Liver X receptors (LXRs) are nuclear receptors that play an important role in the regulation of gene expression involved in lipid metabolism, cholesterol homeostasis and inflammation. Certain modifications in endogenous LXR activity have been associated with pathological conditions and diseases. The dysregulation of LXR activity can lead to vascular and metabolic diseases, neurological degeneration and formation of malignant tumors. One of the solutions to treat the diseases or at least to slow down the progression is the development of different strategies to modulate LXR signaling for the treatment or prevention of these pathological conditions. Many researchers and pharmaceutical companies are developing selective LXR modulators to precisely control receptor activity and achieve specific pharmacological effects. The activation of LXRs has been linked to the inhibition of cell proliferation and metastasis, the promotion of apoptosis and re-differentiation of tumor cells that drives exosome release to stimulate anti-tumor immunity in certain cancer types. The main challenge is to minimize the adverse effects of LXR induction on lipid metabolism via selective activation of LXRβ isoform. Several lead compounds were identified and tested in clinical trials for few pathologies discussed above. The discovery of a potent and selective LXR ligand involves a combination of multiple steps from lead discovery, optimization, preclinical and clinical testing before getting to marketing authorization. This review offers an overview of synthetic agonists for LXRs developed over the past three decades. Though multiple investigations have been in progress, no FDA-approved LXR-targeting drug is available so far.
Estrogens play an important role in regulating mitochondrial function and cellular metabolism. Most of the effects of estrogens are mediated by estrogen receptors α and β (ERα and ERβ), located in various cellular compartments, including the mitochondria. The aim of this study was to validate the mitochondrial accumulation of mitochondria-targeted estrogens and determine whether their biological effects are mediated by ERs. Two mitochondria-targeted estrogen derivatives, mitoE2 and mitoEE2, were synthesized by covalently coupling a triphenylphosphonium (TPP) moiety to 17β-estradiol (E2) and 17α-ethynylestradiol (EE2), respectively, and were structurally characterized. Mitochondrial and cytosolic accumulation in MCF-7 cells was quantified by high resolution mass spectrometry. Cell viability assays were performed to assess the cytocompatibility of TPP. To investigate the binding of mitochondria-targeted estrogens to ERs molecular dynamics simulations were performed. Functional assays were assessed in vitro in the MCF-7 cell line through Manganese-dependent Superoxide Dismutase (MnSOD) activity assays using selective ER antagonists, and by gene expression analyses of mitochondrial-encoded cytochrome c oxidase COX1 and nuclear-encoded COX4. Both mitoE2 and mitoEE2 preferentially accumulated in mitochondria compared with the cytosol. The molecular dynamic simulations showed a thermodynamically stable binding to the ligand-binding domain of ERα and ERβ. In vitro analyses revealed that ERβ selectively mediates the inhibition of MnSOD activity induced by mitoE2 and that mitochondria-targeted estrogens differentially modulate COX1 and COX4 expression in a time-dependent manner. In conclusion, this work contributes to understanding the molecular mechanisms underlying the effects of mitochondria-targeted estrogens on mitochondrial function. Our findings support the need for further investigation to determine their role as modulators of mitochondrial processes and provide a mechanistic basis for future studies in additional cellular and disease-specific models.
Breast cancer continues to be a major threat to women’s health on a global scale. Innovative treatments that are effective and well-tolerated are greatly needed. Nature-derived compounds, such as phytochemicals, have gained widespread interest owing to their diverse anti-tumor actions on cancer cells. Murrayanine, found in Murraya koenigii, has shown efficacy as an anticancer agent by promoting apoptosis, inducing reactive oxygen species, and regulating pivotal pathways like PI3K/Akt and NF-κB. This investigation evaluated the anti-breast cancer efficacy of Murrayanine by combining cytotoxicity assays, molecular docking and the production of solid lipid nanoparticles for improved delivery. Machine learning models, such as Random Forest, identified Murrayanine as a possible PARP10 inhibitor, suggesting molecules known as PARP inhibitors can pass through the cell barrier. Both network pharmacology and protein interaction analyses identified the proteins PARP, PTEN and TP53 as potential targets for breast cancer therapy. Molecular simulations revealed stable protein-drug interactions, suggesting its potential to produce a favorable effect on the molecular level. Murrayanine SLNs exhibited excellent biochemical features, controlled drug release and stability in in vivo settings. Dose-dependent cytotoxic effects were observed against MCF7 breast cancer cells, with an IC₅₀ of 37.35 µg/mL determined. The experimental data suggest that Murrayanine delivered as SLNs is a promising preclinical candidate for breast cancer treatment. The PARP10-inhibitory mechanism and the PARP/PTEN/TP53 network targets proposed here are derived from machine learning classification and molecular modelling and therefore represent computational predictions rather than biochemically or in vivo confirmed outcomes. Conclusively, it is necessary to carry out biochemical validation, pharmacokinetic, and clinical evaluations to ascertain the anticancer efficacy of Murrayanine delivered by SLNs. An integrated use of computational and experimental approaches highlights the ability of natural product drug discovery to address breast cancer. Quantitatively, the Random Forest classifier achieved 91.23% accuracy (Kappa 0.8684, MCC 0.869, weighted F1 0.912) on the class-balanced training partition but only 80.0% accuracy (Kappa 0.2793, MCC 0.287) on an independent, natively imbalanced test partition, indicating overfitting; Murrayanine docked into the PARP10 catalytic domain (PDB 5LX6) with a binding affinity of −8.2 kcal/mol in a protocol validated by redocking veliparib (RMSD 0.86 Å), and the complex remained stable over 100 ns (Rg 16.84 ± 0.21 Å, ALA921 hydrogen-bond occupancy 78.4%, MM-GBSA ΔG −52.00 kcal/mol). The SLNs showed an encapsulation efficiency of 82.64 ± 1.37%, a particle size of 179.0 ± 5.3 nm, a zeta potential of −27.4 ± 1.6 mV and 88.17% cumulative release at 72 h, and were stable for 90 days at 4 °C.
