Process intensification of pharmaceutical powder blending at commercial throughputs by utilizing semi-continuous mini-blending.

Process intensification involves the miniaturization of equipment while retaining process throughput and performance. The pharmaceutical industry can benefit from this approach especially during drug product development, where the availability of active pharmaceutical ingredients (API) is often limited. It reduces the need for process scale up, as equipment used during product development and commercial production is identical. However, applications of process intensification for processing pharmaceutical powders are limited so far. Here we show that semi-continuous mini-blending can be utilized for process intensification of blending of API and excipients. Uniform blending at commercially relevant throughputs was achieved through mini-blends with a volume of less than ten liters. Our results demonstrate that blending speed, cycle time and blender fill level can be optimized without compromising blending performance. Acceptable blend uniformity is obtained over a broad range of operating parameters, by choosing the right excipients. The optimized throughput of the mini-blending process is in line with the desired throughput of a commercial Continuous Direct Compression (CDC) process.

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.

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.

Using AI/ML to predict blending performance and process sensitivity for Continuous Direct Compression (CDC).

Utilising three artificial intelligence (AI)/machine learning (ML) tools, this study explores the prediction of fill level in inclined linear blenders at steady state by mapping a wide range of bulk powder characteristics to processing parameters. Predicting fill levels enables the calculation of blade passes (strain), known from existing literature to enhance content uniformity. We present and train three AI/ML models, each demonstrating unique predictive capabilities for fill level. These models collectively identify the following rank order of feature importance: RPM, Mixing Blade Region (MB) size, Wall Friction Angle (WFA), and Feed Rate (FR). Random Forest Regression, a machine learning algorithm that constructs a multitude of decision trees at training time and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees, develops a series of individually useful decision trees. but also allows the extraction of logic and breakpoints within the data. A novel tool which utilises smart optimisation and symbolic regression to model complex systems into simple, closed-form equations, is used to build an accurate reduced-order model. Finally, an Artificial Neural Network (ANN), though less interrogable emerges as the most accurate fill level predictor, with an r2 value of 0.97. Following training on single-component mixtures, the models are tested with a four-component powdered paracetamol formulation, mimicking an existing commercial drug product. The ANN predicts the fill level of this formulation at three RPMs (250, 350 and 450) with a mean absolute error of 1.4%. Ultimately, the modelling tools showcase a framework to better understand the interaction between process and formulation. The result of this allows for a first-time-right approach for formulation development whilst gaining process understanding from fewer experiments. Resulting in the ability to approach risk during product development whilst gaining a greater holistic understanding of the processing environment of the desired formulation.

A Commentary on Co-Processed API as a Promising Approach to Improve Sustainability for the Pharmaceutical Industry.

Pharmaceutical products represent a meaningful target for sustainability improvement and emissions reduction. It is proposed here that rethinking the standard, and often linear, approach to the synthesis of Active Pharmaceutical Ingredients (API) and subsequent formulation and drug product processing will deliver transformational sustainability opportunities. The greatest potential arguably involves API that have challenging physico-chemical properties. These can require the addition of excipients that can significantly exceed the weight of the API in the final dosage unit, require multiple manufacturing steps to achieve materials amenable to delivering final dosage units, and need highly protective packaging for final product stability. Co-processed API are defined as materials generated via addition of non-covalently bonded, non-active components during drug substance manufacturing steps, differing from salts, solvates and co-crystals. They are an impactful example of provocative re-thinking of historical regulatory and quality precedents, blurring drug substance and drug product operations, with sustainability opportunities. Successful examples utilizing co-processed API can modify properties with use of less excipient, while simultaneously reducing processing requirements by delivering material amenable to continuous manufacturing. There are also opportunities for co-processed API to reduce the need for highly protective packaging. This commentary will detail the array of sustainability impacts that can be delivered, inclusive of business, regulatory, and quality considerations, with discussion on potential routes to more comprehensively commercialize co-processed API technologies.

Application of Positron Emission Particle Tracking (PEPT) for the evaluation of powder behaviour in an incline linear blender for Continuous Direct Compression (CDC).

Positron Emission Particle Tracking (PEPT) is a non-invasive measurement technique which offers the ability to track the motion of individual particles with high temporal and spatial resolution, and thus build up an understanding of the bulk behaviour of a system from its microscopic (particle level) dynamics. Using this measurement technique, we have developed a series of novel metrics to better understand the behaviours of powders during the steady-state operation of a continuous blender system. Results are presented concerning the response of particle motion to processing parameters (mixing blade configuration and RPM), quantifying the motion in terms of predicted mixing performance. It was found that both increasing rpm and increasing hold-up mass (by selecting fewer transport blades and more mixing blades) provided improved mixing conditions. Interestingly, under specific conditions, there is evidence of convection-like mixing occurring at the interface of the transport and mixing region. This suggests the existence of a potential 'folding region' whereby powder is transported up the barrel (and away from the powder bulk bed) before being reconstituted back into the bulk mass. The results also provide valuable experimental data for the development, calibration and validation of future Discrete Element Method (DEM) simulations.

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.

Continuous Feeding and Blending Demonstration with Co-Processed Drug Substance.

Continuous direct compression (CDC) of solid oral dosage forms requires materials exhibiting acceptable flow and compression properties. The desired active pharmaceutical ingredient (API) powder properties can be difficult to achieve through conventional particle engineering approaches, such as particle size and habit modification during crystallization. Co-processing of API with excipients can significantly improve the powder properties to overcome these difficulties. In this manuscript, performance of a co-processed API was evaluated in a continuous feeding and blending process using GEA ConsiGma® Continuous Dosing and Blending Unit (CDB1). The co-processed theophylline was generated via a methodology in which polymer was precipitated and coated the crystalline theophylline particles resulting in nearly spherical agglomerates. A range of drug loads (1-25% w/w), flow rates (15-40 kg/h) and blender speeds (220-400 rpm) were studied. The results demonstrated that the co-processed API can be successfully fed through a loss-in-weight feeder and blended with other excipients in a high shear blender to generate tablets with acceptable content uniformity at 1-25% w/w drug loads. This study supports that using co-processed API with enhanced powder properties is a promising approach to enable continuous manufacturing for APIs with challenging properties.

Digital twin of a continuous direct compression line for drug product and process design using a hybrid flowsheet modelling approach.

The pharmaceutical industry is continuously overcoming ways to reduce its development times to market and bring new medicines to patients with the highest quality standards faster. This can be achieved with continuous manufacturing and digital design by minimising the amount of active pharmaceutical ingredient (API) needed in drug product design, early project de-risking, and reducing the use of clinical manufacturing equipment, rework, and quality investigations. This paper presents the digital twin of a continuous direct compression line combining first-principles models, residence time distribution (RTD) models obtained from discrete element method (DEM) simulations, science of scale tools and data-driven models from process data in a hybrid flowsheet approach. The flowsheet predicts critical process parameters in the feeders, blender, and tablet press, and critical quality attributes, like tablet composition, weight, thickness, and hardness. It allows the study of the steady state operation in the design space, the impact of operating conditions, material and process parameters, and the dynamic response to disturbances. This is used to de-risk and optimise drug product and process development while reducing the number of experiments. The digital twin also has the potential to guide manufacturing runs and respond to new drug product market approval queries using flowsheet modelling.

Development and Use of a Residence Time Distribution (RTD) Model Control Strategy for a Continuous Manufacturing Drug Product Pharmaceutical Process.

Residence-time-distribution (RTD)-based models are key to understanding the mixing dynamics of continuous manufacturing systems. Such models can allow for material traceability throughout the process and can provide the ability for removal of non-conforming material from the finished product. These models have been implemented in continuous pharmaceutical manufacturing mainly for monitoring purposes, not as an integral part of the control strategy and in-process specifications. This paper discusses the steps taken to develop an RTD model design space and how the model was statistically incorporated into the product's control strategy. To develop the model, experiments were conducted at a range of blender impeller speeds and total system mass flow rates. RTD parameters were optimized for each condition tested using a tank-in-series-type model with a delay. Using the experimental RTD parameters, an equation was derived relating the mean residence time to the operating conditions (i.e., blender impeller speed and mass flow rate). The RTD parameters were used in combination with real-time upstream process data to predict downstream API concentration, where these predictions allowed validation across the entire operating range of the process by comparison to measured tablet assay. The standard in-process control limits for the product were statistically tightened using the validation acceptance criteria. Ultimately, this model and strategy were accepted by regulatory authorities.

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.

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 (RSDBU) 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 RSDBU. 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.

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 (FCCmax), the relative standard deviation on the maximum feed capacity (RSDFCmax), the short term variability (STRSD) and feed capacity decay (FCdecay) were determined. The gravimetric feeder trials generated values for the mass flow rate variability (RSDLC), short term variability (STRSD) and refill responses (Vrefill and RSDrefill). 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.

Effects of process parameters on tablet critical quality attributes in continuous direct compression: a case study of integrating data-driven statistical models and mechanistic compaction models.

Continuous manufacturing of oral-dosage drug products is increasing the need for rigorous process understanding both from a process design and control perspective. The purpose of this study is to develop a methodology that analyzes the effects of upstream process parameters on continuous tablet compaction and then correlates associated upstream variables to the final tablet attributes (e.g. relative density and hardness). The impact of three process parameters (system throughput, blender speed, and compaction force) on tablet attributes is investigated using a full factorial experimental design. As expected, the compaction force was found to be the most significant process parameter. However, importantly, throughput was discovered to have a non-negligible impact which was previously unaccounted for. This impact is proposed to be related to differing levels of powder pre-compression. An empirical model for this relationship is regressed and incorporated into a flowsheet model. The flowsheet model is then used to develop an in silico design space which is compared favorably to that built from experiments. Moreover, in the future, the in silico design space based on the validated flowsheet model can provide better manufacturing flexibility and make control strategy development simpler.

The Comparison of Two Challenging Low Dose APIs in a Continuous Direct Compression Process.

Segregation is a common problem in batch-based direct compression (BDC) processes, especially with low-dose tablet products, as is the preparation of a homogenous mixture. The scope of the current work was to explore if a continuous direct compression (CDC) process could serve as a solution for these challenges. Furthermore, the principle of a platform formulation was demonstrated for low dose tablets. The combination of filler excipients and the API in the formulation used was suitable for direct compression, but also prone to induce segregation in BDC process. The CDC process was found to be very promising; it was shown that tablets with the desired quality parameters could be manufactured successfully with both of the APIs studied. Powder analysis indicated that the APIs display some fundamental differences in their physical properties, which was also reflected in powder mixture properties and, hence, eventually in processing. However, process parameters, especially mixer impeller speed, were not found to have any significant influence on end product quality. The study suggests that a CDC process can be a viable solution to resolve the challenges described. Moreover, manufacturing by using a universal platform formulation seems to be a feasible way for producing low-dose tablets.

Systematic development of a high dosage formulation to enable direct compression of a poorly flowing API: A case study.

In this work, the transfer of oral solid dosage forms, currently manufactured via wet granulation, to a continuous direct compression process was considered. Two main challenges were addressed: (1) a poorly flowing API (Canagliflozin) and (2) high drug loading (51 wt%). A scientific approach was utilised for formulation development, targeting flow and compaction behaviour suitable for manufacturing scale. This was achieved through systematic screening of excipients to identify feasible formulations. Targeted design of experiments based on factors such as formulation mixture and processing parameters were utilised to investigate key responses for tablet properties, flow and compaction behaviour. Flow behaviour was primarily evaluated from percentage compressibility and shear cell testing on a powder flow rheometer (FT4). The compaction behaviour was studied using a compaction simulator (Gamlen). The relationships between tablet porosity, tensile strength and compaction pressure were used to evaluate tabletability, compactibility and compressibility to assess scale-up. The success of this design procedure is illustrated by scaling up from the compaction simulator to a Riva Piccola rotary tablet press, while maintaining critical quality attributes (CQAs). Compactibility was identified as a suitable scale-up relationship. The developed procedure should allow accelerated development of formulations for continuous direct compression.

Impact of blend properties on die filling during tableting.

Based on characterization of a wide range of fillers and APIs, thirty divergent blends were composed and subsequently compressed on a rotary tablet press, varying paddle speed and turret speed. The tablet weight variability was determined of 20 grab samples consisting of each 20 tablets. Additionally, the bulk residence time, ejection force, pre-compression displacement, main compression force, die fill fraction and feed frame fill fraction were determined during each run. Multivariate data analysis was applied to investigate the relation between the process parameters, blend characteristics, product and process responses. Blends with metoprolol tartrate as API showed high ejection forces. This behavior could be linked to the high wall friction value of metoprolol tartrate. The main responses related to the die filling could be predicted via a PLS model based on blend characteristics. Tablet weight variability was highly correlated with the variability on pre-compression displacement and main compression force. A good predictive model for tablet weight variability was obtained taking the porosity, wall friction angle, flowability, density, compressibility and permeability into account. Additionally, turret speed and paddle speed were included in the calibration of the model. The applied approach can save resources (material, time) during early drug product development.

A multivariate raw material property database to facilitate drug product development and enable in-silico design of pharmaceutical dry powder processes.

In current study a holistic material characterization approach was proposed and an extensive raw material property database was developed including a wide variety of APIs and excipients with different functionalities. In total 55 different materials were characterized and described by over 100 raw material descriptors related to particle size and shape distribution, specific surface area, bulk, tapped and true density, compressibility, electrostatic charge, moisture content, hygroscopicity, permeability, flowability and wall friction. Principal component analysis (PCA) was applied to reveal similarities and dissimilarities between materials and to identify overarching properties. The developed PCA model allows to rationalize the number of critical characterization techniques in routine characterization and to identify surrogates for use during early drug product development stages when limited amounts of active pharmaceutical ingredients are available. Additionally, the developed database will be the basis to build predictive models for in silico process and formulation development based on (a selection of) property descriptors.

Development of a continuous direct compression platform for low-dose drug products.

In this work a continuous direct compression process was developed for a low-dosed drug product. Each unit operation of the GEA CDC-50 system was thoroughly investigated. This paper aimed to tackle the macroscopic and microscopic blend uniformity challenges inherently associated with continuous direct compression of cohesive and agglomerated APIs formulated at low dose. Density, compressibility and flow were identified as key material properties at the feeding stage. The screw speed coupled with powder flow regulated the gravimetric feeding performance. The impact of process and design variables was elucidated at the blending stage. The impeller configuration (number and pattern of radial mixing blades) and speed were key variables to steer the residence time distribution at the blending stage. An impeller configuration with distributed radial mixing blades could sufficiently filter the steady state feeding variability at low mixer speed, but exerted limited strain and shear on the blend. Hence micro-agglomerates persisted through the blending process and occasionally resulted in super potent tablets. Therefore, a new configuration was evaluated with more radial mixing blades centered on the impeller. This configuration resulted in a long mixing time at high tip speed which induced a maximized strain and shear. Consequently, excellent uniformity of the blend and tablets at macroscopic and microscopic level was achieved. Besides, this impeller improved robustness towards feeding disturbances, changes in process settings and variable blend properties. Next, it was demonstrated that the lubrication step requires critical attention during the design of the equipment, formulation and process. This study provided abundant evidence that an optimized continuous direct compression process allows direct compression of challenging low-dose drug products.