The Utility of Miniaturized Adsorbers in Exploring the Cellular and Molecular Effects of Blood Purification: A Pilot Study with a Focus on Immunoadsorption in Multiple Sclerosis.

Immunoadsorption (IA) has proven to be clinically effective in the treatment of steroid-refractory multiple sclerosis (MS) relapses, but its mechanism of action remains unclear. We used miniaturized adsorber devices with a tryptophan-immobilized polyvinyl alcohol (PVA) gel sorbent to mimic the IA treatment of patients with MS in vitro. The plasma was screened before and after adsorption with regard to disease-specific mediators, and the effect of the IA treatment on the migration of neutrophils and the integrity of the endothelial cell barrier was tested in cell-based models. The in vitro IA treatment with miniaturized adsorbers resulted in reduced plasma levels of cytokines and chemokines. We also found a reduced migration of neutrophils towards patient plasma treated with the adsorbers. Furthermore, the IA-treated plasma had a positive effect on the endothelial cell barrier's integrity in the cell culture model. Our findings suggest that IA results in a reduced infiltration of cells into the central nervous system by reducing leukocyte transmigration and preventing blood-brain barrier breakdown. This novel approach of performing in vitro blood purification therapies on actual patient samples with miniaturized adsorbers and testing their effects in cell-based assays that investigate specific hypotheses of the pathophysiology provides a promising platform for elucidating the mechanisms of action of those therapies in various diseases.

Vaccination status and self-reported side effects after SARS-CoV-2 vaccination in relation to psychological and clinical variables in patients with multiple sclerosis.

The recent SARS-CoV-2 pandemic and the vaccination campaign posed a challenge to patients with autoimmune disease, such as multiple sclerosis (MS). We aimed for investigating whether psychological/sociodemographic/clinical characteristics of MS patients are associated with SARS-CoV-2 vaccination status and self-reported vaccination side effects (SEs). We have asked patients with MS about their willingness to receive recommended standard vaccinations pre-pandemically since June 2019. Between 10/2021 and 01/2022, we surveyed 193 of these MS patients about their current SARS-CoV-2 vaccination status, their perception of vaccination-related SEs, and reasons for and against SARS-CoV-2 vaccination. 75.6% of the patients declared their willingness to receive standard vaccinations before the pandemic. 84.5%, 78.2%, and 13.0% of the patients had received the first, second, and third SARS-CoV-2 vaccination, respectively, until the follow-up survey. The most common reason for not getting vaccinated against SARS-CoV-2 was concern about possible side effects (82.1%), followed by the belief that the vaccines had not been adequately tested (64.3%). Vaccination-related SEs were reported by 52.8% of the patients. Younger age, higher education, lower degree of disability, relapsing disease course, shorter disease duration, not receiving a disease-modifying therapy and higher anxiety and depression levels were associated with the occurrence of certain vaccination-related SEs. Concerns about novel vaccines are widespread among MS patients and necessitate targeted education of the patients, especially to those with more severe psychopathological symptoms (anxiety or depression) and those who are generally skeptical of vaccination.

Differential gene expression in B cells and T helper cells following high-dose glucocorticoid therapy for multiple sclerosis relapse.

BACKGROUND: Despite remarkable advances in the therapy of multiple sclerosis (MS), patients with MS may still experience relapses. High-dose short-term methylprednisolone (MP) remains the standard treatment in the acute management of MS relapses due to its potent anti-inflammatory and immunosuppressive properties. However, there is a lack of studies on the cell type-specific transcriptome changes that are induced by this synthetic glucocorticoid (GC). Moreover, it is not well understood why some patients do not benefit adequately from MP therapy. METHODS: We collected peripheral blood from MS patients in relapse immediately before and after ∼3-5 days of therapy with MP at 4 study centers. CD19+ B cells and CD4+ T cells were then isolated for profiling the transcriptome with high-density arrays. The patients' improvement of neurological symptoms was evaluated after ∼2 weeks by the treating physicians. We finally analyzed the data to identify genes that were differentially expressed in response to the therapy and whose expression differed between clinical responders and non-responders. RESULTS: After MP treatment, a total of 33 genes in B cells and 55 genes in T helper cells were significantly up- or downregulated. The gene lists overlap in 10 genes and contain genes that have already been described as GC-responsive genes in the literature on other cell types and diseases. Their differential expression points to a rapid and coordinated modulation of multiple signaling pathways that influence transcription. Genes that were previously suggested as potential prognostic biomarkers of the clinical response to MP therapy could not be confirmed in our data. However, a greater increase in the expression of genes encoding proteins with antimicrobial activity was detected in CD4+ T cells from non-responders compared to responders. CONCLUSION: Our study delved into the cell type-specific effects of MP at the transcriptional level. The data suggest a therapy-induced ectopic expression of some genes (e.g., AZU1, ELANE and MPO), especially in non-responders. The biological consequences of this remain to be explored in greater depth. A better understanding of the molecular mechanisms underlying clinical recovery from relapses in patients with MS will help to optimize future treatment decisions.

Update and Application of a Deep Learning Model for the Prediction of Interactions between Drugs Used by Patients with Multiple Sclerosis.

Patients with multiple sclerosis (MS) often take multiple drugs at the same time to modify the course of disease, alleviate neurological symptoms and manage co-existing conditions. A major consequence for a patient taking different medications is a higher risk of treatment failure and side effects. This is because a drug may alter the pharmacokinetic and/or pharmacodynamic properties of another drug, which is referred to as drug-drug interaction (DDI). We aimed to predict interactions of drugs that are used by patients with MS based on a deep neural network (DNN) using structural information as input. We further aimed to identify potential drug-food interactions (DFIs), which can affect drug efficacy and patient safety as well. We used DeepDDI, a multi-label classification model of specific DDI types, to predict changes in pharmacological effects and/or the risk of adverse drug events when two or more drugs are taken together. The original model with ~34 million trainable parameters was updated using >1 million DDIs recorded in the DrugBank database. Structure data of food components were obtained from the FooDB database. The medication plans of patients with MS (n = 627) were then searched for pairwise interactions between drug and food compounds. The updated DeepDDI model achieved accuracies of 92.2% and 92.1% on the validation and testing sets, respectively. The patients with MS used 312 different small molecule drugs as prescription or over-the-counter medications. In the medication plans, we identified 3748 DDIs in DrugBank and 13,365 DDIs using DeepDDI. At least one DDI was found for most patients (n = 509 or 81.2% based on the DNN model). The predictions revealed that many patients would be at increased risk of bleeding and bradycardic complications due to a potential DDI if they were to start a disease-modifying therapy with cladribine (n = 242 or 38.6%) and fingolimod (n = 279 or 44.5%), respectively. We also obtained numerous potential interactions for Bruton's tyrosine kinase inhibitors that are in clinical development for MS, such as evobrutinib (n = 434 DDIs). Food sources most often related to DFIs were corn (n = 5456 DFIs) and cow's milk (n = 4243 DFIs). We demonstrate that deep learning techniques can exploit chemical structure similarity to accurately predict DDIs and DFIs in patients with MS. Our study specifies drug pairs that potentially interact, suggests mechanisms causing adverse drug effects, informs about whether interacting drugs can be replaced with alternative drugs to avoid critical DDIs and provides dietary recommendations for MS patients who are taking certain drugs.