(189b) Advancing from QbD to Operational Excellence in Continuous Pharmaceutical Manufacturing | AIChE

(189b) Advancing from QbD to Operational Excellence in Continuous Pharmaceutical Manufacturing


Ganesh, S. - Presenter, Purdue University
Reklaitis, G. V. R., Purdue University
Nagy, Z. K., Purdue University
Vo, L. B. D., Purdue University
Rentz, B., Purdue University
The advent of Industry 4.0 (Smart Manufacturing) in the 2010s, is a manifestation of cultural and mindset change across multiple industries towards the acceptance of digital tools in process design and operations, resulting in highly automated and data-rich systems to realize lean manufacturing. These process intelligence tools leverage cross-industry advances in manufacturing methods, data analytics, fault management and implementation technologies, for sustainable process operations, safety, quality risk assessment, and enhanced customer relations. In pharmaceutical systems, such digitalization tools facilitate the implementation of continuous manufacturing and real-time release testing by tracking product quality in real-time, thereby enabling the release of life critical medication with reduced off-line quality testing compared to existing practices.

Continuous manufacturing of drug product is the outcome of targeted process intensification with systematic integration of product and process knowledge, instrumentation and automation systems, quality control protocols and real-time process management for a solids processing system handling pharmaceutical materials. Success in the technology necessitate appropriate infrastructure for Information and Operations Technology integration.

This poster discusses an integrated data-driven and model-based framework for robust process monitoring and maintenance management, as continuous manufacturing advances from technology development to adoption and implementation. The infrastructure for digital process operations implemented at Purdue University uses an analytically redundant sensor network and automation systems such as Emerson DeltaV, OSIsoft PI System and Applied Materials Smart Factory Rx for continuous tableting via direct compaction and dry granulation. A hierarchical systems integration architecture closely following ISA-95 is setup for the implementation of Quality-by-Design. Further, the poster discusses leveraging the systems for implementation of supervisory control and reporting for QbD implementation.

Further Reading:

  1. S. Ganesh, G. Reklaitis et al. Leveraging the digital infrastructure for real-time operations management in continuous pharmaceutical manufacturing: Condition-based maintenance for sensor network robustness (in preparation).
  2. M. Moreno, S. Ganesh, Y. Shah, Q. Su, M. Gonzalez, Z. Nagy, G. Reklaitis. Sensor Network Robustness using Model-based Data Reconciliation for Continuous Tablet Manufacturing. J Pharm Sci (2019).
  3. Q. Su, S. Ganesh, M. Moreno, Y. Bommireddy, M. Gonzalez, G. Reklaitis, Z. Nagy. A perspective on Quality-by-Control (QbC) in pharmaceutical continuous manufacturing. Comput Chem Eng (2019).
  4. Su Q, Bommireddy Y, Shah Y, et al. Data reconciliation in the Quality-by-Design (QbD) implementation of pharmaceutical continuous tablet manufacturing. International Journal of Pharmaceutics. (2019).