Dienogest

Pseudomonas aeruginosa is a leading cause of nosocomial infections, particularly in individuals with a compromised immune system. Due to its strong adaptive ability, P. aeruginosa tends to develop antibiotic resistance and establish chronic infection, making its eradication through traditional antibiotics challenging. Thus, the development of novel therapeutic targets and corresponding inhibitors is urgently required. We conducted bioinformatics analyses of gene-chip datasets (GSE10362, GSE21966) of P. aeruginosa from patients with cystic fibrosis (CF) in the Gene Expression Omnibus (GEO) database, ranging from early to late stages of infection. Differentially expressed gene (DEG) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and Weighted Gene Co-expression Network Analysis (WGCNA) were performed to identify key functional DEGs. Among these genes, candidate genes were refined using three machine learning algorithms: LASSO regression, random forest (RF), and support vector machine (SVM). Using this integrated approach and published reports, target gene was identified consequently. With reported inhibitors, a structure-based virtual screening model against the target protein was established to screen inhibitors from an FDA-approved drug library. Screened hits were experimentally validated through in vitro MIC and checkerboard broth microdilution assays to determine their antibacterial activity and the synergistic effect of tobramycin (TOB) and amikacin (AMK). Enzyme activity was measured to evaluate target protein inhibition. Transcriptome sequencing analysis was performed to explain the effect of target inhibition and reveal the possible mechanism of inhibition. We identified 210 upregulated genes in the late stage of infection. KEGG functional enrichment demonstrated that the up-regulated genes were mainly enriched in metabolic pathways. Eight metabolism-related key DEGs were obtained by the intersection of genes from metabolic pathways and genes from the key module of WGCNA. Machine learning algorithms (LASSO, RF, and SVM) and literature investigation results identified glcB, which encodes malate synthase G (MS), as a therapeutic target for developing inhibitors. In virtual screening, we formulated four rules for a screening model based on the docking results, and finally identified three FDA-approved drugs prednisolone, dienogest and carbinoxamine maleate salt (CAR) as candidates. In vitro MIC assay and checkerboard assay suggested that CAR had an antibacterial effect with a concentration-dependent trend and could enhance the effect of tobramycin and amikacin. Consistent with MIC assay, the inhibition rate in MS activity assay showed a concentration-dependent trend. Transcriptome sequencing analysis showed that carbon metabolism was remodeled when MS was inhibited, thus impairing the growth of P. aeruginosa, and the synergistic effect with TOB and AMK may be explained by the impaired ability to combat oxidative stress. This study identified and validated drug target glcB (encodes MS) and potential inhibitor CAR in treating chronic P. aeruginosa infection based on an integrated method of bioinformatics, virtual screening, and in vitro experiments.

To compare the efficacy, safety and tolerability of elagolix with dienogest in women with moderate‐to‐severe endometriosis‐associated pain.

A multicentre, double‐blind, double‐dummy, randomised, parallel‐group, active‐controlled, non‐inferiority phase III study.

Nineteen clinical centres across India.

Women (18–49 years) diagnosed with endometriosis and experiencing moderate‐to‐severe pain.

Participants were randomised (1:1) to receive oral elagolix (150 mg once daily) or dienogest (2 mg once daily) for 24 weeks.

The primary outcome was change in endometriosis‐related pain (Numeric Rating Scale [NRS]) from baseline to Day 85. Secondary outcomes included changes in NRS (Day 169), dysmenorrhoea, non‐menstrual pelvic pain (NMPP) scores (Days 85 and 169), rescue medication use, patient global impression of change (PGIC), adverse events and bone mineral density.

Of 340 patients screened, 230 were randomised (115 per group). At Day 85, both arms showed similar reductions in NRS pain scores with a treatment difference of 0.04 (95% CI: −0.3, 0.37) [ p  = 0.9747] demonstrating non‐inferiority as upper 95% CI was below pre‐specified margin of 1.5. At Day 169, both arms showed comparable improvements in overall pain, dysmenorrhoea and NMPP from baseline ( p  = 0.9372, p  = 0.8884, and p  = 0.9616, respectively). Rescue medication use and PGIC were comparable between treatment arms. Adverse event incidence was similar (elagolix: 14.8%; dienogest: 19.1%), with no serious TEAEs or discontinuations. No significant bone mineral density changes were observed.

Elagolix demonstrated non‐inferiority to dienogest with an acceptable safety and tolerability profile, supporting its use in managing endometriosis‐associated pain.

ClinicalTrials.gov identifier: CTRI/2023/01/049292