GUT MICROBIOTE DISTINCTIVE FEATURES IN PARKINSON DISEASE: FOCUS ON DUODOPA AND OTHER ANTIPARKINSONIAN DRUGS

Background: Recent data suggest that imbalances in the composition of the intestinal microbiome could trigger and / or exacerbate the progression of PD. It is also hypothesized that such modifications are influenced by extrinsic factors and the specific effect of Levodopa (LD) and in particular of LD-carbidopa intestinal gel (LCIG) has not been evaluated so far Objective: The aim of this study was to confirm whether the faecal microbiota of PD patients differs from that of control subjects and to identify the effect of LD and LCIG on the gut microbiota composition. Methods: We analyzed the gut microbiota in 107 PD cases and 25 healthy controls by next-generation sequencing of the variable V3 and V4 regions of the 16S rRNA gene. Subjects were divided in three treatment groups, LD-Group, LCIG-Group and Naïve-Group, patients who have not assumed any medicaments containing LD at the moment of recruitment. The fecal samples were collected and stored at -80°C before DNA extraction, library preparation and sequencing by MiSeq platform (Illumina). Statistical analyses were performed including the corrections for several potential confounders and multiple comparisons. Results: Independent microbial signatures were detected for treated and untreated PD versus Control-Group. After correction for confounders LD-Group showed a reduction of Bacteroides and Firmicutes phyla, while Veillonella genus (Firmicutes) and Serratia entomophila (Proteobacteria) were increased. LCIG-Group showed an increase in the abbundance in Proteobacteria, a reduction of Brevibacteriaceae and of Blautia. LCIG-Group showed a significant higher abbundance of Enterobacteriaceae family, Escherichia and Serratia genera compared to LD-Group. Conclusion: Our results suggest that LD and mostly its intraudodenal injection (LCIG) might significantly influence microbiota composition with a potential pro-inflammatory effect. Further studies with larger cohorts and high-resolution sequencing methods are required to better define the causal link between these changes and PD pathogenesis

A class of two-sample nonparametric statistics for binary and time-to-event outcomes

© The Author(s) 2021We propose a class of two-sample statistics for testing the equality of proportions and the equality of survival functions. We build our proposal on a weighted combination of a score test for the difference in proportions and a Weighted Kaplan-Meier statistic-based test for the difference of survival functions. The proposed statistics are fully non-parametric and do not rely on the proportional hazards assumption for the survival outcome. We present the asymptotic distribution of these statistics, propose a variance estimator and show their asymptotic properties under fixed and local alternatives. We discuss different choices of weights including those that control the relative relevance of each outcome and emphasize the type of difference to be detected in the survival outcome. We evaluate the performance of these statistics with a simulation study, and illustrate their use with a randomized phase III cancer vaccine trial. We have implemented the proposed statistics in the R package SurvBin, available on GitHub (this https URL).This work was supported by the Ministerio de Ciencia e Innovación (Spain) under Grants PID2019-104830RB-I00; the Departament d’Empresa i Coneixement de la Generalitat de Catalunya (Spain) under Grant 2017 SGR 622 (GRBIO); and the Ministerio de Economía y Competitividad (Spain), through the María de Maeztu Programme for Units of Excellence in R&D under Grant MDM-2014-0445 to M. Bofill Roig.Peer ReviewedPostprint (published version

Design of phase III trials with long-term survival outcomes based on short-term binary results

Pathologic complete response (pCR) is a common primary endpoint for a phase II trial or even accelerated approval of neoadjuvant cancer therapy. If granted, a two-arm confirmatory trial is often required to demonstrate the efficacy with a time-to-event outcome such as overall survival. However, the design of a subsequent phase III trial based on prior information on the pCR effect is not straightforward. Aiming at designing such phase III trials with overall survival as primary endpoint using pCR information from previous trials, we consider a mixture model that incorporates both the survival and the binary endpoints. We propose to base the comparison between arms on the difference of the restricted mean survival times, and show how the effect size and sample size for overall survival rely on the probability of the binary response and the survival distribution by response status, both for each treatment arm. Moreover, we provide the sample size calculation under different scenarios and accompany them with the R package survmixer where all the computations have been implemented. We evaluate our proposal with a simulation study, and illustrate its application through a neoadjuvant breast cancer trial.Generalitat de Catalunya, 2017 SGR 622; Ministerio de Ciencia e Innovación, MTM2015-64465-C2-1-R; PID2019-104830RB-I00; Ministerio de Economía y Competitividad, MDM-2014-0445; National Cancer Institute, National Institutes of Health, CA016672Peer ReviewedPostprint (author's final draft

Design of Trials with Composite Endpoints with the R Package CompAREdesign

Composite endpoints are widely used as primary endpoints in clinical trials. Designing trials with time-to-event endpoints can be particularly challenging because the proportional hazard assumption usually does not hold when using a composite endpoint, even when the premise remains true for their components. Consequently, the conventional formulae for sample size calculation do not longer apply. We present the R package CompAREdesign by means of which the key elements of trial designs, such as the sample size and effect sizes, can be computed based on the information on the composite endpoint components. CompAREdesign provides the functions to assess the sensitivity and robustness of design calculations to variations in initial values and assumptions. Furthermore, we describe other features of the package, such as functions for the design of trials with binary composite endpoints, and functions to simulate trials with composite endpoints under a wide range of scenarios

Design of trials with composite endpoints with the R package compAREdesign

Composite endpoints are widely used as primary endpoints in clinical trials. Designing trials with time-to-event endpoints can be particularly challenging because the proportional hazard assumption usually does not hold when using a composite endpoint, even when the premise remains true for their components. Consequently, the conventional formulae for sample size calculation do not longer apply. We present the R package CompAREdesign by means of which the key elements of trial designs, such as the sample size and effect sizes, can be computed based on the information on the composite endpoint components. CompAREdesign provides the functions to assess the sensitivity and robustness of design calculations to variations in initial values and assumptions. Furthermore, we describe other features of the package, such as functions for the design of trials with binary composite endpoints, and functions to simulate trials with composite endpoints under a wide range of scenarios.This work was supported by the Ministerio de Economía y Competitividad (Spain) under Grant PID2019- 104830RB-I00 and the Departament d’Empresa i Coneixement de la Generalitat de Catalunya (Spain) under Grant 2017 SGR 622 (GRBIO). Marta Bofill Roig is a member of the EU Patient Centric Clinical Trial Platforms (EU-PEARL). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA and Children’s Tumor Foundation, Global Alliance for TB Drug Development nonprofit organization, Springworks Therapeutics Inc. This publication reflects the authors’ views. Neither IMI nor the European Union, EFPIA, nor any Associated Partners are responsible for any use that may be made of the information contained herein.Peer ReviewedPostprint (author's final draft

An integrated framework for non-traditional machining process technology selection in healthcare applications

In spite of continuous progress in technical advancement, the conventional machining process became unsatisfactory in the healthcare field due to its disadvantages. This inadequacy lead researchers to consider using the application of nontraditional machining that can machine extremely hard and brittle materials into complicated shapes such as medical devices and implants in healthcare. In this study, the three most popular nontraditional machining process technologies: Laser Beam Machining, Water Jet Machining, and Electrocautery are evaluated to determine the most appropriate technology using the Health Technology Assessment based Multi-criteria Decision-Making framework. HTA is organized evaluation of effects and properties of health technology that enables the application of systematic skills to solve a health problem. HTA's main goal is to raise awareness of new health technologies among decision makers. For these reasons, the HTA core model that enables the production of HTA-related information was utilized.The comparison of selected technologies was carried out via integrating the HTA core model, Best Worst, and Evaluation Based on Distance from Average Solution methods. Finally, a comparison was made to find the most suitable technology to create the necessary infrastructure. As a result, evaluation scores were computed as 0,673; 0,538 and 0,500 for WJM, LBM, and EC, respectively.Vedecká Grantová Agentúra MŠVVaŠ SR a SA

Sex-specific tonic 2-arachidonoylglycerol signaling at inhibitory inputs onto dopamine neurons of Lister Hooded rats

Addiction as a psychiatric disorder involves interaction of inherited predispositions and environmental factors. Similarly to humans, laboratory animals self-administer addictive drugs, whose appetitive properties result from activation and suppression of brain reward and aversive pathways, respectively. The ventral tegmental area (VTA) where dopamine (DA) cells are located is a key component of brain reward circuitry, whereas the rostromedial tegmental nucleus (RMTg) critically regulates aversive behaviors. Reduced responses to either aversive intrinsic components of addictive drugs or to negative consequences of compulsive drug taking might contribute to vulnerability to addiction. In this regard, female Lister Hooded (LH) rats are more vulnerable than male counterparts to cannabinoid self-administration. We, therefore, took advantage of sex differences displayed by LH rats, and studied VTA DA neuronal properties to unveil functional differences. Electrophysiological properties of DA cells were examined performing either single cell extracellular recordings in anesthetized rats or whole-cell patch-clamp recordings in slices. In vivo, DA cell spontaneous activity was similar, though sex differences were observed in RMTg-induced inhibition of DA neurons. In vitro, DA cells showed similar intrinsic and synaptic properties. However, females displayed larger depolarization-induced suppression of inhibition (DSI) than male LH rats. DSI, an endocannabinoid-mediated form of short term plasticity, was mediated by 2-arachidonoylglycerol (2-AG) activating type 1-cannabinoid (CB1) receptors. We found that sex-dependent differences in DSI magnitude were not ascribed to CB1 number and/or function, but rather to a tonic 2-AG signaling. We suggest that sex specific tonic 2-AG signaling might contribute to regulate responses to aversive intrinsic properties to cannabinoids, thus resulting in faster acquisition/initiation of cannabinoid taking and, eventually, in progression to addiction