Methods for estimating supersaturation in antisolvent crystallization systems

Schall, Jennifer M.; Capellades, Gerard; Myerson, Allan S.

doi:10.1039/c9ce00843h

Cited by 24 publications

(33 citation statements)

References 15 publications

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“…In this context, crystal growth and nucleation kinetics of APIs are typically obtained for each solvent−solute system by conducting a series of batch (seeded) crystallization experiments at different conditions, in which the decay of the concentration of the API in the solution is measured, e.g., by in-line process analytical technology (PAT) tools such as attenuated total reflectance fourier transform infrared (ATR-FTIR) spectroscopy. 5 Subsequently, the collected PAT data, complemented by off-line analytics measuring API solubility, solid API form (thermoanalytical data), and seed attributes (e.g., initial seed PSD) are combined to compute approximations of the "true" crystallization driving force (supersaturation), which carries a pivotal role in the determination of crystallization kinetics. The kinetic parameters can then be estimated by regression in averaged-size models 6 or in a population balance model (PBM) 7,8 after the data from different sources are parsed and curated, and appropriate equations for the crystallization mechanisms are identified.…”

Section: ■ Introductionmentioning

confidence: 99%

Crystal Growth Kinetics of an Industrial Active Pharmaceutical Ingredient: Implications of Different Representations of Supersaturation and Simultaneous Growth Mechanisms

Quilló

Bhonsale

Gielen

et al. 2021

Crystal Growth & Design

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The crystal growth kinetics of a proprietary active pharmaceutical ingredient (API) was investigated by isothermal seeded batch de-supersaturation experiments in solvent mixtures using the "true" thermodynamic representation of the supersaturation driving force, which considers the activities of the saturated and supersaturated states. Three approaches to approximate the experimentally inaccessible activity coefficients of the supersaturated state were assessed, as well as the most common approximation, which omits the activity coefficients altogether. Subsequently, the supersaturation data from the different expressions were fed into a population balance model to estimate kinetic parameters for the empirical, Burton− Cabrera−Frank, and birth-and-spread growth models. The results demonstrate that the approach used to compute the supersaturation alters the estimated kinetic parameters significantly, having potentially serious implications for their physical interpretation and for extracting the physical properties they represent in lumped form. Moreover, including the chemical activities in the supersaturation leads to kinetic parameters with a tighter joint confidence interval and weaker parameter correlation that can better explain the experimental observation of the API growing appreciably only under higher antisolvent amounts. Finally, the simultaneous occurrence of multiple crystal growth mechanisms is investigated, concluding that the additive contribution of B+S and BCF best explains the supersaturation decay observed in the experiments for this API.

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Section: ■ Introductionmentioning

confidence: 99%

Crystal Growth Kinetics of an Industrial Active Pharmaceutical Ingredient: Implications of Different Representations of Supersaturation and Simultaneous Growth Mechanisms

Quilló

Bhonsale

Gielen

et al. 2021

Crystal Growth & Design

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“…Note that superscript * denotes quantities in saturation. As shown previously in several accounts, − the latter definition may not accurately represent the actual driving force in solution crystallization, especially for electrolytes such as sodium chlorate. A thermodynamically more appropriate expression uses the chemical potential of the supersaturated state and that at solid–liquid equilibrium to define σ.…”

Section: Theorymentioning

confidence: 99%

Crystal Growth, Dissolution, and Agglomeration Kinetics of Sodium Chlorate

Fytopoulos

Kavousanakis

Gerven

et al. 2021

Ind. Eng. Chem. Res.

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The analysis, development, and implementation of novel complex industrial crystallization processes requires kinetic knowledge of not only crystal growth and nucleation but also breakage, dissolution, and agglomeration processes. In this work, the crystal growth, dissolution, and agglomeration kinetics of sodium chlorate (NaClO3) in aqueous solutions are estimated via seeded batch experiments with in-line particle size distribution measurements. Contrary to previous works, the growth/dissolution kinetics are expressed in terms of the fundamental driving force of crystallization calculated from the activity of supersaturated solutions. The activity-based driving force is roughly 2-fold higher than the commonly used representation, which assumes ideal solutions. By fitting experimental desupersaturation data to mechanistic and empirical growth models, we show that the growth of sodium chlorate is surface integration controlled and is best described by a two-dimensional birth and spread surface nucleation mechanism. The dissolution of sodium chlorate crystals is diffusion controlled and is ∼4 times faster than the growth at an equal initial driving force. Particle agglomeration is substantial in the early stages of crystallization experiments, likely due to a strong increase in the particle number due to initial breeding secondary nucleation upon seeding. The agglomeration rate constant increases with supersaturation and decreases at higher energy dissipation rate (by increased agitation) due to a strong decrease in the efficiency of interparticle collisions. Seeding with material of different sizes does not influence the agglomeration rate constant, although substantial amount of small particles was present in all seeding materials.

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“…7, like the use of a more accurate phase balance for high solid concentrations, 108 and other simplifications to the thermodynamic definition of supersaturation that have become a standard practice in crystallization modelling, but have been shown to introduce unnecessary uncertainties in the predicted kinetics. 109,110 In the context of impurity incorporation, additional equations have been introduced to the crystallization models, often relating predictable metrics like liquid concentrations or crystal growth rates to an average partition coefficient, DC i , for the impurity (eqn ( 1)).…”

Section: Predictive Modelsmentioning

confidence: 99%

Section: Prevention and Controlmentioning

confidence: 99%

Impurity incorporation in solution crystallization: diagnosis, prevention, and control

2022

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Methods for estimating supersaturation in antisolvent crystallization systems

Abstract: Common simplifying assumptions to the thermodynamic expression of supersaturation can impose large errors on kinetics, yield, and process design.

Cited by 24 publications

References 15 publications

Crystal Growth Kinetics of an Industrial Active Pharmaceutical Ingredient: Implications of Different Representations of Supersaturation and Simultaneous Growth Mechanisms

Crystal Growth Kinetics of an Industrial Active Pharmaceutical Ingredient: Implications of Different Representations of Supersaturation and Simultaneous Growth Mechanisms

Crystal Growth, Dissolution, and Agglomeration Kinetics of Sodium Chlorate

Impurity incorporation in solution crystallization: diagnosis, prevention, and control

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