(697b) Flowsheet Modeling for Oral Solid Drug Product Manufacturing

Authors: 
Wang, Z., Rutgers, The State University of New Jersey
Escotet-Espinoza, M. S., Rutgers, The State University of New Jersey
Singh, R., Rutgers, The State University of New Jersey
Muzzio, F. J., Rutgers University
Ierapetritou, M., Rutgers, The State University of New Jersey

QbD is a
systematic approach to pharmaceutical development based on scientific knowledge
and risk management [1]. Continuous manufacturing is a promising alternative and
important for the implementation of QbD because it reduces the need of scale-up
studies [2]; it requires smaller equipment size and thus minimizes the plant
footprint [3]; and it decreases human factor using automated operation and thus
decreases labor cost
[4]. To facilitate the
development of continuous pharmaceutical manufacturing, each unit operation
must be well understood in terms of the effect of different material properties
and operational conditions on the final product quality attributes. This goal can
be facilitated by the utilization of computer-aided process systems engineering
(PSE) approaches. Flowsheet modeling is an efficient way to understand
manufacturing process dynamics. An integrated flowsheet model can be used in
the design, analysis, and optimization of a chemical process [3]. Simulation
results can help identify the possible process integration requirements, study
multiple designs and test control strategies. In pharmaceutical process
modeling, a computationally efficient equation-oriented process simulator
(gPROMS) has been used to develop
models for unit operations and build
flowsheet models [3] [4].

Among
different modeling methods, residence time distribution (RTD) theory has been commonly
used in developing pharmaceutical unit operation models. RTD is defined as the
probability distribution of the time that the particles stay inside one or more
unit operations in a continuous flow system [5]. With RTD, process changes
(e.g., material changes, process disturbances) can be traced throughout the
system. This helps determine disturbance dissipation
along the process and thus help decide when to collect final products with
desired quality. Consequently mathematical models based on RTD theory are very useful
for modeling the continuous pharmaceutical manufacturing process.

We focus
on building validated integrated flowsheet model with RTD-based unit operation
models for direct compression and granulation lines. Major unit operations such
as feeders, comil, blenders, transfer pipes, feed frames, tablet presses, and
granulation equipment were modeled and properly integrated. These flowsheet
models are used to simulate and predict the dynamics of entire continuous
direct compression processes given input such as material properties and
operational conditions. Moreover, the behavior of different processes in
response to disturbances can also be simulated. Flowsheet models can also be
easily customized. Users can drag and drop necessary unit models and connect
them based on their process design or to test different designs and different
processing strategies. Graphic user interfaces (GUIs) have been implemented in
our flowsheet simulations to provide users with an icon-based interface  (i.e.,
no exposure to model code) and set parameters for each unit. Multiple process
configurations can be easily set with GUIs, which makes it convenient to run
different simulations.

The
objective of this presentation is to highlight the use of flowsheet modeling
tools in the pharmaceutical tablet manufacturing process and to demonstrate their
applications for process
design. Because the process models can have thousands of differential and
algebraic equations (DAEs) and integral expressions, they can be difficult to solve
with deterministic solvers. In these cases, surrogate modeling methodologies
are adopted to address the challenges. However with the high-dimension
problems, the application of surrogate modeling is limited. For example, Kriging
method is computationally expensive for high dimensional problems because it
needs to numerically solve a maximum likelihood estimation problem to fit the
model [7]. In contrast, Radial Basis Functions (RBF) is a promising alternative
because it can provide simple and efficient model to approximate the original
model [8]. We focus on solving high-dimensional design problems by using
RBF-based surrogate modeling strategies. We introduce flexibility analysis for
the design problem to quantify the ability of a process to maintain feasible
operation in the presence of inherent variability or external disturbances [6].
The final goal is to achieve a more robust design for the continuous
pharmaceutical manufacturing process.

 

 

References

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