Model development and prediction of particle size distribution, density and friability of a comilling operation in a continuous pharmaceutical manufacturing process

Metta, Nirupaplava; Verstraeten, Maxim; Ghijs, Michaël; Kumar, Ashish; Schäfer, Elisabeth; Singh, Ravendra; Beer, Thomas De; Cappuyns, Philippe; Assche, Ivo Van; Ierapetritou, Marianthi G.; Ramachandran, Rohit

doi:10.1016/j.ijpharm.2018.07.056

Cited by 27 publications

(10 citation statements)

References 25 publications

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“…Granules that are broken to the required size exit the comill through a screen. In this work, the comill model published in Metta et al [16] is used. Briefly, the mill model is a hybrid model that includes a PBM approach and a partial least squares (PLS) approach.…”

Section: Millmentioning

confidence: 99%

“…The blender blade speed influences the mass holdup in the blender, as explained in Section 2.1.2, but not the steady state flow rate, as this depends on the incoming feed flowrate. The mill impeller speed does not have an effect on granules obtained from a FBD [16]. Overall, the factors considered, their corresponding lower and upper bounds, and number of levels for each factor is listed in Table 4.…”

Section: Scenario Analysismentioning

confidence: 99%

“…Specifically, the units involved are feeders, blenders, twin-screw wet granulator (TSWG), fluid bed dryer (FBD), comill, and tablet press. Models for these processes are developed [12][13][14][15][16] and included. Besides these, models for intermediate feeding and blending operations are also included.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Flowsheet Model Development and Sensitivity Analysis of a Continuous Pharmaceutical Tablet Manufacturing Process Using the Wet Granulation Route

et al. 2019

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In view of growing interest and investment in continuous manufacturing, the development and utilization of mathematical model(s) of the manufacturing line is of prime importance. These models are essential for understanding the complex interplay between process-wide critical process parameters (CPPs) and critical quality attributes (CQAs) beyond the individual process operations. In this work, a flowsheet model that is an approximate representation of the ConsiGma TM -25 line for continuous tablet manufacturing, including wet granulation, is developed. The manufacturing line involves various unit operations, i.e., feeders, blenders, a twin-screw wet granulator, a fluidized bed dryer, a mill, and a tablet press. The unit operations are simulated using various modeling approaches such as data-driven models, semi-empirical models, population balance models, and mechanistic models. Intermediate feeders, blenders, and transfer lines between the units are also simulated. The continuous process is simulated using the flowsheet model thus developed and case studies are provided to demonstrate its application for dynamic simulation. Finally, the flowsheet model is used to systematically identify critical process parameters (CPPs) that affect process responses of interest using global sensitivity analysis methods. Liquid feed rate to the granulator, and air temperature and drying time in the dryer are identified as CPPs affecting the tablet properties.

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Section: Millmentioning

confidence: 99%

Section: Scenario Analysismentioning

confidence: 99%

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Dynamic Flowsheet Model Development and Sensitivity Analysis of a Continuous Pharmaceutical Tablet Manufacturing Process Using the Wet Granulation Route

et al. 2019

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“…The main drawback of mechano-chemical activation is the heterogeneous nature of collisions between drug and excipients during co-milling, which may lead to large batch-to-batch variations [7]. While a number of studies have been conducted on aqueous solubility using mechano-chemical activation, the issue of variability in the resulting drug solubilities has been overlooked [8]. An important question is what is the impact of the processing method (co-milling vs. solvent evaporation) on molecular interactions.…”

Section: Introductionmentioning

confidence: 99%

Solubility Improvement of Progesterone from Solid Dispersions Prepared by Solvent Evaporation and Co-milling

et al. 2020

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The aim of this contribution was to evaluate the impact of processing methods and polymeric carriers on the physicochemical properties of solid dispersions of the poorly soluble drug progesterone (PG). Five polymers: hydroxypropyl methylcellulose (HPMC), hydroxypropyl methylcellulose acetate succinate (HPMCAS), microcrystalline cellulose (MCC), polyvinylpyrrolidone (PVP) and silica (SiO2), and two processing methods: solvent evaporation (SE) and mechano-chemical activation by co-milling (BM) were applied. H-bonding was demonstrated by FTIR spectra as clear shifting of drug peaks at 1707 cm−1 (C20 carbonyl) and 1668 cm−1 (C3 carbonyl). Additionally, spectroscopic and thermal analysis revealed the presence of unstable PG II polymorphic form and a second heating DSC cycle, the presence of another polymorph possibly assigned to form III, but their influence on drug solubility was not apparent. Except for PG–MCC, solid dispersions improved drug solubility compared to physical mixtures. For SE dispersions, an inverse relationship was found between drug water solubility and drug–polymer Hansen solubility parameter difference (Δδt), whereas for BM dispersions, the solubility was influenced by both the intermolecular interactions and the polymer Tg. Solubility improvement with SE was demonstrated for all except PG–MCC dispersions, whereas improvement with BM was demonstrated by the PG–HPMC, PG–PVP and PG–HPMCAS dispersions, the last showing impressive increase from 34.21 to 82.13 μg/mL. The extensive H-bonding between PG and HPMCAS was proved by FTIR analysis of the dispersion in the liquid state. In conclusion, although SE improved drug solubility, BM gave more than twice greater improvement. This indicates that directly operating intermolecular forces are more efficient than the solvent mediated.

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“…To implement Quality by Design (QbD) concepts, and to ensure optimally designed processes, over the last two decades model-based process design has become an important tool in pharmaceutical manufacturing and process systems engineering [1][2][3][4][5]. For instance, dynamic process models support recent activities of the Food and Drug Administration (FDA) [6] and the International Council for Harmonisation (ICH) Q11 guideline [7] regarding QbD, and the quantification of process variability [8].…”

Section: Introductionmentioning

confidence: 99%

Robust Process Design in Pharmaceutical Manufacturing under Batch-to-Batch Variation

Xie

Schenkendorf

2019

Processes

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Model-based concepts have been proven to be beneficial in pharmaceutical manufacturing, thus contributing to low costs and high quality standards. However, model parameters are derived from imperfect, noisy measurement data, which result in uncertain parameter estimates and sub-optimal process design concepts. In the last two decades, various methods have been proposed for dealing with parameter uncertainties in model-based process design. Most concepts for robustification, however, ignore the batch-to-batch variations that are common in pharmaceutical manufacturing processes. In this work, a probability-box robust process design concept is proposed. Batch-to-batch variations were considered to be imprecise parameter uncertainties, and modeled as probability-boxes accordingly. The point estimate method was combined with the back-off approach for efficient uncertainty propagation and robust process design. The novel robustification concept was applied to a freeze-drying process. Optimal shelf temperature and chamber pressure profiles are presented for the robust process design under batch-to-batch variation.Processes 2019, 7, 509 2 of 14 operating mode when producing active pharmaceutical ingredients (APIs) and drugs [18], i.e., all materials are charged before the start of processing and discharged at the end of processing. Thus, slight experimental deviations or the degradation of the process equipment might result in batch-to-batch variation [18][19][20]. The source of batch-to-batch variation is difficult to predict, but can be quantified with process analytical technology (PAT) and multivariate statistical analysis [19,20]. In the literature, it is well-known that batch-to-batch variation causes severe problems in pharmaceutical manufacturing, drug quality, clinical studies, and therapeutics [6,17,21,22]. To lower batch-to-batch variation in pharmaceutical, and to improve QbD measures, analyzing the effect of measurement noise and batch-to-batch variation is essential. The adverse effect of batch-to-batch variation in pharmaceutical manufacturing is studied experimentally for various processing steps, e.g., fermentation, crystallization, and (nanomaterial) formulation [17,19,23]. In model-based process design, in turn, recent studies try to analysis and control batch-to-batch variation effects too [24,25]. For instance, in the case of model-based process design, model parameters can be derived for each batch data set separately. When each batch run is fit individually, batch-to-batch variation leads to different sets of model parameter estimates and parameter uncertainties. Please note that the variability in the model parameters is not exclusively the result of measurement noise, but the joint effect of measurement errors and slight differences in the experimental settings and the raw materials of the batches [6,17]. Thus, simulation studies should consider imprecise uncertainties [26-28] as well. These imprecise uncertainties cannot be described via a single PDF, but via a set of PDFs that is known as the a...

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Model development and prediction of particle size distribution, density and friability of a comilling operation in a continuous pharmaceutical manufacturing process

Cited by 27 publications

References 25 publications

Dynamic Flowsheet Model Development and Sensitivity Analysis of a Continuous Pharmaceutical Tablet Manufacturing Process Using the Wet Granulation Route

Dynamic Flowsheet Model Development and Sensitivity Analysis of a Continuous Pharmaceutical Tablet Manufacturing Process Using the Wet Granulation Route

Solubility Improvement of Progesterone from Solid Dispersions Prepared by Solvent Evaporation and Co-milling

Robust Process Design in Pharmaceutical Manufacturing under Batch-to-Batch Variation

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