Influence of crystal packing on the thermal properties of cocrystals and cocrystal solvates of olanzapine: insights from computations

Surampudi, Anuja Venkata Sai Durga; Rajendrakumar, Satyasree; Nanubolu, Jagadeesh Babu; Sridhar, Balasubramanian; Surov, Artem O.; Voronin, Alexander P.; Perlovich, German L.

doi:10.1039/d0ce00914h

Cited by 23 publications

(24 citation statements)

References 91 publications

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“…17 There are few studies on the solvates which are built entirely by weak interactions, like C-H•••O and C-H•••π. 18,19 Despite the increasing interest in solvates, 15,16 we still lack a general understanding of the solvate formation and structure−stability relationships, especially the role of weak interactions.…”

Section: Introductionmentioning

confidence: 99%

Insights into Intermolecular Interactions of Spironolactone Solvates

Yin

Zhang

Yang

et al. 2021

Crystal Growth & Design

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To gain insights into the effect of weak interactions in solvate formation and structure−property correlation, experimental (thermal and structural analysis) and computational (lattice and pairwise energy calculations) studies were undertaken to relate intermolecular interactions to packing arrangements, formation mechanism, stability, and the desolvation process of spironolactone (SPI) solvates. Herein, four novel solvates were found, and the crystal structures of five solvates (formic acid, acetic acid, benzene, toluene, and acetonitrile) were resolved for the first time. Structural similarity and diversity of SPI forms are apparent according to the crystal structures, where the strongest interaction is the stacking of the steroid ring system via van der Waals forces. Although the solvent molecules are located in channels (acetonitrile molecules in isolated sites), they form hydrogen bonds and/or dispersion interactions with SPI frameworks. These findings can rationalize the relatively superior stability of SPI solvates. Both increased packing coefficient and additional SPI•••solvent interactions are the important driving forces for solvate formation, especially for the van der Waals force controlled solvates. Besides, the thermodynamic stability determines the desolvation of SPI solvates to form II, while the desolvation pattern between formic acid solvate to form I is guided by the isostructural pairs.

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Section: Introductionmentioning

confidence: 99%

Insights into Intermolecular Interactions of Spironolactone Solvates

Yin

Zhang

Yang

et al. 2021

Crystal Growth & Design

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“…Table reveals the summary of olanzapine screening that had 25 positive results out of 52 experiments, including the previously reported cocrystals/salts of Olanzapine. ,− Figure S2 shows the experimental evidence for olanzapine multicomponent solid forms. To qualitatively compare these predictive approaches, the ROC plot and the respective area under the curve (AUC) for each method are illustrated in Figure .…”

Section: Resultsmentioning

confidence: 99%

Virtual Multicomponent Crystal Screening: Hydrogen Bonding Revisited

Ahmadi

Mondal

et al. 2021

Crystal Growth & Design

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Pharmaceutical cocrystals, salts, and multicomponent crystals, in general, have increasingly come under the spotlight in recent years. A fast and efficient a priori theoretical classifier to identify potential coformers is highly sought after to complement the experimental brute force screening methods. This research examines the qualitative approaches that are based on hydrogen bonding strength. First, molecular electrostatic potential (MEP) maps of 330 coformers were obtained from density functional theory simulations, using two geometries: experimentally determined crystal structures and gas-phase optimization. An in-depth comparison of MEPs revealed the potential pitfalls of these two geometries that are deliberated at length in the manuscript. Next, six APIs and their reported salts/cocrystals on the Cambridge Structural Database (CSD) were inversely predicted with MEP analysis. For two of these APIs, the prediction showed systematic errors that are resolved with suggestions provided in the manuscript. Subsequently, hydrogen bond energy (HBE) and hydrogen bond propensity (HBP) calculations were put to the test with two APIs and 52 organic coformers. Finally, multivariate logistic regression, a linear machine learning (ML) algorithm, showed how a combination of HBE and HBP can be a superior classifier, for which 18 out of 25 positive cases were uninterruptedly identified at the top of the list. Provided that a database of failed attempts of cocrystallization is compiled within the scientific community to supplement the existing positive results (multicomponent crystals in the CSD), the combination of chemistry-based parameters and ML can be a promising classifier for coformer selection.

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“…37 This endothermal desolvation during the photodimerization is accompanied by reduction of density lowering burst speeds. 38 The splitting into pieces of CP 2 may be due to the strain induced by the loss of guest solvent as well as photodimerization. 29 Importantly, the crisscross aligned CQC bonds in the CH 3 -bpeb pairs (CP 3 ) may slow down or even prohibit the reaction though it can undergo the pedal-like motion to a parallel orientation for photodimerization.…”

Section: And Fig S13 S14 Esi †)mentioning

confidence: 99%

“…37 Endothermal desolvation also buffers the strain of reaction by dissipation of energy. [37][38][39] We herein report the modulation of the PS effects by governing the ). Thermogravimetric analysis (TGA) revealed that CP 1 -CP 4 were stable up to 340 1C (Fig.…”

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confidence: 99%