Type 1 diabetes prevention clinical trial simulator: Case reports of model-informed drug development tool.

Clinical trials seeking to delay or prevent the onset of type 1 diabetes (T1D) face a series of pragmatic challenges. Despite more than 100 years since the discovery of insulin, teplizumab remains the only FDA-approved therapy to delay progression from Stage 2 to Stage 3 T1D. To increase the efficiency of clinical trials seeking this goal, our project sought to inform T1D clinical trial designs by developing a disease progression model-based clinical trial simulation tool. Using individual-level data collected from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies, we previously developed a quantitative joint model to predict the time to T1D onset. We then applied trial-specific inclusion/exclusion criteria, sample sizes in treatment and placebo arms, trial duration, assessment interval, and dropout rate. We implemented a function for presumed drug effects. To increase the size of the population pool, we generated virtual populations using multivariate normal distribution and ctree machine learning algorithms. As an output, power was calculated, which summarizes the probability of success, showing a statistically significant difference in the time distribution until the T1D diagnosis between the two arms. Using this tool, power curves can also be generated through iterations. The web-based tool is publicly available: https://app.cop.ufl.edu/t1d/. Herein, we briefly describe the tool and provide instructions for simulating a planned clinical trial with two case studies. This tool will allow for improved clinical trial designs and accelerate efforts seeking to prevent or delay the onset of T1D.

Do we perceive sensations inside and outside of our body differently? Perceptual, emotional, and behavioral differences between visceral and somatic sensation, discomfort, and pain.

BACKGROUND: Experimental research evaluating differences between the visceral and somatic stimulation is limited to pain and typically uses different induction methods for visceral and somatic stimulation (e.g., rectal balloon distention vs. tactile hand stimulation). Our study aimed to compare differences in response time, intensity, unpleasantness, and threat between identical electrical visceral and somatic stimulations at both painful and non-painful perceptual thresholds. METHODS: Electrical stimulation was applied to the wrist and distal esophagus in 20 healthy participants. A double pseudorandom staircase determined perceptual thresholds of Sensation, Discomfort, and Pain for the somatic and visceral stimulations, separately. Stimulus reaction time (ms, via button press), and intensity, unpleasantness, and threat ratings were recorded after each stimulus. General linear mixed models compared differences in the four outcomes by stimulation type, threshold, and the stimulation type-by-threshold interaction. Sigmoidal maximum effect models evaluated differences in outcomes across all delivered stimulation intensities. KEY RESULTS: Overall, visceral stimulations were perceived as more intense, threatening, and unpleasant compared to somatic stimulations, but participants responded faster to somatic stimulations. There was no significant interaction effect, but planned contrasts demonstrated differences at individual thresholds. Across all delivered intensities, higher intensity stimulations were needed to reach the half-maximum effect of self-reported intensity, unpleasantness, and threat ratings in the visceral domain. CONCLUSIONS AND INFERENCES: Differences exist between modalities for both non-painful and painful sensations. These findings may have implications for translating paradigms and behavioral treatments from the somatic domain to the visceral domain, though future research in larger clinical samples is needed.

Drug Repurposing for Targeting Myeloid-Derived Suppressor-Cell-Generated Immunosuppression in Ovarian Cancer: A Literature Review of Potential Candidates.

The lethality of patients with ovarian cancer (OC) remains high. Current treatment strategies often do not lead to the desired outcome due to the development of therapy resistance, resulting in high relapse rates. Additionally, clinical trials testing immunotherapy against OC have failed to reach significant results to date. The OC tumor microenvironment and specifically myeloid-derived suppressor cells (MDSC) are known to generate immunosuppression and inhibit the anti-tumor immune response following immunotherapy treatment. Our review aims to characterize potential candidate treatments to target MDSC in OC through drug-repurposing. A literature search identified repurposable compounds with evidence of their suppressing the effect of MDSC. A total of seventeen compounds were withheld, of which four were considered the most promising. Lurbinectedin, metformin, celecoxib, and 5-azacytidine have reported preclinical effects on MDSC and clinical evidence in OC. They have all been approved for a different indication, characterizing them as the most promising candidates for repurposing to treat patients with OC.