Accelerated Discovery of Macrocyclic CDK2 Inhibitor QR-6401 by Generative Models and Structure-Based Drug Design
Selective CDK2 inhibitors have the potential to provide effective therapeutics for CDK2-dependent cancers and for combating drug resistance due to high cyclin E1 (CCNE1) expression intrinsically or CCNE1 amplification induced by treatment of CDK4/6 inhibitors. Generative models that take advantage o...
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| Veröffentlicht in: | ACS medicinal chemistry letters 2023-03, Vol.14 (3), p.297-304 |
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| creator | Yu, Yang Huang, Junhong He, Hu Han, Jing Ye, Geyan Xu, Tingyang Sun, Xianqiang Chen, Xiumei Ren, Xiaoming Li, Chunlai Li, Huijuan Huang, Wei Liu, Yangyang Wang, Xinjuan Gao, Yongzhi Cheng, Nianhe Guo, Na Chen, Xibo Feng, Jianxia Hua, Yuxia Liu, Chong Zhu, Guoyun Xie, Zhi Yao, Lili Zhong, Wenge Chen, Xinde Liu, Wei Li, Hailong |
| description | Selective CDK2 inhibitors have the potential to provide effective therapeutics for CDK2-dependent cancers and for combating drug resistance due to high cyclin E1 (CCNE1) expression intrinsically or CCNE1 amplification induced by treatment of CDK4/6 inhibitors. Generative models that take advantage of deep learning are being increasingly integrated into early drug discovery for hit identification and lead optimization. Here we report the discovery of a highly potent and selective macrocyclic CDK2 inhibitor QR-6401 (23) accelerated by the application of generative models and structure-based drug design (SBDD). QR-6401 (23) demonstrated robust antitumor efficacy in an OVCAR3 ovarian cancer xenograft model via oral administration. |
| doi_str_mv | 10.1021/acsmedchemlett.2c00515 |
| format | Article |
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QR-6401 (23) demonstrated robust antitumor efficacy in an OVCAR3 ovarian cancer xenograft model via oral administration.</description><identifier>ISSN: 1948-5875</identifier><identifier>EISSN: 1948-5875</identifier><identifier>DOI: 10.1021/acsmedchemlett.2c00515</identifier><identifier>PMID: 36923916</identifier><language>eng</language><publisher>United States: American Chemical Society</publisher><ispartof>ACS medicinal chemistry letters, 2023-03, Vol.14 (3), p.297-304</ispartof><rights>2023 American Chemical Society</rights><rights>2023 American Chemical Society.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a354t-8596811aca1b6c90935c77b74b8489b49d1769428292c1de0b815726357931663</citedby><cites>FETCH-LOGICAL-a354t-8596811aca1b6c90935c77b74b8489b49d1769428292c1de0b815726357931663</cites><orcidid>0000-0002-6021-9448 ; 0000-0003-2125-3229</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/acsmedchemlett.2c00515$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/acsmedchemlett.2c00515$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>314,778,782,2754,27059,27907,27908,56721,56771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36923916$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yu, Yang</creatorcontrib><creatorcontrib>Huang, Junhong</creatorcontrib><creatorcontrib>He, Hu</creatorcontrib><creatorcontrib>Han, Jing</creatorcontrib><creatorcontrib>Ye, Geyan</creatorcontrib><creatorcontrib>Xu, Tingyang</creatorcontrib><creatorcontrib>Sun, Xianqiang</creatorcontrib><creatorcontrib>Chen, Xiumei</creatorcontrib><creatorcontrib>Ren, Xiaoming</creatorcontrib><creatorcontrib>Li, Chunlai</creatorcontrib><creatorcontrib>Li, Huijuan</creatorcontrib><creatorcontrib>Huang, Wei</creatorcontrib><creatorcontrib>Liu, Yangyang</creatorcontrib><creatorcontrib>Wang, Xinjuan</creatorcontrib><creatorcontrib>Gao, Yongzhi</creatorcontrib><creatorcontrib>Cheng, Nianhe</creatorcontrib><creatorcontrib>Guo, Na</creatorcontrib><creatorcontrib>Chen, Xibo</creatorcontrib><creatorcontrib>Feng, Jianxia</creatorcontrib><creatorcontrib>Hua, Yuxia</creatorcontrib><creatorcontrib>Liu, Chong</creatorcontrib><creatorcontrib>Zhu, Guoyun</creatorcontrib><creatorcontrib>Xie, Zhi</creatorcontrib><creatorcontrib>Yao, Lili</creatorcontrib><creatorcontrib>Zhong, Wenge</creatorcontrib><creatorcontrib>Chen, Xinde</creatorcontrib><creatorcontrib>Liu, Wei</creatorcontrib><creatorcontrib>Li, Hailong</creatorcontrib><title>Accelerated Discovery of Macrocyclic CDK2 Inhibitor QR-6401 by Generative Models and Structure-Based Drug Design</title><title>ACS medicinal chemistry letters</title><addtitle>ACS Med. Chem. Lett</addtitle><description>Selective CDK2 inhibitors have the potential to provide effective therapeutics for CDK2-dependent cancers and for combating drug resistance due to high cyclin E1 (CCNE1) expression intrinsically or CCNE1 amplification induced by treatment of CDK4/6 inhibitors. Generative models that take advantage of deep learning are being increasingly integrated into early drug discovery for hit identification and lead optimization. Here we report the discovery of a highly potent and selective macrocyclic CDK2 inhibitor QR-6401 (23) accelerated by the application of generative models and structure-based drug design (SBDD). QR-6401 (23) demonstrated robust antitumor efficacy in an OVCAR3 ovarian cancer xenograft model via oral administration.</description><issn>1948-5875</issn><issn>1948-5875</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNqFkEtPwkAUhSdGI4j-BTJLN8WZaee1RFAkSoyvdTOdXqCktDjTkvDvHQIaXbm6N7nnnJvzIdSnZEAJozfG-jXkdgnrEppmwCwhnPIT1KU6URFXkp_-2jvowvsVIUJLSc5RJxaaxZqKLtoMrYUSnGkgx-PC23oLbofrOZ4Z62q7s2Vh8Wj8yPC0WhZZ0dQOv7xGIiEUZzs8gWpvLraAZ3UOpcemyvFb41rbtA6iW-P3wa5d4DH4YlFdorO5KT1cHWcPfdzfvY8eoqfnyXQ0fIpMzJMmUlwLRamxhmbCaqJjbqXMZJKpROks0TmVQidMMc0szYFkinLJRMyljqkQcQ9dH3I3rv5swTfpOrSDsjQV1K1PmeJJsOsAoofEQRoKe-9gnm5csTZul1KS7mmnf2mnR9rB2D_-aLNw_7F94w0CdhCEgHRVt64Klf9L_QLMk463</recordid><startdate>20230309</startdate><enddate>20230309</enddate><creator>Yu, Yang</creator><creator>Huang, Junhong</creator><creator>He, Hu</creator><creator>Han, Jing</creator><creator>Ye, Geyan</creator><creator>Xu, Tingyang</creator><creator>Sun, Xianqiang</creator><creator>Chen, Xiumei</creator><creator>Ren, Xiaoming</creator><creator>Li, Chunlai</creator><creator>Li, Huijuan</creator><creator>Huang, Wei</creator><creator>Liu, Yangyang</creator><creator>Wang, Xinjuan</creator><creator>Gao, Yongzhi</creator><creator>Cheng, Nianhe</creator><creator>Guo, Na</creator><creator>Chen, Xibo</creator><creator>Feng, Jianxia</creator><creator>Hua, Yuxia</creator><creator>Liu, Chong</creator><creator>Zhu, Guoyun</creator><creator>Xie, Zhi</creator><creator>Yao, Lili</creator><creator>Zhong, Wenge</creator><creator>Chen, Xinde</creator><creator>Liu, Wei</creator><creator>Li, Hailong</creator><general>American Chemical Society</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-6021-9448</orcidid><orcidid>https://orcid.org/0000-0003-2125-3229</orcidid></search><sort><creationdate>20230309</creationdate><title>Accelerated Discovery of Macrocyclic CDK2 Inhibitor QR-6401 by Generative Models and Structure-Based Drug Design</title><author>Yu, Yang ; 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Chem. Lett</addtitle><date>2023-03-09</date><risdate>2023</risdate><volume>14</volume><issue>3</issue><spage>297</spage><epage>304</epage><pages>297-304</pages><issn>1948-5875</issn><eissn>1948-5875</eissn><abstract>Selective CDK2 inhibitors have the potential to provide effective therapeutics for CDK2-dependent cancers and for combating drug resistance due to high cyclin E1 (CCNE1) expression intrinsically or CCNE1 amplification induced by treatment of CDK4/6 inhibitors. Generative models that take advantage of deep learning are being increasingly integrated into early drug discovery for hit identification and lead optimization. Here we report the discovery of a highly potent and selective macrocyclic CDK2 inhibitor QR-6401 (23) accelerated by the application of generative models and structure-based drug design (SBDD). 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| title | Accelerated Discovery of Macrocyclic CDK2 Inhibitor QR-6401 by Generative Models and Structure-Based Drug Design |
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