Modeling of Microneedle Arrays in Transdermal Drug Delivery Applications

Multi-material 3D printed eutectogel microneedle patches integrated with fast customization and tunable drug delivery

Microneedle patches are emerging multifunctional platforms for transdermal diagnostics and drug delivery. However, it still remains challenging to develop smart microneedles integrated with customization, sensing, detection and drug delivery by 3D printing strategy. Here, we present an innovative but facile strategy to rationally design and fabricate multifunctional eutectogel microneedle (EMN) patches via multi-material 3D printing. Polymerizable deep eutectic solvents (PDES) were selected as printing inks for rapid one-step fabrication of 3D printing functional EMN patches due to fast photopolymerization rate and ultrahigh drug solubility. Moreover, stretchable EMN patches incorporating rigid needles and flexible backing layers were easily realized by changing PDES compositions of multi-material 3D printing. Meanwhile, we developed multifunctional smart multi-material EMN patches capable of performing wireless monitoring of body movements, painless colorimetric glucose detection, and controlled transdermal drug delivery. Thus, such multi-material EMN system could provide an effective platform for the painless diagnosis, detection, and therapy of a variety of diseases.

Simulation and Optimization: A New Direction in Supercritical Technology Based Nanomedicine

In recent years, nanomedicines prepared using supercritical technology have garnered widespread research attention due to their inherent attributes, including structural stability, high bioavailability, and commendable safety profiles. The preparation of these nanomedicines relies upon drug solubility and mixing efficiency within supercritical fluids (SCFs). Solubility is closely intertwined with operational parameters such as temperature and pressure while mixing efficiency is influenced not only by operational conditions but also by the shape and dimensions of the nozzle. Due to the special conditions of supercriticality, these parameters are difficult to measure directly, thus presenting significant challenges for the preparation and optimization of nanomedicines. Mathematical models can, to a certain extent, prognosticate solubility, while simulation models can visualize mixing efficiency during experimental procedures, offering novel avenues for advancing supercritical nanomedicines. Consequently, within the framework of this endeavor, we embark on an extensive review encompassing the application of mathematical models, artificial intelligence (AI) methodologies, and computational fluid dynamics (CFD) techniques within the medical domain of supercritical technology. We undertake the synthesis and discourse of methodologies for calculating drug solubility in SCFs, as well as the influence of operational conditions and experimental apparatus upon the outcomes of nanomedicine preparation using supercritical technology. Through this comprehensive review, we elucidate the implementation procedures and commonly employed models of diverse methodologies, juxtaposing the merits and demerits of these models. Furthermore, we assert the dependability of employing models to compute drug solubility in SCFs and simulate the experimental processes, with the capability to serve as valuable tools for aiding and optimizing experiments, as well as providing guidance in the selection of appropriate operational conditions. This, in turn, fosters innovative avenues for the development of supercritical pharmaceuticals.