Multi support vector models to estimate solubility of Busulfan drug in supercritical carbon dioxide

Particle size, shape and morphology can be considered as the most significant functional parameters, their effects on increasing the performance of oral solid dosage formulation are indisputable. Supercritical Carbon dioxide fluid (SCCO2) technology is an effective approach to control the above-mentioned parameters in oral solid dosage formulation. In this study, drug solubility measuring is investigated based on artificial intelligence model using carbon dioxide as a common supercritical solvent, at different pressure and temperature, 120–400 bar, 308–338 K. The results indicate that pressure has a strong effect on drug solubility. In this investigation, Decision Tree (DT), Adaptive Boosted Decision Trees (ADA-DT), and Nu-SVR regression models are used for the first time as a novel model on the available data, which have two inputs, including pressure, X1 = P(bar) and temperature, X2 = T(K). Also, output is Y = solubility. With an R-squared score, DT, ADA-DT, and Nu-SVR showed results of 0.836, 0.921, and 0.813. Also, in terms of MAE, they showed error rates of 4.30E−06, 1.95E−06, and 3.45E−06. Another metric is RMSE, in which DT, ADA-DT, and Nu-SVR showed error rates of 4.96E−06, 2.34E−06, and 5.26E−06, respectively. Due to the analysis outputs, ADA-DT selected as the best and novel model and the find optimal outputs can be shown via vector: (x1 = 309, x2 = 317.39, Y1 = 7.03e−05).

show abstract

“…It should also be as flat as feasible. Since we want to avoid over-fitted models in this investigation 36 – 38 . …”

Section: Methodsmentioning

confidence: 99%

“…Also, ξ, ξ i are extra slack variables depicted in 42 , declares that distance of value ξ above ɛ error are reasonable. The parameter C , define as the regularization amount, indicates an equilibrium on the tolerance of error ɛ and flatness of f 38 .…”

Section: Methodsmentioning

confidence: 99%

Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models

Alshahrani

Alshetaili

Alfadhel

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The second approach is empirical or semi-empirical correlations [31][32][33][34][35][36] and the third is intelligent computer techniques. [37][38][39][40][41][42] All these modelling approaches rely on experimental data.…”

Section: Introductionmentioning

confidence: 99%

“…This can be divided into three main classes: cubic EoS, complex non‐cubic EoS and conductor‐like screening model (COSMO) based on quantum mechanical calculation results to determine the interaction between molecules in a mixture, which can be used to predict the solubility of a solid solute in SC−CO 2 . The second approach is empirical or semi‐empirical correlations [31–36] and the third is intelligent computer techniques [37–42] . All these modelling approaches rely on experimental data.…”

Section: Introductionmentioning

confidence: 99%

QSPR Modelling of the Solubility of Drug and Drug‐like Compounds in Supercritical Carbon Dioxide

Euldji

Si‐Moussa

Hamadache

et al. 2022

Molecular Informatics

View full text Add to dashboard Cite

Quantitative structure–property relationship (QSPR) modeling was investigated to predict drug and drug‐like compounds solubility in supercritical carbon dioxide. A dataset of 148 drug\drug‐like compounds, accounting for 3971 experimental data points (EDPs), was collected and used for modelling the relationship between selected molecular descriptors and solubility fraction data achieved by a nonlinear approach (Artificial neural network, ANN) based on molecular descriptors. Experimental solubility data for a given drug were published as a function of temperature and pressure. In the present study, 11 significant PaDEL descriptors (AATS3v, MATS2e, GATS4c, GATS3v, GATS4e, GATS3 s, nBondsM, AVP‐0, SHBd, MLogP, and MLFER_S), the temperature and the pressure were statistically proved to be sufficient inputs. The architecture of the optimized model was found to be {13,10,1}. Several statistical metrics, including average absolute relative deviation (AARD=3.7748 %), root mean square error (RMSE=0.5162), coefficient of correlation (r=0.9761), coefficient of determination (R2=0.9528), and robustise (Q2=0.9528) were used to validate the obtained model. The model was also subjected to an external test by using 143 EDPs. Sensitivity analysis and domain of application were examined. The overall results confirmed that the optimized ANN‐QSPR model is suitable for the correlation and prediction of this property.

show abstract

“…Drug nanonization has been used as an appealing technique for optimization of drugs thru solubility enhancement withinside the body. Supercritical solvents are extra appealing techniques due to the advanced characteristics of this process for preparation of nanosized drug particles 3 , 6 , 9 – 12 . The use of superficial CO 2 as the secure solvent in pharmaceutical has been accredited by authorities.…”

Section: Introductionmentioning

confidence: 99%

Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent

Alshahrani

Almutairy

Alfadhel

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO2 was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308–338 K, and pressure 120–400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian process (GPR), which are used for the first time as a novel model. These models are optimized using their hyper-parameters tuning and then assessed using standard metrics, which shows R2-score, KRR, DTR, and GPR equal to 0.806, 0.891, and 0.998. Also, the MAE metric shows 1.08E−04, 7.40E−05, and 9.73E−06 error rates in the same order. The other metric is MAPE, in which the KRR error rate is 4.64E−01, DTR shows an error rate equal to 1.63E−01, and GPR as the best mode illustrates 5.06E−02. Finally, analysis using the best model (GPR) reveals that increasing both inputs results in an increase in the solubility of Decitabine. The optimal values are (P = 400, T = 3.38E + 02, Y = 1.07E−03).

show abstract

Multi support vector models to estimate solubility of Busulfan drug in supercritical carbon dioxide

Cited by 19 publications

References 37 publications

Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models

Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models

QSPR Modelling of the Solubility of Drug and Drug‐like Compounds in Supercritical Carbon Dioxide

Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent

Contact Info

Product

Resources

About