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Introduction: In recent years, there has been a growing trend among regulatory agencies to consider the use of historical controls in clinical trials as a means of improving the efficiency of trial design. In this paper, to enhance the statistical operating characteristic of Phase I dose-finding trials, we propose a novel model-assisted design method named “MEM-Keyboard”.Methods: The proposed design is based on the multisource exchangeability models (MEMs) that allows for dynamic borrowing of information from multiple supplemental data sources, including historical trial data, to inform the dose-escalation process. Furthermore, with the frequent occurrence of delayed toxicity in novel anti-cancer drugs, we extended our proposed method to handle late-onset toxicity by incorporating historical data. This extended method is referred to as “MEM-TITE-Keyboard” and aims to improve the efficiency of early clinical trials.Results: Simulation studies have indicated that the proposed methods can improve the probability of correctly selecting the maximum tolerated dose (MTD) with an acceptable level of risk, compared to designs that do not account for information borrowing and late-onset toxicity.Discussion: The MEM-Keyboard and MEM-TITE-Keyboard, easy to implement in practice, provide a useful tool for identifying MTD and accelerating drug development.

Due to the small sample sizes in early-phase clinical trials, the toxicity and efficacy profiles of the dose-schedule regimens determined for subsequent trials may not be well established. The recent development of novel anti-tumor treatments and combination therapies further complicates the problem. Therefore, there is an increasing recognition of the essential place of optimizing dose-schedule regimens, and new strategies are now urgently needed. Bayesian adaptive designs provide a potentially effective way to evaluate several doses and schedules simultaneously in a single clinical trial with higher efficiency, but real-world implementation examples of such adaptive designs are still few. In this paper, we cover the critical factors associated with dose-schedule optimization and review the related innovative Bayesian adaptive designs. The assumptions, characteristics, limitations, and application scenarios of those designs are introduced. The review also summarizes some unresolved issues and future research opportunities for dose-schedule optimization.

The endoplasmic reticulum (ER) forms direct membrane contact sites with the plasma membrane (PM) in eukaryotic cells. These ER-PM contact sites play essential roles in lipid homeostasis, ion dynamics, and cell signaling, which are carried out by protein-protein or protein-lipid interactions. Distinct tethering factors dynamically control the architecture of ER-PM junctions in response to intracellular signals or external stimuli. The physiological roles of ER-PM contact sites are dependent on a variety of regulators that individually or cooperatively perform functions in diverse cellular processes. This review focuses on proteins functioning at ER-PM contact sites and highlights the recent progress in their mechanisms and physiological roles.