Supercritical fluid technology for solubilization of poorly water soluble drugs via micro- and naonosized particle generation

Microencapsulation for Pharmaceutical Applications: A Review

In order to improve bioavailability, stability, control release, and target delivery of active pharmaceutical ingredients (APIs), as well as to mask their bitter taste, to increase their efficacy, and to minimize their side effects, a variety of microencapsulation (including nanoencapsulation, particle size <100 nm) technologies have been widely used in the pharmaceutical industry. Commonly used microencapsulation technologies are emulsion, coacervation, extrusion, spray drying, freeze-drying, molecular inclusion, microbubbles and microsponge, fluidized bed coating, supercritical fluid encapsulation, electro spinning/spray, and polymerization. In this review, APIs are categorized by their molecular complexity: small APIs (compounds with low molecular weight, like Aspirin, Ibuprofen, and Cannabidiol), medium APIs (compounds with medium molecular weight like insulin, peptides, and nucleic acids), and living microorganisms (such as probiotics, bacteria, and bacteriophages). This article provides an overview of these microencapsulation technologies including their processes, matrix, and their recent applications in microencapsulation of APIs. Furthermore, the advantages and disadvantages of these common microencapsulation technologies in terms of improving the efficacy of APIs for pharmaceutical treatments are comprehensively analyzed. The objective is to summarize the most recent progresses on microencapsulation of APIs for enhancing their bioavailability, control release, target delivery, masking their bitter taste and stability, and thus increasing their efficacy and minimizing their side effects. At the end, future perspectives on microencapsulation for pharmaceutical applications are highlighted.

Development of Cholinium-Based API Ionic Liquids with Enhanced Drug Solubility: Biological Evaluation and Interfacial Properties

We report an efficient sustainable two-step anion exchange synthetic procedure for the preparation of choline API ionic liquids (Cho-API-ILs) that contain active pharmaceutical ingredients (APIs) as anions combined with choline-based cations. We have evaluated the in vitro cytotoxicity for the synthesized compounds using three different cells lines, namely, HEK293 (normal kidney cell line), SW480, and HCT 116 (colon carcinoma cells). The solubility of APIs and Cho-API-ILs was evaluated in water/buffer solutions and was found higher for Cho-API-ILs. Further, we have investigated the antimicrobial potential of the pure APIs, ILs, and Cho-API-ILs against clinically relevant microorganisms, and the results demonstrated the promise of Cho-API-ILs as potent antimicrobial agents to treat bacterial infections. Moreover, the aggregation and adsorption properties of the Cho-API-ILs were observed by using a surface tension technique. The aggregation behavior of these Cho-API-ILs was further supported by conductivity and pyrene probe fluorescence. The thermodynamics of aggregation for Cho-API-ILs has been assessed from the temperature dependence of surface tension. The micellar size and their stability have been studied by dynamic light scattering, transmission electron microscopy, and zeta potential. Therefore, the duality in the nature of Cho-API-ILs has been explored with the upgradation of their physical, chemical, and biopharmaceutical properties, which enhance the opportunities for advances in pharmaceutical sciences.

Design of predictive model to optimize the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug

These days, many efforts have been made to increase and develop the solubility and bioavailability of novel therapeutic medicines. One of the most believable approaches is the operation of supercritical carbon dioxide fluid (SC-CO₂). This operation has been used as a unique method in pharmacology due to the brilliant positive points such as colorless nature, cost-effectives, and environmentally friendly. This research project is aimed to mathematically calculate the solubility of Oxaprozin in SC-CO₂ through artificial intelligence. Oxaprozin is a nonsteroidal anti-inflammatory drug which is useful in arthritis disease to improve swelling and pain. Oxaprozin is a type of BCS class II (Biopharmaceutical Classification) drug with low solubility and bioavailability. Here in order to optimize and improve the solubility of Oxaprozin, three ensemble decision tree-based models including random forest (RF), Extremely random trees (ET), and gradient boosting (GB) are considered. 32 data vectors are used for this modeling, moreover, temperature and pressure as inputs, and drug solubility as output. Using the MSE metric, ET, RF, and GB illustrated error rates of 6.29E-09, 9.71E-09, and 3.78E-11. Then, using the R-squared metric, they demonstrated results including 0.999, 0.984, and 0.999, respectively. GB is selected as the best fitted model with the optimal values including 33.15 (K) for the temperature, 380.4 (bar) for the pressure and 0.001242 (mole fraction) as optimized value for the solubility.

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.

An overview on the application of supercritical carbon dioxide for the processing of pharmaceuticals

Supercritical carbon dioxide (scCO2) application in the pharmaceutical industry is still undeveloped regardless of significant research interests in this processing medium shown in the last decades. ScCO2 technologies can improve drug solubility, bioavailability, and therapeutic effect. These technologies can lead to the development of new formulations that will contribute to a decrease in drug dose, medication frequency, and increase patients' well-being. Considering the significant decrease in the price of high-pressure equipment and society's growing need for cleaner production and safer products, it is expected that symbiosis between supercritical fluid and pharmaceutical technologies will happen soon. Therefore, this review was focused on the latest contributions of scCO2 technologies to the pharmaceutical field. The main aim was to bring these technologies closer to pharmaceutical specialists. For this purpose, the most commonly used technologies were explained and discussed: the preparation of solid dispersions, polymer impregnation with drugs, and drug micro/nanoparticle production using scCO2.

Nanoparticles and Nanocrystals by Supercritical CO2-Assisted Techniques for Pharmaceutical Applications: A Review

Many active ingredients currently prescribed show limited therapeutic efficacy, mainly due to their dissolution rate inadequate to treat the pathology of interest. A large drug particle size creates an additional problem if a specific site of action in the human body has to be reached. For this reason, active ingredient size reduction using micronization/nanonization techniques is a valid approach to improve the efficacy of active compounds. Supercritical carbon-dioxide-assisted technologies enable the production of different morphologies of different sizes, including nanoparticles and nanocrystals, by modulating operating conditions. Supercritical fluid-based processes have numerous advantages over techniques conventionally employed to produce nanosized particles or crystals, such as reduced use of toxic solvents, which are completely removed from the final product, ensuring safety for patients. Active compounds can be processed alone by supercritical techniques, although polymeric carriers are often added as stabilizers, to control the drug release on the basis of the desired therapeutic effect, as well as to improve drug processability with the chosen technology. This updated review on the application of supercritical micronization/nanonization techniques in the pharmaceutical field aims at highlighting the most effective current results, operating conditions, advantages, and limitations, providing future perspectives.