Perspectives on the continuous manufacturing of powder-based pharmaceutical processes

A framework for the in silico assessment of the robustness of an MPC in a CDC line in function of process variability

The shift from batch manufacturing towards continuous manufacturing for the production of oral solid dosages requires the development and implementation of process models and process control. Previous work focused mainly on developing deterministic models for the investigated system. Furthermore, the in silico tuning and analysis of a control strategy are mostly done based on deterministic models. This deterministic approach could lead to wrong actions in diversion strategies and poor transferability of the controller performance if the system behaves differently than the deterministic model. This work introduces a framework that explicitly includes the process variability which is characteristic of powder handling processes and tests it on a novel continuous feeding-blending unit (i.e., the FE continuous processing system (CPS)), followed by a tablet press (i.e., the FE 55). It employs a stochastic model by allowing the model parameters to have a probability distribution. The performance of a model predictive control (MPC), steering the feed rate of the main excipient feeder to compensate for the feed rate deviations of the active pharmaceutical ingredient (API) feeder to keep the API concentration close to the desired value, is evaluated and the impact of process variability is assessed in a Monte Carlo (MC) analysis. Next to the process variability, a model for the prediction error of the chemometric model and realistic feed rate disturbances were included to increase the transferability of the results to the real system. The obtained results show that process variability is inherently present and that wrong conclusions can be drawn if it is not taken into account in the in silico analysis.

Batch vs. continuous direct compression - a comparison of material processability and final tablet quality

In this study, an in-depth comparison was made between batch and continuous direct compression using similar compression set-ups. The overall material processability and final tablet quality were compared and evaluated. Correlations between material properties, process parameters and final tablet properties were made via multivariate data analyses. In total, 10 low-dosed (1% w/w) and 10 high-dosed (40% w/w) formulations were processed, using a total of 10 different fillers/filler combinations. The trials indicated that the impact of filler type, drug load or process settings was similar for batch and continuous direct compression. The main differentiator between batch and continuous was the flow dynamics in the operating system, where properties related to flow, compressibility and permeability played a crucial role. The less consistent flow throughout a batch process resulted in a significantly higher variability within the tablet press (σCF) and for the tablet quality responses (σMass, σTS). However, the better controlled blending procedure prior to batch processing was reflected in a more consistent API concentration variability. Overall, the comparison showed the benefits of selecting appropriate excipients and process settings to achieve a specific outcome, keeping in mind some key differentiators between both processes.

Feed factor profile prediction model for two-component mixed powder in the twin-screw feeder

In continuous pharmaceutical manufacturing processes, it is crucial to control the powder flow rate. The feeding process is characterized by the amount of powder delivered per screw rotation, referred to as the feed factor. This study aims to develop models for predicting the feed factor profiles (FFPs) of two-component mixed powders with various formulations, while most previous studies have focused on single-component powders. It further aims to identify the suitable model type and to determine the significance of material properties in enhancing prediction accuracy by using several FFP prediction models with different input variables. Four datasets from the experiment were generated with different ranges of the mass fraction of active pharmaceutical ingredients (API) and the powder weight in the hopper. The candidates for the model inputs are (a) the mass fraction of API, (b) process parameters, and (c) material properties. It is desirable to construct a high-performance prediction model without the material properties because their measurement is laborious. The results show that using (c) as input variables did not improve the prediction accuracy as much, thus there is no need to use them.

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.

Analytical comparison between batch and continuous direct compression processes for pharmaceutical manufacturing using an innovative UV-Vis reflectance method and chemometrics

Advancements in industrial technologies and the application of quality by design (QbD) guidelines are shifting the attention of manufacturers towards innovative production techniques. In the pharmaceutical field, there is a significant focus on the implementation of continuous processes, in which the production stages are carried out continuously, without the need to interrupt the process and store the production intermediates, as in traditional batch production. Such innovative production techniques also require the development of proper analytical methods able to analyze the products in-line, while still being processed. The present study aims to compare a traditional batch manufacturing process with an alternative continuous one. To this end, a real pharmaceutical formulation was used, substituting the active pharmaceutical ingredient (API) with riboflavin, at the concentration of 2 %w/w. Moreover, a direct and non-destructive analytical method based on UV-Vis reflectance spectroscopy was applied for the quantification of riboflavin in the final tablets, and compared with a traditional absorbance analysis. Good results were obtained in the comparison of both the two manufacturing processes and the two analytical methods, with R2 higher than 0.9 for all the calculated calibration models and predicted riboflavin concentrations that never significantly overcame the 15 % limits recommended by the pharmacopeia. The continuous production method demonstrated to be as reliable as the batch one, allowing to save time and money in the production step. Moreover, UV-Vis reflectance was proved to be an interesting alternative to absorption spectroscopy, which, with the proper technology, could be implemented for in-line process control.

Developing a Modular Continuous Drug Product Manufacturing System with Real Time Quality Assurance for Producing Pharmaceutical Mini-Tablets

The pharmaceutical industry has shown keen interest in developing small-scale modular manufacturing systems for producing medicinal products. These systems offer agile and flexible manufacturing, and are well-suited for use in situations requiring rapid production of drugs such as pandemics and humanitarian disasters. The creation of such systems requires the development of modular facilities for making solid oral drug products. In recent years, however, the development of such facilities has seen limited progress. This study presents a development of a prototype modular system that uses drop on demand (DoD) printing to produce personalized solid oral drug products. The system's operation is demonstrated for manufacturing mini-tablets, a category of pediatric drug products, in continuous and semi-batch modes. In this process, the DoD printer is used to generate molten formulation drops that are solidified into mini-tablets. These dosages are then extracted, washed and dried in a continuous filtration and drying unit which is integrated with the printer. Process monitoring tools are also incorporated in the system to track the critical quality attributes of the product and the critical process parameters of the manufacturing operation in real time. Future areas of innovation are also proposed to improve this prototype unit and to enable the development of advanced drug manufacturing systems based on this platform.

Two-dimensional printing of nanoparticles as a promising therapeutic method for personalized drug administration

The necessity for personalized patient treatment has drastically increased since the contribution of genes to the differences in physiological and metabolic state of individuals have been exposed. Different approaches have been considered so far in order to satisfy all of the diversities in patient needs, yet none of them have been fully implemented thus far. In this framework, various types of 2D printing technologies have been identified to offer some potential solutions for personalized medication, which development is increasing rapidly. Accurate drug-on-demand deposition, the possibility of consuming multiple drug substances in one product and adjusting individual drug concentration are just some of the few benefits over existing bulk pharmaceuticals manufacture, which printing technologies brings. With inclusion of nanotechnology by printing nanoparticles from its dispersions some further opportunities such as controlled and stimuli-responsive drug release or targeted and dose depending on drug delivery were highlighted. Yet, there are still some challenges to be solved before such products can reach the pharmaceutical market. In those terms mostly chemical, physical as well as microbiological stability concerns should be answered, with which 2D printing technology could meet the treatment needs of every individual and fulfill some existing drawbacks of large-scale batch production of pharmaceuticals we possess today.

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.

Reviewing the Impact of Powder Cohesion on Continuous Direct Compression (CDC) Performance

The pharmaceutical industry is undergoing a paradigm shift towards continuous processing from batch, where continuous direct compression (CDC) is considered to offer the most straightforward implementation amongst powder processes due to the relatively low number of unit operations or handling steps. Due to the nature of continuous processing, the bulk properties of the formulation will require sufficient flowability and tabletability in order to be processed and transported effectively to and from each unit operation. Powder cohesion presents one of the greatest obstacles to the CDC process as it inhibits powder flow. As a result, there have been many studies investigating potential manners in which to overcome the effects of cohesion with, to date, little consideration of how these controls may affect downstream unit operations. The aim of this literature review is to explore and consolidate this literature, considering the impact of powder cohesion and cohesion control measures on the three-unit operations of the CDC process (feeding, mixing, and tabletting). This review will also cover the consequences of implementing such control measures whilst highlighting subject matter which could be of value for future research to better understand how to manage cohesive powders for CDC manufacture.

Flowsheet modelling of a powder continuous feeder-mixer system

In this study, an integrated flowsheet model of the continuous feeder-mixer system was calibrated, simulated and compared against experimental data. The feeding process was first investigated using two major components (ibuprofen and microcrystalline cellulose (MCC)), in a formulation comprised of: 30 wt% of ibuprofen, 67.5 wt% MCC, 2 wt% of sodium starch glycolate and 0.5 wt% of magnesium stearate. The impact of a refill on feeder performance was experimentally evaluated for different operating conditions. Results showed that it had no influence on feeder performance. While simulations with the feeder model fairly reproduced the material behaviour observed in the feeder, unintended disturbances were underpredicted due to the model's low complexity. Experimentally, mixer's efficiency was assessed based on ibuprofen residence time distribution. Mean residence time pointed to a higher mixer's efficiency at lower flow rates. Blend homogeneity results showed that for the entire set of experiments, ibuprofen RSD <5%, irrespective of process variables. A feeder-mixer flowsheet model was calibrated, after regressing the axial model coefficients. The regression curves exhibited a R² above 0.96, whereas the RMSE varied from 1.58x10^-4 to 1.06x10^-3 s^-1 across all fitted curves. Simulations confirmed that flowsheet model captured the powder dynamics inside the mixer and qualitatively predicted the mixer's filtering ability against feeding composition fluctuations, as well as ibuprofen RSD in blend, in line with real experiments.

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.

Terahertz frequency-domain sensing combined with quantitative multivariate analysis for pharmaceutical tablet inspection

Near infrared (NIR) and Raman spectroscopy combined with multivariate analysis are established techniques for the identification and quantification of chemical properties of pharmaceutical tablets like the concentration of active pharmaceutical ingredients (API). However, these techniques suffer from a high sensitivity to particle size variations and are not ideal for the characterization of physical properties of tablets such as tablet density. In this work, we have explored the feasibility of terahertz frequency-domain spectroscopy, with the advantage of low scattering effects, combined with multivariate analysis to quantify API concentration and tablet density. We studied 33 tablets, consisting of Ibuprofen, Mannitol, and a lubricant with API concentration and filler particle size as the design factors. The terahertz signal was measured in transmission mode across the frequency range 750 GHz to 1.5 THz using a vector network analyzer, frequency extenders, horn antennas, and four off-axis parabolic mirrors. The attenuation spectral data were pre-processed and orthogonal partial least square (OPLS) regression was applied to the spectral data to obtain quantitative prediction models for API concentration and tablet density. The performance of the models was assessed using test sets. While a fair model was obtained for API concentration, a high-quality model was demonstrated for tablet density. The coefficient of determination (R²) for the calibration set was 0.97 for tablet density and 0.98 for API concentration, while the relative prediction errors for the test set were 0.7% and 6% for tablet density and API concentration models, respectively. In conclusion, terahertz spectroscopy demonstrated to be a complementary technique to Raman and NIR spectroscopy, which enables the characterization of physical properties of tablets like tablet density, and the characterization of API concentration with the advantage of low scattering effects.

Quality-by-control of intensified continuous filtration-drying of active pharmaceutical ingredients

Continuous manufacturing and closed-loop quality control are emerging technologies that are pivotal for next-generation pharmaceutical modernization. We develop a process control framework for a continuous carousel for integrated filtration-drying of crystallization slurries. The proposed control system includes model-based monitoring and control routines, such as state estimation and real-time optimization, implemented in a hierarchical, three-layer quality-by-control (QbC) framework. We implement the control system in ContCarSim, a publicly available carousel simulator. We benchmark the proposed control system against simpler methods, comprising a reduced subset of the elements of the overall control system, and against open-loop operation (the current standard in pharmaceutical manufacturing). The proposed control system demonstrates superior performance in terms of higher consistency in product quality and increased productivity, proving the benefits of closed-loop control and of model-based techniques in pharmaceutical manufacturing. This study represents a step forward toward end-to-end continuous pharmaceutical processing, and in the evolution of quality-by-design toward quality-by-control.

Continuous direct compression: Development of an empirical predictive model and challenges regarding PAT implementation

In this study, an empirical predictive model was developed based on the quantitative relationships between blend properties, critical quality attributes (CQA) and critical process parameters (CPP) related to blending and tableting. The blend uniformity and API concentration in the tablets were used to elucidate challenges related to the processability as well as the implementation of PAT tools. Thirty divergent ternary blends were evaluated on a continuous direct compression line (ConsiGma™ CDC-50). The trials showed a significant impact of the impeller configuration and impeller speed on the blending performance, whereas a limited impact of blend properties was observed. In contrast, blend properties played a significant role during compression, where changes in blend composition significantly altered the tablet quality. The observed correlations allowed to develop an empirical predictive model for the selection of process configurations based on the blend properties, reducing the number of trial runs needed to optimize a process and thus reducing development time and costs of new drug products. Furthermore, the trials elucidated several challenges related to blend properties that had a significant impact on PAT implementation and performance of the CDC-platform, highlighting the importance of further process development and optimization in order to solve the remaining challenges.

Deep learning in drug discovery: an integrative review and future challenges

Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug-target interactions (DTIs), drug-drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.

Linked experimental and modelling approaches for tablet property predictions

Recent years have seen the advent of Quality-by-Design (QbD) as a philosophy to ensure the quality, safety, and efficiency of pharmaceutical production. The key pharmaceutical processing methodology of Direct Compression to produce tablets is also the focus of some research. The traditional Design-of-Experiments and purely experimental approach to achieve such quality and process development goals can have significant time and resource requirements. The present work evaluates potential for using combined modelling and experimental approach, which may reduce this burden by predicting the properties of multicomponent tablets from pure component compression and compaction model parameters. Additionally, it evaluates the use of extrapolation from binary tablet data to determine theoretical pure component model parameters for materials that cannot be compacted in the pure form. It was found that extrapolation using binary tablet data - where one known component can be compacted in pure form and the other is a challenging material which cannot be - is possible. Various mixing rules have been evaluated to assess which are suitable for multicomponent tablet property prediction, and in the present work linear averaging using pre-compression volume fractions has been found to be the most suitable for compression model parameters, while for compaction it has been found that averaging using a power law equation form produced the best agreement with experimental data. Different approaches for estimating component volume fractions have also been evaluated, and using estimations based on theoretical relative rates of compression of the pure components has been found to perform slightly better than using constant volume fractions (that assume a fully compressed mixture). The approach presented in this work (extrapolation of, where necessary, binary tablet data combined with mixing rules using volume fractions) provides a framework and path for predictions for multicomponent tablets without the need for any additional fitting based on the multicomponent formulation composition. It allows the knowledge space of the tablet to be rapidly evaluated, and key regions of interest to be identified for follow-up, targeted experiments that that could lead to an establishment of a design and control space and forgo a laborious initial Design-of-Experiments.

A review on the modernization of pharmaceutical development and manufacturing - Trends, perspectives, and the role of mathematical modeling

Recently, the pharmaceutical industry has been facing several challenges associated to the use of outdated development and manufacturing technologies. The return on investment on research and development has been shrinking, and, at the same time, an alarming number of shortages and recalls for quality concerns has been registered. The pharmaceutical industry has been responding to these issues through a technological modernization of development and manufacturing, under the support of initiatives and activities such as quality-by-design (QbD), process analytical technology, and pharmaceutical emerging technology. In this review, we analyze this modernization trend, with emphasis on the role that mathematical modeling plays within it. We begin by outlining the main socio-economic trends of the pharmaceutical industry, and by highlighting the life-cycle stages of a pharmaceutical product in which technological modernization can help both achieve consistently high product quality and increase return on investment. Then, we review the historical evolution of the pharmaceutical regulatory framework, and we discuss the current state of implementation and future trends of QbD. The pharmaceutical emerging technology is reviewed afterwards, and a discussion on the evolution of QbD into the more effective quality-by-control (QbC) paradigm is presented. Further, we illustrate how mathematical modeling can support the implementation of QbD and QbC across all stages of the pharmaceutical life-cycle. In this respect, we review academic and industrial applications demonstrating the impact of mathematical modeling on three key activities within pharmaceutical development and manufacturing, namely design space description, process monitoring, and active process control. Finally, we discuss some future research opportunities on the use of mathematical modeling in industrial pharmaceutical environments.

Microbial acoustic sensor test-system based on a piezoelectric resonator with a lateral electric field for kanamycin detection in liquid

A microbial test-system for real-time determination of low/residual concentrations of kanamycin in a liquid without the need for special labels is presented. The main element of the system was a piezoelectric resonator excited by a lateral electric field based on an X-cut lithium niobate plate 0.5 mm thick with two rectangular electrodes on one side. On the other side of the resonator, there was a 1.5 ml liquid container. As a sensory element we used Escherichia coli B-878 microbial cells, which are sensitive to kanamycin. For measurement 1 ml of this cells suspension was placed in a liquid container and then the test liquid in the amount of 2 μl containing kanamycin was added. The change in the real part of the electrical impedance of the resonator before and after the test liquid addition was used as an analytical signal which indicated the presence of kanamycin. The lower limit of determination of kanamycin turned out to be 1.0 μg/ml with an analysis time of 10 min. The test-system allows to detect kanamycin in the presence of such antibiotic as ampicillin and polymixin.

In-depth analysis of the long-term processability of materials during continuous feeding

This study determined the feasibility of long-term continuous powder feeding and its effect on the overall process performance. Additionally, quantitative relationships were established between material properties, process settings and screw feeding responses during gravimetric feeding. Twelve divergent raw materials were processed over longer periods using a GEA Compact Feeder integrated in a continuous direct compression line (ConsiGma™ CDC-50). The resulting gravimetric feeding responses were combined with the material properties and process settings into an overall PLS model. The model elucidated the impact of the material descriptors for density; powder flow; particle size; compressibility; permeability and wall friction angle on the feeding process. Furthermore, long-term processing of the materials exhibited challenges related to the processability and refill consistency where a significant impact of the compressibility and cohesive/adhesive properties of the materials was observed. Overall, this approach provided insights into the feasibility of long-term continuous feeding which is not possible through 'short-term' feeding trials. Additionally, throughout this study, the need for material-specific adjustments of the feeding and refill equipment was highlighted.

Real-Time Monitoring of Powder Mass Flowrates for Plant-Wide Control of a Continuous Direct Compaction Tablet Manufacturing Process

While measurement and monitoring of powder/particulate mass flow rate are not essential to the execution of traditional batch pharmaceutical tablet manufacturing, in continuous operation, it is an important additional critical process parameter. It has a key role both in establishing that the process is in a state of control, and as a controlled variable in process control system design. In current continuous tableting line operations, the pharmaceutical community relies on loss-in-weight feeders to monitor and understand upstream powder flow dynamics. However, due to the absence of established sensing technologies for measuring particulate flow rates, the downstream flow of the feeders is monitored and controlled using various indirect strategies. For example, the hopper level of the tablet press is maintained as a controlled process output by adjusting the turret speed of the tablet press, which indirectly controlling the flow rate. This gap in monitoring and control of the critical process flow motivates our investigation of a novel PAT tool, a capacitance-based sensor (ECVT), and its effective integration into the plant-wide control of a direct compaction process. First, the results of stand-alone experimental studies are reported, which confirm that the ECVT sensor can provide real-time measurements of mass flow rate with measurement error within -1.8 ~ 3.3% and with RMSE of 0.1 kg/h over the range of flow rates from 2 to 10 kg/h. The key caveat is that the powder flowability has to be good enough to avoid powder fouling on the transfer line walls. Next, simulation case studies are carried out using a dynamic flowsheet model of a continuous direct compression line implemented in Matlab/Simulink to demonstrate the potential structural and performance advantages in plant-wide process control enabled by mass flow sensing. Finally, experimental studies are performed on a direct compaction pilot plant in which the ECVT sensor is located at the exit of the blender, to confirm that the powder flow can be monitored instantaneously and controlled effectively at the specified setpoint within a plant-wide feedback controller system.

Application of MHE-based NMPC on a Rotary Tablet Press under Plant-Model Mismatch

Active control strategies play a vital role in modern pharmaceutical manufacturing. Automation and digitalization are revolutionizing the pharmaceutical industry and are particularly important in the shift from batch operations to continuous operation. Active control strategies provide real-time corrective actions when departures from quality targets are detected or even predicted. Under the concept of Quality-by-Control (QbC), a three-level hierarchical control structure can be applied to achieve effective setpoint tracking and disturbance rejection in the tablet manufacturing process through the development and implementation of a moving horizon estimation-based nonlinear model predictive control (MHE-NMPC) framework. When MHE is coupled with NMPC, historical data in the past time window together with real-time data from the sensor network enable model parameter updating and control. The adaptive model in the NMPC strategy compensates for process uncertainties, further reducing plant-model mismatch effects. The frequency and constraints of parameter updating in the MHE window should be determined cautiously to maintain control robustness when sensor measurements are degraded or unavailable. The practical applicability of the proposed MHE-NMPC framework is demonstrated via using a commercial scale tablet press, Natoli NP-400, to control tablet properties, where the nonlinear mechanistic models used in the framework can predict the essential powder properties and provide physical interpretations.

Impact of blend properties and process variables on the blending performance

In this study, quantitative relationships were established between blend properties, process settings and blending responses via multivariate data-analysis. Four divergent binary blends were composed in three different ratios and processed at various throughputs and impeller speeds. Additionally, different impeller configurations were tested to see their impact on the overall blending performance. During each run, feeder mass flows were compared with the API concentration (BU) in order to investigate the dampening potential of the blender. The blender hold-up mass (HM), mean residence time (MRT), strain on the powder (#BP) and BU variability (RSD_BU) were determined as blending descriptors and analyzed via PLS-regression. This elucidated the correlation between process settings (i.e. throughput and impeller speed) and blending responses, as well as the impact of blend properties on MRT and RSD_BU. Furthermore, the study revealed that HM does not need to be in steady state conditions to assure a stable BU, while it became clear that long/large feeder deviations can only be dampened by the blender when using dedicated impeller configurations. Overall, this study demonstrated the generic application of the blender, while the developed PLS models could be used to predict the blender performance based on the blend properties.

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; Teżyk 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.

Mathematical modeling and digital design of an intensified filtration-washing-drying unit for pharmaceutical continuous manufacturing

This paper introduces a comprehensive mathematical model of a novel integrated filter-dryer carousel system, designed for continuously filtering, washing and drying a slurry stream into a crystals cake. The digital twin includes models for dead-end filtration, cake washing and convective cake drying, based on dynamic multi-component mass, energy and momentum balances. For set of feed conditions and control inputs, the model allows tracking the solvents and impurities content in the cake (critical quality attributes, CQAs) throughout the whole process. The model parameters were identified for the isolation of paracetamol from a multi-component slurry, containing a non-volatile impurity. The calibrated model was used for identifying the probabilistic design space and maximum throughput for the process, expressing the combinations of the carousel feed conditions and control inputs for which the probability of meeting the target CQAs is acceptable.

Analysis of the Effects of Process Parameters on Start-Up Operation in Continuous Wet Granulation

Toward further implementation of continuous tablet manufacturing, one key issue is the time needed for start-up operation because it could lead to lower product yield and reduced economic performance. The behavior of the start-up operation is not well understood; moreover, the definition of the start-up time is still unclear. This work investigates the effects of process parameters on the start-up operation in continuous wet granulation, which is a critical unit operation in solid drug manufacturing. The profiles of torque and granule size distribution were monitored and measured for the first hour of operation, including the start-up phase. We analyzed the impact of process parameters based on design of experiments and performed an economic assessment to see the effects of the start-up operation. The torque profiles indicated that liquid-to-solid ratio and screw speed would affect the start-up operation, whereas different start-up behavior resulted in different granule size. Depending on the indicator used to define the start-up operation, the economic optimal point was significantly different. The results of this study stress that the start-up time differs according to the process parameters and used definition, e.g., indicators and criteria. This aspect should be considered for the further study and regulation of continuous manufacturing.

Determination of a quantitative relationship between material properties, process settings and screw feeding behavior via multivariate data-analysis

In this study, a quantitative relationship between material properties, process settings and screw feeding responses of a high-throughput feeder was established via multivariate models (PLS). Thirteen divergent powders were selected and characterized for 44 material property descriptors. During volumetric feeder trials, the maximum feed capacity (FCC_max), the relative standard deviation on the maximum feed capacity (RSD_FCmax), the short term variability (STRSD) and feed capacity decay (FC_decay) were determined. The gravimetric feeder trials generated values for the mass flow rate variability (RSD_LC), short term variability (STRSD) and refill responses (V_refill and RSD_refill). The developed PLS models elucidated that the material properties and process settings were clearly correlated to the feeding behavior. The extended volumetric feeder trials pointed out that there was a significant influence of the chosen screw type and screw speed on the feeding process. Furthermore, the process could be optimized by reducing the feeding variability through the application of optimized mass flow filters, high frequency vibrations, independent agitator control and optimized top-up systems. Overall, the models could allow the prediction of the feeding performance for a wide range of materials based on the characterization of a subset of material properties greatly reducing the number of required feeding experiments.

Soft sensor for real-time estimation of tablet potency in continuous direct compression manufacturing operation

One of the critical quality attributes of the solid oral dosage forms produced in continuous direct compression operations is the tablet potency. A novel soft sensor comprising of a combination of first principle-based and empirical models has been developed to enable real-time monitoring of blend and tablet potency, and concentrations of other excipients at various stream levels along the direct compression line. The soft sensor model has only three adjustable parameters, primarily associated with the equipment design and operation, so the model is product agnostic which is key to enable flexible manufacturing. The estimation accuracy of the soft sensor is demonstrated through a series of real time experiments which include steady state and dynamic transitions of potency during the runs, compared with offline analytically tested tablet cores. The results indicate that the proposed soft sensor can be utilized as a robust tool for real-time monitoring of potency, suggesting an extension of its utilization to higher levels of control. Two potential applications of the soft sensor are: 1. An element of a control strategy for product diversion; 2. A predictive model for advanced process control strategy to minimize the variability in tablet composition.

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.

Bacteria-based electro-optical platform for ampicillin detection in aquatic solutions

We have shown for the first time that it is possible to use a bacteria-based sensory system consisting of the bacterium Pseudomonas putida TSh-18 and an electro-optical sensor to detect ampicillin in the concentration range 0.5-600 μg/mL. Changes in the anisotropy of cell polarizability were detected at 900 and 2100 kHz; these represented the state of the cytoplasm and of the cell membrane, respectively. The changes indicate the quickest cell response to changes in the characteristics of the bacterial culture exposed to ampicillin. We have also shown that it is possible to monitor the ampicillin in the presence of kanamycin. In control experiments, we examined the effects of ampicillin and kanamycin on bacterial cells by phase-contrast microscopy and by standard microbiological tests on solid media. P. putida TSh-18 is recommended as a sensor system for ampicillin detection. Electro-optical analysis ensures detection of ampicillin in aquatic solutions in real-time, takes 10 min, and offers a lower limit of ampicillin detection of 0.5 μg/mL, which is lower than the European Community's maximum residue limit standards for penicillin antibiotics.

Loss-in-weight feeding, powder flow and electrostatic evaluation for direct compression hydroxypropyl methylcellulose (HPMC) to support continuous manufacturing

Minimizing variability in the feeding process is important for continuous manufacturing since materials are fed individually and can impact the final product. This study demonstrates the importance of measuring powder properties and highlights the need to characterize the feeding performance both offline with multiple refills and in the intended configuration for the continuous manufacturing equipment. The standard grade hydroxypropyl methylcellulose (HPMC) had material buildup on the loss-in-weight feeder barrel from triboelectric charging and resulted in more mass flow excursions and failed refills which were not observed with the direct compression grades. The location of the electrostatic buildup changed when the feeder was connected to a hopper instead of feeding offline into a collection bucket. Overall, the direct compression HPMC exhibited better flow which resulted in more accurate loss-in-weight feeding with less excursions from the target mass flow and all refills were completed in the first attempt. The improvements with the direct compression HPMC would be beneficial when running any continuous process (wet granulation, roller compaction, or direct compression) or other processes where loss-in-weight feeding is utilized, such as melt extrusion or twin screw granulation.

Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press

The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects.

Design of condition-based maintenance framework for process operations management in pharmaceutical continuous manufacturing

Continuous manufacturing, an emerging technology in the pharmaceutical industry, has the potential to increase the efficiency, and agility of pharmaceutical manufacturing processes. To realize these potential benefits of continuous operations, effectively managing materials, equipment, analyzers, and data is vital. Developments for continuous pharmaceutical manufacturing have led to novel technologies and methods for processing material, designing and configuring individual equipment and process analyzers, as well as implementing strategies for active process control. However, limited work has been reported on managing abnormal conditions during operations to prevent unplanned deviations and downtime and sustain system capabilities. Moreover, although the sourcing, analysis, and management of real-time data have received growing attention, limited discussion exists on the continued verification of the infrastructure for ensuring reliable operations. Hence, this work introduces condition-based maintenance (CBM) as a general strategy for continually verifying and sustaining advanced pharmaceutical manufacturing systems, with a focus on the continuous manufacture of oral solid drug products (OSD-CM). Frameworks, such as CBM, benefit unified efforts towards continued verification and operational excellence by leveraging process knowledge and the availability of real-time data. A vital implementation consideration for manufacturing operations management applications, such as CBM, is a systems architecture and an enabling infrastructure. This work outlines the systems architecture design for CBM in OSD-CM and highlights sample fault scenarios involving equipment and process analyzers. For illustrative purposes, this work also describes the infrastructure implemented on an OSD-CM testbed, which uses commercially available automation systems and leverages enterprise architecture standards. With the increasing digitalization of manufacturing operations in the pharmaceutical industry, proactively using process data towards modernizing maintenance practices is relevant to a single unit operation as well as to a series of physically integrated unit operations.

Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review

The development and application of emerging technologies of Industry 4.0 enable the realization of digital twins (DT), which facilitates the transformation of the manufacturing sector to a more agile and intelligent one. DTs are virtual constructs of physical systems that mirror the behavior and dynamics of such physical systems. A fully developed DT consists of physical components, virtual components, and information communications between the two. Integrated DTs are being applied in various processes and product industries. Although the pharmaceutical industry has evolved recently to adopt Quality-by-Design (QbD) initiatives and is undergoing a paradigm shift of digitalization to embrace Industry 4.0, there has not been a full DT application in pharmaceutical manufacturing. Therefore, there is a critical need to examine the progress of the pharmaceutical industry towards implementing DT solutions. The aim of this narrative literature review is to give an overview of the current status of DT development and its application in pharmaceutical and biopharmaceutical manufacturing. State-of-the-art Process Analytical Technology (PAT) developments, process modeling approaches, and data integration studies are reviewed. Challenges and opportunities for future research in this field are also discussed.