Setting up multivariate specifications on critical raw material attributes to ensure consistent drug dissolution from high drug-load sustained-release matrix tablet

A novel strategy for the quality control of carbonized Typhae pollen using colorimeter, liquid chromatography-mass spectrometry, and efficacy evaluation coupled with multivariate statistical analysis

In this study, a novel quality control strategy was proposed, aiming to establish a multivariate specification for the processing step by exploring the correlation between colors, chemical components, and hemostatic effects of the carbonized Typhae pollen (CTP) using multivariate statistical analysis. The CTP samples were stir-fried at different durations. Afterward, the colorimeter and LC-MS techniques were applied to characterize the CTP samples, followed by the determination of bleeding time and clotting time using mice to evaluate their hemostatic effect. Then, principal component analysis, hierarchical cluster analysis, and multi-block partial least squares were used for data analysis on colors, chemical components, and their correlation with the hemostatic effect. Consequently, 13 critical quality attributes (CQAs) of CTP were identified via multivariate statistical analysis-L*, a*, b*, 3,4-dihydroxybenzoic acid, 4-hydroxybenzoic acid, 3-hydroxybenzoic acid, quercetin-3-O-glucoside, azelaic acid, kaempferol-3-O-glucoside, quercetin, naringenin, kaempferol, and isorhamnetin. The multivariate specification method involving the 13 CQAs was developed and visualized in the latent variable space of the partial least squares model, indicating that the proposed method was successfully applied to assess the quality of CTP and the degree of carbonization. Most importantly, this study offers a novel insight into the control of processing for carbonized Chinese herbal medicines.

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.

Bottom-up design of hydrogels for programmable drug release

Hydrogels are a promising drug delivery system for biomedical applications due to their biocompatibility and similarity to native tissue. Programming the release rate from hydrogels is critical to ensure release of desired dosage over specified durations, particularly with the advent of more complicated medical regimens such as combinatorial drug therapy. While it is known how hydrogel structure affects release, the parameters that can be explicitly controlled to modulate release ab initio could be useful for hydrogel design. In this review, we first survey common physical models of hydrogel release. We then extensively go through the various input parameters that we can exercise direct control over, at the levels of synthesis, formulation, fabrication and environment. We also illustrate some examples where hydrogels can be programmed with the input parameters for temporally and spatially defined release. Finally, we discuss the exciting potential and challenges for programming release, and potential implications with the advent of machine learning.

Application of Multivariate Methods to Evaluate Differential Material Attributes of HPMC from Different Sources

The aim of the present study is to achieve differential material attributes (DMAs) of hydroxypropyl methylcellulose (HPMC) with different viscosity grades (K4M, K15M, and K100M) from different manufacturers (Anhui Shanhe and Dow Chemical). Two kinds of multivariate methods, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), were adopted. The physicochemical properties of HPMC were systematically investigated via various techniques (e.g., SEM, particle size detection, and SeDeM characterization). Data from 33 characterization variables were applied to the multivariate methods. The PCA and OPLS-DA results indicated the differences between the HPMC from two manufacturers by the common variables that include the tablet hardness (HD), tensile strength (TS), bulk density, interparticle porosity, Carr index, cohesion index, Hausner ratio, flowability, and the width of the particle size distribution (span). Interestingly, these variables showed a certain correlation with each other, supporting the characterization results. Except for these different variables of the HPMC obtained by multivariate analysis results, distinguishable shapes and surface morphologies also appeared between different sources. To sum up, the powder properties (particle size, surface topography, dimension, flowability, and compressibility) and the tablet properties (HD and TS) were recognized as the DMAs of HPMC samples. This work provided the multivariate methods for the physicochemical characterization of HPMC, with potential in the quality control and formulation development.

Effect of Spray Drying Conditions on Physical Properties of Panax notoginseng Saponin (PNS) Powder and the Intra-Batch Dissolution Variability of PNS Hydrophilic Matrix Tablet

Purpose

Understanding raw material variability and its impact on product quality are crucial for developing robust pharmaceutical processes. This work aimed to study the effects of spray drying conditions on properties of the spray dried Panax notoginseng saponin (PNS) powders as well as the subsequent intra-batch dissolution variability of PNS hydrophilic matrix tablets.

Methods

The Plackett-Burman design was applied to screen the critical process parameters (CPPs). Then, the Box-Behnken design was used to investigate the relationship between the CPPs and the physiochemical properties of spray dried PNS powders. The PNS hydrophilic matrix tablets containing 57% spray dried PNS powders were directly compressed. The partial least squares (PLS) regression was used to uncover the hidden multivariate relationships among the CPPs, intermediate powder properties, and tablet quality attributes.

Results

The identified CPPs were the feed concentration, the inlet air temperature, and the atomization pressure. It was found that the CPPs exerted little impact on chemical properties of spray dried PNS powders, but had significant impact on physical properties, such as particle size, specific surface area, bulk density, hygroscopicity, and inter-particle porosity, etc. Latent variable modeling results revealed that the high inlet air temperature of spray drying process could produce PNS powders with low moisture content and high hygroscopicity, which were beneficial to reduce the intra-batch dissolution variability of PNS hydrophilic matrix tablets. Finally, a design space of the spray drying process was built in order to ensure the dissolution consistency.

Conclusion

Our research provided a reference for improving the spray drying conditions in order to ensure the dissolution consistency of the PNS hydrophilic matrix tablet.

An overview on the mathematical modeling of hydrogels' behavior for drug delivery systems

Hydrogels-based systems (HBSs) for drug delivery are nowadays extensively used and the interest in modeling their behavior is dramatically increasing. In this review a critical overview on the modeling approaches is given, quantitatively and qualitatively analyzing the publications on the subject, the trend of the publications per year and the type of modeling approaches. It was found that, despite the drug release fitting models (i.e. Higuchi's equation) are the most abundant, their use for HBSs is decreasing in the last years and luckily, considering the limiting assumption on which they were built, they will be confined to simple mathematical fitting equations. Within the mechanistic models the "multi-component" with the swelling approximation (mass transport only) and with the mechanics (fully coupled) are experiencing the highest growth rate, with much more interest toward the last one that, in the next years could be able to provide a first principles model. Statistical models, especially based on the response surface methodology, are rapidly spreading in the scientific community mainly thanks to their ability to be predictive, regardless of the phenomenology, in the analyzed design space with very low efforts. Neural Networks models for HBSs, in countertrend with their use in the pharmaceutical industry, have never take off preferring less data demanding statistical models.

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.

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.