Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction.

Introduction: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics. Methods: Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival. Results: SNP-based fine-mapping, TWAS and PWAS identified 117 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified five drugs significantly enriched for interactions with ALS associated genes, with directional analyses highlighting α-glucosidase inhibitors may exacerbate ALS pathology. Additionally, drug class enrichment analysis showed calcium channel blockers may reduce ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R2 = 4%; p-value = 2.1×10-21). Conclusions: Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.

To wait, or too late? Modeling the effects of delayed ofatumumab treatment in relapsing-remitting multiple sclerosis.

BACKGROUND: Several disease-modifying treatments (DMTs) for relapsing-remitting multiple sclerosis (RRMS) reduce relapse rates and slow disease progression. RRMS DMTs have varying efficacy and administration routes; DMTs prescribed first may not be the most effective on relapses or disease progression. Here, we aimed to quantify the benefit of initiating ofatumumab, a high-efficacy DMT, earlier in the treatment pathway. METHODS: Aggregate data from a real-world cohort of patients with RRMS, who were eligible for dimethyl fumarate (DMF) or ofatumumab treatment within the UK National Health Service (N = 615), were used to produce a simulated patient cohort. The cohort was tracked through a discrete event simulation (DES) model, based on the Expanded Disability Status Scale (EDSS), with a lifetime time horizon. Outcomes assessed were: mean number of relapses, time to wheelchair (EDSS ≥7), and time to death. Two modeling approaches were used. The first compared outcomes between two treatment sequences (base case: ofatumumab to natalizumab versus DMF to ofatumumab). The second incorporated a time-specific delay of 1-5 years for switching from DMF to ofatumumab; the difference in outcomes as a function of increasing delay to ofatumumab are reported. RESULTS: Compared with delayed ofatumumab, fewer relapses and increased time to wheelchair were predicted for earlier ofatumumab in the treatment-sequence approach (mean relapses over the lifetime time horizon: 8.63 versus 9.00; time to wheelchair: 17.55 versus 16.60 years). Time to death was similar for both sequences. At Year 10, a numerically greater proportion of patients receiving earlier ofatumumab had mild disease (EDSS 0-3: 44.12% versus 40.06%). Greater differences, reflecting poorer outcomes, were predicted for relapses and time to wheelchair with increasing delays to ofatumumab treatment. CONCLUSIONS: The DES model provided a means by which the magnitude of benefit associated with earlier ofatumumab initiation could be quantified; fewer relapses and a prolonged time to wheelchair were predicted.

Exposure to an Immersive Virtual Reality Environment can Modulate Perceptual Correlates of Endogenous Analgesia and Central Sensitization in Healthy Volunteers.

Virtual reality (VR) has been shown to produce analgesic effects during different experimental and clinical pain states. Despite this, the top-down mechanisms are still poorly understood. In this study, we examined the influence of both a real and sham (ie, the same images in 2D) immersive arctic VR environment on conditioned pain modulation (CPM) and in a human surrogate model of central sensitization in 38 healthy volunteers. CPM and acute heat pain thresholds were assessed before and during VR/sham exposure in the absence of any sensitization. In a follow-on study, we used the cutaneous high frequency stimulation model of central sensitization and measured changes in mechanical pain sensitivity in an area of heterotopic sensitization before and during VR/sham exposure. There was an increase in CPM efficiency during the VR condition compared to baseline (P < .01). In the sham condition, there was a decrease in CPM efficiency compared to baseline (P < .01) and the real VR condition (P < .001). Neither real nor sham VR had any effect on pain ratings reported during the conditioning period or on heat pain threshold. There was also an attenuation of mechanical pain sensitivity during the VR condition indicating a lower sensitivity compared to sham (P < .05). We conclude that exposure to an immersive VR environment has no effect over acute pain thresholds but can modulate dynamic CPM responses and mechanical hypersensitivity in healthy volunteers. PERSPECTIVE: This study has demonstrated that exposure to an immersive virtual reality environment can modulate perceptual correlates of endogenous pain modulation and secondary hyperalgesia in a human surrogate pain model. These results suggest that virtual reality could provide a novel mechanism-driven analgesic strategy in patients with altered central pain processing.