Network-Based Prediction of Side Effects of Repurposed Antihypertensive Sartans against COVID-19 via Proteome and Drug-Target Interactomes.
Despoina P KiouriCharalampos NtallisKonstantinos KelaidonisMassimiliano PeanaSotirios TsiodrasThomas MauromoustakosAlessandro GiulianiHarry RidgwayGraham J MooreJohn M MatsoukasChristos T ChasapisPublished in: Proteomes (2023)
The potential of targeting the Renin-Angiotensin-Aldosterone System (RAAS) as a treatment for the coronavirus disease 2019 (COVID-19) is currently under investigation. One way to combat this disease involves the repurposing of angiotensin receptor blockers (ARBs), which are antihypertensive drugs, because they bind to angiotensin-converting enzyme 2 (ACE2), which in turn interacts with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. However, there has been no in silico analysis of the potential toxicity risks associated with the use of these drugs for the treatment of COVID-19. To address this, a network-based bioinformatics methodology was used to investigate the potential side effects of known Food and Drug Administration (FDA)-approved antihypertensive drugs, Sartans. This involved identifying the human proteins targeted by these drugs, their first neighbors, and any drugs that bind to them using publicly available experimentally supported data, and subsequently constructing proteomes and protein-drug interactomes. This methodology was also applied to Pfizer's Paxlovid, an antiviral drug approved by the FDA for emergency use in mild-to-moderate COVID-19 treatment. The study compares the results for both drug categories and examines the potential for off-target effects, undesirable involvement in various biological processes and diseases, possible drug interactions, and the potential reduction in drug efficiency resulting from proteoform identification.
Keyphrases
- coronavirus disease
- sars cov
- angiotensin converting enzyme
- respiratory syndrome coronavirus
- angiotensin ii
- human health
- drug administration
- blood pressure
- drug induced
- public health
- endothelial cells
- adverse drug
- binding protein
- risk assessment
- electronic health record
- cancer therapy
- oxidative stress
- combination therapy
- machine learning
- climate change
- network analysis
- sensitive detection
- smoking cessation
- pluripotent stem cells
- small molecule