Preparing anti-SARS-CoV-2 agent EIDD-2801 by a practical and scalable approach, and quick evaluation via machine learning
EIDD-2801 is an orally bioavailable prodrug, which will be applied for emergency use authorization from the U.S. Food and Drug Administration for the treatment of COVID-19. To investigate the optimal parameters, EIDD-2801 was optimized via a four-step synthesis with high purity of 99.9%. The hydroxy...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-11-01
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| Series: | Acta Pharmaceutica Sinica B |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211383521004032 |
| _version_ | 1818998467755769856 |
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| author | Zhen Qin Bin Dong Renbing Wang Dechun Huang Jubo Wang Xi Feng Jinlei Bian Zhiyu Li |
| author_facet | Zhen Qin Bin Dong Renbing Wang Dechun Huang Jubo Wang Xi Feng Jinlei Bian Zhiyu Li |
| author_sort | Zhen Qin |
| collection | DOAJ |
| description | EIDD-2801 is an orally bioavailable prodrug, which will be applied for emergency use authorization from the U.S. Food and Drug Administration for the treatment of COVID-19. To investigate the optimal parameters, EIDD-2801 was optimized via a four-step synthesis with high purity of 99.9%. The hydroxylamination procedure was telescoped in a one-pot and the final step was precisely controlled on reagents, temperature and reaction time. Compared to the original route, the yield of the new route was enhanced from 17% to 58% without column chromatography. The optimized synthesis has been successfully determinated on a decagram scale: the first step at 200 g and the final step at 20 g. Besides, the relationship between yield and temperature, time, and reagents in the deprotection step was investigated via Shapley value explanation and machine learning approach-decision tree method. The results revealed that reagents have the greatest impact on yield estimation, followed by the temperature. |
| first_indexed | 2024-12-20T22:02:00Z |
| format | Article |
| id | doaj.art-e311f1d537714e31bf747cecb30787ef |
| institution | Directory Open Access Journal |
| issn | 2211-3835 |
| language | English |
| last_indexed | 2024-12-20T22:02:00Z |
| publishDate | 2021-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Acta Pharmaceutica Sinica B |
| spelling | doaj.art-e311f1d537714e31bf747cecb30787ef2022-12-21T19:25:20ZengElsevierActa Pharmaceutica Sinica B2211-38352021-11-01111136783682Preparing anti-SARS-CoV-2 agent EIDD-2801 by a practical and scalable approach, and quick evaluation via machine learningZhen Qin0Bin Dong1Renbing Wang2Dechun Huang3Jubo Wang4Xi Feng5Jinlei Bian6Zhiyu Li7Jiangsu Key Laboratory of Drug Design and Optimization, Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 211100, ChinaDepartment of Pharmaceutical Engineering, School of Engineering, China Pharmaceutical University, Nanjing 211100, ChinaJiangsu Key Laboratory of Drug Design and Optimization, Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 211100, ChinaDepartment of Pharmaceutical Engineering, School of Engineering, China Pharmaceutical University, Nanjing 211100, ChinaJiangsu Key Laboratory of Drug Design and Optimization, Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 211100, China; Corresponding authors. Tel.: +86 15151865295 (Jinlei Bian), +86 13951678592 (Zhiyu Li).Jiangsu Key Laboratory of Drug Design and Optimization, Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 211100, ChinaJiangsu Key Laboratory of Drug Design and Optimization, Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 211100, China; Corresponding authors. Tel.: +86 15151865295 (Jinlei Bian), +86 13951678592 (Zhiyu Li).Jiangsu Key Laboratory of Drug Design and Optimization, Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, Nanjing 211100, China; Corresponding authors. Tel.: +86 15151865295 (Jinlei Bian), +86 13951678592 (Zhiyu Li).EIDD-2801 is an orally bioavailable prodrug, which will be applied for emergency use authorization from the U.S. Food and Drug Administration for the treatment of COVID-19. To investigate the optimal parameters, EIDD-2801 was optimized via a four-step synthesis with high purity of 99.9%. The hydroxylamination procedure was telescoped in a one-pot and the final step was precisely controlled on reagents, temperature and reaction time. Compared to the original route, the yield of the new route was enhanced from 17% to 58% without column chromatography. The optimized synthesis has been successfully determinated on a decagram scale: the first step at 200 g and the final step at 20 g. Besides, the relationship between yield and temperature, time, and reagents in the deprotection step was investigated via Shapley value explanation and machine learning approach-decision tree method. The results revealed that reagents have the greatest impact on yield estimation, followed by the temperature.http://www.sciencedirect.com/science/article/pii/S2211383521004032EIDD-2801SARS-CoV-2Antiviral drugDecision treeShapley value |
| spellingShingle | Zhen Qin Bin Dong Renbing Wang Dechun Huang Jubo Wang Xi Feng Jinlei Bian Zhiyu Li Preparing anti-SARS-CoV-2 agent EIDD-2801 by a practical and scalable approach, and quick evaluation via machine learning Acta Pharmaceutica Sinica B EIDD-2801 SARS-CoV-2 Antiviral drug Decision tree Shapley value |
| title | Preparing anti-SARS-CoV-2 agent EIDD-2801 by a practical and scalable approach, and quick evaluation via machine learning |
| title_full | Preparing anti-SARS-CoV-2 agent EIDD-2801 by a practical and scalable approach, and quick evaluation via machine learning |
| title_fullStr | Preparing anti-SARS-CoV-2 agent EIDD-2801 by a practical and scalable approach, and quick evaluation via machine learning |
| title_full_unstemmed | Preparing anti-SARS-CoV-2 agent EIDD-2801 by a practical and scalable approach, and quick evaluation via machine learning |
| title_short | Preparing anti-SARS-CoV-2 agent EIDD-2801 by a practical and scalable approach, and quick evaluation via machine learning |
| title_sort | preparing anti sars cov 2 agent eidd 2801 by a practical and scalable approach and quick evaluation via machine learning |
| topic | EIDD-2801 SARS-CoV-2 Antiviral drug Decision tree Shapley value |
| url | http://www.sciencedirect.com/science/article/pii/S2211383521004032 |
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