Publications

Authors: Jun Zhang, Ye Ying, Barbara Pielecha-Safira, Ecevit Bilgili, Rohit Ramachandran, Rodolfo Romañach, Rajesh N.Davé, Zafar Iqbal

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Abstract: Raman spectroscopy was used as a process analytical technology (PAT) tool for in-line measurement of active pharmaceutical ingredient (API) content during continuous manufacturing of strip films containing nanoparticles of poorly water-soluble APIs. Fenofibrate and naproxen were used as model APIs, whose concentrations ranged from 3% to 26% (w/w) in the model calibration. For both in-line and off-line measurements, calibration models employed partial least square (PLS) analysis, yielding correlation coefficients (R2) greater than 0.9946 and root mean squared error of calibration (RMSEC) of about 0.44%, indicating the validity and accuracy of the calibration. The robustness of Raman spectroscopy as a PAT tool was established by considering three processing parameters after substrate interference correction: sensing location, substrate speed and film thickness. Calibration models for each API were validated using a separate batch of strip films by predicting the API concentrations to within ±1.3%. Principal component analysis (PCA) was used to explain the interactions between processing variables and calibration models, which suggest that besides API concentration, film thickness could also be monitored using Raman spectroscopy. The results demonstrate the potential of Raman spectroscopy as an effective PAT tool for novel strip film manufacturing process, facilitating detection of drug form and concentration in real-time.

Authors: Maitraye Sen, A. Chaudhury, Ravendra Singh, R. Ramachandran

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Abstract: In this study a crystallization model has been developed using a 2D population balance model (PBM). A cooling crystallization process with initial seed induction has been adapted. The crystal growth has been considered to take place in two length directions. This model is able to track the crystal size distribution (CSD), aspect ratio and average diameter (number based) as a function of time. The model has been validated with experimental data of solute concentration as a function of time. A parameter estimation framework has been set up such that the experimental data are itted to the model. The various kinetic parameters related to the crystal growth and nucleation have been estimated. The developed model is robust and has been used to study the evolution of various crystal attributes (i.e. CSD, average diameter and aspect ratio). This model can be used as a tool for virtual experimentation to study the dynamics of a crystallization process. This model can be further applied for model based design, control and optimization of crystallization processes.

Authors: Dana Barrasso, Ashutosh Tamrakar, Rohit Ramachandran

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Abstract: Wet granulation is a particle design process, often used in the pharmaceutical, consumer product, food, and fertilizer industries. A better process understanding is needed to improve process design, control, and optimization. Predominantly, two modeling frameworks are implemented to simulate granulation processes: population balance modeling (PBM) and discrete element methods (DEM). While PBM simulates changes in the number of particles in each size class due to rate processes such as aggregation, DEM tracks each particle individually, with the abilities to simulate spatial variations and collect mechanistic data. In this bi-directional coupled approach, the computational expenditure of the full model is overwhelmed by the high-fidelity DEM algorithm that needs to solve a set of ODEs for each and every particle being handled in the system for very small time intervals. To mitigate this computational inefficiency, reduced order modeling (ROM) is used to replace the computationally expensive DEM step. An artificial neural network (ANN) was trained using DEM results to relate particle size, size distribution, and impeller speed to the collision frequency. Results showed a high correlation between the trained ANN predictions and DEM-generated data. The ANN was coupled with a PBM as a key component of the aggregation rate kernel. The coupled model showed a different development of average particle size and size distribution over time from that of a constant aggregation rate kernel. In addition, the coupled model demonstrated sensitivity to the impeller speed via the ANN rate kernel. When compared with the fully coupled PBM–DEM model for accuracy and computation time savings, the hybrid PBM–ANN model demonstrated excellent agreement with DEM simulations at fractions of the original computational time.

Authors: Ravendra Singh, Abhishek Sahay, Krizia M.Karry, Fernando Muzzio, Marianthi Ierapetritou, Rohit Ramachandran

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Abstract: It is desirable for a pharmaceutical final dosage form to be manufactured through a quality by design (QbD)-based approach rather than a quality by testing (QbT) approach. An automatic feedback control system coupled with PAT tools that is part of the QbD paradigm shift, has the potential to ensure that the pre-defined end product quality attributes are met in a time and cost efficient manner. In this work, an advanced hybrid MPC–PID control architecture coupled with real time inline/online monitoring tools and principal components analysis (PCA) based additional supervisory control layer has been proposed for a continuous direct compaction tablet manufacturing process. The advantages of both MPC and PID have been utilized in a hybrid scheme. The control hardware and software integration and implementation of the control system has been demonstrated using feeders and blending unit operation of a continuous tablet manufacturing pilot plant and an NIR based PAT tool. The advanced hybrid MPC–PID control scheme leads to enhanced control loop performance of the critical quality attributes in comparison to a regulatory (e.g. PID) control scheme indicating its potential to improve pharmaceutical product quality.

Authors: Maitraye Sen, Ravendra Singh and Rohit Ramachandran

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Abstract: In this work, a hybrid MPC (model predictive control)-PID (proportional-integral-derivative) control system has been designed for the continuous purification and processing framework of active pharmaceutical ingredients (APIs). The specific unit operations associated with the purification and processing of API have been developed from first-principles and connected in a continuous framework in the form of a flowsheet model. These integrated unit operations are highly interactive along with the presence of process delays. Therefore, a hybrid MPC-PID is a promising alternative to achieve the desired control loop performance as mandated by the regulatory authorities. The integrated flowsheet model has been simulated in gPROMSTM (Process System Enterprise, London, UK). This flowsheet model has been linearized in order to design the control scheme. The ability to track the set point and reject disturbances has been evaluated. A comparative study between the performance of the hybrid MPC-PID and a PID-only control scheme has been presented. The results show that an enhanced control loop performance can be obtained under the hybrid control scheme and demonstrate that such a scheme has high potential in improving the efficiency of pharmaceutical manufacturing operations.

Authors: Ravendra Singh, Abhishek Sahay, Fernando Muzzio, Marianthi Ierapetritou, Rohit Ramachandran

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Abstract: A novel manufacturing strategy based on continuous processing integrated with online/inline monitoring tools coupled with an advanced control system is highly desired for efficient Quality by Design (QbD)-based pharmaceutical manufacturing. A control system ensures the predefined end product quality, satisfies the high regulatory constraints, facilitates real time release of the product, and optimizes the resources. In this work, a systematic framework for the onsite design and implementation of the control system in continuous tablet manufacturing process has been developed. The framework includes a generic methodology and supporting tools through which the control system can be designed at the manufacturing site and can be implemented for closed-loop operation. The control framework has different novel features such as the option to run the plant in closed-loop (MPC/PID), open-loop and simulation mode. NIR sensor, an online prediction tool, a PAT data management tool, and a control platform have been used to close the control loop.

Authors: Maitraye Sen, Dana Barrasso, Ravendra Singh and Rohit Ramachandran

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Abstract: In this study, a hybrid multi-scale model has been developed for a continuous fluid bed wet granulation process by dynamically coupling computational fluid dynamics (CFD) with a discrete element model (DEM) and population balance model (PBM). In this process, the granules are formed by spraying the liquid binder on the fluidized powder bed. The fluid flow field has been solved implementing CFD principles and the behavior of the solid particles has been modeled using DEM techniques whereas the change in particle size has been quantified with the help of PBM. The liquid binder droplets have been modeled implicitly in DEM. A detailed understanding of the process aids in the development of better design, optimization and control strategies. The model predicts the evolution of important process variables (i.e., average particle diameter, particle size distribution (PSD) and particle liquid content) over time, which have qualitative similarity with experimentally observed trends. The advantage of incorporating the multi-scale approach is that the model can be used to study the distributions of collision frequencies, particle velocity and particle liquid content in different sections of the fluid bed granulator (FBG), in a more mechanistic manner.

Authors: Maitraye Sen, Ravendra Singh & Rohit Ramachandran

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Abstract: In this study, an efficient system-wide controlsystem has been designed for the integrated continuous purification and processing of the active pharmaceutical ingredient (API). The control strategy is based on the regulatory PID controller which is most widely used in the manufacturing industry because of its simplicity and robustness. The designed control system consists of single and cascade (nested) control loops. The control system has been simulated in gPROMSTM (Process System Enterprise). The ability of the control system to track the specified set point changes as well as to reject disturbances has been evaluated. Results demonstrate that the model shows an enhanced performance in the presence of random disturbances under closed-loop control compared to an open-loop operation. The control system is also able to track the set point changes effectively. This proves that closed-loop feedback control can be used in improving pharmaceutical manufacturing operations based on the Quality by Design (QbD) paradigm.

Authors: Anwesha Chaudhury, Dana Barrasso, Preetanshu Pandey, Huiquan Wu & Rohit Ramachandran

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Abstract: This paper focuses on the predictive model development for a pharmaceutically relevant model granulation process. A population balance modeling (PBM) framework has been employed for modeling purposes which is then utilized to obtain accurate predictions of the process. The model is aligned to adequately describe the high-shear mode of granulation operation in a batch process. The model is calibrated using the particle swarm algorithm (PSA) in the form of a multiobjective optimization problem. The multiobjective optimization problem was implemented based on the ε-constraint method which involves the handling of multiple cost functions in the form of constraints with the minimization of one primary objective function from the entire set of cost functions. The resultant solutions obtained from the model are Pareto optimal. The effects of the impeller speed, liquid-to-solid ratio, and wet massing time on the particle size distributions were characterized, and predicted size distributions were in agreement with experimental results. The predictive model framework lends itself to the quality by design (QbD) initiative undertaken by the US Food and Drug Administration (US FDA).

Authors: Ravendra Singh, Dana Barrasso, Anwesha Chaudhury, Maitraye Sen, Marianthi Ierapetritou & Rohit Ramachandran

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Abstract: The wet granulation route of tablet manufacturing in a pharmaceutical manufacturing process is very common due to its numerous processing advantages such as enhanced powder flow and decreased segregation. However, this route is still operated in batch mode with little (if any) usage of an automatic control system. Tablet manufacturing via wet granulation, integrated with online/inline real time sensors and coupled with an automatic feedback control system, is highly desired for the transition of the pharmaceutical industry toward quality by design as opposed to quality by testing. In this manuscript, an efficient, plant-wide control strategy for an integrated continuous pharmaceutical tablet manufacturing process via wet granulation has been designed in silico. An effective controller parameter tuning strategy involving an integral of time absolute error method coupled with an optimization strategy has been used. The designed control system has been implemented in a flowsheet model that was simulated in gPROMS (Process System Enterprise) to evaluate its performance. The ability of the control system to reject the unknown disturbances and track the set point has been analyzed. Advanced techniques such as anti-windup and scale-up factor have been used to improve controller performance. Results demonstrate enhanced achievement of critical quality attributes under closed-loop operation, thus illustrating the potential of closed-loop feedback control in improving pharmaceutical tablet manufacturing operations.