2020 Research Retrospective

As we enter into the last month of 2020, EVO NANO has progressed past its second yearly review with the European Union. The technical report is currently being finalized for submission and our project received favourable feedback from the reviewers who we presented to.

Given how tumultuous the year 2020 has been for everyone around the globe, I thought it would be a worthwhile endeavor to catch up with all the members of the project and get a measure of their current work, in order to reflect on how much we have been able to progress despite 2020’s best efforts to the contrary:


University of Novi Sad have developed an open-ended, agent-based model for the generation of anti-cancer therapies. In the model, nano-agents evolve by simultaneously developing novel properties and optimizing existing ones. They showed that this strategy is especially efficient when dealing with the emergence of tumour resistance. In such scenarios, nano-agents are able to continuously track down newly emerging tumour mutations and keep them under control.

Åbo Akademi University is working to develop a deep learning model to predict the efficiency of the nanoparticle design for drug delivery. The ultimate goal is to use “explainable AI (XAI)” approaches to identify the most influential features in designing an efficient nanoparticle. Such an outcome is an important step in designing cost-effective drug-carrier nanoparticles.

IMDEA Nanoscience has constructed tumour microvessel-on-a-chip devices that include an artificial vessel-like endothelium, which allow us to investigate the dynamic interactions and transport characteristics of nanoparticle based anticancer drugs at physiological flow conditions.

ProChimia have produced a number of functionalized gold-core nanoparticles with fluorescent tracking capability, both with and without the addition of anti-cancer drug molecules, that can be implemented in the in vitro and in vivo studies by the other partners.

University of Bristol have produced a codebase script that can automatically track and analyze the penetration of nanoparticles through a biological matrix using fluorescent microscopy. Custom-built millifluidic chips have been fabricated to allow multiple experiments to be run in parallel. By using a commercially available hydrogel matrix and fluorescent particles, we can determine the penetration depth and time dynamics of how nanoparticles diffuse over a period of hours.

University of the West of England are investigating the in silico optimization of cancer treatments, utilizing an open-source agent based simulator, in terms of the design parameters of multiple NPs comprising one treatment and their application times. Because the number of different NPs that will achieve the best performance is not known, the evolutionary algorithm uses a variable length genome approach, namely a metameric representation and accordingly modified operators.

VHIR have been validating the synthesized nanoparticles from Prochimia in models of cancer stem cells (CSC). For this purpose, 2D and 3D cell models were used in which CSC can be tracked in situ. Also, they have been optimizing a similar in vivo model and evaluating its capacity for CSC-targeted nanomedicine preclinical validation.