| Project Detail |
Drug delivery systems are often needed for delivering drugs in a targeted organ or tissue at a desired concentration range and without toxic side effects. Metal-organic frameworks (MOFs) have emerged as promising drug delivery systems due to their high loading capacity, but currently this is also their main limitation as it results in too rapid drug release in the human body, causing an excessive response and potentially toxic side effects. Slowing down the drug release rate, while maintaining high loading capacity and biocompatibility, would enable applications within especially cancer therapy.
Disordered MOFs (i.e. MOFs without an ordered crystalline structure) have smaller pores and more torturous pathways for drug release. The project’s proposed strategy is to load anti-cancer drugs in the crystalline MOF with high surface area and thus high loading capacity and then subsequently subject this material to an order-to-disorder transition, giving rise to slower drug release. This transition can be induced by either mechanical ball milling or thermal melt-quenching. Molecular dynamics and classification-based machine learning will be used to identify which local atomic structures are best correlated with drug release rate in disordered MOFs. Experimental validation will be performed to establish design principles for MOF structures with a tailored drug release profile.
The project builds on complementary expertise of the fellow applicant (disordered biomaterials, in vitro studies) and supervisor (MOFs, simulations, machine learning). Combined with the research and training environment offered by the host organization (Aalborg University, Denmark), this will ensure the achievement of this novel project as well as the dissemination and exploitation of the results. The goal is to develop principles for designing disordered MOFs for drug delivery. The fellow applicant will emerge from the project with new skills, and the capability to launch his own research group. |
