Transcriptomic and genomic studies classify NKL54 as a histone deacetylase inhibitor with indirect influence on MEF2-dependent transcription.

In leiomyosarcoma class IIa HDACs (histone deacetylases) bind MEF2 and convert these transcription factors into repressors to sustain proliferation. Disruption of this complex with small molecules should antagonize cancer growth. NKL54, a PAOA (pimeloylanilide o-aminoanilide) derivative, binds a hydrophobic groove of MEF2, which is used as a docking site by class IIa HDACs. However, NKL54 could also act as HDAC inhibitor (HDACI). Therefore, it is unclear which activity is predominant. Here, we show that NKL54 and similar derivatives are unable to release MEF2 from binding to class IIa HDACs. Comparative transcriptomic analysis classifies these molecules as HDACIs strongly related to SAHA/vorinostat. Low expressed genes are upregulated by HDACIs, while abundant genes are repressed. This transcriptional resetting correlates with a reorganization of H3K27 acetylation around the transcription start site (TSS). Among the upregulated genes there are several BH3-only family members, thus explaining the induction of apoptosis. Moreover, NKL54 triggers the upregulation of MEF2 and the downregulation of class IIa HDACs. NKL54 also increases the binding of MEF2D to promoters of genes that are upregulated after treatment. In summary, although NKL54 cannot outcompete MEF2 from binding to class IIa HDACs, it supports MEF2-dependent transcription through several actions, including potentiation of chromatin binding.

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.

Pinus mugo Essential Oil Impairs STAT3 Activation through Oxidative Stress and Induces Apoptosis in Prostate Cancer Cells.

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.

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.

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.

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.

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.

A Comparative Analysis of Punicalagin Interaction with PDIA1 and PDIA3 by Biochemical and Computational Approaches.

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 anti-STAT1 polyphenol myricetin inhibits M1 microglia activation and counteracts neuronal death.

Microglia activation toward M1 pro-inflammatory phenotype represents one of the earliest events of neurological disorders. Therefore, reducing microglia activation should inhibit neuroinflammation, thereby delaying the progression of neurodegeneration. Recently, we pointed out the role of STAT1 signaling in hypoxia-induced M1 activation and proposed STAT1 as a suitable molecular target for the prevention and treatment of neurodegeneration. Myricetin (MYR) is a natural flavonoid that exhibits a specific anti-STAT1 activity correlated with its direct interaction with STAT1 protein itself. Herein, we investigated the anti-inflammatory effect of MYR and its ability to protect neurons from death in an in vitro model of neurotoxicity using the neuroblast-like SH-SY5Y cells that were exposed to conditioned media from hypoxia-activated microglia BV2 cells. We demonstrate that MYR pretreatment is able to switch off hypoxia-induced M1 microglia polarization through the inhibition of STAT1 signaling. The analysis of the molecular mechanism suggests that the direct interaction of MYR with STAT1 impairs its S-glutathionylation and phosphorylation. Moreover, treatment of SH-SY5Y cells with conditioned medium from hypoxia-activated microglia pretreated with MYR produced a significant reduction in neuronal viability. Our data indicate that MYR may represent a promising candidate for prevention and treatment of neuroinflammation in neurodegenerative disorders.

First-in-Class Inhibitors of the Ribosomal Oxygenase MINA53.

MINA53 is a JmjC domain 2-oxoglutarate-dependent oxygenase that catalyzes ribosomal hydroxylation and is a target of the oncogenic transcription factor c-MYC. Despite its anticancer target potential, no small-molecule MINA53 inhibitors are reported. Using ribosomal substrate fragments, we developed mass spectrometry assays for MINA53 and the related oxygenase NO66. These assays enabled the identification of 2-(aryl)alkylthio-3,4-dihydro-4-oxoypyrimidine-5-carboxylic acids as potent MINA53 inhibitors, with selectivity over NO66 and other JmjC oxygenases. Crystallographic studies with the JmjC demethylase KDM5B revealed active site binding but without direct metal chelation; however, molecular modeling investigations indicated that the inhibitors bind to MINA53 by directly interacting with the iron cofactor. The MINA53 inhibitors manifest evidence for target engagement and selectivity for MINA53 over KDM4-6. The MINA53 inhibitors show antiproliferative activity with solid cancer lines and sensitize cancer cells to conventional chemotherapy, suggesting that further work investigating their potential in combination therapies is warranted.

Development of alkyl glycerone phosphate synthase inhibitors: Structure-activity relationship and effects on ether lipids and epithelial-mesenchymal transition in cancer cells.

In aggressive tumors, alkylglyceronephosphate synthase (AGPS) controls cellular ether phospholipid utilization and metabolism to promote cancer cell proliferation and motility. SAR studies on the first-in-class AGPS inhibitor 1, discovered by our group, led to the 2,6-difluoro analog 2i which showed higher binding affinity than 1in vitro. In 231MFP cancer cells, 2i reduced ether lipids levels and cell migration rate. When tested in PC-3 and MDA-MB-231 cancer cells, 2i specifically impaired epithelial to mesenchymal transition (EMT) by modulating E-cadherin, Snail and MMP2 expression levels. Moreover, the combination of siRNAs against AGPS and 2i provided no additive effect, confirming that the modulation of 2i on EMT specifically relies on AGPS inhibition. Finally, this compound also affected cancer cell proliferation especially in MDA-MB-231 cells expressing higher AGPS level, whereas it provided negligible effects on MeT5A, a non-tumorigenic cell line, thus showing cancer specificity.