A Novel Computational Approach for the Discovery of Drug Delivery System Candidates for COVID-19
In order to treat Coronavirus Disease 2019 (COVID-19), we predicted and implemented a drug delivery system (DDS) that can provide stable drug delivery through a computational approach including a clustering algorithm and the Schrödinger software. Six carrier candidates were derived by the proposed m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/22/6/2815 |
| _version_ | 1797541843742752768 |
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| author | Taeheum Cho Hyo-Sang Han Junhyuk Jeong Eun-Mi Park Kyu-Sik Shim |
| author_facet | Taeheum Cho Hyo-Sang Han Junhyuk Jeong Eun-Mi Park Kyu-Sik Shim |
| author_sort | Taeheum Cho |
| collection | DOAJ |
| description | In order to treat Coronavirus Disease 2019 (COVID-19), we predicted and implemented a drug delivery system (DDS) that can provide stable drug delivery through a computational approach including a clustering algorithm and the Schrödinger software. Six carrier candidates were derived by the proposed method that could find molecules meeting the predefined conditions using the molecular structure and its functional group positional information. Then, just one compound named glycyrrhizin was selected as a candidate for drug delivery through the Schrödinger software. Using glycyrrhizin, nafamostat mesilate (NM), which is known for its efficacy, was converted into micelle nanoparticles (NPs) to improve drug stability and to effectively treat COVID-19. The spherical particle morphology was confirmed by transmission electron microscopy (TEM), and the particle size and stability of 300–400 nm were evaluated by measuring DLSand the zeta potential. The loading of NM was confirmed to be more than 90% efficient using the UV spectrum. |
| first_indexed | 2024-03-10T13:21:23Z |
| format | Article |
| id | doaj.art-888cc4983dc6447aa13fe62bd7dbc998 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T13:21:23Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-888cc4983dc6447aa13fe62bd7dbc9982023-11-21T09:57:49ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672021-03-01226281510.3390/ijms22062815A Novel Computational Approach for the Discovery of Drug Delivery System Candidates for COVID-19Taeheum Cho0Hyo-Sang Han1Junhyuk Jeong2Eun-Mi Park3Kyu-Sik Shim4MODNBIO Inc., Digital Road 34, Kolon Science Valley I, Guro-gu, Seoul 08378, KoreaDepartment of Health Administration, Joongbu University, Chungnam 31713, KoreaMODNBIO Inc., Digital Road 34, Kolon Science Valley I, Guro-gu, Seoul 08378, KoreaMODNBIO Inc., Digital Road 34, Kolon Science Valley I, Guro-gu, Seoul 08378, KoreaMODNBIO Inc., Digital Road 34, Kolon Science Valley I, Guro-gu, Seoul 08378, KoreaIn order to treat Coronavirus Disease 2019 (COVID-19), we predicted and implemented a drug delivery system (DDS) that can provide stable drug delivery through a computational approach including a clustering algorithm and the Schrödinger software. Six carrier candidates were derived by the proposed method that could find molecules meeting the predefined conditions using the molecular structure and its functional group positional information. Then, just one compound named glycyrrhizin was selected as a candidate for drug delivery through the Schrödinger software. Using glycyrrhizin, nafamostat mesilate (NM), which is known for its efficacy, was converted into micelle nanoparticles (NPs) to improve drug stability and to effectively treat COVID-19. The spherical particle morphology was confirmed by transmission electron microscopy (TEM), and the particle size and stability of 300–400 nm were evaluated by measuring DLSand the zeta potential. The loading of NM was confirmed to be more than 90% efficient using the UV spectrum.https://www.mdpi.com/1422-0067/22/6/2815COVID-19in silicomachine learningclusteringunsupervised learningdrug delivery system |
| spellingShingle | Taeheum Cho Hyo-Sang Han Junhyuk Jeong Eun-Mi Park Kyu-Sik Shim A Novel Computational Approach for the Discovery of Drug Delivery System Candidates for COVID-19 International Journal of Molecular Sciences COVID-19 in silico machine learning clustering unsupervised learning drug delivery system |
| title | A Novel Computational Approach for the Discovery of Drug Delivery System Candidates for COVID-19 |
| title_full | A Novel Computational Approach for the Discovery of Drug Delivery System Candidates for COVID-19 |
| title_fullStr | A Novel Computational Approach for the Discovery of Drug Delivery System Candidates for COVID-19 |
| title_full_unstemmed | A Novel Computational Approach for the Discovery of Drug Delivery System Candidates for COVID-19 |
| title_short | A Novel Computational Approach for the Discovery of Drug Delivery System Candidates for COVID-19 |
| title_sort | novel computational approach for the discovery of drug delivery system candidates for covid 19 |
| topic | COVID-19 in silico machine learning clustering unsupervised learning drug delivery system |
| url | https://www.mdpi.com/1422-0067/22/6/2815 |
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