The Glycolytic Pathway as a Target for Novel Onco-Immunology Therapies in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDA) is one of the most lethal forms of human cancer, characterized by unrestrained progression, invasiveness and treatment resistance. To date, there are limited curative options, with surgical resection as the only effective strategy, hence the urgent need to discover novel therapies. A platform of onco-immunology targets is represented by molecules that play a role in the reprogrammed cellular metabolism as one hallmark of cancer. Due to the hypoxic tumor microenvironment (TME), PDA cells display an altered glucose metabolism—resulting in its increased uptake—and a higher glycolytic rate, which leads to lactate accumulation and them acting as fuel for cancer cells. The consequent acidification of the TME results in immunosuppression, which impairs the antitumor immunity. This review analyzes the genetic background and the emerging glycolytic enzymes that are involved in tumor progression, development and metastasis, and how this represents feasible therapeutic targets to counteract PDA. In particular, as the overexpressed or mutated glycolytic enzymes stimulate both humoral and cellular immune responses, we will discuss their possible exploitation as immunological targets in anti-PDA therapeutic strategies.     

Development of a method for quantitative determination of the cytotoxic agent piplartine (piperlongumine) in multiple skin layers

This study reports the development of a simple and reproducible method, with high rates of recovery, to extract the cytotoxic agent piplartine from skin layers, and a sensitive and rapid UV‐HPLC method for its quantification. Considering the potential of piplartine for topical treatment of skin cancer, this method may find application for formulation development and pharmacokinetics studies to assess cutaneous bioavailability. Porcine skin was employed as a model for human tissue. Piplartine was extracted from the stratum corneum (SC) and remaining viable skin layers (VS) using methanol, vortex homogenization and bath sonication, and subsequently assayed by HPLC using a C₁₈ column, and 1:1 (v/v) acetonitrile–water (adjusted to pH 4.0 with acetic acid 0.1%) as mobile phase. The quantification limit of piplartine was 0.2 μg/mL (0.6 μm ), and the assay was linear up to 5 μg/mL (15.8 μm ), with within‐day and between‐days assay coefficients of variation and relative errors <15%. Piplartine recovery from SC and VS varied from 86 to 96%. The method was suitable to assay samples from skin penetration studies, enabling detection of differences in cutaneous delivery in different skin compartments resulting from treatment with various formulations and time periods.     

Selective MAO-B inhibitors: a lesson from natural products

Monoamine oxidases (MAOs) are mitochondrial bound enzymes, which catalyze the oxidative deamination of monoamine neurotransmitters. Inside the brain, MAOs are present in two isoforms: MAO-A and MAO-B. The activity of MAO-B is generally higher in patients affected by neurodegenerative diseases like Alzheimer’s and Parkinson’s. Therefore, the search for potent and selective MAO-B inhibitors is still a challenge for medicinal chemists. Nature has always been a source of inspiration for the discovery of new lead compounds. Moreover, natural medicine is a major component in all traditional medicine systems. In this review, we present the latest discoveries in the search for selective MAO-B inhibitors from natural sources. For clarity, compounds have been classified on the basis of structural analogy or source: flavonoids, xanthones, tannins, proanthocyanidins, iridoid glucosides, curcumin, alkaloids, cannabinoids, and natural sources extracts. MAO inhibition values reported in the text are not always consistent due to the high variability of MAO sources (bovine, pig, rat brain or liver, and human) and to the heterogeneity of the experimental protocols used.     

Computing the Longtime Behaviour of NMR Propagators in Porous Media Using a Pore Network Random Walk Model

We present a pore network model combined with a random walk algorithm allowing the simulation of molecular displacement distributions in porous media as measured by NMR. A particular feature of this technique is the ability to probe the time evolution of these distributions. The objective is to predict the displacement behaviour for time intervals larger than the experimental observation time and explore the asymptotic dispersion regime at long times. Starting from 3D micro-CT images, we computed the variance of displacement distributions of water molecules in a Fontainebleau sand and found very good agreement of the time evolution of the variance with experimental data, without fitting parameter. The model confirms a weak superdispersion in the asymptotic regime. In addition, we conclude that, since pore network models do not take into account small scale features of the porous medium (e.g., surface roughness and grain shape), the origin of the observed superdispersion is mainly due to the topology and geometry of the porous medium.