Drug Release Kinetics of DOX-Loaded Graphene-Based Nanocarriers for Ovarian and Breast Cancer Therapeutics
Taylor K., Tabish TA., Narayan RJ.
Cancer remains one of the leading causes of death worldwide despite extensive efforts at developing curative treatments. Chemotherapy, one of the most common forms of treatment, lacks specificity and can induce collateral damages to healthy surrounding tissues/cells and elicit off-target toxic side effects. The carbon-based nanomaterial graphene, can load aromatic drugs with high efficiency, has good biocompatibility, and can be easily functionalised with targeting ligands, antibodies, and biomolecules to increase the accuracy of targeting specific areas; graphene has therefore been explored as a nanocarrier for classical chemotherapy drugs. In this work, seventeen publications that report the release of doxorubicin (DOX) from 2D graphene-based nanohybrids (graphene oxide and reduced graphene oxide) for the treatment of breast and ovarian cancers have been identified based on a range of inclusion and exclusion criteria. To aid in the clinical translation of proof-of-concept studies, this work identifies the pre-clinical experimental protocols and analyses the release kinetics of these publications. Fifteen of the papers utilised a change in pH as the stimulus for drug release, and two utilised either near infrared (NIR) or ultrasound as the stimulus. The extracted drug release data from these publications were fit to four known kinetic models. It was found that the majority of these data best fit the Weibull kinetic model. The agreement between the kinetic data in previously published literature provides a predictable estimation of DOX release from graphene-based nanocarriers. This study demonstrates the potential conjugation of graphene and DOX in drug delivery applications, and this knowledge can help improve to the design and formulation of future graphene-based nanocarriers. In addition, the use of further experimental testing and the standardisation of experimental protocols will be beneficial for future work. The incorporation of computational modelling prior to pre-clinical testing will also aid in the development of controlled and sustained DOX release systems that offer efficient and efficacious results.