitoring and management procedures in a trial dashboard. METHODS: We performed a literature review to identify risk indicators and trial monitoring approaches followed by a contextual analysis involving local, national and international stakeholders. Based on this work we developed a risk-tailored management approach with integrated monitoring for RCTs and including a visualizing trial dashboard. We piloted the approach and refined it in an iterative process based on feedback from stakeholders and performed formal user testing with investigators and staff of two clinical trials. RESULTS: The developed risk assessment comprises four domains (patient safety and rights, overall trial management, intervention management, trial data). An accompanying manual provides rationales and detailed instructions for the risk assessment. We programmed two trial dashboards tailored to one medical and one surgical RCT to manage identified trial risks based on daily exports of accumulating trial data. We made the code for a generic dashboard available on GitHub that can be adapted to individual trials. CONCLUSIONS: The presented trial management approach with integrated monitoring enables user-friendly, continuous checking of critical elements of trial conduct to support trial teams in the academic setting. Further work is needed in order to show effectiveness of the dashboard in terms of safe trial conduct and successful completion of clinical trials.

Intrathecal Immunoglobulin M Synthesis is an Independent Biomarker for Higher Disease Activity and Severity in Multiple Sclerosis.

OBJECTIVE: We aimed to determine in relapsing multiple sclerosis (MS) whether intrathecal synthesis of immunoglobulin (Ig) M and IgG is associated with outcomes reflecting inflammatory activity and chronic worsening. METHODS: We compared cerebrospinal fluid analysis, clinical and magnetic resonance imaging data, and serum neurofilament light chain (sNfL) levels at baseline and follow-up in 530 patients with relapsing MS. Patients were categorized by the presence of oligoclonal IgG bands (OCGB) and intrathecal synthesis of IgG and IgM (intrathecal fraction [IF]: IgGIF and IgMIF ). Relationships with the time to first relapse, sNfL concentrations, T2-weighted (T2w) lesions, MS Severity Score (MSSS), and time to initiation of high-efficacy therapy were analyzed in covariate-adjusted statistical models. RESULTS: By categorical analysis, in patients with IgMIF the median time to first relapse was 28 months shorter and MSSS on average higher by 1.11 steps compared with patients without intrathecal immunoglobulin synthesis. Moreover, patients with IgMIF had higher sNfL concentrations, more new/enlarging T2w lesions, and higher total T2w lesion counts (all p ≤ 0.01). These associations were absent or equally smaller in patients who were positive for only OCGB or OCGB/IgGIF . Furthermore, quantitative analyses revealed that in patients with IgMIF ≥ median, the time to first relapse and to initiation of high-efficacy therapy was shorter by 32 and by 203 months, respectively (both p < 0.01), in comparison to patients with IgMIF < median. Dose-dependent associations were also found for IgMIF but not for IgGIF with magnetic resonance imaging-defined disease activity and sNfL. INTERPRETATION: This large study supports the value of intrathecal IgM synthesis as an independent biomarker of disease activity and severity in relapsing MS. ANN NEUROL 2021;90:477-489.

Using Yeast Synthetic Lethality To Inform Drug Combination for Malaria.

Combinatorial chemotherapy is necessary for the treatment of malaria. However, finding a suitable partner drug for a new candidate is challenging. Here we develop an algorithm that identifies all of the gene pairs of Plasmodium falciparum that possess orthologues in yeast that have a synthetic lethal interaction but are absent in humans. This suggests new options for drug combinations, particularly for inhibitors of targets such as P. falciparum calcineurin, cation ATPase 4, or phosphatidylinositol 4-kinase.

Serum Glial Fibrillary Acidic Protein Compared With Neurofilament Light Chain as a Biomarker for Disease Progression in Multiple Sclerosis.

Importance: There is a lack of validated biomarkers for disability progression independent of relapse activity (PIRA) in multiple sclerosis (MS). Objective: To determine how serum glial fibrillary acidic protein (sGFAP) and serum neurofilament light chain (sNfL) correlate with features of disease progression vs acute focal inflammation in MS and how they can prognosticate disease progression. Design, Setting, and Participants: Data were acquired in the longitudinal Swiss MS cohort (SMSC; a consortium of tertiary referral hospitals) from January 1, 2012, to October 20, 2022. The SMSC is a prospective, multicenter study performed in 8 centers in Switzerland. For this nested study, participants had to meet the following inclusion criteria: cohort 1, patients with MS and either stable or worsening disability and similar baseline Expanded Disability Status Scale scores with no relapses during the entire follow-up; and cohort 2, all SMSC study patients who had initiated and continued B-cell-depleting treatment (ie, ocrelizumab or rituximab). Exposures: Patients received standard immunotherapies or were untreated. Main Outcomes and Measures: In cohort 1, sGFAP and sNfL levels were measured longitudinally using Simoa assays. Healthy control samples served as the reference. In cohort 2, sGFAP and sNfL levels were determined cross-sectionally. Results: This study included a total of 355 patients (103 [29.0%] in cohort 1: median [IQR] age, 42.1 [33.2-47.6] years; 73 female patients [70.9%]; and 252 [71.0%] in cohort 2: median [IQR] age, 44.3 [33.3-54.7] years; 156 female patients [61.9%]) and 259 healthy controls with a median [IQR] age of 44.3 [36.3-52.3] years and 177 female individuals (68.3%). sGFAP levels in controls increased as a function of age (1.5% per year; P < .001), were inversely correlated with BMI (-1.1% per BMI unit; P = .01), and were 14.9% higher in women than in men (P = .004). In cohort 1, patients with worsening progressive MS showed 50.9% higher sGFAP levels compared with those with stable MS after additional sNfL adjustment, whereas the 25% increase of sNfL disappeared after additional sGFAP adjustment. Higher sGFAP at baseline was associated with accelerated gray matter brain volume loss (per doubling: 0.24% per year; P < .001) but not white matter loss. sGFAP levels remained unchanged during disease exacerbations vs remission phases. In cohort 2, median (IQR) sGFAP z scores were higher in patients developing future confirmed disability worsening compared with those with stable disability (1.94 [0.36-2.23] vs 0.71 [-0.13 to 1.73]; P = .002); this was not significant for sNfL. However, the combined elevation of z scores of both biomarkers resulted in a 4- to 5-fold increased risk of confirmed disability worsening (hazard ratio [HR], 4.09; 95% CI, 2.04-8.18; P < .001) and PIRA (HR, 4.71; 95% CI, 2.05-9.77; P < .001). Conclusions and Relevance: Results of this cohort study suggest that sGFAP is a prognostic biomarker for future PIRA and revealed its complementary potential next to sNfL. sGFAP may serve as a useful biomarker for disease progression in MS in individual patient management and drug development.

Development, validation and clinical usefulness of a prognostic model for relapse in relapsing-remitting multiple sclerosis.

BACKGROUND: Prognosis for the occurrence of relapses in individuals with relapsing-remitting multiple sclerosis (RRMS), the most common subtype of multiple sclerosis (MS), could support individualized decisions and disease management and could be helpful for efficiently selecting patients for future randomized clinical trials. There are only three previously published prognostic models on this, all of them with important methodological shortcomings. OBJECTIVES: We aim to present the development, internal validation, and evaluation of the potential clinical benefit of a prognostic model for relapses for individuals with RRMS using real-world data. METHODS: We followed seven steps to develop and validate the prognostic model: (1) selection of prognostic factors via a review of the literature, (2) development of a generalized linear mixed-effects model in a Bayesian framework, (3) examination of sample size efficiency, (4) shrinkage of the coefficients, (5) dealing with missing data using multiple imputations, (6) internal validation of the model. Finally, we evaluated the potential clinical benefit of the developed prognostic model using decision curve analysis. For the development and the validation of our prognostic model, we followed the TRIPOD statement. RESULTS: We selected eight baseline prognostic factors: age, sex, prior MS treatment, months since last relapse, disease duration, number of prior relapses, expanded disability status scale (EDSS) score, and number of gadolinium-enhanced lesions. We also developed a web application that calculates an individual's probability of relapsing within the next 2 years. The optimism-corrected c-statistic is 0.65 and the optimism-corrected calibration slope is 0.92. For threshold probabilities between 15 and 30%, the "treat based on the prognostic model" strategy leads to the highest net benefit and hence is considered the most clinically useful strategy. CONCLUSIONS: The prognostic model we developed offers several advantages in comparison to previously published prognostic models on RRMS. Importantly, we assessed the potential clinical benefit to better quantify the clinical impact of the model. Our web application, once externally validated in the future, could be used by patients and doctors to calculate the individualized probability of relapsing within 2 years and to inform the management of their disease.