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المؤلفNajmi, Maryam
المؤلفAyari, Mohamed A.
المؤلفSadeghsalehi, Hamidreza
المؤلفVaferi, Behzad
المؤلفKhandakar, Amith
المؤلفChowdhury, Muhammad E. H.
المؤلفRahman, Tawsifur
المؤلفJawhar, Zanko H.
تاريخ الإتاحة2023-04-17T06:57:42Z
تاريخ النشر2022
اسم المنشورPharmaceutics
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.3390/pharmaceutics14081632
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41947
الملخصSynthesizing micro-/nano-sized pharmaceutical compounds with an appropriate size distribution is a method often followed to enhance drug delivery and reduce side effects. Supercritical CO2 (carbon dioxide) is a well-known solvent utilized in the pharmaceutical synthesis process. Reliable knowledge of a drug's solubility in supercritical CO2 is necessary for feasible study, modeling, design, optimization, and control of such a process. Therefore, the current study constructs a stacked/ensemble model by combining three up-to-date machine learning tools (i.e., extra tree, gradient boosting, and random forest) to predict the solubility of twelve anticancer drugs in supercritical CO2. An experimental databank comprising 311 phase equilibrium samples was gathered from the literature and applied to design the proposed stacked model. This model estimates the solubility of anticancer drugs in supercritical CO2 as a function of solute and solvent properties and operating conditions. Several statistical indices, including average absolute relative deviation (AARD = 8.62%), mean absolute error (MAE = 2.86 x 10-6), relative absolute error (RAE = 2.42%), mean squared error (MSE = 1.26 x 10-10), and regression coefficient (R2 = 0.99809) were used to validate the performance of the constructed model. The statistical, sensitivity, and trend analyses confirmed that the suggested stacked model demonstrates excellent performance for correlating and predicting the solubility of anticancer drugs in supercritical CO2. 2022 by the authors.
راعي المشروعThe publication of this article was funded by the Qatar National Library.
اللغةen
الناشرMDPI
الموضوعanticancer solid drugs
artificial intelligence technique
ensemble model
solubility
supercritical CO2
العنوانEstimating the Dissolution of Anticancer Drugs in Supercritical Carbon Dioxide with a Stacked Machine Learning Model
النوعArticle
رقم العدد8
رقم المجلد14
dc.accessType Open Access


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