Raman-based multivariate calibration models have been developed for real-time in situ monitoring of multiple process parameters within cell culture bioreactors. Developed models are generic, in the sense that they are applicable to various products, media, and cell lines based on Chinese Hamster Ovarian (CHO) host cells, and are scalable to large pilot and manufacturing scales. Several batches using different CHO-based cell lines and corresponding proprietary media and process conditions have been used to generate calibration datasets, and models have been validated using independent datasets from separate batch runs. All models have been validated to be generic and capable of predicting process parameters with acceptable accuracy. The developed models allow monitoring multiple key bioprocess metabolic variables, and hence can be utilized as an important enabling tool for Quality by Design approaches which are strongly supported by the U.S. Food and Drug Administration.

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Raman spectroscopy was used to design and monitor a lysozyme protein batch crystallization process in a lab scale study to facilitate the design of a pharmaceutical protein manufacturing process. A D-optimal design that consisted of 18 experiments was performed to elucidate the effect of temperature, concentration of the precipitating agent, time of crystallization, and possible interactions between these three factors on the Raman scattering changes. A polynomial mathematical model was calculated relating the scattering of the lysozyme solutions measured at individual Raman shifts to the significant factors obtained in the previous crystallization experiment. The 2,940-cm −1 band provided the highest correlation values indicative of small prediction errors and good predictive ability for the crystallization model. Raman scattering signals obtained during the experiments were used as input to obtain a response surface for the factors studied and elucidate the relationship between the crystallization process conditions and the crystals obtained. The main factors affecting the crystallization process were the sodium chloride concentration and temperature.

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A novel manufacturing strategy based on continuous processing, integrated with online/inline monitoring tools, coupled with an advanced automatic feedback control system is highly desired for efficient Quality by Design (QbD)-based manufacturing of the next generation of pharmaceutical products with optimal consumption of time, space and resources. In this work, an advanced hybrid MPC-PID control system as well as a simpler PID controller for a direct compaction continuous tablet manufacturing process has been designed and implemented for a pilot-scale pharmaceutical process. An NIR sensor, an online NIR prediction tool, a PAT data management tool, an OPC communication protocol, a standard control platform and control hardware have been used to close the control loop. A systematic methodology to design and implement the control system has been also proposed. A control framework with features such as the option to run the plant in open-loop as well as in a closed-loop scenario has been developed. Furthermore, within the closed-loop scenario, options for a simpler PID, a dead time compensator (Smith predictor) as well as an advanced model predictive controller have been included. The feature to run the control strategy in simulation mode has been added to the control platform to facilitate virtual control system design and performance evaluation. Two case studies involving a direct compaction continuous tablet manufacturing process have been considered to demonstrate the closed-loop operation. Case Study 1 was completed at Rutgers University and constituted the use of a continuous cylindrical blender with a rotating screw. Case Study 2 was based on a continuous tumble mixer and was completed at the University of Puerto Rico-Mayaguez Campus (UPRM).

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