Inhibition of GSK_3β by Iridoid Glycosides of Snowberry (Symphoricarpos albus) Effective in the Treatment of Alzheimer’s Disease Using Computational Drug Design Methods

The inhibition of glycogen synthase kinase-3β (GSK-3β) activity prevents tau hyperphosphorylation and binds it to the microtubule network. Therefore, a GSK-3β inhibitor may be a recommended drug for Alzheimer’s treatment. In silico methods are currently considered as one of the fastest and most cost-effective available alternatives for drug/design discovery in the field of treatment. In this study, computational drug design was conducted to introduce compounds that play an effective role in inhibiting the GSK-3β enzyme by molecular docking and molecular dynamics simulation. The iridoid glycosides of the common snowberry (Symphoricarpos albus), including loganin, secologanin, and loganetin, are compounds that have an effect on improving memory and cognitive impairment and the results of which on Alzheimer’s have been studied as well. In this study, in the molecular docking phase, loganin was considered a more potent inhibitor of this protein by establishing a hydrogen bond with the ATP-binding site of GSK-3β protein and the most negative binding energy to secologanin and loganetin. Moreover, by molecular dynamics simulation of these ligands and GSK-3β protein, all structures were found to be stable during the simulation. In addition, the protein structure represented no change and remained stable by binding ligands to GSK-3β protein. Furthermore, loganin and loganetin have higher binding free energy than secologanin; thus, these compounds could effectively bind to the active site of GSK-3β protein. Hence, loganin and loganetin as iridoid glycosides can be effective in Alzheimer’s prevention and treatment, and thus, further in vitro and in vivo studies can focus on these iridoid glycosides as an alternative treatment.

Synthetic and semi-synthetic pathways for the realisation of phenothiazine derivatives

This paper focuses on simple synthetic pathways that may help achieve two compounds previously designed by our group, which should preferably target the prion protein involved in Sporadic Fatal Insomnia. This step is necessary for in vitro studies whose relevance is linked to their potential empirical support of the hitherto computational drug design analysis carried out. (The Chlorine in meta position was not indicated in all steps, for simplicity).

Phytochemicals for drug discovery in Alzheimer’s disease: In silico Advances

: The search for novel drugs that can prevent or control Alzheimer’s disease has attracted lot of attention from researchers across the globe. Phytochemicals are increasingly being used to provide scaffolds to design drugs for AD. In silico techniques, have proven to be a game-changer in this drug design and development process. In this review, the authors have focussed on current advances in the field of in silico medicine, applied to phytochemicals, to discover novel drugs to prevent or cure AD. After giving a brief context of the etiology and available drug targets for AD, authors have discussed the latest advances and techniques in computational drug design of AD from phytochemicals. Some of the prototypical studies in this area are discussed in detail. In silico phytochemical analysis is a tool of choice for researchers all across the globe and helps integrate chemical biology with drug design.

Computational approaches for the discovery of natural pancreatic lipase inhibitors as antiobesity agents

Obesity is becoming one of the greatest threats to global health in the 21st century and therefore the development of novel antiobesity drugs is one of the top priorities of global drug research. An important treatment strategy includes the reduction of intestinal fat absorption through the inhibition of pancreatic lipase (PL). Natural products provide a vast pool of PL inhibitors with novel scaffolds that can possibly be developed into clinical products. Computational drug design methods have become increasingly invaluable in the drug discovery process. In recent years, the discovery of new antiobesity PL inhibitors has been facilitated by the application of computational methods. This review highlights some computer-aided drug design techniques utilized in the discovery of natural PL inhibitors.

Computational Drug Design against Ebola Virus Targeting Viral Matrix Protein VP30

Ebola viral disease (EVD) is a deadly infectious hemorrhagic viral fever caused by the Ebola virus with a high mortality rate. Until date, there is no effective drug or vaccination available to combat this condition. This study focuses on designing an effective antiviral drug for Ebola viral disease targeting viral protein 30 (VP30) of Ebola virus, highly required for transcription initiation. The lead molecules were screened for Lipinski rule of five, ADMET study following which molecular docking and bioactivity prediction was carried out. The compounds with the least binding energy were analyzed using interaction software. The results revealed that 6-Hydroxyluteolin and (-)-Arctigenin represent active lead compounds that inhibit the activity of VP30 protein and exhibits efficient pharmacokinetics. Both these compounds are plant-derived flavonoids and possess no known adverse effects on human health. In addition, they bind strongly to the predicted binding site centered on Lys180, suggesting that these two lead molecules can be imperative in designing a potential drug for EVD.

The development of novel HPV Drugs by HDAC inhibitor based upon applied bioinformatics tool and their specialized algorithm

The HPV infection, which is the sole cause of cervical cancer, is a malicious threat for developing countries, for instance, Indonesia. Drug for coping HPV infection have been sold in the market namely SAHA or Vorinostat. Mainly, it works by inhibiting the Human Deacetylase Enzyme (HDAC) class II Homo sapiens. However, due to the contradicting report on its efficacy, a novel drug should be developed to remedy the resistance problem. Some groups have successfully developed novel HDAC inhibitors, based upon computational drug design.