Continuous direct compression: Development of an empirical predictive model and challenges regarding PAT implementation.
Int J Pharm X
; 4: 100110, 2022 Dec.
Article
en En
| MEDLINE
| ID: mdl-35024605
ABSTRACT
In this study, an empirical predictive model was developed based on the quantitative relationships between blend properties, critical quality attributes (CQA) and critical process parameters (CPP) related to blending and tableting. The blend uniformity and API concentration in the tablets were used to elucidate challenges related to the processability as well as the implementation of PAT tools. Thirty divergent ternary blends were evaluated on a continuous direct compression line (ConsiGma™ CDC-50). The trials showed a significant impact of the impeller configuration and impeller speed on the blending performance, whereas a limited impact of blend properties was observed. In contrast, blend properties played a significant role during compression, where changes in blend composition significantly altered the tablet quality. The observed correlations allowed to develop an empirical predictive model for the selection of process configurations based on the blend properties, reducing the number of trial runs needed to optimize a process and thus reducing development time and costs of new drug products. Furthermore, the trials elucidated several challenges related to blend properties that had a significant impact on PAT implementation and performance of the CDC-platform, highlighting the importance of further process development and optimization in order to solve the remaining challenges.
#BP, Number of blade passes; #RMB1, Number of radial mixing blades of the main blender; API, Active pharmaceutical ingredient; API_sd, Spray dried API; BRT, Bulk residence time; BU, Blend uniformity; CDC, Continuous direct compression; CDC-50; CU, Content uniformity; C_P, Caffeine anhydrous powder; Continuous direct compression; Continuous manufacturing; DCP, Dicalcium phosphate / Emcompress AN; FD, Fill depth; HM1/HM2, Hold-up mass main blender/Hold-up mass lubricant blender; Imp1, Impeller speed main blender; LC, Percentage label claim; MCF, Main compression force; MCH, Main compression height; MPT_µ, Metoprolol micronized; MgSt, Magnesium stearate/Ligamed MF-2-V; Multivariate data-analysis; NIR, Near infrared; PAT; PAT, Process Analytical Technology; PC, Principle component; PCA, Principle component analysis; PCD, Pre-compression displacement; PCF, Pre-compression force; PCH, Pre-compression height; PH101, Microcrystalline cellulose / Avicel PH-101; PH200, Microcrystalline cellulose / Avicel PH-200; PLS, Partial least squares; P_DP, Paracetamol dense powder; P_P, Paracetamol powder; P_µ, Paracetamol micronized; Predictive modeling; Q2, Goodness of prediction; R2Y, Goodness of fit; RMSEcv, Root mean squared error of cross validation; RSDTW, Relative standard deviation of tablet weight; SD100, Mannitol / Pearlitol 100 SD; T80, Lactose / Tablettose 80; T_P, Theophylline anhydrous powder; rpm, Revolutions per minute; σForce, Main compression force variability; σPCD, Variability in pre-compression displacement
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Int J Pharm X
Año:
2022
Tipo del documento:
Article
País de afiliación:
Bélgica