Essential oils (EOs) and their components have been reported to possess anticancer properties and to increase the sensitivity of cancer cells to chemotherapy. The aim of this work was to select EOs able to downregulate STAT3 signaling using Western blot and RT-PCR analyses. The molecular mechanism of anti-STAT3 activity was evaluated through spectrophotometric and fluorometric analyses, and the biological effect of STAT3 inhibition was analyzed by flow cytometry and wound healing assay. Herein, Pinus mugo EO (PMEO) is identified as an inhibitor of constitutive STAT3 phosphorylation in human prostate cancer cells, DU145. The down-modulation of the STAT3 signaling cascade decreased the expression of anti-proliferative as well as anti-apoptotic genes and proteins, leading to the inhibition of cell migration and apoptotic cell death. PMEO treatment induced a rapid drop in glutathione (GSH) levels and an increase in reactive oxygen species (ROS) concentration, resulting in mild oxidative stress. Pretreatment of cells with N-acetyl-cysteine (NAC), a cell-permeable ROS scavenger, reverted the inhibitory action of PMEO on STAT3 phosphorylation. Moreover, combination therapy revealed that PMEO treatment displayed synergism with cisplatin in inducing the cytotoxic effect. Overall, our data highlight the importance of STAT3 signaling in PMEO cytotoxic activity, as well as the possibility of developing adjuvant therapy or sensitizing cancer cells to conventional chemotherapy.

In the last decade essential oils have attracted scientists with a constant increase rate of more than 7% as witnessed by almost 5000 articles. Among the prominent studies essential oils are investigated as antibacterial agents alone or in combination with known drugs. Minor studies involved essential oil inspection as potential anticancer and antiviral natural remedies. In line with the authors previous reports the investigation of an in-house library of extracted essential oils as a potential blocker of HSV-1 infection is reported herein. A subset of essential oils was experimentally tested in an in vitro model of HSV-1 infection and the determined IC₅₀s and CC₅₀s values were used in conjunction with the results obtained by gas-chromatography/mass spectrometry chemical analysis to derive machine learning based classification models trained with the partial least square discriminant analysis algorithm. The internally validated models were thus applied on untested essential oils to assess their effective predictive ability in selecting both active and low toxic samples. Five essential oils were selected among a list of 52 and readily assayed for IC₅₀ and CC₅₀ determination. Interestingly, four out of the five selected samples, compared with the potencies of the training set, returned to be highly active and endowed with low toxicity. In particular, sample CJM1 from Calaminta nepeta was the most potent tested essential oil with the highest selectivity index (IC₅₀ = 0.063 mg/mL, SI > 47.5). In conclusion, it was herein demonstrated how multidisciplinary applications involving machine learning could represent a valuable tool in predicting the bioactivity of complex mixtures and in the near future to enable the design of blended essential oil possibly endowed with higher potency and lower toxicity.

In a previous work, it was shown that punicalagin, an active ingredient of pomegranate, is able to bind to PDIA3 and inhibit its disulfide reductase activity. Here we provide evidence that punicalagin can also bind to PDIA1, the main expressed form of protein disulfide isomerase (PDI). In this comparative study, the affinity and the effect of punicalagin binding on each protein were evaluated, and a computational approach was used to identify putative binding sites. Punicalagin binds to either PDIA1 or PDIA3 with a similar affinity, but the inhibition efficacy on protein reductase activity is higher for PDIA3. Additionally, punicalagin differently affects the thermal denaturation profile of both proteins. Molecular docking and molecular dynamics simulations led to propose a punicalagin binding mode on PDIA1 and PDIA3, identifying the binding sites at the redox domains a’ in two different pockets, suggesting different effects of punicalagin on proteins’ structure. This study provides insights to develop punicalagin-based ligands, to set up a rational design for PDIA3 selective inhibitors, and to dissect the molecular determinant to modulate the protein activity.

The estrogen receptor α (ERα) is an important biological target mediating 17β-estradiol driven breast cancer (BC) development. Aiming to develop innovative drugs against BC, either wild-type or mutated ligand-ERα complexes were used as source data to build structure-based 3-D pharmacophore and 3-D QSAR models, afterward used as tools for the virtual screening of National Cancer Institute datasets and hit-to-lead optimization. The procedure identified Brefeldin A (BFA) as hit, then structurally optimized toward twelve new derivatives whose anticancer activity was confirmed both in vitro and in vivo. Compounds as SERMs showed picomolar to low nanomolar potencies against ERα and were then investigated as antiproliferative agents against BC cell lines, as stimulators of p53 expression, as well as BC cell cycle arrest agents. Most active leads were finally profiled upon administration to female Wistar rats with pre-induced BC, after which 3DPQ-12, 3DPQ-3, 3DPQ-9, 3DPQ-4, 3DPQ-2, and 3DPQ-1 represent potential candidates for BC therapy.

Scientific investigation on essential oils composition and the related biological profile are continuously growing. Nevertheless, only a few studies have been performed on the relationships between chemical composition and biological data. Herein, the investigation of 61 assayed essential oils is reported focusing on their inhibition activity against Microsporum spp. including development of machine learning models with the aim of highlining the possible chemical components mainly related to the inhibitory potency. The application of machine learning and deep learning techniques for predictive and descriptive purposes have been applied successfully to many fields. Quantitative composition–activity relationships machine learning-based models were developed for the 61 essential oils tested as Microsporum spp. growth modulators. The models were built with in-house python scripts implementing data augmentation with the purpose of having a smoother flow between essential oils’ chemical compositions and biological data. High statistical coefficient values (Accuracy, Matthews correlation coefficient and F₁ score) were obtained and model inspection permitted to detect possible specific roles related to some components of essential oils’ constituents. Robust machine learning models are far more useful tools to reveal data augmentation in comparison with raw data derived models. To the best of the authors knowledge this is the first report using data augmentation to highlight the role of complex mixture components, in particular a first application of these data will be for the development of ingredients in the dermo-cosmetic field investigating microbial species considering the urge for the use of natural preserving and acting antimicrobial agents.

Chagas disease is an illness caused by the protozoan parasite Trypanosoma cruzi, affecting more than 7 million people in the world. Benznidazole and nifurtimox are the only drugs available for treatment and in addition to causing several side effects, are only satisfactory in the acute phase of the disease. Sirtuins are NAD⁺-dependent deacetylases involved in several biological processes, which have become drug target candidates in various disease settings. T. cruzi presents two sirtuins, one cytosolic (TcSir2rp1) and the latter mitochondrial (TcSir2rp3). Here, we characterized the effects of human sirtuin inhibitors against T. cruzi sirtuins as an initial approach to develop specific parasite inhibitors. We found that, of 33 compounds tested, two inhibited TcSir2rp1 (15 and 17), while other five inhibited TcSir2rp3 (8, 12, 13, 30, and 32), indicating that specific inhibitors can be devised for each one of the enzymes. Furthermore, all inhibiting compounds prevented parasite proliferation in cultured mammalian cells. When combining the most effective inhibitors with benznidazole at least two compounds, 17 and 32, demonstrated synergistic effects. Altogether, these results support the importance of exploring T. cruzi sirtuins as drug targets and provide key elements to develop specific inhibitors for these enzymes as potential targets for Chagas disease treatment.