Supercritical Processing Research Articles

Analysis of nanonization and purification of organic compounds via green supercritical processing: Model development using advanced hybrid techniques

Complex relationship between process parameters and critical quality attributes in nanonization of drugs based on supercritical processing demands for developing advanced hybrid computational models. In this research, the complicated relationships between temperature, pressure, solvent density, and solubility of Nystatin in supercritical carbon dioxide for green processing of nanoparticles was explored via advanced hybrid models. For the first time, three base regression models: K-Nearest Neighbors (KNN), Bayesian Ridge Regression (BRR), and Polynomial Regression (PR) in combination with Political Optimizer (PO) for correlation of solubility of organic compound as well as solvent density was developed. Additionally, an ensemble approach, Bagging, is employed to enhance predictive performance of the models. Bagging ensemble technique combined with BRR predicts Nystatin solubility with an R2 of 0.89459. Bagging combined with KNN produces a score of 0.9162, showcasing its strong predictive capabilities. Moreover, bagging coupled with PR exhibits exceptional predictive performance, attaining a score of 0.99819. When predicting solvent density, bagging in conjunction with BRR yields an R2 value of 0.81069, while bagging paired with KNN achieves a higher score of 0.90542, demonstrating its proficiency in modeling solvent density. Bagging with PR surpasses the other models, attaining a significant R2 of 0.99035, highlighting its superior predictive accuracy.

Advances in Aerogels Formulations for Pulmonary Targeted Delivery of Therapeutic Agents: Safety, Efficacy and Regulatory Aspects.

Aerogels are the 3D network of organic, inorganic, composite, layered, or hybrid-type materials that are used to increase the solubility of Class 1 (low solubility and high permeability) and Class 4 (poor solubility and low permeability) molecules. This approach improves systemic drug absorption due to the alveoli's broad surface area, thin epithelial layer, and high vascularization. Local therapies are more effective and have fewer side effects than systemic distribution because inhalation treatment targets the specific location and raises drug concentration in the lungs. The present manuscript aims to explore various aspects of aerogel formulations for pulmonary targeted delivery of active pharmaceutical agents. The manuscript also discusses the safety, efficacy, and regulatory aspects of aerogel formulations. According to projections, the global respiratory drug market is growing 4-6% annually, with short-term development potential. The proliferation of literature on pulmonary medicine delivery, especially in recent years, shows increased interest. Aerogels come in various technologies and compositions, but any aerogel used in a biological system must be constructed of a material that is biocompatible and, ideally, biodegradable. Aerogels are made via "supercritical processing". After many liquid phase iterations using organic solvents, supercritical extraction, and drying are performed. Moreover, the sol-gel polymerization process makes inorganic aerogels from TMOS or TEOS, the less hazardous silane. The resulting aerogels were shown to be mostly loaded with pharmaceutically active chemicals, such as furosemide-sodium, penbutolol-hemisulfate, and methylprednisolone. For biotechnology, pharmaceutical sciences, biosensors, and diagnostics, these aerogels have mostly been researched. Although aerogels are made of many different materials and methods, any aerogel utilized in a biological system needs to be made of a substance that is both biocompatible and, preferably, biodegradable. In conclusion, aerogel-based pulmonary drug delivery systems can be used in biomedicine and non-biomedicine applications for improved sustainability, mechanical properties, biodegradability, and biocompatibility. This covers scaffolds, aerogels, and nanoparticles. Furthermore, biopolymers have been described, including cellulose nanocrystals (CNC) and MXenes. A safety regulatory database is necessary to offer direction on the commercialization potential of aerogelbased formulations. After that, enormous efforts are discovered to be performed to synthesize an effective aerogel, particularly to shorten the drying period, which ultimately modifies the efficacy. As a result, there is an urgent need to enhance the performance going forward.

Preparation and properties of aluminosilicate aerogels from boehmite and waterglass

Aluminosilicate aerogels are promising thermal insulation materials for high temperature applications. In this study, aluminosilicate aerogels were effectively synthesised using affordable precursors: dispersible boehmite powder and waterglass solutions, with subsequent drying achieved by supercritical CO2 processing. According to the evaluation of their properties, the Al–Si aerogels exhibited low densities (0.11 g/cm3), low thermal conductivities (<0.021 W/m·K) and high specific surface areas (SSA) (516 m2/g for an Al:Si molar ratio of 3:1). When waterglass-based aerogel and aluminosilicate samples were treated at high temperature (1200 °C), the formation of mullite was demonstrated for the aluminosilicate aerogel with a molar ratio of Al:Si 3:1, which showed a higher value for SSA compared to the waterglass-based aerogel.

Lightweight, porous, stretchable nanocomposite foams with sandwich-like and bimodal cell structure by supercritical fluids-assisted processing for flexible strain sensor

Developing strain sensors with wide detection range and high sensitivity at a low conductive nanofillers content still faces challenges. Herein, thermoplastic polyurethane (TPU)/multi-walled carbon nanotubes (MWCNTs) nanocomposite foams with a multilayer alternating and bimodal cell structure is prepared by stacking hot pressing and supercritical carbon dioxide batch foaming process. Consequently, the synergistic effect of sandwich-like and bimodal cell structure endows the foam sensor of FTPU-C3-L10 T80P10 (TPU/3wt.%MWCNTs nanocomposite foams with ten layers alternating structure foamed at 80℃ and 10MPa) 0.34 vol.% MWCNTs loading, broad response range of 0~150%, high sensitivity with a gauge factor (GF) of 101, fast response speed of 180 ms, as well as cyclic reproducibility and durability of 1000 cycles. Furthermore, the fabricated foam sensor can effectively monitor basic human motion from tiny to large. This work provides a facile strategy for designing and producing strain sensors applied in various fields, including human motion detection, artificial intelligence devices, and medical diagnosis.

Biodegradable nanocomposite poly(lactic acid) foams containing carvacrol-based cocrystal prepared by supercritical CO2 processing for controlled release in active food packaging

Compounds derived from essential oils have been used in active packaging, but their volatility and degradability negatively affect stability and leads to high release rates. The present study aimed to develop PLA bionanocomposite foams loaded with carvacrol cocrystal by supercritical CO2 and its release into a food simulant for control release in food packaging. For this purpose, 4,4′-bipyridine was used as coformer and carvacrol as active agent. Cocrystallized closed cell foams were obtained using supercritical CO2 and were characterized in terms of their physicochemical and mechanical properties, and release kinetics to a D1 simulant were evaluated as well as the antioxidant ability. A better overall mechanical behavior due to the nanoclay promoting a higher interfacial adhesion with the polymeric matrix was revealed. A higher incorporation of carvacrol was observed in samples with higher C30B content. The incorporated cocrystals showed a decrease of one order of magnitude in the estimated effective diffusion coefficient of carvacrol and showed antioxidant activity. These results suggest that the nanocomposite foam containing carvacrol-based cocrystals could be used in active packaging systems with controlled release characteristics, especially with highly volatile compounds, and can be proposed for other fields such as biomedical applications.

Pharmaceutical nanonization by green supercritical processing: Investigation of Exemestane anti-estrogenic medicine solubility using machine learning

Oral administration is known as the most prevalently-employed approach of drug delivery because of possessing different benefits like simplicity of use, affordability and negligible sterility restrictions. Despite the noteworthy interests of scientists to manufacture oral therapeutic medicines, poor solubility/bioavailability of orally-administered drugs remains a big challenge in recent years. Therefore, various scientists are trying to develop novel techniques to enhance the solubility of orally-administered therapeutic agents. One of the promising ways, which has recently attracted the global attentions is the use of supercritical fluids (SCFs). This technique could overcome the unfavorable/detrimental influences of organic solvents on the environment due to its environmentally-benign characteristics, non-inflammability and low toxicity. In this research, the solubility of Exemestane (EXE) drug is modeled based on two parameters of temperature and pressure. In order to handle the modeling process, the AdaBoost method with three core models of linear regression (LR), K-Nearest Neighbor (KNN) and Gaussian Process (GPR) are considered, which are tuned by AEO method in terms of their hyper-parameters. Based on the evaluations carried out in this research, ADA GPR, ADA KNN, and ADA LR models showed 0.996, 0.991, and 0.925 values in terms of R-square score. Also, in terms of the error rate, the three models ADA GPR, ADA KNN, and ADA LR have an RMSE error equal to 1.3459, 1.8967, and 5.8875, respectively. Based on these facts and some other analyses, the Gaussian process model optimized and boosted with AdaBoost can be accepted as the most accurate model of this research.

Supercritical CO2 Processing of White Grape Must as a Strategy to Reduce the Addition of SO2.

In winemaking, sulfur dioxide addition is the most common procedure to prevent enzymatic and microbial alterations. However, the enological industry looks for safer alternatives to preserve enological products, and high-pressure treatments with supercritical CO2 are a suitable alternative. This study evaluates the effectiveness of this process in the stabilization and preservation of white grape must, studying the influence of time, pressure, and CO2 percentage on must characteristics. In spite of the percentage of CO2 turned out to be the variable that affects the most the process, no remarkable differences were observed in pH, acidity, and color intensity between untreated and treated musts. Moreover, this technique has proven to be very efficient in the reduction of aerobic mesophilic microorganisms as well as in the reduction of residual polyphenol oxidase activities, being lower than those obtained with SO2 addition (60 and 160 mg/L). Based on the results, the most convenient conditions were 100 bar and 10% CO2, for 10 min treatment.

Targeted Chemical Processing Initiating Biosome Action-Potential-Matched Artificial Synapses for the Brain-Machine Interface.

A great gap still exists between artificial synapses and their biological counterparts in operation voltage or stimulation duration. Here, an artificial synaptic device based on a thin-film transistor with an operating voltage (-50-50 mV) analogous to biological action potential is developed by targeted chemical processing with the help of supercritical fluids. Chemical molecules [hexamethyldisilazane (HMDS)] are elaborately chosen and brought into the target interface to form charge receptors through supercritical processing. These charge receptors with the ability of capturing electrons mimic neurotransmitter receptors in terms of mechanism and constitute key players accounting for the synaptic behaviors. The relatively lower electrical barrier height contributes to an action-potential-matched operating voltage and considerably low power consumption (∼1 pJ/synaptic event), minimizing the divide with biological synapse for a seamless linkage to the biosystem or brain-machine interface. The stable synaptic behaviors also lead to near-ideal accuracy in pattern recognition. Moreover, this methodology that introduces chemical groups into a target interface can be viewed as a platform technology that could be adapted to other conventional devices with suitable chemical molecules to reach designed synaptic behaviors. This environmentally friendly and low-temperature processing method, which can be performed even after device fabrication, has the potential to play an important role in the future development of bionic devices.

Thermodynamic and experimental analysis of drug nanoparticles preparation using supercritical thermal processing: Solubility of Chlorothiazide in different Co-solvents

Background/purposeChlorothiazide is a well-known diuretic and an antihypertensive drug with poor solubility and permeability and thus low bioavailability. Producing nanoparticles of this drug via a suitable supercritical method can enhance its therapeutic efficiency. For this purpose, supercritical solubility of Chlorothiazide must be known. At 308- 338 K and 130- 290 bar, Majrashi obtained this parameter between the mole fraction of 0.417×10-5 to 1.012×10-5. The poor supercritical solubility of Chlorothiazide can be enhanced by adding a little polar co-solvent to scCO2. Model/designIn the current study, the solubility of this drug in the ternary systems of Chlorothiazide, scCO2, and different co-solvents of ethanol, DMSO, and acetone was measured. Also, the obtained experimental data were correlated by some empirical models presented by the research teams of González, Mendez-Santiago-Teja, Li, Soltani-Mazloumi, and Jouyban. FindingThe supercritical solubility of Chlorothiazide in the presence of ethanol, DMSO, and acetone was found in the mole fraction range of 1.115×10-5 to 11.895×10-5, 0.778×10-5 to 9.25×10-5, and 0.668 ×10-5 to 9.04×10-5, respectively. It has been shown that addition of these co-solvents can improve the supercritical solubility of Chlorothiazide, and in the meantime, ethanol with the greatest effect can increase it by about 2.02-11.75 times. Furthermore, the Joyban model has the most accuracy for the correlation of the obtained solubility data, and the data calculated by this model are more consistent with the experimental data. NoveltyFor the first time at this work, the effect of three different co-solvents of ethanol, DMSO, and acetone on the solubility of Chlorothiazide in scCO2 was studied both experimentally and theoretically.

Computational intelligence modeling of hyoscine drug solubility and solvent density in supercritical processing: gradient boosting, extra trees, and random forest models

This work presents the results of using tree-based models, including Gradient Boosting, Extra Trees, and Random Forest, to model the solubility of hyoscine drug and solvent density based on pressure and temperature as inputs. The models were trained on a dataset of hyoscine drug with known solubility and density values, optimized with WCA algorithm, and their accuracy was evaluated using R2, MSE, MAPE, and Max Error metrics. The results showed that Gradient Boosting and Extra Trees models had high accuracy, with R2 values above 0.96 and low MAPE and Max Error values for both solubility and density output. The Random Forest model was less accurate than the other two models. These findings demonstrate the effectiveness of tree-based models for predicting the solubility and density of chemical compounds and have potential applications in determination of drug solubility prior to process design by correlation of solubility and density to input parameters including pressure and temperature.

Advanced AI modeling and optimization for determination of pharmaceutical solubility in supercritical processing for production of nanosized drug particles

This study presents a comparative analysis of three different models, namely Deep Neural Network (DNN), Quantile Regression (QR), and K-Nearest Neighbors (KNN) for the prediction of SC-CO2 density and solubility of rivaroxaban. The models were tuned using the Fireworks Algorithm (FWA) to optimize their hyperparameters. The dataset used in this analysis consisted of temperature (T), pressure (P), SC-CO2 density, and mole fractions of rivaroxaban. The results indicate that the DNN model exhibited outstanding performance in both prediction tasks. For the prediction of SC-CO2 density, the DNN model achieved an impressive R2 score of 0.99667, with a mean absolute error (MAE) of 7.61812E+00. Similarly, for the prediction of mole fractions of rivaroxaban, the DNN model achieved an excellent R2 score of 0.99831, with a very low MAE of 2.49870E-02. The KNN model also demonstrated good performance, with R2 scores of 0.9799 and 0.98029 for SC-CO2 density and mole fractions, respectively. However, it exhibited slightly higher MAE values compared to the DNN model. On the other hand, the QR model showed relatively lower accuracy in both prediction tasks, with R2 scores of 0.90873 and 0.87362 for SC-CO2 density and mole fractions, respectively. The QR model had higher MAE values, indicating larger average deviations from the true values. Overall, the DNN model outperformed both the KNN and QR models in predicting SC-CO2 density and mole fractions of rivaroxaban. These findings highlight the effectiveness of DNN models in accurately modeling and predicting complex chemical properties.

Production of Antioxidant Transfersomes by a Supercritical CO2 Assisted Process for Transdermal Delivery Applications.

Transfersomes are deformable vesicles that can transport drugs across difficult-to-permeate barriers in human tissues. In this work, nano-transfersomes were produced for the first time by a supercritical CO2 assisted process. Operating at 100 bar and 40 °C, different amounts of phosphatidylcholine (2000 and 3000 mg), kinds of edge activators (Span® 80 and Tween® 80), and phosphatidylcholine to edge activator weight ratio (95:5, 90:10, 80:20) were tested. Formulations prepared using Span® 80 and phosphatidylcholine at an 80:20 weight ratio produced stable transfersomes (-30.4 ± 2.4 mV ζ-potential) that were characterized by a mean diameter of 138 ± 55 nm. A prolonged ascorbic acid release of up to 5 h was recorded when the largest amount of phosphatidylcholine (3000 mg) was used. Moreover, a 96% ascorbic acid encapsulation efficiency and a quasi-100% DPPH radical scavenging activity of transfersomes were measured after supercritical processing.

Advanced modeling and intelligence-based evaluation of pharmaceutical nanoparticle preparation using green supercritical processing: Theoretical assessment of solubility

Modeling and simulations based on machine learning techniques were conducted in this research for determination of pharmaceutical solubility in supercritical solvent for the sake of green processing development in the area of pharmaceutics. By this method, drug nanoparticles can be formed and controlled for better solubility and consequently bioavailability. We modeled the solubility of the drug namely desoxycorticosterone acetate (DA). The temperature and pressure are two inputs for the developed models, whereas the target output is the solubility of DA in the solvent. Indeed, models with two inputs and one output are developed in this study. This dataset contains 45 rows of data collected at five distinct temperatures and nine pressure levels. Three novel models are employed here including: HHO-MLP, HHO-RR, and HHO–KNN for theoretical determination of the drug solubility in the solvent. They are in fact MLP, Ridge Regression, and KNN models coupled with Harris Hawks optimizer (HHO) for hyper-parameter optimization. The HHO-MLP model has the best performance among models with R2 of 0.995, RMSE error rate of 0.194 and maximum error equal to 0.327. The model's findings can be used for estimation of the solubility data in wide range of T and P.

Assessment of solid-dosage drug nanonization by theoretical advanced models: Modeling of solubility variations using hybrid machine learning models

Production of solid-dosage drug nanoparticles was assessed by theoretical models to investigate the possibility of drug treatment via supercritical green processing. Nanonization can enhance drug solubility and consequently its bioavailability which is of great importance for pharmaceutical industry. This research presents a comparative study of three different regression models including Gaussian process regression, k-nearest neighbors, and multi-layer perceptron for predicting solvent density and solubility of Hyoscine drug. The models optimized using political optimizer (PO) algorithm. The results showed that all three optimized methods were able to predict density and solubility with high accuracy. PO-GPR achieved the highest R2 score for solubility (0.9984) and same for density (0.9999). The PO-MLP model achieved the high R2 score for density (0.9997) and the second-highest score for solubility (0.9945). PO-KNN also showed good performance for density (R2 = 0.9557) and solubility (R2 = 0.9783) but was outperformed by the other two models. In terms of RMSE and AARD%, PO-GPR and PO-MLP achieved lower error rates compared to PO-KNN. Overall, the results suggest that PO-GPR and PO-MLP are promising methods for predicting density and solubility of values. The models were useful for the application of drug nanonization and can be used to optimize the process.

Supercritical Carbon Dioxide Technology for Recovering Valuable Phytochemicals from Cannabis sativa L. and Valorization of Its Biomass for Food Applications.

Supercritical carbon dioxide (CO2) extraction techniques meet all-new consumer market demands for health-promoting phytochemical compound-rich extracts produced from green and sustainable technology. In this regard, this review is dedicated to discussing is the promise of integrating high-pressure CO2 technologies into the Cannabis sativa L. processing chain to valorize its valuable pharmaceutical properties and food biomass. To do this, the cannabis plant, cannabinoids, and endocannabinoid system were reviewed to understand their therapeutic and side effects. The supercritical fluid extraction (SFE) technique was presented as a smart alternative to producing cannabis bioproducts. The impact of SFE operating conditions on cannabis compound extraction was examined for aerial parts (inflorescences, stems, and leaves), seeds, and byproducts. Furthermore, the opportunities of using non-thermal supercritical CO2 processing on cannabis biomass were addressed for industrial hemp valorization, focusing on its biorefinery to simultaneously produce cannabidiol and new ingredients for food applications as plant-based products.

Investigating the Effect of Rosemary Essential Oil, Supercritical CO2 Processing and Their Synergism on the Quality and Microbial Inactivation of Chicken Breast Meat.

Fresh chicken meat is a very perishable good, even at refrigerated storage conditions, due to psychrophilic microbial growth and physicochemical changes. The present study focuses on the use of rosemary (Rosmarinus officinalis L.) essential oil (REO), supercritical CO2 processing and their synergism to increase the microbial inactivation in chicken breast meat. E. coli and L. innocua were inoculated on the chicken breast surface, and the inactivation effects of two different processes, namely SC-CO2 and SC-MAPCO2, were compared with or without the addition of REO. Moreover, the impact of the treatments on the superficial color of the meat was considered. The study demonstrated a synergic effect with 1% REO and supercritical CO2 for the inactivation of E. coli on chicken meat, while for L. innocua, there was no synergism. Regarding SC-CO2 treatment, the E. coli reduction was 1.29 and 3.31 log CFU/g, while for L. innocua, it was 1.42 and 1.11 log CFU/g, respectively, without and with the addition of 1.0% of REO. The same amount of REO allowed us to obtain a reduction of 1.3 log CFU/g of E. coli when coupled with SC-MAPCO2. For L. innocua, no reduction was obtained, either with SC-MAPCO2 or together with REO. The synergism of SC-MAPCO2 with 1% REO was confirmed for the total psychrophilic bacteria, demonstrating a strong dependence on the microorganism. The color modification induced by the SC-MAPCO2 process was lower than the SC-CO2 treatment. Overall, this study demonstrated a possible synergism of the technologies which can support the development of innovative methods to improve the safety and shelf-life of chicken breast meat.

Controlled Release of Flurbiprofen from 3D-Printed and Supercritical Carbon Dioxide Processed Methacrylate-Based Polymer.

The ability to engineer and predict drug release behavior during treatment is critical to the design and implementation of effective drug delivery systems. In this study, a drug delivery system consisting of a methacrylate-based polymer and flurbiprofen was studied, and its release profile in a controlled phosphate-buffered saline solution was characterized. The polymer, which was 3D printed and processed in supercritical carbon dioxide under different temperature and pressure settings, showed sustained drug release over a prolonged period. A computer algorithm was used to determine the drug release time duration before reaching steady state and the maximum drug release at steady state. Several empirical models were applied to fit the release kinetic data to gain information about the drug release mechanism. The diffusion coefficients for each system were also estimated using Fick's law. Based on the results, the influence of supercritical carbon dioxide processing conditions on the diffusion behavior is interpreted, providing insights into the effective and tunable design of drug delivery systems for targeted treatment specifications.

Experimental validation and modeling study on the drug solubility in supercritical solvent: Case study on Exemestane drug

Green processing based on supercritical solvents has attracted much attention recently in different fields such as pharmaceutical industry due to its superior characteristics. Comprehensive modeling was performed in this study to analyze the preparation of nanomedicine using green supercritical processing. Computational analysis was performed in order to estimate the solubility at different pressures and temperatures. The model was developed based on the input parameters and can estimate the only output of the process which is drug solubility in the supercritical solvent. In this work, we examined how temperature and pressure affect EXE (Exemestane) drug solubility using different tree-based ensemble methods. The models used in this analysis are the Random Forest (RF), the Extremely Randomized Tree (ET), and the Gradient Boosting (GB). Model optimization and hyper-parameter tuning are also accomplished with the aid of Golden eagle optimizer (GEOA). The R2 values for the test phases of ET, GB, and RF were 0.993, 0.985, and 0.978, respectively. The scores are 0.9945, 0.9758, and 0.9904 in train phases. Specifically, the ET model was chosen since it is the most accurate one. Error rates for this model are 2.317 with MSE, 1.522 with RMSE, and 0.2113 with MAPE.

Polymorphism of Praziquantel: Role of Cooling Crystallization in Access to Solid Forms and Discovery of New Polymorphs

Praziquantel (PZQ) is an anthelmintic first-line drug to treat schistosomiasis, a neglected tropical disease strongly related to poverty. PZQ crystalline structures and polymorphs have been studied mainly via mechanochemical approaches. This work applies a systematic study to investigate the polymorphism of PZQ using cooling crystallization experiments under different conditions not yet explored in the literature for this compound. We accessed all forms previously reported so far obtained via mechanochemistry and the hydrate found through supercritical CO2 processing. A novel dimethylacetamide (DMA) solvate was obtained, and a new form was discovered after desolvation of the DMA solvate by aging and exposure to water-vapor atmosphere. Toluene and triethylamine were solvents capable of enabling the formation of different forms depending on the employed experimental condition. A new form was discovered using triethylamine as solvent, which differs from all known polymorphs. The results of this work demonstrate that solvent selection and variation in the rate of supersaturation generation can generate forms obtained from more complicated techniques as well as potentially finding new forms.

Nanocarrier of Pin1 inhibitor based on supercritical fluid technology inhibits cancer metastasis by blocking multiple signaling pathways.

Cancer metastasis is the primary cause of all cancer-related deaths due to the lack of effective targeted drugs that simultaneously block multiple signaling pathways that drive the dissemination and growth of cancer cells. The unique proline isomerase Pin1 activates numerous cancer pathways, but its role in cancer metastasis and the inhibitory efficacy of Pin1 inhibitors on cancer metastasis are unknown. Moreover, the applicability of Pin1 inhibitor-all-trans retinoic acid (ATRA) is limited due to its several drawbacks. Herein, uniform ATRA-loaded polylactic acid-polyethylene glycol block copolymer nanoparticles (ATRA-NPs) with high encapsulation efficiency, good cellular uptake, excellent controlled release performance and pharmacokinetics are developed using supercritical carbon dioxide processing combined with an optimized design. ATRA-NPs exhibited excellent biosafety and significant inhibition on the growth and metastasis of hepatocellular carcinoma. Pin1 played a key role in cancer metastasis and was the main target of ATRA-NPs. ATRA-NPs exerted their potent anti-metastatic effect by inhibiting Pin1 and then simultaneously blocking multiple signaling pathways and cancer epithelial-mesenchymal progression. Since ATRA-NPs could effectively couple the inhibition of cancer cell dissemination with cancer growth, it provided a novel therapeutic strategy for efficiently inhibiting cancer metastasis.