Comparison of Alternative Methods to Assess the Cost-Effectiveness of Tumor-Agnostic Therapies: A Triangulation Approach Using Larotrectinib as a Case Study.

OBJECTIVES: The study objective was to investigate the economic value of tumor-agnostic therapies when only single-arm effectiveness data are available at launch by applying multiple methodologies to establish comparative effectiveness. METHODS: In the absence of direct comparative data, 3 methods were used to estimate the counterfactual: (1) a historical control based on a systematic literature review for each tumor site from the larotrectinib trials, (2) an intracohort comparison using the previous line of therapy time to progression from larotrectinib trials, and (3) a nonresponder control that applied outcomes for larotrectinib nonresponders. Cost-effectiveness was modeled using the partitioned survival approach. Stochastic parameter uncertainty was assessed in a probabilistic sensitivity analysis (PSA). A triangulated estimate of the mean cost-effectiveness result was generated combining all 3 counterfactual estimates. RESULTS: Incremental cost-effectiveness ratios were similar across the 3 methodologies in the deterministic analysis ranging from £83 868 (95% uncertainty interval [UI] £65 698-£107 668) to £104 922 per quality-adjusted life-year (95% UI £80 132-£139 658). PSA results for each method substantially overlapped when plotted on the cost-effectiveness plane. Weighting PSA results for each method equally in the triangulation method produced an incremental cost-effectiveness ratios of £95 587 per quality-adjusted life-year gained (95% UI £70 449-£137 431). CONCLUSIONS: In the absence of direct comparative data, different methods of estimating a counterfactual are possible, each with strengths and limitations. Triangulating results across the methods provides a composite view of the total uncertainty and a more consistent estimation of the cost-effectiveness of the tumor-agnostic intervention compared with choosing a single method.

Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure.

Simulating the brain-body-environment trinity in closed loop is an attractive proposal to investigate how perception, motor activity and interactions with the environment shape brain activity, and vice versa. The relevance of this embodied approach, however, hinges entirely on the modeled complexity of the various simulated phenomena. In this article, we introduce a software framework that is capable of simulating large-scale, biologically realistic networks of spiking neurons embodied in a biomechanically accurate musculoskeletal system that interacts with a physically realistic virtual environment. We deploy this framework on the high performance computing resources of the EBRAINS research infrastructure and we investigate the scaling performance by distributing computation across an increasing number of interconnected compute nodes. Our architecture is based on requested compute nodes as well as persistent virtual machines; this provides a high-performance simulation environment that is accessible to multi-domain users without expert knowledge, with a view to enable users to instantiate and control simulations at custom scale via a web-based graphical user interface. Our simulation environment, entirely open source, is based on the Neurorobotics Platform developed in the context of the Human Brain Project, and the NEST simulator. We characterize the capabilities of our parallelized architecture for large-scale embodied brain simulations through two benchmark experiments, by investigating the effects of scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a large-scale balanced network, while the second one is a multi-region embodied brain simulation consisting of more than a million neurons and a billion synapses. Both benchmarks clearly show how scaling compute resources improves the aforementioned performance metrics in a near-linear fashion. The second benchmark in particular is indicative of both the potential and limitations of a highly distributed simulation in terms of a trade-off between computation speed and resource cost. Our simulation architecture is being prepared to be accessible for everyone as an EBRAINS service, thereby offering a community-wide tool with a unique workflow that should provide momentum to the investigation of closed-loop embodiment within the computational neuroscience community.

Brain simulation as a cloud service: The Virtual Brain on EBRAINS.

The Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic conversion of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation.

The economic burden of bronchiectasis - known and unknown: a systematic review.

BACKGROUND: The increasing prevalence and recognition of bronchiectasis in clinical practice necessitates a better understanding of the economic disease burden to improve the management and achieve better clinical and economic outcomes. This study aimed to assess the economic burden of bronchiectasis based on a review of published literature. METHODS: A systematic literature review was conducted using MEDLINE, Embase, EconLit and Cochrane databases to identify publications (1 January 2001 to 31 December 2016) on the economic burden of bronchiectasis in adults. RESULTS: A total of 26 publications were identified that reported resource use and costs associated with management of bronchiectasis. Two US studies reported annual incremental costs of bronchiectasis versus matched controls of US$5681 and US$2319 per patient. Twenty-four studies reported on hospitalization rates or duration of hospitalization for patients with bronchiectasis. Mean annual hospitalization rates per patient, reported in six studies, ranged from 0.3-1.3, while mean annual age-adjusted hospitalization rates, reported in four studies, ranged from 1.8-25.7 per 100,000 population. The average duration of hospitalization, reported in 12 studies, ranged from 2 to 17 days. Eight publications reported management costs of bronchiectasis. Total annual management costs of €3515 and €4672 per patient were reported in two Spanish studies. Two US studies reported total costs of approximately US$26,000 in patients without exacerbations, increasing to US$36,00-37,000 in patients with exacerbations. Similarly, a Spanish study reported higher total annual costs for patients with > 2 exacerbations per year (€7520) compared with those without exacerbations (€3892). P. aeruginosa infection increased management costs by US$31,551 to US$56,499, as reported in two US studies, with hospitalization being the main cost driver. CONCLUSIONS: The current literature suggests that the economic burden of bronchiectasis in society is significant. Hospitalization costs are the major driver behind these costs, especially in patients with frequent exacerbations. However, the true economic burden of bronchiectasis is likely to be underestimated because most studies were retrospective, used ICD-9-CM coding to identify patients, and often ignored outpatient burden and cost. We present a conceptual framework to facilitate a more comprehensive assessment of the true burden of bronchiectasis for individuals, healthcare systems and society.

Review: High-performance computing to detect epistasis in genome scale data sets.

It is becoming clear that most human diseases have a complex etiology that cannot be explained by single nucleotide polymorphisms (SNPs) or simple additive combinations; the general consensus is that they are caused by combinations of multiple genetic variations. The limited success of some genome-wide association studies is partly a result of this focus on single genetic markers. A more promising approach is to take into account epistasis, by considering the association of multiple SNP interactions with disease. However, as genomic data continues to grow in resolution, and genome and exome sequencing become more established, the number of combinations of variants to consider increases rapidly. Two potential solutions should be considered: the use of high-performance computing, which allows us to consider a larger number of variables, and heuristics to make the solution more tractable, essential in the case of genome sequencing. In this review, we look at different computational methods to analyse epistatic interactions within disease-related genetic data sets created by microarray technology. We also review efforts to use epistatic analysis results to produce biomarkers for diagnostic tests and give our views on future directions in this field in light of advances in sequencing technology and variants in non-coding regions.

The Budget Impact of Biosimilar Infliximab (Remsima®) for the Treatment of Autoimmune Diseases in Five European Countries.

INTRODUCTION: Inflammatory autoimmune diseases (rheumatoid arthritis, ankylosing spondylitis, Crohn's disease, ulcerative colitis, psoriasis, and psoriatic arthritis) have a considerable impact on patients' quality of life and healthcare budgets. Biosimilar infliximab (Remsima(®)) has been authorized by the European Medicines Agency for the management of inflammatory autoimmune diseases based on a data package demonstrating efficacy, safety, and quality comparable to the reference infliximab product (Remicade(®)). This analysis aims to estimate the 1-year budget impact of the introduction of Remsima in five European countries. METHODS: A budget impact model for the introduction of Remsima in Germany, the UK, Italy, the Netherlands, and Belgium was developed over a 1-year time horizon. Infliximab-naïve and switch patient groups were considered. Only direct drug costs were included. The model used the drug-acquisition cost of Remicade. The list price of Remsima was not known at the time of the analysis, and was assumed to be 10-30% less than that of Remicade. Key variables were tested in the sensitivity analysis. RESULTS: The annual cost savings resulting from the introduction of Remsima were projected to range from €2.89 million (Belgium, 10% discount) to €33.80 million (Germany, 30% discount). If any such savings made were used to treat additional patients with Remsima, 250 (Belgium, 10% discount) to 2602 (Germany, 30% discount) additional patients could be treated. The cumulative cost savings across the five included countries and the six licensed disease areas were projected to range from €25.79 million (10% discount) to €77.37 million (30% discount). Sensitivity analyses showed the number of patients treated with infliximab to be directly correlated with projected cost savings, with disease prevalence and patient weight having a smaller impact, and incidence the least impact. CONCLUSION: The introduction of Remsima could lead to considerable drug cost-related savings across the six licensed disease areas in the five European countries. FUNDING: Mundipharma International Ltd.

Using evolutional properties of gene networks in understanding survival prognosis of glioblastoma.

Previously, we investigated survival prognosis of glioblastoma by applying a gene regulatory approach to a human glioblastoma dataset. Here, we further extend our understanding of survival prognosis of glioblastoma by refining the network inference technique we apply to the glioblastoma dataset with the intent of uncovering further topological properties of the networks. For this study, we modify the approach by specifically looking at both positive and negative correlations separately, as opposed to absolute correlations. There is great interest in applying mathematical modeling approaches to cancer cell line datasets to generate network models of gene regulatory interactions. Analysis of these networks using graph theory metrics can identify genes of interest. The principal approach for modeling microarray datasets has been to group all the cell lines together into one overall network, and then, analyze this network as a whole. As per the previous study, we categorize a human glioblastoma cell line dataset into five categories based on survival data, and analyze each category separately using both negative and positive correlation networks constructed using a modified version of the WGCNA algorithm. Using this approach, we identified a number of genes as being important across different survival stages of the glioblastoma cell lines.