Currently, there is a high level of interest in the pharmaceutical industry in continuous-manufacturing strategies, integrated
with online-monitoring tools, that are designed, optimized, and controlled using advanced, model-predictive systems. These
strategies can accelerate the full implementation of the quality-by-design (QbD) paradigm for the next generation of pharmaceutical
products. In addition to its flexibility and time- and cost-saving features, continuous manufacturing is intrinsically steady
and therefore easily amenable to model predictive design, optimization, and control methods. These methods have proven to
be effective approaches to improve operational efficiency and have been widely used in various process industries. Excitingly,
in the pharmaceutical industry, the application of the model-predictive design, optimization, and control is virgin territory,
wide open to researchers and technology providers.
Using modeling methods
Recently, the pharmaceutical industry as well as FDA have recognized the need for modernizing pharmaceutical manufacturing
and have launched an initiative for enhancing process understanding through QbD and process analytical technology (PAT) tools
(1-4). Major goals of these efforts include development of the scientific mechanistic understanding of a wide range of processes;
harmonization of processes and equipment; development of technologies to perform online measurements of critical material
properties during processing; performance of real-time control and optimization; minimization of the need for empirical experimentation
and, finally, exploration of process flexibility or design space (5). In many cases, these goals can be achieved effectively
and efficiently by the joint application of designed experiments and modeling tools such as discrete element modeling (DEM),
computational fluid dynamics (CFD), statistical models, and population balance models (PBM). DEM and CFD are mechanistic in
nature and can effectively capture the motion of particles within equipment or their interaction with a stream of fluid. The
DEM approach has been implemented in various pharmaceutically relevant unit operations, such as blending, granulation, and
coating (6). The application of CFD has been observed in unit operations, such as mixing, granulation, and crystallization
(7). Statistical models, such as response-surface methodologies, have been largely used to determine design space and to a
lesser extent for optimization (8). PBMs (hyperbolic partial differential equations representing mesoscopic framework) have
also been widely implemented on particle-based unit operations, such as granulation, crystallization, and mixing (9).
While application of model-predictive design in pharmaceutical applications is only in its infancy, several successes can
be reported. Examples of applications at Rutgers University include stirred tank agitators used as bioreactors (10), roller
bottle reactors used for mammalian cell culture of viruses (11), and understanding wall losses in the Anderson Cascade Impactor
(12). Many other groups have since used CFD tools for many design applications, including chemical reactors of many different
scales, crystallizers, liquid-liquid and liquid-gas reactors, and cleanroom design.
Similarly, use of DEM applications go back well over a decade. Some of the earliest examples of the use of DEM for design
purposes in pharmaceutical applications focused on the design of powder blenders, including the V-blender, the double cone,
and the bin blender. Since then, many other examples have followed, focusing on hoppers, tablet coating, and tableting, for
An additional modeling tool is the use of response-surface models, which are typically developed using data from designed
experiments and subsequently used to select process optima and to design control algorithms. While the data-driven models
used in this application are largely empirical and do not require a mechanistic understanding of the processes to which they
are applied, they are invaluable as tools to aid understanding of the relative importance of process and formulation variables
and thus help narrow down the scope of work required to advance process understanding.
These tools are increasingly becoming common components of the pharmaceutical process-design toolbox. They have spread from
academia to the largest pharmaceutical companies, many of which have formed process-modeling groups in which researchers are
using these tools to design and optimize processes in silico prior to expensive equipment acquisition or to reduce the complexity of designed experiments.
To date, efforts have been piecemeal and typically have focused on individual process components. The emergence of continuous
manufacturing as a central focus of attention, however, is now motivating the need to develop modeling frameworks capable
of simulating all process components simultaneously, using a variety of tools suited for each specific process. The flowsheet
framework meets this need.