Discovery of novel indole derivatives as potent and selective inhibitors of proMMP-9 activation.

Matrix metalloproteinase-9 (MMP-9) is a secreted zinc-dependent endopeptidase that degrades the extracellular matrix and basement membrane of neurons, and then contributes to synaptic plasticity by remodeling the extracellular matrix. Inhibition of MMP-9 activity has therapeutic potential for neurodegenerative diseases such as fragile X syndrome. This paper reports the molecular design, synthesis, and in vitro studies of novel indole derivatives as inhibitors of proMMP-9 activation. High-throughput screening (HTS) of our internal compound library and subsequent merging of hit compounds 1 and 2 provided compound 4 as a bona-fide lead. X-ray structure-based design and subsequent lead optimization led to the discovery of compound 33, a highly potent and selective inhibitor of proMMP-9 activation.

The Contribution of Chest X-Ray to Predict Extubation Failure in Mechanically Ventilated Patients Using Machine Learning-Based Algorithms.

OBJECTIVES: To evaluate the contribution of a preextubation chest X-ray (CXR) to identify the risk of extubation failure in mechanically ventilated patients. DESIGN: Retrospective cohort study. SETTINGS: ICUs in a tertiary center (the Medical Information Mart for Intensive Care IV database). PATIENTS: Patients greater than or equal to 18 years old who were mechanically ventilated and extubated after a spontaneous breathing trial. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 1,066 mechanically ventilated patients, 132 patients (12%) experienced extubation failure, defined as reintubation or death within 48 hours of extubation. To predict extubation failure, we developed the following models based on deep learning (EfficientNet) and machine learning (LightGBM) with the training data: 1) model using only the rapid-shallow breathing index (RSBI), 2) model using RSBI and CXR, 3) model using all candidate clinical predictors (i.e., patient demographics, vital signs, laboratory values, and ventilator settings) other than CXR, and 4) model using all candidate clinical predictors with CXR. We compared the predictive abilities between models with the test data to investigate the predictive contribution of CXR. The predictive ability of the model using CXR as well as RSBI was not significantly higher than that of the model using only RSBI (c-statistics, 0.56 vs 0.56; p = 0.95). The predictive ability of the model using clinical predictors with CXR was not significantly higher than that of the model using all clinical predictors other than CXR (c-statistics, 0.71 vs 0.70; p = 0.12). Based on SHapley Additive exPlanations values to interpret the model using all clinical predictors with CXR, CXR was less likely to contribute to the predictive ability than other predictors (e.g., duration of mechanical ventilation, inability to follow commands, and heart rate). CONCLUSIONS: Adding CXR to a set of other clinical predictors in our prediction model did not significantly improve the predictive ability of extubation failure in mechanically ventilated patients.