Prediction of solubility of sunitinib malate (an anti-cancer drug) in supercritical carbon dioxide (SC–CO2): Experimental correlations and thermodynamic modeling

Solubility of lumiracoxib in supercritical carbon dioxide

This study aims to use a static-based solubility method for measuring the solubility of lumiracoxib at a temperature of 308-338 K and pressure of 120-400 bar for the first time. The obtained solubility data for lumiracoxib is between 4.74 × 10-5 and 3.46 × 10-4 (mole fraction) for the studied ranges of pressure and temperature. The solubility values reveal that the lumiracoxib experiences a crossover pressure of about 160 bar. Moreover, the measured solubility data of these two drugs are correlated with density-based semi-empirical correlations namely Bartle et al., Mendez-Santiago-Teja, Kumar and Johnstone, Chrastil and modified Chrastil models with an average absolute relative deviation of 10.7%, 9.5%, 9.8%, 7.8%, and 8.7% respectively for lumiracoxib. According to these findings, it is obvious that all of the examined models are rather accurate and there is no superiority between these models for both examined drugs although the Chrastil model is slightly better in the overall view.

Pretreatment and analysis techniques development of TKIs in biological samples for pharmacokinetic studies and therapeutic drug monitoring

Tyrosine kinase inhibitors (TKIs) have emerged as the first-line small molecule drugs in many cancer therapies, exerting their effects by impeding aberrant cell growth and proliferation through the modulation of tyrosine kinase-mediated signaling pathways. However, there exists a substantial inter-individual variability in the concentrations of certain TKIs and their metabolites, which may render patients with compromised immune function susceptible to diverse infections despite receiving theoretically efficacious anticancer treatments, alongside other potential side effects or adverse reactions. Therefore, an urgent need exists for an up-to-date review concerning the biological matrices relevant to bioanalysis and the sampling methods, clinical pharmacokinetics, and therapeutic drug monitoring of different TKIs. This paper provides a comprehensive overview of the advancements in pretreatment methods, such as protein precipitation (PPT), liquid-liquid extraction (LLE), solid-phase extraction (SPE), micro-SPE (μ-SPE), magnetic SPE (MSPE), and vortex-assisted dispersive SPE (VA-DSPE) achieved since 2017. It also highlights the latest analysis techniques such as newly developed high performance liquid chromatography (HPLC) and high-resolution mass spectrometry (HRMS) methods, capillary electrophoresis (CE), gas chromatography (GC), supercritical fluid chromatography (SFC) procedures, surface plasmon resonance (SPR) assays as well as novel nanoprobes-based biosensing techniques. In addition, a comparison is made between the advantages and disadvantages of different approaches while presenting critical challenges and prospects in pharmacokinetic studies and therapeutic drug monitoring.

Studies on solubility measurement of codeine phosphate (pain reliever drug) in supercritical carbon dioxide and modeling

In this study, the solubilities of codeine phosphate, a widely used pain reliever, in supercritical carbon dioxide (SC-CO2) were measured under various pressures and temperature conditions. The lowest determined mole fraction of codeine phosphate in SC-CO2 was 1.297 × 10-5 at 308 K and 12 MPa, while the highest was 6.502 × 10-5 at 338 K and 27 MPa. These measured solubilities were then modeled using the equation of state model, specifically the Peng-Robinson model. A selection of density models, including the Chrastil model, Mendez-Santiago and Teja model, Bartle et al. model, Sodeifian et al. model, and Reddy-Garlapati model, were also employed. Additionally, three forms of solid-liquid equilibrium models, commonly called expanded liquid models (ELMs), were used. The average solvation enthalpy associated with the solubility of codeine phosphate in SC-CO2 was calculated to be - 16.97 kJ/mol. The three forms of the ELMs provided a satisfactory correlation to the solubility data, with the corresponding average absolute relative deviation percent (AARD%) under 12.63%. The most accurate ELM model recorded AARD% and AICc values of 8.89% and - 589.79, respectively.

A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical CO2

Nilotinib hydrochloride monohydrate (NHM) is an anti-cancer drug whose solubility was statically determined in supercritical carbon dioxide (SC-CO2) for the first time at various temperatures (308-338 K) and pressures (120-270 bar). The mole fraction of the drug dissolved in SC-CO2 ranged from 0.1 × 10-5 to 0.59 × 10-5, corresponding to the solubility range of 0.016-0.094 g/L. Four sets of models were employed to evaluate the correlation of experimental data; (1) ten empirical and semi-empirical models with three to six adjustable parameters, such as Chrastil, Bartle, Sparks, Sodeifian, Mendez-Santiago and Teja (MST), Bian, Jouyban, Garlapati-Madras, Gordillo, and Jafari-Nejad; (2) Peng-Robinson equation of state (Van der Waals mixing rule, had an AARD% of 10.73); (3) expanded liquid theory (modified Wilson model, on average, the AARD of this model was 11.28%); and (4) machine learning (ML) algorithms (random forest, decision trees, multilayer perceptron, and deep neural network with respective R2 values of 0.9933, 0.9799, 0.9724 and 0.9701). All the models showed an acceptable agreement with the experimental data, among them, the Bian model exhibited excellent performance with an AARD% of 8.11. Finally, the vaporization (73.49 kJ/mol) and solvation (- 21.14 kJ/mol) enthalpies were also calculated for the first time.

Solubility measurement and modeling of hydroxychloroquine sulfate (antimalarial medication) in supercritical carbon dioxide

A supercritical fluid, such as supercritical carbon dioxide (scCO₂) is increasingly used for the micronization of pharmaceuticals in the recent past. The role of scCO₂ as a green solvent in supercritical fluid (SCF) process is decided by the solubility information of the pharmaceutical compound in scCO₂. The commonly used SCF processes are the rapid expansion of supercritical solution (RESS) and supercritical antisolvent precipitation (SAS). To implement micronization process, solubility of pharmaceuticals in scCO₂ is required. Present study is aimed at both measuring and modeling of solubilities of hydroxychloroquine sulfate (HCQS) in scCO₂. Experiments were conducted at various conditions (P = 12 to 27 MPa and T = 308 to 338 K), for the first time. The measured solubilities were found to be ranging between (0.0304 × 10^-4 and 0.1459 × 10^-4) at 308 K, (0.0627 × 10^-4 and 0.3158 × 10^-4) at 318 K, (0.0982 × 10^-4 and 0.4351 × 10^-4) at 328 K, (0.1398 × 10^-4 and 0.5515 × 10^-4) at 338 K. To expand the usage of the data, various models were tested. For the modelling task existing models (Chrastil, reformulated Chrastil, Méndez-Santiago and Teja (MST), Bartle et al., Reddy-Garlapati, Sodeifian et al., models) and new set of solvate complex models were considered. Among the all models investigated Reddy-Garlapati and new solvate complex models are able to fit the data with the least error. Finally, the total and solvation enthalpies of HCQS in scCO₂ were calculated with the help of model constants obtained from Chrastil, reformulated Chrastil and Bartle et al., models.

pH-sensitive biocompatible chitosan/sepiolite-based cross-linked citric acid magnetic nanocarrier for efficient sunitinib release

In this study, the magnetite nanoparticles were immobilized on the sepiolite needles via co-precipitation of iron ions. Then, the resulted magnetic sepiolite (mSep) nanoparticles were coated with chitosan biopolymer (Chito) in the presence of citric acid (CA) to prepare mSep@Chito core-shell drug nanocarriers (NCs). TEM images showed magnetic Fe₃O₄ nanoparticles with small sizes (less than 25 nm) on the sepiolite needles. Sunitinib anticancer drug loading efficiencies were ⁓45 and 83.7 % for the NCs with low and high content of Chito, respectively. The in-vitro drug release results exhibited that the mSep@Chito NCs have a sustained release behavior with high pH-dependent properties. Cytotoxic results (MTT assay) showed that the sunitinib-loaded mSep@Chito2 NC had a significant cytotoxic effect on the MCF-7 cell lines. Also, the in-vitro compatibility of erythrocytes, physiological stability, biodegradability, and antibacterial and antioxidant activities of NCs was evaluated. The results showed that the synthesized NCs had excellent hemocompatibility, good antioxidant properties, and were sufficiently stable and biocompatible. Based on the antibacterial data, the minimal inhibitory concentration (MIC) values for mSep@Chito1, mSep@Chito2, and mSep@Chito3 were obtained as 125, 62.5, and 31.2 μg/mL towards S. aureus, respectively. All in all, the prepared NCs could be potentially used as a pH-triggered system for biomedical applications.

Computational simulation and target prediction studies of solubility optimization of decitabine through supercritical solvent

Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO₂ was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308-338 K, and pressure 120-400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian process (GPR), which are used for the first time as a novel model. These models are optimized using their hyper-parameters tuning and then assessed using standard metrics, which shows R²-score, KRR, DTR, and GPR equal to 0.806, 0.891, and 0.998. Also, the MAE metric shows 1.08E-04, 7.40E-05, and 9.73E-06 error rates in the same order. The other metric is MAPE, in which the KRR error rate is 4.64E-01, DTR shows an error rate equal to 1.63E-01, and GPR as the best mode illustrates 5.06E-02. Finally, analysis using the best model (GPR) reveals that increasing both inputs results in an increase in the solubility of Decitabine. The optimal values are (P = 400, T = 3.38E + 02, Y = 1.07E-03).

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.

Developing an accurate empirical correlation for predicting anti-cancer drugs’ dissolution in supercritical carbon dioxide

This study introduces a universal correlation based on the modified version of the Arrhenius equation to estimate the solubility of anti-cancer drugs in supercritical carbon dioxide (CO₂). A combination of an Arrhenius-shape term and a departure function was proposed to estimate the solubility of anti-cancer drugs in supercritical CO₂. This modified Arrhenius correlation predicts the solubility of anti-cancer drugs in supercritical CO₂ from pressure, temperature, and carbon dioxide density. The pre-exponential of the Arrhenius linearly relates to the temperature and carbon dioxide density, and its exponential term is an inverse function of pressure. Moreover, the departure function linearly correlates with the natural logarithm of the ratio of carbon dioxide density to the temperature. The reliability of the proposed correlation is validated using all literature data for solubility of anti-cancer drugs in supercritical CO₂. Furthermore, the predictive performance of the modified Arrhenius correlation is compared with ten available empirical correlations in the literature. Our developed correlation presents the absolute average relative deviation (AARD) of 9.54% for predicting 316 experimental measurements. On the other hand, the most accurate correlation in the literature presents the AARD = 14.90% over the same database. Indeed, 56.2% accuracy improvement in the solubility prediction of the anti-cancer drugs in supercritical CO₂ is the primary outcome of the current study.

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.

A universal methodology for reliable predicting the non-steroidal anti-inflammatory drug solubility in supercritical carbon dioxide

Understanding the drug solubility behavior is likely the first essential requirement for designing the supercritical technology for pharmaceutical processing. Therefore, this study utilizes different machine learning scenarios to simulate the solubility of twelve non-steroidal anti-inflammatory drugs (NSAIDs) in the supercritical carbon dioxide (SCCO₂). The considered NSAIDs are Fenoprofen, Flurbiprofen, Ibuprofen, Ketoprofen, Loxoprofen, Nabumetone, Naproxen, Nimesulide, Phenylbutazone, Piroxicam, Salicylamide, and Tolmetin. Physical characteristics of the drugs (molecular weight and melting temperature), operating conditions (pressure and temperature), and solvent property (SCCO₂ density) are effectively used to estimate the drug solubility. Monitoring and comparing the prediction accuracy of twelve intelligent paradigms from three categories (artificial neural networks, support vector regression, and hybrid neuro-fuzzy) approves that adaptive neuro-fuzzy inference is the best tool for the considered task. The hybrid optimization strategy adjusts the cluster radius of the subtractive clustering membership function to 0.6111. This model estimates 254 laboratory-measured solubility data with the AAPRE = 3.13%, MSE = 2.58 × 10^–9, and R² = 0.99919. The leverage technique confirms that outliers may poison less than four percent of the experimental data. In addition, the proposed hybrid paradigm is more reliable than the equations of state and available correlations in the literature. Experimental measurements, model predictions, and relevancy analyses justified that the drug solubility in SCCO₂ increases by increasing temperature and pressure. The results show that Ibuprofen and Naproxen are the most soluble and insoluble drugs in SCCO₂, respectively.

Functional block copolymer micelles based on poly (jasmine lactone) for improving the loading efficiency of weakly basic drugs

Functionalization of polymers is an attractive approach to introduce specific molecular forces that can enhance drug–polymer interaction to achieve higher drug loading when used as drug delivery systems. The novel amphiphilic block copolymer of methoxy poly(ethylene glycol) and poly(jasmine lactone) i.e., mPEG-b-PJL, derived from renewable jasmine lactone provides free allyl groups on the backbone thus, allowing flexible and facile post-synthesis functionalization. In this study, mPEG-b-PJL and its carboxyl functionalized polymer mPEG-b-PJL-COOH were utilised to explore the effect of ionic interactions on the drug–polymer behaviour. Various drugs with different pK_a values were employed to prepare drug-loaded polymeric micelles (PMs) of mPEG-b-PJL, mPEG-b-PJL-COOH and Soluplus® (polyvinyl caprolactam–polyvinyl acetate–polyethylene glycol graft copolymer) via a nanoprecipitation method. Electrostatic interactions between the COOH pendant on mPEG-b-PJL-COOH and the basic drugs were shown to influence the entrapment efficiency. Additionally, molecular dynamics (MD) simulations were employed to understand the polymer–drug interactions at the molecular level and how polymer functionalization influenced these interactions. The release kinetics of the anti-cancer drug sunitinib from mPEG-b-PJL and mPEG-b-PJL-COOH was assessed, and it demonstrated a sustainable drug release pattern, which depended on both pH and temperature. Furthermore, the cytotoxicity of sunitinib-loaded micelles on cancer cells was evaluated. The drug-loaded micelles exhibited dose-dependent toxicity. Also, haemolysis capacity of these polymers was investigated. In summary, polymer functionalization seems a promising approach to overcome challenges that hinder the application of polymer-based drug delivery systems such as low drug loading degree.

Block copolymer micelles with a functional core have been synthesized and evaluated for their drug delivery capability. High drug loading was observed due to strong ionic interactions, while cytotoxicity of polymers was found to be low.

Investigation of temperature-responsive tocosomal nanocarriers as the efficient and robust drug delivery system for Sunitinib malate anti-cancer drug: Effects of MW and chain length of PNIPAAm on LCST and dissolution rate

In this study, for the first time, the coated tocosome by blend of chitosan, CS, and poly(N-isopropylacrylamide), PNIPAAm, was developed as the efficient and robust drug delivery system with improved drug encapsulation efficiency, extended stability, proper particle size and industrial upscaling for Sunitinib malate anti-cancer drug. Tocosome was synthesized by using Mozafari method as a scalable and robust method and without the need for organic solvents. The effects of tocosome composition and drug concentration on the stability, particle size of tocosome, zeta potential, encapsulation efficacy and loading of drug into it were investigated by Taguchi method, and optimum composition was selected for combining with the polymeric blend. Homopolymer of PNIPAAm was synthesized by two different polymerization methods, including free radical and reversible addition-fragmentation chain transfer (RAFT). Effects of molecular weight (MW) and chain length of the polymers on lower critical solution temperature (LCST) were examined. The developed nanocarrier in this research, CS-Raft-PNIPAAm-tocosome, indicated LCST value beyond 37°C (about 45°C) and this is suitable for hyperthermia and spatio-temporal release of drug particles.

A critical review on the particle generation and other applications of rapid expansion of supercritical solution

The novel particle generation processes of Active Pharmaceutical Ingredient (API)/drug have been extensively explored in recent decades due to their wide-range applications in the pharmaceutical industry. The Rapid Expansion of Supercritical Solutions (RESS) is one of the promising techniques to obtain the fine particles (micro to nano-size) of APIs with narrow particle size distribution (PSD). In RESS, supercritical carbon dioxide (SC CO₂) and API are used as solvent and solute respectively. In this literature survey, the application of RESS in the formation of fine particles is critically reviewed. Solubility of API in SC CO₂ and supersaturation are the key factors in tuning the particle size. The different approaches to model and predict the solubility of API in SC CO₂ are discussed. Then, the effect of process parameters on mean particle size and the particle size distribution are interpreted in the context of solubility and supersaturation. Furthermore, the less-explored applications of RESS in preparation of solid-lipid nanoparticles, liposome, polymorphic conversion, cocrystallization and inclusion complexation are compared with traditional processes. The solubility enhancement of API in SC CO₂ using co-solvent and its applications in particle generation are explored in published literature. The development and modifications in the conventional RESS process to overcome the limitations of RESS are presented. Finally, the perspective on RESS with special attention to its commercial operation is highlighted.