Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning

Deep Eutectic Solvents Enhancing Drug Solubility and Its Delivery

Deep eutectic solvents (DES) are environmentally friendly solvents with the potential to dissolve bioactive compounds without affecting their characteristics. DES has special qualities that can be customized to meet the unique characteristics of a biomolecule/active pharmaceutical ingredient (API) in accordance with various therapeutic needs, providing a reliable approach in opening the door for the creation of cutting-edge drug formulations by resolving solubility issues in pharmaceutics. This study outlines newly developing approaches to solve the problem of inefficient API extraction due to poor solubility. These emerging strategies also have the capacity to alter the chemical and physical stability of API, which triggers drug's shelf life and their possible health benefits. It is anticipated that the highlighted methods and processes will be developed to capitalize on the DES potential to improve drug solubility and delivery in the pharmaceutical sector.

Mathematical modeling and numerical simulation of supercritical processing of drug nanoparticles optimization for green processing: AI analysis

In recent decades, unfavorable solubility of novel therapeutic agents is considered as an important challenge in pharmaceutical industry. Supercritical carbon dioxide (SCCO2) is known as a green, cost-effective, high-performance, and promising solvent to develop the low solubility of drugs with the aim of enhancing their therapeutic effects. The prominent objective of this study is to improve and modify disparate predictive models through artificial intelligence (AI) to estimate the optimized value of the Oxaprozin solubility in SCCO2 system. In this paper, three different models were selected to develop models on a solubility dataset. Pressure (bar) and temperature (K) are the two inputs for each vector, and each vector has one output (solubility). Selected models include NU-SVM, Linear-SVM, and Decision Tree (DT). Models were optimized through hyper-parameters and assessed applying standard metrics. Considering R-squared metric, NU-SVM, Linear-SVM, and DT have scores of 0.994, 0.854, and 0.950, respectively. Also, they have RMSE error rates of 3.0982E-05, 1.5024E-04, and 1.1680E-04, respectively. Based on the evaluations made, NU-SVM was considered as the most precise method, and optimal values can be summarized as (T = 336.05 K, P = 400.0 bar, solubility = 0.00127) employing this model. Fig 4.

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.

Computational intelligence modeling of hyoscine drug solubility and solvent density in supercritical processing: gradient boosting, extra trees, and random forest models

This work presents the results of using tree-based models, including Gradient Boosting, Extra Trees, and Random Forest, to model the solubility of hyoscine drug and solvent density based on pressure and temperature as inputs. The models were trained on a dataset of hyoscine drug with known solubility and density values, optimized with WCA algorithm, and their accuracy was evaluated using R², MSE, MAPE, and Max Error metrics. The results showed that Gradient Boosting and Extra Trees models had high accuracy, with R² values above 0.96 and low MAPE and Max Error values for both solubility and density output. The Random Forest model was less accurate than the other two models. These findings demonstrate the effectiveness of tree-based models for predicting the solubility and density of chemical compounds and have potential applications in determination of drug solubility prior to process design by correlation of solubility and density to input parameters including pressure and temperature.