A rational approach to elucidate human monoamine oxidase molecular selectivity

Designing highly selective human monoamine oxidase (hMAO) inhibitors is a challenging goal on the road to a more effective treatment of depression and anxiety (inhibition of hMAO-A isoform) as well as neurodegenerative diseases (inhibition of hMAO-B isoform). To uncover the molecular rationale of hMAOs selectivity, two recently prepared 2H-chromene-2-ones, namely compounds 1 and 2, were herein chosen as molecular probes being highly selective toward hMAO-A and hMAO-B, respectively. We performed molecular dynamics (MD) studies on four different complexes, cross-simulating one at a time the two hMAO-isoforms (dimer embedded in a lipid bilayer) with the two considered probes. Our comparative analysis on the obtained 100 ns trajectories discloses a stable H-bond interaction between 1 and Gln215 as crucial for ligand selectivity toward hMAO-A whereas a water-mediated interaction might explain the observed hMAO-B selectivity of compound 2. Such hypotheses are further supported by binding free energy calculations carried out applying the molecular mechanics generalized Born surface area (MM-GBSA) method and allowing us to evaluate the contribution of each residue to the observed isoform selectivity. Taken as whole, this study represents the first attempt to explain at molecular level hMAO isoform selectivity and a valuable yardstick for better addressing the design of new and highly selective MAO inhibitors

Exhaled and arterial levels of endothelin-1 are increased and correlate with pulmonary systolic pressure in COPD with pulmonary hypertension

BACKGROUND: Endothelin-1 (ET-1) and Nitric Oxide (NO) are crucial mediators for establishing pulmonary artery hypertension (PAH). We tested the hypothesis that their imbalance might also occur in COPD patients with PAH. METHODS: The aims of the study were to measure exhaled breath condensate (EBC) and circulating levels of ET-1, as well as exhaled NO (FENO) levels by, respectively, a specific enzyme immunoassay kit, and by chemiluminescence analysis in 3 groups of subjects: COPD with PAH (12), COPD only (36), and healthy individuals (15). In order to evaluate pulmonary-artery systolic pressure (PaPs), all COPD patients underwent Echo-Doppler assessment. RESULTS: Significantly increased exhaled and circulating levels of ET-1 were found in COPD with PAH compared to both COPD (p < 0.0001) only, and healthy controls (p < 0.0001). In COPD with PAH, linear regression analysis showed good correlation between ET-1 in EBC and PaPs (r = 0.621; p = 0.031), and between arterial levels of ET-1 and PaPs (r = 0.648; p = 0.022), while arterial levels of ET-1 inversely correlated with FEV1%, (r = -0.59, p = 0.043), and PaPs negatively correlated to PaO2 (r = -0.618; p = 0.032). Significantly reduced levels of FENO were found in COPD associated with PAH, compared to COPD only (22.92 +/- 11.38 vs.35.07 +/- 17.53 ppb; p = 0.03). Thus, we observed an imbalanced output in the breath between ET-1 and NO, as expression of pulmonary endothelium and epithelium impairment, in COPD with PAH compared to COPD only. Whether this imbalance is an early cause or result of PAH due to COPD is still unknown and deserves further investigations

Applicability Domain for QSAR models: where theory meets reality

Quantitative Structure-Activity Relationships are widely acknowledged predictive methods employed, for years, in organic and medicinal chemistry. More recently, they have assumed a central role also in the context of the explorative toxicology for the protection of environment and human health. However, their real-life application has not been always enthusiastically welcomed, being often retrospectively used and, thus, of limited importance for prospective goals. The need of making more trustable predictions has thus addressed studies on the so-called Applicability Domain, which represents the chemical space from which a model is derived and where a prediction is considered to be reliable. In the present study, the authors survey a number of approaches used to build the Applicability Domain. In particular, they will focus on strategies based on: a) physico chemical, b) structural and c) response domains. Moreover, some examples integrating different strategies will be also discussed to meet the needs of both model developers and downstream users

Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques

&lt;p&gt;Drug-induced cardiotoxicity is a common side effect of drugs in clinical use or under postmarket surveillance and is commonly due to off-target interactions with the cardiac human-ether-a-go-go-related (hERG) potassium channel. Therefore, prioritizing drug candidates based on their hERG blocking potential is a mandatory step in the early preclinical stage of a drug discovery program. Herein, we trained and properly validated 30 ligandbased classifiers of hERG-related cardiotoxicity based on 7,963 curated compounds extracted by the freely accessible repository ChEMBL (version 25). Different machine learning algorithms were tested, namely, random forest, K-nearest neighbors, gradient boosting, extreme gradient boosting, multilayer perceptron, and support vector machine. The application of 1) the best practices for data curation, 2) the feature selection method VSURF, and 3) the synthetic minority oversampling technique (SMOTE) to properly handle the unbalanced data, allowed for the development of highly predictive models (BAMAX = 0.91, AUCMAX = 0.95). Remarkably, the undertaken temporal validation approach not only supported the predictivity of the herein presented classifiers but also suggested their ability to outperform those models commonly used in the literature. From a more methodological point of view, the study put forward a new computational workflow, freely available in the GitHub repository (https://github.com/PDelre93/hERG-QSAR), as valuable for building highly predictive models of hERG-mediated cardiotoxicity.&lt;/p&gt

Early treatment with noninvasive positive pressure ventilation prolongs survival in Amyotrophic Lateral Sclerosis patients with nocturnal respiratory insufficiency

<p>Abstract</p> <p>Background</p> <p>Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease, which rapidly leads to chronic respiratory failure requiring mechanical ventilation. Currently, forced vital capacity (FVC) < 50% is considered as physiologic marker for admitting patients to Noninvasive Positive Pressure Ventilation (NPPV) intervention, although it has been recently shown the median survival of patients with baseline FVC < 75% much shorter than median survival of patients with baseline FVC > 75%, independently by any treatment.</p> <p>Aim</p> <p>To assess the role of NPPV in improving outcome of ALS, a retrospective analysis was performed to investigate 1 year survival of ALS patients with FVC < 75% and nocturnal respiratory insufficiency, treated with NPPV, compared to a well-matched population of ALS patients, who refused or was intolerant to NPPV.</p> <p>Methods</p> <p>We investigated seventy-two consecutive ALS patients who underwent pulmonary function test. Forty-four presented a FVC > 75% and served as control group. Twenty-eight patients presented a FVC < 75% and showed, at polysomnography analysis, nocturnal respiratory insufficiency, requiring NPPV; sixteen were treated with NPPV, while twelve refused or were intolerant.</p> <p>Results</p> <p>Increased survival rate at 1 year in patients with FVC < 75% treated with NPPV, as compared to those who refused or could not tolerate NPPV (p = 0.02), was observed. The median rate of decline in FVC% was slower in NPPV patients than in patients who did not use NPPV (95% CI: 0.72 to 1.85; p < 0.0001).</p> <p>Conclusion</p> <p>This report demonstrates that early treatment with NPPV prolongs survival and reduces decline of FVC% in ALS.</p