Osteoporosis, characterized by reduced bone density and increased fracture risk, affects over 200 million people worldwide, predominantly older adults and postmenopausal women. The disruption of the balance between bone-forming osteoblasts and bone-resorbing osteoclasts underlies osteoporosis pathophysiology. Standard treatment includes lifestyle modifications, calcium and vitamin D supplementation and specific drugs that either inhibit osteoclasts or stimulate osteoblasts. However, these treatments have limitations, including side effects and compliance issues. Natural products have emerged as potential osteoporosis therapeutics, but their mechanisms of action remain poorly understood. In this study, we investigate the efficacy of natural compounds in modulating molecular targets relevant to osteoporosis, focusing on the Mitogen-Activated Protein Kinase (MAPK) pathway and the gut microbiome’s influence on bone homeostasis. Using an in silico and in vitro methodology, we have identified quercetin as a promising candidate in modulating MAPK activity, offering a potential therapeutic perspective for osteoporosis treatment.

Corticotropin-releasing factor or hormone (CRF or CRH) and the urocortins regulate a plethora of physiological functions and are involved in many pathophysiological processes. CRF and urocortins belong to the family of CRF peptides (CRF family), which includes sauvagine, urotensin, and many synthetic peptide and non-peptide CRF analogs. Several of the CRF analogs have shown considerable therapeutic potential in the treatment of various diseases. The CRF peptide family act by interacting with two types of plasma membrane proteins, type 1 (CRF₁R) and type 2 (CRF₂R), which belong to subfamily B1 of the family B G-protein-coupled receptors (GPCRs). This work describes the structure of CRF peptides and their receptors and the activation mechanism of the latter, which is compared with that of other GPCRs. It also discusses recent structural information that rationalizes the selective binding of various ligands to the two CRF receptor types and the activation of receptors by different agonists.

Diabetes mellitus affects more than 400 million people worldwide, and the incidence of disease is rising. Current anti-hyperglycemic agents share major drawbacks, such as hypoglycemia and low potency due to a lack of target specificity. Drug repurposing accelerates drug research and development pipelines and empowers chemical space enrichment. Herein, we propose a data-driven approach towards drug repurposing in diabetes mellitus by integrating heterogeneous biomedical data in a unified knowledge graph. Through extensive data mining in public repositories, diabetes-related multimodal data have been retrieved. Several data analysis techniques were employed to extract information and define semantic associations, followed by data parsing and, next, descriptive statistics, regression, and cluster analysis. Biomedical entity recognition and negation detection were performed by natural language processing. Predefined biological ontologies served as reference endpoints for class definition upon data integration. Graph analytics were performed, and drug–drug, protein–protein, drug–protein, and drug–disease interactions were established. A majority vote-based machine learning framework for the prediction of human cytochrome P450 inhibitors was also integrated into the proposed enhanced knowledge graph analysis that facilitates data-driven ranking for drug repurposing candidates in diabetes mellitus. The presented method yields a ranked list of repurposing candidates.

Background and Purpose: Growth hormone-releasing hormone (GHRH), a 44-residue neuropeptide produced in the hypothalamus, stimulates the synthesis and release of pituitary growth hormone via binding to its cognate receptor, the growth hormone-releasing hormone receptor (GHRHR). The GHRHR belongs to Class B1 of G protein-coupled receptors (GPCRs). Class B1 GPCR peptides have been proposed to bind in a two-step model, where firstly the C-terminal region of the peptide interacts with the extracellular domain of the receptor and subsequently, the N-terminus interacts with the seven transmembrane domain of the receptor, resulting in activation. No non-peptide compounds targeting the GHRHR have been identified, despite observations indicating that it may be an effective therapeutic target due to overexpression in several disorders including prostate, breast, pancreatic and ovarian cancer, and peptide antagonists displaying promising results in many cancer models. Experimental Approach: We have utilized several computational tools to target the GHRHR and identify potential small-molecule compounds directed at this receptor. These compounds were validated in vitro using a cAMP ELISA to measure activity at the GHRHR. Key Results: In vitro results suggest that several of the novel small-molecule compounds could inhibit GHRH-induced cAMP accumulation. Preliminary analysis of the specificity/selectivity of one of the most effective hit compounds indicated that the effect was via antagonism of the GHRHR. Conclusion and Implications: We therefore report the first non-peptide antagonists of GHRHR and propose a structural basis for antagonism, which may assist in the future design of lead GHRHR compounds for treating disorders attributed to dysregulated/aberrant GHRHR signalling.

Drug repurposing is a state-of-the-art pipeline that expands pharmaceutical research and development portfolia. Despite computational tools are rapidly advancing, they predominantly rely on the examination of textual (1D) data. Herein, we present Cloudscreen ^®, a "one-stop-shop" platform for drug repurposing, which seamlessly integrates 3D (structural data through predictive protein-ligand modeling) and 1D data alongside state-of-the-art machine learning algorithms. Our platform leverages a knowledge graph database, curated from diverse sources, including publicly available repositories and databases coupled to in-house computations. We are harnessing the power of artificial intelligence to predict and assess the efficacy and safety of repurposed biomolecules for novel therapeutic indications while interrogating the human variome. Moreover, Cloudscreen ^® expands such prediction capabilities based on AlphaFold models, ADMETox and pharmacogenomics (emphasis on missense variants). Cloudscreen ^® is a powerful tool that results from the synergy of wet- and dry-lab validated datasets and hence, provides “one-stop-shop” predictive insights into the uncharted therapeutic possibilities for repurposed drugs.