The influence of API concentration on the roller compaction process: modeling and prediction of the post compacted ribbon, granule and tablet properties using multivariate data analysis

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.

Specific surface area of mannitol rather than particle size dominant the dissolution rate of poorly water-soluble drug tablets: A study of binary mixture

The dissolution behavior of tablets, particularly those containing poorly water-soluble drugs, is a critical factor in determining their absorption and therapeutic efficacy. Traditionally, the particle size of excipients has been considered a key property affecting tablet dissolution. However, lurasidone hydrochloride (LH) tablets prepared by similar particle size mannitol, namely M200 (D90 = 209.68 ± 1.42 μm) and 160C (D90 = 195.38 ± 6.87 μm), exhibiting significant differences in their dissolution behavior. In order to find the fundamental influential factors of mannitol influencing the dissolution of LH tablets, the properties (particle size, water content, true density, bulk density, tapped density, specific surface area, circularity, surface free energy, mechanical properties and flowability) of five grades mannitol including M200 and 160C were investigated. Principal component analysis (PCA) was used to establish a relationship between mannitol properties and the dissolution behavior of LH. The results demonstrated that specific surface area (SSA) emerged as the key property influencing the dissolution of LH tablets. Moreover, our investigation based on the percolation theory provided further insights that the SSA of mannitol influences the probability of LH-LH bonding and LH infinite cluster formation, resulting in the different percolation threshold states, then led to different dissolution behaviors. Importantly, it is worth noting that these findings do not invalidate previous conclusions, as reducing particle size generally increases SSA, thereby affecting the percolation threshold and dissolution behavior of LH. Instead, this study provides a deeper understanding of the underlying role played by excipient SSA in the dissolution of drug tablets. This study provides valuable guidance for the development of novel excipients aimed at improving drug dissolution functionality.

Process Analytical Technology Tools for Monitoring Pharmaceutical Unit Operations: A Control Strategy for Continuous Process Verification

Various frameworks and methods, such as quality by design (QbD), real time release test (RTRT), and continuous process verification (CPV), have been introduced to improve drug product quality in the pharmaceutical industry. The methods recognize that an appropriate combination of process controls and predefined material attributes and intermediate quality attributes (IQAs) during processing may provide greater assurance of product quality than end-product testing. The efficient analysis method to monitor the relationship between process and quality should be used. Process analytical technology (PAT) was introduced to analyze IQAs during the process of establishing regulatory specifications and facilitating continuous manufacturing improvement. Although PAT was introduced in the pharmaceutical industry in the early 21st century, new PAT tools have been introduced during the last 20 years. In this review, we present the recent pharmaceutical PAT tools and their application in pharmaceutical unit operations. Based on unit operations, the significant IQAs monitored by PAT are presented to establish a control strategy for CPV and real time release testing (RTRT). In addition, the equipment type used in unit operation, PAT tools, multivariate statistical tools, and mathematical preprocessing are introduced, along with relevant literature. This review suggests that various PAT tools are rapidly advancing, and various IQAs are efficiently and precisely monitored in the pharmaceutical industry. Therefore, PAT could be a fundamental tool for the present QbD and CPV to improve drug product quality.

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.

Integrating QbD Tools for Flexible Scale-Up Batch Size Selection for Solid Dosage Forms

The pilot scale batch size for solid oral dosage forms is currently defined by major regulatory agencies as one-tenth of the full production, or 100,000 units, whichever is larger. The current criterion is arbitrary and is not based on scientific and risk assessment principles. The approach does not consider geometric, kinematic, and dynamic changes that come into play on scale-up. Even if this criterion is met, impact of scale-up on critical quality attributes cannot be ruled out and also reproducibility cannot be assured simply by restricting the scale-up size. In keeping with the vision for the 21st Century Good Manufacturing Practice initiative to build quality into the product, it is imperative that the selection of scale-up batch size be based on science and risk assessment principles and be part of the product development program. Scale-up should never be seen as an isolated activity. This article will review various tools that can be integrated with quality by design for flexible batch size selection during scale-up.

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.

Enhanced Understanding of Pharmaceutical Materials Through Advanced Characterisation and Analysis

The impact of pharmaceutical materials properties on drug product quality and manufacturability is well recognised by the industry. An ongoing effort across industry and academia, the Manufacturing Classification System consortium, aims to gather the existing body of knowledge in a common framework to provide guidance on selection of appropriate manufacturing technologies for a given drug and/or guide optimization of the physical properties of the drug to facilitate manufacturing requirements for a given processing route. Simultaneously, material scientists endeavour to develop characterisation methods such as size, shape, surface area, density, flow and compactibility that enable a stronger understanding of materials powder properties. These properties are routinely tested drug product development and advances in instrumentation and computing power have enabled novel characterisation methods which generate larger, more complex data sets leading to a better understanding of the materials. These methods have specific requirements in terms of data management and analysis. An appropriate data management strategy eliminates time-consuming data collation steps and enables access to data collected for multiple methods and materials simultaneously. Methods ideally suited to extract information from large, complex data sets such as multivariate projection methods allow simpler representation of the variability contained within the data and easier interpretation of the key information it contains. In this review, an overview of the current knowledge and challenges introduced by modern pharmaceutical material characterisation methods is provided. Two case studies illustrate how the incorporation of multivariate analysis into the material sciences workflow facilitates a better understanding of materials.

Characterising Drug Release from Immediate-Release Formulations of a Poorly Soluble Compound, Basmisanil, Through Absorption Modelling and Dissolution Testing

The study aimed to characterise the mechanism of release and absorption of Basmisanil, a biopharmaceutics classification system (BCS) class 2 compound, from immediate-release formulations via mechanistic absorption modelling, dissolution testing, and Raman imaging. An oral absorption model was developed in GastroPlus® and verified with single-dose pharmacokinetic data in humans. The properties and drug release behaviour of different oral Basmisanil formulations were characterised via biorelevant dissolution and Raman imaging studies. Finally, an in vitro-in vivo correlation (IVIVC) model was developed using conventional and mechanistic deconvolution methods for comparison. The GastroPlus model accurately simulated oral Basmisanil exposure from tablets and granules formulations containing micronized drug. Absorption of oral doses below 200 mg was mostly dissolution rate-limited and thus particularly sensitive to formulation properties. Indeed, reduced exposure was observed for a 120-mg film-coated tablet and the slower dissolution rate measured in biorelevant media was attributed to differences in drug load. This hypothesis was confirmed when Raman imaging showed that the percolation threshold was exceeded in this formulation. This biorelevant dissolution method clearly differentiated between the formulations and was used to develop a robust IVIVC model. The study demonstrates the applicability and impact of mechanistic absorption modelling and biopharmaceutical in vitro tools for rational drug development.

Effect of roll compaction on granule size distribution of microcrystalline cellulose-mannitol mixtures: computational intelligence modeling and parametric analysis

Dry granulation using roll compaction is a typical unit operation for producing solid dosage forms in the pharmaceutical industry. Dry granulation is commonly used if the powder mixture is sensitive to heat and moisture and has poor flow properties. The output of roll compaction is compacted ribbons that exhibit different properties based on the adjusted process parameters. These ribbons are then milled into granules and finally compressed into tablets. The properties of the ribbons directly affect the granule size distribution (GSD) and the quality of final products; thus, it is imperative to study the effect of roll compaction process parameters on GSD. The understanding of how the roll compactor process parameters and material properties interact with each other will allow accurate control of the process, leading to the implementation of quality by design practices. Computational intelligence (CI) methods have a great potential for being used within the scope of quality by design approach. The main objective of this study was to show how the computational intelligence techniques can be useful to predict the GSD by using different process conditions of roll compaction and material properties. Different techniques such as multiple linear regression, artificial neural networks, random forest, Cubist and k-nearest neighbors algorithm assisted by sevenfold cross-validation were used to present generalized models for the prediction of GSD based on roll compaction process setting and material properties. The normalized root-mean-squared error and the coefficient of determination (R²) were used for model assessment. The best fit was obtained by Cubist model (normalized root-mean-squared error =3.22%, R²=0.95). Based on the results, it was confirmed that the material properties (true density) followed by compaction force have the most significant effect on GSD.

MCC-mannitol mixtures after roll compaction/dry granulation: percolation thresholds for ribbon microhardness and granule size distribution

In roll compaction, the specific compaction force, the gap width and the roll speed are the most important settings as they have a high impact in the products obtained. However the mechanical properties of the mixture being compacted are also critical. For this reason, a multilevel full factorial design including these parameters as factors plus three repetitions of the center point was performed for microcrystalline cellulose, mannitol and five binary mixtures (15, 30, 50, 70 and 85% MCC). These two reference excipients were chosen in order to investigate the plastic/brittle behavior of mixtures for the roll compaction process. These materials were roll compacted in a 3-W-Polygran^® 250/50/3 (Gerteis) and the ribbons obtained were collected and milled into granules which were characterized regarding granule size distribution. After statistical evaluation, it was found that the most critical factors affecting the D10, D50, D90 and the fines fraction from the granules were the gap width and the specific compaction force, as well as the proportion of MCC together with its quadratic effect and the interaction between force and proportion of MCC. The microhardness of the ribbons from the center point as well as the D10, D50, D90 and the fines fraction from the granules produced at these same conditions were characterized. In all the cases, the proportion of MCC, i.e. the composition of the mixture, showed also an important effect on these properties measured. In this sense, the percolation theory was applied in order to study further the importance of the plastic/brittle ratio by calculating the percolation threshold or the limit over which the behavior of the system changes. This resulted in values of 34% for the HU (expression of microhardness), 27% and 28% for the D10 and fines, respectively (percolation of MCC) and 84% and 85% for the D50 and D90, respectively (percolation of mannitol).