Facile synthesis of Zn-based metal-organic framework in the presence of carboxymethyl cellulose: A safe carrier for ibuprofen

Folic acid functionalized Ag@MOF(Ag) decorated carboxymethyl starch nanoparticles as a new doxorubicin delivery system with inherent antibacterial activity

Considering the benefits of controlled drug delivery in cancer treatment, as well as the importance of biological macromolecules in this area, herein, the pre-synthesized carboxymethyl starch (CMS) was converted to CMS nanoparticles (CMS NPs) in one easy nanoprecipitation way. Thereafter, the Ag@MOF(Ag) was in situ synthesized in the presence of pre-prepared CMS NPs (CMS NPs/Ag@MOF(Ag)). Eventually, the functionalization with folic acid (FA) obtained the CMS NPs/Ag@MOF(Ag)-FA. The success of the accomplished process was approved by doing several techniques, including FT-IR, XRD, EDX, AFM, etc. The SEM analysis showed a combination of rod-like and spherical-like morphology for the fabricated bio-nanocomposite. The generated CMS NPs/Ag@MOF(Ag)-FA with a surface area of 10.595 m2/g displayed a pore size of 13.666 nm and 82.99 % of doxorubicin (DOX) loading efficiency (DOX@CMS NPs/Ag@MOF(Ag)-FA). The 38.46 % and 58.19 % of loaded DOX were released respectively within 240 h at pH 7.4 and pH 5.0, referring to the pH-responsivity of the constructed system. 27.25 % of inhibitory effects on HeLa cells were obtained for the drug-loaded bio-nanocomposite. The CMS NPs/Ag@MOF(Ag)-FA also displayed an inherent antibacterial activity towards two common gram-negative and gram-positive bacteria. All of these results can contribute to developing polysaccharide-based porous systems in controlled cancer therapy.

Preparation and in vitro evaluation of pH and glutathione dual-responsive drug delivery system based on sodium carboxymethyl cellulose

Stimuli-responsive drug delivery systems based on sodium carboxymethyl cellulose (NaCMC) for drug release encounter inherent challenges. In this research, a novel pH and glutathione (GSH) dual-responsive system, CPT-S-S-NaCMC@ZIF-8/SP-PEG, was constructed. Firstly, the prodrug CPT-S-S-OH was synthesized and combined with NaCMC to form GSH-responsive micelles CPT-S-S-NaCMC, significantly enhancing the drug loading and grafting rates to 63.79 % and 91.99 %, respectively. Subsequently, zinc ions and dimethylimidazole can be assembled into porous materials (ZIF-8) on the surface of the micelles. This system exhibits dual pH-GSH responsiveness and effectively reduces the drug release from 84.76 % to 28.71 % at pH = 7.4. Moreover, incorporating pH-responsive spiropyran (SP)-modified polyethylene glycol (PEG) can reduce drug leakage to 16.09 % at pH = 7.4 and exhibit good fluorescence intensity at 722 nm.

Carboxymethyl chitosan modification of cobalt-zinc bimetallic MOF for tetracycline hydrochloride removal: Exploration of the enhancement mechanism of the process

This study synthesized a carboxymethyl chitosan-modified bimetallic Co/Zn-ZIF (CZ@CMC) with strong hydrophilicity and adsorption performance via the one-pot method. Tetracycline hydrochloride (TCH) was used as the model contaminant to evaluate the adsorption and peroxymonosulfate (PMS) activation properties of CZ@CMC. Mechanism showed that the adsorption behavior occurred through pore filling, electrostatic attraction, surface complexation, hydrogen bonding, and π-π stacking. In addition, a CZ@CMC/PMS system was constructed, which had excellent catalytic performance. The hydrophilicity and selective adsorption properties of CMC conferred a greatly accelerated CZ@CMC in catalyzing the PMS process with kobs of 0.095 min-1, in which OH, 1O2, SO4-, O2-, and Co(III) were the main ROS which quenching tests, EPR, and chemical probe experiments verified. In addition, the degradation pathways of TCH were obtained utilizing DFT and HPLC-MS and analyzed to show that the system possessed a good detoxification capacity. This work is expected to provide a green, efficient, and stable strategy to enhance the adsorption properties of catalytic materials and subsequently their co-catalytic properties.

Prediction of the Ibuprofen Loading Capacity of MOFs by Machine Learning

Metal-organic frameworks (MOFs) have been widely researched as drug delivery systems due to their intrinsic porous structures. Herein, machine learning (ML) technologies were applied for the screening of MOFs with high drug loading capacity. To achieve this, first, a comprehensive dataset was gathered, including 40 data points from more than 100 different publications. The organic linkers, metal ions, and the functional groups, as well as the surface area and the pore volume of the investigated MOFs, were chosen as the model’s inputs, and the output was the ibuprofen (IBU) loading capacity. Thereafter, various advanced and powerful machine learning algorithms, such as support vector regression (SVR), random forest (RF), adaptive boosting (AdaBoost), and categorical boosting (CatBoost), were employed to predict the ibuprofen loading capacity of MOFs. The coefficient of determination (R²) of 0.70, 0.72, 0.66, and 0.76 were obtained for the SVR, RF, AdaBoost, and CatBoost approaches, respectively. Among all the algorithms, CatBoost was the most reliable, exhibiting superior performance regarding the sparse matrices and categorical features. Shapley additive explanations (SHAP) analysis was employed to explore the impact of the eigenvalues of the model’s outputs. Our initial results indicate that this methodology is a well generalized, straightforward, and cost-effective method that can be applied not only for the prediction of IBU loading capacity, but also in many other biomaterials projects.