Zachariassenbernard9737

The global spread of antimicrobial resistance (AMR) and lack of novel alternative treatments have been declared a global public health emergency by WHO. The greatest impact of AMR is experienced in resource-poor settings, because of lack of access to alternative antibiotics and because the prevalence of multidrug-resistant bacterial strains may be higher in low-income and middle-income countries (LMICs). Intelligent surveillance of AMR infections is key to informed policy decisions and public health interventions to counter AMR. Molecular surveillance using whole-genome sequencing (WGS) can be a valuable addition to phenotypic surveillance of AMR. WGS provides insights into the genetic basis of resistance mechanisms, as well as pathogen evolution and population dynamics at different spatial and temporal scales. Due to its high cost and complexity, WGS is currently mainly carried out in high-income countries. However, given its potential to inform national and international action plans against AMR, establishing WGS as a surveillance tool in LMICs will be important in order to produce a truly global picture. Here, we describe a roadmap for incorporating WGS into existing AMR surveillance frameworks, including WHO Global Antimicrobial Resistance Surveillance System, informed by our ongoing, practical experiences developing WGS surveillance systems in national reference laboratories in Colombia, India, Nigeria and the Philippines. Challenges and barriers to WGS in LMICs will be discussed together with a roadmap to possible solutions.Volume of distribution at steady state (VD,ss) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict VD,ss, accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set ( less then 150) of compounds. To address these issues, a series of in silico methods for predicting human VD,ss directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict VD,ss directly and to predict input parameters required for mechanistic and empirical VD,ss predictions. In addition, log D, fraction unbound in plasma (fup), and blood-to-plasma partition ratio (BPR) were measured on 254 compounds to estimate the impact of measured data on predictive performance of mechanistic models. Furthermore, the impact of novel methodologies such as measuring partition (Kp) in adipocytes and myocytes (n = 189) on VD,ss predictions was also investigated. In prediented. The novel data set (n = 254) generated using a single protocol for each in vitro assay reported in this study could further aid in advancing VD,ss prediction methodologies.Meclofenamate is a nonsteroidal anti-inflammatory drug used in the treatment of mild-to-moderate pain yet poses a rare risk of hepatotoxicity through an unknown mechanism. Nonsteroidal anti-inflammatory drug (NSAID) bioactivation is a common molecular initiating event for hepatotoxicity. Thus, we hypothesized a similar mechanism for meclofenamate and leveraged computational and experimental approaches to identify and characterize its bioactivation. Analyses employing our XenoNet model indicated possible pathways to meclofenamate bioactivation into 19 reactive metabolites subsequently trapped into glutathione adducts. We describe the first reported bioactivation kinetics for meclofenamate and relative importance of those pathways using human liver microsomes. The findings validated only four of the many bioactivation pathways predicted by modeling. For experimental studies, dansyl glutathione was a critical trap for reactive quinone metabolites and provided a way to characterize adduct structures by mass spect and yield qualitative insights that do not scale relative bioactivation risks. We developed and applied innovative computational modeling and quantitative kinetics to identify and validate meclofenamate bioactivation pathways and relevance as a function of time and concentration. This strategy yielded novel insights on meclofenamate bioactivation and provides a tractable approach to more accurately and efficiently assess other drug bioactivations and correlate risks to toxicological outcomes.A first-in-class cannabinoid analog called lenabasum that is a CB2 agonist is being developed as an inflammation-resolving drug candidate. Thus far, specific therapeutic targets include scleroderma, cystic fibrosis, dermatomyositis, and lupus, all of which represent unmet medical needs. Two somewhat-independent molecular mechanisms for this type of action are here proposed. Both pathways initially involve the release of free arachidonic acid after activation of the CB2 receptor and phospholipase A2 by lenabasum. The pathways then diverge into a cyclooxygenase 2-mediated and a lipoxygenase-mediated route. The former leads to increased levels of the cyclopentenone prostaglandin 15-deoxy-Δ12,14-prostaglandin-J2 that can activate the NLPR3 inflammasome, which in turn releases caspase-3, leading to apoptosis and the resolution of chronic inflammation. The lipoxygenase-mediated pathway stimulates the production of lipoxin A4 as well as other signaling molecules called specialized proresolving mediators. These also have inflammation-resolving actions. It is not well understood under which conditions each of these mechanisms operates and whether there is crosstalk between them. Thus, much remains to be learned about the mechanisms describing the actions of lenabasum. SIGNIFICANCE STATEMENT The resolution of chronic inflammation is a major unmet medical need. https://www.selleckchem.com/products/rbn-2397.html The synthetic nonpsychoactive cannabinoid lenabasum could provide a safe and effective drug for this purpose. Two putative molecular mechanisms are suggested to better understand how lenabasum produces this action. In both, different metabolites of arachidonic acid act as mediators.Cardiac fibrosis is characterized by accumulation and activation of fibroblasts and excessive production of extracellular matrix, which results in myocardial stiffening and eventually leads to heart failure. Although previous work suggests that protein kinase C (PKC) isoforms play a role in cardiac fibrosis and remodeling, the results are conflicting. Moreover, the potential of targeting PKC with pharmacological tools to inhibit pathologic fibrosis has not been fully evaluated. Here we investigated the effects of selected PKC agonists and inhibitors on cardiac fibroblast (CF) phenotype, proliferation, and gene expression using primary adult mouse CFs, which spontaneously transdifferentiate into myofibroblasts in culture. A 48-hour exposure to the potent PKC activator phorbol 12-myristate 13-acetate (PMA) at 10 nM concentration reduced the intensity of α-smooth muscle actin staining by 56% and periostin mRNA levels by 60% compared with control. The decreases were inhibited with the pan-PKC inhibitor Gö6983 and the inhibitor of classical PKC isoforms Gö6976, suggesting that classical PKCs regulate CF transdifferentiation.