Study of Hyoscine solubility in scCO2: Experimental measurement and thermodynamic modeling

Hani, Umme; Alosaimi, Hani E.; Huwaimel, Bader; Alharby, Tareq Nafea; Alanazi, Jowaher; Alanazi, Muteb

doi:10.1016/j.molliq.2023.121821

Cited by 10 publications

(2 citation statements)

References 38 publications

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“…The input variables considered for the dataset are temperature in Kelvin and pressure in bar, whereas the output variables are density and solubility 3 . The entire data set is displayed in Table 1 which has been obtained from 37 . The ρsc CO2 stand for the density of solvent and y is the solubility in this table.…”

Section: Data Descriptionmentioning

confidence: 99%

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

Ghazwani

Begum

2023

Sci Rep

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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.

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Section: Data Descriptionmentioning

confidence: 99%

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

Ghazwani

Begum

2023

Sci Rep

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“…Generally, a greater number of adjustable parameters leads to better-fitting results. Table 1 presents empirical and semi-empirical models used in the past five years for calculating drug solubility in SCCO 2 [36][37][38]. These 32 models are categorized into five groups based on influencing factors (variables): density; temperature and density; temperature and pressure, density; temperature and pressure; and pressure and density.…”

Section: Empirical Modelsmentioning

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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Binary and ternary approach of solubility of Rivaroxaban for preparation of developed nano drug using supercritical fluid

Askarizadeh,

Esfandiari,

Honarvar

et al. 2024

Arabian Journal of Chemistry

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Study of Hyoscine solubility in scCO2: Experimental measurement and thermodynamic modeling

Cited by 10 publications

References 38 publications

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

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

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

Binary and ternary approach of solubility of Rivaroxaban for preparation of developed nano drug using supercritical fluid

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