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H.S. Pordal (email@example.com) is principal of Stress Engineering Services, Inc., 7030 Stress Engineering Way, Mason, OH 45040; (513) 336-6741.
Clint A. Haynes is vice-president of Stress Engineering Services, Inc., 7030 Stress Engineering Way, Mason OH 45040, (513)336-6741.
Predictive computational methods are finding wider use in pharmaceutical solid dosage development, particularly in mixing and blending, to improve tech transfer and equipment selection, and speed the acquisition of process understanding.
Powder mixing, a key step in the manufacturing of solid dosage pharmaceuticals, is more challenging than mixing liquids. While it can be relatively easy to move liquids around by optimizing pumping and piping systems, bulk solids do not flow easily, making materials transfer more difficult. Processing solids depends on controlling and optimizing particle-size distribution and dynamic flow properties such as flowability and compressibility. These properties are inherent material characteristics and determined experimentally. In the past few years, pharmaceutical manufacturers have adopted computer-based simulation approaches to reduce the cost and improve the results of technology transfer and equipment selection, and to improve the overall solids mixing process itself (Figure 1) prior to tableting or encapsulation.
In technology transfer, a product moves from the laboratory to a pilot plant or the manufacturing floor, or from one equipment scale or type to another. Previously, this transition was a weak link in drug development. R&D professionals used to speak of throwing processes “over the wall” to manufacturing. Traditionally, the most common approach to technology transfer has relied primarily on a reiterated cycle of testing, trying, learning, then retesting, a costly and time-consuming approach. Such testing was done manually, using bench-scale testing methods to mimic manufacturing operations (e.g., blending, solids dissolution, and filling).
However, manual techniques often failed to ensure that processes scaled well to a production setting. In addition, the processes could be tedious, error-prone, expensive, and time consuming. Often, a large amount of design-of-experiment (DoE) data are needed to determine the conditions that will maximize product yield, another aspect of traditional technology transfer that adds complexity, cost, and time.
Simulation tools can be used to develop a roadmap for technology transfer involving changes in materials, scale, and process variability based on a fundamental understanding of the processes and science involved (1). Simulation can benefit internal transfers or those to a contract manufacturing organization (CMO) or contract development and manufacturing organization (CDMO). Digital tools are increasingly being used to improve mixing. The mixing equipment used at one site such as R&D lab may be different from the equipment that will be used at the site where the process will be transferred such as a CDMO’s manufacturing site. This particular tool focuses on differences between impeller-generated flow patterns in two mixing vessels at the different sites, as illustrated in Figure 2. It uses digital solutions-based analysis to characterize mixing behavior and extract key performance parameters for the process, allowing the technology transfer team to achieve parity between parameters in the two vessels by adjusting individual process parameters and/or equipment configuration. This approach minimizes expensive testing and reduces development time and cost.
Digital tools can also be used to determine the suitability of a specific type of equipment for the given process and product. For example, skid-mounted systems are designed to offer portability, flexibility, and quick changeover, and to allow the same equipment to be used in multiple applications. Mixing and blending operations are commonly placed on a skid.In deploying a skid-based mixing system, the following questions must be considered:
In a traditional setting, these questions would be answered during equipment procurement or process development and deployment stages. However, in a flexible manufacturing environment, these questions arise more frequently and must be answered quickly. For these situations, digital solutions-based analysis can be coupled with selective rheological or bench-scale testing to provide a rapid way to determine whether equipment will be suitable for a specific application. This approach minimizes risk and waste, allowing informed decisions related to suitability of equipment to be made well in advance of deployment.
For example, consider a skid-based system used to manufacture a suspension product. Sufficient agitation is necessary to prevent settling of particulates from the suspension. At the time of skid deployment, it is crucial to measure the impeller’s revolutions per minute (rpm) for the process. However, excessive agitation will lead to air entrapment and affect the efficacy of the product. Optimal rpm is therefore necessary to ensure suspension uniformity while minimizing air entrapment.
Digital analysis can be used to determine the impact of rpm on particle concentration in the vessel. This analysis can also be applied to extract key parameters that result in air entrapment. Figure 3 shows how modeling can be used to optimize the balance of required properties.
For powder/granular flow analysis, the digital simulation technique is selected depending on the powder flow regime. For situations where powder/granular flow is rapid, the predictive methods developed for fluid flow apply. In cases where friction dominates, both discrete element methods as well as fluid flow techniques apply. Use of these methods is illustrated in the following example.
For tablet and capsule manufacturing process, binders, flavoring agents, colorant, disintegrants, anti-adherent agents, glidants, and lubricants must all be added to the primary excipient and API and mixed. The application of digital simulations to this type of mixing application is shown in Figure 1. In this case, the mixing of two granular ingredients is simulated using discrete element methods. The interactions between the granular particles and the enclosing vessel are simulated based on the fundamental laws of physics. In this example, the equipment is not optimal for mixing and the simulation shows the segregation of ingredients. Simulations like this can be applied, not only to select the best equipment, but to optimize process conditions to achieve optimal mixing.
Digital simulation-based analysis coupled with selective bench-scale testing provide a rapid means to determine suitability of equipment for different applications. The use of predictive analysis minimizes risk and waste so informed decisions related to suitability of equipment can be made prior to deployment. Other processes where digital simulations coupled with selective customized testing have been leveraged for process development and technology transfer include lyophilization, spray drying. and powder-filling systems. The heat, mass, and momentum transfer associated with particle transport during spray drying can be simulated with a great deal of accuracy providing high fidelity digital models that are leveraged to optimize and troubleshoot the process and associated equipment. Similarly, conveying of powder during filling can be evaluated to prevent issues associated with fillers such as dust generation and deposition of powder over sealing surfaces of packages.
As the industry moves toward Pharma 4.0, digitization, data analytics, machine learning, and artificial intelligence will increasingly be used to improve productivity and quality while reducing costs. It must be recognized, however, that this cannot be achieved through data alone; a hybrid approach leveraging physics and data science is necessary to realize the benefits. Solid-dosage forms are manufactured using ingredients in powder form. Digital simulations provide a means to quantify critical process parameters (CPP). Powder materials exhibit complex behaviors and it is not possible to relate the critical quality attributes (CQA) of the final product to CPP based purely on computational methods. Customized testing is required to make the connection between input parameters and CQA.
In the near future, the industry may see increased use of a hybrid approach that combines physics-based models, customized test data, and artificial intelligence algorithms to generate surrogate models that relate ingredients, process equipment attributes, and process conditions to CPP and CQA. At this point, however, digital simulation tools continue to gain ground in solid dosage form development and process optimization.
1. S. Yeom and D. Chiu, Pharmaceutics 11(6), 2019.
Supplement: Solid Dosage Drug Development and Manufacturing
When referring to this article, please cite it as H. Prodal and C. A. Haynes, “Harnessing Predictive Simulation to Improve Mixing” Pharmaceutical Technology Solid Dosage Drug Development and Manufacturing Supplement (April 2021).