Computational design of multifunctional bioactive glass nanoparticles: A multi-objective optimization approach to balance osteogenesis, anticancer activity, and antibacterial efficacy

Abstract

Developing multifunctional biomaterials that can simultaneously support bone regeneration, suppress tumor growth, and prevent infection is still a major challenge in treating bone cancer. Naruphontjirakul et al. recently showed that bioactive glass nanoparticles co-doped with zinc and silver (Zn/Ag-BGNPs)-, specifically the 0.5Ag–1Zn and 1Ag–1Zn formulations-demonstrated encouraging triple functionality: osteogenic differentiation in hFOB 1.19 cells, selective cytotoxicity against MG-63 osteosarcoma cells, and broad-spectrum antibacterial properties while remaining non-toxic to hFOB 1.19 cells at a concentration of 125 µg/mL. The design of such materials has focused on empirical methods with little systematic consideration of the trade-offs between competing biological objectives. Therefore, in this study, we develop and implement an innovative data-driven multi-objective optimization framework that utilizes the entire experimental data set from the study noted above, to identify Pareto-optimal Ag/Zn compositions. By predicting an Ag-Zn effect that favored one composition over another, this study provides a rationale for the predictive design of multifunctional biomaterials—a study that demonstrates that high-performance therapeutic platforms can be optimized computationally without new experimentation, thereby increasing the speed for clinical translation to orthopedic applications in bone regeneration.