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Rapid release and diminished stability are two of the limitations associated with the growth factors that are essentially used in dental applications. These growth factors are employed to enhance the quality and quantity of tissue or bone matter during regeneration. Therefore, drug delivery devices and systems have been developed to address these limitations. In this study, bovine serum albumin (BSA), as a representative growth factor, was successfully sustained by encapsulation with the medium-absorbable copolymer, poly(L-lactide-co-glycolide) (PLG) 70:30% mol, via the multiple emulsion method. Different PLG, PVA, and BSA concentrations were used to investigate their effects on the BSA encapsulation efficiency. The suitable ratios leading to a better characterization of microparticles and a higher encapsulation efficiency in producing encapsulated PLG microparticles were 8% (w/v) of PLG, 0.25% (w/v) of PVA, and 8% (w/v) of BSA. Furthermore, an in vitro release study revealed a bursting release of BSA from the encapsulated PLG microsphere in the early phase of development. Subsequently, a gradual release was observed over a period of eight weeks. Furthermore, to encapsulate LL-37, different proteins were used in conjunction with PLG under identical conditions with regard to the loading efficiency and morphology, thereby indicating high variations and poor reproducibility. In conclusion, the encapsulated PLG microparticles could effectively protect the protein during encapsulation and could facilitate sustainable protein release over a period of 60 days. Importantly, an optimal method must be employed in order to achieve a high degree of encapsulation efficiency for all of the protein or growth factors. Accordingly, the outcomes of this study will be useful in the manufacture of drug delivery devices that require medium-sustained release growth factors, particularly in dental treatments.

Meso-scale simulations have been widely used to probe aggregation caused by structural formation in macromolecular systems. However, the limitations of the long-length scale, resulting from its simulation box, cause difficulties in terms of morphological identification and insufficient classification. In this study, structural knowledge derived from meso-scale simulations based on parameters from atomistic simulations were analyzed in dissipative particle dynamic (DPD) simulations of PS-b-PI diblock copolymers. The radial distribution function and its Fourier-space counterpart or structure factor were proposed using principal component analysis (PCA) as key characteristics for morphological identification and classification. Disorder, discrete clusters, hexagonally packed cylinders, connected clusters, defected lamellae, lamellae and connected cylinders were effectively grouped.