Modeling the effects of material properties on tablet compaction: A building block for controlling both batch and continuous pharmaceutical manufacturing processes

Analysis of the impact of material properties on tabletability by principal component analysis and partial least squares regression

Principal component analysis (PCA) and partial least squares regression (PLS) were combined in this study to identify key material descriptors determining tabletability in direct compression and roller compaction. An extensive material library including 119 material descriptors and tablet tensile strengths of 44 powders and roller compacted materials with varying drug loads was generated to systematically elucidate the impact of different material descriptors, raw API and filler properties as well as process route on tabletability. A PCA model was created which highlighted correlations between different powder descriptors and respective characterization methods and, thus, can enable reduction of analyses to save resources to a certain extent. Subsequently, PLS models were established to identify key material attributes for tabletability such as density and particle size but also surface energy, work of cohesion and wall friction, which were for the first time demonstrated by PLS as highly relevant for tabletability in roller compaction and direct compression. Further, PLS based on extensive material characterization enabled the prediction of tabletability of materials unknown to the model. Thus, this study highlighted how PCA and PLS are useful tools to elucidate the correlations between powder and tabletability, which will enable more robust prediction of manufacturability in formulation development.

A Rational Hierarchy to Capture Raw Material Attribute Variability in the Pharmaceutical Drug Product Development and Manufacturing Lifecycle

Assessing the robustness of a drug product formulation and manufacturing process to variations in raw material (RM) properties is an essential aspect of pharmaceutical product development. Motivated by the need to demonstrate understanding of attribute-performance relationships at the time of new product registration and for subsequent process maintenance, we review practices to explore RM variations. We describe limitations that can arise when active ingredients and excipients invariably undergo changes during a drug product lifecycle. Historical approaches, such as Quality-by-Design (QbD) experiments, are useful for initial evaluations but can be inefficient and cumbersome to maintain once commercial manufacturing commences. The relatively miniscule data sets accessible in product development - used to predict response to a hypothetical risk of variation - become less relevant as real-world experience of actual variability in the commercial landscape grows. Based on our observations of development and manufacturing, we instead propose a holistic framework exploiting a hierarchy of RM variability, and challenge this with common failure modes. By explicitly incorporating higher ranking RM variations as perturbations, material-conserving experiments are shown to provide powerful and enduring robustness data. Case studies illustrate how correctly contextualizing such data in formulation and process development can avoid the traps of historical QbD approaches and become valuable for evaluating changes occurring later in the drug product lifecycle.

Influence of the drug deformation behaviour on the predictability of compressibility and compactibility of binary mixtures

Various studies investigate the predictability of the compressibility and compactibility of tablet formulations based on the behaviour of the pure materials. However, these studies are limited to a few materials so far probably because of the complexity of the powder compaction process. One approach preventing the excessive increase in complexity is the extension of the investigations from pure materials to binary powder mixtures. The focus of this study is on the predictability of the compressibility and compactibility of binary mixtures consisting of an active pharmaceutical ingredient (API) and the excipient microcrystalline cellulose. Three APIs with markedly different deformation behaviour were used. The API concentration and type are systematically varied. For all three material combinations it is found that the in-die compressibility of the binary mixtures can be precisely predicted based on the characteristic compression parameters of the raw materials using the extended in-die compression function in combination with a volume-based linear mixing rule. Since the tablet porosity (out-of-die) also follows a linear mixing rule, the predictability can be further extended using the method of Katz et al. In contrast, the influence of the API concentration on compactibility or rather on tablet tensile strength is non-linear and strongly dependent on the deformation behaviour of the API, making the predictability more difficult. Neither the approach of Reynolds et al. nor this of Kuentz and Leuenberger are able to predict the compactibility when clear deviations from a linear mixing rule appear.

Process Model Approach to Predict Tablet Weight Variability for Direct Compression Formulations at Pilot and Production Scale

Optimizing processing conditions to achieve a critical quality attribute (CQA) is an integral part of pharmaceutical quality by design (QbD). It identifies combinations of material and processing parameters ensuring that processing conditions achieve a targeted CQA. Optimum processing conditions are formulation and equipment-dependent. Therefore, it is challenging to translate a process design between formulations, pilot-scale and production-scale equipment. In this study, an empirical model was developed to determine optimum processing conditions for direct compression formulations with varying flow properties, across pilot- and production-scale tablet presses. The CQA of interest was tablet weight variability, expressed as percentage relative standard deviation. An experimental design was executed for three model placebo blends with varying flow properties. These blends were compacted on one pilot-scale and two production-scale presses. The process model developed enabled the optimization of processing parameters for each formulation, on each press, with respect to a target tablet weight variability of <1%RSD. The model developed was successfully validated using data for additional placebo and active formulations. Validation formulations were benchmarked to formulations used for model development, employing permeability index values to indicate blend flow.

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.

An insight into predictive parameters of tablet capping by machine learning and multivariate tools

Capping is the frequently observed mechanical defect in tablets arising from the sub-optimal selection of the formulation composition and their robustness of response toward process parameters. Hence, overcoming capping propensity based on the understanding of suitable process and material parameters is of utmost importance to expedite drug product development. In the present work, 26 diverse formulations were characterized at commercial tableting condition to identify key tablet properties influencing capping propensity, and a predictive model based on threshold properties was established using machine learning and multivariate tools. It was found that both the compaction parameters (i.e., compaction pressure, radial stress transmission characteristics, and Poisson's ratio), and the material properties, (i.e., brittleness, yield strength, particle bonding strength and elastic recovery) strongly dictate the capping propensity of a tablet. In addition, ratio of elastic modulus in the orthogonal direction in a tablet and its variation with porosity were notable quantitative metrics of capping occurrence.

Pharmaceutical application of multivariate modelling techniques: a review on the manufacturing of tablets

The tablet manufacturing process is a complex system, especially in continuous manufacturing (CM). It includes multiple unit operations, such as mixing, granulation, and tableting. In tablet manufacturing, critical quality attributes are influenced by multiple factorial relationships between material properties, process variables, and interactions. Moreover, the variation in raw material attributes and manufacturing processes is an inherent characteristic and seriously affects the quality of pharmaceutical products. To deepen our understanding of the tablet manufacturing process, multivariable modeling techniques can replace univariate analysis to investigate tablet manufacturing. In this review, the roles of the most prominent multivariate modeling techniques in the tablet manufacturing process are discussed. The review mainly focuses on applying multivariate modeling techniques to process understanding, optimization, process monitoring, and process control within multiple unit operations. To minimize the errors in the process of modeling, good modeling practice (GMoP) was introduced into the pharmaceutical process. Furthermore, current progress in the continuous manufacturing of tablets and the role of multivariate modeling techniques in continuous manufacturing are introduced. In this review, information is provided to both researchers and manufacturers to improve tablet quality.

Data-Driven Modelling of a Pelleting Process and Prediction of Pellet Physical Properties

In the manufacture of pelleted catalyst products, controlling physical properties of the pellets and limiting their variability is of critical importance. To achieve tight control over these critical quality attributes (CQAs), it is necessary to understand their relationship with the properties of the powder feed and the pelleting process parameters (PPs). This work explores the latter, using standard multivariate methods to gain a better understanding of the sources of process variability and the impact of PPs on the density and strength of the resulting pellets. A Kilian STYL’ONE EVO Compaction Simulator machine was used to produce over 1000 pellets, whose properties were measured, with varied powder feed mechanism and powder feed rate. Process data recorded by the Compaction Simulator machine were analysed using Principal Component Analysis (PCA) to understand the key aspects of variability in the process. This was followed by Partial Least Squares (PLS) regression to predict pellet density and hardness from the Compaction Simulator data. Pellet density was predicted accurately, achieving an R2 metric of 0.87 in 10-fold cross-validation, and 0.86 in an independent hold-out test. Pellet hardness proved more difficult to predict accurately, with an R2 of 0.67 in 10-fold cross-validation, and 0.63 in an independent hold-out test. This may however simply be highlighting measurement quality issues in pellet hardness data. The PLS models provided direct insights into the relationships between pelleting PPs and pellet CQAs and highlighted the potential for such models in process monitoring and control applications. Furthermore, the overall modelling process boosted understanding of the key sources of process and product variability, which can guide future efforts to improve pelleting performance.

Determination of Residence Time Distribution in a Continuous Powder Mixing Process With Supervised and Unsupervised Modeling of In-line Near Infrared (NIR) Spectroscopic Data

Successful implementation of continuous manufacturing processes requires robust methods to assess and control product quality in a real-time mode. In this study, the residence time distribution of a continuous powder mixing process was investigated via pulse tracer experiments using near infrared spectroscopy for tracer detection in an in-line mode. The residence time distribution was modeled by applying the continuous stirred tank reactor in series model for achieving the tracer (paracetamol) concentration profiles. Partial least squares discriminant analysis and principal component analysis of the near infrared spectroscopy data were applied to investigate both supervised and unsupervised chemometric modeling approaches. Additionally, the mean residence time for three powder systems was measured with different process settings. It was found that a significant change in the mean residence time occurred when comparing powder systems with different flowability and mixing process settings. This study also confirmed that the partial least squares discriminant analysis applied as a supervised chemometric model enabled an efficient and fast estimate of the mean residence time based on pulse tracer experiments.

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.

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.

Using a Material Library to Understand the Impacts of Raw Material Properties on Ribbon Quality in Roll Compaction

The purpose of this study is to use a material library to investigate the effect of raw material properties on ribbon tensile strength (TS) and solid fraction (SF) in the roll compaction (RC) process. A total of 81 pharmaceutical materials, including 53 excipients and 28 natural product powders (NPPs), were characterized by 22 material descriptors and were compacted under five different hydraulic pressures. The transversal and longitudinal splitting behaviors of the ribbons were summarized. The TS-porosity and TS-pressure relationships were used to explain the roll compaction behavior of powdered materials. Through defining the target ribbon quality (i.e., 0.6 ≤ SF ≤ 0.8 and TS ≥ 1 MPa), the roll compaction behavior classification system (RCBCS) was built and 81 materials were classified into three categories. A total of 24 excipients and five NPPs were classified as Category I materials, which fulfilled the target ribbon quality and had less occurrence of transversal splitting. Moreover, the multivariate relationships between raw material descriptors, the hydraulic pressure and ribbon quality attributes were obtained by PLS regression. Four density-related material descriptors and the cohesion index were identified as critical material attributes (CMAs). The multi-objective design space summarizing the feasible material properties and operational region for the RC process were visualized. The RCBCS presented in this paper enables a formulator to perform the initial risk assessment of any new materials, and the data modeling method helps to predict the impact of formulation ingredients on strength and porosity of compacts.

Feed frame: The last processing step before the tablet compaction in pharmaceutical manufacturing

The feed frame is a force-feeding device used in the die filling process. The die filling process is crucial within pharmaceutical manufacturing to guarantee the critical quality attributes of the tablets. In recent years, interest in this unit has increased because it can affect the properties of the powder blend and tablets, and because of the success in real time monitoring of powder blend uniformity potential for Process Analytical Technology as described in this review. The review focuses on the recent advances in understanding the powder flow behavior inside the feed frame and how the residence time distribution of the powder within the feed frame is affected by the operating conditions and design parameters. Furthermore, this review also highlights the effect of the paddle wheel design and feed frame process parameters on the tablet weight, the principal variable for measuring die filling performance.

A compression behavior classification system of pharmaceutical powders for accelerating direct compression tablet formulation design

In this paper, a compression behavior classification system (CBCS) for direct compression (DC) pharmaceutical powders is presented. Seven descriptors from a series of compression models for powder compressibility, compactibility and tabletability analysis were included in the CBCS. A new tabletability index d was proposed to differentiate three categories of tensile strength (TS) vs. pressure relationships, and its physical meaning was explained thoroughly. 130 materials containing diverse pharmaceutical excipients and natural product powders (NPPs) were fully characterized and were compiled into an in-house developed material library, in which 70 materials with potential DC applications were used to justify the effectiveness of the CBCS. Principle component analysis (PCA) was used to uncover the latent structure of compression variables. Moreover, partial least squares (PLS) regression models are established in prediction of both tablet TS and solid fraction (SF) based on the raw materials' physical characteristics, the compression behavior indices and the compression force. The obtained scores and loadings are used to group the materials and the compression variables, respectively. Different categories of tabletability for DC powders were clearly clustered along two orthogonal directions pointing to the index d and the compression force. Finally, a multi-objective design space was identified under the latent variable space, summarizing the operationally possible region for both material properties and compression pressure required in DC tablet formulation design.

Mixing Cell: a Device to Mimic Extent of Lubrication and Shear in Continuous Tubular Blenders

Continuous manufacturing (CM) has clear potential for manufacturing solid oral dosages. It provides several advantages that may aid the manufacturing and quality of drug products. However, one of the main challenges of this technology is the relatively large amount of knowledge required and the amounts of material needed to develop the process during the early stages of development. Early process development evaluations of continuous manufacturing equipment often require larger amounts of material compared with batch, which hinder CM prospect for drugs during the early stages of process development. In this work, a small-scale evaluation of the mixing process occurring in a continuous mixing system was performed. The evaluation involved the use of a small-scale "mixing cell" which was able to replicate the lubrication process of a continuous mixer. It is worth mentioning that we designed the mixing cell by reconfiguration of an existing continuous tubular blender. The extent of lubrication evaluation was performed for three example formulations and was done by mimicking the amount of shear provided to a formulation by means of matching the number of paddle-passes that a formulation experiences within a continuous blending process in the batch mixing cell. The evaluation showed that the small-scale mixing cell was able to replicate the extent of lubrication-evaluated by measuring the tensile strength of compacts being made with both the continuous and mixing cell experiments-occurring in the continuous mixer using a fraction of the amount of materials needed to perform the same evaluation in the continuous blending process.

Managing API raw material variability in a continuous manufacturing line - Prediction of process robustness

Many studies on continuous twin-screw granulation only focus on the granulator without linking this process step to the upstream and downstream unit operations. Product critical quality attributes (CQAs) are however not only determined by the granulation step. In this study, the possibility to manage the batch-to-batch variability of an active pharmaceutical ingredient (API) in a high drug loaded formulation on a continuous line was investigated to obtain consistent tablet CQAs. As the ultimate goal of continuous manufacturing is to produce 24/7, current study also aimed at guaranteeing long term stability of the process. To do so, previously identified API critical material attributes (CMAs) were varied together with granulation, drying and milling critical process parameters (CPPs) in a screening design of experiments to understand the influence of these factors upon product CQAs and process stability. To evaluate the factors affecting the process stability with a reduced amount of materials, process deviations recorded by process sensors were used. While product CQAs only depended on process CPPs, process stability was strongly affected by API CMAs. The effect of API batch-to-batch variability on process stability could nonetheless be managed by applying suitable granulation conditions. Therefore, appropriate ranges of CPPs were defined to ensure both product CQAs and process stability. By studying the fully integrated continuous manufacturing line, it was possible to highlight the interactions between the different unit operations and the API CMAs and to design a robust process.

Integrated continuous manufacturing in pharmaceutical industry: current evolutionary steps toward revolutionary future

Continuous manufacturing (CM) has the potential to provide pharmaceutical products with better quality, improved yield and with reduced cost and time. Moreover, ease of scale-up, small manufacturing footprint and on-line/in-line monitoring and control of the process are other merits for CM. Regulating authorities are supporting the adoption of CM by pharmaceutical manufacturers through issuing proper guidelines. However, implementation of this technology in pharmaceutical industry is encountered by a number of challenges regarding the process development and quality assurance. This article provides a background on the implementation of CM in pharmaceutical industry, literature survey of the most recent state-of-the-art technologies and critically discussing the encountered challenges and its future prospective in pharmaceutical industry.

Optimal Selection of Incoming Materials from the Inventory for Achieving the Target Drug Release Profile of High Drug Load Sustained-Release Matrix Tablet

In the pharmaceutical process, raw material (including APIs and excipients) variability can be delivered to the final product, and lead to batch-to-batch and lot-to-lot variances in its quality, finally impacting the efficacy of the drug. In this paper, the Panax notoginseng saponins (PNS) sustained-release matrix tablet was taken as the model formulation. Hydroxypropyl methylcellulose with the viscosity of 4000 mPa·s (HPMCK4M) from different vendors and batches were collected and their physical properties were characterized by the SeDeM methodology. The in-vitro dissolution profiles of active pharmaceutical ingredients (APIs) from matrix tablets made up of different batches HPMC K4M displayed significant variations. Multi-block partial least squares (MB-PLS) modeling results further demonstrated that physical properties of excipients played dominant roles in the drug release. In order to achieve the target drug release profile with respect to those far from the criteria, the optimal selection method of incoming materials from the available was established and validated. This study provided novel insights into the control of the input variability of the process and amplified the application of the SeDeM expert system, emphasizing the importance of the physical information of the raw materials in the drug manufacturing process.