PAT-Enabled Determination of Design Space for Seeded Cooling Crystallization

Yu, Zai Qun; Tan, Reginald B. H.; Ang, Wei Han

doi:10.1021/op300319t

Cited by 10 publications

(16 citation statements)

References 22 publications

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“…Characterization of Unmilled Cocrystals. Before the milling experiments, the CA−GA cocrystals obtained by seeded cooling crystallization 22 were analyzed by PXRD to confirm that they belonged to Form II. As shown in Figure 1, the PXRD pattern of the unmilled crystals corresponded to that of Form II.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Stability of Pharmaceutical Cocrystal During Milling: A Case Study of 1:1 Caffeine–Glutaric Acid

Lau

Vangala

2017

Crystal Growth & Design

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Despite the rising interest in pharmaceutical cocrystals in the past decade, there is a lack of research on the solid processing of cocrystals downstream to crystallization. Mechanical stress induced by unit operations such as milling could affect the integrity of the material. The purpose of this study is to investigate the effect of milling on pharmaceutical cocrystals and to compare the performance of ball mill and jet mill, using caffeine−glutaric acid (1:1) cocrystal as the model compound. Our results show that ball milling induced polymorphic transformation from the stable Form II to the metastable Form I, whereas Form II remained intact after jet milling. Jet milling was found to be effective in reducing particle size, but ball milling was unable to reduce the particle size beyond a certain limit even with increasing milling intensity. The heating effect during ball milling was proposed as a possible explanation for the difference in the performance of the two types of mill. The local increase in temperature beyond the polymorphic transformation temperature may lead to the conversion from stable to metastable form. At longer ball milling duration, the local temperature could exceed the melting point of Form I, leading to surface melting and subsequent recrystallization of Form I from the melt and agglomeration of the crystals. The findings in this study have broader implications on the selection of mill and interpretation of milling results for not only pharmaceutical cocrystals but pharmaceutical compounds in general.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Stability of Pharmaceutical Cocrystal During Milling: A Case Study of 1:1 Caffeine–Glutaric Acid

Lau

Vangala

2017

Crystal Growth & Design

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“…The prior knowledge and understanding of crystallization accumulated over the years, , when combined with Design of Experiment (DoE) studies, afford efficient identification of critical process parameters and determination of the design space for tight particle size control in some processes on the bench scale. , Scale-up effects may be one of the biggest obstacles to achieving consistent particle size control in crystallization. It is not a surprise that the optimal conditions determined on the bench scale, when translated to larger vessels according to some criteria, fail to produce crystals in the target particle size range.…”

Section: Introductionmentioning

confidence: 99%

Particle Size Control in Batch Crystallization of Pyrazinamide on Different Scales

Yeoh

Tan

2016

Org. Process Res. Dev.

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A methodology to achieve consistent particle size in seeded batch crystallization on two scales (0.5 and 10 L) was demonstrated using pyrazinamide as a model compound. An empirical process model was established from a Design of Experiment study on the 500 mL scale and subsequently verified by correlating D 50 of the crystals with stirrer speed, seed size, and seed loading. Experiments on the 10 L scale showed that the criterion of equivalent impeller tip speed produced a larger D 50 than that of equivalent input energy. The process model derived from the 0.5 L scale was demonstrated to work well for process parameter adjustment to obtain the target product size on the 10 L scale. Some practical issues in crystallization development are discussed.

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“…The model predictions are generally in good agreement with measured values, aside from a significant broadening of measured PSD as compared to predicted for plant batch 4. This batch was agitated at a higher rate than the first three batches (105 rpm for batch 4, as compared to 75 rpm for batches 1–3), which may have resulted in an increased number of fines, unaccounted for in the prediction because of omission of crystal breakage from the model . The predicted PSD also underestimates the fraction of larger particles for this batch.…”

Section: Resultsmentioning

confidence: 99%

“…This batch was agitated at a higher rate than the first three batches (105 rpm for batch 4, as compared to 75 rpm for batches 1−3), which may have resulted in an increased number of fines, unaccounted for in the prediction because of omission of crystal breakage from the model. 35 The predicted PSD also underestimates the fraction of larger particles for this batch. This could be an indication that the model overpredicts the impact of agitator rpm on PSD for this particular vessel configuration, but additional batches prepared in this reactor with different rpm would be needed to confirm this.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Population Balance Modeling To Predict Particle Size Distribution upon Scale-Up of a Combined Antisolvent and Cooling Crystallization of an Active Pharmaceutical Ingredient

Rosenbaum

Tan

Dummeldinger

et al. 2019

Org. Process Res. Dev.

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Herein, a population balance model (PBM) for a combined cooling and antisolvent crystallization process for an active pharmaceutical ingredient (API) has been developed and utilized to predict the product particle size distribution (PSD) for two sets of four ∼35 kg scale plant batches, with good agreement to data. The PBM was constructed from lab-scale (∼10 g) crystallization runs using seed and product PSD measurements along with concentration measurements of the API during batch desupersaturation experiments. The PBM was then used to predict the product PSD for two sets of four plant batches, run using different reactors equipped with different agitator types operated at different agitation rates. Analysis of the crystallization kinetics reveals that secondary nucleation due to attrition has a strong influence on the PSD in the crystallization process of the API, and thus mixing conditions (agitator type, agitator speed, pumping, and power numbers) have a strong effect on PSD. The model provides a more robust particle size control strategy than design of experiment (DOE) studies alone by incorporating fundamental crystallization kinetics, with data from a small set of lab experiments in lieu of extensive DOE studies. This firstprinciple-based approach was useful for enhancing the robustness of the technical transfer process by accounting for impacts on product PSD stemming from process scale-up and parameter changes.

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PAT-Enabled Determination of Design Space for Seeded Cooling Crystallization

Cited by 10 publications

References 22 publications

Stability of Pharmaceutical Cocrystal During Milling: A Case Study of 1:1 Caffeine–Glutaric Acid

Stability of Pharmaceutical Cocrystal During Milling: A Case Study of 1:1 Caffeine–Glutaric Acid

Particle Size Control in Batch Crystallization of Pyrazinamide on Different Scales

Population Balance Modeling To Predict Particle Size Distribution upon Scale-Up of a Combined Antisolvent and Cooling Crystallization of an Active Pharmaceutical Ingredient

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