Investigation of the Storage and Stability as Well as the Dissolution Rate of Novel Ilaprazole/Xylitol Cocrystal DOI Open Access

Sihyun Nam,

Changjin Lim, Yong‐Dae Kim

и другие.

Опубликована: Янв. 4, 2024

Reflux esophagitis, a treatment for gastric ulcers known as Ilaprazole (Ila), is not stable during storage and handling at room temperature, requiring 5 degrees celsius. In this study, to address these issues with Ila, coformers rich in oxygen (O) hydroxyl (OH) groups capable of forming hydrogen bonds were selected. These included Xylitol (Xyl), Meglumine (Meg), Nicotinic acid (Nic), L-Aspartic (Asp), L-Glutamic (Glu). A 1:1 physical mixture Ila each coformer was prepared, the potential cocrystal for-mation predicted using differential scanning calorimetry (DSC) screening. The results indi-cated formation Ila/Xyl mixture. Subsequently, Xyl mixed ethyl acetate (EA) ratio, after 28 hours slurry, confirmed through solid-state CP/MAS 13C NMR spectrum analysis, showing intermolecular bonding conformational changes. Furthermore, ratio solution-state (1H, 13C, 2D) molecular structure analysis. To assess stability it stored com-pared 25±2°C relative humidity (RH) 65±5% over three months. showed that purity remained 99.8% from initial 99.75% months, while decrease an 90% Additionally, RH 65±5%, specific impurity B observed be 0.03% whereas increase 0.032% 2.28% evaluate dissolution rate cocrystal, formulation prepared compared pH 10, dosage equivalent 10mg Ila. reached 55% within 15 minutes 100% 45 minutes, reach 32% only 60 minutes. However, overall, or exceeding Therefore, will maximize its effectiveness more convenient crystal development, allowing preservation temperature without need problematic 5°C refrigeration ambient con-ditions storage, addressing associated

Язык: Английский

Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials DOI Creative Commons
Gregory J. O. Beran

Chemical Science, Год журнала: 2023, Номер 14(46), С. 13290 - 13312

Опубликована: Янв. 1, 2023

Molecular crystal structure prediction has matured to the point where it can routinely facilitate discovery and design of new organic materials.

Язык: Английский

Процитировано

41
Crystal engineering: from promise to delivery DOI Creative Commons
Dario Braga

Chemical Communications, Год журнала: 2023, Номер 59(95), С. 14052 - 14062

Опубликована: Янв. 1, 2023

Twenty years ago, I wrote a Chem. Commun. feature article entitled “ Crystal Engineering: where from? Where to? ”: an update is in order.

Язык: Английский

Процитировано

24
Virtual Screening, Structural Analysis, and Formation Thermodynamics of Carbamazepine Cocrystals DOI Creative Commons
Artem O. Surov, Anna G. Ramazanova, Alexander P. Voronin

и другие.

Pharmaceutics, Год журнала: 2023, Номер 15(3), С. 836 - 836

Опубликована: Март 3, 2023

In this study, the existing set of carbamazepine (CBZ) cocrystals was extended through successful combination drug with positional isomers acetamidobenzoic acid. The structural and energetic features CBZ 3- 4-acetamidobenzoic acids were elucidated via single-crystal X-ray diffraction followed by QTAIMC analysis. ability three fundamentally different virtual screening methods to predict correct cocrystallization outcome for assessed based on new experimental results obtained in study data available literature. It found that hydrogen bond propensity model performed worst distinguishing positive negative experiments 87 coformers, attaining an accuracy value lower than random guessing. method utilizes molecular electrostatic potential maps machine learning approach named CCGNet exhibited comparable terms prediction metrics, albeit latter resulted superior specificity overall while requiring no time-consuming DFT computations. addition, formation thermodynamic parameters newly evaluated using temperature dependences Gibbs energy. reactions between selected coformers be enthalpy-driven, entropy being statistically from zero. observed difference dissolution behavior aqueous media thought caused variations their stability.

Язык: Английский

Процитировано

22
Polymorphism of Carbamazepine Pharmaceutical Cocrystal: Structural Analysis and Solubility Performance DOI Creative Commons
Artem O. Surov, Ksenia V. Drozd, Anna G. Ramazanova

и другие.

Pharmaceutics, Год журнала: 2023, Номер 15(6), С. 1747 - 1747

Опубликована: Июнь 15, 2023

Polymorphism is a common phenomenon among single- and multicomponent molecular crystals that has significant impact on the contemporary drug development process. A new polymorphic form of carbamazepine (CBZ) cocrystal with methylparaben (MePRB) in 1:1 molar ratio as well drug's channel-like containing highly disordered coformer molecules have been obtained characterized this work using various analytical methods, including thermal analysis, Raman spectroscopy, single-crystal high-resolution synchrotron powder X-ray diffraction. Structural analysis solid forms revealed close resemblance between novel II previously reported I [CBZ + MePRB] (1:1) terms hydrogen bond networks overall packing arrangements. The was found to belong distinct family isostructural CBZ cocrystals coformers similar size shape. Form appeared be related by monotropic relationship, being proven thermodynamically more stable phase. dissolution performance both polymorphs aqueous media significantly enhanced when compared parent CBZ. However, considering superior thermodynamic stability consistent profile, discovered seems promising reliable for further pharmaceutical development.

Язык: Английский

Процитировано

18
Speeding Up the Cocrystallization Process: Machine Learning-Combined Methods for the Prediction of Multicomponent Systems DOI Creative Commons
Rebecca Birolo, Federica Bravetti, Eugenio Alladio

и другие.

Crystal Growth & Design, Год журнала: 2023, Номер 23(11), С. 7898 - 7911

Опубликована: Окт. 19, 2023

Pharmaceutical cocrystals are crystalline materials composed of at least two molecules, i.e., an active pharmaceutical ingredient (API) and a coformer, assembled by noncovalent forces. Cocrystallization is successfully applied to improve the physicochemical properties APIs, such as solubility, dissolution profile, pharmacokinetics, stability. However, choosing ideal coformer challenging task in terms time, efforts, laboratory resources. Several computational tools machine learning (ML) models have been proposed mitigate this problem. challenge achieving robust generalizable predictive method still open. In study, we propose new approach quickly predict formation cocrystals, employing partial squares-discriminant analysis, random forest, neural networks. The were based on data sets 13 structurally different APIs with both positive negative cocrystallization outcomes. At same features specially selected from variety molecular descriptors explain phenomenon cocrystallization. All ML showed cross-validation accuracy higher than 83%. Furthermore, was drive experimental tests 2-phenylpropionic acid, showcasing high potential practice.

Язык: Английский

Процитировано

17
On the Road to Cocrystal Prediction: A Screening Study for the Validation of In Silico Methods DOI Creative Commons
Amy A. Sarjeant, Heba Abourahma, Stephen R. Thomas

и другие.

Crystal Growth & Design, Год журнала: 2024, Номер 24(13), С. 5486 - 5493

Опубликована: Июнь 20, 2024

The pharmaceutical industry is increasingly exploring cocrystals as a solution to provide improved material properties for otherwise intractable active ingredients (APIs). Researchers have attempted streamline the experimental process of screening by developing in silico predictive tools. These tools use intermolecular interactions, primarily hydrogen bonding, well other molecular descriptors quickly assess likelihood cocrystal formation between an API and set small-molecule coformers. We developed web-based application using three such help us prioritize against library nearly 300 individual In order validate our algorithms, molecules from compound were screened, experimentally with application, subset 40 Here, we present design app, work used its predictions, relative success techniques.

Язык: Английский

Процитировано

6
Combination of Machine Learning and COSMO-RS Thermodynamic Model in Predicting Solubility Parameters of Coformers in Production of Cocrystals for Enhanced Drug Solubility DOI
Wael A. Mahdi, Ahmad J. Obaidullah

Chemometrics and Intelligent Laboratory Systems, Год журнала: 2024, Номер 253, С. 105219 - 105219

Опубликована: Авг. 23, 2024

Язык: Английский

Процитировано

5
In silico co-crystal design: Assessment of the latest advances DOI
Carolina von Eßen,

David Luedeker

Drug Discovery Today, Год журнала: 2023, Номер 28(11), С. 103763 - 103763

Опубликована: Сен. 7, 2023

Язык: Английский

Процитировано

11
Exploring the Cocrystal Landscape of Posaconazole by Combining High-Throughput Screening Experimentation with Computational Chemistry DOI Creative Commons
Matteo Guidetti,

Rolf Hilfiker,

Martin Kuentz

и другие.

Crystal Growth & Design, Год журнала: 2022, Номер 23(2), С. 842 - 852

Опубликована: Дек. 23, 2022

The development of multicomponent crystal forms, such as cocrystals, represents a means to enhance the dissolution and absorption properties poorly water-soluble drug compounds. However, successful discovery new pharmaceutical cocrystals remains time- resource-consuming process. This study proposes use combined computational-experimental high-throughput approach tool accelerate improve efficiency cocrystal screening exemplified by posaconazole. First, we employed COSMOquick software preselect rank candidates (coformers). Second, crystallization experiments (HTCS) were conducted on selected coformers. HTCS results successfully reproduced liquid-assisted grinding reaction crystallization, ultimately leading synthesis thirteen posaconazole (7 anhydrous, 5 hydrates, 1 solvate). characterized PXRD, 1H NMR, Fourier transform-Raman, thermogravimetry-Fourier transform infrared spectroscopy, differential scanning calorimetry. In addition, prediction performance was compared that two alternative knowledge-based methods: molecular complementarity (MC) hydrogen bond propensity (HBP). Although HBP does not perform better than random guessing for this case study, both MC show good discriminatory ability, suggesting their potential virtual screening.

Язык: Английский

Процитировано

16
What Has Carbamazepine Taught Crystal Engineers? DOI Creative Commons
Amy V. Hall, Aurora J. Cruz‐Cabeza, Jonathan W. Steed

и другие.

Crystal Growth & Design, Год журнала: 2024, Номер 24(17), С. 7342 - 7360

Опубликована: Июнь 21, 2024

The antiepilepsy drug carbamazepine is one of the most studied pharmaceuticals in world. rich story its solid forms, cocrystals, and formulation a microcosm topical world pharmaceutical materials. Understanding has required time, money, dedication from numerous researchers companies worldwide. This wealth knowledge provides opportunity to reflect on progress within crystal engineering field general. Perspective covers extensive form landscape applies these examples discuss answer fundamental questions discipline. encompasses screening methods, computational discovery, power influence understanding controlling crystals amorphous state, environmental legacy modern pharmaceuticals. broad but in-depth analysis vehicle into engineering, not only own right across spectrum organic materials science formulation. Discoveries demonstrate potential richness chemistry every drug.

Язык: Английский

Процитировано

3