In Silico Analysis of the Effect of Hydrastis canadensis on Controlling Breast Cancer.

Background and Objectives: Breast cancer is a significant type of cancer among women worldwide. Studies have reported the anti-carcinogenic activity of Hydrastis Canadensis (Goldenseal) in cancer cell lines. Hydrastis Canadensis could help eliminate toxic substances due to its anti-cancer, anti-inflammatory, and other properties. The design phase includes the identification of potential and effective molecules through modern computational techniques. Objective: This work aims to study Hydrastis Canadensis's effect in controlling hormone-independent breast cancer through in-silico analysis. Materials and Methods: The preliminary screening of reported phytochemicals includes biomolecular networking. Identifying functionally relevant phytochemicals and the respective target mutations/genes leads to selecting 3D proteins of the desired mutations being considered the target. Interaction studies have been conducted using docking. The kinetic and thermodynamic stability of complexes was studied through molecular dynamic simulation and MM-PBSA/GBSA analysis. Pharmacodynamic and pharmacokinetic features have been predicted. The mechanism-wise screening, functional enrichment, and interactional studies suggest that canadaline and Riboflavin effectively interact with the target proteins. Results: Hydrastis Canadensis has been identified as the effective formulation containing all these constituents. The phytoconstituents; Riboflavin and Canadensis showed good interaction with the targets of hormone-independent breast cancer. The complexes were found to be kinetically and thermodynamically stable. Conclusions: Hydrastis Canadensis has been identified as effective in controlling 'hormone-independent or basal-like breast cancer' followed by 'hormone-dependent breast cancer: Luminal A' and Luminal B.

Identification of (2R,3R)-2-(3,4-dihydroxyphenyl)chroman-3-yl-3,4,5-trihydroxy benzoate as multiple inhibitors of SARS-CoV-2 targets; a systematic molecular modelling approach.

Coronavirus disease of 2019 (COVID-19) is a zoonotic disease caused by a new severe acute respiratory syndrome (SARS-CoV-2) which has quickly resulted in a pandemic. Recent anti-COVID-19 drug discoveries are leaning towards repurposing phytochemicals which have been previously reported for SARS and MERS-CoV outbreaks. However, they have been either virtually screened or tested so far against mono targets and the potent derivatives of virtually sorted lead molecules remain elusive. We aimed to identify the phytochemicals having potentials to inhibit SARS CoV-2 infection via multiple targets. The selected 132 phytochemicals were virtually screened using a structure based in silico technique against main protease (Mpro) which is a potential target of SARS CoV-2. Six compounds were selected based on the LibDock scores and further subjected to induced fit docking using the CDOCKER module of DS. Two compounds namely cinnamtannin-B and gallocatechin gallate were identified as top HITS against main protease (Mpro). Based on the Lipinski rule of five (L-ROF) and synthetic feasibility, gallocatechin gallate was taken for our further studies. Six analogues of gallocatechin gallate were screened against the next important targets such as RNA-dependent RNA polymerase (RdRp), angiotensin converting enzyme-2 (ACE2), transmembrane protease serine -2 (TMPRSS2) and interleukin-6 (IL-6) along with main protease (Mpro). Our molecular docking results reveal that a gallocatechin analogue (GC-2) namely (2R,3R)-2-(3,4-dihydroxyphenyl)chroman-3-yl-3,4,5-trihydroxy benzoate has shown potential to inhibit multiple targets of SARS CoV-2. Further, the molecular dynamics study was carried out to ascertain the stability of the GC-2 and RdRp complex.