Brightline-2: a phase IIa/IIb trial of brigimadlin (BI 907828) in advanced biliary tract cancer, pancreatic ductal adenocarcinoma or other solid tumors.

Mouse double minute 2 homolog (MDM2) is a key negative regulator of the tumor suppressor p53. Blocking the MDM2-p53 interaction, and restoring p53 function, is therefore a potential therapeutic strategy in MDM2-amplified, TP53 wild-type tumors. MDM2 is amplified in several tumor types, including biliary tract cancer (BTC), pancreatic ductal adenocarcinoma (PDAC), lung adenocarcinoma and bladder cancer, all of which have limited treatment options and poor patient outcomes. Brigimadlin (BI 907828) is a highly potent MDM2-p53 antagonist that has shown promising activity in preclinical and early-phase clinical studies. This manuscript describes the rationale and design of an ongoing phase IIa/IIb Brightline-2 trial evaluating brigimadlin as second-line treatment for patients with advanced/metastatic BTC, PDAC, lung adenocarcinoma, or bladder cancer.

Brightline-2: a phase IIa/IIb trial of brigimadlin (BI 907828) in advanced BTC, PDAC, or other solid tumorsIn some types of cancer, including cancers of the bile duct, pancreas, bladder and lung, the number of copies of a gene called MDM2 is abnormally increased (MDM2 amplification). MDM2 usually regulates p53, a protein that stops cancer cells from growing uncontrollably. When MDM2 is amplified, the cell makes too much of the MDM2 protein, which prevents p53 from stopping cancer growth. Blocking the interaction between MDM2 and p53 may allow p53 to do its job again and stop cancer cells from growing.Brightline-2 is a clinical trial that is currently in progress. This trial is assessing the efficacy and safety of an investigational drug, brigimadlin (or BI 907828), in patients with selected advanced or metastatic cancers. To be included, patients must have advanced biliary tract cancer, pancreatic ductal adenocarcinoma, bladder cancer, or lung adenocarcinoma. The tumor must show amplification of MDM2 when tested by a laboratory. Patients will take a 45 mg tablet of brigimadlin by mouth, once every 3 weeks. In this trial, researchers are investigating the ability of the drug to shrink tumors, the side effects of the drug, and the impact of the drug on a patients' quality of life.The goal of this trial is to assess the potential of brigimadlin as a new treatment option for patients with advanced biliary tract cancer, pancreatic ductal adenocarcinoma, bladder cancer, or lung adenocarcinoma.Clinical Trial Registration: NCT05512377 (ClinicalTrials.gov).

Modeling and maximum likelihood based inference of interval-censored data with unknown upper limits and time-dependent covariates.

Due to the nature of study design or other reasons, the upper limits of the interval-censored data with multiple visits are unknown. A naïve approach is to treat the last observed time as the exact event time, which may induce biased estimators of the model parameters. In this paper, we first develop a Cox model with time-dependent covariates for the event time and a proportional hazards model with frailty for the gap time. We then construct the upper limits using the latent gap times to resolve the issue of interval-censored event time data with unknown upper limits. A data-augmentation technique and a Monte Carlo EM (MCEM) algorithm are developed to facilitate computation. Theoretical properties of the computational algorithm are also investigated. Additionally, new model comparison criteria are developed to assess the fit of the gap time data as well as the fit of the event time data conditional on the gap time data. Our proposed method compares favorably with competing methods in both simulation study and real data analysis.

Bayesian spatial homogeneity pursuit for survival data with an application to the SEER respiratory cancer data.

In this work, we propose a new Bayesian spatial homogeneity pursuit method for survival data under the proportional hazards model to detect spatially clustered patterns in baseline hazard and regression coefficients. Specially, regression coefficients and baseline hazard are assumed to have spatial homogeneity pattern over space. To capture such homogeneity, we develop a geographically weighted Chinese restaurant process prior to simultaneously estimating coefficients and baseline hazards and their uncertainty measures. An efficient Markov chain Monte Carlo (MCMC) algorithm is designed for our proposed methods. Performance is evaluated using simulated data, and further applied to a real data analysis of respiratory cancer in the state of Louisiana.

Interface-exfoliated graphene-based conductive screen-printing inks: low-loading, low-cost, and additive-free.

Paper diagnostics are of growing interest due to their low cost and easy accessibility. Conductive inks, necessary for manufacturing the next generation diagnostic devices, currently face challenges such as high cost, high sintering temperatures, or harsh conditions required to remove stabilizers. Here we report an effective, inexpensive, and environmentally friendly approach to graphene ink that is suitable for screen printing onto paper substrates. The ink formulation contains only pristine graphite, water, and non-toxic alkanes formed by an interfacial trapping method in which graphite spontaneously exfoliates to graphene. The result is a viscous graphene stabilized water-in-oil emulsion-based ink. This ink does not require sintering, but drying at 90 °C or brief microwaving can improve the conductivity. The production requires only 40 s of shaking to form the emulsion. The sheet resistance of the ink is approximately 600 Ω/sq at a thickness of less than 6 µm, and the ink can be stabilized by as little as 1 wt% graphite.

Look before you leap: systematic evaluation of tree-based statistical methods in subgroup identification.

Subgroup analysis, as the key component of personalized medicine development, has attracted a lot of interest in recent years. While a number of exploratory subgroup searching approaches have been proposed, informative evaluation criteria and scenario-based systematic comparison of these methods are still underdeveloped topics. In this article, we propose two evaluation criteria in connection with traditional type I error and power concepts, and another criterion to directly assess recovery performance of the underlying treatment effect structure. Extensive simulation studies are carried out to investigate empirical performance of a variety of tree-based exploratory subgroup methods under the proposed criteria. A real data application is also included to illustrate the necessity and importance of method evaluation.