(697f) Active Process Control in the Quality-By-Design (QbD) Implementation of Pharmaceutical Continuous Tablet Manufacturing

Su, Q., Purdue University
Bommireddy, Y., Purdue University
Ganesh, S., Purdue University
Gonzalez, M., Purdue University
Reklaitis, G. V., Purdue University
Nagy, Z. K., Purdue University
Continuous manufacturing has been routinely implemented and systematically improved in the chemical, petroleum and refinery industries. In the last decade, the stringently regulated pharmaceutical industry has undergone a shift in paradigm from the conventional batch manufacturing to continuous manufacturing.1 Under this trend, real-time product quality assurance in pharmaceutical continuous manufacturing (PCM) has been widely investigated under the Quality-by-Design (QbD) guidelines recommended by the US Food and Drug Administration. Active process control was highlighted recently2 as a key component to enable QbD implementation. Specifically, it was proposed that the active control of critical quality attributes (CQA), such as drug loading in oral solid dosage, can address variations in critical material properties (CMA), such as bulk density of active pharmaceutical ingredients (APIs) by manipulating critical process parameters (CPP), such as tablet press dosing position. Despite the feasibility of implementing active process control, which has been demonstrated for a number of continuous manufacturing facilities, including continuous direct compaction, there remains hesitation in widespread adoption of active process control strategies due to existing investments in established and mature batch technologies.3 In this work we investigate the development of an efficient active process control design for a rotary tablet press.

A rotary tablet press has a number of punch stations (e.g., 16) mounted on a round turret. Each punch station undergoes the following steps: die filling and metering, pre-compaction, main-compaction, tablet ejection, and tablet take-off from lower punch. Conventional batch manufacturing employs pre-batch testing of in-process CQAs, such as API composition and mixing uniformity of the blend, and post-batch testing of tablet CQAs, such as weight and tensile strength. This operating procedure minimizes the importance of active process control and thus requires CPPs (such as main compression force, dosing position, turret speed) to be kept within a predefined design space during the entire run. By contrast, continuous manufacturing relies on active process control, under QbD guidance, to ensure consistent quality production in real time and with less trial and error testing. Thus, active process control is a key component to enabling widespread adoption of pharmaceutical continuous manufacturing. However, an efficient active process control system design for rotary tablet press has not been fully investigated and its full benefits have not been elaborated. Specifically, the advantages of active process control algorithms and structures for rotary tablet press over established and mature batch manufacturing strategies in terms of product quality variances or risks merit investigation. It is worth noting that the robust design of traditional manufacturing equipment (such as a rotary tablet press, a roller compactor, etc.) has resulted in minimum variation of CPPs and CQAs during operation and thus allowed batch pharmaceutical manufacturing to assess quality using post-batch statistical quality control (SQC) methods. An active process control system, by contrast, can use possibly noisy and biased CQA measurements to effectively supervise the control of CPPs and thus minimize the need for batch-end SQC. An important aspect to investigate is the extent to which this integration imposes additional dynamics on the process, and how this could potentially amplify variations in CPPs and thus in CQAs.

In this study, a hierarchical three-level active process control, designed according to the ANSI/ISA 95 standard, was implemented on a Natoli BLP-16 rotary tablet press integrated within a continuous direct compaction pilot plant.4 A Level 0 control consisted of the CPP controls built-into the programmable logic control (PLC) panel provided by the equipment vendor for CPPs (turret speed, dosing position, etc.). A Level 1 control was employed at the OPC server level using cascaded PID close-loop control of CQAs (drug loading, tablet weight, and tensile strength) by means of tablet weight and API composition measurements, obtained with load cell and NIR spectroscopy in-house designed sensors, respectively. A Level 2 controller was designed, using data reconciliation (DR) and model predictive control (MPC) techniques, with the objective of reconciling and supervising the control of CQAs and CPPs at the lower levels. In addition, an at-line Sotax AT4 tester was employed to collect a string of 16 tablets every five minutes with the purpose of determining the variance of tablet CQAs over time. The Level 2 DR implementation was found to be necessary to reconcile noisy sensor measurements of CQA used in the process control systems.5, 6 Furthermore, it was also demonstrated that, under predetermined nominal material variances, the Level 0 control commonly used in batch manufacturing showed promising control performance due to the reliable mechanical design of the equipment. Under conservative tuning, both Level 1 and Level 2 active process control schemes achieved comparable promising performance in the presence of inherent system and sensor noise. However, when materials were subject to significant variations in, e.g., bulk density, the active process control with Level 1 and 2 exhibited better performance. In addition, the Level 2 MPC control showed better performance than the Level 1 PID loops in process automation roles. For example, Level 2 MPC can rapidly reach a CQA set point during process start-up.

The results of this work conclusively demonstrate that an active process control strategy, with process knowledge7 and advanced model-based control techniques, is indispensable to the QbD implementation of pharmaceutical continuous manufacturing, and that it ensures more robustness and efficiency than the techniques used in conventional batch manufacturing, providing the key ingredient towards the new paradigm of Quality-by-Control (QbC).8References


Ierapetritou M, Muzzio F, Reklaitis G. Perspectives on the continuous manufacturing of powder-based pharmaceutical processes. AIChE Journal. 2016;62(6):1846-1862.


Yu LX, Amidon G, Khan MA, et al. Understanding pharmaceutical quality by design. The AAPS Journal. 2014;16(4):771-783.


Diab S, Gerogiorgis DI. Process modelling, simulation and technoeconomic evaluation of crystallization antisolvents for the continuous pharmaceutical manufacturing of rufinamide. Computers and Chemical Engineering. 2018;111:102-114.


Su Q, Moreno M, Giridhar A, Reklaitis GV, Nagy ZK. A systematic framework for process control design and risk analysis in continuous pharmaceutical solid-dosage manufacturing. Journal of Pharmaceutical Innovation. 2017;12:327-346.


Moreno M, Liu J, Ganesh S, et al. Steady-state data reconciliation of a direct compression tableting line. Paper presented at: AIChE Annual Meeting, 2017; Minneapolis, US.


Liu J, Su Q, Moreno M, Laird C, Nagy Z, Reklaitis G. Robust state estimation of feeding-blending systems in continuous pharmaceutical manufacturing. Chemical Engineering Research and Design. 2018:Accepted.


Su Q, Bommireddy Y, Gonzalez M, Reklaitis GV, Nagy ZK. Variation and risk analysis in tablet press control for continuous manufacturing of solid dosage via direct compaction. Paper presented at: The 13th International Symposium on Process Systems Engineering PSE 2018, 2018; San Diego, CA.


Koswara A, Nagy ZK. On-Off feedback control of plug-flow crystallization: a case of Quality-by-Control in continuous manufacturing. IEEE Life Sciences Letters. 2017;3(1):1-4.