Medicine relies on the use of pharmacologically active agents to treat diseases. However, drugs are not inherently effective; their benefit is directly coupled to the manner by which they are administered. It affects drug pharmacokinetics (PK), absorption, distribution, toxicity, duration of therapeutic effect, metabolism and excretion. In the ideal case, drugs would be applied at exactly the therapeutic concentration and would precisely target desired cells or tissues. However, drug delivery, release rate, targeting and stability are not easily controlled and are difficult to predict. To address these limitations, drug delivery systems (DDS) have been designed using a wide array of materials and chemical strategies.
Recently, much progress has been made in developing nanoparticle (NP)- based DDS with the specific aim to improve site-specific targeting of drugs, thus improving efficacy and reducing side-effects. However, in the present state of the art, the rates at which new efficient NP-based DDS are designed are exceedingly small and costly. As a consequence, even though revolutionary new drug delivery strategies can be conceived, throughputs are far too low to approach widespread medical applications. Therefore, our targeted scientific breakthrough is to establish, for the first time, an integrated platform for artificial intelligence (AI)-powered design of novel NP-based DDS, their synthesis and rapid testing, both in vivo and in vitro on a newly developed microfluidic-based platform.
In short, overall goal of EVO-NANO is to create new foundations for rapid development and assessment of new anti-cancer treatments. Since developing new DDSs costs much less and is much shorter than developing new drugs, EVO-NANO’s focus on accelerating production of new DDSs is in line with one of the key strategic challenges defined in the European Commission’s Strategic Plan for Health and Food Safety: (Specific objective 1.3 Achieving greater cost-effectiveness).
Overall objectives of the project are:
Objective 1: To develop a new class of open-ended evolutionary algorithms to creatively assess different cancer scenarios and autonomously engineer effective NP-based solutions to them in a novel way.
Objective 2: To implement a computational platform for the autonomous generation of new strategies for targeting CSC surface receptors using functionalized NPs. In its final form our model will simulate all the main aspects of NP dynamics: their travel via blood streams, extravasation, tumour penetration and endocytosis.
Objective 3: To streamline synthesis of functionalized NPs suggested by the computational platform.
Objective 4: To develop an integrated platform for validation of efficacy of the artificially evolved nanoparticle designs. It will be composed of (i) tumour microenvironments on microfluidic chips that will mimic major physiological barriers for NP tumour delivery and (ii) in vivo pre-clinical tests.
