Advances in ATR-FTIR Spectroscopic Imaging for the Analysis of Tablet Dissolution and Drug Release

Acceleration of chondrogenic differentiation utilizing biphasic core-shell alginate sulfate electrospun nanofibrous scaffold

Engineering new biomedical materials with tailored physicochemical, mechanical and biological virtues in order to differentiate stem cells into chondrocytes for cartilage regeneration has garnered much scientific interest. In this study, core/shell nanofibrous scaffold based on poly(ɛ-caprolactone) (PCL) as a core material and alginate sulfate (AlgS)-poly(vinyl alcohol) (PVA) blend as shell materials (AlgS-PVA/PCL) was fabricated by emulsion electrospinning. In this vein, the influence of AlgS to PVA ratio (30:70, 50:50), organic to aqueous phase ratio (1:2, 1:3 and 1:5) and acid concentration (0, 10, 20, 30, 40 and 50 %) on nanofibers morphology were investigated. SEM images depicted that AlgS to PVA ratio of 30:70 and 50:50, organic to aqueous phase ratio of 1:3 and 1:5 and acid concentration of 30 % led to uniform, bead-free fibrous mats. AlgS-PVA/PCL scaffolds with AlgS to PVA ratio of 30:70 and organic to aqueous phase ratio of 1:3, showed admirable mechanical features, high porosity (>90 %) with desirable swelling ratio in wet condition. In vitro assays indicated that the AlgS-PVA/PCL scaffold surface had desirable interaction with stem cells and promotes cells attachment, proliferation and differentiation. Thus, we envision that this salient structure could be an intriguing construction as a cartilage tissue-engineered scaffold.

Application of shadowgraph imaging (SGI) particle characterisation data to interpret the impact of varying test conditions on power dissolution and to develop an automated agglomeration identification method (AIM) in the USP flow through apparatus

The aims of this work were 1) to explore the application of shadowgraph imaging (SGI) as a real time monitoring tool to characterize ibuprofen particle behaviour during dissolution testing under various conditions in the USP 4 flow-through apparatus and 2) to investigate the potential to develop an SGI-based automated agglomeration identification method (AIM) for real time agglomerate detection during dissolution testing. The effect of surfactant addition, changes in the drug mass and flow rate, the use of sieved and un-sieved powder fractions, and the use of different drug crystal habits were investigated. Videos at every sampling time point during dissolution were taken and analysed by SGI. The AIM was developed to characterize agglomerates based on two criteria - size and solidity. All detections were confirmed by manual video observation and a reference agglomerate data set. The method was validated under new dissolution conditions with un-sieved particles. Characterisation of particle dispersion behaviour by SGI enabled interpretation of the impact of dissolution test conditions. Higher numbers of early detections reflected greater dissolution rates with increased surfactant concentration, using sieved fraction or plate-shaped crystals, but was impacted by drug mass tested. An AIM was successfully developed and applied to detect agglomerates during dissolution, suggesting potential, with appropriate method development, for application in quality control.

Analysis of the Dissolution Behavior of Theophylline and Its Cocrystal Using ATR-FTIR Spectroscopic Imaging

Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopic imaging is a powerful tool to visualize the distribution of components, and it has been used to analyze drug release from tablets. In this work, ATR-FTIR spectroscopic imaging was applied for observing the dissolution of molecular crystals from tablet compacts. The IR spectra provided chemically specific information about the transformation of crystal structures during the dissolution experiments. Theophylline (TPL) anhydrate and its cocrystals were used as model systems of molecular crystals. The IR spectra during the dissolution of TPL revealed information about the crystal structure of TPL, which transformed from anhydrate to monohydrate in water. During a dissolution test of a model cocrystal system, it was suggested that an active pharmaceutical ingredient (API) and a coformer were dissolved in water simultaneously. The IR spectra that were acquired during the dissolution of a cocrystal tablet showed new spectral bands attributed to the API after 5 min. This suggested that the precipitation of API was observed during the dissolution experiment. Measurements from ATR-FTIR spectroscopic imaging can visualize the drug release from the tablet and determine the transformation of molecular crystals during their dissolution. These results will have an impact on clarifying the dissolution mechanism of molecular crystals.

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.