The aim of the present study was to improve the recovery of polyphenols from olive leaves (OL) by optimizing a multistage extraction scheme; provided that the olive leaves have been previously steam blanched. The maximum total phenol content expressed in ppm caffeic acid equivalents was obtained at pH 2, particle size 0.315 mm, solid-liquid ratio 1:7 and aqueous ethanol concentration 70% (v/v). The optimum duration time of each extraction stage and the operation temperature, were chosen based on qualitative and quantitative analysis of oleuropein (OLE), verbascoside, luteolin-7-O-glucoside and apigenin-7-O-glucoside performed by high performance liquid chromatography with diode array detector (HPLC-DAD). The optimum conditions for multistage extraction were 30 min total extraction time (10 min × 3 stages) at 85 °C. The 80% of the total yield of polyphenols was obtained at the 1st stage of the extraction. The total extraction yield of oleuropein was found 23 times higher (103.1 mg OLE/g dry weight (d.w.) OL) compared to the yield (4.6 mg OLE/g d.w. OL) obtained by the conventional extraction method (40 °C, 48 h). However, from an energetic and hence from an economical point of view it is preferable to work at 40 °C, since the total extraction yield of polyphenolic compounds was only 17% higher for a double increase in the operating temperature (i.e., 85 °C).

The performance of solid oral dosage forms targeting the colon is typically evaluated using standardised pharmacopeial dissolution apparatuses. However, these fail to replicate colonic hydrodynamics. This study develops a digital twin of the Dynamic Colon Model; a physiologically representative in vitro model of the human proximal colon. Magnetic resonance imaging of the Dynamic Colon Model verified that the digital twin robustly replicated flow patterns under different physiological conditions (media viscosity, volume, and peristaltic wave speed). During local contractile activity, antegrade flows of 0.06–0.78 cm s⁻¹ and backflows of −2.16–−0.21 cm s⁻¹ were measured. Mean wall shear rates were strongly time and viscosity dependent although peaks were measured between 3.05–10.12 s⁻¹ and 5.11–20.34 s⁻¹ in the Dynamic Colon Model and its digital twin respectively, comparable to previous estimates of the USPII with paddle speeds of 25 and 50 rpm. It is recommended that viscosity and shear rates are considered when designing future dissolution test methodologies for colon-targeted formulations. In the USPII, paddle speeds >50 rpm may not recreate physiologically relevant shear rates. These findings demonstrate how the combination of biorelevant in vitro and in silico models can provide new insights for dissolution testing beyond established pharmacopeial methods.

The algorithm behind particle methods is extremely versatile and used in a variety of applications that range from molecular dynamics to astrophysics. For continuum mechanics applications, the concept of ‘particle’ can be generalized to include discrete portions of solid and liquid matter. This study shows that it is possible to further extend the concept of ‘particle’ to include artificial neurons used in Artificial Intelligence. This produces a new class of computational methods based on ‘particle-neuron duals’ that combines the ability of computational particles to model physical systems and the ability of artificial neurons to learn from data. The method is validated with a multiphysics model of the intestine that autonomously learns how to coordinate its contractions to propel the luminal content forward (peristalsis). Training is achieved with Deep Reinforcement Learning. The particle-neuron duality has the advantage of extending particle methods to systems where the underlying physics is only partially known, but we have observations that allow us to empirically describe the missing features in terms of reward function. During the simulation, the model evolves autonomously adapting its response to the available observations, while remaining consistent with the known physics of the system.

This work used in vivo MRI images of human colon wall motion to inform a biorelevant Dynamic Colon Model (DCM) to understand the interplay of wall motion, volume, viscosity, fluid, and particle motion within the colon lumen. Hydrodynamics and particle motion within the DCM were characterized using Positron Emission Tomography (PET) and Positron Emission Particle Tracking (PEPT), respectively. In vitro PET images showed that fluid of higher viscosity follows the wall motion with poor mixing, whereas good mixing was observed for a low viscosity fluid. PEPT data showed particle displacements comparable to the in vivo data. Increasing fluid viscosity favors the net forward propulsion of the tracked particles. The use of a floating particle demonstrated shorter residence times and greater velocities on the liquid surface, suggesting a surface wave that was moving faster than the bulk liquid. The DCM can provide an understanding of flow motion and behavior of particles with different buoyancy, which in turn may improve the design of drug formulations, whereby fragments of the dosage form and/or drug particles are suspended in the proximal colon.