Objective:To identify differences in the metabolomic profile in the serum of patients with multiple sclerosis (MS) compared to controls and to identify biomarkers of disease severity.Methods:We studied 2 cohorts of patients with MS: a retrospective longitudinal cohort of 238 patients and 74 controls and a prospective cohort of 61 patients and 41 controls with serial serum samples. Patients were stratified into active or stable disease based on 2 years of prospective assessment accounting for presence of clinical relapses or changes in disability measured with the Expanded Disability Status Scale (EDSS). Metabolomic profiling (lipids and amino acids) was performed by ultra-high-performance liquid chromatography coupled to mass spectrometry in serum samples. Data analysis was performed using parametric methods, principal component analysis, and partial least square discriminant analysis for assessing the differences between cases and controls and for subgroups based on disease severity.Results:We identified metabolomics signatures with high accuracy for classifying patients vs controls as well as for classifying patients with medium to high disability (EDSS >3.0). Among them, sphingomyelin and lysophosphatidylethanolamine were the metabolites that showed a more robust pattern in the time series analysis for discriminating between patients and controls. Moreover, levels of hydrocortisone, glutamic acid, tryptophan, eicosapentaenoic acid, 13S-hydroxyoctadecadienoic acid, lysophosphatidylcholines, and lysophosphatidylethanolamines were associated with more severe disease (non-relapse-free or increase in EDSS).Conclusions:We identified metabolomic signatures composed of hormones, lipids, and amino acids associated with MS and with a more severe course.
Background: Recent developments have meant that network theory is making an important contribution to the topological study of biological networks, such as protein-protein interaction (PPI) networks. The identification of differentially expressed genes in DNA array experiments is a source of information regarding the molecular pathways involved in disease. Thus, considering PPI analysis and gene expression studies together may provide a better understanding of multifactorial neurodegenerative diseases such as Multiple Sclerosis (MS) and Alzheimer disease (AD). The aim of this study was to assess whether the parameters of degree and betweenness, two fundamental measures in network theory, are properties that differentiate between implicated (seed-proteins) and non-implicated nodes (neighbors) in MS and AD. We used experimentally validated PPI information to obtain the neighbors for each seed group and we studied these parameters in four networks: MS-blood network; MS-brain network; AD-blood network; and AD-brain network.
BackgroundChronic spontaneous urticaria (CSU) negatively impacts patient quality of life and productivity and is associated with considerable indirect costs to society.ObjectiveThe aim of this study was to assess the cost utility of add-on omalizumab treatment compared with standard of care (SOC) in moderate or severe CSU patients with inadequate response to SOC, from the UK societal perspective.MethodsA Markov model was developed, consisting of health states based on Urticaria Activity Score over 7 days (UAS7) and additional states for relapse, spontaneous remission and death. Model cycle length was 4 weeks, and total model time horizon was 20 years in the base case. The model considered early discontinuation of non-responders (response: UAS7 ≤6) and retreatment upon relapse (relapse: UAS7 ≥16) for responders. Clinical and cost inputs were derived from omalizumab trials and published sources, and cost utility was expressed as incremental cost-effectiveness ratios (ICERs). Scenario analyses included no early discontinuation of non-responders and an altered definition of response (UAS7 <16).ResultsWith a deterministic ICER of £3183 in the base case, omalizumab was associated with increased costs and benefits relative to SOC. Probabilistic sensitivity analysis supported this result. Productivity inputs were key model drivers, and individual scenarios without early discontinuation of non-responders and adjusted response definitions had little impact on results. ICERs were generally robust to changes in key model parameters and inputs.ConclusionsIn this, the first economic evaluation of omalizumab in CSU from a UK societal perspective, omalizumab consistently represented a treatment option with societal benefit for CSU in the UK across a range of scenarios.Electronic supplementary materialThe online version of this article (doi:10.1007/s40273-016-0412-1) contains supplementary material, which is available to authorized users.
BACKGROUNDLantus, the reference insulin glargine used for the treatment of diabetes, lost its patent protection in 2014, opening the market to biosimilar competitors. OBJECTIVEFirst, to analyze the adoption rates of insulin glargine biosimilars in primary care in England and estimate the savings realized and missed, since an insulin glargine biosimilar was first used, and second, to assess potential variations in adoption rates across Clinical Commissioning Groups (CCGs). RESEARCH DESIGN AND METHODSData sets capturing information on all insulin glargine items prescribed by all general practitioners up to December 2018 were used. Total costs of insulin glargine and uptake rates of biosimilars were calculated. The real-world budget impact was estimated assuming the cost of reference insulin glargine for all items and comparing the total costs in this scenario with the total costs in the real world. The missed savings were estimated assuming the cost of biosimilars for all insulin glargine items. Choropleth maps were generated to assess potential variations in uptake across CCGs. RESULTSInsulin glargine biosimilars generated savings of £900,000 between October 2015 (time of first prescription) and December 2018. The missed savings amounted to £25.6 million in this period, indicating that only 3.42% of the potential savings were achieved. The analyses demonstrated a large level of variation in the uptake of insulin glargine biosimilars across CCGs, with market shares ranging from 0 to 53.3% (December 2018). CONCLUSIONSThese results may encourage decision makers in England to promote the use of bestvalue treatments in primary care and to reevaluate variation across CCGs.Biosimilars, as defined by the European Medicines Agency (EMA), are medicines considered to be highly similar to another medicine already marketed in the European Union (i.e., the reference product). Owing to the natural variability associated with the production of biological medicines, the EMA acknowledges that minor differences can exist between the biosimilar and its reference product but that these are not
Objective To predict the real-world (RW) cost-effectiveness of carfilzomib in combination with lenalidomide and dexamethasone (KRd) versus lenalidomide and dexamethasone (Rd) in relapsed multiple myeloma (MM) patients after one to three prior therapies. Methods A partitioned survival model that included three health states (progression-free, progressed disease and death) was built. Progression-free survival (PFS), overall survival (OS) and time to discontinuation (TTD) data for the Rd arm were derived using the Registry of Monoclonal Gammopathies in the Czech Republic; the relative treatment effects of KRd versus Rd were estimated from the phase 3, randomised, ASPIRE trial, and were used to predict PFS, OS and TTD for KRd. The model was developed from the payer perspective and included drug costs, administration costs, monitoring costs, palliative care costs and adverse-event related costs collected from Czech sources. Results The base case incremental cost effectiveness ratio for KRd compared with Rd was €73,156 per quality-adjusted life year (QALY) gained. Patients on KRd incurred costs of €117,534 over their lifetime compared with €53,165 for patients on Rd. The QALYs gained were 2.63 and 1.75 for patients on KRd and Rd, respectively. Conclusions Combining the strengths of randomised controlled trials and observational databases in cost-effectiveness models can generate policy-relevant results to allow well-informed decision-making. The current model showed that KRd is likely to be cost-effective versus Rd in the RW and, therefore, the reimbursement of KRd represents an efficient allocation of resources within the healthcare system.
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