Human plasma derived exosomes: Impact of active and passive drug loading approaches on drug delivery.

The aim of the current study was to explore the potential of human plasma-derived exosomes as versatile carriers for drug delivery by employing various active and passive loading methods. Exosomes were isolated from human plasma using differential centrifugation and ultrafiltration method. Drug loading was achieved by employing sonication and freeze thaw methods, facilitating effective drug encapsulation within exosomes for delivery. Each approach was examined for its effectiveness, loading efficiency and ability to preserve membrane stability. Methotrexate (MTX), a weak acid model drug was loaded at a concentration of 2.2 µM to exosomes underwent characterization using various techniques such as particle size analysis, transmission electron microscopy and drug loading capacity. Human plasma derived exosomes showed a mean size of 162.15 ± 28.21 nm and zeta potential of -30.6 ± 0.71 mV. These exosomes were successfully loaded with MTX demonstrated a better drug encapsulation of 64.538 ± 1.54 % by freeze thaw method in comparison 55.515 ± 1.907 % by sonication. In-vitro drug release displayed 60 % loaded drug released within 72 h by freeze thaw method that was significantly different from that by sonication method i.e., 99 % within 72 h (p value 0.0045). Moreover, cell viability of exosomes loaded by freeze thaw method was significantly higher than that by sonication method (p value 0.0091) suggested that there was membrane disruption by sonication method. In conclusion, this study offers valuable insights into the potential of human plasma-derived exosomes loaded by freeze thaw method suggest as a promising carrier for improved drug loading and maintenance of exosomal membrane integrity.

The drug loading capacity prediction and cytotoxicity analysis of metal-organic frameworks using stacking algorithms of machine learning.

Metal-organic frameworks (MOFs) have shown excellent performance in the field of drug delivery. Despite the synthesis of a vast array of MOFs exceeding 100,000 varieties, certain formulations have exhibited suboptimal performance characteristics. Therefore, there is a pressing need to enhance their efficacy by identifying MOFs with superior drug loading capacities and minimal cytotoxicity, which can be achieved through machine learning (ML). In this study, a stacking regression model was developed to predict drug loading capacity and cytotoxicity of MOFs using datasets compiled from various literature sources. The model exhibited exceptional predictive capabilities, achieving R2 values of 0.907 for drug loading capacity and 0.856 for cytotoxicity. Furthermore, various model interpretation methods including partial dependence plots, individual conditional expectation, Shapley additive explanation, decision tree, random forest, CatBoost Regressor, and light gradient-boosting machine were employed for feature importance analysis. The results revealed that specific metal atoms such as Zn, Cr, Fe, Zr, and Cu significantly influenced the drug loading capacity and cytotoxicity of MOFs. Through model validation encompassing experimental validation and computational verification, the reliability of the model was thoroughly established. In general, it is a good practice to use ML methods for predicting drug loading capacity and cytotoxicity analysis of MOFs, guiding the development of future property prediction methods for MOFs.

Improving and controlling the drug loading capacity of seven quinolones in minced fish matrix based on microfluidics.

Traditional spiking methods for preparing matrix reference material of aquatic products is difficult to control the drug content in the matrix, especially one matrix containing multiple drugs. Minced fish is commonly used for the preparation of matrix reference materials in aquatic products, which is a relatively complex matrix with stickiness and difficult handling. Drug loading capacity is a key factor affecting the effectiveness of matrix reference materials. Here, we proposed a new spiking approach to improve the drug loading capacity of seven quinolones based on microfluidics, simultaneously. Fresh grass carp tissue underwent grinding, fine filtration, centrifugation and reconstituted in distilled water to form a liquid sample, which was subsequently mixed with a sodium alginate solution (1 %) at a ratio of 1:1.2. The mixed solution was supplemented with seven quinolones of equal concentration, followed by the preparation of uniform fish gel microspheres using microfluidic technology. The results indicated that the recoveries of seven quinolones ranged from 82.54 % to 114.17 %, demonstrating a significant improvement in the drug loading capacity of these quinolones compared to traditional methods. Moreover, the drug concentration in the matrix can be precisely controlled. A strong linear relationship was observed between the concentration of seven quinolones in the matrix and its initial concentration, which could serve as a reference for the development of other matrix reference materials.