Reliable manufacturing of products capable of consistent performance requires the understanding of complex interacting phenomena occurring at different scales from the molecular to the macro scale. Moreover, effective and reliable manufacturing requires understanding how the sequential steps used to make the product interact with one another and contribute to the performance of the finished product. This can be a daunting task, since the manufacturing process can be affected by the properties of multiple raw materials and their blends, as well as environmental variables and human error and consist of a number of interacting processing steps. Thus, manufacturing processes are rarely optimized, and often lack robustness and are prone to unexpected failures. At present, these problems in the pharmaceutical industry are addressed largely ad hoc, relying heavily on heuristics. Fortunately, the industry as well as the Food and Drug Administration (FDA) have recognized the need for modernizing pharmaceutical manufacturing and the FDA has launched an initiative for enhancing process understanding through Quality by Design (QbD) and Process Analytical Technology (PAT) tools. The major goals of these efforts include the development of 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 evaluation of process design space. Predictive models are extremely useful tools for achieving these goals. A predictive model can be used to design, trouble shoot, and optimize a process, and assess and mitigate risks associated with different types of perturbations, whether due to changes in material properties, process variability, or human error. Modeling of the tablet manufacturing process requires a comprehensive understanding of the physical phenomena underlying the correlation between the properties of the starting particulates and the mechanical characteristics of the end product. This proposal will focus on developing this framework. In particular the following tasks have been identified: (a) to develop flowsheet modeling tools for continuous pharmaceutical manufacturing of solid based drug products;(b) to develop and integrate within the overall framework the sensitivity analysis tools required to perform systematic risk assessment and failure mode analysis needed to determine the robustness of a process. In this phase we will also demonstrate the use of the flowsheet simulation tools in the development of efficient control strategies;and (c) to validate the models and analysis tools using the experimental facility and the data in our pilot plant and from our industrial partners. The ultimate target is to deliver to FDA a generic modeling tool that can be used to simulate, monitor, control and optimize pharmaceutical manufacturing processes for solid based drug products. We propose a close collaboration with the agency so that the developed tools meet their expectations and maximize their usability.

Public Health Relevance

The $600 billion/year pharmaceutical industry is an important sector in which the U.S. remains a global leader. The manufacture of a new drug product involves a wide range of steps, which results in the appropriate delivery of the right amount of the active ingredient at the right site over the right time horizon. Pharmaceutical manufacturing, however, currently relies on empirical methods to achieve these goals. This proposal targets the development of a predictive computational tool that will enable the virtual simulation and optimization of pharmaceutical manufacturing before the actual manufacturing begins, speeding up reliable drug development.

National Institute of Health (NIH)
Food and Drug Administration (FDA)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZFD1)
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Rutgers University
New Brunswick
United States
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