Rational drug repurposing for alzheimer's treatment using in-silico ligand and structure-based approaches

Abstract Alzheimer's disease is a devastating neurodegenerative disorder characterized by memory loss and cognitive decline. New AD treatments are essential, and drug repositioning is a promising approach. In this study, we combined ligand-based and structure-based approaches to identify potential candidates among FDA-approved drugs for AD treatment. We used the human acetylcholinesterase receptor structure (PDB ID: 4EY7) and applied Rapid Overlay of Chemical Structures and Swiss Similarity for ligand-based screening.Computational shape-based screening revealed 20 out of 760 FDA approved drugs with promising structural similarity to Donepezil, an AD treatment AChE inhibitor and query molecule. The screened hits were further analyzed using docking analysis with Autodock Vina and Schrodinger glide. Predicted binding affinities of hits to AChE receptor guided prioritization of potential drug candidates. Doxazosin, Oxypertine, Cyclopenthiazide, Mestranol, and Terazosin exhibited favorable properties in shape similarity, docking energy, and molecular dynamics stability.Molecular dynamics simulations confirmed the stability of the complexes over 100 ns. Binding free energy analysis using MM-GBSA indicated favourable binding energies for the selected drugs. ADME, formulation studies offered insights into therapeutic applications and predicted toxicity.This comprehensive computational approach identified potential FDA-approved drugs (especially Doxazosin) as candidates for repurposing in AD treatment, warranting further investigation and clinical assessment.

Changes of disease structure in a tertiary general hospital during the COVID-19 pandemic / 中华医院管理杂志

Objective:To explore the changes of disease structure in a tertiary general hospital during the COVID-19 pandemic.Methods:A database of 783 diagnosis-related groups(DRG) patients in a tertiary general hospital from 2017 to 2020 was used. The rank sum test was used to compare the number of patients among different years, and the Chi-square test was used to compare the composition of patients among different years. With the patient composition ratio as the main index, the thermal cluster analysis was used to analyze the changes of disease structure during the COVID-19 pandemic, from the perspectives of major diagnostic categories(MDC) and the key DRG(the number of patients in any year more than 2 000)respectively. All analyses were performed in R software, with P<0.05 indicating significance. Results:There were significant differences in the number and composition of patients in MDC groups and key DRG groups among different years( P<0.05). The results of thermal clustering analysis showed that the MDC composition of patients in 2020 was significantly different from those in 2017 to 2019; the 26 MDC groups were classified into four main categories. The results of thermal clustering analysis also showed that the DRG composition of patients in 2020 were significantly different from those in 2017 to 2019; The RU14 group and the other 19 key DRG groups were classified into different groups; and the other 19 key DRG groups except RU14 were classified into five main categories. Conclusions:The disease structure of tertiary general hospitals has changed significantly during the COVID-19 pandemic.