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

The effect of material properties and process parameters on die filling at varying throughputs: A pls-model-based analysis

When tablets are manufactured on a rotary tablet press and the throughput is increased, it leads to changes in powder dynamics during die filling due to formulation characteristics and changing powder flow in the feed frame. This may result, a.o. in increased tablet weight variability, poorer content uniformity, capping and lamination. This research focuses on explaining the die filling performance depending on material properties and process settings, including throughput for small and large tablets. It was concluded that throughput had a negative impact on die filling variability, which is related to reduced residence time and lower fill fraction of feed frame and dies. Furthermore, the die filling mechanism was inherently different for large tablets in comparison to small tablets. Higher die filling consistency was observed for dense, less porous, less compressible and better flowing powders. As a result of this work, a model was developed to predict impact of formulation properties and process settings on die filling variability and its dependency on changes in throughput. This model will benefit formulation development at an early stage when active ingredient availability may be challenging as it will avoid the need to conduct experiments at high throughputs.

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.

Ten years of the manufacturing classification system: a review of literature applications and an extension of the framework to continuous manufacture

The MCS initiative was first introduced in 2013. Since then, two MCS papers have been published: the first proposing a structured approach to consider the impact of drug substance physical properties on manufacturability and the second outlining real world examples of MCS principles. By 2023, both publications had been extensively cited by over 240 publications. This article firstly reviews this citing work and considers how the MCS concepts have been received and are being applied. Secondly, we will extend the MCS framework to continuous manufacture. The review structure follows the flow of drug product development focussing first on optimisation of API properties. The exploitation of links between API particle properties and manufacturability using large datasets seems particularly promising. Subsequently, applications of the MCS for formulation design include a detailed look at the impact of percolation threshold, the role of excipients and how other classification systems can be of assistance. The final review section focusses on manufacturing process development, covering the impact of strain rate sensitivity and modelling applications. The second part of the paper focuses on continuous processing proposing a parallel MCS framework alongside the existing batch manufacturing guidance. Specifically, we propose that continuous direct compression can accommodate a wider range of API properties compared to its batch equivalent.

Feature extraction of particle morphologies of pharmaceutical excipients from scanning electron microscope images using convolutional neural networks

Scanning electron microscopy (SEM) images are the most widely used tool for evaluating particle morphology; however, quantitative evaluation using SEM images is time-consuming and often neglected. In this study, we aimed to extract features related to particle morphology of pharmaceutical excipients from SEM images using a convolutional neural network (CNN). SEM images of 67 excipients were acquired and used as models. A classification CNN model of the excipients was constructed based on the SEM images. Further, features were extracted from the middle layer of this CNN model, and the data was compressed to two dimensions using uniform manifold approximation and projection. Lastly, hierarchical clustering analysis (HCA) was performed to categorize the excipients into several clusters and identify similarities among the samples. The classification CNN model showed high accuracy, allowing each excipient to be identified with a high degree of accuracy. HCA revealed that the 67 excipients were classified into seven clusters. Additionally, the particle morphologies of excipients belonging to the same cluster were found to be very similar. These results suggest that CNN models are useful tools for extracting information and identifying similarities among the particle morphologies of excipients.

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.

Sample Size Requirements of a Pharmaceutical Material Library: A Case in Predicting Direct Compression Tablet Tensile Strength by Latent Variable Modeling

The material library is an emerging, new data-driven approach for developing pharmaceutical process models. How many materials or samples should be involved in a particular application scenario is unclear, and the impact of sample size on process modeling is worth discussing. In this work, the direct compression process was taken as the research object, and the effects of different sample sizes of material libraries on partial least squares (PLS) modeling in the prediction of tablet tensile strength were investigated. A primary material library comprising 45 materials was built. Then, material subsets containing 5 × i (i = 1, 2, 3, …, 8) materials were sampled from the primary material library. Each subset underwent sampling 1000 times to analyze variations in model fitting performance. Both hierarchical sampling and random sampling were employed and compared, with hierarchical sampling implemented with the help of the tabletability classification index d. For each subset, modeling data were organized, incorporating 18 physical properties and tableting pressure as the independent variables and tablet tensile strength as the dependent variable. A series of chemometric indicators was used to assess model performance and find important materials for model training. It was found that the minimum R2 and RMSE values reached their maximum, and the corresponding values were kept almost unchanged when the sample sizes varied from 20 to 45. When the sample size was smaller than 15, the hierarchical sampling method was more reliable in avoiding low-quality few-shot PLS models than the random sampling method. Two important materials were identified as useful for building an initial material library. Overall, this work demonstrated that as the number of materials increased, the model's reliability improved. It also highlighted the potential for effective few-shot modeling on a small material library by controlling its information richness.

A tabletability change classification system in supporting the tablet formulation design via the roll compaction and dry granulation process

In this paper, the material library approach was used to uncover the pattern of tabletability change and related risk for tablet formulation design under the roll compaction and dry granulation (RCDG) process. 31 materials were fully characterized using 18 physical parameters and 9 compression behavior classification system (CBCS) parameters. Then, each material was dry granulated and sieved into small granules (125-250 μm) and large granules (630-850 μm), respectively. The compression behavior of granules was characterized by the CBCS descriptors, and were compared with that of ungranulated powders. The relative change of tabletability (CoTr) index was used to establish the tabletability change classification system (TCCS), and all materials were classified into three types, i.e. loss of tabletability (LoT, Type I), unchanged tabletability (Type II) and increase of tabletability (Type III). Results showed that approximately 65% of materials presented LoT, and as the granules size increased, 84% of the materials exhibited LoT. A risk decision tree was innovatively proposed by joint application of the CBCS tabletability categories and the TCCS tabletability change types. It was found that the LoT posed little risk to the tensile strength of the final tablet, when Category 1 or 2A materials, or Category 2B materials with Type II or Type III change of tabletability were used. Formulation risk happened to Category 2C or 3 materials, or Category 2B materials with Type I change of tabletability, particularly when high proportions of these materials were involved in tablet formulation. In addition, the risk assessment results were verified in the material property design space developed from a latent variable model in prediction of tablet tensile strength. Overall, results suggested that a combinational use of CBCS and TCCS could aid the decision making in selecting materials for tablet formulation design via RCDG.

T-shaped partial least squares for high-dosed new active pharmaceutical ingredients in continuous twin-screw wet granulation: Granule size prediction with limited material information

This work presents a granule size prediction approach applicable to diverse formulations containing new active pharmaceutical ingredients (APIs) in continuous twin-screw wet granulation. The approach consists of a surrogate selection method to identify similar materials with new APIs and a T-shaped partial least squares (T-PLS) model for granule size prediction across varying formulations and process conditions. We devised a surrogate material selection method, employing a combination of linear pre-processing and nonlinear classification algorithms, which effectively identified suitable surrogates for new materials. Using only material properties obtained through four characterization methods, our approach demonstrated its predictive prowess. The selected surrogate methods were seamlessly integrated with our developed T-PLS model, which was meticulously validated for high-dose formulations involving three new APIs. When surrogating new APIs based on Gaussian process classification, we achieved the lowest prediction errors, signifying the method's robustness. The predicted d-values were within the range of uncertainty bounds for all cases, except for d90 of API C. Notably, the approach offers a direct and efficient solution for early-phase formulation and process development, considerably reducing the need for extensive experimental work. By relying on just four material characterization methods, it streamlines the research process while maintaining a high degree of accuracy.

Leveraging a multivariate approach towards enhanced development of direct compression extended release tablets

Extended release formulations play a crucial role in the pharmaceutical industry by maintaining steady plasma levels, reducing side effects, and improving therapeutic efficiency and compliance. One commonly used method to develop extended release formulations is direct compression, which offers several advantages, such as simplicity, time savings, and cost-effectiveness. However, successful direct compression-based extended release formulations require careful assessment and an understanding of the excipients' attributes. The scope of this work is the characterization of the compaction behavior of some matrix-forming agents and diluents for the development of extended release tablets. Fifteen excipients commonly used in extended release formulations were evaluated for physical, compaction and tablet properties. Powder properties (e.g., particle size, flow properties, bulk density) were evaluated and linked to the tablet's mechanical properties in a fully integrated approach, and data were analyzed by constructing a principal component analysis (PCA). Significant variability was observed among the various excipients. The present work successfully demonstrates the applicability of PCA as an effective tool for comparative analysis, pattern and clustering recognition and correlations between excipients and their properties, facilitating the development and manufacturing of direct compressible extended release formulations.

Application of unsupervised and supervised learning to a material attribute database of tablets produced at two different granulation scales

The purpose of this study is to demonstrate the usefulness of machine learning (ML) for analyzing a material attribute database from tablets produced at different granulation scales. High shear wet granulators (scale 30 g and 1000 g) were used and data were collected according to the design of experiments at different scales. In total, 38 different tablets were prepared, and the tensile strength (TS) and dissolution rate after 10 min (DS10) were measured. In addition, 15 material attributes (MAs) related to particle size distribution, bulk density, elasticity, plasticity, surface properties, and moisture content of granules were evaluated. By using unsupervised learning including principal component analysis and hierarchical cluster analysis, the regions of tablets produced at each scale were visualized. Subsequently, supervised learning with feature selection including partial least squares regression with variable importance in projection and elastic net were applied. The constructed models could predict the TS and DS10 from the MAs and the compression force with high accuracy (R²= 0.777 and 0.748, respectively), independent of scale. In addition, important factors were successfully identified. ML can be used for better understanding of similarity/dissimilarity between scales, for constructing predictive models of critical quality attributes, and for determining critical factors.

Morphological distribution mapping: Utilisation of modelling to integrate particle size and shape distributions

The aim of this work was to develop approaches to utilize whole particle distributions for both particle size and particle shape parameters to map the full range of particle properties in a curated dataset. It is hoped that such an approach may enable a more complete understanding of the particle landscape as a step towards improving the link between particle properties and processing behaviour. A 1-dimensional principal component analysis (PCA) approach was applied to create a 'morphological distribution landscape'. A dataset of imaged APIs, intermediates and excipients encompassing particle size, particle shape (elongation, length and width) and distribution shape was curated between 2008 and 2022. The curated dataset encompassed over 200 different materials, which included over 150 different APIs, and approximately 3500 unique samples. For the purposes of the current work, only API samples were included. The morphological landscape enables differentiation of materials of equivalent size but varying shape and vice versa. It is hoped that this type of approach can be utilised to better understand the influence of particle properties on pharmaceutical processing behaviour and thereby enable scientists to leverage historical knowledge to highlight and mitigate risks associated to materials of similar morphological nature.

Elucidation of the powder flow pattern in a twin-screw LIW-feeder for various refill regimes

The importance of residence time distribution modeling is acknowledged as a tool for enabling material tracking and control within a continuous manufacturing line in order to safeguard both product quality and production efficiency. One of the first unit-operations into a continuous direct compression line (i.e. CDC-line) worthwhile doing extensive RTD-analysis upon are the LIW-feeders since they dose the ingredients in a controlled way following the label claim and hence can directly influence critical quality attributes like content uniformity. An NIR measurement method was developed determining the RTD of selected powders at specific feeder settings. Step-change experiments using sodium saccharin as a tracer were conducted. In order to gain and in depth understanding of the material flow, spatial samples throughout the hopper were taken at predefined timepoints during the step change experiments. This revealed the presence of a bypass trajectory along the edges of the agitator, while in the center of the agitator an inner mixing volume in which the tracer concentration lags behind seemed to be present. Finally, a model based on a plug flow and continuous stirred tank reactor was evaluated. The fitted model was not able to capture this complex flow behavior and shows the need for an extended compartmental model distinguishing between a bypass trajectory formed by the agitator and an inner mixing volume.

Using a Material Library to Understand the Change of Tabletability by High Shear Wet Granulation

Understanding the tabletability change of materials after granulation is critical for the formulation and process design in tablet development. In this paper, a material library consisting of 30 pharmaceutical materials was used to summarize the pattern of change of tabletability during high shear wet granulation and tableting (HSWGT). Each powdered material and the corresponding granules were characterized by 19 physical properties and nine compression behavior classification system (CBCS) parameters. Principal component analysis (PCA) was used to compare the physical properties and compression behaviors of ungranulated powders and granules. A new index, namely the relative change of tabletability (CoT_r), was proposed to quantify the tabletability change, and its advantages over the reworking potential were demonstrated. On the basis of CoT_r values, the tabletability change classification system (TCCS) was established. It was found that approximately 40% of materials in the material library presented a loss of tabletability (i.e., Type I), 50% of materials had nearly unchanged tabletability (i.e., Type II), and 10% of materials suffered from increased tabletability (i.e., Type III). With the help of tensile strength (TS) vs. compression pressure curves implemented on both powders and granules, a data fusion method and the PLS2 algorithm were further applied to identify the differences in material properties requirements for direct compression (DC) and HSWGT. Results indicated that increasing the plasticity or porosity of the starting materials was beneficial to acquiring high TS of tablets made by HSWGT. In conclusion, the presented TCCS provided a means for the initial risk assessment of materials in tablet formulation design and the data modeling method helped to predict the impact of formulation ingredients on the strength of compacts.

Understanding the Multidimensional Effects of Polymorphism, Particle Size and Processing for D-Mannitol Powders

The relevance of the polymorphic form, particle size, and processing of mannitol for the mechanical properties of solid oral dosage forms was examined. Thus, particle and powder properties of spray granulated β D-mannitol, β D-mannitol, and δ D-mannitol were assessed in this study with regards to their manufacturability. D-mannitol is a commonly used excipient in pharmaceutical formulations, especially in oral solid dosage forms, and can be crystallized as three polymorphic forms, of which β is the thermodynamically most stable form and δ is a kinetically stabilized polymorph. A systematic analysis of the powders as starting materials and their respective roller compacted granules is presented to elucidate the multidimensional effects of powder and granules characteristics such as polymorphic form, particle size, and preprocessing on the resulting tablets’ mechanical properties. In direct compression and after roller compaction, δ polymorph displayed superior tableting properties over β mannitol, but was outperformed by spray granulated β mannitol. This could be primarily correlated to the higher specific surface area, leading to higher bonding area and more interparticle bonds within the tablet. Hence, it was shown that surface characteristics and preprocessing can prevail over the impact of polymorphism on manufacturability for oral solid dosage forms.

Development of a predictive model for gravimetric powder feeding from an API-rich materials properties library

A model was developed for predicting the feed factor profile of a powder, processed through a gravimetric feeder, as a function of material properties and process parameters. Predictive models proposed in existing literature have often used excipients and active pharmaceutical ingredients (APIs) with good powder flow characteristics in their development. In this work, a material properties library containing a large proportion of APIs, as well as excipients and co-processed blends, was used to build the model and enhance the prediction of feed factor profile for cohesive powders. Gravimetric feeder trials were performed at varying mass flow rates and screw geometries to determine the feed factor profiles. A semi-empirical exponential model, with parameters f_max, f_min, and β, was then used to fit the experimental feed factor profiles. Bayesian optimisation and Support Vector Regression (SVR) modelling techniques were utilised to optimise and predict the exponential model parameters as a function of material properties. The parameters found to strongly influence the model were particle size, bulk density, FFC and FT4 rheometer parameters. Results showed low prediction errors between the estimated and experimental data. The final model produces good estimations of the feed factor profile and requires minimal powder consumption.

A Multivariate Formulation and Process Development Platform for Direct Compression

The efficient development of robust tableting processes is challenging due to the lack of mechanistic understanding on the impact of raw material properties and process parameters on tablet quality. The experimental determination of the effect of process and formulation parameters on tablet properties and subsequent optimization is labor-intensive, expensive and time-consuming. The combined use of an extensive raw material property database, process simulation tools and multivariate modeling allows more efficient and more optimized development of the direct compression (DC) process. In this study, key material attributes and in-process mechanical properties with a potential effect on tablet processability and tablet properties were identified. In a first step, an extensive characterization of 55 raw materials (over 100 material descriptors) (Van Snick et al., 2018) and 26 formulation blends (31 material descriptors) (Dhondt et al., 2022) was performed. These blends were subsequently compacted on a compaction simulator under multiple process conditions through a design of experiments (DoE) approach. A T-shaped partial least squares (T-PLS) model was established which correlates tablet quality attributes with process settings, raw material properties and blend ratios. During future development of the DC formulation and process for a new active pharmaceutical ingredient (API), this model can then be used to provide a preliminary formulation and compaction process settings as starting point to be further optimized during development trials based on well-defined raw material characteristics and compaction tests. This study hence contributes to a better understanding on the impact of raw material properties and process settings on a DC process and final properties of the produced tablets; and provides a platform allowing a more efficient and more optimized development of a robust tableting process.

Improvements on multiple direct compaction properties of three powders prepared from Puerariae Lobatae Radix using surface and texture modification: comparison of microcrystalline cellulose and two nano-silicas

It has been reported that hydrophilic nano-silica (N) markedly improved direct compaction (DC) properties of Zingiberis Rhizoma alcoholic extract. This study aims to examine the broader scope and generality of the previous work by investigating (i) three powders, i.e., the directly pulverized product, ethanol extract, and water extract prepared from the same medicinal herb-Puerariae Lobatae Radix (named DP, EE, and WE) and (ii) the effects on their DC properties of co-processing with N, hydrophobic nano-silica (BN), or microcrystalline cellulose (C). Unexpectedly, C provided the best improvement on tabletability for WE, while N for both DP and EE. More importantly, only N could move all parent powders to a regime suitable for DC, and BN rather than C enabled parent WE to be directly compressed. Typically, 6/9 N-modified powders simultaneously met the requirements of DC on bulk density, flowability, and tablet tensile strength (σ_t). Principal component analysis indicated that DC properties were mainly governed by flowability and texture properties. The partial least-squares regression model revealed that flowability, texture parameters, and deformation behavior of powders were dominating factors impacting tablet σ_t and solid fraction. Overall, the findings are promising for the manufacture of high drug loading tablets of herbs by DC.

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.

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.

Investigating key properties of model excipients and binary powder blends using ultrasonic in-die measurements on a compaction simulator

In this work, the recently introduced Kilian Inline Measurement System (KIM) that enables ultrasonic measurements during powder compaction was studied using three pharmaceutical excipients with different properties and particle sizes, applying various amounts of lubricant and different compaction pressures. It was shown that the generated results were highly reproducible, not only in series but also on different days including dismantling and reassembling of the components. The relation between ultrasonic velocity and increasing compact density differed among the investigated materials and was independent of initial particle size and applied maximum pressure. Since the velocity through a porous solid is dependent on pore volume and shape, velocity profiles have the potential to track changes in the microstructures of the materials. Furthermore, ultrasonic velocities through binary mixtures (50:50 (w/w)) at a given solid fraction were between the values of the plain excipients, but more closely resembled one of their components. This may be indicative of the compaction behavior and performance of the blend. Overall, ultrasonic instrumentation seems to be a robust and promising tool for the characterization of powders and blends in compaction processes. However, its practical value must still be investigated further including multi-component blends, underlying densification mechanisms, and decompression.

Application of machine learning to a material library for modeling of relationships between material properties and tablet properties

This study investigates the usefulness of machine learning for modeling complex relationships in a material library. We tested 81 types of active pharmaceutical ingredients (APIs) and their tablets to construct the library, which included the following variables: 20 types of API material properties, one type of process parameter (three levels of compression pressure), and two types of tablet properties (tensile strength (TS) and disintegration time (DT)). The machine learning algorithms boosted tree (BT) and random forest (RF) were applied to analysis of our material library to model the relationships between input variables (material properties and compression pressure) and output variables (TS and DT). The calculated BT and RF models achieved higher performance statistics compared with a conventional modeling method (i.e., partial least squares regression), and revealed the material properties that strongly influence TS and DT. For TS, true density, the tenth percentile of the cumulative percentage size distribution, loss on drying, and compression pressure were of high relative importance. For DT, total surface energy, water absorption rate, polar surface energy, and hygroscopicity had significant effects. Thus, we demonstrate that BT and RF can be used to model complex relationships and clarify important material properties in a material library.

Systematic study of paracetamol powder mixtures and granules tabletability: Key role of rheological properties and dynamic image analysis

The aim of this systematic study was to analyze the granulometric and rheological behavior of tableting mixtures in relation to tabletability by single tablet and lab-scale batch compression with an eccentric tablet machine. Three mixtures containing 33, 50, and 66% of the cohesive drug paracetamol were prepared. The high compressibility of the powder mixtures caused problems with overcompaction or lamination in the single tablet compression method; due to jamming of the material during the filling of the die, the lab-scale batch compression was impossible. Using high shear granulation, the flow properties and tabletability were adjusted. A linear relationship between the span of granules and the specific energy measured by FT4 powder rheometer was detected. In parallel, a linear relationship between conditioned bulk density and the tensile strength of the tablets at lab-scale batch tableting was noted. The combination of dynamic image analysis and powder rheometry was useful for predicting the tabletability of pharmaceutical mixtures during the single tablet (design) compression and the lab-scale batch compression.

Continuous downstream processing of milled electrospun fibers to tablets monitored by near-infrared and Raman spectroscopy

Electrospinning is a technology for manufacture of nano- and micro-sized fibers, which can enhance the dissolution properties of poorly water-soluble drugs. Tableting of electrospun fibers have been demonstrated in several studies, however, continuous manufacturing of tablets have not been realized yet. This research presents the first integrated continuous processing of milled drug-loaded electrospun materials to tablet form supplemented by process analytical tools for monitoring the active pharmaceutical ingredient (API) content. Electrospun fibers of an amorphous solid dispersion (ASD) of itraconazole and poly(vinylpyrrolidone-co-vinyl acetate) were produced using high speed electrospinning and afterwards milled. The milled fibers with an average fiber diameter of 1.6 ± 0.9 µm were continuously fed with a vibratory feeder into a twin-screw blender, which was integrated with a tableting machine to prepare tablets with ~ 10 kN compression force. The blend of fibers and excipients leaving the continuous blender was characterized with a bulk density of 0.43 g/cm³ and proved to be suitable for direct tablet compression. The ASD content, and thus the API content was determined in-line before tableting and at-line after tableting using near-infrared and Raman spectroscopy. The prepared tablets fulfilled the USP <905> content uniformity requirement based on the API content of ten randomly selected tablets. This work highlights that combining the advantages of electrospinning (e.g. less solvent, fast and gentle drying, low energy consumption, and amorphous products with high specific surface area) and the continuous technologies opens a new and effective way in the field of manufacturing of the poorly water-soluble APIs.

Effect of binder type and lubrication method on the binder efficacy for direct compression

The effect of different binders for direct compression on tablet critical quality attributes was investigated. Dicalcium phosphate, lactose and microcrystalline cellulose were used as fillers and combined with ten binders (10, 20 and 30% w/w). Binder properties were linked to tensile strength via partial least square analysis. Tablets containing VA64F and PH105 exhibited the highest tensile strength which was linked to their compaction properties (specific work of compaction, elasticity, cohesion index) and particle size. In contrast, S1500 and E15 exhibited the lowest tensile strength of all binders. Lubrication method influenced the tensile strength as lubricant sensitivity was observed to some extent for all binders. Tensile strength was significantly higher applying external compared to internal lubrication. Fast disintegration was observed for MCC (PH105 and PH200) and starch (S1500 and NMSt) grades, whereas HPC (KEXF and KEF) and E15 resulted in delayed disintegration. Wettability measurements, via determination of contact angle, correlated well with the disintegration behaviour of the binders and can therefore be used as an indicative measurement for tablet disintegration. This study revealed the effect of binder properties, filler type and lubrication method on tablet critical quality attributes. In addition, the potential of dry binder addition for direct compression was highlighted.

TPLS as predictive platform for twin-screw wet granulation process and formulation development

In recent years, the interest in continuous manufacturing techniques, such as twin-screw wet granulation, has increased. However, the understanding of the influence of the combination of raw material properties and process settings upon the granule quality attributes is still limited. In this study, a T-shaped partial least squares (TPLS) model was developed to link raw material properties, the ratios in which these raw materials were combined and the applied process parameters for the twin-screw wet granulation process with the granule quality attributes. In addition, the predictive ability of the TPLS model was used to find a suitable combination of formulation composition and twin-screw granulation process settings for a new API leading to desired granule quality attributes. Overall, this study helped to better understand the link between raw material properties, formulation composition and process settings on granule quality attributes. Moreover, as TPLS can provide a reasonable starting point for formulation and process development for new APIs, it can reduce the experimental development efforts and consequently the consumption of expensive (and often limited available) new API.

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.

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.

An Investigation into the Relationship between Xanthan Gum Film Coating Materials and Predicted Oro-Esophageal Gliding Performance for Solid Oral Dosage Forms

Oral drug therapy is generally provided in the form of solid oral dosage forms (SODF) that have to be swallowed and move throughout the oro-esophageal system. Previous studies have provided evidence that the oro-esophageal transit of SODF depends on their shape, size, density, and surface characteristics. To estimate the impact of SODF surface coatings during esophageal transit, an in vitro system was implemented to investigate the gliding performance across an artificial mucous layer. In this work, formulations comprised of different slippery-inducing agents combined with a common film forming agent were evaluated using the artificial mucous layer system. Xanthan gum (XG) and polyethylene glycol 1500 (PEG) were applied as film-forming agents, while carnauba wax (CW), lecithin (LE), carrageenan (CA), gellan gum (GG) and sodium alginate (SA), and their combination with sodium lauryl sulfate (SLS), were applied as slippery-inducing components. All tested formulations presented lower static friction (SF) as compared to the negative control (uncoated disc, C, F0), whereas only CW/SLS-based formulations showed similar performance to F0 regarding dynamic friction (DF). The applied multivariate analysis approach allowed a higher level of detail to the evaluation and supported a better identification of excipients and respective concentrations that are predicted to improve in vivo swallowing safety.

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.

Continuous twin screw granulation: Robustness of lactose/MCC-based formulations

In recent years, significant progress has been made in the field of continuous twin screw granulation. However, only limited knowledge is currently available on the impact of active pharmaceutical ingredient (API) properties on granule quality and processability. In this study, the response behavior of four formulations containing APIs (5-10% drug load) with diverse characteristics was compared to the behavior of the corresponding placebo formulation consisting of lactose, microcrystalline cellulose (MCC) and hydroxypropylmethylcellulose (HPMC). API selection was based on extensive material characterization, combining conventional testing with in silico descriptors. For each formulation, a design of experiments was set up, evaluating the impact of liquid to solid (L/S) ratio and screw speed. Response ranges, response behavior and processability of each of the four formulations proved very similar to the placebo formulation when an appropriate center point L/S ratio was chosen. Hence, this robust placebo formulation could prove useful by decreasing drug product development time and consequently providing patients with a faster access to innovative medicine. Additionally, APIs with similar properties exhibited highly comparable response behavior at similar L/S ratios, indicating the potential use of surrogate APIs in novel drug product development.

Evaluation of an external lubrication system implemented in a compaction simulator

The internal blending of magnesium stearate is often associated with decreasing tensile strengths and longer disintegration and dissolution times. Therefore, external lubrication has gained interest in the pharmaceutical industry as these negative effects could be minimized using this method. In this study, an external lubrication system implemented in a compaction simulator was investigated. The influence of 2 process parameters related to the external lubrication system, spraying time and atomizing pressure, on the responses was studied using 4 common fillers and 2 model drugs. While the parameters of the external lubrication system had a significant impact on the ejection forces, no negative effect was observed on the tensile strength and disintegration time as similar values were obtained compared to non-lubricated experiments. Moreover, equal or lower ejection forces were obtained for external lubrication using a lower concentration of magnesium stearate compared to internal lubrication, where a decrease in tensile strength and prolonged disintegration was noticed for most formulations. The observed results could be correlated to the wall friction angle, compaction properties and tablet brittleness index of the raw materials and blends. This study showed the potential of external lubrication as an alternative lubrication method for lubricant-sensitive formulations.

Recent progress in continuous manufacturing of oral solid dosage forms

Continuous drug product manufacturing is slowly being implemented in the pharmaceutical industry. Although the benefits related to the quality and cost of continuous manufacturing are widely recognized, several challenges hampered the widespread introduction of continuous manufacturing of drug products. Current review presents an overview of state-of-the art research, equipment, process analytical technology implementations and advanced control strategies. Additionally, guidelines and regulatory viewpoints on implementation of continuous manufacturing in the pharmaceutical industry are discussed.

New advances in the characterization of lyophilised orally disintegrating tablets

Orally disintegrating tablets (ODTs) produced by lyophilisation have a unique porous structure which leads to a favorable orodispersable functionality. They possess ultra-fast disintegration kinetics, have acceptable mechanical strength and give a smooth mouth texture. Only limited literature is available on the characterization of ODTs and even fewer on lyophilised tablets. The suitability of a broad range of characterization methods for lyophilised tablets was evaluated since they possess other mechanical properties compared to compressed tablets. Four ODTs with diverse properties were carefully selected and thoroughly analysed using various characterization techniques leading to a multitude of descriptors. The functionality of these four lyophilised tablets was examined and the relation between the different descriptors was evaluated using a Principal Component Analysis (PCA). Furthermore, μCT images of two ODTs were acquired to study the internal structure and active pharmaceutical ingredient (API) distribution inside the tablets. The important descriptors, disintegration and mechanical strength, interacted inversely indicating the importance of a well-balanced ODT formulation to achieve favorable properties. μCT data can improve the process knowledge as it yields very diverse descriptors. Ultimately, the different tablets were ranked at their performance.

Determining key parameters of continuous wet granulation for tablet quality and productivity: A case in ethenzamide

This paper aims to determine key parameters that affect tablet quality and productivity in continuous tablet manufacturing. Experiments were performed based on design of experiments using a continuous high-shear granulator and ethenzamide as the active pharmaceutical ingredient. To guide a systematic and comprehensive parameter analysis, a parameter framework was defined that comprised five input parameters on raw material properties and process parameters, 11 intermediate parameters on granule properties, and 11 output parameters on tablet quality and productivity. The interrelationships were analyzed statistically and were described as matrix functions. The liquid/solid ratio was the key parameter that affected circularity, density, and flowability as the granule properties, and disintegration and dissolution as the tablet quality. The maximum acceptable manufacturing rate that governs productivity was also affected by the liquid/solid ratio. Circularity was found to affect disintegration and dissolution. This result was specific to the setup of the study, but suggested development opportunities for a new process analytical technology system/quality-by-design application based on circularity. In addition, practical findings were obtained as follows: (1) high-speed manufacturing favored a lower liquid/solid ratio, and (2) high circularity slowed down disintegration/dissolution. This obtained knowledge will enhance the applicability of continuous technology in an actual manufacturing environment.

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.