(744g) Continuous Direct Compaction Tablet Manufacturing Characterization Using Data-Reconciliation and Residence Time Distribution Functions

Authors: 
Medina-González, S., Purdue University
Huang, Y. S., Purdue University
Bachawala, S., Purdue University
Bommireaddy, Y., Purdue University
Gonzalez, M., Purdue University
Reklaitis, G. V., Purdue University
Nagy, Z. K., Purdue University
The success in pharmaceutical continuous manufacturing depends on the accurate, reliable, and real-time information flow from and to the unit operations. In real industrial applications, in addition to the complexities associated with the selection and set-up of measurement devices (in-situ sensors), the coordination and correction of disturbances or gross errors in the generated process data represents a major concern. The process is typically monitored using multivariate statistical process control (MSPC) the effectiveness of MSPC depends critically on the quality of the data from which the PCA model is built. In addition to the complexity to build a sufficiently reliable model, the MSPC method fails to capture variables measured or calculated indirectly and it cannot distinguish between an actual variation of the product quality attributes and an imperfection in the measurement. Data reconciliation (DR) has been advanced as a compelling alternative to reduce data mismatch/uncertainty considering both process knowledge and data measurement simultaneously [1]. DR also has the potential to estimate variables that cannot be measured directly[2].

Direct compaction (DC) represents the simplest solid-dosage pharmaceutical manufacturing process consisting of a series of powder-based unit operations, i.e., feeding, blending and tableting. The DC system in Purdue University’s Continuous Solids Processing Pilot Plant has been used for both computational and experimental studies[3,4], in which the feeder is modeled and simulated using a first-order plus time-delay function and the blender was described using a two-dimensional compartment model. The main compression force applied in the rotary tablet press was characterized using the Kawakita model for a pre-defined turret speed. Su et al., (2019)[3] used a DR analysis to correlate the tablet weight and main compression force (characterized off-line out of the Kawakita model) while minimizing the discrepancy between the estimated and measured data. It is worth noting that the DR was performed only at the tablet press and to estimate a single attribute. However, the same strategy needs to be used to simultaneously analyze all the unit operations across the DC continuous line. Specifically, in addition to tablet weight, the estimation of other quality attributes such as tensile strength and elastic recovery using real-time main compression force measurement is productive direction of research.

Monitoring and control of a continuous manufacturing systems not only depends on the accuracy of plant models but also on having a detailed understanding of the process dynamics. In a continuous DC line, any physical changes in the material can occur at the timescale of seconds and thus, regardless of the sensors’ accuracy, the process dynamics may not be fully captured. Since perturbations propagate across the entire system following different distributions, residence time distribution (RTD) models have been used to evaluate the impact and duration of these disturbances[5]. Traditional RTD models use steady-state values to capture the propagation of disturbances. Recently, Escotet-Espinoza et al., (2019)[6,7] characterized the effects of material types, properties, tracer materials and equipment configuration in the residence time distribution for a powder feeding-blending system. The main goal of tis set of studies was to provide some standard methods for conducting, interpreting, and using RTD results in continuous pharmaceutical manufacturing. However, it bears emphasis that RTDs are dependent on details of dynamics and, therefore, if material properties or unit operations change then RTD parameters must be updated.

In this study, the DC system in Purdue University’s Continuous Solids Processing Pilot Plant was characterized pursuing two main objectives. First, the unit operations constituting the continuous DC line were modeled and characterized using experimental campaigns. The loss-in-weight feeder was described by a feed factor model, and other unit operations were modeled using the formulation presented by Tian et al., (2019). The mismatch between the experimental measured data and the model prediction was corrected through DR. The second part of this study characterizes the powder dynamics across the pilot plant using RTDs. In particular, the DC line consists of two loss-in-weight feeders connected in parallel, which are further connected in series to a continuous blender and a Natoli NP-400 continuous rotary tablet press. By running experiments, RTDs were calibrated from tracer concertation measurements at different times after tracer insertion at the feeders and at different locations in the line. Specifically, powder samples were taken at the exit of each unit operation and the amount of tracer material was measured using spectrometry.

References

[1] Su, Q., Bommireddy, Y., Shah, Y., Ganesh, S., Moreno, M., Liu, J., Gonzalez, M., Yazdanpanah, N., O’Connor, T., Reklaitis, G.V., Nagy, Z.K., 2019. Data reconciliation in the Quality-by-Design (QbD) implementation of pharmaceutical continuous tablet manufacturing. International Journal of Pharmaceutics. 563, 259-272.

[2] Liu, J., Su, Q., Moreno, M., Laird, C., Nagy, Z.K., Reklaitis, G.V., 2019. Robust state estimation of feeding–blending systems in continuous pharmaceutical manufacturing. Chemical Engineering Research and Design. 134, 140-153.

[3] Su, Q., Moreno, M., Giridhar, A., Reklaitis, G.V., Nagy, Z.K., 2017. A systematic framework for process control design and risk analysis in continuous pharmaceutical solid-dosage manufacturing. J. Pharm. Innov. 12, 327–346.

[4] Su, Q., Ganesh, S., Moreno, M., Bommireddy, Y., Gonzalez, M., Reklaitis, G. V., & Nagy, Z. K. (2019). A perspective on Quality-by-Control (QbC) in pharmaceutical continuous manufacturing. Computers & Chemical Engineering, 125, 216-231.

[5] Tian, G., Koolivand, A., Arden, N.S., Lee, S., O’Connor, T.F., 2019. Quality risk assessment and mitigation of pharmaceutical continuous manufacturing using flowsheet modeling approach. Computers and Chemical Engineering 106508.

[6] Escotet-Espinoza, M.S., Moghtadernejad, S., Oka, S., Wang, Y., Roman-Ospino, A., Schäfer, E., Cappuyns, P., Assche, I.V., Futran, M., Ierapetritou, M., Muzzio, F., 2019. Effect of tracer material properties on the residence time distribution (RTD) of continuous powder blending operations. Part I of II: Experimental evaluation. Powder Technology 342, 744-763.

[7] Escotet-Espinoza, M.S., Moghtadernejad, S., Oka, S., Wang, Y., Roman-Ospino, A., Schäfer, E., Cappuyns, P., Assche, I.V., Futran, M., Ierapetritou, M., Muzzio, F., 2019. Effect of tracer material properties on the residence time distribution (RTD) of continuous powder blending operations. Part II of II: Application of models. Powder Technology 344, 525-544.