On Drug-Membrane Permeability of Antivirals for SARS-CoV-2
One of the key parameters required to identify effective drugs is membrane permeability, as a compound intended for an intracellular target with poor permeability will have low efficacy. In this paper, we leverage a computational approach recently developed by our group to study the interactions between nanoparticles and mammalian membranes to study the time of entry of a variety of drugs into the viral envelope of coronavirus as well as cellular organelles. Using a combination of all-atoms molecular dynamics simulations and statistical analysis, we consider both drug characteristics and membrane properties to determine the behavior of 79 drugs and their interactions with the viral envelope, composed of the membrane and spike protein, as well as five other membranes that correspond to various mammalian compartments (lysosome, plasma, Golgi, mitochondrial, and endoplasmic reticulum membranes). The results highlight important trends that can be exploited for drug design, from the relatively high permeability of the viral envelope and the effect of transmembrane proteins, to the differences in permeability between organelles. When compared with bioavailability data present in the literature, the model results suggest a negative correlation between time of permeation and bioavailability of promising drugs. The method is general and flexible and can be employed for a variety of molecules, from small drugs to small nanoparticles, as well to a variety of biological membranes. Overall, the results indicate that this model can contribute to the identification of successful drugs as it predicts the ability of compounds to reach both intended and unintended intracellular targets.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
The journal of physical chemistry letters - 12(2021), 5 vom: 11. Feb., Seite 1384-1389 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Changjiang [VerfasserIn] |
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| Links: |
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| Themen: |
Antiviral Agents |
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| Anmerkungen: |
Date Completed 01.03.2021 Date Revised 29.08.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1021/acs.jpclett.0c02397 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM320698084 |
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| 500 | |a published: Print-Electronic | ||
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| 520 | |a One of the key parameters required to identify effective drugs is membrane permeability, as a compound intended for an intracellular target with poor permeability will have low efficacy. In this paper, we leverage a computational approach recently developed by our group to study the interactions between nanoparticles and mammalian membranes to study the time of entry of a variety of drugs into the viral envelope of coronavirus as well as cellular organelles. Using a combination of all-atoms molecular dynamics simulations and statistical analysis, we consider both drug characteristics and membrane properties to determine the behavior of 79 drugs and their interactions with the viral envelope, composed of the membrane and spike protein, as well as five other membranes that correspond to various mammalian compartments (lysosome, plasma, Golgi, mitochondrial, and endoplasmic reticulum membranes). The results highlight important trends that can be exploited for drug design, from the relatively high permeability of the viral envelope and the effect of transmembrane proteins, to the differences in permeability between organelles. When compared with bioavailability data present in the literature, the model results suggest a negative correlation between time of permeation and bioavailability of promising drugs. The method is general and flexible and can be employed for a variety of molecules, from small drugs to small nanoparticles, as well to a variety of biological membranes. Overall, the results indicate that this model can contribute to the identification of successful drugs as it predicts the ability of compounds to reach both intended and unintended intracellular targets | ||
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| 650 | 7 | |a Spike Glycoprotein, Coronavirus |2 NLM | |
| 700 | 1 | |a Elvati, Paolo |e verfasserin |4 aut | |
| 700 | 1 | |a Violi, Angela |e verfasserin |4 aut | |
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