Treatment of Obesity Through Glial Cell-Derived Neurotrophic Factor Lipid Nanoparticle Delivery in Mice.

BACKGROUND AND AIMS: The overexpression of glial cell-derived neurotrophic factor (GDNF) in the liver and adipose tissues offers strong protection against high-fat diet (HFD)-induced obesity in mice. We hypothesize that sustainably enhancing GDNF expression in the liver may provide a therapeutic effect that can prevent the progression of HFD-induced obesity in mice. METHODS: Expression lentivector encoding mouse GDNF (GDNF(pDNA) or empty vector (pDNA, control) were encapsulated in lipid nanoparticles (LNPs) using the thin-film hydration method. Mice were fed with regular diet (RD) or HFD for 20 weeks prior to injection and the GDNF and control vector-loaded LNPs were administered by intravenous (IV) injection to mice once weekly for 5 weeks. Changes in body weight were monitored and mice tissues were collected and imaged for fluorescence using an IVIS in vivo imaging system. Post-treatment abdominal fat weight, colon length, and spleen weight were obtained. GDNF protein levels in the liver and serum were quantified by enzyme-linked immunosorbent assay, while liver AKT serine/threonine kinase and AMP-activated protein kinase phosphorylation levels were evaluated by Western blotting. RESULTS: IV-injected GDNF(pDNA)-loaded LNPs targeted the liver and remained in there for up to 15 days postinjection. A single injection of GDNF(pDNA)-loaded LNPs significantly increased GDNF expression for 7 days and consequently increased the levels of phosphorylated AKT serine/threonine kinase and AMP-activated protein kinase. Once weekly injections of GDNF(pDNA)-loaded LNPs for 5 weeks slowed increase in body weight, reduced abdominal fat, and modulated the gut microbiota toward a healthier composition in HFD-fed mice. CONCLUSION: GDNF(pDNA)-loaded LNPs could potentially be developed as a therapeutic strategy to reverse weight gain in obese patients.

Detecting asthma exacerbations using daily home monitoring and machine learning.

OBJECTIVE: Acute exacerbations contribute significantly to the morbidity of asthma. Recent studies have shown that early detection and treatment of asthma exacerbations leads to improved outcomes. We aimed to develop a machine learning algorithm to detect severe asthma exacerbations using easily available daily monitoring data. METHODS: We analyzed daily peak expiratory flow and symptom scores recorded by participants in the SAKURA study (NCT00839800), an international multicentre randomized controlled trial comparing budesonide/formoterol as maintenance and reliever therapy versus budesonide/formoterol maintenance plus terbutaline as reliever, in adults with persistent asthma. The dataset consisted of 728,535 records of daily monitoring data in 2010 patients, with 576 severe exacerbation events. Data post-processing techniques included normalization, standardization, calculation of differences or slopes over time and the use of smoothing filters. Principal components analysis was used to reduce the large number of derived variables to a smaller number of linearly independent components. Logistic regression, decision tree, naïve Bayes, and perceptron algorithms were evaluated. Model accuracy was assessed using stratified cross-validation. The primary outcome was the detection of exacerbations on the same day or up to three days in the future. RESULTS: The best model used logistic regression with input variables derived from post-processed data using principal components analysis. This had an area under the receiver operating characteristic curve of 0.85, with a sensitivity of 90% and specificity of 83% for severe asthma exacerbations. CONCLUSION: Asthma exacerbations may be detected using machine learning algorithms applied to daily self-monitoring of peak expiratory flow and asthma symptoms.

In Vitro and In Vivo Models for Evaluating the Oral Toxicity of Nanomedicines.

Toxicity studies for conventional oral drug formulations are standardized and well documented, as required by the guidelines of administrative agencies such as the US Food & Drug Administration (FDA), the European Medicines Agency (EMA) or European Medicines Evaluation Agency (EMEA), and the Japanese Pharmaceuticals and Medical Devices Agency (PMDA). Researchers tend to extrapolate these standardized protocols to evaluate nanoformulations (NFs) because standard nanotoxicity protocols are still lacking in nonclinical studies for testing orally delivered NFs. However, such strategies have generated many inconsistent results because they do not account for the specific physicochemical properties of nanomedicines. Due to their tiny size, accumulated surface charge and tension, sizeable surface-area-to-volume ratio, and high chemical/structural complexity, orally delivered NFs may generate severe topical toxicities to the gastrointestinal tract and metabolic organs, including the liver and kidney. Such toxicities involve immune responses that reflect different mechanisms than those triggered by conventional formulations. Herein, we briefly analyze the potential oral toxicity mechanisms of NFs and describe recently reported in vitro and in vivo models that attempt to address the specific oral toxicity of nanomedicines. We also discuss approaches that may be used to develop nontoxic NFs for oral drug delivery.

In vitro Intestinal Epithelial Wound-healing Assays Using Electric Cell-Substrate Impedance Sensing Instrument.

Here, we describe an in vitro epithelial wound-healing assay using Electric Cell-Substrate Impedance Sensing (ECIS) technology. The ECIS technology is a real time cell growth assay based on a small (250 µm diameter) active gold electrode which resistance is measured continuously. When intestinal epithelial cells reach confluency on the gold electrode, resistances reach a plateau. For the wound-healing assays, confluent intestinal epithelial monolayers are subjected to a current of 40 kHz frequency, 1,400 µA amplitude, and 30-second duration. This kills the cells around the small active gold electrode, causing detachment and generating a wound that is healed by surrounding cells that have not been submitted to the current pulse. Wound healing is then assessed by continuous resistance measurements for approximately 30 h after wound. Both cell wounding and measurements of the subsequent healing process are carried out under computer control that takes online measurements each 30 s and stores the data. ECIS technology can be used to study the underlying causes for impaired mucosal healing and to test the efficacy of drugs in mucosal healing.