Project

Goal

EVO-NANO will create an integrated platform for the artificial evolution and validation of novel strategies for treatment of cancer using nanoparticles (NPs). We expect our proposed framework to provide a full pipeline for the development of effective NP-based therapies that are safe, have optimal bio-distribution and delivery characteristics and can be personalised to specific patients and health risks.

Scenario

We focus on designing nanoparticle-based strategies that specifically target cancer stem cells (CSC) of breast and colon origin, with the aim of improved NP bio-distribution, tumour penetration and cellular uptake in target tissues.

Approach

EVO-NANO is organised around two main research hubs: in silico computational modelling (PFNS, UB, UWE and AAU) and in vitro and in vivo experimental work (IMDEA, VHIR and PCS). Partners with cross-disciplinary expertise collaborate across hubs to evolve, produce, and validate novel nanoparticle designs.

EVO-NANO uses the most recent advances in evolutionary algorithms to explore a wide range of nanoparticle designs, considering the effect of different shapes, sizes, coatings and charges on their ability to reach and penetrate tumours. Our algorithms, jointly developed by PFNS, UB and UWE, require many simulations with many millions of individual nanoparticles and cells to explore the huge design space and find the optimal nanoparticle design. To achieve this, we use the expertise in high performance computing at AAU . Based on results from simulation, PCS will develop customised functional NPs using their interdisciplinary expertise in the fields of chemistry, nanotechnology, and biotechnology. Validation of the evolved anti-cancer nanoparticles will be done both in vitro thanks to IMDEA’s tumour-on-a-chip micro-fluidic system mimicing major physiological barriers for NP tumour delivery (NP transport and extravasation, tissue penetration and selective cellular uptake), and VHIR’s expertise in targeted drug delivery in vivo towards preclinical translation.

Our Objectives

Objective 1: To develop a new class of open-ended evolutionary algorithms that can assess various cancer scenarios and autonomously engineer NP-based solutions in a novel and creative way.
Objective 2: To implement a computational platform for the autonomous generation of new strategies for specific targeting of CSC surface receptors using NPs. In its final form, our platform 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 our computational platform.
Objective 4: To develop an integrated platform for the validation of the efficacy of artificially evolved NP designs, composed of in vitro micro-fluidics that mimic major physiological barriers for NP tumour delivery and (ii) in vivo pre-clinical tests.