Publications

Authors: Dana Barrasso & Rohit Ramachandran

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Abstract: Wet granulation processes play a crucial role in solid oral dosage manufacturing processes. However, they are often designed empirically with poor efficiency. To implement quality-by-design, a more scientific understanding is desired to predict the effects of process and equipment design and material properties on the rate mechanisms governing wet granulation processes. In this study, a multi-dimensional compartmental population balance model of a twin screw granulation process is coupled with discrete element method simulations to evaluate mechanistic rate expressions describing aggregation, breakage, consolidation, and particle flow. Steady-state results are presented for various configurations of the screw elements. The effects of screw element configuration on product size distribution, porosity, and liquid distribution are presented and compared with experimental trends described in literature. Simulated results are consistent with experimental findings, demonstrating the model’s qualitative ability to predict the effects of screw element design and configuration on the particle-scale phenomena and process outcomes.

Authors: Ravendra Singh, Andrés D.Román-Ospino, Rodolfo J.Romañach, Marianthi Ierapetritou, Rohit Ramachandran

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Abstract: The pharmaceutical industry is strictly regulated, where precise and accurate control of the end product quality is necessary to ensure the effectiveness of the drug products. For such control, the process and raw materials variability ideally need to be fed-forward in real time into an automatic control system so that a proactive action can be taken before it can affect the end product quality. Variations in raw material properties (e.g., particle size), feeder hopper level, amount of lubrication, milling and blending action, applied shear in different processing stages can affect the blend density significantly and thereby tablet weight, hardness and dissolution. Therefore, real time monitoring of powder bulk density variability and its incorporation into the automatic control system so that its effect can be mitigated proactively and efficiently is highly desired. However, real time monitoring of powder bulk density is still a challenging task because of different level of complexities. In this work, powder bulk density which has a significant effect on the critical quality attributes (CQA’s) has been monitored in real time in a pilot-plant facility, using a NIR sensor. The sensitivity of the powder bulk density on critical process parameters (CPP’s) and CQA’s has been analyzed and thereby feed-forward controller has been designed. The measured signal can be used for feed-forward control so that the corrective actions on the density variations can be taken before they can influence the product quality. The coupled feed-forward/feed-back control system demonstrates improved control performance and improvements in the final product quality in the presence of process and raw material variations.

Authors: Ravendra Singh, Fernando J. Muzzio, Marianthi Ierapetritou, and Rohit Ramachandran

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Abstract: Continuous pharmaceutical manufacturing together with PAT (Process Analytical Technology) provides a suitable platform for automatic control of the end product quality as desired by QbD (quality by design)-based efficient manufacturing. The precise control of the quality of the pharmaceutical product requires corrective actions in the process/raw material variability before product quality can be influenced. In this manuscript, a combined feed-forward/feed-back control system has been developed for a direct compaction continuous tablet manufacturing process. The feed-forward controller takes into account the effect of process disturbances proactively while the feed-back control system ensures the end product quality consistently. The coupled feed-forward/feed-back control system ensures the minimum variability in the final product quality irrespective of process and raw material variations. The performance of the combined control strategy has been evaluated through process simulation and is found to be more effective in comparison with a feed-back only control strategy and, therefore, demonstrates potential to further improve pharmaceutical tablet manufacturing operations.

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

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Abstract: The pharmaceutical industry is strictly regulated, where precise control of the end product quality is necessary to ensure the efficiency of the drug products. In this work, a combined feed-forward/feedback (FF/FB) control system has been developed for a direct compaction continuous tablet manufacturing pilot-plant. The feed-forward controller takes into account the effect of process disturbances and raw material variability proactively while the feedback control system ensures the end product quality consistently. The feed-forward control loop is based on real time monitoring of the powder bulk density while the feedback control loops are based on the powder level of instrumented hopper, drug concentration, tablet weight and hardness. Powder blend density has significant effects on the end product quality of the pharmaceutical tablets and therefore has been selected as the feed-forward variable. The coupled FF/FB control system ensures minimum variability in the final product quality irrespective of process and raw material variations.

Authors: Ravendra Singh, Maitraye Sen, Marianthi Ierapetritou & Rohit Ramachandran

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Abstract: In this manuscript, a moving horizon-based real-time optimization (MH-RTO) has been integrated with a hybrid model predictive control (MPC) system for a continuous tablet manufacturing process for quality by design (QbD)-based efficient continuous manufacturing. In the proposed approach, the integrated MH-RTO provides the optimal operational set points for the tablet production rate in real time. The MH-RTO takes into consideration the capital and operating cost, the market fluctuations, the product inventory, the product quality assurance strategy, the regulatory constraints, and the product rejections. An advanced hybrid model predictive control system then ensures that the required production rate with desired quality is met with minimum resources and time. A robust optimization strategy and an efficient control system have been integrated to achieve the maximum profit. The MH-RTO integrated with a hybrid control strategy ensures the maximum possible profit irrespective of the market demand fluctuations. The basic advantage of the MH-RTO framework is that it takes into consideration the future demand and thus can lead to increased profit compared to a standard real-time optimization approach.

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. Two modeling frameworks are available to simulate granulation processes: population balance modeling (PBM) and discrete element methods (DEM). PBM simulates changes in the number of particles in each size class due to rate processes such as aggregation, often relying on empirical rate kernels or require additional mechanistic information, such as flux data, collision frequencies, and impact forces. DEM tracks each particle individually, with the abilities to simulate spatial variations and collect mechanistic data. DEM does not inherently simulate particle size changes and is highly computationally expensive. While DEM can determine collision rates between particles of various sizes, PBM can use this data to determine aggregation rates and calculate a net change in the number of particles in each size class. As the size distribution develops, the collision rates change, resulting in a time- and size-dependent aggregation rate kernel. To solve this complex model, 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.

Authors: Sarang Oka, Heather Emady, Ondřej Kašpar, Viola Tokárová, Fernando Muzzio, František Štěpánek, Rohit Ramachandran

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Abstract: This work focuses on the content non-homogeneity in granules across size classes in a high shear wet granulation process as a result of powder segregation during dry mixing coupled with preferential wettability of one of the ingredients with the binder fluid.

Authors: Sarang Oka, Ondřej Kašpar, Viola Tokárová, Koushik Sowrirajan, Huiquan Wu, Mansoor Khan, Fernando Muzzio, František Štěpánek, Rohit Ramachandran

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Abstract: The objective of the current work was to investigate the effect of liquid to solid ratio (L/S), impeller speed and the wet massing time on the critical quality attributes of granules in a high shear wet granulation process for a two component (API and excipient) system. The parameters were evaluated for their effect on granule properties using a design of experiment based approach. Granules were characterized for their particle size distribution, content uniformity, morphology and porosity.

Authors: Anwesha Chaudhury, Ashutosh Tamrakar, Marek Schöngut‡, David Smrčka, František Štěpánek, and Rohit Ramachandran

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Abstract: Reactive granulation is a complex process which brings about a physical and chemical change in the raw materials. It is extensively used for the production of detergents through the “dry neutralization” reaction. In this paper, the dynamics of the reactive granulation process is captured using a coupled multidimensional population balance model (PBM) for the size enlargement and a diffusion-reaction (DR) equation for the kinetics of the reaction. This framework can capture the experimental observations that have also been reported in Schöngut et al. (Ind. Eng. Chem. Res.2011, 50, 11576). There are a few empirical parameters involved in the modeling framework for which a dynamic sensitivity analysis was employed to shortlist the parametric quantities that have the largest influence on the critical quality attributes. The model is calibrated against experimental results via the Nelder–Mead simplex algorithm for estimating the empirical parameters, and the model predictions were compared with experimental results. Both the parameter estimation and prediction results showed good agreement with the experimental results. The development in terms of a comprehensive mathematical model and the results as presented in this paper suggest the ability of this approach to make suitable model predictions which can significantly reduce the number of experimental trials required for process optimization and control.

Authors: Dana Barrasso, Thomas Eppinger, Frances E.Pereira, Ravindra Aglave, Kristian Debus, Sean K.Bermingham, Rohit Ramachandran

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Abstract: In this study, a novel mechanistic model for a wet granulation process is presented, combining the techniques of population balance modeling and discrete element methods to predict critical quality attributes of the granule product, such as porosity and size distribution. When applied to a twin screw granulation process, the model shows sensitivities to the screw element type and geometry, as well as material properties (binder viscosity, pore saturation) and process parameters (screw speed, liquid-to-solid ratio). Predicted trends are consistent with experimental observations in the literature. Using this modeling framework, a model-based approach can be used to implement Quality by Design, establishing a design space to transition towards a quantitative mechanistic understanding of wet granulation processes.

Authors: Mehdi Ghodbane, Anthony Kulesa, Henry H. Yu, Tim J. Maguire, Rene S. Schloss, Rohit Ramachandran, Jeffrey D. Zahn & Martin L. Yarmush

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Abstract: Immunoassays are one of the most versatile and widely performed biochemical assays and, given their selectivity and specificity, are used in both clinical and research settings. However, the high cost of reagents and relatively large sample volumes constrain the integration of immunoassays into many applications. Scaling the assay down within microfluidic devices can alleviate issues associated with reagent and sample consumption. However, in many cases, a new device is designed and empirically optimized for each specific analyte, a costly and time-consuming approach. In this paper, we report the development of a microfluidic bead-based immunoassay that, using antibody-coated microbeads, can potentially detect any analyte or combination of analytes for which antibody-coated microbeads can be generated. We also developed a computational reaction model and optimization algorithm that can be used to optimize the device for any analyte. We applied this technique to develop a low-volume IL-6 immunoassay with high sensitivity (358 fM, 10 pg/mL) and a large dynamic range (four orders of magnitude). This device design and optimization technique can be used to design assays for any protein with an available antibody and can be used with a large number of applications including biomarker discovery, temporal in vitro studies using a reduced number of cells and reagents, and analysis of scarce biological samples in animal studies and clinical research settings.

Authors: Dana Barrasso, Arwa El Hagrasy, James D.Litster, Rohit Ramachandran

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Abstract: In this study, a novel multi-component population balance model (PBM) for the twin-screw granulation (TSG) process was developed, taking into account the rate processes of aggregation, breakage, liquid addition, and consolidation. Interactions between multiple solid components (e.g. active pharmaceutical ingredient and excipient) and the amount of liquid were accounted for in quantifying the aggregation and breakage rates. Experimental data was obtained for the TSG process, whereby the effect of initial particle size distribution and liquid-to-solid ratios on key granule properties was studied. The data was used to estimate adjustable parameters in the proposed mechanistic kernels and to validate the calibrated process model as a predictive tool. The simulation results showed a good agreement with experimental data.

Authors: DanaBarrasso, Rohit Ramachandran

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Abstract: Wet granulation is a complex particle design process often operated inefficiently in industrial applications. Enhanced process understanding is required to facilitate design, control, and optimization. In this study, a hybrid multi-scale model is presented using a bi-directional coupling approach between DEM and PBM. The hybrid model takes into account particle collision frequencies and liquid distribution, providing a framework suitable for the complex sub-processes in wet granulation. The effect of particle size distribution on the collision frequency function was demonstrated, indicating the need for a multi-scale model. Results of the hybrid model show an increase in particle size over time from an average diameter of 0.98 mm to 2.5 mm, which qualitatively agrees with experimental trends observed during the liquid addition and wet massing stages. Two-dimensional distributions in particle size and liquid fraction are also presented incorporating the key effect of liquid distribution on the evolution of granule PSD.

Authors: Maximilian. O. Besenhard, Anwesha Chaudhury, Thomas Vetter, Rohit Ramachandran, Johannes G.Khinast

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Abstract: Population balance equations (PBE) coupled with mass and energy balance equations represent the common modeling framework for crystallization processes. Often the expressions required for crystal growth, nucleation, as well as aggregation and breakage rates contain parameters that need to be estimated from experimental data. To establish a process model, parameter estimation (PE) is applied to determine an optimal set of parameters by minimizing the sum of squared errors between the experimental results and the model output. Inappropriate selection of the objective function, the optimization routine itself and inaccurate or limited experimental data might severely handicap the parameter estimation procedure.

Authors: Anwesha Chaudhury, Marco Euclide Armenante, Rohit Ramachandran

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Abstract: Population balance models (PBMs) are used extensively to model various particulate processes such as granulation. A high shear granulation process is often assumed to be well mixed and is represented using a single compartment PBM. However, the inhomogeneities existent within the granulator are not effectively addressed using the single PBM representation for the process. Thus, a multi-compartment model is needed to account for the inhomogeneities within the granulator. In this study, the multiple compartments are identified from data mining methods (e.g. clustering) and their average values are thereby obtained. Using regression analysis, a general expression is obtained for the size of the compartments and the average values for different operating conditions. These expressions are then used within a multi-compartment PBM formulation to describe the process dynamics. Validation for the regression expressions also showed good agreement against the varying operating conditions. Also, the multi-compartment model is able to account for mechanical dispersion behaviors that is typically associated with a high-shear process. This study shows that the assumption of a single compartment for a high-shear granulator is often inadequate and the multi-compartment based approach can offer a better physical representation of the process.