In vitro and in silico methods to investigate the effect of moderately increasing medium viscosity and density on ibuprofen dissolution rate.

Medium viscosity can affect drug dissolution rate, however, it is not usually considered in routine dissolution testing or less complex biorelevant media. The effects of moderately increasing medium viscosity on the in vitro and in silico dissolution of ibuprofen were investigated with two viscosity enhancing agents (VEA) (hydroxypropyl methylcellulose (HPMC) and sucrose), three viscosity levels (range 0.7-5.5 mPa.s), two solubilities and two fluid velocities in the paddle, flow-through and intrinsic dissolution apparatuses. A factorial design analysis highlighted which factors significantly affected key dissolution metrics. Experimental results in the flow-through apparatus (FTA) were compared with in silico dissolution profiles generated by an in-house simulation code (SIMDISSOTM). Increasing viscosity reduced the intrinsic dissolution rate of ibuprofen for both VEAs. The dissolution rate reduction was also observed in the FTA with sucrose, but less so with HPMC, suggesting particle wetting, motion and surface area effects. Particle motion simulations suggested reduced particle lifting times as viscosity increased, indicating an effect of viscosity on particle dispersal. The viscosity- and fluid density-mediated reduction in the dissolution rate observed with sucrose was accurately simulated by SIMDISSOTM, in particular at higher velocities. Velocity had a significant impact on dissolution rates in the paddle apparatus, with a significant viscosity-related reduction in dissolution observed in the low solubility-low velocity scenario. Even small increases in medium viscosity can reduce the dissolution rate of a BCS class II drug, and in silico particle motion and dissolution data can assist interpretation of particulate dissolution behaviour.

Application of an AI image analysis and classification approach to characterise dissolution and precipitation events in the flow through apparatus.

Imaging and artificial intelligence (AI) approaches have been used with increasing frequency in pharmaceutical industry in recent years. Characterisation of processes such as drug dissolution and precipitation is vital in quality control testing and drug manufacture. To support existing techniques like in vitro dissolution testing, novel process analytical technologies (PATs) can give an insight into these processes. The aim of this study was to create and explore the potential of an automated image classification model based on image analysis to identify events (dissolution and precipitation) occurring in the flow-through apparatus (FTA) test cell, and the ability to characterise a dissolution process over time. Several precipitation conditions were tested in a USP 4 FTA test cell with images recorded during early (plume formation) and late (particulate re-formation) stages of precipitation. An available MATLAB code was used as a base to develop and validate an anomaly classification model able to detect different events occurring during the precipitation process in the dissolution cell. Two variants of the model were tested on images from a dissolution test in the FTA, with a view to application of the image analysis system to quantitative characterization of the dissolution process over time. It was found that the classification model is highly accurate (>90%) in detecting events occurring in the FTA test cell. The model showed potential to be used to characterise the stages of dissolution and precipitation processes, and as a proof of concept demonstrates potential for deep machine learning image analysis to be applied to kinetics of other pharmaceutical processes.

Exploring bulk volume, particle size and particle motion definitions to increase the predictive ability of in vitro dissolution simulations.

The definition of the local dissolution environment is central to accurate particle dissolution simulation, and is determined by the apparatus and conditions used. In the flow-through apparatus dissolution occurs in the cell, often in a low velocity environment, with the reservoir considered the relevant volume for dissolution kinetics. Dissolution simulations were conducted using a reduced-order model based on the Ranz-Marshall correlation for mass transfer from spherical particles. Using ibuprofen as a model drug, the effect of defining a local volume to simulate dynamic bulk concentration conditions in the flow-through and paddle apparatus was assessed by comparing use of a near particle volume (NPV), extending a distance of one radius from the particle surface, with a flow-through apparatus cell volume or paddle apparatus vessel volume as the relevant instantaneous volume for dissolution. The instantaneous inlet concentration to NPV or cell volume is the reservoir/vessel concentration at that simulation time point, reflecting the continuous input to the cell of more dilute solution from the reservoir (closed system). Additionally, inputting particle size distribution (PSD) instead of a median particle size (MPS) and enabling or disabling particle motion were investigated, in two media (resulting in low and high solubility) and with two fluid velocity conditions in each apparatus. The NPV predicted effects of fluid velocity differences on dissolution in the high solubility medium in the flow-through apparatus, but had no effect on predictive ability in the paddle apparatus. In both apparatuses, simulations were reasonable for the high solubility environment but underpredicted dissolution in the low solubility environment. The PSD option and disabling particle motion increased the predictive ability of the simulations in low solubility media in the flow-through apparatus. The results highlight the necessity to incorporate the local dynamic dissolution conditions in the flow-through apparatus for accurate dissolution simulation, and the challenges of defining an effective particle size for dissolution simulation and of reflecting hydrodynamic complexity in simulating dissolution in the paddle apparatus.

Understanding the Potential for Dissolution Simulation to Explore the Effects of Medium Viscosity on Particulate Dissolution.

Viscosity, influenced by medium composition, will affect the hydrodynamics of a dissolution system. Dissolution simulation methods are valuable tools to explore mechanistic dissolution effects, with an understanding of limitations of any simulation method essential to its appropriate use. The aims of this paper were a) to explore, using dissolution simulation, the effects of slightly viscous media on particulate dissolution and b) to illustrate approaches to, and limitations of, the dissolution simulations. A lumped parameter fluid dynamics dissolution simulation model (SIMDISSO™) was used to simulate particulate (20 and 200 µm diameter) dissolution in media with viscosity at 37 °C of water (0.7 mPa.s), milk (1.4 mPa.s) and a nutrient drink (12.3 mPa.s). Effects of flow rate, modality (constant vs pulsing), viscosity and gravitational and particle motion/sedimentation effects on simulated dissolution were explored, in the flow through and paddle apparatuses as appropriate. Shadowgraph imaging (SGI) was used to visualise particle suspension behaviour. Flow rate, hydrodynamic viscous effects and disabling particle motion and gravitational effects affected simulated dissolution of larger particles. SGI imaging revealed retention of particles in suspension in 1.4 mPa.s medium, which sedimented in water. The effect of diffusion adjusted for viscosity was significant for both particle sizes. The limitations of this 1D simulation approach would be greater for larger particles in low velocity regions of the paddle apparatus. Even slightly viscous media can affect dissolution of larger particles with dissolution simulation affording insight into the mechanisms involved, provided the assumptions and limitations of the simulation approach are clarified and understood.

Modelling and shadowgraph imaging of cocrystal dissolution and assessment of in vitro antimicrobial activity for sulfadimidine/4-aminosalicylic acid cocrystals.

PURPOSE: The aim of this work was to evaluate the influence of crystal habit on the dissolution and in vitro antibacterial and anitiprotozoal activity of sulfadimidine:4-aminosalicylic acid cocrystals. METHODS: Cocrystals were produced via milling or solvent mediated processes. In vitro dissolution was carried out in the flow-through apparatus, with shadowgraph imaging and mechanistic mathematical models used to observe and simulate particle dissolution. In vitro activity was tested using agar diffusion assays. RESULTS: Cocrystallisation via milling produced small polyhedral crystals with antimicrobial activity significantly higher than sulfadimidine alone, consistent with a fast dissolution rate which was matched only by cocrystals which were milled following solvent evaporation. Cocrystallisation by solvent evaporation (ethanol, acetone) or spray drying produced flattened, plate-like or quasi-spherical cocrystals, respectively, with more hydrophobic surfaces and greater tendency to form aggregates in aqueous media, limiting both the dissolution rate and in vitro activity. Deviation from predicted dissolution profiles was attributable to aggregation behaviour, supported by observations from shadowgraph imaging. CONCLUSIONS: Aggregation behaviour during dissolution of cocrystals with different habits affected the dissolution rate, consistent with in vitro activity. Combining mechanistic models with shadowgraph imaging is a valuable approach for dissolution process analysis.

Mechanistic modelling and mechanistic monitoring: simulation and shadowgraph imaging of particulate dissolution in the flow-through apparatus.

Accurate mechanistic modelling of a complex system requires insight into the process being simulated, in addition to a theoretical 'first-principles' approach. The current work uses a numerical mechanistic model to simulate dissolution of a particulate system in the flow-through dissolution apparatus. A shadowgraph imaging method is also used to monitor the dissolution process, providing real-time estimates of particle motion, number and total dissolution time. Experimental dissolution studies of ibuprofen particles are used to assess the accuracy of the model. The numerical model adequately predicts the ibuprofen particle dissolution rate at 16 mL min(-1) . Parameter sensitivity analysis identified dissolution test circumstances requiring more, or less, accuracy in the particle size and density calculations. The shadowgraph imaging method successfully determined the total dissolution time and decreasing particle numbers over time. The images confirmed the pulsing particle motion of the numerical model but revealed some more complex velocity patterns, assisting numerical model development. Further optimisation of the sampling window is required to capture all relevant particle motion and changing particle size distribution. A mechanistic model can successfully simulate particulate dissolution in the flow-through apparatus, and when used along with shadowgraph imaging, can give valuable insight into the dissolution process mechanisms and environment.

High dynamic velocity range particle image velocimetry using multiple pulse separation imaging.

The dynamic velocity range of particle image velocimetry (PIV) is determined by the maximum and minimum resolvable particle displacement. Various techniques have extended the dynamic range, however flows with a wide velocity range (e.g., impinging jets) still challenge PIV algorithms. A new technique is presented to increase the dynamic velocity range by over an order of magnitude. The multiple pulse separation (MPS) technique (i) records series of double-frame exposures with different pulse separations, (ii) processes the fields using conventional multi-grid algorithms, and (iii) yields a composite velocity field with a locally optimized pulse separation. A robust criterion determines the local optimum pulse separation, accounting for correlation strength and measurement uncertainty. Validation experiments are performed in an impinging jet flow, using laser-Doppler velocimetry as reference measurement. The precision of mean flow and turbulence quantities is significantly improved compared to conventional PIV, due to the increase in dynamic range. In a wide range of applications, MPS PIV is a robust approach to increase the dynamic velocity range without restricting the vector evaluation methods.