Design Considerations for Factorial Adaptive Multi-Arm Multi-Stage (FAST) Clinical Trials

Multi-Arm, Multi-Stage (MAMS) clinical trial designs allow for multiple therapies to be compared across a spectrum of clinical trial phases. MAMS designs can be categorized into several overarching design groups, including adaptive designs (AD) and multi-arm (MA) designs. Factorial clinical trials designs represent an additional group of designs which can provide increased efficiency relative to fixed, traditional designs. In this work, we explore design choices associated with Factorial Adaptive Multi-Arm Multi-Stage (FAST) designs, which represent the combination of factorial and MAMS designs. This category of trial can potentially offer benefits similar to both MAMS and factorial designs. This work is motivated by a proposed clinical trial under development

Propranolol Blocks Cardiac and Neuronal Voltage-Gated Sodium Channels

Propranolol is a widely used, non-selective β-adrenergic receptor antagonist with proven efficacy in treating cardiovascular disorders and in the prevention of migraine headaches. At plasma concentrations exceeding those required for β-adrenergic receptor inhibition, propranolol also exhibits anti-arrhythmic (“membrane stabilizing”) effects that are not fully explained by β-blockade. Previous in vitro studies suggested that propranolol may have local anesthetic effects. We directly tested the effects of propranolol on heterologously expressed recombinant human cardiac (NaV1.5) and brain (NaV1.1, NaV1.2, NaV1.3) sodium channels using whole-cell patch-clamp recording. We found that block was not stereospecific as we observed approximately equal IC50 values for tonic and use-dependent block by R-(+) and S-(−) propranolol (tonic block: R: 21.4 μM vs S: 23.6 μM; use-dependent block: R: 2.7 μM vs S: 2.6 μM). Metoprolol and nadolol did not block NaV1.5 indicating that sodium channel block is not a class effect of β-blockers. The biophysical effects of R-(+)-propranolol on NaV1.5 and NaV1.1 resembled that of the prototypical local anesthetic lidocaine including the requirement for a critical phenylalanine residue (F1760 in NaV1.5) in the domain 4 S6 segment. Finally, we observed that brain sodium channels exhibited less sensitivity to R-(+)-propranolol than NaV1.5 channels. Our findings establish sodium channels as targets for propranolol and may help explain some beneficial effects of the drug in treating cardiac arrhythmias, and may explain certain adverse central nervous system effects

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing