Solubility of some statin drugs in supercritical carbon dioxide and representing the solute solubility data with several density-based correlations

Determination of morphine sulfate anti-pain drug solubility in supercritical CO2 with machine learning method

Accurate solute solubility measuring and modeling in supercritical carbon dioxide (ScCO2) would address the best working conditions and thermodynamic boundaries for material processing with this type of fluid. Theory- and data-driven methods are two general modeling approaches. Using theory-driven methods, the solubility is estimated based on the principles of thermodynamics, while data-driven methods are developed by training the algorithms. Despite acceptance of each of these methods, more experimental solubility data are still needed to promote modeling performances. In this study, for the first time, solubility of morphine sulfate is determined and modeled by a set of 13 semi-empirical (theory-driven) and random forest (data-driven) models. Using a laboratory system with an ultraviolet-visible (UV-Vis) spectroscopy, the experimental solubilities including 48 data points were obtained at different temperatures (308-338 K) and pressures (12-27 MPa). The minimum (0.806 × 10-5) and maximum (5.902 × 10-5) equilibrium mole fractions were observed at working pressures of 12 and 27 MPa, respectively, both at the same temperature of 338 K. It was indicated that random forest model (with AARD% of 1.29%) had an excellent predictive performance against semi-empirical models (with AARD% from 9.33 to 19.76%). The results showed that solute molecular weight had the highest effect on random forest modeling. Using modeling results from Chrastil and Bartle models, total and vaporization enthalpies of dissolution of morphine sulfate in ScCO2 were found to be 35.12 and 59.04 kJ/mole, 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.

Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning

Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO₂, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO₂SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241).

Solubility Optimization of Loxoprofen as a Nonsteroidal Anti-Inflammatory Drug: Statistical Modeling and Optimization

Industrial-based application of supercritical CO₂ (SCCO₂) has emerged as a promising technology in numerous scientific fields due to offering brilliant advantages, such as simplicity of application, eco-friendliness, and high performance. Loxoprofen sodium (chemical formula C₁₅H₁₈O₃) is known as an efficient nonsteroidal anti-inflammatory drug (NSAID), which has been long propounded as an effective alleviator for various painful disorders like musculoskeletal conditions. Although experimental research plays an important role in obtaining drug solubility in SCCO₂, the emergence of operational disadvantages such as high cost and long-time process duration has motivated the researchers to develop mathematical models based on artificial intelligence (AI) to predict this important parameter. Three distinct models have been used on the data in this work, all of which were based on decision trees: K-nearest neighbors (KNN), NU support vector machine (NU-SVR), and Gaussian process regression (GPR). The data set has two input characteristics, P (pressure) and T (temperature), and a single output, Y = solubility. After implementing and fine-tuning to the hyperparameters of these ensemble models, their performance has been evaluated using a variety of measures. The R-squared scores of all three models are greater than 0.9, however, the RMSE error rates are 1.879 × 10⁻⁴, 7.814 × 10⁻⁵, and 1.664 × 10⁻⁴ for the KNN, NU-SVR, and GPR models, respectively. MAE metrics of 1.116 × 10⁻⁴, 6.197 × 10⁻⁵, and 8.777 × 10⁻⁵errors were also discovered for the KNN, NU-SVR, and GPR models, respectively. A study was also carried out to determine the best quantity of solubility, which can be referred to as the (x₁ = 40.0, x₂ = 338.0, Y = 1.27 × 10⁻³) vector.

Enhancing Atorvastatin In Vivo Oral Bioavailability in the Presence of Inflammatory Bowel Disease and Irritable Bowel Syndrome Using Supercritical Fluid Technology Guided by wbPBPK Modeling in Rat and Human

Inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS) are common disorders that can change the body's physiology and drugs pharmacokinetics. Solid dispersion (SD) preparation using supercritical fluid technology (SFT) has many advantages. Our study aimed to explore the effect of IBS and IBD on atorvastatin (ATV) pharmacokinetics, enhance ATV oral bioavailability (BCS II drug) using SFT, and analyze drug-disease-formulation interaction using a whole-body physiologically based pharmacokinetic (wbPBPK) model in rat and human. A novel ATV formulation was prepared using SFT and characterized in vitro and in vivo in healthy, IBS, and IBD rats. The resulting ATV plasma levels were analyzed using a combination of conventional and wbPBPK approaches. The novel formulation increased ATV solubility by 20-fold and resulted in a zero-order release of up to 95%. Both IBS and IBD increased ATV exposure after oral and intravenous administration by more than 30%. The novel SFT formulation increased ATV bioavailability by 28, 14, and 18% in control, IBD, and IBD rat groups and resulted in more consistent exposure as compared to raw ATV solution. Higher improvements in ATV bioavailability of more than 2-fold upon receiving the novel SFT formulation were predicted by the human wbPBPK model as compared to receiving the conventional tablets. Finally, the established wbPBPK model could describe ATV ADME in the presence of IBS and IBD after oral administration of raw ATV and using the novel SFT formula and can help scale the optimized ATV dosing regimens in the presence of IBS and IBD from rats to humans.

Solubility of favipiravir (as an anti-COVID-19) in supercritical carbon dioxide: An experimental analysis and thermodynamic modeling

Favipiravir is one of the most commonly prescribed drugs in the treatment of COVID-19 in the early stages of the disease. In this work, the solubility of favipiravir was measured in supercritical CO2 at temperatures ranging from 308 to 338 K and pressures ranging from 12 to 30 MPa. The mole fraction solubility of favipiravir was in the range of 3.0 × 10-6 to 9.05 × 10-4. The solubility data were correlated with three types of methods including; (a) density-based models (Chrastil, Garlapati and Madras, Sparks et al., Sodeifian et al., K-J and Keshmiri et al.), (b) Equations of states SRK with quadratic mixing rules) and (c) expanded liquid theory (modified Wilson model). According to the results, modified Wilson and K-J models are generally capable of providing good correlation of solubility. Finally, the approximate values of total (Δ H total ), vaporization (Δ H vap ), and solvation (Δ H sol ) enthalpies were computed.

Experimental and thermodynamic modeling decitabine anti cancer drug solubility in supercritical carbon dioxide

Design and development of efficient processes for continuous manufacturing of solid dosage oral formulations is of crucial importance for pharmaceutical industry in order to implement the Quality-by-Design paradigm. Supercritical solvent-based manufacturing can be utilized in pharmaceutical processing owing to its inherent operational advantages. However, in order to evaluate the possibility of supercritical processing for a particular medicine, solubility measurement needs to be carried out prior to process design. The current work reports a systematic solubility analysis on decitabine as an anti-cancer medicine. The solvent is supercritical carbon dioxide at different conditions (temperatures and pressures), while gravimetric technique is used to obtain the solubility data for decitabine. The results indicated that the solubility of decitabine varies between 2.84 × 10^–05 and 1.07 × 10^–03 mol fraction depending on the temperature and pressure. In the experiments, temperature and pressure varied between 308–338 K and 12–40 MPa, respectively. The solubility of decitabine was plotted against temperature and pressure, and it turned out that the solubility had direct relation with the pressure due to the effect of pressure on solvating power of solvent. The effect of temperature on solubility was shown to be dependent on the cross-over pressure. Below the cross-over pressure, there is a reverse relation between temperature and solubility, while a direct relation was observed above the cross-over pressure (16 MPa). Theoretical study was carried out to correlate the solubility data using several thermodynamic-based models. The fitting and model calibration indicated that the examined models were of linear nature and capable to predict the measured decitabine solubilities with the highest average absolute relative deviation percent (AARD %) of 8.9%.

Application of PC-SAFT EOS for Pharmaceuticals: Solubility, Co-Crystal, and Thermodynamic Modeling

In this study, the applicability of the perturbed chain statistical associating fluid theory (PC-SAFT) was evaluated for pharmaceutical compounds. For this purpose, the parameters of the PC-SAFT equation of state (EOS) were regressed by applying the experimental solubility data of 54 pharmaceuticals in pure solvents. The reported errors for train and test data show the suitability of the PC-SAFT EOS. The applicability of the PC-SAFT EOS was explored by its prediction accuracy for the ternary system of medicine in solvent mixtures. The model errors were 23 and 26% for two ternary systems of salicylic acid and lidocaine HCl in solvent mixtures of ethanol and water, respectively. The co-crystal formation of salicylic acid-sulfamethazine in methanol was investigated by the PC-SAFT EOS. Finally, the reaction and crystallization sections for acetaminophen production from p-aminophenol were simulated by accompanying the PC-SAFT EOS and equilibrium assumption. The purification efficiencies for acetaminophen were obtained to be 93-98% for different temperatures.

Supercritical carbon dioxide-based technologies for the production of drug nanoparticles/nanocrystals - A comprehensive review

Low drug bioavailability, which is mostly a result of poor aqueous drug solubilities and of inadequate drug dissolution rates, is one of the most significant challenges that pharmaceutical companies are currently facing, since this may limit the therapeutic efficacy of marketed drugs, or even result in the discard of potential highly effective drug candidates during developmental stages. Two of the main approaches that have been implemented in recent years to overcome poor drug solubility/dissolution issues have frequently involved drug particle size reduction (i.e., micronization/nanonization) and/or the modification of some of the physicochemical and structural properties of poorly water soluble drugs. A large number of particle engineering methodologies have been developed, tested, and applied in the synthesis and control of particle size/particle-size distributions, crystallinities, and polymorphic purities of drug micro- and nano-particles/crystals. In recent years pharmaceutical processing using supercritical fluids (SCF), in general, and supercritical carbon dioxide (scCO₂), in particular, have attracted a great attention from the pharmaceutical industry. This is mostly due to the several well-known advantageous technical features of these processes, as well as to other increasingly important subjects for the pharmaceutical industry, namely their "green", sustainable, safe and "environmentally-friendly" intrinsic characteristics. In this work, it is presented a comprehensive state-of-the-art review on scCO₂-based processes focused on the formation and on the control of the physicochemical, structural and morphological properties of amorphous/crystalline pure drug nanoparticles. It is presented and discussed the most relevant scCO₂, scCO₂-based fluids and drug physicochemical properties that are pertinent for the development of successful pharmaceutical products, namely those that are critical in the selection of an adequate scCO₂-based method to produce pure drug nanoparticles/nanocrystals. scCO₂-based nanoparticle formation methodologies are classified in three main families, and in terms of the most important role played by scCO₂ in particle formation processes: as a solvent; as an antisolvent or a co-antisolvent; and as a "high mobility" additive (a solute, a co-solute, or a co-solvent). Specific particle formation methods belonging to each one of these families are presented, discussed and compared. Some selected amorphous/crystalline drug nanoparticles that were prepared by these methods are compiled and presented, namely those studied in the last 10-15 years. A special emphasis is given to the formation of drug cocrystals. It is also discussed the fundamental knowledge and the main mechanisms in which the scCO₂-based particle formation methods rely on, as well as the current status and urgent needs in terms of reliable experimental data and of robust modeling approaches. Other addressed and discussed topics include the currently available and the most adequate physicochemical, morphological and biological characterization methods required for pure drug nanoparticles/nanocrystals, some of the current nanometrology and regulatory issues associated to the use of these methods, as well as some scale-up, post-processing and pharmaceutical regulatory subjects related to the industrial implementation of these scCO₂-based processes. Finally, it is also discussed the current status of these techniques, as well as their future major perspectives and opportunities for industrial implementation in the upcoming years.

A NEW SIMPLE CORRELATION FOR CALCULATING SOLUBILITY OF DRUGS IN SUPERCRITICAL CARBON DIOXIDE

During the past 20 years, supercritical fluid (SCF) based technologies have been gaining an increasing attention through the academic and industrial communities due to its advantages. One of the most important parameter for any supercritical-based technologies is the knowledge of the solute solubility at different pressures and temperatures. But, due to several concerns e.g. time and expense, measuring the solubility of all compounds in wide ranges of temperature and pressure is not possible. Respect to this, a new empirical correlation with four fitting parameters has been proposed to correlate the solubility of pharmaceuticals in different temperatures and pressures. The obtained results compared with four widely used density based correlations including Mendez-Santiago and Teja (MST), Bartle et al., Chrastil, Kumar and Johnston (KJ) revealed rather good capability of the proposed simple correlation for predicting the solubility of solutes in supercritical carbon dioxide (SC- CO 2). At last, the obtained results compared with the results of three Equations of State (EoS's) with three different mixing rules.