A Hybrid Model to Predict Formulation Dependent Granule Growth in a Bi-Component Wet Granulation Process
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Pharmaceutics |
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| Online Access: | https://www.mdpi.com/1999-4923/13/12/2063 |
| _version_ | 1797501485767983104 |
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| author | Indu Muthancheri Rohit Ramachandran |
| author_facet | Indu Muthancheri Rohit Ramachandran |
| author_sort | Indu Muthancheri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:19:06Z |
| format | Article |
| id | doaj.art-0d456a9f30a449f0b793999ab2af099f |
| institution | Directory Open Access Journal |
| issn | 1999-4923 |
| language | English |
| last_indexed | 2024-03-10T03:19:06Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceutics |
| spelling | doaj.art-0d456a9f30a449f0b793999ab2af099f2023-11-23T10:05:22ZengMDPI AGPharmaceutics1999-49232021-12-011312206310.3390/pharmaceutics13122063A Hybrid Model to Predict Formulation Dependent Granule Growth in a Bi-Component Wet Granulation ProcessIndu Muthancheri0Rohit Ramachandran1Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USADepartment of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USAIn 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.https://www.mdpi.com/1999-4923/13/12/2063wet granulationmulticomponentpopulation balance modelcontent uniformity |
| spellingShingle | Indu Muthancheri Rohit Ramachandran A Hybrid Model to Predict Formulation Dependent Granule Growth in a Bi-Component Wet Granulation Process Pharmaceutics wet granulation multicomponent population balance model content uniformity |
| title | A Hybrid Model to Predict Formulation Dependent Granule Growth in a Bi-Component Wet Granulation Process |
| title_full | A Hybrid Model to Predict Formulation Dependent Granule Growth in a Bi-Component Wet Granulation Process |
| title_fullStr | A Hybrid Model to Predict Formulation Dependent Granule Growth in a Bi-Component Wet Granulation Process |
| title_full_unstemmed | A Hybrid Model to Predict Formulation Dependent Granule Growth in a Bi-Component Wet Granulation Process |
| title_short | A Hybrid Model to Predict Formulation Dependent Granule Growth in a Bi-Component Wet Granulation Process |
| title_sort | hybrid model to predict formulation dependent granule growth in a bi component wet granulation process |
| topic | wet granulation multicomponent population balance model content uniformity |
| url | https://www.mdpi.com/1999-4923/13/12/2063 |
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