Statistical data treatment for residence time distribution studies in pharmaceutical manufacturing.

Residence time distribution (RTD) method has been widely used in the pharmaceutical manufacturing for understanding powder dynamics within unit operations and continuous integrated manufacturing lines. The dynamics thus captured is then used to develop predictive models for unit operations and important RTD-based applications ensuring product quality assurance. Despite thorough efforts in tracer selection, data acquisition, and calibration model development to obtain tracer concentration profiles for RTD studies, there can exist significant noise in these profiles. This noise can make it challenging to identify the underlying signal and get a representative RTD of the system under study. Such concerns have previously indicated the importance of noise handling for RTD measurements in literature. However, the literature does not provide sufficient information on noise handling or data treatment strategies for RTD studies. To this end, we investigate the impact of varying levels of noise using different tracers on measurement of RTD profile and its applications. We quantify the impact of different denoising methods (time and frequency averaging methods). Through this investigation, we see that Savitsky Golay filtering turns out to a good method for denoising RTD profiles despite varying noise levels. The investigation is performed such that the key features of the RTD profile (which are important for RTD based applications) are preserved. Subsequently, we also investigate the impact of denoising on RTD-based applications such as out-of-specification (OOS) analysis and RTD modeling. The results show that the degree of noise levels considered in this work do not significantly impact the RTD-based applications.

Design space determination of pharmaceutical processes: Effects of control strategies and uncertainty.

The identification of process Design Space (DS) is of high interest in highly regulated industrial sectors, such as pharmaceutical industry, where assurance of manufacturability and product quality is key for process development and decision-making. If the process can be controlled by a set of manipulated variables, the DS can be expanded in comparison to an open-loop scenario, where there are no controls in place. Determining the benefits of control strategies may be challenging, particularly when the available model is complex and computationally expensive - which is typically the case of pharmaceutical manufacturing. In this study, we exploit surrogate-based feasibility analysis to determine whether the process satisfies all process constraints by manipulating the process inputs and reduce the effect of uncertainty. The proposed approach is successfully tested on two simulated pharmaceutical case studies of increasing complexity, i.e., considering (i) a single pharmaceutical unit operation, and (ii) a pharmaceutical manufacturing line comprised of a sequence of connected unit operations. Results demonstrate that different control actions can be effectively exploited to operate the process in a wider range of inputs and mitigate uncertainty.

Hybrid model development for parameter estimation and process optimization of hydrophobic interaction chromatography.

Hydrophobic Interaction Chromatography (HIC) is often employed as a polishing step to remove aggregates for the purification of therapeutic proteins in the biopharmaceutical industry. To accelerate the process development and save the costs of performing time- and resource-intensive experiments, advanced model-based process design and optimization are necessary. Due to the unclear adsorption mechanism of the salt-dependent interaction between the protein and resin, the development of an accurate mechanistic model to describe the complex HIC behavior is challenging. In this work, an isotherm derived from Wang et al. is modified by adding three extra parameters together with an equilibrium dispersive model to represent the HIC process. To reduce the development effort of isotherm equations and extract missing information from the available data, a hybrid model is constructed by combining a simple and well-known multi-component Langmuir isotherm (MCL) with a neural network (NN). It is observed that the structure of the hybrid model is of critical importance to the accuracy of the developed model. During parameter estimation, a regularization strategy is incorporated to prevent overfitting. Furthermore, the impact of NN structures and regularization rates are comprehensively investigated. One of the interesting findings was that a simple NN with one hidden layer with two nodes and sigmoid as the activation function, significantly outperforms the mechanistic model, with a 62% improvement in accuracy in calibration and 31.4% in validation. To ensure the generalizability of the developed hybrid model, an in-silico dataset is generated using the mechanistic model to test the extrapolation capability of the hybrid model. Process optimization is also carried out to find the optimal operating conditions under product quality constraints using the developed hybrid model.

Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation.

In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.

An integrated data management and informatics framework for continuous drug product manufacturing processes: A case study on two pilot plants.

The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. In this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.

Optimal quantification of residence time distribution profiles from a quality assurance perspective.

Residence time distribution (RTD) has been widely applied across various fields of chemical engineering, including pharmaceutical manufacturing, for applications such as material traceability, quality assurance, system health monitoring, and fault detection. Determination of a representative RTD, in principle, requires an accurate process analytical technology (PAT) procedure capturing the entire range of tracer concentrations from zero to maximum. Such a wide concentration range creates at least two problems: i) decreased accuracy of the model across the entire range of concentrations, relating to limit of quantification, and ii) ambiguity associated with the detection of the tracer for low concentration levels, relating to limit of detection (LOD). These problems affect not only the RTD profile itself, but also RTD-based applications, which can potentially lead to erroneous conclusions. This article seeks to minimize the impact of these problems by understanding the relative importance of different features of RTD on the detection of out-of-specification (OOS) products. In this work, the RTD obtained experimentally was truncated at different levels, to investigate the impact of the truncation of RTD on funnel plots for OOS detection. The main finding is that the tail of the RTD can be truncated with no loss of accuracy in the determination of exclusion intervals. This enables the manufacturing scientist to focus entirely on the peak region, maximizing the accuracy of chemometric models.

Optimization of key energy and performance metrics for drug product manufacturing.

During the development of pharmaceutical manufacturing processes, detailed systems-based analysis and optimization are required to control and regulate critical quality attributes within specific ranges, to maintain product performance. As discussions on carbon footprint, sustainability, and energy efficiency are gaining prominence, the development and utilization of these concepts in pharmaceutical manufacturing are seldom reported, which limits the potential of pharmaceutical industry in maximizing key energy and performance metrics. Based on an integrated modeling and techno-economic analysis framework previously developed by the authors (Sampat et al., 2022), this study presents the development of a combined sensitivity analysis and optimization approach to minimize energy consumption while maintaining product quality and meeting operational constraints in a pharmaceutical process. The optimal input process conditions identified were validated against experiments and good agreement resulted between simulated and experimental data. The results also allowed for a comparison of the capital and operational costs for batch and continuous manufacturing schemes under nominal and optimized conditions. Using the nominal batch operations as a basis, the optimized batch operation results in a 71.7% reduction of energy consumption, whereas the optimized continuous case results in an energy saving of 83.3%.

Characterization and propagation of RTD uncertainty for continuous powder blending processes.

Residence time distribution (RTD) is a probability density function that describes the time materials spend inside a system. It is a promising tool for mixing behavior characterization, material traceability, and real-time quality control in pharmaceutical manufacturing. However, RTD measurements are accompanied with some degree of uncertainties because of process fluctuation and variation, measurement error, and experimental variation among different replicates. Due to the strict quality control requirements of drug manufacturing, it is essential to consider RTD uncertainty and characterize its effects on RTD-based predictions and applications. Towards this end, two approaches were developed in this work, namely model-based and data-based approaches. The model-based approach characterizes the RTD uncertainty via RTD model parameters and uses Monte Carlo sampling to propagate and analyze the effects on downstream processes. To avoid bias and possible reduction of uncertainty during model fitting, the data-based approach characterizes RTD uncertainty using the raw experimental data and utilizes interval arithmetic for uncertainty propagation. A constrained optimization approach was also proposed to overcome the drawback of interval arithmetic in the data-based approach. Results depict probability intervals around the upstream disturbance tracking profile and the funnel plot, facilitating better decision-making for quality control under uncertainty.

Process design of a fully integrated continuous biopharmaceutical process using economic and ecological impact assessment.

Continuous biomanufacturing is a promising alternative to current batch operation as it offers benefits in terms of improved productivity, product quality, and reduced footprint. This study aims to build a fully integrated continuous platform for monoclonal antibody (mAb) production incorporating novel technologies (like intensified seed expansion and continuous high cell density perfusion operations, single-pass tangential flow filtration, and single-use technologies) as well as media and buffer preparation steps. Economic assessment is performed on the basis of the total cost of goods (COGs), which is $102.2/g in the base-case scenario with a bioreactor scale of 500 L. E-factor is used as an environmental indicator and the result shows that 4865.6kg of process water and 11.1 kg of consumables are required to produce 1 kg mAbs. After the development and analysis of the benchmark process, scenario analysis is performed to assess the impacts of the bioreactor scale (60-2000 L) and upstream titers (1.12-2.08 g/L) on the process economics as well as on the environmental footprint. With the increase of bioreactor scale and mAb titer, the operating COGs per unit product decrease. Moreover, increasing the mAb titer is more favorable in terms of the ecological impacts. To investigate the production capacity, the upstream production is increased and the downstream bottlenecks are determined. It is found that only the multicolumn chromatographic (MCC) operations become the process bottleneck and the order of the MCC unit operation that becomes the process bottleneck depends on capacity utilization for that step. Finally, a new platform is built with the integration of membrane chromatography and the two designed processes are compared in terms of economic and ecological impacts.

Ambient-pressure lignin valorization to high-performance polymers by intensified reductive catalytic deconstruction.

Chemocatalytic lignin valorization strategies are critical for a sustainable bioeconomy, as lignin, especially technical lignin, is one of the most available and underutilized aromatic feedstocks. Here, we provide the first report of an intensified reactive distillation­reductive catalytic deconstruction (RD-RCD) process to concurrently deconstruct technical lignins from diverse sources and purify the aromatic products at ambient pressure. We demonstrate the utility of RD-RCD bio-oils in high-performance additive manufacturing via stereolithography 3D printing and highlight its economic advantages over a conventional reductive catalytic fractionation/RCD process. As an example, our RD-RCD reduces the cost of producing a biobased pressure-sensitive adhesive from softwood Kraft lignin by up to 60% in comparison to the high-pressure RCD approach. Last, a facile screening method was developed to predict deconstruction yields using easy-to-obtain thermal decomposition data. This work presents an integrated lignin valorization approach for upgrading existing lignin streams toward the realization of economically viable biorefineries.

Using residence time distribution in pharmaceutical solid dose manufacturing - A critical review.

While continuous manufacturing (CM) of pharmaceutical solid-based drug products has been shown to be advantageous for improving the product quality and process efficiency in alignment with FDA's support of the quality-by-design paradigm (Lee, 2015; Ierapetritou et al., 2016; Plumb, 2005; Schaber, 2011), it is critical to enable full utilization of CM technology for robust production and commercialization (Schaber, 2011; Byrn, 2015). To do so, an important prerequisite is to obtain a detailed understanding of overall process characteristics to develop cost-effective and accurate predictive models for unit operations and process flowsheets. These models are utilized to predict product quality and maintain desired manufacturing efficiency (Ierapetritou et al., 2016). Residence time distribution (RTD) has been a widely used tool to characterize the extent of mixing in pharmaceutical unit operations (Vanhoorne, 2020; Rogers and Ierapetritou, 2015; Tezyk et al., 2015) and manufacturing lines and develop computationally cheap predictive models. These models developed using RTD have been demonstrated to be crucial for various flowsheet applications (Kruisz, 2017; Martinetz, 2018; Tian, 2021). Though extensively used in the literature (Gao et al., 2012), the implementation, execution, evaluation, and assessment of RTD studies has not been standardized by regulatory agencies and can thus lead to ambiguity regarding their accurate implementation. To address this issue and subsequently prevent unforeseen errors in RTD implementation, the presented article aims to aid in developing standardized guidelines through a detailed review and critical discussion of RTD studies in the pharmaceutical manufacturing literature. The review article is divided into two main sections - 1) determination of RTD including different steps for RTD evaluation including experimental approach, data acquisition and pre-treatment, RTD modeling, and RTD metrics and, 2) applications of RTD for solid dose manufacturing. Critical considerations, pertaining to the limitations of RTDs for solid dose manufacturing, are also examined along with a perspective discussion of future avenues of improvement.

A novel framework of surrogate-based feasibility analysis for establishing design space of twin-column continuous chromatography.

Multi-column periodic counter-current chromatography (PCC) has attracted wide attention for the primary capture for the purpose of achieving continuous biomanufacturing. Consequently, determining the design space of the continuous capture process is very important to facilitate process understanding and improving product quality. In this work, we proposed a novel approach to identify the design space of continuous chromatography to balance the computational complexity and model predictions. Specifically, surrogate-based feasibility analysis with adaptive sampling is applied to establish the design space of twin-column CaptureSMB process. The surrogate model is constructed based on the developed mechanistic model for the identification of the design space. The effects of process variables (including interconnected loading time, interconnected flowrate, and batch flowrate) on the design space are comprehensively examined based on an active set strategy. Besides, essential factors like recovery-regeneration time and constraints of column performance parameters (yield, productivity, and capacity utilization) are thoroughly investigated. The impact of design variables such as column length is also studied.

Experimental investigation and modelling of tensile strength of pharmaceutical tablets based on shear force applied by feed frame paddles.

The feed frame is an essential device used in a rotary tablet press and it improves the performance of the powder filling process into dies. However, the feed frame affects critical quality attributes such as a tensile strength and a dissolution negatively due to a shear applied to powders from feed frame paddles, leading to over-lubrication. This effects may be significant for shear sensitive materials. The work focuses on the effect of tablet press parameters (die disk speed and feed frame speed) and mixture composition (amount of magnesium stearate) on the tensile strength and the prediction of the tensile strength by considering the extent of shear. It is found that within the investigated range of tablet press parameters and the amount of magnesium stearate, the feed frame speed and the amount of magnesium stearate have an impact on the tensile strength. Furthermore, a lubrication model based on the extent of shear is presented to predict the decreasing trend of the tensile strength of tablets during tableting process and the results demonstrate that the prediction of tensile strength is in good agreement with experimental measurements.

Techno-economic and Life Cycle Analysis of MixAlco® Processes for Mixed Alcohol Production from Brown Algae.

The need for producing renewable fuels from biomass has increased due to depleting fossil resources and environmental concerns. However, the low fraction of biomass carbon converted to product is an undeniable drawback for most current biofuel productions from fermentation due to undecomposed lignin in biomass composition and carbon loss as CO2. In this work, two main production routes of the MixAlco® process, the ketonization route (KR) and esterification route (ER) are evaluated for the mixed alcohol production by brown algae, a third-generation biomass without lignin. A novel fermentation process using syntrophic bacteria consortia (SBC) is developed to produce acetic acid from waste gas produced by KR and ER process. The paper investigates the integrated flowsheet for these alternative routes, using techno-economic and life cycle analysis to compare the minimum selling price and environmental impacts. From TEA, we find that the overall costs for KR and ER are lower than the SBC processes. The cost of ketonization routes is lower than esterification routes. The capital cost and operating cost for the ER+SBC process are the highest. Raw materials and utilities are the two major costs for all the processing routes examined. The MSP for the ER+SBC process is the lowest out of all four routes. ER process performs the best in terms of environmental impacts except in water depletion compared with other processes, while the KR process performs the worst regarding the environmental metrics.

Hybrid multi-zonal compartment modeling for continuous powder blending processes.

To modernize drug manufacturing, the pharmaceutical industry has been moving towards implementing emerging technologies to enhance manufacturing robustness and process reliability for production of regulation compliant drug products. Although different science and risk based technologies, like Quality-by-Design, have been used to illustrate their potential, there still exist some underlying obstacles. Specifically, for the production of oral solid drug products, an in-depth process understanding, and predictive modeling of powder mixing in continuous powder blenders is one such major obstacle and originates from the current limitations of the experimental and modeling approaches. Though first principle based discrete element modeling (DEM) approach can address the above issues, it can get very computationally intensive which limits its applications for predictive modeling. In the proposed work, we aim to address this limitation using a multi-zonal compartment modeling approach, which is constructed from DEM. The approach provides a computationally efficient and mechanistically informed hybrid model. The application of the proposed approach is first demonstrated for a periodic section of the blender, followed by its extension for the entire continuous powder blender and the obtained model predictions are validated. The proposed approach provides an overall assessment of powder mixing along axial and radial directions, which is an important requirement for the quantification of blend uniformity. Given the low computational cost, the developed model can further be integrated within the predictive flowsheet model of the manufacturing line.

Residence time distribution modelling and in line monitoring of drug concentration in a tablet press feed frame containing dead zones.

The presence of a 'significant dead zone' in any continuous manufacturing equipment may affect the product quality and need to be investigated systematically. Dead zone will affect the residence time distribution (RTD) of continuous manufacturing and thus the mixing and product quality. Tablet press (feed frame) is one of unit operations that directly influence the critical quality attributes (CQA's). However, currently no systematic methods and tools are available to characterize and model the feed frame dead zone. In this manuscript, the RTD of the tablet press feed frame containing dead zone is investigated. Step-change experiments revealed that the feed frame could be expressed as a traditional continuous stirred tank model. The volume fractions of the dead zones are determined experimentally as well as using RTD model. In addition, an in-line NIR method for drug concentration monitoring inside the feed frame is also developed. The developed NIR calibration model enables to monitor the drug concentration precisely and detect the variation immediately with the probe positioned right above the left paddle. It is also found that the feed frame paddle speed slightly affects the predictive accuracy of NIR, while the die disc speed has no significant effect.

Effect of material properties on the residence time distribution (RTD) of a tablet press feed frame.

In continuous manufacturing (CM) of pharmaceutical tablets, the residence time distribution (RTD) of the tablet press feed frame plays an important role in ensuring the critical quality attributes (CQAs) of the final product. Knowledge of factors affecting the RTD of the feed frame is necessary for sufficient RTD model development. The aim of this work is to investigate the effect of material properties on the mean residence time (MRT) and the lag time. Seven materials with different powder properties were used and the tracer concentration as a function of time were obtained. The RTD model obtained by tracer experiments is approximated using a plug flow reactor (PFR) and a continuous stirred tank reactor (CSTR). The loading plots of principle component analysis (PCA) indicated that the powder bulk density is correlated with the RTD model parameters. Therefore, we focus on establishing the relationship between the bulk density and the MRT. As a result, a linear correlation is obtained to describe the relation between the MRT and the powder bulk density. The simulated results show that the material with lower bulk density had higher risk of producing out-of-specification (OOS) products in comparison to higher bulk density powders.

Discrete element modeling for continuous powder feeding operation: Calibration and system analysis.

Research emphases on extensive experimental studies and modeling efforts have been on the rise for the development of accurate predictive models of pharmaceutical unit operations and 'digital-twin' framework for continuous manufacturing lines. These exhaustive studies have been conducted at different process conditions to acquire comprehensive knowledge of effects of process parameters on the overall process dynamics. However, there still lacks a detailed understanding of material property effects of pharmaceutical powders on process operation. To address this issue, a discrete element modeling (DEM) approach combined with material calibration is applied for simulation of feeder unit to obtain particle-level insight into effects of material properties on feeder performance with focus on particle flow and powder mixing within the feeder unit. Bulk calibration is implemented to accurately represent powder material properties within the DEM framework. Different refill situations are simulated using DEM to observe powder mixing, measured at the outlet. Feeder DEM simulations are further applied to understand correlations of material properties on feeder operation. These studies provide a detailed physical insight and particle-scale information into the powder mechanics during powder feeding operation.

Economic Analysis of Batch and Continuous Biopharmaceutical Antibody Production: A Review.

PURPOSE: There is a growing interest in continuous biopharmaceutical processing due to the advantages of small footprint, increased productivity, consistent product quality, high process flexibility and robustness, facility cost-effectiveness, and reduced capital and operating cost. To support the decision making of biopharmaceutical manufacturing, comparisons between conventional batch and continuous processing are provided. METHODS: Various process unit operations in different operating modes are summarized. Software implementation, as well as computational methods used, are analyzed pointing to the advantages and disadvantages that have been highlighted in the literature. Economic analysis methods and their applications in different parts of the processes are also discussed with examples from publications in the last decade. RESULTS: The results of the comparison between batch and continuous process operation alternatives are discussed. Possible improvements in process design and analysis are recommended. The methods used here do not reflect Lilly's cost structures or economic evaluation methods. CONCLUSION: This paper provides a review of the work that has been published in the literature on computational process design and economic analysis methods on continuous biopharmaceutical antibody production and its comparison with a conventional batch process.

Renewable lubricants with tailored molecular architecture.

We present a strategy to synthesize three types of renewable lubricant base oils with up to 90% yield using 2-alkylfurans, derived from nonfood biomass, and aldehydes, produced from natural oils or biomass through three chemistries: hydroxyalkylation/alkylation (HAA), HAA followed by hydrogenation, and HAA followed by hydrodeoxygenation. These molecules consist of (i) furan rings, (ii) saturated furan rings, and (iii) deoxygenated branched alkanes. The structures of these molecules can be tailored in terms of carbon number, branching length, distance between branches, and functional groups. The site-specific, energy-efficient C-C coupling chemistry in oxygenated biomass compounds, unmatched in current refineries, provides tailored structure and tunable properties. Molecular simulation demonstrates the ability to predict properties in agreement with experiments, proving the potential for molecular design.