Discovery of potential sclerostin inhibitors from plants with loop2 region of sclerostin inhibition by interacting with residues outside Pro-Asn-Ala-Ile-Gly motif

Antibacterial activities of Miang extracts against selected pathogens and the potential of the tannin-free extracts in the growth inhibition of Streptococcus mutans

Bacterial pathogens have remained a major public health concern for several decades. This study investigated the antibacterial activities of Miang extracts (at non-neutral and neutral pH) against Bacillus cereus TISTR 747, Escherichia coli ATCC 22595, Salmonella enterica serovar Typhimurium TISTR 292 and Streptococcus mutans DMST 18777. The potential of Polyvinylpolypyrrolidone (PVPP)-precipitated tannin-free Miang extracts in growth-inhibition of the cariogenic Streptococcus mutans DMST 18777 and its biofilms was also evaluated. The tannin-rich fermented extracts had the best bacterial growth inhibition against S. mutans DMST 18777 with an MIC of 0.29 and 0.72 mg/mL for nonfilamentous fungi (NFP) Miang and filamentous-fungi-processed (FFP) Miang respectively. This observed anti-streptococcal activity still remained after PVPP-mediated precipitation of bioactive tannins especially, in NFP and FFP Miang. Characterization of the PVPP-treated extracts using High performance liquid chromatography quadrupole-time of flight-mass spectrometry (HPLC-QToF-MS) analysis, also offered an insight into probable compound classes responsible for the activities. In addition, Crystal violet-staining also showed better IC50 values for NFP Miang (4.30 ± 0.66 mg/mL) and FFP Miang (12.73 ± 0.11 mg/mL) against S. mutans DMST 18777 biofilms in vitro. Homology modeling and molecular docking analysis using HPLC-MS identified ligands in tannin-free Miang supernatants, was performed against modelled S. mutans DMST 18777 sortase A enzyme. The in silico analysis suggested that the inhibition by NFP and FFP Miang might be attributed to the presence of ellagic acid, flavonoid aglycones, and glycosides. Thus, these Miang extracts could be optimized and explored as natural active pharmaceutical ingredients (NAPIs) for applications in oral hygienic products.

Mechanistic advances in osteoporosis and anti-osteoporosis therapies

Osteoporosis is a type of bone loss disease characterized by a reduction in bone mass and microarchitectural deterioration of bone tissue. With the intensification of global aging, this disease is now regarded as one of the major public health problems that often leads to unbearable pain, risk of bone fractures, and even death, causing an enormous burden at both the human and socioeconomic layers. Classic anti-osteoporosis pharmacological options include anti-resorptive and anabolic agents, whose ability to improve bone mineral density and resist bone fracture is being gradually confirmed. However, long-term or high-frequency use of these drugs may bring some side effects and adverse reactions. Therefore, an increasing number of studies are devoted to finding new pathogenesis or potential therapeutic targets of osteoporosis, and it is of great importance to comprehensively recognize osteoporosis and develop viable and efficient therapeutic approaches. In this study, we systematically reviewed literatures and clinical evidences to both mechanistically and clinically demonstrate the state-of-art advances in osteoporosis. This work will endow readers with the mechanistical advances and clinical knowledge of osteoporosis and furthermore present the most updated anti-osteoporosis therapies.

Baicalin Nanocomplexes with an In Situ-Forming Biomimetic Gel Implant for Repair of Calvarial Bone Defects via Localized Sclerostin Inhibition

In situ-forming hydrogels are highly effective in covering complex and irregular tissue defects. Herein, a biomimetic gel implant (CS-GEL) consisting of methacrylated chondroitin sulfate and gelatin is obtained via visible light irradiation, which displays rapid gelation (∼30 s), suitable mechanical properties, and biological features to support osteoblast attachment and proliferation. Sclerostin is proven to be a viable target to promote osteogenesis. Hence, baicalin, a natural flavonoid with a high affinity to sclerostin, is selected as the therapeutic compound to achieve localized neutralization of sclerostin. To overcome its poor solubility and permeability, a baicalin nanocomplex (BNP) is synthesized using Solutol HS15, which is then dispersed in the CS-GEL to afford a nanocomposite delivery system, i.e., BNP-loaded gel (BNP@CS-GEL). In vitro, BNP significantly downregulated the level of sclerostin in MLO-Y4 osteocytes. In vivo, either CS-GEL or BNP@CS-GEL is proven to effectively promote osteogenesis and angiogenesis in a calvarial critical-sized bone defect rat model, with BNP@CS-GEL showing the best pro-healing effect. Specifically, the BNP@CS-GEL-treated group significantly downregulated the sclerostin level as compared to the sham group (p < 0.05). RANKL expression was also significantly suppressed by BNP in MLO-Y4 cells and BNP@CS-GEL in vivo. Collectively, our study offers a facile and viable gel platform in combination with nanoparticulated baicalin for the localized neutralization of sclerostin to promote bone regeneration and repair.

Drug discovery of sclerostin inhibitors

Sclerostin, a protein secreted from osteocytes, negatively regulates the WNT signaling pathway by binding to the LRP5/6 co-receptors and further inhibits bone formation and promotes bone resorption. Sclerostin contributes to musculoskeletal system-related diseases, making it a promising therapeutic target for the treatment of WNT-related bone diseases. Additionally, emerging evidence indicates that sclerostin contributes to the development of cancers, obesity, and diabetes, suggesting that it may be a promising therapeutic target for these diseases. Notably, cardiovascular diseases are related to the protective role of sclerostin. In this review, we summarize three distinct types of inhibitors targeting sclerostin, monoclonal antibodies, aptamers, and small-molecule inhibitors, from which monoclonal antibodies have been developed. As the first-in-class sclerostin inhibitor approved by the U.S. FDA, the monoclonal antibody romosozumab has demonstrated excellent effectiveness in the treatment of postmenopausal osteoporosis; however, it conferred high cardiovascular risk in clinical trials. Furthermore, romosozumab could only be administered by injection, which may cause compliance issues for patients who prefer oral therapy. Considering these above safety and compliance concerns, we therefore present relevant discussion and offer perspectives on the development of next-generation sclerostin inhibitors by following several ways, such as concomitant medication, artificial intelligence-based strategy, druggable modification, and bispecific inhibitors strategy.

Treasuring the computational approach in medicinal plant research

Medicinal plants serve as a valuable source of secondary metabolites since time immemorial. Computational Research in 21st century is giving more attention to medicinal plants for new drug design as pharmacological screening of bioactive compound was time consuming and expensive. Computational methods such as Molecular Docking, Molecular Dynamic Simulation and Artificial intelligence are significant Insilico tools in medicinal plant research. Molecular docking approach exploits the mechanism of potential phytochemicals into the target active site to elucidate its interactions and biological therapeutic properties. MD simulation illuminates the dynamic behavior of biomolecules at atomic level with fine quality representation of biomolecules. Dramatical advancement in computer science is illustrating the biological mechanism via these tools in different diseases treatment. The advancement comprises speed, the system configuration, and other software upgradation to insights into the structural explanation and optimization of biomolecules. A probable shift from simulation to artificial intelligence has in fact accelerated the art of scientific study to a sky high. The most upgraded algorithm in artificial intelligence such as Artificial Neural Networks, Deep Neural Networks, Neuro-fuzzy Logic has provided a wide opportunity in easing the time required in classical experimental strategy. The notable progress in computer science technology has paved a pathway for understanding the pharmacological functions and creating a roadmap for drug design and development and other achievement in the field of medicinal plants research. This review focus on the development and overview in computational research moving from static molecular docking method to a range of dynamic simulation and an advanced artificial intelligence such as machine learning.

In-silico identification of new inhibitors for Low-density lipoprotein receptor-related protein6 (LRP6)

Low-density lipoprotein receptor-related protein 6 (LRP6) is an important therapeutic target for diseases such as osteoporosis, Alzheimer, cancer, and neurodegenerative disease. Computational methods such as ligand-based and structure-based virtual screening have been introduced as an extremely efficient and accurate tool for finding new drug targets and candidates. In this study, we aimed to screen the National Cancer Institute (NCI) Diversity Set II and parts of the ZINC database by virtual screening to identify potential and safe compounds that can inhibit the LRP6 protein. By utilizing various screening methods such as rigid and flexible molecular docking and Lipinski's rule of five, we identified 10 potential compounds. Then, their validity was further tested by molecular dynamics simulation and MMPBSA binding free energy calculations. Eventually, it was concluded that ZINC03954520, ZINC01729523, ZINC03898665, ZINC13152226, ZINC26730911 and ZINC01069082 compounds can be potential drug compounds for inhibiting LRP6 protein. These compounds in complex with β-propeller domains of LRP6 showed that they are capable of altering the backbone of these domains and interfere with their structural dynamics which may lead to the inhibition of the signal transmission. Communicated by Ramaswamy H. Sarma.