Advantages of Utilizing Population Balance Modeling of Crystallization Processes for Particle Size Distribution Prediction of an Active Pharmaceutical Ingredient

Rosenbaum, Tamar; Tan, Li; Engstrom, Joshua D.

doi:10.3390/pr7060355

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…The current unimodal PSD is considered suitable for the application of microcapsules in the pharmaceutical field. 21 Table 8 groups the main statistical parameters for the microcapsules of the seventh formulation chosen. The coefficient of variation was less than 15 %, so the particle size values were homogeneous.…”

Section: Statistical Parametersmentioning

confidence: 99%

Optimisation of the microencapsulation of an active ingredient by crosslinking and the coating method to target colon diseases

et al. 2021

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The aim of this study was to prepare microcapsules based on a natural polymer chitosan solution (high degree of deacetylation (DDA), low molecular weight (MW), and low viscosity)/sodium alginate in the presence of a crosslinking agent (glutaraldehyde), in order to encapsulate and vectorise the active principle towards the diseased organ (colon), without being diffused into other levels of the digestive tract, to increase the therapeutic effectiveness of treatment by chemotherapy and to reduce undesirable effects. The method of preparation of the microcapsules obtained from the sodium alginate/chitosan solution/active ingredients system was examined by conventional optical microscopy. In addition, an in vitro study was carried out on the active ingredients’ release profiles, depending on the pH simulating the gastric and intestinal media for the seven systems proposed. It should be mentioned that, in the basic medium (pH(colon) = 8), the release of the active ingredients is of the utmost importance. Nevertheless, control of this release can be improved by a crosslinking agent and the coating method. The dry [sodium alginate / chitosan solution / active ingredients + crosslinking 2 %] formulation coated with non-crosslinked chitosan (Formulation 7) is the standard formula that meets all the criteria from our earlier work, with a core release rate of 67 %. The PSD was unimodal, with sizes ranging from 750 µm to 900 µm.

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Section: Statistical Parametersmentioning

confidence: 99%

Optimisation of the microencapsulation of an active ingredient by crosslinking and the coating method to target colon diseases

et al. 2021

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“…Many innovative approaches to API crystallization process design have emerged, offering a significant improvement over the conventional methodology. , These approaches leverage the use of process modeling and/or feedback control strategies to optimize the crystallization process development. Process modeling involves the development of mathematical models that describe the underlying physics and kinetics of crystallization under different process conditions.…”

Section: Introductionmentioning

confidence: 99%

ANFIS-Driven Machine Learning Automated Platform for Cooling Crystallization Process Development

Jong,

Mittal,

Tristan

et al. 2024

Org. Process Res. Dev.

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Manual crystallization trials have historically posed significant challenges, demanding substantial expertise for process development and often offering unpredictable outcomes. This study addresses these difficulties by introducing an automated system that alleviates the need for manual iterations and intuitive deductions. The system leverages machine learning algorithms capable of learning from high-quality data to discern patterns and recommend optimal actions for subsequent runs. The automation process commences with a direct chord length (DCL) control system, generating system-specific training data via universal crystallization rules. After that, the automation process will progress into a machine learning iteration loop using adaptive neuro-fuzzy inference system (ANFIS) models. In this iteration loop, multiple models will be built (with accumulative historical data) and deployed to the crystallization process until predefined exit criteria are met or a maximum of five iterative cycles are reached. Results from the two campaigns are presented. It is evident that the automated crystallization platform with machine learning's ability can confidently explore the operational space, proposing credible processing conditions that yield desirable process outcomes.

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“…Crystal size distribution (CSD) is an important critical quality attribute in downstream pharmaceutical processing. − Process models (such as population balance models), which quantitatively account for crystal growth kinetics, are crucial for the control of crystallization. − To solve the mass and population balance equations, crystal population density and solution concentration measurements are needed and enabled by process analytical technology probes. Focused beam reflectance measurement (FBRM) and attenuated total reflectance Fourier-transform infrared spectroscopy probes inserted within a batch crystallizer enable crystal population density and solution concentration measurements, respectively. ,− However, the fidelity of crystallization models is dependent on the accuracy of such measurements.…”

Section: Introductionmentioning

confidence: 99%

Rapid, Automated Measurement of Dynamic Size Distributions and Size-Dependent Growth Rates of Crystal Ensembles within Microfluidic Flow Cells

Chua

Tan

Yeap

et al. 2022

Crystal Growth & Design

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In-process monitoring of crystal sizes and extraction of crystal growth kinetics during crystallization, enabled by process analytical technologies, are crucial for the development of crystal size distribution (CSD) control strategies in pharmaceutical manufacturing. In this paper, we demonstrate a well-based microfluidic flow cell platform for rapid and direct measurements of evolving crystal sizes and size-dependent growth rates within crystal ensembles exposed to welldefined flow fields. We present detailed growth measurements of two high-aspect-ratio model drugscelecoxib and glycine, where growing crystal ensembles are trapped in pseudo-static fashion within wells in a microfluidic flow cell under controlled laminar shear fields and observed via polarized microscopy over sustained time intervals. Time-varying CSDs are extracted via a novel image segmentationbased length detection algorithm, which then allow rapid estimation of shear and size-dependent growth rates through a simple optimization scheme. We also demonstrate discrimination between different regimes of crystal growth in the two model drugs. We envision the platform to be applicable both as a process development and analytics tool. The obtained growth kinetics data can be directly incorporated into computational fluid dynamics-coupled population balance models for improved accuracy in CSD prediction. The platform can also be retrofitted to crystallization vessels for rapid online measurements of evolving crystal sizes.

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Advantages of Utilizing Population Balance Modeling of Crystallization Processes for Particle Size Distribution Prediction of an Active Pharmaceutical Ingredient

Cited by 7 publications

References 22 publications

Optimisation of the microencapsulation of an active ingredient by crosslinking and the coating method to target colon diseases

Optimisation of the microencapsulation of an active ingredient by crosslinking and the coating method to target colon diseases

ANFIS-Driven Machine Learning Automated Platform for Cooling Crystallization Process Development

Rapid, Automated Measurement of Dynamic Size Distributions and Size-Dependent Growth Rates of Crystal Ensembles within Microfluidic Flow Cells

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