Estimating the Dissolution of Anticancer Drugs in Supercritical Carbon Dioxide with a Stacked Machine Learning Model.

Synthesizing 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 × 10-6), relative absolute error (RAE = 2.42%), mean squared error (MSE = 1.26 × 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.

Efficient functionalization of alginate biomaterials.

Peptide coupled alginates obtained by chemical functionalization of alginates are commonly used as scaffold materials for cells in regenerative medicine and tissue engineering. We here present an alternative to the commonly used carbodiimide chemistry, using partial periodate oxidation followed by reductive amination. High and precise degrees of substitution were obtained with high reproducibility, and without formation of by-products. A protocol was established using l-Tyrosine methyl ester as a model compound and the non-toxic pic-BH3 as the reducing agent. DOSY was used to indirectly verify covalent binding and the structure of the product was further elucidated using NMR spectroscopy. The coupling efficiency was to some extent dependent on alginate composition, being most efficient on mannuronan. Three different bioactive peptide sequences (GRGDYP, GRGDSP and KHIFSDDSSE) were coupled to 8% periodate oxidized alginate resulting in degrees of substitution between 3.9 and 6.9%. Cell adhesion studies of mouse myoblasts (C2C12) and human dental stem cells (RP89) to gels containing various amounts of GRGDSP coupled alginate demonstrated the bioactivity of the material where RP89 cells needed higher peptide concentrations to adhere.