Inclisiran, a small interfering RNA (siRNA), reduces the levels of low-density lipoproteins (LDL) in the body by preventing the hepatic synthesis of proprotein convertase subtilisin/kexin type 9 (PCSK Show more
Inclisiran, a small interfering RNA (siRNA), reduces the levels of low-density lipoproteins (LDL) in the body by preventing the hepatic synthesis of proprotein convertase subtilisin/kexin type 9 (PCSK9). However, there is limited pooled data regarding the efficacy and safety of inclisiran in patients with hypercholesterolemia. PubMed/MEDLINE, Embase and the Cochrane Library were searched by investigators from inception till July 2024 to identify randomised controlled trials (RCTs) that investigated inclisiran in patients with hypercholesterolemia. Weighted mean differences (MDs) for continuous outcomes and risk ratios (RRs) for the dichotomous outcomes were pooled. The analysis was conducted using the random effects model, and a p-value of < 0.05 was considered statistically significant. A total of 8 RCTs reporting data for 5016 patients were included in the pooled analysis. Our pooled analysis demonstrated that inclisiran was associated with a significant decline in the % of LDL-C levels (MD = -50.42, 95% CI: -56.15 to -44.70), % of PCSK9 levels (MD = -78.57, 95% CI: -81.64 to -75.50), % of total cholesterol levels in the body (MD = -31.22, 95% CI: -33.08.15 to -29.37), and apo B levels (MD = -41.47, 95% CI: -44.83 to -38.11) when compared with the control group. The risk of all-cause death, cardiovascular death, major adverse cardiovascular events, myocardial infarction, stroke, and serious adverse events remained comparable (p > 0.05) across the two groups. Inclisiran reduces LDL-C, PCSK9, cholesterol and apo-B levels in the body without increasing the risk of serious adverse events. Show less
Developing highly potent covalent inhibitors of Fibroblast growth factor receptors 1 (FGFR1) has always been a challenging task. In the current study, various computational techniques, such as 3D-QSAR Show more
Developing highly potent covalent inhibitors of Fibroblast growth factor receptors 1 (FGFR1) has always been a challenging task. In the current study, various computational techniques, such as 3D-QSAR, covalent docking, fingerprinting analysis, MD simulation followed by MMGB/PBSA, and per-residue energy decomposition analysis were used to explore the binding mechanism of pyrazolo[3,4-d]pyridazinone derivatives to FGFR1. The high q2 and r2 values for the CoMFA and CoMSIA models, suggest that the constructed 3D-QSAR models could reliably predict the bioactivities of FGFR1 inhibitors. The structural requirements revealed by the model's contour maps were strategically used to computationally create an in-house library of more than 100 new FGFR1 inhibitors using the R-group exploration technique implemented in the Spark Show less