Model-Predictive Design, Control, and Optimization

Applying model-predictive methods and a continuous process-control framework to a continuous tablet-manufacturing process. This article contains bonus online-exclusive material.
Jun 02, 2013
Volume 37, Issue 6

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 example.

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.

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