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

The introduction of nanotopography suppresses bacterial adhesion and enhances osteoinductive capacity of plasma deposited polyoxazoline surface

The plasma deposited polyoxazoline (PPOx) has been emerging in biomedical applications, especially for the surface modification of bone tissue engineering scaffold and/or bone implants. Herein, PPOx surfaces were generated by plasma polymerization with the introduction of surface nanotopography gradient, achieved by immobilization of different density of 16 nm gold nanoparticles. The introduction of surface nanotopography suppressed the adhesion of S. aureus on PPOx surface. Moreover, the introduction of surface nanotopography enhanced the initial attachment and spreading of hMSCs, as well as promoted the osteogenic differentiation of hMSCs. RhoA/ROCK signaling pathway may be involved in the enhancement of osteoinductive capacity of PPOx surface by nanotopography