Fisetin for COVID-19 in skilled nursing facilities: Senolytic trials in the COVID era

The burden of senescent cells (SnCs), which do not divide but are metabolically active and resistant to death by apoptosis, is increased in older adults and those with chronic diseases. These individuals are also at the greatest risk for morbidity and mortality from SARS-CoV-2 infection. SARS-CoV-2 complications include cytokine storm and multiorgan failure mediated by the same factors as often produced by SnCs through their senescence-associated secretory phenotype (SASP). The SASP can be amplified by infection-related pathogen-associated molecular profile factors. Senolytic agents, such as Fisetin, selectively eliminate SnCs and delay, prevent, or alleviate multiple disorders in aged experimental animals and animal models of human chronic diseases, including obesity, diabetes, and respiratory diseases. Senolytics are now in clinical trials for multiple conditions linked to SnCs, including frailty; obesity/diabetes; osteoporosis; and cardiovascular, kidney, and lung diseases, which are also risk factors for SARS-CoV-2 morbidity and mortality. A clinical trial is underway to test if senolytics decrease SARS-CoV-2 progression and morbidity in hospitalized older adults. We describe here a National Institutes of Health-funded, multicenter, placebo-controlled clinical trial of Fisetin for older adult skilled nursing facility (SNF) residents who have been, or become, SARS-CoV-2 rtPCR-positive, including the rationale for targeting fundamental aging mechanisms in such patients. We consider logistic challenges of conducting trials in long-term care settings in the SARS-CoV-2 era, including restricted access, consent procedures, methods for obtaining biospecimens and clinical data, staffing, investigational product administration issues, and potential solutions for these challenges. We propose developing a national network of SNFs engaged in interventional clinical trials.     

Predicting In-Hospital Mortality at Admission to the Medical Ward: A Big-Data Machine Learning Model

BackgroundGeneral medical wards admit high-risk patients. Artificial intelligence algorithms can use big data for developing models to assess patients’ risk stratification. The aim of this study was to develop a mortality prediction machine learning model using data available at the time of admission to the medical ward.MethodsWe included consecutive patients (ages 18-100) admitted to medical wards at a single medical center (January 1, 2013-December 31, 2018). We constructed a machine learning model using patient characteristics, comorbidities, laboratory tests, and patients’ emergency department (ED) management. The model was trained on data from the years 2013 to 2017 and validated on data from the year 2018. The area under the curve (AUC) for mortality prediction was used as an outcome metric. Youden index was used to find an optimal sensitivity-specificity cutoff point.ResultsOf the 118,262 patients admitted to the medical ward, 6311 died (5.3%). The single variables with the highest AUCs were medications administered in the ED (AUC = 0.74), ED diagnosis (AUC = 0.74), and albumin (AUC = 0.73). The machine learning model yielded an AUC of 0.924 (95% confidence interval [CI]: 0.917-0.930). For Youden index, a sensitivity of 0.88 (95% CI: 0.86-0.89) and specificity of 0.83 (95% CI: 0.83–0.83) were observed. This corresponds to a false-positive rate of 1:5.9 and negative predictive value of 0.99.ConclusionA machine learning model outperforms single variables predictions of in-hospital mortality at the time of admission to the medical ward. Such a decision support tool has the potential to augment clinical decision-making regarding level of care needed for admitted patients.     

Ketamine’s Antidepressant Efficacy is Extended for at Least Four Weeks in Subjects with a Family History of an Alcohol Use Disorder

Background:

A single subanesthetic infusion of the N-methyl-D-aspartate (NMDA) receptor antagonist ketamine has rapid and potent antidepressant properties in treatment-resistant major depressive disorder (TRD). As a family history of an alcohol use disorder is a positive predictor of ketamine’s antidepressant response and the strength of the association increases over time, we hypothesized that depressed subjects with a family history of an alcohol use disorder would have greater antidepressant durability and that riluzole would augment and/or extend ketamine’s antidepressant efficacy.

Methods:

Fifty-two TRD subjects received an open-label infusion of ketamine (0.5mg/kg over 40 minutes), and, four to six hours post-infusion, were randomized to either flexible-dose (100–200mg/day) riluzole or placebo in the following proportions: Family History Positive (FHP) riluzole (n = 10), FHP placebo (n = 9), Family History Negative (FHN) riluzole (n = 16), and FHN placebo (n = 17).

Results:

FHP subjects randomized to placebo had a greater antidepressant response than FHN subjects; however, contrary to our initial hypothesis, there was no significant difference in antidepressant efficacy with riluzole. Although potentially underpowered, there was no difference in overall time-to-relapse based on randomization status (riluzole responders: n = 15, placebo responders: n = 17). Yet, time-to-relapse was longer in FHP placebo responders (n = 8) compared to FHN placebo responders (n = 9) with, again, no significant difference in time-to-relapse in FHP riluzole responders (n = 6) compared to FHN riluzole responders (n = 9).

Conclusions:

Ketamine’s extended antidepressant durability in FHP TRD should be considered in the design and analysis of ketamine depression trials.     

Neural Correlates of Suicidal Ideation and Its Reduction in Depression

Background:

The neural correlates of suicidal ideation and its reduction after treatment are unknown. We hypothesized that increased regional cerebral glucose metabolism in the infralimbic cortex (Brodmann area 25), amygdala, and subgenual anterior cingulate cortex would be associated with suicidal ideation and its reduction after ketamine infusion.

Methods:

Medication-free patients (n=19) with treatment-resistant major depressive disorder underwent positron emission tomography imaging at baseline and 230 minutes after an open-label ketamine infusion (0.5mg/kg for 40 minutes).

Results:

Baseline suicidal ideation and regional cerebral glucose metabolism in the infralimbic cortex were significantly correlated (r=.59, P=.007); but not overall mood scores (r=−.07, P=.79). Reductions in suicidal ideation after ketamine infusion were correlated with decreased regional cerebral glucose metabolism in the infralimbic cortex (r=.54, P=.02). Metabolism in other areas of interest was not significantly correlated with suicidal ideation or depression.

Conclusion:

The infralimbic cortex may be implicated in suicidal ideation.