Advanced AI modeling and optimization for determination of pharmaceutical solubility in supercritical processing for production of nanosized drug particles

Obaidullah, Ahmad J.

doi:10.1016/j.csite.2023.103199

Cited by 7 publications

(1 citation statement)

References 26 publications

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“…[21,67] In recent years, significant progress has been made in the fields of AI-SCF technology and nanomedicine, owing to the substantial advancements in computer hardware that have greatly enhanced computational capabilities. In the realm of SCF technology, AI models are not only applied to predict drug solubility but also assist in optimizing experimental design and parameters [72]. In the medical domain, AI finds widespread use in medical imaging research, encompassing improvements in image quality and the segmentation of tumor and healthy region boundaries to aid in surgical procedures [73][74][75].…”

Section: K-nearest Neighbors (Knn)mentioning

confidence: 99%

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

Huang,

Zheng,

et al. 2023

Bioengineering

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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.

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