Our Approach

EVO-NANO is organised around two research hubs: computing sciences and modelling (PFNS, UB, UWE and AAU) and experimental in vitro and in vivo cancer and nanoparticle research (IMDEA, VHIR and PCS). Partners with cross-disciplinary expertise are expected to work between these hubs to produce an integrated synthesis between in-silico models and their material realisations.

Algorithms jointly developed by PFNS, UB and UWE will need to be distributed and heavily rely on parallel processing to accommodate simulations of large numbers of agents. This will be done using expertise of AAU whose specialty is massive parallel computation and multi-core processes. AAU has free access to the several powerful heterogeneous HPC platforms maintained by the Finnish IT centre for science, which will enable efficient simulations.

To verify evolved anti-cancer strategies we will test them in vitro and in vivo thanks to expertise of three additional partners. PCS is a company that develops customised functionalised NPs and has interdisciplinary expertise in the fields of chemistry, nanotechnology, and biotechnology. IMDEA works on miniaturised micro-patterned devices and nano-scale platforms for single-cell manipulation and surfaces for biomedical applications. VHIR is the drug delivery and targeting group working on designing new drug delivery systems and their in-vitro and in-vivo preclinical validation. 


The Process

We will focus on designing nanoparticle strategies targeted to cancer stem cells (CSC) of breast and colon origin, with improved NP bio-distribution, tumour penetration and cellular uptake in target tissues. In parallel with the computational platform, we will generate the experimental testbed.

Testbed will consist of in vitro microfluidics with controlled cell patterning and in vivo mouse cancer xenografts. Microfluidic systems will mimic major physiological barriers for NP tumour delivery (NP transport and extravasation, tissue penetration and selective cellular uptake).

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With them, we will be able to test large libraries of NP strategies produced by our new evolutionary platform. These microfluidic systems integrated with the computational platform and in vivo tests will offer an unprecedented opportunity to improve our understanding of the physicochemical properties of NPs that influence their distribution, tumour penetration and nonspecific interactions upstream of tumour accumulation. Although most of the components in the proposed system have their precedents, the overall ensemble is entirely novel and represents a major challenge.


The targeted breakthrough will represent a milestone for more rapid development of new NP-based DDS because it can be adapted to other diseases and many different NPs. Therefore, our long term vision is to create the integrated platform for the artificial evolution and validation of novel strategies for cancer treatment using NPs. We expect the proposed framework to provide a full pipeline for development of effective NP-based cancer therapies that are safe, have adequate biodistribution and delivery characteristics and can be personalized to specific patients.


Our Objectives

  • Objective 1: To develop a new class of open-ended evolutionary algorithms that will creatively assess different cancer scenarios and autonomously engineer NP-based solutions to them in a novel and creative way.

  • Objective 2: To implement a computational platform for the autonomous generation of new strategies for targeting CSC surface receptors using NPs. In its final form, our model will globally simulate all the main aspects of NPs dynamics: their travel via blood streams, extravasation, tumour penetration and endocytosis.

  • Objective 3: To streamline synthesis of functionalised NPs suggested by the computational platform.

  • Objective 4: To develop an integrated platform for validation of efficacy of artificially evolved nanoparticle designs. It will be composed of (i) in vitro microfluidics that will mimic major physiological barriers for NP tumour delivery and (ii) in vivo pre-clinical tests.

This project has received funding from the European Union's Horizon 2020 FET Open programme under grant agreement. No. 800983.