Laser triangulation as a fast and reliable method for determining ribbon solid fraction; focus on accuracy, precision, and measurement time.

Roller compaction and dry granulation represent well-established unit operations in the pharmaceutical industry. The ribbon solid fraction is classified as a critical quality attribute, that directly impacts final product quality and performance. The development and evaluation of novel methods measuring ribbon solid fraction represent a subject of current research, since novel analyses strategies need to be established for at-, on-, or in-line process monitoring to overcome limitations of end product testing and to set the course for continuous manufacturing. In this study, a novel analytical device, using the principle of laser triangulation, was investigated to asses its potential being used as at-line process analytical technology tool during a roller compaction process. To this end, the laser triangulation device was compared with X-ray micro-computed tomography and powder based volume displacement measurement techniques using different statistical evaluation methods. Special focus was given to accuracy, precision, and total measurement time. The laser triangulation device was confirmed as highly accurate and precise, enabling the shortest total measurement time compared to the other methods. The findings of this study support the idea of implementing the laser triangulation device as a novel at-line process analytical technology tool into a roller compaction process.

A linear scale-up approach to fluid bed granulation.

Fluid bed granulation (FBG) is used extensively in the pharmaceutical industry and it is known to be a complex process, because the final product quality of the FBG process is determined by a complex interplay between the process parameters, fluid dynamics, and material properties. Due to this complexity, the FBG process is inherently nonlinear and as such difficult to scale-up. The field of chemical engineering has shown that complex nonlinear processes can be assumed to be linear under limiting conditions. We leverage this idea and present a linear scale-up approach (LiSA) to the FBG process. We derive the key LiSA equation from first principles, and then use it in combination with the similarity principle for scale-up purposes. Furthermore, we present a novel regression-based LiSA. The regression-based LiSA is founded on the hypothesis that there is a linear relationship between the moisture content and a scaling parameter called the Maus factor. This hypothesis is based on our experience and it is shown to be plausible due to high R2 values ranging from 0.86 to 0.98. Moreover, we successfully demonstrate that LiSA is effective under typical industrial process settings by applying it to two different formulations during pharmaceutical drug product development.

A semi-theoretical model for simulating the temporal evolution of moisture-temperature during industrial fluidized bed granulation.

Moisture plays a major role in determining the attributes of granules prepared by fluidized bed granulation (FBG). Here, a semi-theoretical droplet-based evaporation rate model was developed and incorporated into moisture mass-enthalpy balances to simulate the temporal evolution of bed moisture-temperature. Experimental data from a GPCG30 unit were used to fit the model parameters. With only two fitting parameters, the model demonstrated excellent capability to describe the moisture-temperature evolution for a wide range of operating conditions. Then, in a global process model (GPM) approach, the evaporation parameters were fitted to multi-linear functions of inlet air temperature, binder concentration, and spray rate. The GPM was validated successfully by simulating a different data set which was not used in its calibration. As the GPM demonstrated a good predictive capability, it was further used to investigate the impacts of process parameters. Numerical simulations suggest that the proposed GPM predicts the experimentally well-established trends of moisture-temperature profiles in previously published data, proving the applicability of the GPM approach. This study has demonstrated the capabilities of simple process models as a practical approach to predict time-wise evolution of bed moisture-temperature profiles in industrial FBG modeling, while also pointing out their limitations.

Mechanistic modelling of fluid bed granulation, Part I: Agglomeration in pilot scale process.

The present study aims to develop a mechanistic model to predict the performance of a fluid bed granulation process. Therefore, the behavior of the bed was investigated experimentally for various operating conditions. It was observed that the granule Loss on Drying (LoD) and granule size are strongly interrelated. In detail, the maximum final granule size was observed at an intermediate final LoD. Consequently, there is an optimum spray rate and inlet temperature with respect to the granule size. Besides, it was demonstrated that the experiments delivering lower LoD result in a more elongated final granule. Aimed at enabling the prediction of the bed performance numerically, a single-compartment, population-balance-based model was developed and validated against experimental data. The model parameters associated with the growth rate of granule were estimated and mechanistically correlated to the relevant operating conditions. Detailed analysis of the experimental results suggested that these model parameters may be partially connected to the granule LoD. Subsequently, in order to examine the accuracy of the developed model, a simulation was performed for a new set of operating conditions not previously accounted for in the correlations. The comparison of the simulated bed performance, when compared to the experimental results, proved with reasonable accuracy the reliability of the developed model in predicting the temporal evolution of granule size. Therefore, this study can be a step forward in developing a stand-alone granulation model, via modeling heat and mass transfer, to simulate evaporation and drying in a fluid bed granulator.

Mechanistic modelling of fluid bed granulation, Part II: Eased process development via degree of wetness.

The performance of a fluid bed granulator was investigated through experimental and numerical study to develop a stand-alone fluid bed granulation model. The single-compartment model proposed in part I (for agglomeration modeling) was extended to account for i) evaporation of freely-flowing droplets, and ii) particle drying. This model enables us to predict the granule liquid content and temperature besides the granule size. Accurately, the equations of heat and mass conservation were solved in parallel to the population balance calculation of the agglomeration. In the same manner as for the agglomeration model, the model parameters associated with the drying model were estimated and correlated to the relevant quantities. The analysis of the experimental results revealed the significant contribution of the system "degree of wetness" to the bed performance, i.e., granule size and loss on drying (LoD). As the agglomeration model parameters were partially correlated to LoD in Part I, the presented model was revisited by inclusion of the degree of wetness. The reliability of the developed model in predicting the temporal evolution of granule size, liquid content, and temperature was proven through comparing the bed performance between simulation and experiment. Subsequently, to lowering the costs associated with experimental run, an approach was proposed based on the degree of wetness, aimed at reducing the number of experiments required for the design of experiment (DoE). The results of our simulation using reduced experiments demonstrated that the degree of wetness can be a promising indicator for the performance of the fluid bed granulator as well as for more efficient design of experiment.

Prediction of solid fraction from powder mixtures based on single component compression analysis.

The aim of this study was to provide a systematic evaluation of various compression models (Percolation, Kawakita, Exponential model) in respect to predict tablet́s solid fraction for direct compression mixtures, based on single component compression analysis. Four mixtures were compressed over a wide pressure range at various fractions of microcrystalline cellulose (MCC) and pre-agglomerated lactose monohydrate (LAC) to compare an adjusted Percolation, Kawakita and a simple Exponential model. Based on single compression analysis of the pure excipients and application of these models, it was possible to predict the solid fraction of all mixtures. The Kawakita model showed overall superior prediction accuracy, whereas the Percolation model resulted in the best fit for mixtures containing microcrystalline cellulose in a range of 72%-48%. Both models were in good agreement at residuals below 3%.