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GSK3358699 is a mononuclear myeloid-targeted bromodomain and extra-terminal domain (BET) family inhibitor which demonstrates immunomodulatory effects in vitro. This phase 1, randomized, first-in-human study evaluated the safety, pharmacokinetics, and pharmacodynamics of GSK3358699 in healthy male participants (NCT03426995).Part A (N = 23) included three dose-escalating periods of 1-40 mg of GSK3358699 or placebo in two cohorts in a single ascending-dose crossover design. Part C (N = 25) was planned as an initial dose of 10 mg of GSK3358699 or placebo daily for 14 days followed by selected doses in four sequential cohorts.In part A, exposure to GSK3358699 and its metabolite GSK3206944 generally increased with increasing doses. The median initial half-life ranged from 0.7 to 1.1 (GSK3358699) and 2.1 to 2.9 (GSK3206944) hours after a single dose of 1-40 mg. GSK3206944 concentrations in monocytes were quantifiable at 1-hour post-dose following 10 mg of GSK3358699 and 1 and 4 hours post-dose following 20-40 mg. Mean predicted percentage inhibition of ex vivo lipopolysaccharide-induced monocyte chemoattractant protein (MCP)-1 reached 75% with 40 mg of GSK3358699. GSK3358699 did not inhibit interleukin (IL)-6 and tumour necrosis factor (TNF). The most common adverse event (AE) was headache. Four AEs of nonsustained ventricular tachycardia were observed across parts A and C. One serious AE of atrial fibrillation (part C) required hospitalization.Single doses of GSK3358699 are generally well tolerated with significant metabolite concentrations detected in target cells. A complete assessment of pharmacodynamics was limited by assay variability. A causal relationship could not be excluded for cardiac-related AEs, resulting in an inability to identify a suitable repeat-dose regimen and study termination.

AIM: Plants that host root‐symbiotic nitrogen‐fixing bacteria have an important role in driving terrestrial ecosystem processes, but N‐fixing ability is unequally distributed among plant taxa and ecosystems. Here we explore the large‐scale distribution of N‐fixing plant species worldwide. LOCATION: Global. TIME PERIOD: Present. MAJOR TAXA STUDIED: Vascular plants. METHODS: We estimated root‐symbiotic N‐fixing plant species diversity (as Shannon entropy) and relative richness (log‐ratio of N‐fixing to non‐fixing plant species) for c. 7,800 km² hexagonal grid cells using the NodDB and Global Biodiversity Information Facility (GBIF) databases. Additionally, we explored the distributions of plant species associated with rhizobia, actinobacteria or cyanobacteria (relative to other plant species), and the relative richness of N‐fixing trees (log‐ratio of N‐fixing to non‐fixing trees). We related N‐fixing plant species distribution to environmental (climate, soil) and biogeographical (biome, realm) variables using multiple linear regression. RESULTS: N‐fixing plant diversity and relative richness showed unimodal relationships with latitude. Diversity of N‐fixing plants was highest in warm and wet climates, but in dry biomes and in Australasia. The relative richness of N‐fixing plants was highest in warm and dry climates, in tropical and temperate grasslands and in Eurasia. Plants associated with cyanobacteria were more widely distributed near the equator, while those associated with rhizobia were more prevalent at the edge of the tropics, and those associated with actinobacteria at higher latitudes (especially in boreal forests). The relative richness of N‐fixing tree species was highest in cold and dry areas and in boreal forests, with contrasting peaks in the Northern and Southern Hemispheres. MAIN CONCLUSIONS: The distribution of N‐fixing plant species exhibits regional hotspots and coldspots related to both environmental conditions and biogeographical history. Global N‐fixing plant distributions are different for the key root‐symbiotic bacterial groups. Information about N‐fixing plant distribution can improve global models of ecosystem functions and contribute to understanding how plants respond to global change.

Summary Randomised controlled trials are considered the gold standard in trial design. However, phase II oncology trials with a binary outcome are often single-arm. Although a number of reasons exist for choosing a single-arm trial, the primary reason is that single-arm designs require fewer participants than their randomised equivalents. Therefore, the development of novel methodology that makes randomised designs more efficient is of value to the trials community. This article introduces a randomised two-arm binary outcome trial design that includes stochastic curtailment (SC), allowing for the possibility of stopping a trial before the final conclusions are known with certainty. In addition to SC, the proposed design involves the use of a randomised block design, which allows investigators to control the number of interim analyses. This approach is compared with existing designs that also use early stopping, through the use of a loss function comprised of a weighted sum of design characteristics. Comparisons are also made using an example from a real trial. The comparisons show that for many possible loss functions, the proposed design is superior to existing designs. Further, the proposed design may be more practical, by allowing a flexible number of interim analyses. One existing design produces superior design realisations when the anticipated response rate is low. However, when using this design, the probability of rejecting the null hypothesis is sensitive to misspecification of the null response rate. Therefore, when considering randomised designs in phase II, we recommend the proposed approach be preferred over other sequential designs.

Objective: There is scarce research on the effects of mindfulness in individual therapy. As many practitioners integrate mindfulness exercises into individual therapy, empirical evidence is of high clinical relevance. Method: We investigated the effects of a session?introducing intervention with mindfulness elements (SIIME) in a randomized, controlled design. The effects of SIIME on therapeutic alliance and symptomatic outcome were compared with progressive muscle relaxation (PMR) and treatment?as?usual (TAU) control conditions. The sample comprised 162 patients with anxiety and depression. Results: Multilevel modeling revealed a significant symptom reduction and significant increase of alliance over the course of therapy. There were no significant time-condition interactions on outcome and alliance, indicating the comparable efficiency of all three treatment conditions. Conclusions: We found no advantage of SIIME versus PMR and TAU. Add?on mindfulness might not improve individual therapy related to alliance and outcome. (DIPF/Orig.)

Funder: National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre, A standard two-arm randomised controlled trial usually compares an intervention to a control treatment with equal numbers of patients randomised to each treatment arm and only data from within the current trial are used to assess the treatment effect. Historical data are used when designing new trials and have recently been considered for use in the analysis when the required number of patients under a standard trial design cannot be achieved. Incorporating historical control data could lead to more efficient trials, reducing the number of controls required in the current study when the historical and current control data agree. However, when the data are inconsistent, there is potential for biased treatment effect estimates, inflated type I error and reduced power. We introduce two novel approaches for binary data which discount historical data based on the agreement with the current trial controls, an equivalence approach and an approach based on tail area probabilities. An adaptive design is used where the allocation ratio is adapted at the interim analysis, randomising fewer patients to control when there is agreement. The historical data are down-weighted in the analysis using the power prior approach with a fixed power. We compare operating characteristics of the proposed design to historical data methods in the literature: the modified power prior; commensurate prior; and robust mixture prior. The equivalence probability weight approach is intuitive and the operating characteristics can be calculated exactly. Furthermore, the equivalence bounds can be chosen to control the maximum possible inflation in type I error.

Numerous publications have now addressed the principles of designing, analyzing, and reporting the results of stepped-wedge cluster randomized trials. In contrast, there is little research available pertaining to the design and analysis of multiarm stepped-wedge cluster randomized trials, utilized to evaluate the effectiveness of multiple experimental interventions. In this paper, we address this by explaining how the required sample size in these multiarm trials can be ascertained when data are to be analyzed using a linear mixed model. We then go on to describe how the design of such trials can be optimized to balance between minimizing the cost of the trial and minimizing some function of the covariance matrix of the treatment effect estimates. Using a recently commenced trial that will evaluate the effectiveness of sensor monitoring in an occupational therapy rehabilitation program for older persons after hip fracture as an example, we demonstrate that our designs could reduce the number of observations required for a fixed power level by up to 58%. Consequently, when logistical constraints permit the utilization of any one of a range of possible multiarm stepped-wedge cluster randomized trial designs, researchers should consider employing our approach to optimize their trials efficiency.

Numerous publications have now addressed the principles of designing, analyzing, and reporting the results of stepped-wedge cluster randomized trials. In contrast, there is little research available pertaining to the design and analysis of multiarm stepped-wedge cluster randomized trials, utilized to evaluate the effectiveness of multiple experimental interventions. In this paper, we address this by explaining how the required sample size in these multiarm trials can be ascertained when data are to be analyzed using a linear mixed model. We then go on to describe how the design of such trials can be optimized to balance between minimizing the cost of the trial and minimizing some function of the covariance matrix of the treatment effect estimates. Using a recently commenced trial that will evaluate the effectiveness of sensor monitoring in an occupational therapy rehabilitation program for older persons after hip fracture as an example, we demonstrate that our designs could reduce the number of observations required for a fixed power level by up to 58%. Consequently, when logistical constraints permit the utilization of any one of a range of possible multiarm stepped-wedge cluster randomized trial designs, researchers should consider employing our approach to optimize their trials efficiency.

In order for a tumour to continue to grow and disseminate, it must acquire a new blood supply. Neovascularisation can be enacted by a number of different mechanisms. This dependence of tumour progression on an augmented vascular supply has been exploited by the development of anti-angiogenic drugs, which are designed to inhibit new blood vessel formation or disrupt existing tumour-associated vasculature, both leading to ischaemic-hypoxic tumour cell death. However, the clinical benefits of these therapeutic approaches are frequently variable and often transient, the neoplasm sometimes being able to use other neovascularisation mechanisms to maintain its blood supply and thus evade the current anti-angiogenic therapy. Tumours may also develop a more malignant phenotype following this treatment. Clinical outcomes may be improved by simultaneously inhibiting different angiogenic pathways, abetted by more effective drug delivery regimens such as metronomic chemotherapy and the concurrent use of other antitumour modalities.

The determination of the sample size required by a crossover trial typically depends on the specification of one or more variance components. Uncertainty about the value of these parameters at the design stage means that there is often a risk a trial may be under‐ or overpowered. For many study designs, this problem has been addressed by considering adaptive design methodology that allows for the re‐estimation of the required sample size during a trial. Here, we propose and compare several approaches for this in multitreatment crossover trials. Specifically, regulators favor reestimation procedures to maintain the blinding of the treatment allocations. We therefore develop blinded estimators for the within and between person variances, following simple or block randomization. We demonstrate that, provided an equal number of patients are allocated to sequences that are balanced for period, the proposed estimators following block randomization are unbiased. We further provide a formula for the bias of the estimators following simple randomization. The performance of these procedures, along with that of an unblinded approach, is then examined utilizing three motivating examples, including one based on a recently completed four‐treatment four‐period crossover trial. Simulation results show that the performance of the proposed blinded procedures is in many cases similar to that of the unblinded approach, and thus they are an attractive alternative.

Rationale The doubly labelled water (DLW) method is the reference method for the estimation of free-living total energy expenditure (TEE). In this method, where both 2H and 18O are employed, different approaches have been adopted to deal with the non-conformity observed regarding the distribution space for the labels being non-coincident with total body water. However, the method adopted can have a significant effect on the estimated TEE. Methods We proposed a Bayesian reasoning approach to modify an assumed prior distribution for the space ratio using experimental data to derive the TEE. A Bayesian hierarchical approach was also investigated. The dataset was obtained from 59 adults (37 women) who underwent a DLW experiment during which the 2H and 18O enrichments were measured using isotope ratio mass spectrometry (IRMS). Results The TEE was estimated at 9925 (9106-11236) [median and interquartile range], 9646 (9167-10540), and 9,638 (9220-10340) kJ·day-1 for women and at 13961 (12851-15347), 13353 (12651-15088) and 13211 (12653-14238) kJ·day-1 for men, using normalized non-Bayesian, independent Bayesian and hierarchical Bayesian approaches, respectively. A comparison of hierarchical Bayesian with normalized non-Bayesian methods indicated a marked difference in behaviour between genders. The median difference was -287 kJ·day-1 for women, and -750 kJ·day-1 for men. In men there is an appreciable compression of the TEE distribution obtained from the hierarchical model compared with the normalized non-Bayesian methods (range of TEE 11234 – 15431 kJ·day-1 vs 10786 – 18221 kJ·day-1). An analogous, yet smaller, compression is seen in women (7081 – 12287 kJ·day-1 vs 6989 – 13775 kJ·day-1). Conclusions The Bayesian analysis is an appealing method to estimate TEE during DLW experiments. The principal advantages over those obtained using the classical least-squares method is the generation of potentially more useful estimates of total energy expenditure, and improved handling of outliers and missing data scenarios, particularly if a hierarchical model is used.