Machine learning simulation of pharmaceutical solubility in supercritical carbon dioxide: Prediction and experimental validation for busulfan drug

Deep Eutectic Solvents Enhancing Drug Solubility and Its Delivery

Deep eutectic solvents (DES) are environmentally friendly solvents with the potential to dissolve bioactive compounds without affecting their characteristics. DES has special qualities that can be customized to meet the unique characteristics of a biomolecule/active pharmaceutical ingredient (API) in accordance with various therapeutic needs, providing a reliable approach in opening the door for the creation of cutting-edge drug formulations by resolving solubility issues in pharmaceutics. This study outlines newly developing approaches to solve the problem of inefficient API extraction due to poor solubility. These emerging strategies also have the capacity to alter the chemical and physical stability of API, which triggers drug's shelf life and their possible health benefits. It is anticipated that the highlighted methods and processes will be developed to capitalize on the DES potential to improve drug solubility and delivery in the pharmaceutical sector.

Advanced modeling of pharmaceutical solubility in solvents using artificial intelligence techniques: assessment of drug candidate for nanonization processing

This research is an analysis of multiple regression models developed for predicting ketoprofen solubility in supercritical carbon dioxide under different levels of T(K) and P(bar) as input features. Solubility of the drug was correlated to pressure and temperature as major operational variables. Selected models for this study are Piecewise Polynomial Regression (PPR), Kernel Ridge Regression (KRR), and Tweedie Regression (TDR). In order to improve the performance of the models, hyperparameter tuning is executed utilizing the Water Cycle Algorithm (WCA). Among, the PPR model obtained the best performance, with an R2 score of 0.97111, alongside an MSE of 1.6867E-09 and an MAE of 3.01040E-05. Following closely, the KRR model demonstrated a good performance with an R2 score of 0.95044, an MSE of 2.5499E-09, and an MAE of 3.49707E-05. In contrast, the TDR model produces a lower R2 score of 0.84413 together with an MSE of 7.4249E-09 and an MAE of 5.69159E-05.

Solubility of palbociclib in supercritical carbon dioxide from experimental measurement and Peng-Robinson equation of state

Palbociclib is a poorly water-soluble medicine which acts against metastatic breast cancer cells. Among various techniques to improve the solubility of this medicine, applying supercritical technologies to produce micro- and nano-sized particles is a possible option. For this purpose, extraction of solubility data is required. In this research, the solubility of palbociclib in supercritical carbon dioxide (ScCO₂) at different equilibrium conditions was measured at temperatures between 308 and 338 K and pressures within 12-27 MPa, for the first time. The minimum and maximum solubility data were found to be 8.1 × 10^-7 (at 338 K and 12 MPa) and 2.03 × 10^-5 (at 338 K and 27 MPa), respectively. Thereafter, two sets of models, including ten semi-empirical equations and three Peng-Robinson (PR) based integrated models were used to correlate the experimental solubility data. Bian's model and PR equation of state using van der Waals mixing rules (PR + vdW) showed better accuracy among the examined semi-empirical and integrated models, respectively. Furthermore, the self-consistency of the obtained data was confirmed using two distinct semi-empirical models. At last, the total and vaporization enthalpies of palbociclib solubility in ScCO₂ were calculated from correlation results of semi-empirical equations and estimated to be 40.41 and 52.67 kJ/mol, respectively.

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.

Experimental solubility and thermodynamic modeling of empagliflozin in supercritical carbon dioxide

The solubility of empagliflozin in supercritical carbon dioxide was measured at temperatures (308 to 338 K) and pressures (12 to 27 MPa), for the first time. The measured solubility in terms of mole faction ranged from 5.14 × 10^–6 to 25.9 × 10^–6. The cross over region was observed at 16.5 MPa. A new solubility model was derived to correlate the solubility data using solid–liquid equilibrium criteria combined with Wilson activity coefficient model at infinite dilution for the activity coefficient. The proposed model correlated the data with average absolute relative deviation (AARD) and Akaike’s information criterion (AIC_c), 7.22% and − 637.24, respectively. Further, the measured data was also correlated with 11 existing (three, five and six parameters empirical and semi-empirical) models and also with Redlich-Kwong equation of state (RKEoS) along with Kwak-Mansoori mixing rules (KMmr) model. Among density-based models, Bian et al., model was the best and corresponding AARD% was calculated 5.1. The RKEoS + KMmr was observed to correlate the data with 8.07% (correspond AIC_c is − 635.79). Finally, total, sublimation and solvation enthalpies of empagliflozin were calculated.