Mechanistic understanding of granule growth behavior in bi-component wet granulation processes with wettability differentials

Muthancheri, Indu; Ramachandran, Rohit

doi:10.1016/j.powtec.2020.04.016

Cited by 16 publications

(12 citation statements)

References 41 publications

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“…e for pore is assumed to be 0. The work of capillary force (W c ) is experimentally determined from the regime map analysis carried out Muthancheri and Ramachandran [22]. Figure 4 plots the capillary number which is the ratio between capillary force and viscous force as a function of API fraction.…”

Section: Particle Aggregationmentioning

confidence: 99%

“…Experimental data from Muthancheri and Ramachandran [22] is used to estimate the unknown parameters in the model. The cumulative granule size fraction and content uniformity measurements were used for the estimation.…”

Section: Optimization and Parameter Estimationmentioning

confidence: 99%

“…The model was trained and validated using the experiment data from Muthancheri and Ramachandran [22]. In the aforementioned study, two formulations were considered such that there exists a wettability differential between the two components in a formulation.…”

Section: Model Training and Validationmentioning

confidence: 99%

“…The model predicted a decrease in the extend of de-mixing with increase in percentage API for ibuprofen formulation and an increase in the extent of de-mixing with increase in percentage API for APAP formulation. Muthancheri and Ramachandran [22] studied the effect of increase in hydrophobic API percentage on the granule size. It was shown that in an insoluble system (all constituent materials were insoluble in the binder liquid), an increase in hydrophobic component results in an overall increase in granule size.…”

Section: Model Training and Validationmentioning

confidence: 99%

“…The time scale of the nucleation process is relatively faster than the rest of the granulation rate mechanisms [20], and thus, it is not necessary to incorporate the dynamics of the nucleation process into the granulation modeling. However, the internal properties of nuclei such as size, deformability, surface liquid content, and content uniformity affect the granulation growth kinetics, and the final granule quality attributes [21,22]. Thus, it is important to have an experimental or modeling framework to predict the properties of the initial nuclei to simulate granule properties other than size distribution using PBM.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Hybrid Model to Predict Formulation Dependent Granule Growth in a Bi-Component Wet Granulation Process

Muthancheri

Ramachandran

2021

Pharmaceutics

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In this study, a hybrid modeling framework was developed for predicting size distribution and content uniformity of granules in a bi-component wet granulation system with components of differing hydrophobicities. Two bi-component formulations, (1) ibuprofen-USP and micro-crystalline cellulose and (2) micronized acetaminophen and micro-crystalline cellulose, were used in this study. First, a random forest method was used for predicting the probability of nucleation mechanism (immersion and solid spread), depending upon the formulation hydrophobicity. The predicted nucleation mechanism probability is used to determine the aggregation rate as well as the initial particle distribution in the population balance model. The aggregation process was modeled as Type-I: Sticking aggregation and Type-II: Deformation driven aggregation. In Type-I, the capillary force dominant aggregation mechanism is represented by the particles sticking together without deformation. In the case of Type-II, the particle deformation causes an increase in the contact area, representing a viscous force dominant aggregation mechanism. The choice between Type-I and II aggregation is determined based on the difference in nucleation mechanism that is predicted using the random forest method. The model was optimized and validated using the granule content uniformity data and size distribution data obtained from the experimental studies. The proposed framework predicted content non-uniform behavior for formulations that favored immersion nucleation and uniform behavior for formulations that favored solid-spreading nucleation.

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Section: Particle Aggregationmentioning

confidence: 99%

Section: Optimization and Parameter Estimationmentioning

confidence: 99%

Section: Model Training and Validationmentioning

confidence: 99%

Section: Model Training and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Hybrid Model to Predict Formulation Dependent Granule Growth in a Bi-Component Wet Granulation Process

Muthancheri

Ramachandran

2021

Pharmaceutics

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Enabling energy‐efficient manufacturing of pharmaceutical solid oral dosage forms via integrated techno‐economic analysis and advanced process modeling

Sampat

Kotamarthy

Bhalode

et al. 2022

J Adv Manuf & Process

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The global pharmaceutical industry is a trillion-dollar market. However, the pharmaceutical sector often lags in manufacturing innovation and automation which limits its potential to maximize energy efficiency. The integration of technoeconomic analysis (TEA) with advanced process models as part of an overarching smart manufacturing platform, can help industries create business models, which can be adapted for manufacturing to reduce energy consumption and operating costs while ensuring product quality which can further enable a more sustainable process operation. In this study, a rational design of experiment on three unitoperations (wet granulation, drying, and milling) was performed on a batch (case 1) and continuous (case 2) pharmaceutical process to obtain experimental data. Process models for predicting product quality and energy efficiency of each of the three-unit operations were developed. The experimental data were used to validate the models and good agreement was observed. The energy consumption of each unit operation was calculated using statistical models relating the power consumption and the process parameters. The developed process models and energy models were further integrated into a TEA framework, which quantified the energy and monetary cost of manufacturing for both batch and continuous manufacturing cases. With this integrated framework, energy costs savings of ~33% was obtained in the continuous manufacturing process (case 2) over the batch process (case 1). K E Y W O R D Sadvanced process models, energy efficiency, Industry 4.0, smart manufacturing, technoeconomic analysis Chaitanya Sampata and Lalith Kotamarthy contributed equally to this study.

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A compartment based population balance model for the prediction of steady and induction granule growth behavior in high shear wet granulation

Muthancheri

Chaturbedi

Bétard

et al. 2021

Advanced Powder Technology

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Mechanistic understanding of granule growth behavior in bi-component wet granulation processes with wettability differentials

Cited by 16 publications

References 41 publications

A Hybrid Model to Predict Formulation Dependent Granule Growth in a Bi-Component Wet Granulation Process

A Hybrid Model to Predict Formulation Dependent Granule Growth in a Bi-Component Wet Granulation Process

Enabling energy‐efficient manufacturing of pharmaceutical solid oral dosage forms via integrated techno‐economic analysis and advanced process modeling

A compartment based population balance model for the prediction of steady and induction granule growth behavior in high shear wet granulation

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