The Food and Drug Administration

The Food and Drug Administration (FDA) tries to warrant an incessant supply of high-quality drugs in the USA. Drug shortages are a critical health care issue which affects a large number of patients across the USA. Drug shortages are due to disruption in supply related to product or facility quality. Thus FDA is focusing on encouraging and supporting developments in pharmaceutical engineering. The industry currently has a limited ability to increase production during drug shortages and pandemics. This is because it takes several years in setting up a new plant or expanding the capacity of the existing plant for the manufacturing of drugs. However, continuous manufacturing can potentially permit increasing production volume. It is one such revolution that has the excessive potential to improve flexibility and sturdiness in the production of pharmaceuticals.1
Traditionally, pharmaceutical manufacturing has used the batch manufacturing mode. It has several advantages like flexibility provided by a readily reconfigurable set of multipurpose unit operations, the feasibility of operation with empirically determined recipes with limited first-principles knowledge, and the convenience of tracking many different drug products of well-defined provenance. However, batch operations also have some limitations like batch to batch variability due to the human element, scaling of manufacturing operations from pilot scale to lab scale and finally the need to handle inventory between successive batch operations. On the other hand, continuous manufacturing is a more recent technology in pharmaceutical manufacturing that enables economic and faster production as compared to the traditional method that is quite inefficient and causes a delay in the time-to-market product. There are numerous advantages to a continuous manufacturing mode which include high equipment/utilization cost, lower operating costs per kilogram and simplification in online measurement, modeling and automation requirement compared to batch manufacturing mode. For our subsequent discussion, let’s look into the features of the unit operations used in continuous solid oral-dosage production. The most common unit operations are powder feeding, continuous blending, hopper operation, roller compaction, milling, wet granulation, drying, and tablet pressing.2
The operation begins with powder feeding done by loss in weight (LIW) feeders. It is critical to achieving a constant flow rate even if the operational flow rate of lubricant and active pharmaceutical ingredient (API) is low. Continuous powder blenders combine several powder feeds into a mixture of uniform composition. The mixing performance is influenced by design parameters such as the agitator size, configuration, and geometry of the vessel. Operating parameters like the rotation rate of agitator and powder flow rate also play important roles in continuous blending operation. Hoppers are used as a means of storage before a unit operation. The hoppers should provide a consistent flow. The main operating variable that requires attention is the level of material in the hopper which again plays a great role in hopper refilling strategies. Roller compactors operate continuously and convert the powder blend into ribbon by compression between two rolls. There is a mill integrated with the compaction unit that mills the ribbon into granules to produce a granular material with flow characteristics favorable for feeding into the tablet press. The operating variables for a roller compactor are feed speed, roll speed and the gap between rolls and roll pressure. The tablet press serves to convert a granular input into the tablets. A tablet press consists of a hopper through which the material is fed into the series of dies with a feed frame. The butterfly valve below the hopper regulates the flow of material according to the level of opening. The shape of the die and depth of die cavity determine the shape and size of a tablet. Material within the die is compressed using upper and lower punch to make the tablet and then the tablet is ejected. The dies rotate continuously guided by cam tracks so that they can be filled, compacted and discharged at a high speed. The operating variables for a tablet press are feed rate, turret speed, compression, and ejection force.2
Continuous manufacturing provides the ability to monitor and rectify data in real time. Thus, it is considered a data-rich manufacturing process. However, the amount of data that can be generated from a continuous process can be enormous. In pharmaceutical drug development and manufacturing, the amount of information of different types ranging from raw experimental data to lab reports to complex mathematical models that need to be stored, accessed, validated manipulated, managed and used for decision making is staggering. This vast information is generated from different analytical instruments, images, spectra, lab notes, chemometric models or simulation tools. This information is stored in different formats like JPEG files and MPEG movies. Thus, there is a requirement of a sophisticated data management system. In order to represent, manage and analyze a large amount of complex information, an ontological informatics structure is necessary for the product and process development.3 Now, we need to understand what an ontology is.
“An ontology defines the basic terms and relations comprising the vocabulary of a topic area as well as the rules of combining terms and relations to define extensions to the vocabulary. It is a formal, explicit specification of a shared conceptualization. Conceptualization refers to an abstract model of some phenomenon in the world by having identified the relevant concepts of that phenomenon. Explicit means that the type of concepts used and the constraints on their use are explicitly defined. Formal refers to the fact that the ontology should be machine-readable. Shared reflects the notion that an ontology captures consensual knowledge, that is, it is not private of some individual, but accepted by a group.”3
To support decision making in the pharmaceutical industry, the Purdue Ontology for Pharmaceutical Engineering has been the first attempt in developing an ontology. It is based on the concepts of materials, experiments, and properties. The aim is to develop a precise model that could be used to assist pharmaceutical product development for information exchange and provide a common information template for data, information, knowledge, and tool integration as well as information processing for better pharmaceutical product development.4
There has been a tremendous growth in the amount of information being generated and managed both within the technical domain and in the broader business setting since the last decade. In order to derive maximum benefit, it is necessary that the information is captured and stored in a structured way. It should also be machine accessible because only then we can ensure that such a vast amount of information be processed by computer-assisted methods to provide effective and timely decision support. If the information is not stored in a structured, semantically rich fashion to begin with, then it becomes very expensive and sometimes impossible, to retrieve the desired items of information later.5
The ISA-88 Batch Control Standard is an international standard addressing batch process control, which has already been implemented in other industries, for instance, the petroleum industry, for years. Therefore, adapting this industrial standard into pharmaceutical manufacturing could provide a design philosophy for describing equipment, material, personnel, as well as reference models. The combination of the ISA-88 recipe model and the data warehouse informatics strategy leads to the “recipe data warehouse” strategy which can lead to data management across multiple execution systems. It also provides the ability for data analysis and visualization. In addition to the data collection and the integration of the continuous manufacturing plant, we also need to capture and store the highly variable and unpredictable properties of raw materials in a database because they could have an impact on the quality of the product. Cloud computing is suitable for data management in the pharmaceutical industry because of its advantages like flexibility, security, and efficiency. Data generated from both process equipment and PAT tools will be sent into the DeltaV system and organized according to the ISA-88 recipe model. PI system, playing the role of historian, is able to receive data from PCS and build up recipe hierarchical structure using PI Event Frame. In the final step, cloud storage is chosen for permanent data storage and the portal of ERP.