Design Of Crystallization Processes Research Articles

Utilizing the phase-field method to investigate liquid-liquid phase separation in the ternary system of water/ethanol/butylparaben

Small-molecule compounds targeted for drug development have frequently exhibited poor solubility in water, leading to instances of liquid-liquid phase separation (LLPS) during cooling crystallization and anti-solvent crystallization processes. Addressing strategies for managing crystallization processes involving LLPS has become a significant concern. Predicting changes in the structure and concentration of each separated phase over time would contribute to setting conditions and guiding process design to prevent oiling-out and achieve particles with desired morphology. In this study, we assessed the potential of the phase-field method to predict LLPS through simulations in a typical LLPS system, especially a ternary water/ethanol/butylparaben system. Consequently, the phase states in each zone (stable, metastable, and unstable) were determined to be thermodynamically valid. Additionally, the size of the spherical dispersed phase was confirmed to change in proportion to one-third of the time according to the Ostwald ripening rule. Furthermore, the qualitative analysis of factors influencing phase structure, local composition, delay time for phase separation in spinodal decomposition, and the localized LLPS during anti-solvent addition is also feasible. These findings suggest that the phase-field method holds potential as a tool to aid in the design of crystallization processes.

Automated Growth Rate Measurement of the Facet Surfaces of Single Crystals of the β-Form of l-Glutamic Acid Using Machine Learning Image Processing.

Precision measurement of the growth rate of individual single crystal facets (hkl) represents an important component in the design of industrial crystallization processes. Current approaches for crystal growth measurement using optical microscopy are labor intensive and prone to error. An automated process using state-of-the-art computer vision and machine learning to segment and measure the crystal images is presented. The accuracies and efficiencies of the new crystal sizing approach are evaluated against existing manual and semi-automatic methods, demonstrating equivalent accuracy but over a much shorter time, thereby enabling a more complete kinematic analysis of the overall crystallization process. This is applied to measure in situ the crystal growth rates and through this determining the associated kinetic mechanisms for the crystallization of β-form l-glutamic acid from the solution phase. Growth on the {101} capping faces is consistent with a Birth and Spread mechanism, in agreement with the literature, while the growth rate of the {021} prismatic faces, previously not available in the literature, is consistent with a Burton-Cabrera-Frank screw dislocation mechanism. At a typical supersaturation of σ = 0.78, the growth rate of the {101} capping faces (3.2 × 10-8 m s-1) is found to be 17 times that of the {021} prismatic faces (1.9 × 10-9 m s-1). Both capping and prismatic faces are found to have dead zones in their growth kinetic profiles, with the capping faces (σc = 0.23) being about half that of the prismatic faces (σc = 0.46). The importance of this overall approach as an integral component of the digital design of industrial crystallization processes is highlighted.

Digital Design of Crystallization Process: Why particle size and shape matters!

Populationbalance modelling is a widely used tool to understand the mechanism and kineticparameters of crystal growth at the bench, to facilitate scale-up to Production.In this work, we present recommendations for particle size and shape analyticalmethods to improve the predictive capability of 1D and 2D population balancemodels for crystallisation of contemporary active ingredients. For 1D models, wereport that the optical model for laser diffraction, a common technique formeasuring particle size distribution, can significantly impact the growth parametersresolved from the model. For 2D models, particle image analysis of powders andsingle crystals give valuable length and width data to predict how the aspectratio of particles evolves. Future work will be directed towards applying thesetools when scaling a crystallization process.

An NMR-based approach for the design of crystallization processes for rigid molecules and effects of molecular rigidity and impurities on crystallization pathways

A methodology based on NMR analysis to enable development of crystallization procedures for relatively rigid molecules is presented. The number of relatively stable conformers in solution and their relative proportions are determined from peak splitting of equivalent protons and their relative areas as measured by NMR. Experimental data indicated that slow conformational interconversion was limiting crystallization at low temperatures. Variable temperature NMR analysis was utilized to determine rotamers’ peak coalescence and identify a suitable crystallization solvent. This NMR-based approach led to the development of a manufacturing crystallization recipe of a relatively rigid molecule that was impossible to crystallize via a scalable process. Furthermore, prediction of relatively stable conformer in gas phase, determined by Molecular Dynamics (MD) calculations were in acceptable agreement with data obtained from NMR analyses. Finally, the effects of molecular rigidity and impurities on crystallization and oiling-out pathways are described and an approach to avoid oiling-out is presented.

Case Studies in the Application of a Workflow-Based Crystallization Design for Optimized Impurity Rejection in Pharmaceutical Development

Crystallization is an important unit operation in pharmaceutical drug substance manufacturing for the isolation of organic products. Impurity control is a recurring critical quality attribute in most commercial pharmaceutical crystallization processes. A systematic workflow that can reveal the incorporation mechanism of various impurities during crystallization and guide us to understand impurity rejection is useful for crystallization process design. Such a workflow was recently published by Urwin et al. Herein, we present three case studies implementing this workflow, covering surface deposition, conglomerate formation, and agglomeration as principal routes for impurity incorporation during crystallization of a drug substance’s active pharmaceutical ingredient and its intermediates. We demonstrate the experimental steps to identify various impurity incorporation mechanisms and discuss the strategy for impurity rejection in each case. We built upon the previous approach to demonstrate a material sparing approach through our case studies. We highlight the value of stepwise dissolution and show the importance of doing it early in the impurity incorporation identification stage. At this stage, we introduce a missing incorporation mechanism in prior work, namely, conglomerate systems. We show how to identify and resolve this mode of incorporation mechanism through stepwise dissolution and solubility-limited rejection maps.

Solid-liquid equilibrium and dissolution thermodynamic properties of L-isoleucine in binary solvent mixtures

Thermodynamic properties, which play an important role in design and optimization of crystallization processes, are the essential data for the study of crystal nucleation and growth. In this study, the solubility of L-isoleucine were firstly determined by gravimetric method in three binary solvent mixtures including water + (ethanol, acetonitrile or DMSO) in the temperature range of 303.20 K to 338.05 K at atmospheric pressure. The effects of temperatures and solvent types on the dissolution process of L-isoleucine were investigated. It was found that the solubility of L-isoleucine increased dramatically with the increasing of the mole fraction of water in each binary solvent mixture and increased monotonically with the increasing of temperature. To extend the applicability of the solubility data, the experimental solubility data of L-isoleucine were fitted and analyzed using the modified Apelblat equation, the CNIBS/R-K model and the Jouyban-Acree model. Finally, based on the Van’t Hoff equation and the experimental solubility data, the apparent dissolution thermodynamic properties of L-isoleucine including apparent dissolution enthalpy, apparent dissolution entropy, and apparent Gibbs free energy change of dissolution process were calculated and discussed.

Characterization of a Modular Microfluidic Section for Seeded Nucleation in Multiphase Flow

We present a proof-of-concept modular nucleation section for seeded multiphase flow crystallization of an active pharmaceutical ingredient. The setup can be used in four different modes of operation (with or without off-line prepared seeds and with or without a stream of microbubbles). The setup is characterized to provide insights into the design and operation of crystallization processes. First, the performance of the off-line continuous seeding platform is established via the seed delivery efficiency for constant and oscillatory flows. Second, the yields of seeded and unseeded crystallization are evaluated in the presence and absence of microbubbles. A statistically significant increase in the net crystal mass and net yield was measured when comparing unseeded and seeded crystallization, which can be attributed to the increased nucleation rates as a result of secondary nucleation. Third, the clogging behavior of the presented continuous microcrystallizer is discussed. Finally, also the crystal size distribution is analyzed: the mean crystal size decreases for both single- and two-phase flow when seeds are introduced. The presence of microbubbles resulted in a higher net yield and slightly smaller crystals compared to that of the single-phase flow. This work highlights the importance of a holistic characterization approach for continuous microfluidic crystallizers.

A Workflow for Crystallization Process Design with Simultaneous Process Optimization and Solvent Selection based on the Perturbed‐Chain Statistical Associating Fluid Theory

AbstractSolvent selection is a critical part of crystallization process design and is inherently intertwined with optimization of the operating conditions. Computer‐aided tools can greatly assist in solving these two problems simultaneously. However, the integration of predictive thermodynamic models and process optimization tools is often complicated, which may hamper industry adoption. This work presents a workflow for simultaneous solvent selection and process optimization for solution crystallization processes based on the perturbed‐chain statistical associating fluid theory (PC‐SAFT) equation of state. The workflow is provided with readily executable computational tools and aims to strike a balance between the resources needed to obtain experimental input data and good prediction performance. The use of the workflow is demonstrated through a case study involving aspirin crystallization, which shows that the workflow can provide suitable solvents and operating conditions for the crystallization process based on either cooling, antisolvent, or evaporative crystallization.

Iterative model-based optimal experimental design for mixture-process variable models to predict solubility

Crystallization process design relies heavily on predictive solubility models. However, their calibration is resource- and labour-intensive, especially for multicomponent solvent mixtures at different process temperatures. Additionally, solubility data collection often occurs in a constrained design space due to e.g., polymorphism and solvent miscibility limitations. Optimal experimental design techniques enable the efficient use of resources by specifying a (minimum) number of maximally informative experiments focused on improving a statistical criterion for a given model structure in a constrained design space. This work generates D-, A- and I-optimal experimental designs for the commonly applied Van’t-Hoff Jouyban-Acree (VH-JA) solubility regression model, in which it is demonstrated that I-optimal designs reduce the experimental burden for model calibration by approximately 25 % as compared to a typical screening dataset. Alternatively, existing datasets can be augmented to significantly improve model prediction power. The suggested workflow is applied to two case studies: itraconazole in tetrahydrofuran-water and mesalazine in ethanol-polyethylene glycol-water. The screening datasets of 72 and 212 runs were augmented with 16 additional experiments, resulting in a 33 % and 67 % reduction in the corresponding model prediction variance, respectively, which translates to improved model reliability at unprobed conditions.

Implementation and Application of Image Analysis-Based Turbidity Direct Nucleation Control for Rapid Agrochemical Crystallization Process Design and Scale-Up

Implementation and Application of Image Analysis-Based Turbidity Direct Nucleation Control for Rapid Agrochemical Crystallization Process Design and Scale-Up

Solubility of Organic Salts in Solvent-Antisolvent Mixtures: A Combined Experimental and Molecular Dynamics Simulations Approach.

We combine molecular dynamics simulations with experiments to estimate solubilities of an organic salt in complex growth environments. We predict the solubility by simulations of the growth and dissolution of ions at the crystal surface kink sites at different solution concentrations. Thereby, the solubility is identified as the solution’s salt concentration, where the energy of the ion pair dissolved in solution equals the energy of the ion pair crystallized at the kink sites. The simulation methodology is demonstrated for the case of anhydrous sodium acetate crystallized from various solvent–antisolvent mixtures. To validate the predicted solubilities, we have measured the solubilities of sodium acetate in-house, using an experimental setup and measurement protocol that guarantees moisture-free conditions, which is key for a hygroscopic compound like sodium acetate. We observe excellent agreement between the experimental and the computationally evaluated solubilities for sodium acetate in different solvent–antisolvent mixtures. Given the agreement and the rich data the simulations produce, we can use them to complement experimental tasks, which in turn will reduce time and capital in the design of complicated industrial crystallization processes of organic salts.

Temperature Dependence of Solubility Predicted from Thermodynamic Data Measured at a Single Temperature: Application to α, β, and γ-Glycine.

Understanding of solid–liquid equilibria for polymorphic systems is crucial for rational design and efficient operation of crystallization processes. In this work, we present a framework to determine the temperature dependent solubility based on experimentally accessible thermodynamic data measured at a single temperature. Using this approach, we investigate aqueous solubility of α, β, and γ-glycine, which, despite numerous studies, have considerable quantitative uncertainty, in particular for the most stable (γ) and the least stable (β) solid forms. We benchmark our framework on α-glycine giving predictions in excellent agreement with direct solubility measurements between 273–340 K, using only thermodynamic data measured at the reference temperature (298.15 K). We analyze the sensitivity of solubility predictions with respect to underlying measurement uncertainty, as well as the excess Gibbs free energy models used to derive required thermodynamic quantities before providing solubility predictions for β and γ-glycine between 273–310 and 273–330 K, respectively. Crucially, this approach to predict solubility as a function of temperature does not rely on measurement of solute melting properties which will be particularly useful for compounds that undergo thermal decomposition or polymorph transition prior to melting.

A Simple Population Balance Model for Crystallization of L-Lactide in a Mixture of n-Hexane and Tetrahydrofuran

In this contribution, crystallization was performed to assess the kinetics of nucleation and crystal growth of L-lactide. In most common solvents, this compound shows very high solubility even at low temperatures, which could be challenging for crystallization process design. In the first part of this paper, the anti-solvent effects of n-hexane on solutions of L-lactide in tetrahydrofuran (THF) were investigated through studying the influence of solvent compositions on the solubility. Thanks to these effects, the solubility of the interested compound can be adjusted to desired degrees of supersaturation by adding suitable amounts of the anti-solvent. In the second part, a solvent composition at a mass ratio of 45/55 (n-hexane/THF) was chosen, and an isothermal seeded crystallization process was implemented. The evolution of the particle sizes and changes in the solute concentration profile of this process were monitored. Based on the obtained data, a widely used model, i.e., the population balance equation (PBE), was then utilized to model the crystal size distribution (CSD). Reasonable assumptions were made to reduce the mathematical complexity of the PBE. In the simplified model, only crystal growth and secondary nucleation were considered for model formulation, with assumptions of the size-independent growth rate and negligible size of nuclei. The kinetic parameters were estimated by using the seed and final-time crystal density functions in combination with variations in the concentration of the mother liquor. Indeed, the numerical solution for the one-dimensional problem of the L-lactide crystallization based on the estimated parameters gained a relatively good agreement with the determined CSD. Furthermore, the obtained model also correlated well with the variations in the solute concentration of the mother liquor. In short, this simple approach can be used for predicting the productivity and CSD of the L-lactide crystallization.

Droplet-Based Evaporative System for the Estimation of Protein Crystallization Kinetics.

Crystallization is a potential cost-effective alternative to chromatography for the purification of biotherapeutic proteins. Crystallization kinetics are required for the design and control of such processes, but only a limited quantity of proteins is available during the initial stage of process development. This article describes the design of a droplet-based evaporative system for the evaluation of candidate crystallization conditions and the estimation of kinetics using only a droplet (on the order of μL) of protein solution. The temperature and humidity of air fed to a flow cell containing the droplet are controlled for evaporation and rehydration of the droplet, which are used for manipulating supersaturation. Dual-angle images of the droplet are taken and analyzed on-line to obtain the droplet volume and crystal sizes. Crystallization kinetics are estimated based on a first-principles process model and experimental data. Tight control of temperature and humidity of the air, fast and accurate image analysis, and accurate estimation of crystallization kinetics are experimentally demonstrated for a model protein lysozyme. The estimated kinetics are suitable for the model-based design and control of protein crystallization processes.

Measured Growth Rates of Ibuprofen: Comparing Single Crystal and Bulk Suspensions Data

AbstractThe design of industrial crystallization processes usually employs laboratory‐scale experimental data which must be correlated with the full‐scale process for effective technology transfer. In this context, the measured growth rates of single crystals of ibuprofen in stagnant ethanolic solutions are compared with data recorded for a population of crystals crystallized in an agitated 7‐mL reactor. The single crystal growth rates are found to be rather close to those determined in the agitated reactor, with both being also in good agreement with previously published data at the 750‐mL scale, suggesting that studies on single crystals can have utility for the purpose of crystallization process design and optimization.

Computer Aided Design of Solvent Blends for Hybrid Cooling and Antisolvent Crystallization of Active Pharmaceutical Ingredients.

Choosing a solvent and an antisolvent for a new crystallization process is challenging due to the sheer number of possible solvent mixtures and the impact of solvent composition and crystallization temperature on process performance. To facilitate this choice, we present a general computer aided mixture/blend design (CAMbD) formulation for the design of optimal solvent mixtures for the crystallization of pharmaceutical products. The proposed methodology enables the simultaneous identification of the optimal process temperature, solvent, antisolvent, and composition of solvent mixture. The SAFT-γ Mie group-contribution approach is used in the design of crystallization solvents; based on an equilibrium model, both the crystal yield and solvent consumption are considered. The design formulation is implemented in gPROMS and applied to the crystallization of lovastatin and ibuprofen, where a hybrid approach combining cooling and antisolvent crystallization is compared to each method alone. For lovastatin, the use of a hybrid approach leads to an increase in crystal yield compared to antisolvent crystallization or cooling crystallization. Furthermore, it is seen that using less volatile but powerful crystallization solvents at lower temperatures can lead to better performance. When considering ibuprofen, the hybrid and antisolvent crystallization techniques provide a similar performance, but the use of solvent mixtures throughout the crystallization is critical in maximizing crystal yields and minimizing solvent consumption. We show that our more general approach to rational design of solvent blends brings significant benefits for the design of crystallization processes in pharmaceutical and chemical manufacturing.

An Insight into the Correlation between Chemical Composition Changes of Aluminum-Iron-Polyphosphate Glasses and Thermal Properties.

The present study aimed to investigate the influence of the gradual substitution of Fe2O3 by Al2O3 on the thermal properties of polyphosphate glasses. The conducted considerations based on differential scanning calorimetry (DSC) and heating microscopy thermal analysis provided much essential information about the correlation between glass chemical composition and its characteristic parameters, such as transformation temperature, specific heat, crystallization temperature, crystallization enthalpy, the activation energy of crystal growth, melting temperature, and Angell glass thermal stability. The obtained estimation of viscosity changes as a function of temperature could be very helpful for researchers to correctly plan the vitrification process and thus radioactive waste immobilization. A precise analysis of DSC curves and X-ray diffraction patterns revealed the possibility of crystallization process design in order to create materials with different levels of crystallinity and phase composition. The drawn conclusions allow choosing the glass with the optimal concentration of Al2O3 and Fe2O3, which ensures the relatively low melting temperature, viscosity, and glass crystallization ability, with application potential in nuclear waste immobilization.

Integrated Continuous Crystallization and Spray Drying of Insulin for Pulmonary Drug Delivery

Pulmonary delivery of insulin in the form of a dry powder is an attractive alternative in comparison to conventional subcutaneous delivery. Insulin crystals have potential advantages such as sustained release and higher stability in comparison to amorphous particles. However, the design of crystallization processes that can reliably produce insulin crystals in a suitable size range for pulmonary delivery is challenging. A continuous process involving the integration of a tubular crystallizer and a spray dryer is characterized for the production of inhalable insulin. Supersaturation is created via cooling and a pH shift at the inlet of the crystallizer, aiming at a high nucleation rate. The recovery of insulin in the crystallizer exceeds 90%, and the insulin particles appear crystalline under most of the tested conditions. Integration with a spray dryer produces a dry powder with favorable properties for pulmonary drug delivery. The emitted fraction of insulin from a capsule in a commercial inhaler is in the range of 84.3–93.1%, which shows good powder dispersibility. Furthermore, the fine particle fraction of the insulin crystals ranges from 22.0% to 36.4%, which indicates a good potential for pulmonary delivery.

Crystallization with Sinusoidal Modulation of Stirrer Speed: Frequency Response Analysis and Secondary Nucleation Kinetics

In crystallization, obtaining the proper nucleation and growth parameters is essential to the design of crystallization processes. This paper studies the response of crystal size distribution (CSD) to periodic oscillations in the stirrer speed within a mixed suspension mixed product removal (MSMPR) crystallizer. We start from a model in which all processes are coupled. Stirrer speed oscillations drive oscillations in the secondary nucleation kinetics. Oscillations in the nucleation rate drive damped waves in the population distribution of growing crystals. Changes in the population of crystals drive changes in the supersaturation. Supersaturation along with the total mass of crystals influences the nucleation rate. At low frequencies, including the pseudo-steady-state limit, effects of a change in stirring rate are attenuated by the coupling to other processes. At high frequencies, stirring rate oscillations lead to pronounced waves in the CSD that might facilitate studies of secondary nucleation.

Automated measurement of pH-dependent solid-liquid equilibria of itaconic acid and protocatechuic acid

Crystallization is a highly selective unit operation capable of separating target molecules from complex media. Its design requires precise solid-liquid equilibrium (SLE) data. The SLE of platform chemicals like carboxylic acids in complex aqueous media depends on temperature, pH value as well as on present side components, but predominantly, only the solubility for different temperatures is reported. This work presents a novel automated method for the determination of the pH- and temperature-dependent SLE in complex media using readily available laboratory equipment. Taking the example of itaconic acid, the pH-dependent solubility for different temperatures is measured and validated with results obtained from the classic excess solid method along with literature data. Using the automated setup, the influence of different loadings of sodium chloride on the SLE of itaconic acid is investigated and a correlation between solubility and concentration of the electrolyte is derived. The SLE data acquired for protocatechuic acid in fermentation medium demonstrates the applicability of the method for the determination of comprehensive SLE in multicomponent mixtures without the requirement of specific analytical methods for the target compound. The resulting SLE data can be used for a more precise design and techno-economic evaluation of crystallization processes in biotechnological production routes.