TY - JOUR
T1 - Green processing based on supercritical carbon dioxide for preparation of nanomedicine
T2 - Model development using machine learning and experimental validation
AU - Alshahrani, Saad M.
AU - Fahem Albaghdadi, Mustafa
AU - Yasmin, Sabina
AU - Alosaimi, Manal E.
AU - Alsalhi, Abdullah
AU - Algarni, Mohammed
AU - Felemban, Bassem F.
AU - Abdulhussain Fadhil, Ali
AU - Mourad Mohammed, Ibrahim
N1 - Publisher Copyright:
© 2022 The Authors.
PY - 2023/1
Y1 - 2023/1
N2 - Solubility data for ANA (Anastrozole) drug in supercritical solvent was investigated in this study, and models were developed to estimate the solubility values. The main aim was to provide a predictive methodology for determination of drug solubility in wide range of operational parameters for advanced green pharmaceutical manufacture. The properties used are temperature and pressure which were considered as the models' inputs. Modeling has been done using three models based on the support vector regression. These models include support vector regression (with polynomial kernel), boosted support vector machine with AdaBoost, and improved support vector machine with bagging. These models were evaluated after optimization, and all three models have a coefficient of determination (R2) higher than 0.98. Also considering RMSE, AdaBoosted SVR, Bagging SVR, and SVR have error rates of 2.31E-01, 4.31E-01, and 5.01E-01.
AB - Solubility data for ANA (Anastrozole) drug in supercritical solvent was investigated in this study, and models were developed to estimate the solubility values. The main aim was to provide a predictive methodology for determination of drug solubility in wide range of operational parameters for advanced green pharmaceutical manufacture. The properties used are temperature and pressure which were considered as the models' inputs. Modeling has been done using three models based on the support vector regression. These models include support vector regression (with polynomial kernel), boosted support vector machine with AdaBoost, and improved support vector machine with bagging. These models were evaluated after optimization, and all three models have a coefficient of determination (R2) higher than 0.98. Also considering RMSE, AdaBoosted SVR, Bagging SVR, and SVR have error rates of 2.31E-01, 4.31E-01, and 5.01E-01.
KW - Artificial intelligence
KW - Green technology
KW - Modeling
KW - Nanomedicine
KW - Simulation
UR - https://www.scopus.com/pages/publications/85144192848
U2 - 10.1016/j.csite.2022.102620
DO - 10.1016/j.csite.2022.102620
M3 - Article
AN - SCOPUS:85144192848
SN - 2214-157X
VL - 41
JO - Case Studies in Thermal Engineering
JF - Case Studies in Thermal Engineering
M1 - 102620
ER -
