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Fox R, Klug J, Thompson D, Reilly A. Computational predictions of cocrystal formation: A benchmark study of 28 assemblies comparing five methods from high-throughput to advanced models. J Comput Chem 2024; 45:2465-2475. [PMID: 38958249 DOI: 10.1002/jcc.27454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 07/04/2024]
Abstract
Cocrystals are assemblies of more than one type of molecule stabilized through noncovalent interactions. They are promising materials for improved drug formulation in which the stability, solubility, or biocompatibility of the active pharmaceutical ingredient (API) is improved by including a coformer. In this work, a range of density functional theory (DFT) and density functional tight binding (DFTB) models are systematically compared for their ability to predict the lattice enthalpy of a broad range of existing pharmaceutically relevant cocrystals. These range from cocrystals containing model compounds 4,4'-bipyridine and oxalic acid to those with the well benchmarked APIs of aspirin and paracetamol, all tested with a large set of alternative coformers. For simple cocrystals, there is a general consensus in lattice enthalpy calculated by the different DFT models. For the cocrystals with API coformers the cocrystals, enthalpy predictions depend strongly on the DFT model. The significantly lighter DFTB models predict unrealistic values of lattice enthalpy even for simple cocrystals.
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Affiliation(s)
- Robert Fox
- School of Chemical Sciences, Dublin City University, Dublin, Ireland
| | - Joaquin Klug
- Department of Life Sciences, Faculty of Sciences, Atlantic Technological University, ATU Sligo, Sligo, Ireland
| | - Damien Thompson
- Department of Physics, Bernal Institute, University of Limerick, Limerick, Ireland
| | - Anthony Reilly
- School of Chemical Sciences, Dublin City University, Dublin, Ireland
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2
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Muzioł TM, Bronikowska E. Driving Forces in the Formation of Paracetamol Cocrystals and Solvate with Naphthalene, Quinoline and Acridine. Molecules 2024; 29:4437. [PMID: 39339432 PMCID: PMC11434482 DOI: 10.3390/molecules29184437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2024] [Revised: 09/06/2024] [Accepted: 09/16/2024] [Indexed: 09/30/2024] Open
Abstract
Paracetamol is an important analgesic and antipyretic drug showing poor tabletability. Among the various approaches used to improve this property, understanding the forces that govern the crystal packing is revealed to be crucial. We prepared three stable compounds: (par)2∙(nap) (1), (par)∙(quin) (2), and (par)∙(acr) (3) (nap-naphthalene, quin-quinoline, acr-acridine) being cocrystals or solvate. The structural studies showed that all the reported compounds are composed of alternately arranged layers of paracetamol and coformer. Several supramolecular motifs in the paracetamol layer were identified: R44(22) in (1); R64(20) and R22(8) in (2); and R22(8), R42(12), and R44(26) rings in (3). The stability of the crystal network was studied by interactions analysis performed by Hirshfeld surface and fingerprint approaches and the energy between the closest units in the crystal network was calculated. It showed that the strongest interactions were found between blocks connected by N-H⋯O=C and O-H⋯O/N hydrogen bonds due to an important coulombic factor. The dispersive energy becomes important for tail-to-tail (and head-to-tail) arranged paracetamol units, and it prevails in the case of stacking interactions between coformer molecules. The importance of dispersive forces increases with the size of the aromatic system of the coformer. XAS studies confirmed the successful preparation of compounds and provided some details about electron structure.
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Affiliation(s)
- Tadeusz M Muzioł
- Faculty of Chemistry, Nicolaus Copernicus University in Toruń, Gagarina 7, 87-100 Toruń, Poland
| | - Emilia Bronikowska
- Faculty of Chemistry, Nicolaus Copernicus University in Toruń, Gagarina 7, 87-100 Toruń, Poland
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3
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Sadeghi MS, Guo R, Bellucci MA, Quino J, Buckle EL, Nisbet ML, Yang Z, Greenwell C, Gorka DE, Pickard Iv FC, Wood GPF, Sun G, Wen SH, Krzyzaniak JF, Meenan PA, Hancock BC, Yang XH. Tale of Two Polymorphs: Investigating the Structural Differences and Dynamic Relationship between Nirmatrelvir Solid Forms (Paxlovid). Mol Pharm 2024; 21:3800-3814. [PMID: 39051563 DOI: 10.1021/acs.molpharmaceut.3c01074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
Two anhydrous polymorphs of the novel antiviral medicine nirmatrelvir were discovered during the development of Paxlovid, Pfizer's oral Covid-19 treatment. A comprehensive experimental and computational approach was necessary to distinguish the two closely related polymorphs, herein identified as Forms 1 and 4. This approach paired experimental methods, including powder X-ray diffraction and single-crystal X-ray diffraction, solid-state experimental methods, thermal analysis, solid-state nuclear magnetic resonance and Raman spectroscopy with computational investigations comprising crystal structure prediction, Gibbs free energy calculations, and molecular dynamics simulations of the polymorphic transition. Forms 1 and 4 were ultimately determined to be enantiotropically related polymorphs with Form 1 being the stable form above the transition temperature of ∼17 °C and designated as the nominated form for drug development. The work described in this paper shows the importance of using highly specialized orthogonal approaches to elucidate the subtle differences in structure and properties of similar solid-state forms. This synergistic approach allowed for unprecedented speed in bringing Paxlovid to patients in record time amidst the pandemic.
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Affiliation(s)
| | - Rui Guo
- Pfizer Worldwide R&D, Sandwich CT13 9ND, U.K
| | | | - Jaypee Quino
- Pfizer Worldwide R&D, Groton, Connecticut 06340, United States
| | - Erika L Buckle
- Pfizer Worldwide R&D, Groton, Connecticut 06340, United States
| | | | - Zhuocen Yang
- XtalPi Inc, Cambridge, Massachusetts 02142, United States
| | | | | | | | | | - Guangxu Sun
- XtalPi Inc, Cambridge, Massachusetts 02142, United States
| | - Shu-Hao Wen
- XtalPi Inc, Cambridge, Massachusetts 02142, United States
| | | | - Paul A Meenan
- Pfizer Worldwide R&D, Groton, Connecticut 06340, United States
| | - Bruno C Hancock
- Pfizer Worldwide R&D, Groton, Connecticut 06340, United States
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Wang Y, Jiang Y, Zhou Y, He H, Tang J, Luo A, Liu Z, Ma C, Xiao Q, Guan T, Dai C. Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface. AAPS PharmSciTech 2024; 25:133. [PMID: 38862767 DOI: 10.1208/s12249-024-02846-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 05/20/2024] [Indexed: 06/13/2024] Open
Abstract
Nifedipine (NIF) is a dihydropyridine calcium channel blocker primarily used to treat conditions such as hypertension and angina. However, its low solubility and low bioavailability limit its effectiveness in clinical practice. Here, we developed a cocrystal prediction model based on Graph Neural Networks (CocrystalGNN) for the screening of cocrystals with NIF. And scoring 50 coformers using CocrystalGNN. To validate the reliability of the model, we used another prediction method, Molecular Electrostatic Potential Surface (MEPS), to verify the prediction results. Subsequently, we performed a second validation using experiments. The results indicate that our model achieved high performance. Ultimately, cocrystals of NIF were successfully obtained and all cocrystals exhibited better solubility and dissolution characteristics compared to the parent drug. This study lays a solid foundation for combining virtual prediction with experimental screening to discover novel water-insoluble drug cocrystals.
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Affiliation(s)
- Yuting Wang
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Yanling Jiang
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Yu Zhou
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Huai He
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Jincao Tang
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Anqing Luo
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Zeng Liu
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Chi Ma
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Qin Xiao
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Tianbing Guan
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Chuanyun Dai
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China.
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Pimonova Y, Carpenter JE, Gruenwald M. Thermodynamic Stability Is a Poor Indicator of Cocrystallization in Models of Organic Molecules. J Am Chem Soc 2024; 146:2805-2815. [PMID: 38241026 DOI: 10.1021/jacs.3c13030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Cocrystallizing a given molecule with another can be useful for adjusting the physical properties of molecules in the solid state. However, most combinations of molecules do not readily cocrystallize but form either one-component crystals or amorphous solids. Computational methods of crystal structure prediction can, in principle, identify the thermodynamically stable cocrystal and thus predict if molecules will cocrystallize or not. However, the pronounced polymorphism and tendency of many organic molecules to form disordered solids suggest that kinetic factors can play an important role in cocrystallization. The question remains: if a binary system of molecules has a thermodynamically stable cocrystal, will it indeed cocrystallize? To address this question, we simulate the crystallization of more than 2600 distinct pairs of chiral model molecules of similar size in 2D and calculate accurate crystal energy landscapes for all of them. Our analysis shows that thermodynamic criteria alone are unreliable in the prediction of cocrystallization. While the vast majority of cocrystals that form in our simulations are thermodynamically favorable, most coformer systems that have a thermodynamically stable cocrystal do not cocrystallize. We furthermore show that cocrystallization rates increase 3-fold when coformers are used that do not form well-ordered single-component crystals. Our results suggest that kinetic factors of cocrystallization are decisive in many cases.
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Affiliation(s)
- Yulia Pimonova
- Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - John E Carpenter
- Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - Michael Gruenwald
- Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
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Shah HS, Michelle C, Xie T, Chaturvedi K, Kuang S, Abramov YA. Computational and Experimental Screening Approaches to Aripiprazole Salt Crystallization. Pharm Res 2023; 40:2779-2789. [PMID: 37127778 DOI: 10.1007/s11095-023-03522-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/12/2023] [Indexed: 05/03/2023]
Abstract
INTRODUCTION The screening of multicomponent crystal system (MCC) is a key method for improving physicochemical properties of active pharmaceutical ingredients (APIs). The challenges associated with experimental salt screening include a large number of potential counterions and solvent systems and tendency to undergo disproportionation to produce free form during crystallization. These challenges may be mitigated by a combination of experimental and computational approaches to salt screening. The goal of this study is to evaluate performance of the counterion screening methods and propose and validate novel approaches to virtual solvent screening for MCC crystallization. METHODS The actual performance of the ΔpKa > 3 rule for counterion selection was validated using multiple screenings reports. Novel computational models for virtual solvent screening to avoid MCC incongruent crystallization were proposed. Using the ΔpKa rule, 10 acid counterions were selected for experimental aripiprazole (APZ) salt screening using 10 organic solvents. The experimental results were used to validate the proposed novel virtual solvent screen models. RESULTS Experimental APZ salt screening resulted in a total of eight MCCs which included glucuronate, mesylate, oxalate, tartrate, salicylate and mandelate. The new model to virtually screen solvents provided a general agreement with APZ experimental findings in terms of selecting the optimal solvent for MCC crystallization. CONCLUSION The rational selection of counterions and organic solvents for MCC crystallization was presented using combined novel computational model as well as experimental studies. The current virtual solvent screen model was successfully implemented and validated which can be easily applied to newly discovered APIs.
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Affiliation(s)
- Harsh S Shah
- J-Star Research Inc., 6 Cedarbrook Drive, Cranbury, NJ, 08512, USA.
| | | | - Tian Xie
- J-Star Research Inc., 6 Cedarbrook Drive, Cranbury, NJ, 08512, USA
| | | | - Shanming Kuang
- J-Star Research Inc., 6 Cedarbrook Drive, Cranbury, NJ, 08512, USA
| | - Yuriy A Abramov
- J-Star Research Inc., 6 Cedarbrook Drive, Cranbury, NJ, 08512, USA.
- Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
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Beran GJO. Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials. Chem Sci 2023; 14:13290-13312. [PMID: 38033897 PMCID: PMC10685338 DOI: 10.1039/d3sc03903j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/02/2023] [Indexed: 12/02/2023] Open
Abstract
The reliability of organic molecular crystal structure prediction has improved tremendously in recent years. Crystal structure predictions for small, mostly rigid molecules are quickly becoming routine. Structure predictions for larger, highly flexible molecules are more challenging, but their crystal structures can also now be predicted with increasing rates of success. These advances are ushering in a new era where crystal structure prediction drives the experimental discovery of new solid forms. After briefly discussing the computational methods that enable successful crystal structure prediction, this perspective presents case studies from the literature that demonstrate how state-of-the-art crystal structure prediction can transform how scientists approach problems involving the organic solid state. Applications to pharmaceuticals, porous organic materials, photomechanical crystals, organic semi-conductors, and nuclear magnetic resonance crystallography are included. Finally, efforts to improve our understanding of which predicted crystal structures can actually be produced experimentally and other outstanding challenges are discussed.
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Affiliation(s)
- Gregory J O Beran
- Department of Chemistry, University of California Riverside Riverside CA 92521 USA
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8
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Essen CV, Luedeker D. In silico co-crystal design: Assessment of the latest advances. Drug Discov Today 2023; 28:103763. [PMID: 37689178 DOI: 10.1016/j.drudis.2023.103763] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 08/18/2023] [Accepted: 08/31/2023] [Indexed: 09/11/2023]
Abstract
Pharmaceutical co-crystals represent a growing class of crystal forms in the context of pharmaceutical science. They are attractive to pharmaceutical scientists because they significantly expand the number of crystal forms that exist for an active pharmaceutical ingredient and can lead to improvements in physicochemical properties of clinical relevance. At the same time, machine learning is finding its way into all areas of drug discovery and delivers impressive results. In this review, we attempt to provide an overview of machine learning, deep learning and network-based recommendation approaches applied to pharmaceutical co-crystallization. We also present crystal structure prediction as an alternative to machine learning approaches.
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Deng Y, Liu S, Jiang Y, Martins ICB, Rades T. Recent Advances in Co-Former Screening and Formation Prediction of Multicomponent Solid Forms of Low Molecular Weight Drugs. Pharmaceutics 2023; 15:2174. [PMID: 37765145 PMCID: PMC10538140 DOI: 10.3390/pharmaceutics15092174] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/15/2023] [Accepted: 08/19/2023] [Indexed: 09/29/2023] Open
Abstract
Multicomponent solid forms of low molecular weight drugs, such as co-crystals, salts, and co-amorphous systems, are a result of the combination of an active pharmaceutical ingredient (API) with a pharmaceutically acceptable co-former. These solid forms can enhance the physicochemical and pharmacokinetic properties of APIs, making them increasingly interesting and important in recent decades. Nevertheless, predicting the formation of API multicomponent solid forms in the early stages of formulation development can be challenging, as it often requires significant time and resources. To address this, empirical and computational methods have been developed to help screen for potential co-formers more efficiently and accurately, thus reducing the number of laboratory experiments needed. This review provides a comprehensive overview of current screening and prediction methods for the formation of API multicomponent solid forms, covering both crystalline states (co-crystals and salts) and amorphous forms (co-amorphous). Furthermore, it discusses recent advances and emerging trends in prediction methods, with a particular focus on artificial intelligence.
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Affiliation(s)
- Yuehua Deng
- Guangdong Provincial Key Lab of Green Chemical Product Technology, School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China; (Y.D.); (S.L.)
- Department of Pharmacy, University of Copenhagen, Universitetsparken 2, 2100 Copenhagen, Denmark;
| | - Shiyuan Liu
- Guangdong Provincial Key Lab of Green Chemical Product Technology, School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China; (Y.D.); (S.L.)
| | - Yanbin Jiang
- Guangdong Provincial Key Lab of Green Chemical Product Technology, School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou 510640, China; (Y.D.); (S.L.)
- School of Chemical Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China
| | - Inês C. B. Martins
- Department of Pharmacy, University of Copenhagen, Universitetsparken 2, 2100 Copenhagen, Denmark;
| | - Thomas Rades
- Department of Pharmacy, University of Copenhagen, Universitetsparken 2, 2100 Copenhagen, Denmark;
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Zheng L, Zhu B, Wu Z, Guo M, Chen J, Hong M, Liu G, Li W, Ren G, Tang Y. Pharmaceutical Cocrystal Discovery via 3D-SMINBR: A New Network Recommendation Tool Augmented by 3D Molecular Conformations. J Chem Inf Model 2023. [PMID: 37399241 DOI: 10.1021/acs.jcim.3c00066] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
Cocrystals have significant potential in various fields such as chemistry, material, and medicine. For instance, pharmaceutical cocrystals have the ability to address issues associated with physicochemical and biopharmaceutical properties. However, it can be challenging to find proper coformers to form cocrystals with drugs of interest. Herein, a new in silico tool called 3D substructure-molecular-interaction network-based recommendation (3D-SMINBR) has been developed to address this problem. This tool first integrated 3D molecular conformations with a weighted network-based recommendation model to prioritize potential coformers for target drugs. In cross-validation, the performance of 3D-SMINBR surpassed the 2D substructure-based predictive model SMINBR in our previous study. Additionally, the generalization capability of 3D-SMINBR was confirmed by testing on unseen cocrystal data. The practicality of this tool was further demonstrated by case studies on cocrystal screening of armillarisin A (Arm) and isoimperatorin (iIM). The obtained Arm-piperazine and iIM-salicylamide cocrystals present improved solubility and dissolution rate compared to their parent drugs. Overall, 3D-SMINBR augmented by 3D molecular conformations would be a useful network-based tool for cocrystal discovery. A free web server for 3D-SMINBR can be freely accessed at http://lmmd.ecust.edu.cn/netcorecsys/.
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Affiliation(s)
- Lulu Zheng
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Laboratory of Molecular Modeling & Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Bin Zhu
- State Key Laboratory of Bioreactor Engineering, Engineering Research Centre of Pharmaceutical Process Chemistry, Ministry of Education; Laboratory of Pharmaceutical Crystal Engineering & Technology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Laboratory of Molecular Modeling & Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Mei Guo
- State Key Laboratory of Bioreactor Engineering, Engineering Research Centre of Pharmaceutical Process Chemistry, Ministry of Education; Laboratory of Pharmaceutical Crystal Engineering & Technology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Jinyao Chen
- State Key Laboratory of Bioreactor Engineering, Engineering Research Centre of Pharmaceutical Process Chemistry, Ministry of Education; Laboratory of Pharmaceutical Crystal Engineering & Technology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Minghuang Hong
- State Key Laboratory of Bioreactor Engineering, Engineering Research Centre of Pharmaceutical Process Chemistry, Ministry of Education; Laboratory of Pharmaceutical Crystal Engineering & Technology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Laboratory of Molecular Modeling & Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Laboratory of Molecular Modeling & Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guobin Ren
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Laboratory of Molecular Modeling & Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
- State Key Laboratory of Bioreactor Engineering, Engineering Research Centre of Pharmaceutical Process Chemistry, Ministry of Education; Laboratory of Pharmaceutical Crystal Engineering & Technology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Laboratory of Molecular Modeling & Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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Racher F, Petrick TL, Braun DE. Exploring the Supramolecular Interactions and Thermal Stability of Dapsone:Bipyridine Cocrystals by Combining Computational Chemistry with Experimentation. CRYSTAL GROWTH & DESIGN 2023; 23:4638-4654. [PMID: 37304396 PMCID: PMC10251420 DOI: 10.1021/acs.cgd.3c00387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/21/2023] [Indexed: 06/13/2023]
Abstract
The application of computational screening methodologies based on H-bond propensity scores, molecular complementarity, molecular electrostatic potentials, and crystal structure prediction has guided the discovery of novel cocrystals of dapsone and bipyridine (DDS:BIPY). The experimental screen, which included mechanochemical and slurry experiments as well as the contact preparation, resulted in four cocrystals, including the previously known DDS:4,4'-BIPY (2:1, CC44-B) cocrystal. To understand the factors governing the formation of the DDS:2,2'-BIPY polymorphs (1:1, CC22-A and CC22-B) and the two DDS:4,4'-BIPY cocrystal stoichiometries (1:1 and 2:1), different experimental conditions (such as the influence of solvent, grinding/stirring time, etc.) were tested and compared with the virtual screening results. The computationally generated (1:1) crystal energy landscapes had the experimental cocrystals as the lowest energy structures, although distinct cocrystal packings were observed for the similar coformers. H-bonding scores and molecular electrostatic potential maps correctly indicated cocrystallization of DDS and the BIPY isomers, with a higher likelihood for 4,4'-BIPY. The molecular conformation influenced the molecular complementarity results, predicting no cocrystallization for 2,2'-BIPY with DDS. The crystal structures of CC22-A and CC44-A were solved from powder X-ray diffraction data. All four cocrystals were fully characterized by a range of analytical techniques, including powder X-ray diffraction, infrared spectroscopy, hot-stage microscopy, thermogravimetric analysis, and differential scanning calorimetry. The two DDS:2,2'-BIPY polymorphs are enantiotropically related, with form B being the stable polymorph at room temperature (RT) and form A being the higher temperature form. Form B is metastable but kinetically stable at RT. The two DDS:4,4'-BIPY cocrystals are stable at room conditions; however, at higher temperatures, CC44-A transforms to CC44-B. The cocrystal formation enthalpy order, derived from the lattice energies, was calculated as follows: CC44-B > CC44-A > CC22-A.
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12
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Abramov YA, Iuzzolino L, Jin Y, York G, Chen CH, Shultz CS, Yang Z, Chang C, Shi B, Zhou T, Greenwell C, Sekharan S, Lee AY. Cocrystal Synthesis through Crystal Structure Prediction. Mol Pharm 2023. [PMID: 37279175 DOI: 10.1021/acs.molpharmaceut.2c01098] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Crystal structure prediction (CSP) is an invaluable tool in the pharmaceutical industry because it allows to predict all the possible crystalline solid forms of small-molecule active pharmaceutical ingredients. We have used a CSP-based cocrystal prediction method to rank ten potential cocrystal coformers by the energy of the cocrystallization reaction with an antiviral drug candidate, MK-8876, and a triol process intermediate, 2-ethynylglyclerol. For MK-8876, the CSP-based cocrystal prediction was performed retrospectively and successfully predicted the maleic acid cocrystal as the most likely cocrystal to be observed. The triol is known to form two different cocrystals with 1,4-diazabicyclo[2.2.2]octane (DABCO), but a larger solid form landscape was desired. CSP-based cocrystal screening predicted the triol-DABCO cocrystal as rank one, while a triol-l-proline cocrystal was predicted as rank two. Computational finite-temperature corrections enabled determination of relative crystallization propensities of the triol-DABCO cocrystals with different stoichiometries and prediction of the triol-l-proline polymorphs in the free-energy landscape. The triol-l-proline cocrystal was obtained during subsequent targeted cocrystallization experiments and was found to exhibit an improved melting point and deliquescence behavior over the triol-free acid, which could be considered as an alternative solid form in the synthesis of islatravir.
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Affiliation(s)
- Yuriy A Abramov
- XtalPi Inc., 245 Main Street, Cambridge, Massachusetts 02142, United States
- Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Luca Iuzzolino
- Computational and Structural Chemistry, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States
| | - Yingdi Jin
- XtalPi, Inc., Shenzhen Jingtai Technology Co., Ltd., Futian District, Shenzhen 518100, China
| | - Gregory York
- Analytical Research and Development, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States
| | - Chien-Hung Chen
- Analytical Research and Development, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States
| | - C Scott Shultz
- Analytical Research and Development, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States
| | - Zhuocen Yang
- XtalPi, Inc., Shenzhen Jingtai Technology Co., Ltd., Futian District, Shenzhen 518100, China
| | - Chao Chang
- XtalPi, Inc., Shenzhen Jingtai Technology Co., Ltd., Futian District, Shenzhen 518100, China
| | - Baimei Shi
- XtalPi, Inc., Shenzhen Jingtai Technology Co., Ltd., Futian District, Shenzhen 518100, China
| | - Tian Zhou
- XtalPi, Inc., Shenzhen Jingtai Technology Co., Ltd., Futian District, Shenzhen 518100, China
| | - Chandler Greenwell
- XtalPi Inc., 245 Main Street, Cambridge, Massachusetts 02142, United States
| | - Sivakumar Sekharan
- XtalPi Inc., 245 Main Street, Cambridge, Massachusetts 02142, United States
| | - Alfred Y Lee
- Analytical Research and Development, Merck & Co., Inc., 126 East Lincoln Avenue, Rahway, New Jersey 07065, United States
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13
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Shah HS, Yuan J, Xie T, Yang Z, Chang C, Greenwell C, Zeng Q, Sun G, Read BN, Wilson TS, Valle HU, Kuang S, Wang J, Sekharan S, Bruhn JF. Absolute Configuration Determination of Chiral API Molecules by MicroED Analysis of Cocrystal Powders Formed Based on Cocrystal Propensity Prediction Calculations. Chemistry 2023; 29:e202203970. [PMID: 36744589 PMCID: PMC10089073 DOI: 10.1002/chem.202203970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Indexed: 02/07/2023]
Abstract
Establishing the absolute configuration of chiral active pharmaceutical ingredients (APIs) is of great importance. Single crystal X-ray diffraction (scXRD) has traditionally been the method of choice for such analysis, but scXRD requires the growth of large crystals, which can be challenging. Here, we present a method for determining absolute configuration that does not rely on the growth of large crystals. By examining microcrystals formed with chiral probes (small chiral compounds such as amino acids), absolute configuration can be unambiguously determined by microcrystal electron diffraction (MicroED). Our streamlined method employs three steps: (1) virtual screening to identify promising chiral probes, (2) experimental cocrystal screening and (3) structure determination by MicroED and absolute configuration assignment. We successfully applied this method to analyze two chiral API molecules currently on the market for which scXRD was not used to determine absolute configuration.
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Affiliation(s)
- Harsh S Shah
- J-STAR Research Inc., 6 Cedar Brook Dr, Cranbury, NJ 08512, USA
| | - Jiuchuang Yuan
- XtalPi Inc., Shenzhen Jingtai Technology Co., Ltd International Biomedical Innovation Park II 3F, No. 2 Hongliu Road, Futian District, Shenzhen, 518100, China
| | - Tian Xie
- J-STAR Research Inc., 6 Cedar Brook Dr, Cranbury, NJ 08512, USA
| | - Zhuocen Yang
- XtalPi Inc., Shenzhen Jingtai Technology Co., Ltd International Biomedical Innovation Park II 3F, No. 2 Hongliu Road, Futian District, Shenzhen, 518100, China
| | - Chao Chang
- XtalPi Inc., Shenzhen Jingtai Technology Co., Ltd International Biomedical Innovation Park II 3F, No. 2 Hongliu Road, Futian District, Shenzhen, 518100, China
| | | | - Qun Zeng
- XtalPi Inc., Shenzhen Jingtai Technology Co., Ltd International Biomedical Innovation Park II 3F, No. 2 Hongliu Road, Futian District, Shenzhen, 518100, China
| | - GuangXu Sun
- XtalPi Inc., Shenzhen Jingtai Technology Co., Ltd International Biomedical Innovation Park II 3F, No. 2 Hongliu Road, Futian District, Shenzhen, 518100, China
| | - Brandon N Read
- NanoImaging Services Inc., 4940 Carroll Canyon Road, Suite 115, San Diego, CA 92121, USA
| | - Timothy S Wilson
- NanoImaging Services Inc., 4940 Carroll Canyon Road, Suite 115, San Diego, CA 92121, USA
| | - Henry U Valle
- NanoImaging Services Inc., 4940 Carroll Canyon Road, Suite 115, San Diego, CA 92121, USA
| | - Shanming Kuang
- J-STAR Research Inc., 6 Cedar Brook Dr, Cranbury, NJ 08512, USA
| | - Jian Wang
- J-STAR Research Inc., 6 Cedar Brook Dr, Cranbury, NJ 08512, USA
| | | | - Jessica F Bruhn
- NanoImaging Services Inc., 4940 Carroll Canyon Road, Suite 115, San Diego, CA 92121, USA
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14
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Surov AO, Ramazanova AG, Voronin AP, Drozd KV, Churakov AV, Perlovich GL. Virtual Screening, Structural Analysis, and Formation Thermodynamics of Carbamazepine Cocrystals. Pharmaceutics 2023; 15:pharmaceutics15030836. [PMID: 36986697 PMCID: PMC10052035 DOI: 10.3390/pharmaceutics15030836] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 02/28/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
In this study, the existing set of carbamazepine (CBZ) cocrystals was extended through the successful combination of the drug with the positional isomers of acetamidobenzoic acid. The structural and energetic features of the CBZ cocrystals with 3- and 4-acetamidobenzoic acids were elucidated via single-crystal X-ray diffraction followed by QTAIMC analysis. The ability of three fundamentally different virtual screening methods to predict the correct cocrystallization outcome for CBZ was assessed based on the new experimental results obtained in this study and data available in the literature. It was found that the hydrogen bond propensity model performed the worst in distinguishing positive and negative results of CBZ cocrystallization experiments with 87 coformers, attaining an accuracy value lower than random guessing. The method that utilizes molecular electrostatic potential maps and the machine learning approach named CCGNet exhibited comparable results in terms of prediction metrics, albeit the latter resulted in superior specificity and overall accuracy while requiring no time-consuming DFT computations. In addition, formation thermodynamic parameters for the newly obtained CBZ cocrystals with 3- and 4-acetamidobenzoic acids were evaluated using temperature dependences of the cocrystallization Gibbs energy. The cocrystallization reactions between CBZ and the selected coformers were found to be enthalpy-driven, with entropy terms being statistically different from zero. The observed difference in dissolution behavior of the cocrystals in aqueous media was thought to be caused by variations in their thermodynamic stability.
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Affiliation(s)
- Artem O Surov
- G.A. Krestov Institute of Solution Chemistry RAS, 153045 Ivanovo, Russia
| | - Anna G Ramazanova
- G.A. Krestov Institute of Solution Chemistry RAS, 153045 Ivanovo, Russia
| | | | - Ksenia V Drozd
- G.A. Krestov Institute of Solution Chemistry RAS, 153045 Ivanovo, Russia
| | - Andrei V Churakov
- Institute of General and Inorganic Chemistry RAS, Leninsky Prosp. 31, 119991 Moscow, Russia
| | - German L Perlovich
- G.A. Krestov Institute of Solution Chemistry RAS, 153045 Ivanovo, Russia
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15
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Guidetti M, Hilfiker R, Kuentz M, Bauer-Brandl A, Blatter F. Exploring the Cocrystal Landscape of Posaconazole by Combining High-Throughput Screening Experimentation with Computational Chemistry. CRYSTAL GROWTH & DESIGN 2023; 23:842-852. [PMID: 36747574 PMCID: PMC9896487 DOI: 10.1021/acs.cgd.2c01072] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/05/2022] [Indexed: 06/18/2023]
Abstract
The development of multicomponent crystal forms, such as cocrystals, represents a means to enhance the dissolution and absorption properties of poorly water-soluble drug compounds. However, the successful discovery of new pharmaceutical cocrystals remains a time- and resource-consuming process. This study proposes the use of a combined computational-experimental high-throughput approach as a tool to accelerate and improve the efficiency of cocrystal screening exemplified by posaconazole. First, we employed the COSMOquick software to preselect and rank cocrystal candidates (coformers). Second, high-throughput crystallization experiments (HTCS) were conducted on the selected coformers. The HTCS results were successfully reproduced by liquid-assisted grinding and reaction crystallization, ultimately leading to the synthesis of thirteen new posaconazole cocrystals (7 anhydrous, 5 hydrates, and 1 solvate). The posaconazole cocrystals were characterized by PXRD, 1H NMR, Fourier transform-Raman, thermogravimetry-Fourier transform infrared spectroscopy, and differential scanning calorimetry. In addition, the prediction performance of COSMOquick was compared to that of two alternative knowledge-based methods: molecular complementarity (MC) and hydrogen bond propensity (HBP). Although HBP does not perform better than random guessing for this case study, both MC and COSMOquick show good discriminatory ability, suggesting their use as a potential virtual tool to improve cocrystal screening.
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Affiliation(s)
- Matteo Guidetti
- Solid-State
Development Department, Solvias AG, Römerpark 2, CH-4303Kaiseraugst, Switzerland
| | - Rolf Hilfiker
- Solid-State
Development Department, Solvias AG, Römerpark 2, CH-4303Kaiseraugst, Switzerland
| | - Martin Kuentz
- Institute
of Pharma Technology, University of Applied
Sciences and Arts Northwestern Switzerland, CH-4132Muttenz, Switzerland
| | - Annette Bauer-Brandl
- Department
of Physics, Chemistry and Pharmacy, University
of Southern Denmark, Campusvej 55, 5230Odense, Denmark
| | - Fritz Blatter
- Solid-State
Development Department, Solvias AG, Römerpark 2, CH-4303Kaiseraugst, Switzerland
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16
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Sugden IJ, Braun DE, Bowskill DH, Adjiman CS, Pantelides CC. Efficient Screening of Coformers for Active Pharmaceutical Ingredient Cocrystallization. CRYSTAL GROWTH & DESIGN 2022; 22:4513-4527. [PMID: 35915670 PMCID: PMC9337750 DOI: 10.1021/acs.cgd.2c00433] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Controlling the physical properties of solid forms for active pharmaceutical ingredients (APIs) through cocrystallization is an important part of drug product development. However, it is difficult to know a priori which coformers will form cocrystals with a given API, and the current state-of-the-art for cocrystal discovery involves an expensive, time-consuming, and, at the early stages of pharmaceutical development, API material-limited experimental screen. We propose a systematic, high-throughput computational approach primarily aimed at identifying API/coformer pairs that are unlikely to lead to experimentally observable cocrystals and can therefore be eliminated with only a brief experimental check, from any experimental investigation. On the basis of a well-established crystal structure prediction (CSP) methodology, the proposed approach derives its efficiency by not requiring any expensive quantum mechanical calculations beyond those already performed for the CSP investigation of the neat API itself. The approach and assumptions are tested through a computational investigation on 30 potential 1:1 multicomponent systems (cocrystals and solvate) involving 3 active pharmaceutical ingredients and 9 coformers and one solvent. This is complemented with a detailed experimental investigation of all 30 pairs, which led to the discovery of five new cocrystals (three API-coformer combinations, a polymorphic cocrystal example, and one with different stoichiometries) and a cis-aconitic acid polymorph. The computational approach indicates that, for some APIs, a significant proportion of all potential API/coformer pairs could be investigated with only a brief experimental check, thereby saving considerable experimental effort.
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Affiliation(s)
- Isaac J. Sugden
- Molecular
Systems Engineering Group, Department of Chemical Engineering, Sargent
Centre for Process Systems Engineering, Institute for Molecular Science
and Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Doris E. Braun
- University
of Innsbruck, Institute of Pharmacy,
Pharmaceutical Technology, Josef-Moeller-Haus, Innrain 52c, A-6020 Innsbruck, Austria
| | - David H. Bowskill
- Molecular
Systems Engineering Group, Department of Chemical Engineering, Sargent
Centre for Process Systems Engineering, Institute for Molecular Science
and Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Claire S. Adjiman
- Molecular
Systems Engineering Group, Department of Chemical Engineering, Sargent
Centre for Process Systems Engineering, Institute for Molecular Science
and Engineering, Imperial College London, London SW7 2AZ, United Kingdom
| | - Constantinos C. Pantelides
- Molecular
Systems Engineering Group, Department of Chemical Engineering, Sargent
Centre for Process Systems Engineering, Institute for Molecular Science
and Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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17
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Abramov YA, Sun G, Zeng Q. Emerging Landscape of Computational Modeling in Pharmaceutical Development. J Chem Inf Model 2022; 62:1160-1171. [PMID: 35226809 DOI: 10.1021/acs.jcim.1c01580] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Computational chemistry applications have become an integral part of the drug discovery workflow over the past 35 years. However, computational modeling in support of drug development has remained a relatively uncharted territory for a significant part of both academic and industrial communities. This review considers the computational modeling workflows for three key components of drug preclinical and clinical development, namely, process chemistry, analytical research and development, as well as drug product and formulation development. An overview of the computational support for each step of the respective workflows is presented. Additionally, in context of solid form design, special consideration is given to modern physics-based virtual screening methods. This covers rational approaches to polymorph, coformer, counterion, and solvent virtual screening in support of solid form selection and design.
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Affiliation(s)
- Yuriy A Abramov
- XtalPi, Inc., 245 Main St., Cambridge, Massachusetts 02142, United States.,Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Guangxu Sun
- XtalPi, Inc., Shenzhen Jingtai Technology Co., Ltd., Floor 3, Sf Industrial Plant, No. 2 Hongliu road, Fubao Community, Fubao Street, Futian District, Shenzhen 518100, China
| | - Qun Zeng
- XtalPi, Inc., Shenzhen Jingtai Technology Co., Ltd., Floor 3, Sf Industrial Plant, No. 2 Hongliu road, Fubao Community, Fubao Street, Futian District, Shenzhen 518100, China
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18
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Sarathi P, Padhi S. Insight of the various in silico screening techniques developed for assortment of cocrystal formers and their thermodynamic characterization. Drug Dev Ind Pharm 2022; 47:1523-1534. [PMID: 35164621 DOI: 10.1080/03639045.2022.2042554] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Most of the widely used drugs have problems associated with their oral bioavailability either due to their poor aqueous solubility or due to their poor permeability. Co-crystallization is an efficient and economically feasible approach that offers a great opportunity for improvement in physicochemical properties such as solubility, stability, and bioavailability of such type of therapeutic agent. Selection of the best co-former plays a major role in co-crystallization. Various approaches have been developed for the selection of suitable co-formers with API. In recent years in silico screening, a computational tool paying more attention for screening of co-formers has been developed. Numerous approaches can be used for in silico screening such as the Autodocking tool, COSMORS, COSMOTHERM, etc. Autodocking can predict several numbers of co-former effectively screened in silico method to identify a suitable co-former with an API. Prediction of solubility and dissolution is also important for the development of co-crystal. In this review, we discuss in silico screening of coformer and thermodynamic approaches to determine the dissolution and solubility of co-crystal specially with reference to the drugs belonging to BCS class II group.
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Affiliation(s)
- Parth Sarathi
- Noida Institute of Engineering and Technology (Pharmacy Institute), Greater Noida, India
| | - Swarupanjali Padhi
- Noida Institute of Engineering and Technology (Pharmacy Institute), Greater Noida, India
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19
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Lee MJ, Kim JY, Kim P, Lee IS, Mswahili ME, Jeong YS, Choi GJ. Novel Cocrystals of Vonoprazan: Machine Learning-Assisted Discovery. Pharmaceutics 2022; 14:pharmaceutics14020429. [PMID: 35214161 PMCID: PMC8877905 DOI: 10.3390/pharmaceutics14020429] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/11/2022] [Accepted: 02/14/2022] [Indexed: 02/04/2023] Open
Abstract
Vonoprazan (VPZ) is the first-in-class potassium-competitive acid blocker (P-CAB), and has many advantages over proton pump inhibitors (PPIs). It is administered as a fumarate salt for the treatment of acid-related diseases, including reflux esophagitis, gastric ulcer, and duodenal ulcer, and for eradication of Helicobacter pylori. To discover novel cocrystals of VPZ, we adopted an artificial neural network (ANN)-based machine learning model as a virtual screening tool that can guide selection of the most promising coformers for VPZ cocrystals. Experimental screening by liquid-assisted grinding (LAG) confirmed that 8 of 19 coformers selected by the ANN model were likely to create new solid forms with VPZ. Structurally similar benzenediols and benzenetriols, i.e., catechol (CAT), resorcinol (RES), hydroquinone (HYQ), and pyrogallol (GAL), were used as coformers to obtain phase pure cocrystals with VPZ by reaction crystallization. We successfully prepared and characterized three novel cocrystals: VPZ–RES, VPZ–CAT, and VPZ–GAL. VPZ–RES had the highest solubility among the novel cocrystals studied here, and was even more soluble than the commercially available fumarate salt of VPZ in solution at pH 6.8. In addition, novel VPZ cocrystals had superior stability in aqueous media than VPZ fumarates, demonstrating their potential for improved pharmaceutical performance.
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Affiliation(s)
- Min-Jeong Lee
- Department of Pharmaceutical Engineering, Soonchunhyang University, Asan 31538, Chungnam, Korea;
| | - Ji-Yoon Kim
- Department of Medical Science, Soonchunhyang University, Asan 31538, Chungnam, Korea; (J.-Y.K.); (P.K.); (I.-S.L.)
| | - Paul Kim
- Department of Medical Science, Soonchunhyang University, Asan 31538, Chungnam, Korea; (J.-Y.K.); (P.K.); (I.-S.L.)
| | - In-Seo Lee
- Department of Medical Science, Soonchunhyang University, Asan 31538, Chungnam, Korea; (J.-Y.K.); (P.K.); (I.-S.L.)
| | - Medard E. Mswahili
- Department of ICT Convergence, Soonchunhyang University, Asan 31538, Chungnam, Korea;
| | - Young-Seob Jeong
- Department of Computer Engineering, Chungbuk National University, Cheongju 28644, Chungbuk, Korea;
| | - Guang J. Choi
- Department of Pharmaceutical Engineering, Soonchunhyang University, Asan 31538, Chungnam, Korea;
- Department of Medical Science, Soonchunhyang University, Asan 31538, Chungnam, Korea; (J.-Y.K.); (P.K.); (I.-S.L.)
- Correspondence:
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20
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Ohyama M, Amari S, Takiyama H. Operation Design of Co-Crystallization Using Homogeneity Evaluation Including “Single Component Excess” Index. JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2022. [DOI: 10.1252/jcej.21we090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Mitsuki Ohyama
- Department of Chemical Engineering, Tokyo University of Agriculture and Technology
| | - Shuntaro Amari
- Department of Chemical Engineering, Tokyo University of Agriculture and Technology
| | - Hiroshi Takiyama
- Department of Chemical Engineering, Tokyo University of Agriculture and Technology
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21
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Hughes DS, Bingham AL, Hursthouse MB, Threlfall TL, Bond AD. The extensive solid-form landscape of sulfathiazole: hydrogen-bond topology and node shape. CrystEngComm 2022. [DOI: 10.1039/d2ce00964a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Patterns of hydrogen bonds are described in a set of 101 crystal structures containing sulfathiazole. Topological analysis of the hydrogen-bond nets is augmented by comparison of the shapes of the nodes extracted from each net.
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Affiliation(s)
- David S. Hughes
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
| | - Ann L. Bingham
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
- CHEP, Faculty of Social Sciences, University of Southampton, SO17 1BJ, UK
| | - Michael B. Hursthouse
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
| | - Terry L. Threlfall
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
| | - Andrew D. Bond
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
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22
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Hughes DS, Bingham AL, Hursthouse MB, Threlfall TL, Bond AD. The extensive solid-form landscape of sulfathiazole: geometrical similarity and interaction energies. CrystEngComm 2022. [DOI: 10.1039/d1ce01516h] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Sulfathiazole shows one of the most extensive solid-form landscapes known to date for an active pharmaceutical ingredient. A standardised structure set of 5 polymorphs, 59 co-crystals, 29 salts, and 3 other structures is established.
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Affiliation(s)
- David S. Hughes
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
| | - Ann L. Bingham
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
| | - Michael B. Hursthouse
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
| | - Terry L. Threlfall
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
| | - Andrew D. Bond
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
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23
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Kim P, Lee IS, Kim JY, Mswahili M, Jeong YS, Yoon WJ, Yun H, Lee MJ, Choi GJ. A study to discover novel pharmaceutical cocrystals of pelubiprofen with a machine learning approach compared. CrystEngComm 2022. [DOI: 10.1039/d2ce00153e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Pelubiprofen (PF), a biopharmaceutical classification system (BCS) class II non-steroidal anti-inflammatory drug, has been on the market only in its crystalline form. To discover the first cocrystal form(s) of the...
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24
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Parkan A, Mirzaei M, Tavakoli N, Homayouni A. Molecular interactions of indomethacin and amino acids: Computational approach. MAIN GROUP CHEMISTRY 2021. [DOI: 10.3233/mgc-210157] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Molecular interactions of indomethacin (IND) and amino acids (AA) were investigated in this work by employing the computational approaches. To this aim, the models of IND-AA were stabilized by performing density functional theory (DFT) calculations yielding the most favorable configurations regarding the energy values. Next, the approach of quantum theory of atoms in molecules (QTAIM) was used to recognize the roles of interactions and their significance in the bimolecular models. The results of interaction energies indicate that tryptophan (TRP) and phenylalanine (PHE) could be considered for participating in strong interactions with the IND substance. The results of QTAIM indicated that not only the electronegative atomic centers, but also homo-atomic centers could play significant roles in formations of IND-AA bimolecular models.
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Affiliation(s)
- Ali Parkan
- Isfahan Pharmacy Students’ Research Committee, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mahmoud Mirzaei
- Child Growth and Development Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Naser Tavakoli
- Department of Pharmaceutics, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Alireza Homayouni
- Department of Pharmaceutics, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
- Goldaru Herbal Pharmaceutical Laboratories, Isfahan, Iran
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25
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Jiang Y, Yang Z, Guo J, Li H, Liu Y, Guo Y, Li M, Pu X. Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials. Nat Commun 2021; 12:5950. [PMID: 34642333 PMCID: PMC8511140 DOI: 10.1038/s41467-021-26226-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 09/23/2021] [Indexed: 11/21/2022] Open
Abstract
Cocrystal engineering have been widely applied in pharmaceutical, chemistry and material fields. However, how to effectively choose coformer has been a challenging task on experiments. Here we develop a graph neural network (GNN) based deep learning framework to quickly predict formation of the cocrystal. In order to capture main driving force to crystallization from 6819 positive and 1052 negative samples reported by experiments, a feasible GNN framework is explored to integrate important prior knowledge into end-to-end learning on the molecular graph. The model is strongly validated against seven competitive models and three challenging independent test sets involving pharmaceutical cocrystals, π-π cocrystals and energetic cocrystals, exhibiting superior performance with accuracy higher than 96%, confirming its robustness and generalization. Furthermore, one new energetic cocrystal predicted is successfully synthesized, showcasing high potential of the model in practice. All the data and source codes are available at https://github.com/Saoge123/ccgnet for aiding cocrystal community.
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Affiliation(s)
- Yuanyuan Jiang
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Zongwei Yang
- Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, 621900, China
| | - Jiali Guo
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Hongzhen Li
- Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, 621900, China
| | - Yijing Liu
- College of Computer Science, Sichuan University, Chengdu, 610064, China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, 610064, China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu, 610064, China.
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26
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Guo M, Sun X, Chen J, Cai T. Pharmaceutical cocrystals: A review of preparations, physicochemical properties and applications. Acta Pharm Sin B 2021; 11:2537-2564. [PMID: 34522597 PMCID: PMC8424375 DOI: 10.1016/j.apsb.2021.03.030] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 02/22/2021] [Accepted: 03/10/2021] [Indexed: 12/15/2022] Open
Abstract
Pharmaceutical cocrystals are multicomponent systems in which at least one component is an active pharmaceutical ingredient and the others are pharmaceutically acceptable ingredients. Cocrystallization of a drug substance with a coformer is a promising and emerging approach to improve the performance of pharmaceuticals, such as solubility, dissolution profile, pharmacokinetics and stability. This review article presents a comprehensive overview of pharmaceutical cocrystals, including preparation methods, physicochemical properties, and applications. Furthermore, some examples of drug cocrystals are highlighted to illustrate the effect of crystal structures on the various aspects of active pharmaceutical ingredients, such as physical stability, chemical stability, mechanical properties, optical properties, bioavailability, sustained release and therapeutic effect. This review will provide guidance for more efficient design and manufacture of pharmaceutical cocrystals with desired physicochemical properties and applications.
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Sekharan S, Liu X, Yang Z, Liu X, Deng L, Ruan S, Abramov Y, Sun G, Li S, Zhou T, Shi B, Zeng Q, Zeng Q, Chang C, Jin Y, Shi X. Selecting a stable solid form of remdesivir using microcrystal electron diffraction and crystal structure prediction. RSC Adv 2021; 11:17408-17412. [PMID: 35479679 PMCID: PMC9033196 DOI: 10.1039/d1ra03100g] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 04/27/2021] [Indexed: 12/20/2022] Open
Abstract
Therapeutic options in response to the coronavirus disease 2019 (COVID-19) outbreak are urgently needed. In this communication, we demonstrate how to support selection of a stable solid form of an antiviral drug remdesivir in quick time using the microcrystal electron diffraction (MicroED) technique and a cloud-based and artificial intelligence implemented crystal structure prediction platform. We present the MicroED structures of remdesivir forms II and IV and conclude that form II is more stable than form IV at ambient temperature in agreement with experimental observations. The combined experimental and theoretical study can serve as a template for formulation scientists in the pharmaceutical industry.
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Affiliation(s)
| | - Xuetao Liu
- Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China
| | - Zhuocen Yang
- Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China
| | - Xiang Liu
- Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China
| | - Li Deng
- Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China
| | - Shigang Ruan
- Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China
| | - Yuriy Abramov
- XtalPi Inc. 245 Main St, Floor 11 Cambridge MA 02142 USA
| | - GuangXu Sun
- Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China
| | - Sizhu Li
- Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China
| | - Tian Zhou
- Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China
| | - Baime Shi
- Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China
| | - Qun Zeng
- Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China
| | - Qiao Zeng
- Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China
| | - Chao Chang
- Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China
| | - Yingdi Jin
- Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China
| | - Xuekun Shi
- Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China
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Wong SN, Chen YCS, Xuan B, Sun CC, Chow SF. Cocrystal engineering of pharmaceutical solids: therapeutic potential and challenges. CrystEngComm 2021. [DOI: 10.1039/d1ce00825k] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This highlight presents an overview of pharmaceutical cocrystal production and its potential in reviving problematic properties of drugs in different dosage forms. The challenges and future outlook of its translational development are discussed.
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Affiliation(s)
- Si Nga Wong
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, L2-08B, Laboratory Block, 21 Sassoon Road Pokfulam, Hong Kong SAR, China
| | - Yu Chee Sonia Chen
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, L2-08B, Laboratory Block, 21 Sassoon Road Pokfulam, Hong Kong SAR, China
- Department of Pharmacy, Faculty of Life Sciences & Medicine, King's College London, London, UK
| | - Bianfei Xuan
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, L2-08B, Laboratory Block, 21 Sassoon Road Pokfulam, Hong Kong SAR, China
| | - Changquan Calvin Sun
- Pharmaceutical Materials Science and Engineering Laboratory, Department of Pharmaceutics, College of Pharmacy, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Shing Fung Chow
- Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, L2-08B, Laboratory Block, 21 Sassoon Road Pokfulam, Hong Kong SAR, China
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, Hong Kong SAR, China
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Yuan J, Liu X, Wang S, Chang C, Zeng Q, Song Z, Jin Y, Zeng Q, Sun G, Ruan S, Greenwell C, Abramov YA. Virtual coformer screening by a combined machine learning and physics-based approach. CrystEngComm 2021. [DOI: 10.1039/d1ce00587a] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Cocrystals as a solid form technology for improving physicochemical properties have gained increasing popularity in the pharmaceutical, nutraceutical, and agrochemical industries.
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Affiliation(s)
- Jiuchuang Yuan
- XtalPi Inc., Shenzhen Jingtai Technology Co., Ltd., Floor 3, Sf Industrial Plant, No. 2 hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen, 518100 China
| | - Xuetao Liu
- XtalPi Inc., Shenzhen Jingtai Technology Co., Ltd., Floor 3, Sf Industrial Plant, No. 2 hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen, 518100 China
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogeomics, Peking University Shenzhen Graduate School, Shenzhen, 518055 China
| | - Simin Wang
- XtalPi Inc., Shenzhen Jingtai Technology Co., Ltd., Floor 3, Sf Industrial Plant, No. 2 hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen, 518100 China
| | - Chao Chang
- XtalPi Inc., Shenzhen Jingtai Technology Co., Ltd., Floor 3, Sf Industrial Plant, No. 2 hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen, 518100 China
| | - Qiao Zeng
- XtalPi Inc., Shenzhen Jingtai Technology Co., Ltd., Floor 3, Sf Industrial Plant, No. 2 hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen, 518100 China
| | - Zhengtian Song
- XtalPi Inc., Shenzhen Jingtai Technology Co., Ltd., Floor 3, Sf Industrial Plant, No. 2 hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen, 518100 China
| | - Yingdi Jin
- XtalPi Inc., Shenzhen Jingtai Technology Co., Ltd., Floor 3, Sf Industrial Plant, No. 2 hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen, 518100 China
| | - Qun Zeng
- XtalPi Inc., Shenzhen Jingtai Technology Co., Ltd., Floor 3, Sf Industrial Plant, No. 2 hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen, 518100 China
| | - Guangxu Sun
- XtalPi Inc., Shenzhen Jingtai Technology Co., Ltd., Floor 3, Sf Industrial Plant, No. 2 hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen, 518100 China
| | - Shigang Ruan
- XtalPi Inc., Shenzhen Jingtai Technology Co., Ltd., Floor 3, Sf Industrial Plant, No. 2 hongliu Road, Fubao Community, Fubao Street, Futian District, Shenzhen, 518100 China
| | | | - Yuriy A. Abramov
- XtalPi Inc, Cambridge, Massachusetts 02142, USA
- Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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