**Please note: this event has been postponed due to the poor weather conditions.**

We are pleased to announce that our computational modelling in medicine and biomedical imaging themes have co-organised a seminar from Professor Rebecca Shipley OBE,  Co-Director of the UCL Centre for Nerve Engineering and Professor Simon Walker-Samuel from the UCL Centre for Advanced Biomedical Imaging who will visit the Insigneo Institute on Friday 10 March 2023. 

Title:

Combining mathematical modelling, machine learning and biomedical imaging to better understand drug delivery to solid tumours

Abstract:

We are developing tools to model the structure and function of vascular networks, particularly in tumours and retinas, that incorporate biomedical imaging data. In this talk, we will describe our multiscale modelling approaches, how we validate the predictions of these models, and how we aim to translate these tools into the clinic using deep learning.

Blood vessel networks in solid tumours develop rapidly in response to low tissue oxygenation (hypoxia) and are typically highly disorganised and poorly optimised for delivery. This, alongside highly permeable blood vessel walls, can result in low blood flow and raised interstitial fluid pressure, are often considered to act as a barrier to drug delivery [1]. Alternatively, the same highly permeable blood vessels are sometimes thought to enable drug delivery and are the basis of the enhanced permeability and retention (EPR) effect that is often invoked in the design of large nanoparticle-based treatments [2].

These two scenarios are both potentially valid, but are part of a continuum of behaviours that can have significant influence on treatment outcomes. To better understand these complex, multiscale systems, we have developed mathematical models that predict blood flow and drug delivery, primarily using three-dimensional microscopy data of tumour blood vessel networks from mouse models, alongside in vivo magnetic resonance imaging (MRI) [3,4,5]. Within this framework, named REANIMATE (REAlistic Numerical Image-based Modelling of biologicAl Tissue substratEs), we can predict the delivery of MRI contrast agents to individual tumours. Moreover, we have applied our analysis to both normal retinas and in diabetic retinopathy to predict fluorescin delivery.

These models provide training data that underpin deep learning models for potential application in the clinic, but also provide a framework in which biological systems can be interrogated and the complex kinetics of drug delivery better understood.