Drug Delivery of Anticancer Drugs from Injectable 3D Porous Silk Scaffold for Prevention of Gastric Cancer Growth and Recurrence.
Ankit GangradeBiman B MandalPublished in: ACS biomaterials science & engineering (2020)
Localized cancer chemotherapy through injectable hydrogels is a next-generation advanced substitute for the currently operational systemic route of drug administration. Recently, several hydrogels have been developed for prospective drug delivery applications; however, no in vitro disease model is available to evaluate its long-term bioactivity in real time. In this regard, we have designed a porous silk scaffold that provides a single platform to accommodate both the soft hydrogel and cancer cells together. The stomach cancer (AGS) cells were seeded in the periphery of the silk scaffold, where they sit in the pores and form three-dimensional (3D) spheroids. Furthermore, the anticancer drug cisplatin-loaded nanocomposite injectable silk hydrogel was filled in the central cavity of the scaffold to evaluate its 11 day extended bioactivity. Such an arrangement keeps the released cisplatin in close contact with the spheroids for its sustained therapeutic effects. In an attempt to model cancer recurrence, the AGS cells were reseeded on the second day of treatment. Our data revealed that the shelf life and cytotoxic effects of cisplatin, which was explicitly releasing out from the nanocomposite silk hydrogel, were considerably enhanced. Hence, the reseeded AGS cells did not survive further on the scaffold, which also indicates its ability to inhibit cancer relapse. Conclusively, the current work showed a possible way to evaluate the long-term efficacy and bioactivity of the injectable hydrogel system in vitro for sustained drug delivery application.
Keyphrases
- tissue engineering
- drug delivery
- papillary thyroid
- induced apoptosis
- squamous cell
- cell cycle arrest
- cancer therapy
- wound healing
- emergency department
- endoplasmic reticulum stress
- signaling pathway
- cell death
- drug release
- free survival
- mass spectrometry
- machine learning
- young adults
- childhood cancer
- big data
- deep learning
- carbon nanotubes