tetano
Editor, Senior Moderator
Br J Pharmacol
. 2022 Feb 18.
doi: 10.1111/bph.15819. Online ahead of print.
Hypothesis: How far are we from predicting multi-drug interactions during treatment for COVID-19 infection?
Benjamin Lozano[SUP] 1 [/SUP], Javier Santibañez[SUP] 2 [/SUP], Nicolás Severino[SUP] 3 [/SUP], Cristina Saldias[SUP] 4 [/SUP], Magdalena Vera[SUP] 3 [/SUP], Jaime Retamal[SUP] 3 [/SUP], Soledad Torres[SUP] 5 [/SUP], Nelson P Barrera[SUP] 1 [/SUP]
Affiliations
Abstract
Seriously ill patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and hospitalized in intensive care units (ICUs) are commonly given a combination of drugs, a process known as multi-drug treatment. After extracting data on drug-drug interactions with clinical relevance from available online platforms, we hypothesize that an overall interaction map can be generated for all drugs administered. Furthermore, by combining this approach with simulations of cellular biochemical pathways, we may be able to explain the general clinical outcome. Finally, we postulate that by applying this strategy retrospectively to a cohort of patients hospitalized in ICU, a prediction of the timing of developing acute kidney injury (AKI) could be made. Whether or not this approach can be extended to other diseases is uncertain. Still, we believe it represents a valuable pharmacological insight to help improve clinical outcomes for severely ill patients.
. 2022 Feb 18.
doi: 10.1111/bph.15819. Online ahead of print.
Hypothesis: How far are we from predicting multi-drug interactions during treatment for COVID-19 infection?
Benjamin Lozano[SUP] 1 [/SUP], Javier Santibañez[SUP] 2 [/SUP], Nicolás Severino[SUP] 3 [/SUP], Cristina Saldias[SUP] 4 [/SUP], Magdalena Vera[SUP] 3 [/SUP], Jaime Retamal[SUP] 3 [/SUP], Soledad Torres[SUP] 5 [/SUP], Nelson P Barrera[SUP] 1 [/SUP]
Affiliations
- PMID: 35180811
- DOI: 10.1111/bph.15819
Abstract
Seriously ill patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and hospitalized in intensive care units (ICUs) are commonly given a combination of drugs, a process known as multi-drug treatment. After extracting data on drug-drug interactions with clinical relevance from available online platforms, we hypothesize that an overall interaction map can be generated for all drugs administered. Furthermore, by combining this approach with simulations of cellular biochemical pathways, we may be able to explain the general clinical outcome. Finally, we postulate that by applying this strategy retrospectively to a cohort of patients hospitalized in ICU, a prediction of the timing of developing acute kidney injury (AKI) could be made. Whether or not this approach can be extended to other diseases is uncertain. Still, we believe it represents a valuable pharmacological insight to help improve clinical outcomes for severely ill patients.