Using Model-Predictive Design in Solid-Dosage Manufacturing Processes

June 2, 2013
Jennifer Markarian
Pharmaceutical Technology
Volume 37, Issue 6

Models and modeling software gain a foothold in solid-dosage manufacturing process design.

ALENGO/GETTY IMAGES; DAN WARDUsing model-predictive design to define, predict, and control a process is well-established in many industries, and is beginning to take hold in pharmaceutical process development and manufacturing. Modeling software helps developers to model unit operations and, in some cases, an entire continuous process.

Model-predictive design includes a hierarchy of different approaches, explains Bernhardt Trout, PhD, director of the Novartis–Massachusetts Institute of Technology (MIT) Center for Continuous Manufacturing (CCM) and professor in the Department of Chemical Engineering at MIT. The most basic approach is the use of linear univariate models (e.g., parameterized from a linear design of experiments). A more complex approach is multivariate analysis, followed by mechanistic modeling; the ultimate approach is analysis of first principles (e.g., conservation of mass and energy).

The pharmaceutical industry needs to move beyond statistical models, says Fernando Muzzio, PhD, director of the Engineering Research Center for Structured Organic Particulate Systems (C-SOPS) and professor in the Department of Chemical and Biochemical Engineering at Rutgers University. "Models based on first principles allow you to achieve a greater level of understanding and thus extrapolate outside of the area defined by the statistical models," explains Muzzio, who further discusses modeling tools in an article in this issue (see "Model-Predictive Design, Control, and Optimization").

Generating the level of knowledge necessary to build a model, whether it be statistically or mechanistically based, helps to speed development and mitigate risks during product development, scale-up, and commercial manufacture, adds Howard Stamato, associate director in the Portfolio Enabling Technology Group of Drug Product Science and Technology at Bristol-Myers Squibb. He adds that in those cases where the model can be used in closed-loop control, there are significant advantages for both efficiency and quality of the final product.

Much progress has already been made in employing models in the pharmaceutical industry, driven in part by advances in process analytical technology (PAT). "Advances in computing power and analytical methods, including PAT, have broadened what can feasibly be measured and subsequently modeled," says Stamato. "These advances allow models to be built with reasonable resource commitments and much higher accuracy than previously possible," he adds. In the solid-dosage drug-product manufacturing arena, modeling work has been performed, for example, in scale-up of fluidized-bed processing using computational fluid dynamics (CFD) (1) and in tablet coating using mechanistic models (2) and discrete element modeling (DEM) (3).

Models for continuous, plant-wide solid-dosage processes have also been developed (4–5). The Novartis–MIT CCM, for example, constructed a functioning prototype of an end-to-end continuous manufacturing line that forms coated tablets directly from API and, in late 2012, finished integrating the process with a control system that used models to simulate the entire process. Models for the individual unit operations are described by first principles, such as material balances, energy balances, and chemical kinetics, notes Richard Braatz, the Edwin R. Gilliland Professor of Chemical Engineering at MIT. "An advantage of first-principles models is that you can build a predictive model with less data. Statistical models tend to have poor extrapolation outside of the data range," explains Braatz. MIT used modeling software to connect the individual units and then tuned the process controllers by running simulations of the process as a whole. "Without a model of the process, we would not have been able to complete the project in an appropriate time because it would have taken too much trial and error to tune the controllers," says Braatz, who notes that when starting up a continuous process it is desirable to quickly reach steady-state operations to avoid producing out-of-specification material.

Modeling software

Modeling software tools are available to perform various computer-aided engineering tasks in development and manufacturing in various process industries, including pharmaceutical manufacturing. Models and modeling software are well established in API-production operations, such as solvent selection, crystallization, and separation, for both batch and continuous manufacturing processes, comments Jonathan Kadane, director of industry marketing for Pharmaceuticals/Life Sciences at AspenTech. Modeling for solid-dosage pharmaceutical processes is less mature. Bristol-Myers Squibb's Stamato comments, "The behavior of solids from a surface energy, flow, and compaction perspective is still difficult to characterize. Better understanding of the behavior of solids would help to build more computationally manageable and accurate models." AspenTech's solids-modeling capabilities, acquired through SolidSim in 2012, model specific, solids-handling unit operations (e.g., screens, dryers, and cyclones). Although this technology is, to date, primarily targeted for chemical process industries, the early-users group includes pharmaceutical companies, notes Kadane.

Modeling software can be used in all stages of pharmaceutical production, from R&D through to quality control and manufacturing, notes Michael Doyle, principal scientist and marketing director, Materials Science Segment, at software-provider Accelrys. Process designers can use software to understand excipient–API interactions or optimize mixer speeds, for example. Process-centric software captures numeric inputs about the process (e.g., formulation data, PAT sensor data) and feeds this into predictive models that allow process developers to evaluate "what if" questions, explains Doyle.

CFD software can be used to model common operations, such as scaling up a mixing tank, and for more complex operations, such as three-dimensional modeling of a fluidized bed (e.g., tablet coater), explains Kristian Debus, director of Life Science at CD-adapco.

Some particle flow can be modeled with CFD alone, but to capture more detail about particle behavior (i.e., how particles interact with each other, the surrounding walls and equipment parts, and air flow), developers use discrete element modeling (DEM) software. "DEM tracks the interaction between every particle in a numerically efficient manner, modeling contact forces and energy transfer due to collision and heat transfer between particles," explains Debus. DEM has been used extensively to model mixing and coating, and there is now increased interest in using DEM for granulation and compaction, says Richard LaRoche, PhD, vice-president of Engineering at DEM Solutions.

Another type of CFD is multiphase fluid flow (e.g., volume-of-fluid method). This method can range in complexity from nonmixing to phase mixing, which allows modeling of suspensions, for example. Models can also be done in steady state or in real time (i.e., transient state). As complexity increases, so does the computation time and expense. When choosing how to model a process, companies must balance the accuracy needed with the computational expense that will incur.

In the past, computational capabilities have limited applications to laboratory and pilot scales, says DEM Solutions' LaRoche. This work, however, can be used to help devise scale up-rules for process engineers. The company recently added the capability of running DEM on large, high-performance computing and shared-memory systems, which will enable production-scale modeling.

Another type of software uses flow-sheet modeling to describe individual unit operations and provide mechanisms for exchanging data between these unit operations. This method combines techniques, such as DEM, CFD, and population balance modeling, and can be used, for example, in solid-dosage process development to predict what type of process (i.e., wet granulation, dry granulation, or roller compaction) should be targeted or to predict how upstream API properties affect downstream tablet production (6), adds Douglas Hausner, associate director for industrial relations and business development at C-SOPS.

Looking ahead

An increasing use of modeling in the pharmaceutical industry is being driven both by the FDA mandate to better control the manufacturing process using quality by design and PAT and by the imperative of reducing drug-product development costs and speeding the development process, explains Doyle. A greater focus on efficiency, agrees Kadane, will push the industry toward more process modeling. Modeling the process first, before running experiments, saves time and money because simulated experiments are faster and cheaper than laboratory or pilot-plant experiments. In reality, however, model development and experimental development are run in parallel and correlated with each other, adds Debus. When used together, the process is better understood and error is reduced.

The ultimate goal is to use predictive models to interpret, understand, and control the pharmaceutical process, adds Doyle. Achieving this state would improve consistency and reduce out-of-specification product.

The real power of modeling software for the pharmaceutical industry, says Debus, is in modeling an entire continuous manufacturing process, including three-dimensional models (e.g., multi-phase fluid flow, DEM) of the unit operations and one-dimensional models (e.g., fluid flow through a pipe) of the transfer from one unit to another. Modeling the process as a whole allows advanced process control, in which a parameter change in one unit operation is accounted for in other parts of the process as it affects other unit operations.

Most big pharma companies are using CFD, mostly at a basic level, and smaller pharma companies are just getting started with CFD, comments Debus. Process industries (such as chemical), manufacturing industries (such as automotive), and industries in which flow and particle interaction are crucial (such as aerospace and nuclear), have advanced the use of modeling. The task at hand in the pharmaceutical industry is for all parties (i.e., software and equipment vendors, pharmaceutical producers, and academia) to work together using and improving these tools to better understand pharmaceutical processes.

References

1. J. Parker, et al., Powder Technol. 235 115-120 (2013).

2. W. Chen, et al., J. Pharm. Sci. 99 (7) 3213-3225 (2010).

3. D. Fichana, et al., Pharm. Dev. and Technol. 14 (2) 173-184 (2009).

4. B. Benyahia, R. Lakerveld, and P. Barton, Ind. Eng. Chem. Res. 51 15393-15412 (2012).

5. R. Lakerveld, et al., AIChE J. online, DOI: 10.1002/aic.14107, Mar. 24, 2013.

6. M. Sen, et al., Intl J. Pharm. 445 (1-2) 29-38 (2013).

Join the discussion

How is the pharmaceutical industry using model-predictive design?

Post your comments on www.pharmtech.com/linkedin.