Estimating the Dissolution of Anticancer Drugs in Supercritical Carbon Dioxide with a Stacked Machine Learning Model

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.

Prediction of enhanced drug solubility related to clathrate compositions and operating conditions: Machine learning study

Although complexation technique has been documented as a promising strategy to enhance the dissolution rate and bioavailability of water-insoluble drugs, prediction of the enhanced drug solubility related to clathrate compositions and operating conditions is still a challenge. Herein, clathrate compositions (drug content (DC), drug molecular weight (M) and molar ratio (Ratio)), operating conditions (drug concentration (C), pH, pressure (P), temperature (T) and dissolution time (t)) under the different excipients (PEG, PVP, HPMC and cyclodextrin) as main solubilizers of the clathrates condition as input parameters were used to predict two indexes (drug dissolved percentage and dissolution efficiency) simultaneously through machine learning methodfor the first time. The results show that PVP as the main solubilizer of clathrates had higher prediction accuracy to the drug dissolved percentage, and HPMC as the main solubilizer of clathrates had higher prediction accuracy to the drug dissolution efficiency. In addition, the influence of various factors and interactions on the target variables were analyzed. This study affords achievable hints to the quantitative prediction of the drug solubility affected by various compositions and different operating conditions.