Glioblastoma-targeted, local and sustained drug delivery system based on an unconventional lipid nanocapsule hydrogel.

The objective of this work was to develop an implantable therapeutic hydrogel that will ensure continuity in treatment between surgery and radiochemotherapy for patients with glioblastoma (GBM). A hydrogel of self-associated gemcitabine-loaded lipid nanocapsules (LNC) has shown therapeutic efficacy in vivo in murine GBM resection models. To improve the targeting of GBM cells, the NFL-TBS.40-63 peptide (NFL), was associated with LNC. The LNC-based hydrogels were formulated with the NFL. The peptide was totally and instantaneously adsorbed at the LNC surface, without modifying the hydrogel mechanical properties, and remained adsorbed to the LNC surface after the hydrogel dissolution. In vitro studies on GBM cell lines showed a faster internalization of the LNC and enhanced cytotoxicity, in the presence of NFL. Finally, in vivo studies in the murine GBM resection model proved that the gemcitabine-loaded LNC with adsorbed NFL could target the non-resected GBM cells and significantly delay or even inhibit the apparition of recurrences.

Design of experiment and machine learning inform on the 3D printing of hydrogels for biomedical applications.

In the biomedical field, 3D printing has the potential to deliver on some of the promises of personalized therapy, notably by enabling point-of-care fabrication of medical devices, dosage forms and bioimplants. To achieve this full potential, a better understanding of the 3D printing processes is necessary, and non-destructive characterization methods must be developed. This study proposes methodologies to optimize the 3D printing parameters for soft material extrusion. We hypothesize that combining image processing with design of experiment (DoE) analyses and machine learning could help obtaining useful information from a quality-by-design perspective. Herein, we investigated the impact of three critical process parameters (printing speed, printing pressure and infill percentage) on three critical quality attributes (gel weight, total surface area and heterogeneity) monitored with a non-destructive methodology. DoE and machine learning were combined to obtain information on the process. This work paves the way for a rational approach to optimize 3D printing parameters in the biomedical field.