Essential Oils from Mediterranean Plants Inhibit In Vitro Monocyte Adhesion to Endothelial Cells from Umbilical Cords of Females with Gestational Diabetes Mellitus.

Essential oils (EOs) are mixtures of volatile compounds belonging to several chemical classes derived from aromatic plants using different distillation techniques. Recent studies suggest that the consumption of Mediterranean plants, such as anise and laurel, contributes to improving the lipid and glycemic profile of patients with diabetes mellitus (DM). Hence, the aim of the present study was to investigate the potential anti-inflammatory effect of anise and laurel EOs (AEO and LEO) on endothelial cells isolated from the umbilical cord vein of females with gestational diabetes mellitus (GDM-HUVEC), which is a suitable in vitro model to reproduce the pro-inflammatory phenotype of a diabetic endothelium. For this purpose, the Gas Chromatographic/Mass Spectrometric (GC-MS) chemical profiles of AEO and LEO were first analyzed. Thus, GDM-HUVEC and related controls (C-HUVEC) were pre-treated for 24 h with AEO and LEO at 0.025% v/v, a concentration chosen among others (cell viability by MTT assay), and then stimulated with TNF-α (1 ng/mL). From the GC-MS analysis, trans-anethole (88.5%) and 1,8-cineole (53.9%) resulted as the major components of AEO and LEO, respectively. The results in C- and GDM-HUVEC showed that the treatment with both EOs significantly reduced: (i) the adhesion of the U937 monocyte to HUVEC; (ii) vascular adhesion molecule-1 (VCAM-1) protein and gene expression; (iii) Nuclear Factor-kappa B (NF-κB) p65 nuclear translocation. Taken together, these data suggest the anti-inflammatory efficacy of AEO and LEO in our in vitro model and lay the groundwork for further preclinical and clinical studies to study their potential use as supplements to mitigate vascular endothelial dysfunction associated with DM.

Ligand-based and structure-based studies to develop predictive models for SARS-CoV-2 main protease inhibitors through the 3d-qsar.com portal.

The main protease (Mpro) of SARS-Cov-2 is the essential enzyme for maturation of functional proteins implicated in viral replication and transcription. The peculiarity of its specific cleavage site joint with its high degree of conservation among all coronaviruses promote it as an attractive target to develop broad-spectrum inhibitors, with high selectivity and tolerable safety profile. Herein is reported a combination of three-dimensional quantitative structure-activity relationships (3-D QSAR) and comparative molecular binding energy (COMBINE) analysis to build robust and predictive ligand-based and structure-based statistical models, respectively. Models were trained on experimental binding poses of co-crystallized Mpro-inhibitors and validated on available literature data. By means of deep optimization both models' goodness and robustness reached final statistical values of r2/q2 values of 0.97/0.79 and 0.93/0.79 for the 3-D QSAR and COMBINE approaches respectively, and an overall predictiveness values of 0.68 and 0.57 for the SDEPPRED and AAEP metrics after application to a test set of 60 compounds covered by the training set applicability domain. Despite the different nature (ligand-based and structure-based) of the employed methods, their outcome fully converged. Furthermore, joint ligand- and structure-based structure-activity relationships were found in good agreement with nirmatrelvir chemical features properties, a novel oral Mpro-inhibitor that has recently received U.S. FDA emergency use authorization (EUA) for the oral treatment of mild-to-moderate COVID-19 infected patients. The obtained results will guide future rational design and/or virtual screening campaigns with the aim of discovering new potential anti-coronavirus lead candidates, minimizing both time and financial resources. Moreover, as most of calculation were performed through the well-established web portal 3d-qsar.com the results confirm the portal as a useful tool for drug design.

Human Estrogen Receptor Alpha Antagonists, Part 3: 3-D Pharmacophore and 3-D QSAR Guided Brefeldin A Hit-to-Lead Optimization toward New Breast Cancer Suppressants.

The estrogen receptor α (ERα) is an important biological target mediating 17ß-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.

Human Estrogen Receptor α Antagonists. Part 1: 3-D QSAR-Driven Rational Design of Innovative Coumarin-Related Antiestrogens as Breast Cancer Suppressants through Structure-Based and Ligand-Based Studies.

The estrogen receptor α (ERα) represents a 17ß-estradiol-inducible transcriptional regulator that initiates the RNA polymerase II-dependent transcriptional machinery, pointed for breast cancer (BC) development via either genomic direct or genomic indirect (i.e., tethered) pathway. To develop innovative ligands, structure-based (SB) three-dimensional (3-D) quantitative structure-activity relationship (QSAR) studies have been undertaken from structural data taken from partial agonists, mixed agonists/antagonists (selective estrogen receptor modulators (SERMs)), and full antagonists (selective ERα downregulators (SERDs)) correlated with either wild-type or mutated ERα receptors. SB and ligand-based (LB) alignments allow us to rule out guidelines for the SB/LB alignment of untested compounds. 3-D QSAR models for ERα ligands, coupled with SB/LB alignment, were revealed to be useful tools to dissect the chemical determinants for ERα-based anticancer activity as well as to predict their potency. The herein developed protocol procedure was verified through the design and potency prediction of 12 new coumarin-based SERMs, namely, 3DQ-1a to 3DQ-1e, that upon synthesis turned to be potent ERα antagonists by means of either in vitro or in vivo assays (described in the second part of this study).

Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition-Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils.

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.

Essential Oils Biofilm Modulation Activity, Chemical and Machine Learning Analysis. Application on Staphylococcus aureus Isolates from Cystic Fibrosis Patients.

Bacterial biofilm plays a pivotal role in chronic Staphylococcus aureus (S. aureus) infection and its inhibition may represent an important strategy to develop novel therapeutic agents. The scientific community is continuously searching for natural and "green alternatives" to chemotherapeutic drugs, including essential oils (EOs), assuming the latter not able to select resistant strains, likely due to their multicomponent nature and, hence, multitarget action. Here it is reported the biofilm production modulation exerted by 61 EOs, also investigated for their antibacterial activity on S. aureus strains, including reference and cystic fibrosis patients' isolated strains. The EOs biofilm modulation was assessed by Christensen method on five S. aureus strains. Chemical composition, investigated by GC/MS analysis, of the tested EOs allowed a correlation between biofilm modulation potency and putative active components by means of machine learning algorithms application. Some EOs inhibited biofilm growth at 1.00% concentration, although lower concentrations revealed different biological profile. Experimental data led to select antibiofilm EOs based on their ability to inhibit S. aureus biofilm growth, which were characterized for their ability to alter the biofilm organization by means of SEM studies.

Identification of Inhibitors to Trypanosoma cruzi Sirtuins Based on Compounds Developed to Human Enzymes.

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.

Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils.

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.

Machine Learning Analyses on Data including Essential Oil Chemical Composition and In Vitro Experimental Antibiofilm Activities against Staphylococcus Species.

Biofilm resistance to antimicrobials is a complex phenomenon, driven not only by genetic mutation induced resistance, but also by means of increased microbial cell density that supports horizontal gene transfer across cells. The prevention of biofilm formation and the treatment of existing biofilms is currently a difficult challenge; therefore, the discovery of new multi-targeted or combinatorial therapies is growing. The development of anti-biofilm agents is considered of major interest and represents a key strategy as non-biocidal molecules are highly valuable to avoid the rapid appearance of escape mutants. Among bacteria, staphylococci are predominant causes of biofilm-associated infections. Staphylococci, especially Staphylococcus aureus (S. aureus) is an extraordinarily versatile pathogen that can survive in hostile environmental conditions, colonize mucous membranes and skin, and can cause severe, non-purulent, toxin-mediated diseases or invasive pyogenic infections in humans. Staphylococcus epidermidis (S. epidermidis) has also emerged as an important opportunistic pathogen in infections associated with medical devices (such as urinary and intravascular catheters, orthopaedic implants, etc.), causing approximately from 30% to 43% of joint prosthesis infections. The scientific community is continuously looking for new agents endowed of anti-biofilm capabilities to fight S. aureus and S epidermidis infections. Interestingly, several reports indicated in vitro efficacy of non-biocidal essential oils (EOs) as promising treatment to reduce bacterial biofilm production and prevent the inducing of drug resistance. In this report were analyzed 89 EOs with the objective of investigating their ability to modulate bacterial biofilm production of different S. aureus and S. epidermidis strains. Results showed the assayed EOs to modulated the biofilm production with unpredictable results for each strain. In particular, many EOs acted mainly as biofilm inhibitors in the case of S. epidermidis strains, while for S. aureus strains, EOs induced either no effect or stimulate biofilm production. In order to elucidate the obtained experimental results, machine learning (ML) algorithms were applied to the EOs' chemical compositions and the determined associated anti-biofilm potencies. Statistically robust ML models were developed, and their analysis in term of feature importance and partial dependence plots led to indicating those chemical components mainly responsible for biofilm production, inhibition or stimulation for each studied strain, respectively.

Disruptor of telomeric silencing 1-like (DOT1L): disclosing a new class of non-nucleoside inhibitors by means of ligand-based and structure-based approaches.

Chemical inhibition of chromatin-mediated signaling involved proteins is an established strategy to drive expression networks and alter disease progression. Protein methyltransferases are among the most studied proteins in epigenetics and, in particular, disruptor of telomeric silencing 1-like (DOT1L) lysine methyltransferase plays a key role in MLL-rearranged acute leukemia Selective inhibition of DOT1L is an established attractive strategy to breakdown aberrant H3K79 methylation and thus overexpression of leukemia genes, and leukemogenesis. Although numerous DOT1L inhibitors have been several structural data published no pronounced computational efforts have been yet reported. In these studies a first tentative of multi-stage and LB/SB combined approach is reported in order to maximize the use of available data. Using co-crystallized ligand/DOT1L complexes, predictive 3-D QSAR and COMBINE models were built through a python implementation of previously reported methodologies. The models, validated by either modeled or experimental external test sets, proved to have good predictive abilities. The application of these models to an internal library led to the selection of two unreported compounds that were found able to inhibit DOT1L at micromolar level. To the best of our knowledge this is the first report of quantitative LB and SB DOT1L inhibitors models and their application to disclose new potential epigenetic modulators.

Antimicrobial and Antibiofilm Activity and Machine Learning Classification Analysis of Essential Oils from Different Mediterranean Plants against Pseudomonas aeruginosa.

Pseudomonas aeruginosa is a ubiquitous organism and opportunistic pathogen that can cause persistent infections due to its peculiar antibiotic resistance mechanisms and to its ability to adhere and form biofilm. The interest in the development of new approaches for the prevention and treatment of biofilm formation has recently increased. The aim of this study was to seek new non-biocidal agents able to inhibit biofilm formation, in order to counteract virulence rather than bacterial growth and avoid the selection of escape mutants. Herein, different essential oils extracted from Mediterranean plants were analyzed for their activity against P. aeruginosa. Results show that they were able to destabilize biofilm at very low concentration without impairing bacterial viability. Since the action is not related to a bacteriostatic/bactericidal activity on P. aeruginosa, the biofilm change of growth in presence of the essential oils was possibly due to a modulation of the phenotype. To this aim, application of machine learning algorithms led to the development of quantitative activity-composition relationships classification models that allowed to direct point out those essential oil chemical components more involved in the inhibition of biofilm production. The action of selected essential oils on sessile phenotype make them particularly interesting for possible applications such as prevention of bacterial contamination in the community and in healthcare environments in order to prevent human infections. We assayed 89 samples of different essential oils as P. aeruginosa anti-biofilm. Many samples inhibited P. aeruginosa biofilm at concentrations as low as 48.8 µg/mL. Classification of the models was developed through machine learning algorithms.

Understanding the Molecular Determinant of Reversible Human Monoamine Oxidase B Inhibitors Containing 2H-Chromen-2-One Core: Structure-Based and Ligand-Based Derived Three-Dimensional Quantitative Structure-Activity Relationships Predictive Models.

Monoamine oxidase B (MAO B) catalyzes the oxidative deamination of aryalkylamines neurotransmitters with concomitant reduction of oxygen to hydrogen peroxide. Consequently, the enzyme's malfunction can induce oxidative damage to mitochondrial DNA and mediates development of Parkinson's disease. Thus, MAO B emerges as a promising target for developing pharmaceuticals potentially useful to treat this vicious neurodegenerative condition. Aiming to contribute to the development of drugs with the reversible mechanism of MAO B inhibition only, herein, an extended in silico-in vitro procedure for the selection of novel MAO B inhibitors is demonstrated, including the following: (1) definition of optimized and validated structure-based three-dimensional (3-D) quantitative structure-activity relationships (QSAR) models derived from available cocrystallized inhibitor-MAO B complexes; (2) elaboration of SAR features for either irreversible or reversible MAO B inhibitors to characterize and improve coumarin-based inhibitor activity (Protein Data Bank ID: 2V61 ) as the most potent reversible lead compound; (3) definition of structure-based (SB) and ligand-based (LB) alignment rule assessments by which virtually any untested potential MAO B inhibitor might be evaluated; (4) predictive ability validation of the best 3-D QSAR model through SB/LB modeling of four coumarin-based external test sets (267 compounds); (5) design and SB/LB alignment of novel coumarin-based scaffolds experimentally validated through synthesis and biological evaluation in vitro. Due to the wide range of molecular diversity within the 3-D QSAR training set and derived features, the selected N probe-derived 3-D QSAR model proves to be a valuable tool for virtual screening (VS) of novel MAO B inhibitors and a platform for design, synthesis and evaluation of novel active structures. Accordingly, six highly active and selective MAO B inhibitors (picomolar to low nanomolar range of activity) were disclosed as a result of rational SB/LB 3D QSAR design; therefore, D123 (IC50 = 0.83 nM, Ki = 0.25 nM) and D124 (IC50 = 0.97 nM, Ki = 0.29 nM) are potential lead candidates as anti-Parkinson's drugs.

Essential Oil Extraction, Chemical Analysis and Anti-Candida Activity of Calamintha nepeta (L.) Savi subsp. glandulosa (Req.) Ball-New Approaches.

A comprehensive study on essential oils extracted from different Calamintha nepeta (L.) Savi subsp. glandulosa (Req.) Ball samples from Tarquinia (Italy) is reported. In this study, the 24-h steam distillation procedure for essential oil preparation, in terms of different harvesting and extraction times, was applied. The Gas chromatography-mass spectrometry (GC/MS) analysis showed that C. nepeta (L.) Savi subsp. glandulosa (Req.) Ball essential oils from Tarquinia belong to the pulegone-rich chemotype. The analysis of 44 samples revealed that along with pulegone, some other chemicals may participate in exerting the related antifungal activity. The results indicated that for higher activity, the essential oils should be produced with at least a 6-h steam distillation process. Even though it is not so dependent on the period of harvesting, it could be recommended not to harvest the plant in the fruiting stage, since no significant antifungal effect was shown. The maximum essential oil yield was obtained in August, with the highest pulegone percentage. To obtain the oil with a higher content of menthone, September and October should be considered as the optimal periods. Regarding the extraction duration, vegetative stage material gives the oil in the first 3 h, while material from the reproductive phase should be extracted at least at 6 or even 12 h.

Synthesis, biological evaluation and docking analysis of a new series of methylsulfonyl and sulfamoyl acetamides and ethyl acetates as potent COX-2 inhibitors.

We report herein the synthesis, biological evaluation and docking analysis of a new series of methylsulfonyl, sulfamoyl acetamides and ethyl acetates that selectively inhibit cyclooxygenase-2 (COX-2) isoform. Among the newly synthesized compounds, some of them were endowed with a good activity against COX-2 and a good selectivity COX-2/COX-1 in vitro as well as a desirable analgesic activity in vivo, proving that replacement of the ester moiety with an amide group gave access to more stable derivatives, characterized by a good COX-inhibition.

A Rapid and Consistent Method to Identify Four SARS-CoV-2 Variants during the First Half of 2021 by RT-PCR.

Since 2020, the COVID-19 pandemic has spread worldwide, causing health, economic, and social distress. Containment strategies rely on rapid and consistent methodology for molecular detection and characterization. Emerging variants of concern (VOCs) are currently associated with increased infectivity and immune escape (natural defence mechanisms and vaccine). Several VOCs have been detected, including Alpha variant (B.1.1.7), Beta variant (B.1.351), Gamma variant (P.1/B.1.1.28.1) and Delta variant (B.1.617.2), first identified in the UK, South Africa, Brazil and India, respectively. Here, a rapid and low-cost technique was validated to distinguish the Alpha, Beta, Gamma, and Delta SARS-CoV-2 variants by detecting spike gene mutations using a real-time reverse transcription polymerase chain reaction methodology (RT-PCR). A total of 132 positive patients affected by coronavirus disease-19 (COVID-19) were analysed by employing RT-PCR to target single-nucleotide polymorphisms (SNPs) to screen spike protein mutations. All data were validated by the next-generation sequencing (NGS) methodology and using sequences from a public database. Among 132 COVID-19-positive samples, we were able to discriminate all of the investigated SARS-CoV-2 variants with 100% concordance when compared with the NGS method. RT-PCR -based assays for identifying circulating VOCs of SARS-CoV-2 resulted in a rapid method used to identify specific SARS-CoV-2 variants, allowing for a better survey of the spread of the virus and its transmissibility in the pandemic phase.

In vivo Antiphytoviral Activity of Essential Oils and Hydrosols From Origanum vulgare, Thymus vulgaris, and Rosmarinus officinalis to Control Zucchini Yellow Mosaic Virus and Tomato Leaf Curl New Delhi Virus in Cucurbita pepo L.

In the last decades, the interest in biological activity of natural compounds has been growing. In plant protection, essential oils have been reported to exhibit antiviral, antimycotic, and antiparasitic activities, and are regarded as promising for the formulation of safe antimicrobial agents. Attention has also been focused on hydrosols, the by-products of hydro-distillation of essential oils. Their production is easy, fast, and cheap, and they seem to arise less concern for human health than essential oils. Plant viruses represent a major concern for agricultural crops since no treatment compound is available for virus control. This work was aimed at evaluating the antiphytoviral effectiveness of treatments with three essential oils and corresponding hydrosols extracted from Origanum vulgare, Thymus vulgaris, and Rosmarinus officinalis on Cucurbita pepo plants infected by zucchini yellow mosaic virus or tomato leaf curl New Delhi virus. Treatments were applied either concurrently or after virus inoculation to ascertain an inhibition or curative activity, respectively. Symptoms were observed and samplings were performed weekly. Virus titer and expression levels of phenylalanine ammonia lyase gene (PAL) were measured on treated and untreated infected plants by real-time PCR. PAL gene plays an important role in plant defense response as it is involved in tolerance/resistance to phytopathogens. Results indicated that treatments were effective against tomato leaf curl New Delhi virus whether applied simultaneously with the inoculation or after. A major inhibition was observed with O. vulgare essential oil and hydrosol, resulting in 10-4-fold decrease of virus titer 3 weeks after treatment. Curative activity gave maximum results with all three essential oils and T. vulgaris and R. officinalis hydrosols, recording from 10-2-fold decrease to virus not detected 4 weeks after treatment. An induction of PAL gene expression was recorded at 12 d.p.i. and then was restored to the levels of untreated control. This allows to hypothesize an early plant defense response to virus infection, possibly boosted by treatments. Plant extracts' composition was characterized by gas chromatography-mass spectrometry. Phenols were largely main components of O. vulgare and T. vulgaris extracts (carvacrol and thymol, respectively), while extracts from R. officinalis were based on monoterpene hydrocarbons (essential oil) and oxygenated monoterpenes (hydrosol).

Human estrogen receptor α antagonists, part 2: Synthesis driven by rational design, in vitro antiproliferative, and in vivo anticancer evaluation of innovative coumarin-related antiestrogens as breast cancer suppressants.

New twelve in silico designed coumarin-based ERα antagonists, namely 3DQ-1a to 3DQ-1е, were synthesized and confirmed as selective ERα antagonists, showing potencies ranging from single-digit nanomolar to picomolar. The hits were confirmed as selective estrogen receptor modulators and validated as antiproliferative agents using MCF-7 breast cancer cell lines exerting from picomolar to low nanomolar potency, at the same time showing no agonistic activity within endometrial cell lines. Their mechanism of action was inspected and revealed to be through the inhibition of the Raf-1/MAPK/ERK signal transduction pathway, preventing hormone-mediated gene expression on either genomic direct or genomic indirect level, and stopping the MCF-7 cells proliferation at G0/G1 phase. In vivo experiments, by means of the per os administration to female Wistar rats with pre-induced breast cancer, distinguished six derivatives, 3DQ-4a, 3DQ-2a, 3DQ-1a, 3DQ-1b, 3DQ-2b, and 3DQ-3b, showing remarkable potency as tumor suppressors endowed with optimal pharmacokinetic profiles and no significant histopathological profiles. The presented data indicate the new compounds as potential candidates to be submitted in clinical trials for breast cancer therapy.

Essential Oils Biofilm Modulation Activity and Machine Learning Analysis on Pseudomonas aeruginosa Isolates from Cystic Fibrosis Patients.

The opportunistic pathogen Pseudomonas aeruginosa is often involved in airway infections of cystic fibrosis (CF) patients. It persists in the hostile CF lung environment, inducing chronic infections due to the production of several virulence factors. In this regard, the ability to form a biofilm plays a pivotal role in CF airway colonization by P. aeruginosa. Bacterial virulence mitigation and bacterial cell adhesion hampering and/or biofilm reduced formation could represent a major target for the development of new therapeutic treatments for infection control. Essential oils (EOs) are being considered as a potential alternative in clinical settings for the prevention, treatment, and control of infections sustained by microbial biofilms. EOs are complex mixtures of different classes of organic compounds, usually used for the treatment of upper respiratory tract infections in traditional medicine. Recently, a wide series of EOs were investigated for their ability to modulate biofilm production by different pathogens comprising S. aureus, S. epidermidis, and P. aeruginosa strains. Machine learning (ML) algorithms were applied to develop classification models in order to suggest a possible antibiofilm action for each chemical component of the studied EOs. In the present study, we assessed the biofilm growth modulation exerted by 61 commercial EOs on a selected number of P. aeruginosa strains isolated from CF patients. Furthermore, ML has been used to shed light on the EO chemical components likely responsible for the positive or negative modulation of bacterial biofilm formation.

Targeting the anti-apoptotic Bcl-2 family proteins: machine learning virtual screening and biological evaluation of new small molecules.

Bcl-2 family anti-apoptotic proteins are overexpressed in several hematological and solid tumors, and contribute to tumor formation, progression, and resistance to therapy. They represent a promising therapeutic avenue to explore for cancer treatment. Venetoclax, a Bcl-2 inhibitor is currently used for hematological malignancies or is undergoing clinical trials for either hematological or solid tumors. Despite these progresses, ongoing efforts are focusing on the identification and development of new molecules targeting Bcl-2 protein and/or other family members. Methods: Machine learning guided virtual screening followed by surface plasmon resonance, molecular docking and pharmacokinetic analyses were performed to identify new inhibitors of anti-apoptotic members of Bcl-2 family and their pharmacokinetic profile. The sensitivity of cancer cells from different origin to the identified compounds was evaluated both in in vitro (cell survival, apoptosis, autophagy) and in vivo (tumor growth in nude mice) preclinical models. Results: IS20 and IS21 were identified as potential new lead compounds able to bind Bcl-2, Bcl-xL and Mcl-1 recombinant proteins. Molecular docking investigation indicated IS20 and IS21 could bind into the Beclin-1 BH3 binding site of wild type Bcl-2, Bcl-xL and Mcl-1 proteins. In particular, although the IS21 docked conformation did not show a unique binding mode, it clearly showed its ability in flexibly adapting to either BH3 binding sites. Moreover, both IS20 and IS21 reduced cell viability, clonogenic ability and tumor sphere formation, and induced apoptosis in leukemic, melanoma and lung cancer cells. Autophagosome formation and maturation assays demonstrated induction of autophagic flux after treatment with IS20 or IS21. Experiments with z-VAD-fmk, a pan-caspase inhibitor, and chloroquine, a late-stage autophagy inhibitor, demonstrated the ability of the two compounds to promote apoptosis by autophagy. IS21 also reduced in vivo tumor growth of both human leukemia and melanoma models. Conclusion: Virtual screening coupled with in vitro and in vivo experimental data led to the identification of two new promising inhibitors of anti-apoptotic proteins with good efficacy in the binding to recombinant Bcl-2, Bcl-xL and Mcl-1 proteins, and against different tumor histotypes.

Antitumor effect of Melaleuca alternifolia essential oil and its main component terpinen-4-ol in combination with target therapy in melanoma models.

Essential oils (EOs) have been recently emerging for their promising biological activities in preventing tumorigenesis or progression of different tumor histotypes, including melanoma. In this study, we investigated the antitumor activity of a panel of EOs in different tumor models. The ability of Melaleuca alternifolia (tea tree oil) and its main component, terpinen-4-ol, to sensitize the target therapy currently used for melanoma treatment was also assessed. Our results demonstrated that EOs differently affect the viability of human cancer cells and led us to select six EOs effective in melanoma and lung cancer cells, without toxic effects in human fibroblasts. When combined with dabrafenib and/or trametinib, Melaleuca alternifolia synergistically reduced the viability of melanoma cells by activating apoptosis. Through machine learning classification modeling, α-terpineol, tepinolene, and terpinen-4-ol, three components of Melaleuca alternifolia, were identified as the most likely relevant components responsible for the EO's antitumor effect. Among them, terpinen-4-ol was recognized as the Melaleuca alternifolia component responsible for its antitumor and proapoptotic activity. Overall, our study holds promise for further analysis of EOs as new anticancer agents and supports the rationale for their use to improve target therapy response in melanoma.