In-Silico Aided Screening and Characterization Results in Stability Enhanced Novel Roxadustat Co-Crystal
•ΔpKa, Molecular Complementarity (MC), Molecular Electrostatic Potential (MEP) Maps and Machine Learning approaches were used for the coformer's selection.•MEPS model and Machine learning blend model using MACCs descriptors appears to be the top-performing models for the coformer selection.•Exp...
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creator | Muthusamy, Anantha Rajmohan Singh, Amit Sundaram, Meenakshi Sundaram Soma Wagh, Yogesh Jegorov, Alexandr Jain, Arvind Kumar |
description | •ΔpKa, Molecular Complementarity (MC), Molecular Electrostatic Potential (MEP) Maps and Machine Learning approaches were used for the coformer's selection.•MEPS model and Machine learning blend model using MACCs descriptors appears to be the top-performing models for the coformer selection.•Experimental co-crystal screening resulted in Roxadustat-Nicotinamide (RXD-NA) co-crystal, confirmed by single crystal structure, solid-state FTIR and NMR analysis.•Physical and chemical stability of RXD-NA co-crystal was established.•The phenomenon of photo isomer impurity generation was evaluated by computational analysis.
Roxadustat (RXD) is an approved drug substances for the treatment of renal anemia. It has poor aqueous solubility and photochemical stability. This study employs a comprehensive approach to enhance the stability and physicochemical properties RXD through coformer selection and characterization. The investigation integrates delta pKa analysis, molecular complementary assessment, molecular electrostatic potential surface analysis, and machine learning techniques to predict potential co-crystal formation and binding interactions between drug molecules and coformers. The co-crystal screening which lead to in a novel RXD-nicotinamide co-crystal (RXD-NA). Experimental characterization underscores the physical and chemical stability of the co-crystals. To elucidate the supramolecular synthons and understand the intermolecular interactions in the RXD-NA co-crystal, Hirshfeld surfaces analysis, quantum theory of atoms in molecules (QTAIM) analysis and non-covalent interaction (NCI) analysis were performed. Computational analysis of photo-isomer formation aligns with experimental observations, further enhancing our understanding of RXD-coformer interactions. RXD-NA co-crystal was found photo-chemically stable as compared to free base API drug substance. This integrated methodology provides a systematic framework for informed co-crystal design, holding promise for optimizing RXD formulations based on molecular interactions and stability considerations. Consequently, this study contributes valuable insights to the field of rational drug design and formulation optimization. |
doi_str_mv | 10.1016/j.xphs.2023.10.024 |
format | Article |
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Roxadustat (RXD) is an approved drug substances for the treatment of renal anemia. It has poor aqueous solubility and photochemical stability. This study employs a comprehensive approach to enhance the stability and physicochemical properties RXD through coformer selection and characterization. The investigation integrates delta pKa analysis, molecular complementary assessment, molecular electrostatic potential surface analysis, and machine learning techniques to predict potential co-crystal formation and binding interactions between drug molecules and coformers. The co-crystal screening which lead to in a novel RXD-nicotinamide co-crystal (RXD-NA). Experimental characterization underscores the physical and chemical stability of the co-crystals. To elucidate the supramolecular synthons and understand the intermolecular interactions in the RXD-NA co-crystal, Hirshfeld surfaces analysis, quantum theory of atoms in molecules (QTAIM) analysis and non-covalent interaction (NCI) analysis were performed. Computational analysis of photo-isomer formation aligns with experimental observations, further enhancing our understanding of RXD-coformer interactions. RXD-NA co-crystal was found photo-chemically stable as compared to free base API drug substance. This integrated methodology provides a systematic framework for informed co-crystal design, holding promise for optimizing RXD formulations based on molecular interactions and stability considerations. Consequently, this study contributes valuable insights to the field of rational drug design and formulation optimization.</description><identifier>ISSN: 0022-3549</identifier><identifier>EISSN: 1520-6017</identifier><identifier>DOI: 10.1016/j.xphs.2023.10.024</identifier><identifier>PMID: 37875213</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Co-crystal(s) ; Crystal engineering ; Glycine ; Machine learning ; Photodegradation ; Solid-state stability ; Solubility</subject><ispartof>Journal of pharmaceutical sciences, 2024-05, Vol.113 (5), p.1190-1201</ispartof><rights>2023 American Pharmacists Association</rights><rights>Copyright © 2023 American Pharmacists Association. Published by Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c356t-579d8cdbe2ee84aacaa203887239fd57ffcbbe882323c51ce6af03e2aa6bfca53</citedby><cites>FETCH-LOGICAL-c356t-579d8cdbe2ee84aacaa203887239fd57ffcbbe882323c51ce6af03e2aa6bfca53</cites><orcidid>0000-0002-1948-6130</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,27926,27927</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37875213$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Muthusamy, Anantha Rajmohan</creatorcontrib><creatorcontrib>Singh, Amit</creatorcontrib><creatorcontrib>Sundaram, Meenakshi Sundaram Soma</creatorcontrib><creatorcontrib>Wagh, Yogesh</creatorcontrib><creatorcontrib>Jegorov, Alexandr</creatorcontrib><creatorcontrib>Jain, Arvind Kumar</creatorcontrib><title>In-Silico Aided Screening and Characterization Results in Stability Enhanced Novel Roxadustat Co-Crystal</title><title>Journal of pharmaceutical sciences</title><addtitle>J Pharm Sci</addtitle><description>•ΔpKa, Molecular Complementarity (MC), Molecular Electrostatic Potential (MEP) Maps and Machine Learning approaches were used for the coformer's selection.•MEPS model and Machine learning blend model using MACCs descriptors appears to be the top-performing models for the coformer selection.•Experimental co-crystal screening resulted in Roxadustat-Nicotinamide (RXD-NA) co-crystal, confirmed by single crystal structure, solid-state FTIR and NMR analysis.•Physical and chemical stability of RXD-NA co-crystal was established.•The phenomenon of photo isomer impurity generation was evaluated by computational analysis.
Roxadustat (RXD) is an approved drug substances for the treatment of renal anemia. It has poor aqueous solubility and photochemical stability. This study employs a comprehensive approach to enhance the stability and physicochemical properties RXD through coformer selection and characterization. The investigation integrates delta pKa analysis, molecular complementary assessment, molecular electrostatic potential surface analysis, and machine learning techniques to predict potential co-crystal formation and binding interactions between drug molecules and coformers. The co-crystal screening which lead to in a novel RXD-nicotinamide co-crystal (RXD-NA). Experimental characterization underscores the physical and chemical stability of the co-crystals. To elucidate the supramolecular synthons and understand the intermolecular interactions in the RXD-NA co-crystal, Hirshfeld surfaces analysis, quantum theory of atoms in molecules (QTAIM) analysis and non-covalent interaction (NCI) analysis were performed. Computational analysis of photo-isomer formation aligns with experimental observations, further enhancing our understanding of RXD-coformer interactions. RXD-NA co-crystal was found photo-chemically stable as compared to free base API drug substance. This integrated methodology provides a systematic framework for informed co-crystal design, holding promise for optimizing RXD formulations based on molecular interactions and stability considerations. Consequently, this study contributes valuable insights to the field of rational drug design and formulation optimization.</description><subject>Co-crystal(s)</subject><subject>Crystal engineering</subject><subject>Glycine</subject><subject>Machine learning</subject><subject>Photodegradation</subject><subject>Solid-state stability</subject><subject>Solubility</subject><issn>0022-3549</issn><issn>1520-6017</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kE1rGzEQhkVpaNy0f6CHomMv6-jD2pWhl7CkbSC0ECdnMSvN1jJrrStpQ9xfHy1Oc-xphuF5X5iHkE-cLTnj9eVu-XTYpqVgQpbDkonVG7LgSrCqZrx5SxaMCVFJtVqfk_cp7RhjNVPqHTmXjW6U4HJBtjeh2vjB25FeeYeObmxEDD78phAcbbcQwWaM_i9kPwZ6h2kacqI-0E2GriTzkV6HLQRbwj_HRxzo3fgEbkoZMm3Hqo3Hsg4fyFkPQ8KPL_OCPHy7vm9_VLe_vt-0V7eVlarOlWrWTlvXoUDUKwALIJjUuhFy3TvV9L3tOtRaSCGt4hZr6JlEAVB3vQUlL8iXU-8hjn8mTNnsfbI4DBBwnJIRWvOGc8llQcUJtXFMKWJvDtHvIR4NZ2Y2bHZmNmxmw_OtGC6hzy_9U7dH9xr5p7QAX08Ali8fPUaTrMdZj49os3Gj_1__MyoDjtA</recordid><startdate>202405</startdate><enddate>202405</enddate><creator>Muthusamy, Anantha Rajmohan</creator><creator>Singh, Amit</creator><creator>Sundaram, Meenakshi Sundaram Soma</creator><creator>Wagh, Yogesh</creator><creator>Jegorov, Alexandr</creator><creator>Jain, Arvind Kumar</creator><general>Elsevier Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-1948-6130</orcidid></search><sort><creationdate>202405</creationdate><title>In-Silico Aided Screening and Characterization Results in Stability Enhanced Novel Roxadustat Co-Crystal</title><author>Muthusamy, Anantha Rajmohan ; Singh, Amit ; Sundaram, Meenakshi Sundaram Soma ; Wagh, Yogesh ; Jegorov, Alexandr ; Jain, Arvind Kumar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-579d8cdbe2ee84aacaa203887239fd57ffcbbe882323c51ce6af03e2aa6bfca53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Co-crystal(s)</topic><topic>Crystal engineering</topic><topic>Glycine</topic><topic>Machine learning</topic><topic>Photodegradation</topic><topic>Solid-state stability</topic><topic>Solubility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Muthusamy, Anantha Rajmohan</creatorcontrib><creatorcontrib>Singh, Amit</creatorcontrib><creatorcontrib>Sundaram, Meenakshi Sundaram Soma</creatorcontrib><creatorcontrib>Wagh, Yogesh</creatorcontrib><creatorcontrib>Jegorov, Alexandr</creatorcontrib><creatorcontrib>Jain, Arvind Kumar</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of pharmaceutical sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Muthusamy, Anantha Rajmohan</au><au>Singh, Amit</au><au>Sundaram, Meenakshi Sundaram Soma</au><au>Wagh, Yogesh</au><au>Jegorov, Alexandr</au><au>Jain, Arvind Kumar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>In-Silico Aided Screening and Characterization Results in Stability Enhanced Novel Roxadustat Co-Crystal</atitle><jtitle>Journal of pharmaceutical sciences</jtitle><addtitle>J Pharm Sci</addtitle><date>2024-05</date><risdate>2024</risdate><volume>113</volume><issue>5</issue><spage>1190</spage><epage>1201</epage><pages>1190-1201</pages><issn>0022-3549</issn><eissn>1520-6017</eissn><abstract>•ΔpKa, Molecular Complementarity (MC), Molecular Electrostatic Potential (MEP) Maps and Machine Learning approaches were used for the coformer's selection.•MEPS model and Machine learning blend model using MACCs descriptors appears to be the top-performing models for the coformer selection.•Experimental co-crystal screening resulted in Roxadustat-Nicotinamide (RXD-NA) co-crystal, confirmed by single crystal structure, solid-state FTIR and NMR analysis.•Physical and chemical stability of RXD-NA co-crystal was established.•The phenomenon of photo isomer impurity generation was evaluated by computational analysis.
Roxadustat (RXD) is an approved drug substances for the treatment of renal anemia. It has poor aqueous solubility and photochemical stability. This study employs a comprehensive approach to enhance the stability and physicochemical properties RXD through coformer selection and characterization. The investigation integrates delta pKa analysis, molecular complementary assessment, molecular electrostatic potential surface analysis, and machine learning techniques to predict potential co-crystal formation and binding interactions between drug molecules and coformers. The co-crystal screening which lead to in a novel RXD-nicotinamide co-crystal (RXD-NA). Experimental characterization underscores the physical and chemical stability of the co-crystals. To elucidate the supramolecular synthons and understand the intermolecular interactions in the RXD-NA co-crystal, Hirshfeld surfaces analysis, quantum theory of atoms in molecules (QTAIM) analysis and non-covalent interaction (NCI) analysis were performed. Computational analysis of photo-isomer formation aligns with experimental observations, further enhancing our understanding of RXD-coformer interactions. RXD-NA co-crystal was found photo-chemically stable as compared to free base API drug substance. This integrated methodology provides a systematic framework for informed co-crystal design, holding promise for optimizing RXD formulations based on molecular interactions and stability considerations. Consequently, this study contributes valuable insights to the field of rational drug design and formulation optimization.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>37875213</pmid><doi>10.1016/j.xphs.2023.10.024</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-1948-6130</orcidid></addata></record> |
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subjects | Co-crystal(s) Crystal engineering Glycine Machine learning Photodegradation Solid-state stability Solubility |
title | In-Silico Aided Screening and Characterization Results in Stability Enhanced Novel Roxadustat Co-Crystal |
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