Application of machine learning approach to estimate the solubility of some solid drugs in supercritical CO2

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 12, 2025

Accurate estimation of the solubility solid drugs (SDs) in supercritical carbon dioxide (SC-CO2) plays an essential role related technologies. In this study, artificial intelligence models (AIMs) by gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS) methods were applied to estimate SDs SC-CO2. Hence, a comprehensive database (1816 datasets) comprising operational conditions (T, P) wide ranges (308–348.2 K 80–400 bar), SD's molecular weight (MWSDs), melting point (MPSDs) gathered. Investigation analysis models' strength showed that model developed ANFIS exhibited more satisfactory approximation than GEP model. According optimized model, statistical parameters R2, RMSE, MAE, AARD% obtained, equivalent 0.991, 0.260, 0.167, 13.890% for training 0.990, 0.256, 0.157, 15.273% validation, order. Sensitivity highest effect independent variables on calculating SC-CO2 belong MWSDs, P, MPSDs, T, respectively. Therefore, MWSDs is key factor modeling various Comparing estimated results obtained from AIM with previous semi-empirical AIMs could be accurate

Язык: Английский

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Determination of 5-fluorouracil anticancer drug solubility in supercritical CO2using semi-empirical and machine learning models

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 7, 2025

In order to provide the facilities design supercritical fluid (SCF) processes for micro or nanosizing of solid solute compounds such as drugs, it is essential obtain their solubility in green solvents like pressurized CO2. This important role first stage assessing each SCF technology. A statistical method was developed time and employed determine 5-fluorouracil (5-Fu) solubility. The measurements were performed at different pressures (120-270 bar) temperatures (308-338 K) through UV-vis spectrophotometry, time. obtained between 0.0024 0.0176 g/L. 5-Fu mole fraction constant temperature, increases with an increase pressure. Whereas, a crossover point has been seen. Three models approaches applied correlate model experimental data set: (i) seven density-based models, (ii) PR equations state (vdW2 mixing rule), (iii) machine learning-based namely non-linear regressions, Random Forest, Gradient Boosting, Decision Tree, Kernel Ridge. All tested successfully within acceptable accuracy. Meanwhile, empirical suggested by Sodeifian 2, superior lowest AARD% (AARD = 4.12%). Finally, total, solvation, vaporization enthalpies drug/Sc-CO2 binary system determined using semi-empirical correlations,

Язык: Английский

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Micro and nanosizing of Tamsulosin drug via supercritical CO2 antisolvent (SAS) process

Journal of CO2 Utilization, Год журнала: 2024, Номер 84, С. 102847 - 102847

Опубликована: Июнь 1, 2024

Supercritical anti-solvent (SAS) method is a solvent-free approach where supercritical carbon dioxide (SC-CO2) used as and one can control the output particle size without disturbing nature of particles. The primary objective this study to develop SAS for production Tamsulosin (TML) nanoparticles investigate parameters affecting resultant size. In order optimize nano-sized particles, response surface methodology (RSM) based on Box-Behnken design (BBD (was used. Using dynamic light scattering (DLS) scanning electron microscopy (SEM) analyses, morphologies mean diameter ranges were examined. To look into how process affects TML's chemical physical characteristics, Fourier-transform infrared spectroscopy (FT–IR), X-ray diffraction analysis (XRD) differential calorimetry (DSC) further performed. acquired results demonstrated that pressure imposes largest impact TML production, with exhibiting an inverse correlation pressure. Observations indicated increase in increasing temperature from 35 50 °C. By flow rate drug solution, average exhibited initial decrease followed by trend. Results optimization showed at pressure, temperature, injection 25 MPa, 41.3 °C, 2.9 mL/min, respectively, particles shrink 600 nm.

Язык: Английский

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Thermodynamic modeling and solubility assessment of oxycodone hydrochloride in supercritical CO2: Semi-empirical, EoSs models and machine learning algorithms

Case Studies in Thermal Engineering, Год журнала: 2024, Номер 55, С. 104146 - 104146

Опубликована: Фев. 17, 2024

In this study, the solubility of oxycodone hydrochloride (OXH) in supercritical carbon dioxide (SC–CO2) was investigated at various conditions, temperature (308–338 K) and pressure (120–270 bar), for first time. The ranged from 0.007 to 0.109 g/L, corresponding mole fractions ranging 0.051 × 10−5 0.699 10−5. Three different model groups were used analyze experimental data. group comprised seven semi-empirical models, with 3–6 adjustable parameters. These models include Sparks, Sodeifian 1 2, Bian, Jouyban, Gordillo Jafari-Nejad. second employed two state equations, namely Peng-Robinson (PR) Soave-Redlich-Kwong (SRK) van der Waals mixing rule. average absolute relative deviation percentage (AARD%) 9.73 10.63 PR SRK, respectively. third utilized four machine learning algorithms including DNN, RF, MLP DTs respective R2 values 0.992, 0.980, 0.964 0.961, All exhibited satisfactory agreement Finally, enthalpies vaporization (79.71 kJ/mol) solvation (−19.25 calculated

Язык: Английский

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Solubility of digitoxin in supercritical CO2: Experimental study and modeling

European Journal of Pharmaceutical Sciences, Год журнала: 2024, Номер 195, С. 106731 - 106731

Опубликована: Фев. 20, 2024

In this communication, the solubility of digitoxin drug in supercritical CO

Язык: Английский

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Solubility Measurement and Correlation of Alprazolam in Carbon Dioxide with/without Ethanol at Temperatures from 308 to 338 K and Pressures from 120 to 300 bar

Journal of Chemical & Engineering Data, Год журнала: 2024, Номер 69(4), С. 1718 - 1730

Опубликована: Март 28, 2024

This research investigated the solubility of alprazolam in supercritical carbon dioxide (scCO2) with addition ethanol as a cosolvent. The was examined at various pressures (ranging from 120 to 300 bar) and temperatures 308 338 K). scCO2 ranged 0.027 × 10–4 0.632 10–4. When used cosolvent, increased range 0.157 1.660 (with 1 mol % ethanol) 0.449 3.167 3 ethanol). Under specified operating parameters, highest achieved ternary system bar K. 16.63 times higher than when using pure under same circumstances. Several semiempirical correlations were compute dioxide. These models yielded similar average absolute relative deviations ranging 7.0 8.40%. findings demonstrated superiority Kumar–Johnston model binary systems an deviation 7.0%) Garlapati–Madras 6.19%) compared alternative models.

Язык: Английский

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Solubility of gemcitabine (an anticancer drug) in supercritical carbon dioxide green solvent: Experimental measurement and thermodynamic modeling

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 6, 2025

In order to develop supercritical fluid (SCF) processes for the micro or nanosizing of solid solute components, such as pharmaceuticals, it is essential assess their solubility in solvents including carbon dioxide (SC–CO2). This crucial step fundamental choosing and evaluating SCF technology. A statistical method was developed used determine gemcitabine SC-CO2. The measurements at various pressures temperatures were conducted using UV–vis analysis. Two model types correlate data: semi-empirical (with 3–6 parameters) equation state (EoS) models. SC-CO2 a static 308–338 K 120–270 bar measured modeled first time. Solubility ranged from 0.1274 × 10− 5 1.8128 mole fraction 0.00295 0.08489 kg/L. PR EoS performed best 308 with an AARD 12.58%, SRK excelled 318–338 AARDs between 12.93 15.68%. Application Joback (AARD 12.04–27.13%) COSMO-RS 18.68–31.28%) models also compared. Bian et al. showed fit among density-based 16.62%.

Язык: Английский

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Application of theoretical and semi-empirical equations for modeling of Maprotiline hydrochloride (anti-depressant) drug solubility in SC-CO2

Measurement, Год журнала: 2025, Номер unknown, С. 117508 - 117508

Опубликована: Апрель 1, 2025

Язык: Английский

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Solubility measurement of Ceftriaxone sodium in SC-CO2 and thermodynamic modeling using PR-KM EoS and vdW mixing rules with semi-empirical models

Case Studies in Thermal Engineering, Год журнала: 2024, Номер 61, С. 105074 - 105074

Опубликована: Сен. 1, 2024

The complete investigation of the solubility Ceftriaxone sodium drug has not yet been conducted. This communication investigates in supercritical CO2 (SC-CO2) for first time. measurements were performed using utilizing UV-vis analysis under different operation conditions (at P (bar) = 120-270 and T (K) 308-338). results demonstrated that ranged from 0.90 × 10-6 to 8.01 10-6. mole fraction increases proportionally with an increase pressure, while maintaining a constant temperature. However, crossover was noted. behavior SC-CO2 modeled by Peng-Robinson equation state (PR EoS) Kwak-Mansoori van der Waals mixing rules seven semi-empirical correlations (Bian et al., Kumar-Johnston, Chrastil, Garlapati-Madras, Bartle Sung-Shim Sodeifian al.,). vaporization, total solvation enthalpies sodium/SC-carbon dioxide system obtained KJ, al. correlations. evaluation two methods absolute average relative deviation (AARD%). Both models exhibited satisfactory level agreement experimental data. Finally, PR/KM EoS Chrastil models, AARD% values 8.70% 8.10% respectively, showed superior precision accurately fitting

Язык: Английский

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Estimating Carbon Dioxide Solubility in Brine Using Mixed Effects Random Forest Based on Genetic Algorithm: Implications for Carbon Dioxide Sequestration in Saline Aquifers

SPE Journal, Год журнала: 2024, Номер 29(11), С. 6530 - 6546

Опубликована: Сен. 20, 2024

Summary Accurate prediction of carbon dioxide (CO2) solubility in brine is crucial for the success capture and storage (CCS) by means geological formations like aquifers. This study investigates effectiveness a novel genetic algorithm-mixed effects random forest (GA-MERF) model estimating CO2 brine. The model’s performance compared with established methods group method data handling (GMDH), backpropagation neural networks (BPNN), traditional thermodynamic models. GA-MERF utilizes experimental collected from literature, encompassing key factors influencing solubility: temperature (T), pressure (P), salinity. These are used to train validate ability predict values. results demonstrate superiority other Notably, achieves high coefficient determination (R) 0.9994 unseen data, indicating strong correlation between estimated actual Furthermore, exhibits exceptionally low error metrics, root mean squared (RMSE) 2×10-8 absolute (MAE) 1.8×10-11, signifying outstanding accuracy Beyond its accuracy, offers an additional benefit—reduced computational time models investigated, 65 seconds. efficiency makes particularly attractive tool real-world applications where rapid reliable predictions critical. In conclusion, this presents as powerful efficient predicting Its superior existing previous literature highlights potential valuable researchers engineers working on CCS projects utilizing aquifer storage. rates, reduced make promising candidate advancing development effective technologies.

Язык: Английский

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