Inflammation as A Precursor of Atherothrombosis, Diabetes and Early Vascular Aging

Vascular disease was for a long time considered a disease of the old age, but it is becoming increasingly clear that a cumulus of factors can cause early vascular aging (EVA). Inflammation plays a key role in vascular stiffening and also in other pathologies that induce vascular damage. There is a known and confirmed connection between inflammation and atherosclerosis. However, it has taken a long time to prove the beneficial effects of anti-inflammatory drugs on cardiovascular events. Diabetes can be both a product of inflammation and a cofactor implicated in the progression of vascular disease. When diabetes and inflammation are accompanied by obesity, this ominous trifecta leads to an increased incidence of atherothrombotic events. Research into earlier stages of vascular disease, and documentation of vulnerability to premature vascular disease, might be the key to success in preventing clinical events. Modulation of inflammation, combined with strict control of classical cardiovascular risk factors, seems to be the winning recipe. Identification of population subsets with a successful vascular aging (supernormal vascular aging—SUPERNOVA) pattern could also bring forth novel therapeutic interventions.

Functional Selectivity of Coumarin Derivates Acting via GPR55 in Neuroinflammation

Anti-neuroinflammatory treatment has gained importance in the search for pharmacological treatments of different neurological and psychiatric diseases, such as depression, schizophrenia, Parkinson’s disease, and Alzheimer’s disease. Clinical studies demonstrate a reduction of the mentioned diseases’ symptoms after the administration of anti-inflammatory drugs. Novel coumarin derivates have been shown to elicit anti-neuroinflammatory effects via G-protein coupled receptor GPR55, with possibly reduced side-effects compared to the known anti-inflammatory drugs. In this study, we, therefore, evaluated the anti-inflammatory capacities of the two novel coumarin-based compounds, KIT C and KIT H, in human neuroblastoma cells and primary murine microglia. Both compounds reduced PGE2-concentrations likely via the inhibition of COX-2 synthesis in SK-N-SH cells but only KIT C decreased PGE2-levels in primary microglia. The examination of other pro- and anti-inflammatory parameters showed varying effects of both compounds. Therefore, the differences in the effects of KIT C and KIT H might be explained by functional selectivity as well as tissue- or cell-dependent expression and signal pathways coupled to GPR55. Understanding the role of chemical residues in functional selectivity and specific cell- and tissue-targeting might open new therapeutic options in pharmacological drug development and might improve the treatment of the mentioned diseases by intervening in an early step of their pathogenesis.

Identification of Anticancer and Anti-inflammatory Drugs from Drug-target Interaction Descriptors by Machine Learning..

Background: Drug repositioning is an important subject in drug-disease research. In the past, most studies simply used drug descriptors as the feature vector to classify drugs or targets, or used qualitative data about drug-target or drug-disease to predict drug-target interactions. These data provide limited information for drug repositioning. Objective: Considering both drugs and targets and constructing quantitative drug-target interaction descriptors as a method of drug characteristics are of great significance to the study of drug repositioning. Methods: Taking anticancer and anti-inflammatory drugs as research objects, the interaction sites between drugs and targets were determined by molecular docking. Sixty-seven drug-target interaction descriptors were calculated to describe the drug-target interactions, and 22 important descriptors were screened for drug classification by SVM, LightGBM and MLP. Results: The accuracy of SVM, LightGBM and MLP reached 93.29%, 92.68% and 94.51%, their Matthews correlation coefficients reached 0.852, 0.840 and 0.882, and their areas under the ROC curve reached 0.977, 0.969 and 0.968, respectively. Conclusion: Using drug-target interaction descriptors to build machine learning models can obtain better results for drug classification. Number of atom pairs, force field, hydrophobic interactions and bSASA are the four types of key features for the classification of anticancer and anti-inflammatory drugs.

A personalized FEM model for reproducible measurement of anti-inflammatory drugs in transdermal administration to knee

A personalized model of the human knee for enhancing the inter-individual reproducibility of a measurement method for monitoring Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) after transdermal delivery is proposed. The model is based on the solution of Maxwell Equations in the electric-quasi-stationary limit via Finite Element Analysis. The dimensions of the custom geometry are estimated on the basis of knee circumference at the patella, body mass index, and sex of each individual. An optimization algorithm allows to find out the electrical parameters of each subject by experimental impedance spectroscopy data. Muscular tissues were characterized anisotropically, by extracting Cole–Cole equation parameters from experimental data acquired with twofold excitation, both transversal and parallel to tissue fibers. A sensitivity and optimization analysis aiming at reducing computational burden in model customization achieved a worst-case reconstruction error lower than 5%. The personalized knee model and the optimization algorithm were validated in vivo by an experimental campaign on thirty volunteers, 67% healthy and 33% affected by knee osteoarthritis (Kellgren–Lawrence grade ranging in [1,4]), with an average error of 3%.

Cell Adaptations of Rhodococci to Pharmaceutical Pollutants

Against the background of atense environmental situation, the risk of drug pollution in the natural environment is steadily increasing. Pharmaceuticals entering open ecosystems can cause toxic effects in wildlife from molecular to population levels. The aim of this research was to examine the impact of pharmaceutical pollutants on rhodococci, which are typical representatives of soil actinobacteria and active biodegraders of these compounds. The pharmaceutical products used in this research werediclofenac sodium and ibuprofen, which are non-steroidal anti-inflammatory drugs (NSAIDs) that are widely used and frequently found in the environment. The most common cell adaptations of rhodococci to the effects of NSAIDs were changes in zeta potential, catalase activity, morphometric parameters and degree of hydrophobicity; elevated contents of total cellular lipids; and the formation of cell conglomerates. The findings demonstrated the adaptation mechanisms of rhodococci and their increased resistance to the toxic effects of the pharmaceutical pollutants. Keywords: pharmaceutical pollutants, NSAIDs, diclofenac, ibuprofen, cell responses, Rhodococcus