(260b) Digitalization in Active Process Control of Pharmaceutical Continuous Manufacturing of Oral Solid Dosage

Authors: 
Su, Q., Purdue University
Anderson-Lewis, A. E., Purdue University
Ganesh, S., Purdue University
Gonzalez, M., Purdue University
Bommiready, Y., Purdue University
Reklaitis, G. V. R., Purdue University
Nagy, Z. K., Purdue University
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Qinglin Su Normal Qinglin Su 2 47 2019-04-11T15:38:00Z 2019-04-11T15:38:00Z 2 710 4052 33 9 4753 16.00

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" times new roman>Digitalization in Active Process Control of
Pharmaceutical Continuous Manufacturing of Oral Solid Dosage

" times new roman>Qinglin Su, Alessandra E. Anderson-Lewis, Sudarshan
Ganesh, Yasasvi Bommireddy, Marcial Gonzalez,

Gintaras V. Reklaitis, Zoltan K. Nagy

" times new roman>Davidson School of Chemical Engineering, West
Lafayette, Indiana, United States, 47907

" times new roman>School of Mechanical Engineering, West Lafayette,
Indiana, United States, 47907

11.0pt;line-height:107%;font-family:" times new roman>Abstract " times new roman>: The emerging technology of continuous manufacturing
in the pharmaceutical industry requires the use of active process control
strategies in order to respond to disturbances or
variations in raw material properties or process parameters and thus to
maintain a state of control of in-process material or product critical quality
attributes (CQAs). The development of these control strategies has been
identified as a need in the recent United States Food and Drug Administration¡¯s
(US FDA) draft guidance for industry: Quality Considerations for Continuous
Manufacturing [

1

].
Purdue University, together with Rutgers University, had been funded by US FDA
through the project of ¡°Industry 4.0 Implementation in Continuous
Pharmaceutical Manufacturing¡±, to investigate the digitalization of active
process control, as shown in Figure 1.
One of the specific aims of our work is to develop nonlinear model predictive
control (NMPC) strategies based on high degree understanding of process dynamics
in the product and process for the continuous manufacturing of oral solid
dosage. This advanced control strategy is capable of real-time prediction of the
transition of controlled variables and optimization of the control moves in the
manipulated variables within a receding time horizon, so as
to achieving digital process operation. The actual implementation of
NMPC in pharmaceutical continuous manufacturing lines is still in its infant
stage: not much progress has been as yet reported [

2

].
The sluggish low-level control loops were regarded as a source of medium-level
common risk in our recent study [

3

]. Hence, a second aim of our work is to monitor the
process control system itself, using performance criteria or metrics of control
design analysis to identify and diagnose the root causes of degrading control
performance [

 4

], especially, in the lower-level control loops of the
control hierarchy. Instead of the overall control performance evaluation based
on implicit final product qualities, each individual process control loop
performance can be explicitly and digitally diagnosed. The proposed digitalized
active process control strategy is demonstrated on the Purdue University
Pharmaceutical Continuous Manufacturing pilot plant for oral solid dosage using
the direct compaction route. Specifically, a nonlinear hybrid model based on
classical Kawakita model for powder compressibility is used for NMPC
implementation in the continuous tablet press unit operation. The real-time
model parameter estimation for Kawakita model on the basis of
joint data reconciliation and parameter estimation for sensor network was
recently published in our group [

5

].  In
addition, case studies of using PiControl Apromon for digitalized control
performance monitoring and OSIsoft Pi System for digitalized process
performance visualization are discussed. Finally, perspectives on process model
maintenance and control performance monitoring will be given.

" times new roman> 



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" times new roman>Figure 1. A schematic diagram of the digitalization in
active process control.

" times new roman>


References


x

1.

United States Food and Drug Administration. Quality Considerations for Continuous Manufacturing - Guidance for Industry (Draft Guidance) 2019.

2.

Mesbah A, Paulson JA, Lakerveld R, Braatz RD. Model predictive control of an integrated continuous pharmaceutical manufacturing pilot plant. Organic Process Research & Development. 2017;21:844-854.

3.

Su Q, Ganesh S, Moreno M, et al. A perspective on Quality-by-Control (QbC) in pharmaceutical continuous manufacturing. Computers and Chemical Engineering. 2019;125:216-231.

4.

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.

5.

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;563:259-272.

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