Effects of cooling rate on structural relaxation in amorphous drugs: elastically collective nonlinear langevin equation theory and machine learning study
Theoretical approaches are formulated to investigate the molecular mobility under various cooling rates of amorphous drugs. We describe the structural relaxation of a tagged molecule as a coupled process of cage-scale dynamics and collective molecular rearrangement beyond the first coordination shel...
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| Veröffentlicht in: | RSC advances 2019-12, Vol.9 (69), p.4214-4221 |
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| creator | Phan, Anh D Wakabayashi, Katsunori Paluch, Marian Lam, Vu D |
| description | Theoretical approaches are formulated to investigate the molecular mobility under various cooling rates of amorphous drugs. We describe the structural relaxation of a tagged molecule as a coupled process of cage-scale dynamics and collective molecular rearrangement beyond the first coordination shell. The coupling between local and non-local dynamics behaves distinctly in different substances. Theoretical calculations for the structural relaxation time, glass transition temperature, and dynamic fragility are carried out over twenty-two amorphous drugs and polymers. Numerical results have a quantitatively good accordance with experimental data and the extracted physical quantities using the Vogel-Fulcher-Tammann fit function and machine learning. The machine learning method reveals the linear relation between the glass transition temperature and the melting point, which is a key factor for pharmaceutical solubility. Our predictive approaches are reliable tools for developing drug formulations.
Theoretical approaches are formulated to investigate the molecular mobility under various cooling rates of amorphous drugs. |
| doi_str_mv | 10.1039/c9ra08441j |
| format | Article |
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Theoretical approaches are formulated to investigate the molecular mobility under various cooling rates of amorphous drugs.</description><subject>Amorphous structure</subject><subject>Artificial intelligence</subject><subject>Chemistry</subject><subject>Cooling effects</subject><subject>Cooling rate</subject><subject>Coupling (molecular)</subject><subject>Drugs</subject><subject>Fragility</subject><subject>Glass transition temperature</subject><subject>Machine learning</subject><subject>Melting points</subject><subject>Relaxation time</subject><subject>Temperature</subject><issn>2046-2069</issn><issn>2046-2069</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNpdkkuLFDEURoMozjDOxr0ScCNCa571cCEMzfhiQBBdh1upW93VpJKeJNXYP8V_a9oe29HapMg9OXw3N4Q85ew1Z7J9Y9sIrFGKbx6Qc8FUtRCsah_e-z8jlyltWPkqzUXFH5MzqbUSlarPyc_rYUCbEw0DtSG40a9ohIw0eJpynG2eIzga0cEPyGPZHT2FKcTtOsyJ9nFepbe0VFMeLTi3LxbninHcIfXBFyFCpA78CnflKN7OR01eY4h7Cr6nE9h1wagrpD8ESHnu90_IowFcwsu79YJ8f3_9bflxcfPlw6fl1c3CaqnzAq0QtuYcaiVlpzqLoqnrhg2shqbpoYOutYWpQQIHizB0th-YRq0sU4zJC_Lu6N3O3YS9RZ9Lx2Ybxwni3gQYzb8VP67NKuxMy-qKt6oIXt4JYridMWUzjcmiKz1juSMjqkpo1UrVFPTFf-gmzNGX9oyQoqpbKfUh0asjZWNIKeJwCsOZOQzdLNuvV7-H_rnAz-_HP6F_RlyAZ0cgJnuq_n018hdtbbbV</recordid><startdate>20191204</startdate><enddate>20191204</enddate><creator>Phan, Anh D</creator><creator>Wakabayashi, Katsunori</creator><creator>Paluch, Marian</creator><creator>Lam, Vu D</creator><general>Royal Society of Chemistry</general><general>The Royal Society of Chemistry</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-9147-9939</orcidid><orcidid>https://orcid.org/0000-0002-8667-1299</orcidid></search><sort><creationdate>20191204</creationdate><title>Effects of cooling rate on structural relaxation in amorphous drugs: elastically collective nonlinear langevin equation theory and machine learning study</title><author>Phan, Anh D ; Wakabayashi, Katsunori ; Paluch, Marian ; Lam, Vu D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c535t-ec22c711a7433b4bce287780f07a88dabab9cc227a3a1aceafbcdf05e54c04003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Amorphous structure</topic><topic>Artificial intelligence</topic><topic>Chemistry</topic><topic>Cooling effects</topic><topic>Cooling rate</topic><topic>Coupling (molecular)</topic><topic>Drugs</topic><topic>Fragility</topic><topic>Glass transition temperature</topic><topic>Machine learning</topic><topic>Melting points</topic><topic>Relaxation time</topic><topic>Temperature</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Phan, Anh D</creatorcontrib><creatorcontrib>Wakabayashi, Katsunori</creatorcontrib><creatorcontrib>Paluch, Marian</creatorcontrib><creatorcontrib>Lam, Vu D</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>RSC advances</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Phan, Anh D</au><au>Wakabayashi, Katsunori</au><au>Paluch, Marian</au><au>Lam, Vu D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effects of cooling rate on structural relaxation in amorphous drugs: elastically collective nonlinear langevin equation theory and machine learning study</atitle><jtitle>RSC advances</jtitle><addtitle>RSC Adv</addtitle><date>2019-12-04</date><risdate>2019</risdate><volume>9</volume><issue>69</issue><spage>4214</spage><epage>4221</epage><pages>4214-4221</pages><issn>2046-2069</issn><eissn>2046-2069</eissn><abstract>Theoretical approaches are formulated to investigate the molecular mobility under various cooling rates of amorphous drugs. We describe the structural relaxation of a tagged molecule as a coupled process of cage-scale dynamics and collective molecular rearrangement beyond the first coordination shell. The coupling between local and non-local dynamics behaves distinctly in different substances. Theoretical calculations for the structural relaxation time, glass transition temperature, and dynamic fragility are carried out over twenty-two amorphous drugs and polymers. Numerical results have a quantitatively good accordance with experimental data and the extracted physical quantities using the Vogel-Fulcher-Tammann fit function and machine learning. The machine learning method reveals the linear relation between the glass transition temperature and the melting point, which is a key factor for pharmaceutical solubility. Our predictive approaches are reliable tools for developing drug formulations.
Theoretical approaches are formulated to investigate the molecular mobility under various cooling rates of amorphous drugs.</abstract><cop>England</cop><pub>Royal Society of Chemistry</pub><pmid>35542647</pmid><doi>10.1039/c9ra08441j</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-9147-9939</orcidid><orcidid>https://orcid.org/0000-0002-8667-1299</orcidid><oa>free_for_read</oa></addata></record> |
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| source | DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; PubMed Central Open Access |
| subjects | Amorphous structure Artificial intelligence Chemistry Cooling effects Cooling rate Coupling (molecular) Drugs Fragility Glass transition temperature Machine learning Melting points Relaxation time Temperature |
| title | Effects of cooling rate on structural relaxation in amorphous drugs: elastically collective nonlinear langevin equation theory and machine learning study |
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