Stability of Crystallizer-Producing Shape-Engineered Crystals

Pal, Kanjakha; Chakraborty, Jayanta

doi:10.1021/acs.iecr.5b01165

Cited by 4 publications

(9 citation statements)

References 23 publications

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“…The PBE , for the droplet population is given by eq . Here, ω represents the population density of droplets in the crystallizer, τ represents the droplet age, C ̅ represents the average solute concentration, and C ̅ sol represents the average solvent concentration within the droplets.…”

Section: Theorymentioning

confidence: 99%

“…(c) The emulsifiers attached to the droplet interface 58 are assumed to be removed during the filtration, washing, and drying stages and do not end up in the final product agglomerates. The PBE 53,59 for the droplet population is given by eq 1. Here, ω represents the population density of droplets in the crystallizer, τ represents the droplet age, C̅ represents the average solute concentration, and C̅ sol represents the average solvent concentration within the droplets.…”

Section: Theorymentioning

confidence: 99%

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Mathematical Modeling of Emulsion Solvent Diffusion for Spherical Crystallization: How To Deconvolute Primary Crystal Size Distribution from Agglomerate Size Distribution?

Pal

Ramkrishna

Nagy

2020

Ind. Eng. Chem. Res.

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Spherical crystallization (SC) is a novel process intensification strategy, which can substantially reduce the manufacturing cost for solid oral dosage forms. However, although the manufacturability of the product agglomerates could be controlled, control over the product bioavailability has remained elusive. The major bottleneck over the simultaneous control over product manufacturability and bioavailability is the lack of in situ Process Analytical Technology (PAT) tools, which allow for multiscale monitoring of particulate processes. In this work, mechanistic population balance models (PBMs) have been used to obtain a multiscale understanding of the droplet population, as well as the crystal population within the droplets. The use of mechanistic models enables multiscale process understanding, which is very difficult with in situ PAT tools. The evolution of the crystal population within the droplets is found to be widely different for two different droplet sizes. The time scales of fundamental rate processes, viz. crystal nucleation and growth, have also been elucidated for different droplet sizes. The mathematical modeling framework developed here enhances process understanding and can set the foundation for implementation of a quality-by-design (QbD) framework for SC, which is strongly encouraged by the U.S. Food and Drug Administration (FDA).

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Section: Theorymentioning

confidence: 99%

Section: Theorymentioning

confidence: 99%

Mathematical Modeling of Emulsion Solvent Diffusion for Spherical Crystallization: How To Deconvolute Primary Crystal Size Distribution from Agglomerate Size Distribution?

Pal

Ramkrishna

Nagy

2020

Ind. Eng. Chem. Res.

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“…Crystallization 1 is a widely used separation and purification process, 2,3 which is used to separate the compound of interest from a solvent or a mixture of solvents 4 . As a unit operation, 5 it is used in a multitude of industries including chemical, 6 food processing, 7 petrochemical, and microelectronics 8 .…”

Section: Introductionmentioning

confidence: 99%

Iterative model‐based experimental design for spherical agglomeration processes

et al. 2021

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Spherical agglomeration (SA) is a process intensification strategy, which can reduce the number of unit operations in pharmaceutical manufacturing. SA merges drug substance crystallization with drug product wet granulation, reducing capital, and operating costs. However, SA is a highly nonlinear process, thus for its efficient operation model‐based design and control strategies are beneficial. These require the development of a high‐fidelity process model with appropriately estimated parameters. There are two major problems associated with the development of a high‐fidelity process models—(i) selection of the appropriate model corresponding to the underlying process mechanisms, and (ii) accurate estimation of the parameters. This work focuses on the identification of the best fitting model that correlates with experimental observations using cross‐validation experiments. Further, an iterative model‐based experimental design strategy is developed, which uses D‐optimal experimental design criterion to minimize the number of experiments necessary to obtain accurate parameter estimates.

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“…Mesbah et al designed a closed loop optimal control cooling profile for batch cooling crystallization, using a Luenberger type observer to estimate the state of the system. They found that implementing this closed loop optimal control policy led to a great increase in crystal volume fraction at the end of the batch and the method was also robust to variations between batches Model-based and model-free approaches have also been used to control polymorphic purity during cooling crystallization of small molecules. , There have also been significant advances in modeling the stability of industrial crystallizers, including the case that also considers the evolution of crystals shape, where the principles of optimal control of crystallizers can be used to better predict stability under dynamic conditions.…”

Section: Introductionmentioning

confidence: 99%

“…They found that implementing this closed loop optimal control policy led to a great increase in crystal volume fraction at the end of the batch and the method was also robust to variations between batches Model-based and model-free approaches have also been used to control polymorphic purity during cooling crystallization of small molecules. 17,18 There have also been significant advances in modeling the stability of industrial crystallizers, 19 including the case that also considers the evolution of crystals shape, 20 where the principles of optimal control of crystallizers can be used to better predict stability under dynamic conditions. Although cooling crystallization is the most widely used crystallization strategy, some compounds do not have steep solubility variation with temperature.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Optimization of Cooling Crystallization of Active Pharmaceutical Ingredients Undergoing Thermal Degradation

Pal

Yang

Nagy

2019

Crystal Growth & Design

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Many active pharmaceutical ingredients (APIs) undergo degradation at high temperature. Optimum cooling profiles from first principle models usually do not consider this degradation mechanism. Generally, for maximization of the rate of crystal growth, a slow cooling profile is preferred. This however keeps the API solution at elevated temperatures for a longer time, which promotes thermal degradation. For thermally unstable APIs, fast cooling is preferred to minimize the amount of degradation, leading to excess nucleation and small crystals. In this work, model-based optimization of crystallization processes of thermally unstable APIs are considered. The typical crystallization model is combined with the kinetic model of thermal degradation, and the optimum cooling profiles, which take thermal degradation into account, are computed. An in silico multiobjective optimization study is carried out to find the optimum temperature profile under the competing objectives between maximizing crystal growth and minimizing degradation of the API. Experimental studies have also been carried out to validate our in silico optimization studies.

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Stability of Crystallizer-Producing Shape-Engineered Crystals

Cited by 4 publications

References 23 publications

Mathematical Modeling of Emulsion Solvent Diffusion for Spherical Crystallization: How To Deconvolute Primary Crystal Size Distribution from Agglomerate Size Distribution?

Mathematical Modeling of Emulsion Solvent Diffusion for Spherical Crystallization: How To Deconvolute Primary Crystal Size Distribution from Agglomerate Size Distribution?

Iterative model‐based experimental design for spherical agglomeration processes

Model-Based Optimization of Cooling Crystallization of Active Pharmaceutical Ingredients Undergoing Thermal Degradation

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