APR September/October 2020 - 47

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MANUFACTURING

or predictions. The references cited here72-85 is just a short list and not
meant to be exhaustive.

adopted for such usage include rapid spectroscopic identification and
quantification tools.

In terms of ultimate process control strategy development and
implementation (i.e., advanced manufacturing), significant progress
has been made in pharmaceutical and related industries. Some of
the examples include: (1) advanced process control (APC),86-89 e.g.,
feedforward, decoupling, and inferential control, multivariable
model predictive control (MPC); (2) intelligent control,90 e.g., a class of
control technologies that use various artificial intelligence computing
approaches (neural networks, fuzzy logic, machine learning, and
genetic algorithms). In addition, statistical process control (SPC)91 as a
method for monitoring, controlling, and improving a process through
statistical analysis, has been widely used in the semiconductor industry
and increasingly used in the pharmaceutical industry. Today, PAT serves
as a critical element to pharmaceutical continuous processing, such as:

(3) to collate the sensor data streams for full electronic batch records
and maintain correlation with processing parameters. PAT tools which
may be adopted for such usage includes SPC, Multivariate Data
Analysis (MVDA) techniques, SIMICA-P, etc.

(1) to modulate or adjust the process to accommodate materials
property variability and/or process perturbance, in-line and real-time
process monitoring and product testing with feedback loops to the
controller is essential. PAT tools which may be adopted for such usage
include all real-time spectroscopic tools, real-time non-spectroscopic
tools, intelligent control tools, and RTRT tools.
(2) to allow for online raw material release, eliminating or drastically
reducing the wait time for chemical testing. PAT tools which may be

Given all the above-mentioned progress in various critical aspects of
PAT development and implementation, some critical yet legitimate
questions for the community to consider have been: (1) how PAT
can bring pharmaceutical processes from a 3sigma operation to a
6Sigma one? and (2) what steps may we take to further facilitate PAT
implementation in pharmaceutical manufacturing in the context
of robust control strategy? Obviously, those are open questions and
challenges for the community to collectively address. The author
made an attempt to review the current status and some of the critical
challenges we are facing, in the spirit of stimulating more scientific
and technical discussions in this evolving area.
Identification of critical product attributes (CQAs) and critical
process parameters (CPPs) during pharmaceutical development
as the first step of advanced process monitoring and control
implementation
Development is the first stage in identifying critical product attributes
and process parameters, which can serve as the foundation for

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APR September/October 2020

Table of Contents for the Digital Edition of APR September/October 2020
