Drugs and Drug Candidates

Background: Type 2 diabetes mellitus (T2DM) is a complex metabolic disease requiring multi-targeted therapeutic strategies. Gunnera perpensa and Erythrina zeyheri are traditionally used in diabetes management, but their mechanisms remain poorly understood. Methods: This study used in vitro, metabolomics, and network pharmacology approaches to elucidate their antidiabetic potential. Leaf extracts were screened for glucose utilization in C2C12 cells, and cytotoxicity in Vero cells. Metabolites profiled via GC×GC-TOF-MS and those retrieved from Phytochemical Interaction Database were evaluated for drug-likeness and target prediction using SwissADME and SwissTargetPrediction. Diabetes-related targets were obtained from databases, and overlapping targets were used to construct interaction networks using Cytoscape and STRING. Functional enrichment analyses were conducted via DAVID for GO and KEGG pathways. Results: G. perpensa acetone and methanol extracts enhanced superior glucose utilization (IC₅₀ = 78.5 and 94.8 µg/mL, respectively), with low cytotoxicity (LC₅₀ > 600 µg/mL). Key compounds including arabinose, identified from both plants, showed multi-target binding potential against STAT3, PIK3RI and JAK2. Enrichment analyses revealed pathways related to insulin signaling, inflammation, and glucose metabolism. Conclusions: This study supports the therapeutic relevance of phytochemical synergy in the traditional use of both plants and demonstrated systems-level approaches for elucidating complex drug–target interactions in T2DM.

Background/Objectives: Molnupiravir (MOV) and nirmatrelvir (NMV) are antiviral drugs that were FDA-approved under the emergency use authorization (EUA) for coronavirus disease-2019 (COVID-19) treatment. MOV and NMV target the viral RNA-dependent RNA polymerase and main protease, respectively. Paxlovid is a combination of NMV and ritonavir (RTV), an inhibitor of the human cytochrome P450-3A4 (hCYP3A4). In this study, the structural consequences in the hCYP3A4 caused by MOV-induced mutations (MIM) were evaluated using in silico tools. Methods: MOV-induced mutations (MIM) were inserted into all the possible hotspots in the active site region of the hCYP3A4 gene, and mutant protein models were built. Structural changes in the heme-porphyrin ring of hCYP3A4 were analyzed in the presence and absence of substrates/inhibitors, including RTV. Molecular dynamics (MD) simulations were performed to analyze the effect of MIM-induced structural changes in hCYP3A4 on drug binding. Results: MD simulations confirm that MIMs, R375G and R440G in hCYP3A4 severely affect the heme-porphyrin ring stability by causing a tilt that in turn affects RTV binding, suggesting a possible inefficiency in the function of hCYP3A4. Similar results were seen for amlodipine, atorvastatin, sildenafil and warfarin, which are substrates of hCYP3A4. Conclusions: The current in silico studies indicate that hCYP3A4 containing MIMs can create complications in the treatment of COVID-19 patients, particularly with co-morbidities due to its functional inefficiency. Hence, clinicians must be vigilant when using MOV in combination with other drugs. Further in vitro studies focused on hCYP3A4 containing MIMs are currently in progress to support our current in silico findings.

Background/Objectives: Despite continuous pharmacological advances, the treatment of schizophrenia remains challenging, and suboptimal outcomes are still too frequent. There are currently limited new approved drugs without resistance. Methods: For this reason, drug repurposing presents a promising solution for identifying existing drugs with therapeutic effects for schizophrenia. In this study, we provide a workflow of signature-based drug repurposing methodology. We initially utilized a dataset from Gene Expression Omnibus which consists of RNA sequence data from blood-derived leukocyte samples from individuals with schizophrenia and control subjects, and conducted an analysis. Results: This analysis identified 1205 statistically significant differentially expressed genes, of which 150 upregulated and 150 downregulated genes were used in the CMap and L1000CDS2 tools. Then, each database generated a list of potential compounds that could reverse the disease’s signature and potentially have therapeutic effects for schizophrenia. Subsequently, the compounds associated with the disease, as identified in the research, were chemically clustered, and then their modes of action were predicted. In the last stage, we conducted a literature review to evaluate the relationship of these modes of action with the disease. Conclusions: This systematic analysis provided a list of potential drugs for schizophrenia treatment so that their efficacy can be evaluated in the wet-lab experiments, which is the next stage of drug repurposing.