Application of Principal Component Analysis (PCA) to Evaluating the Deformation Behaviors of Pharmaceutical Powders

Quality-by-design in pharmaceutical development: From current perspectives to practical applications

Current pharmaceutical research directions tend to follow a systematic approach in the field of applied research and development. The concept of quality-by-design (QbD) has been the focus of the current progress of pharmaceutical sciences. It is based on, but not limited, to risk assessment, design of experiments and other computational methods and process analytical technology. These tools offer a well-organized methodology, both to identify and analyse the hazards that should be handled as critical, and are therefore applicable in the control strategy. Once implemented, the QbD approach will augment the comprehension of experts concerning the developed analytical technique or manufacturing process. The main activities are oriented towards the identification of the quality target product profiles, along with the critical quality attributes, the risk management of these and their analysis through in silico aided methods. This review aims to offer an overview of the current standpoints and general applications of QbD methods in pharmaceutical development.

Application of reverse engineering in the field of pharmaceutical tablets using Raman mapping and chemometrics

Raman micro-spectroscopy technique offers a combination of relatively high spatial resolution with identification of components or mixtures of components in different sample areas, e.g. on the surface or the cross-section of a sample. This study is focused on the analysis of the tablets from pharmaceutical development with different technological parameters: (1) the manufacturing technology, (2) the particle size of the input API (active pharmaceutical ingredient) and (3) the quantitative composition of the individual excipients. These three mentioned parameters represent the most frequently solved problems in the field of reverse engineering in pharmacy. The investigation aims to distinguish tablets with the above-described technological parameters with limited subjective steps by Raman microscopy. Furthermore, non-subjective methods of Raman data analysis using advanced statistical analysis have been proposed, namely Principal Component Analysis, Soft Independent Modelling of Class Analogy and Linear Discriminant Analysis. The methods successfully distinguished and identified even very small differences in the analysed tablets within our study and provided objective statistic evaluation of Raman maps. The information on component and particle size distribution including their small differences, which is the critical parameter in the development of the original and generic products, was obtained due to combination of these methods. Even though each of these chemometric methods evaluates the data set from a different perspective, their mutual application on the problem of Raman maps evaluation confirmed and specified results on level that would be unattainable with the use of only one them.

Employing Multivariate Statistics and Latent Variable Models to Identify and Quantify Complex Relationships in Typical Compression Studies

The effect of storage condition (% RH) on flufenamic acid:nicotinamide (FFA:NIC) cocrystal compressibility, compactibility, and tabletability profiles was not observed after visual evaluation or linear regression analysis. However, multivariate statistical analysis showed that storage condition had a significant effect on each compressional profile. Shapiro and Heckel equations were used to determine the compression parameters: porosity, Shapiro's compression parameter (f), densification factor (Da), plastic yield pressure (YPpl), and elastic yield pressure (YPel). Latent variable models such as exploratory factor analysis, principal component analysis, and principal component regression were employed to decode complex hidden main, interaction, and quadratic effects of % RH and the compression parameters on FFA:NIC tablet mechanical strength (TMS). Statistically significant correlations between f and Da, f and YPpl, and Da and YPel supported the idea that both rearrangement and fragmentation, and plastic deformation are important to FFA:NIC TMS. To the authors knowledge, this is the first time that simultaneously operating dual mechanisms of fragmentation and plastic deformation in low and midrange compression, and midrange plastic deformation have been identified and reported. A quantitative PCR model showed that f, Da, and YPel had statistically significant main effects along with a significant antagonist storage condition-porosity "conditional interaction effect". f exhibited a 2.35 times greater impact on TMS compared to Da. The model root-mean-square error at calibration and prediction stages were 0.04 MPa and 0.08 MPa, respectively. The R² values at the calibration stage and at the prediction stage were 0.9005 and 0.7539, respectively. This research demonstrated the need for caution when interpreting the results of bivariate compression data because complex latent inter-relationships may be hidden from visual assessment and linear regression analysis, and result in false data interpretation as illustrated in this report.

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.

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.

In-line monitoring of compaction properties on a rotary tablet press during tablet manufacturing of hot-melt extruded amorphous solid dispersions

As the number of applications for polymers in pharmaceutical development is increasing, there is need for fundamental understanding on how such compounds behave during tableting. This research is focussed on the tableting behaviour of amorphous polymers, their solid dispersions and the impact of hot-melt extrusion on the compaction properties of these materials. Soluplus, Kollidon VA 64 and Eudragit EPO were selected as amorphous polymers since these are widely studied carriers for solid dispersions, while Celecoxib was chosen as BCS class II model drug. Neat polymers and physical mixtures (up to 35% drug load) were processed by hot-melt extrusion (HME), milled and sieved to obtain powders with comparable particle sizes as the neat polymer. A novel approach was used for in-line analysis of the compaction properties on a rotary tablet press (Modul P, GEA) using complementary sensors and software (CDAAS, GEA). By combining 'in-die' and 'out-of-die' techniques, it was possible to investigate in a comprehensive way the impact of HME on the tableting behaviour of amorphous polymers and their formulations. The formation of stable glassy solutions altered the formulations towards more fragmentary behaviour under compression which was beneficial for the tabletability. Principal component analysis (PCA) was applied to summarize the behaviour during compaction of the formulations, enabling the selection of Soluplus and Kollidon VA 64 as the most favourable polymers for compaction of glassy solutions.

Full Out-of-Die Compressibility and Compactibility Profiles From Two Tablets

In this study, a method is presented that can be used to generate full out-of-die compressibility and compactibility profiles using the data from only 2 tablets. For each material, one tablet was compacted at the maximum pressure of interest and a second tablet at a relatively low pressure. The in-die data collected during compaction to the maximum pressure of interest and the solid fraction change after ejection for both tablets were used to generate a profile equivalent to a complete out-of-die compressibility profile. After measuring the tensile strengths of each tablet, a compactibility profile was produced by fitting the out-of-die porosity and tensile strength data to the Ryshkewitch-Duckworth equation. This method generated accurate out-of-die compressibility and compactibility profiles for each of the materials studied. Not only is this technique computationally simple, but in cases where only small amounts of raw material are available, this method allows a detailed understanding of a material's mechanical behavior to be assessed.