Model-based approach to the design of pharmaceutical roller-compaction processes

Abstract

This work presents a new model based approach to process design and scale-up within the same equipment of a roller compaction process. The prediction of the operating space is not performed fully in-silico, but uses low-throughput experiments as input. This low-throughput data is utilized in an iterative calibration routine to describe the behavior of the powder in the roller compactor and improves the predictive quality of the mechanistic models at low and high-throughput. The model has been validated with an experimental design of experiments of two ibuprofen formulations. The predicted sweet spots in the operating space are in good agreement with the experimental results.

Keywords: Design space prediction; Roller compaction; Solid fraction; Throughput.

Graphical abstract

Fig. 1

Schematic of a roller compactor. (1) inlet funnel with agitator; (2) feed screw; (3) tamp screw; (4) small quantity inlet funnel; (5) rollers; and (6) rotor miller. The mechanistic model describes the region of between the rollers.

Fig. 2

Schematic diagram of the compaction process showing the feeding zone and the nip and slip regions. The powder has the maximum density ${\gamma }_{\mathrm{G}}$ at the gap. Past the roller gap, there is a region of elastic recovery where ribbon thickness T increases while reducing the ribbon density to its final value ${\gamma }_{\mathrm{R}}$. The mass flow can be predicted in the feeding zone using either the screw mass flow rate ($\dot{{\mathrm{m}}_{\mathrm{s}}}$) or the gap geometry and the roller speed (${\dot{\mathrm{m}}}_{\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{l}}$). Experimentally, it is the throughput observed after the ribbon relaxation (${\dot{\mathrm{m}}}_{\mathrm{o}\mathrm{b}\mathrm{s}}$).

Fig. 3

Flow chart of the final model. The operation mode decides the process parameters and the model used for predicting solid fraction and throughput. The required material properties are the same. The iterative calibration loop predicts solid fraction for the known low-throughput process conditions and corrects the compression profile to match the low throughput data. With the calibrated compression profile, the rest of the DoE (with high roller speed and throughput) is predicted.

Fig. 4

Comparison of compression profiles obtained from the tableting data (dashed line, ○) and the roller compaction process (solid line). Because no pressure measurement is available during the RC process, the measured ribbon densities are plotted over the predicted pressures (■).

Fig. 5

Initial prediction (■), the prediction after one iteration () and the ribbon solid fraction prediction after a second iteration ().

Fig. 6

Prediction of ribbon solid fraction for formulations IbuMCC and IbuMannitol using the gap-controlled operation model (■) and the screw-controlled operation model using either c_S1 () or c_S2 () as the model input.

Fig. 7

Prediction of throughput for the IbuMCC and IbuMannitol formulations in the gap-controlled model with relaxation factor β (■) and the screw-controlled model with either c_S1 () or c_S2 ().

Fig. 8

c_S1 calculated using Eq. (16) at every experimental point for both formulations. The efficiency of transporting material decreases with increasing screw speeds, indicated by a decreasing c_S1 value.

Fig. 9

Scaled and centered coefficients for ribbon solid fraction for the IbuMCC and IbuMannitol formulations using experimental and simulated data. N = 11.

Fig. 10

Scaled and centered coefficients for throughput for the IbuMCC and IbuMannitol formulatios using experimental and simulated data. N = 11.

Fig. 11

Graphical exploration around the optimal point (γR = 0.725; ${\dot{\mathrm{m}}}_{\mathrm{o}\mathrm{b}\mathrm{s}}$ = 10 kg/h) comparing the MLR model based on the experimental data with the MLR model based on the simulated data for IbuMCC. The four experimental data points used to calibrate the mechanistic model are marked ●.