Measurement and modeling of metoclopramide hydrochloride (anti-emetic drug) solubility in supercritical carbon dioxide

Knowledge of drug solubility data in supercritical carbon dioxide (SC-CO₂) is a fundamental step in producing nano and microparticles through supercritical fluid technology. In this work, for the first time, the solubility of metoclopramide hydrochloride (MCP) in SC-CO₂ was measured in pressure and temperature range of 12 to 27 MPa and 308 to 338 K, respectively. The results represented a range mole fractions of 0.15 × 10^-5 to 5.56 × 10^-5. To expand the application of the obtained data, six semi-empirical models and three models based on the Peng-Robinson equation of state (PR + VDW, PR + WS + Wilson and PR + MHV1 + COSMOSAC) with different mixing rules and various ways to describe intermolecular interactions were investigated. Furthermore, total enthalpy, sublimation enthalpy and solvation enthalpy relevant to MCP solvating in SC-CO₂ were estimated.     

Solubility-dependent complexation of active pharmaceutical ingredients with trimethyl-β-cyclodextrin under supercritical fluid condition

The effect of the solubility of active pharmaceutical ingredients (APIs) in supercritical carbon dioxide (SC-CO₂) on their complexation behavior with trimethyl-β-cyclodextrin (TM-β-CD) has been investigated. Flurbiprofen or naproxen, the solubility of which is lower than that of ibuprofen, was mixed with TM-β-CD and the complexation phenomena on SC-CO₂ processing was evaluated using powder X-ray diffraction, differential scanning calorimetry and IR measurement. Drug complexation depended both on SC-CO₂ treatment time and on drug solubility in CO₂. The inclusion complex formation of flurbiprofen with TM-β-CD proceeded slowly compared with the case of ibuprofen. The slower complexation behavior was also observed when naproxen was used as the guest molecule. These results indicate that dissolution of drug molecules in SC-CO₂ is a rate-determining step for the inclusion complex formation with TM-β-CD and that complexation proceeds after dissolving the both components in SC-CO₂.     

Solubility of amphenicol bacteriostats in CO2

The solubilities of three veterinary amphenicol bacteriostats, chloramphenicol, florfenicol and thiamphenicol, were measured in supercritical carbon dioxide (SC-CO₂) by a re-circulating method at temperatures of 313.15 and 333.15 K and pressures ranging from 11.0 to 49.0 MPa. These compounds displayed very limited solubility in SC-CO₂ (10⁻⁵ to 10⁻⁷ mole fraction) over the range of experimental conditions. Chloramphenicol had the highest observed solubility of the three amphenicols, while the solubilities of florfenicol and thiamphenicol were almost an order of magnitude lower. The experimental solubility data were correlated with seven known density-based models. The density models (ln y versus ln ρ or ln ρ_r) gave better correlation than the semi-log scale of ln y versus ρ_r. Four models for ln E versus density correlations also gave better correlation than the semi-log scale of ln y versus ρ_r by introducing the enhancement factor E. The correlation accuracy of all the seven models mainly depends on the system investigated, measured density and temperature range.     

Theoretical and experimental study on Chloroquine drug solubility in supercritical carbon dioxide via the thermodynamic,multi-layer perceptron neural network (MLPNN), and molecular modeling

The design and development of supercritical carbon dioxide (sc-CO₂) based processes for production of pharmaceutical micro/nanoparticles is one of the interesting research topics of pharmaceutical industries owing to its attractive advantages. The solubility of drugs in sc-CO₂ at different temperatures and pressures is an essential parameter which should be determined for this purpose. Chloroquine as a traditional antirheumatic and antimalarial agent is approved as an effective drug for the treatment of Covid-19. Pishnamazi et al. (2021) measured the solubility of this drug in sc-CO₂ at the pressure range of 120–400 bar and temperature range of 308–338 K, and correlated the obtained data using some empirical models. In this work, a comprehensive computational approach was developed to more accurately study the supercritical solubility of Chloroquine. The thermodynamic models include two equations-of-state based models (Peng-Robinson and Soave-Redlich-Kowang) and two activity coefficient-based models (modified Wilson's and UNIQUAC)), as well as, a multi-layer perceptron neural network (MLPNN)) were used for this purpose. Also, molecular modeling was performed to study the electronic structure of Chloroquine and identify the potential centers of intermolecular interactions during the dissolution process. According to the obtained results, all of the theoretical models can predict Chloroquine solubility in sc-CO₂ with acceptable accuracy. Among these models, the MLPNN model possesses the highest precision with the lowest average absolute relative deviation (AARD%) of 1.76 % and the highest R_adj value of 0.999.     

Effect of supercritical CO2 on bulk hydrogenation of nitrile butadiene rubber catalyzed by RhCl(PPh3)3

The effect of supercritical (SC) CO₂ on the bulk hydrogenation of NBR entrapped with the catalyst (RhCl(PPh₃)₃) was investigated under various reaction times, reaction temperatures, hydrogen pressures and loadings of the catalyst and the thicknesses of the polymer films. CO₂ helps in improving the transport behaviour of catalyst in polymer matrices, as well as helping to move catalyst into or out of the polymer. A method for the measurement of the dissolution extent or the apparent solubility of the Rh based catalyst in SC-CO₂ was developed. It is found that high temperatures and high SC-CO₂ densities would enhance the apparent solubility. Cosolvents, such as acetone, are also found to increase the apparent solubility. Details on the hydrogenation process are also presented.     

Measurement and correlation of antifungal drugs solubility in pure supercritical CO2 using semiempirical models

In the present study the solubilities of two antifungal drugs of ketoconazole and clotrimazole in supercritical carbon dioxide were measured using a simple static method. The experimental data were measured at (308 to 348) K, over the pressure range of (12.2 to 35.5) MPa. The mole fraction solubilities ranged from 0.2 · 10^?6 to 17.45 · 10^?5. In this study five density based models were used to calculate the solubility of drugs in supercritical carbon dioxide. The density based models are Chrastil, modified Chrastil, Bartle, modified Bartle and Mendez-Santiago and Teja (M–T). Interaction parameters for the studied models were obtained and the percentage of average absolute relative deviation (AARD%) in each calculation was displayed. The correlation results showed good agreement with the experimental data. A comparison among the five models revealed that the Bartle and its modified models gave much better correlations of the solubility data with an average absolute relative deviation (AARD%) ranging from 4.8% to 6.2% and from 4.5% to 6.3% for ketoconazole and clotrimazole, respectively. Using the correlation results, the heat of drug–CO₂ solvation and that of drug vaporization was separately approximated in the range of (?22.1 to ?26.4 and 88.3 to 125.9) kJ · mol^?1.     

TP-CSO: A Triptolide Prodrug for Pancreatic Cancer Treatment

Triptolide (TP) is a potential drug candidate for the treatment of cancer, but its use was hampered by its systemic toxicity and poor water solubility. Hence, a TP-CSO prodrug was synthesized by conjugating TP to chitosan oligosaccharide (CSO), and characterized by ¹H NMR, FTIR, DSC and XRD analyses. The TP-CSO containing about 4 wt% of TP exhibited excellent water solubility (15 mg/mL) compared to TP (0.017 mg/mL). Compared with TP, the pharmacokinetics of the conjugate after oral administration showed a three-fold increase in the half-life in the blood circulation and a 3.2-fold increase in AUC _(0–∞). The orally administered TP-CSO could more effectively inhibit tumor progression but with much lower systemic toxicity compared with TP, indicating significant potential for further clinical trials. In conclusion, CSO-based conjugate systems may be useful as a platform for the oral delivery of other sparingly soluble drugs.     

Multiple machine learning models for prediction of CO2 solubility in potassium and sodium based amino acid salt solutions

In this work, we developed artificial intelligence-based models for prediction and correlation of CO₂ solubility in amino acid solutions for the purpose of CO₂ capture. The models were used to correlate the process parameters to the CO₂ loading in the solvent. Indeed, CO₂ loading/solubility in the solvent was considered as the sole model’s output. The studied solvent in this work were potassium and sodium-based amino acid salt solutions. For the predictions, we tried three potential models, including Multi-layer Perceptron (MLP), Decision Tree (DT), and AdaBoost-DT. In order to discover the ideal hyperparameters for each model, we ran the method multiple times to find out the best model. R² scores for all three models exceeded 0.9 after optimization confirming the great prediction capabilities for all models. AdaBoost-DT indicated the highest R² Score of 0.998. With an R² of 0.98, Decision Tree was the second most accurate one, followed by MLP with an R² of 0.9.     

Optimization of Fenoprofen solubility within green solvent through developing a novel and accurate GSO-GPR predictive model

Indisputable importance of drug solubility in various industrial perspectives has motivated the scientists to evaluate different techniques to improve it. Fenoprofen is a significant nonsteroidal anti-inflammatory drug (NSAID), that is the orally administered to relieve mild to moderate pain and the unfavorable symptoms of osteoarthritis and rheumatoid arthritis (i.e., inflammation and stiffness). Supercritical fluids (SCFs) belong to a certain type of fluids, in which their temperature and pressure are higher than the critical point. This property allows the CO₂SCF to simultaneously possess the characteristics of both a liquid and a gas. The prominent target of this paper is to mathematically develop three predictive models via machine learning (ML) technique to optimize the solubility of Fenoprofen in CO₂SCF. In this study, we have 32 data vectors in each dataset, including two input features of pressure and temperature. The output target is solubility, which we are going to model and analyze. Models are constructed through the use of Modular ANN (MANN), Gaussian processes regression (GPR), and the K-Nearest Neighbor technique (KNN) in this body of work. The glowworm swarm optimization (GSO) swarm-based method is utilized in order to carry out the process of model optimization. The root mean squared error (RMSE) rates for GSO-KNN, GSO-MANN, and GSO-GPR are respectively 5.25E-04, 5.46E-04, and 3.01E-05. The aforementioned models were also judged according to a number of other criteria, and since the GSO-GPR model was found to be the most effective according to all of these standards, it is being treated as the conclusive model of this investigation. In addition, the maximum error has been brought down to 5.02E-05 with the help of this model, which has an R2-score of 0.999.     

Formulation optimization and in situ absorption in rat intestinal tract of quercetin-loaded microemulsion

A new microemulsion system has been developed to increase the solubility and oral absorption of quercetin, a poorly water-soluble drug. The formulation of quercetin-loaded microemulsion was optimized by a simplex lattice experiment design. The optimized microemulsion formulation consisted of oil (7%, w/w), surfactant (48%, w/w), and cosurfactant (45%, w/w). Under this condition, the mean droplet diameter of microemulsion was 38.9 nm and solubility of quercetin in the microemulsion was 4.138 mg/ml. The in situ absorption property of quercetin-loaded microemulsion in rat intestine was studied and the results showed there was significant difference in absorption parameters such as K_a, t_1/2 and uptake percentages between microemulsion and micelle solution containing quercetin. The study on absorption percentage in different regions of rat intestine attested that the colon had the best permeability, followed by ileum, duodenum in order. It can be concluded that microemulsion can improve the solubility and oral absorption of quercetin, a poorly water-soluble drug.     

Ibuprofen-Cyclodextrin Inclusion Complex Formation using Supercritical Carbon Dioxide

Supercritical carbon dioxide (SC-CO₂) processing was performed with mixtures of cyclodextrins (CDs) and ibuprofen (IBP) to create inclusion complexes of ibuprofen and CD. Mixtures of IBP and trimethyl-β-CD showed new powder X-ray diffraction peaks after SC-CO₂ processing, although samples after processing with β-CD showed identical X-ray diffraction patterns with the physical mixture. The differential scanning calorimetry curves of samples after processing with trimethyl-β-CD showed no fusion peak of IBP and a new melting peak at around 185 °C. The physicochemical properties are similar to the co-precipitated samples of IBP and trimethyl-β-CD. Therefore, inclusion complex between IBP and trimethyl-β-CD was successfully prepared using SC-CO₂ technique. No inclusion formation was found when nitrogen was used as the supercritical fluid. Complexation of IBP and CD would not occur only on a high-pressure condition. The solubility of cyclodextrin into SC-CO₂ might play an important role in the formation of the inclusion complex.     

Inclusion complexes of fluconazole with β-cyclodextrin: physicochemical characterization and in vitro evaluation of its formulation

Fluconazole (FZ) is a triazole antifungal drug administered orally or intravenously. It is employed for the treatment of mycotic infections. However, the efficacy of FZ is limited with its poor aqueous solubility and low dissolution rate. One of the important pharmaceutical advantages of cyclodextrins is to improve pharmacological efficacy of drugs due to increasing their aqueous solubility. The aim of present study was to prepare an inclusion complex of FZ and β-cyclodextrin (β-CD) to improve the physicochemical and biopharmaceutical properties of FZ. The effects of β-CD on the solubility of FZ were investigated according to the phase solubility technique. Complexes were prepared with 1:1 M ratio by different methods namely, freeze-drying, spray-drying, co-evaporation and kneading. For the characterization of FZ/β-CD complex, FZ amount, practical yield %, thermal, aqueous solubility, XRD, FT-IR and NMR (¹H and ¹³C) analysis were performed. In vitro dissolution from hard cellulose capsules containing FZ/β-CD complexes was compared to pure FZ and its commercial capsules and evaluated by f₁ (difference) and f₂ (similarity) factors. Paddle method defined in USP 31 together with high pressure liquid chromatographic method were used in in vitro dissolution experiments. It was found that solubility enhancement by FZ/β-CD complexes depends on the type of the preparation method. High release of active agent from hard cellulose capsules prepared with β-CD complexes compared to commercial capsules was attributed to the interactions between β-CD and active agent, high energetic amorphous state and inclusion complex formation.     

Computational modeling of Hg/Ni ions separation via MOF/LDH nanocomposite: Machine learning based modeling

Nowadays, sustainable supplement of water has recently been identified as a vital necessity due to the existence of limited drinkable water sources. To do this, various techniques are being developed to remove various types of pollutants from water/wastewater sources. Adsorption of common water pollutants using nanocomposite materials has been of great popularity in recent years due to its high efficiency. This paper aims to develop various models based on machine learning approach to study their efficiency on predicting the experimentally measured results of Hg/Ni ions removal from water sources. To do this, this study attempts regression on a small data set using two parameters as inputs and two parameters as outputs. In this dataset, the inputs are Ion and C0, and the outputs are Ce and Qe. AdaBoost (Adaptive Boosting), a well-known ensemble method, was applied on top of three different models, including Decision Tree Regression (DT), Gaussian Process Regression (GPR), and Linear Regression (LR). After fine-tuning their hyper-parameters, the optimized model was evaluated through various metrics. For example, the R² for ADA + GPR model has a score of 0.998 for Ce and 0.999 for Qe as the best model among these three models. This model in RMSE is the best and illustrates 0.1512 and 1.490 for Ce and Qe as error. Eventually, ADA + GPR has been selected as the optimized model with optimized dataset: (Ion = Ni, C0 = 250, Ce = 206.0). But for Qe, different amounts are illustrated: (Ion = Hg, C0 = 106.7, Ce = 577.35)     

Binary and ternary solubilities of disperse dyes and their blend in supercritical carbon dioxide

Binary and ternary solubilities of C.I. Disperse Blue 134 (1,4-bis(isopropylamino)anthraquinone) C.I. Disperse Yellow 16 (3-methyl-1-phenyl-5-pyrazolone) and their dye mixture in supercritical carbon dioxide (SC-CO₂) were measured by a flow-type apparatus. The solubility measurements were carried out at the pressure ranges from 10.0 to 25.0 MPa for the binary systems at the temperatures from 323.15 to 383.15 K and for the ternary system at 383.15 K. An empirical equation was used to correlate the experimental binary solubilities of the dyes in terms of the density of carbon dioxide. To represent accurately the binary solubility of the dyes in terms of temperature and pressure, we used a modified Peng–Robinson–Stryjek–Vera equation of state (PRSV EOS). The ternary solubilities of the dye blend could be predicted successfully from binary parameters with the modified PRSV EOS.     

Solubility enhancement of decitabine as anticancer drug via green chemistry solvent: Novel computational prediction and optimization

Nowadays, supercritical fluid technology (SFT) has been an interesting scientific subject in disparate industrial-based activities such as drug delivery, chromatography, and purification. In this technology, solubility plays an incontrovertible role. Therefore, achieving more knowledge about the development of promising numerical/computational methods of solubility prediction to validate the experimental data may be advantageous for increasing the quality of research and therefore, the efficacy of novel drugs. Decitabine with the chemical formula C₈H₁₂N₄O₄ is a chemotherapeutic agent applied for the treatment of disparate bone-marrow-related malignancies such as acute myeloid leukemia (AML) by preventing DNA methyltransferase and activation of silent genes. This study aims to predict the optimum value of decitabine solubility in CO₂SCF by employing different machine learning-based mathematical models. In this investigation, we used AdaBoost (Adaptive Boosting) to boost three base models such as Linear Regression (LR), Decision Tree (DT), and GRNN. We used a dataset that has 32 sample points to make solubility models. One of the two input features is P (bar) and the other is T (k). ADA-DT (Adaboost Algorithm-Decision Tree), ADA-LR (Adaboost Algorithm-Linear Regresion), and ADA-GRNN (Generative Regression Neural Network) models showed MAE of 6.54 × 10^?5, 4.66 × 10^?5, and 8.35 × 10^?5, respectively. Also, in terms of R-squared score, these models have 0.986, 0.983, and 0.911 scores, respectively. ADA-LR was selected as the primary model according to numerical and visual analysis. Finally, the optimal values are (P = 400 bar, T = 3.38 K × 102, Y = 1.064 × 10^?3 mol fraction) using this model.     

Use of hydrophobins in formulation of water insoluble drugs for oral administration

The poor water solubility of many drugs requires a specific formulation to achieve a sufficient bioavailability after oral administration. Suspensions of small drug particles can be used to improve the bioavailability. We here show that the fungal hydrophobin SC3 can be used to make suspensions of water insoluble drugs. Bioavailability of two of these drugs, nifedipine and cyclosporine A (CyA), was tested when administered as a SC3-based suspension. SC3 (in a 1:2 (w/w) drug:SC3 ratio) or 100% PEG400 increased the bioavailability of nifedipine to a similar degree (6 ± 2- and 4 ± 3-fold, respectively) compared to nifedipine powder without additives. Moreover, SC3 (in a 7:1 (w/w) drug:hydrophobin ratio) was as effective as a 20-fold diluted Neoral^® formulation by increasing bioavailability of CyA 2.3 ± 0.3-fold compared to CyA in water. Interestingly, using SC3 in the CyA formulation resulted in a slower uptake (p < 0.001 in T_max) of the drug, with a lower peak concentration (C_max 1.8 mg ml^?1) at a later time point (T_max 9 ± 2 h) compared to Neoral^® (C_max 2.2 mg ml^?1; T_max 3.2 ± 0.2). Consequently, SC3 will result in a more constant, longer lasting drug level in the body. Taken together, hydrophobins are attractive candidates to formulate hydrophobic drugs.     

Two Green Micellar HPLC and Mathematically Assisted UV Spectroscopic Methods for the Simultaneous Determination of Molnupiravir and Favipiravir as a Novel Combined COVID-19 Antiviral Regimen

Following the spread of the COVID-19 pandemic crisis, a race was initiated to find a successful regimen for postinfections. Among those trials, a recent study declared the efficacy of an antiviral combination of favipiravir (FAV) and molnupiravir (MLP). The combined regimen helped in a successful 60% eradication of the SARS-CoV-2 virus from the lungs of studied hamster models. Moreover, it prevented viral transmission to cohosted sentinels. Because both medications are orally bioavailable, the coformulation of FAV and MLP can be predicted. The developed study is aimed at developing new green and simple methods for the simultaneous determination of FAV and MLP and then at their application in the study of their dissolution behavior if coformulated together. A green micellar HPLC method was validated using an RP-C18 core-shell column (5 μm, 150 × 4.6 mm) and an isocratic mixed micellar mobile phase composed of 0.1 M SDS, 0.01 M Brij-35, and 0.02 M monobasic potassium phosphate mixture and adjusted to pH 3.1 at 1.0 mL min⁻¹ flow rate. The analytes were detected at 230 nm. The run time was less than five minutes under the optimized chromatographic conditions. Four other multivariate chemometric model methods were developed and validated, namely, classical least square (CLS), principal component regression (PCR), partial least squares (PLS-1), and genetic algorithm–partial least squares (GA–PLS-1). The developed models succeeded in resolving the great similarity and overlapping in the FAV and MLP UV spectra unlike the traditional univariate methods. All methods were organic solvent-free, did not require extraction or derivatization steps, and were applied for the construction of the simultaneous dissolution profile for FAV tablets and MLP capsules. The methods revealed that the amount of the simultaneously released cited drugs increases up until reaching a plateau after 15 and 20 min for FAV and MLP, respectively. The greenness was assessed on GAPI and found to be in harmony with green analytical chemistry concepts.     

Improvement of opipramol base solubility by complexation with β-cyclodextrin

Opipramol (OPI), a tricyclic antidepressant and anxiolytic compound, is administered orally in the form of a dihydrochloride. Salt form of the drug has a higher solubility in water and hence bioavailability and stability. A similar effect can be achieved by closing the hydrophobic part of the drug molecule in the cyclodextrin cavity. The paper presents opipramol inclusion complexes with beta-cyclodextrin (β-CD) in 1:1 molar ratio. Studies on the formation of inclusion complexes were carried out both in solution and in the solid state. The formation and physicochemical characterisation of the complexes were determined by UV spectroscopic measurement (UV–vis), Fourier Transform Infrared (FTIR) Spectroscopy, ¹H Nuclear Magnetic Resonance (¹H NMR, 2D NOESY NMR), thermoanalytical methods (TGA – Termogravimetric analysis, DSC – differential scanning calorimetry), X-ray diffractometry (XRD) and scanning electron microscopy (SEM). The phase solubility profile with β-CD was classified as the A_N- type, indicating the formation of the inclusion complex with a drug.     

C18-attached membrane funnel-based spray ionization mass spectrometry for quantification of anti-diabetic drug from human plasma

In this work, sorbent-attached membrane funnel-based spray ionization mass spectrometry was explored for quantitative analysis of anti-diabetic drugs spiked in human plasma. C₁₈-attached membrane funnel was fabricated for in situ extraction and clean-up to alleviate matrix suppression effect in the ionization process. Repaglinide was used as a target analyte of anti-diabetic drugs. Under optimal working conditions, good linearity (R² > 0.99) was obtained in the concentration range of 1–100 ng mL⁻¹. The method detection limit of target drugs spiked in the human plasma was around 0.30 ng mL⁻¹. Through the application of an isotope-labeled internal standard, the signal fluctuation caused by residual background matrices was largely alleviated and the precision of measurement (RSD) was below 15%. The recovery of repaglinide for 5, 25, and 100 ng mL⁻¹ of spiked human plasma matrixes ranged from 87% to 112%. The developed method was successfully applied to determine repaglinide in plasma volunteers who orally received a dose of drug association. Our results demonstrated that membrane funnel-based spray is a simple and sensitive method for rapid screening analysis of complex biological samples.