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

Alshahrani Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj, 11942, Saudi Arabia|
Mahboubeh (57202900226) | Amal M. (57221218545); Pishnamazi | Kumar (38663737000); Alsubaiyel Medicinal Chemistry Department, Faculty of Pharmacy, Beni-Suef University, Beni-Suef, 62514, Egypt| Abdullah S. (56801887100); Venkatesan The Faculty of Pharmacy, Duy Tan University, Da Nang, 550000, Viet Nam| Ahmed Al. (57217130302); Alshetaili Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam| Mohammed A. S. (56641727000); Saqr Department of Pharmaceutics, College of Pharmacy, Qassim University, Buraidah, 52571, Saudi Arabia| Amany (55749719800); Abourehab Department of Pharmaceutical Chemistry, College of Pharmacy, King Khalid University, Abha, 62529, Saudi Arabia| Munerah M. (57219447846); Belal Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Minia University, Minia, 61519, Egypt| Bjad K. (56845636600); Alfadhel Department of Pharmaceutics, College of Pharmacy, Umm Al-Qura University, Makkah, 21955, Saudi Arabia| Saad M. (56479018200); Almutairy Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, Taif, 21944, Saudi Arabia|

Scientific Reports Số 1, năm 2022 (Tập 12, trang -)

ISSN: 20452322

ISSN: 20452322

DOI:

Tài liệu thuộc danh mục:

Article

English

Từ khóa: Computer Simulation; Decitabine; Machine Learning; Solubility; Solvents; decitabine; solvent; computer simulation; machine learning; solubility
Tóm tắt tiếng anh
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). © 2022, The Author(s).

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