Session 1: Smarter Solid State Research by Prediction and Simulation Archives - Pharma Crystallization Summit https://www.jstar-research.com/2022pcs/category/pcs2020/session-1-smarter-solid-state-research-by-prediction-and-simulation/ Conferences on pharmaceutical crystallization summit Sat, 20 Aug 2022 03:20:43 +0000 en-US hourly 1 https://wordpress.org/?v=6.0.3 https://www.jstar-research.com/2022pcs/wp-content/uploads/2022/05/cropped-PCS-Icon-35-CC-32x32.png Session 1: Smarter Solid State Research by Prediction and Simulation Archives - Pharma Crystallization Summit https://www.jstar-research.com/2022pcs/category/pcs2020/session-1-smarter-solid-state-research-by-prediction-and-simulation/ 32 32 Locality, Strength, and Hierarchy of Intermolecular Interactions in Crystallization https://www.jstar-research.com/2022pcs/2020/06/16/locality-strength-and-hierarchy-of-intermolecular-interactions-in-crystallization/ Tue, 16 Jun 2020 02:32:49 +0000 https://crystallization-summit.jstar-research.com/?p=45 Self-assembling of molecules in crystallization is collectively driven by intermolecular interactions among the solute and solvent. Better understanding of nucleation mechanism may be perceived in light of the locality, strength, and hierarchy of intermolecular interactions.

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Abstract. Self-assembling of molecules in crystallization is collectively driven by intermolecular interactions among the solute and solvent. Better understanding of nucleation mechanism may be perceived in light of the locality, strength, and hierarchy of intermolecular interactions. Primary interactions such as hydrogen bonding decide pre-nucleation dynamics and the secondary such as aromatic stacking and close contacts may effect complementing and competing functions while primary assemblies develop. Additionally, molecular conformation and local preference of a particular interaction can mutually influence the kinetics and structure of self-associates. To demonstrate these principles, several of our recent studies focus on solution chemistry and the interplay between conformation and intermolecular interaction of a series of structurally similar diarylamine compounds.

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Pharmaceutical Crystallization Support by Computational Approaches https://www.jstar-research.com/2022pcs/2020/06/15/pharmaceutical-crystallization-support-by-computational-approaches/ Mon, 15 Jun 2020 15:14:30 +0000 https://crystallization-summit.jstar-research.com/?p=86 Given challenges facing the pharmaceutical industry, an accelerated Drug Development greatly benefits from guidance provided by computational methods.1 This presentation will focus on computational support of the following important tasks related to the pharmaceutical crystallization.

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1 This presentation will focus on computational support of the following important tasks related to the pharmaceutical crystallization. That includes derisking of the stable solid form selection of pharmaceutical API; in silico coformer screening for an API or intermediate cocrystallization; rational solvent selection for solid solvate desolvation; and in silico solvent screening for impurity purge via recrystallization.
  1. Abramov, Y.A., Ed. Computational Pharmaceutical Solid State Chemistry, John Wiley & Sons, 2016.

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Computation Assisted Solvent Selection in Crystal Form Investigation https://www.jstar-research.com/2022pcs/2020/06/14/computation-assisted-solvent-selection-in-crystal-form-investigation/ Sun, 14 Jun 2020 15:15:31 +0000 https://crystallization-summit.jstar-research.com/?p=89 Rational selection of solvents plays a critical role in solid form screening and production. On the one hand, a variety of solvents with different properties are chosen in form screening to maximize the screening space of searching for all potential solid forms because some solvents selectively favor the generation of a particular form as a result of crystallization kinetics.

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Abstract Rational selection of solvents plays a critical role in solid form screening and production. On the one hand, a variety of solvents with different properties are chosen in form screening to maximize the screening space of searching for all potential solid forms because some solvents selectively favor the generation of a particular form as a result of crystallization kinetics. On the other hand, some solvents need to be avoided in crystallization process due to formation of solvates that result in issues in residue solvent control and/or form transformation. We will present a lesson learned from a case study in which the wrong solvent was selected, which created huge pain in the commercialization. Efforts including computational approach utilized to address this issue will also be discussed.

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