Biology subjects

Hubbard, M. E.

Publications and source records attributed to Hubbard, M. E..

3 recordsLinked to original sources

bioRxiv2026

The impact of placental structure on haemodynamics and fetal oxygen uptake

The placenta is a fundamental organ for human reproduction, facilitating fetal growth via the exchange of oxygen, nutrients and waste products between mother and fetus, by means of a dense network of fetal villi bathed in maternal blood that flows through the intervillous space (IVS). However, despite its role in adverse pregnancy outcomes associated with impaired maternal-fetal transfer, the influence of placental structure on maternal haemodynamics and the delivery of oxygen and nutrients to the developing fetus is not well understood. This study employs computational fluid dynamics within physiologically informed 2D and 3D whole-organ-scale placental geometries, whose features are informed by recent ex vivo experimental and {micro}CT data, to examine comprehensively the influence of placental anatomy on maternal flow and transport in the IVS. In particular, we consider in detail the impact of the number and placement of maternal decidual arteries and veins that supply and drain the IVS, the location and height of so-called septal walls that loosely separate the placenta into functional units (cotyledons) and sub-units (lobules), and the density of the fetal villous trees as reflected in the rate of uptake of dissolved solutes from the maternal blood and the resistance to flow. We first exploit the computational efficiency of simulation in a representative 2D geometry to study in detail the sensitivity of haemodynamic markers to these parameters. These results guide our 3D study which reveals that the flow, transport and oxygen uptake are strongly influenced by placental structure, and exposes the vein-to-artery ratio as a key indicator of placental efficiency, regulating a trade-off between a preferential maternal flow environment and fetal oxygen uptake. Conversely, the location and height of the septal walls, a feature that is not well studied, have minimal systematic impact on the macroscopic haemodynamic and transport measures considered here. We also introduce a reduced model, for which analytical progress can be made, and demonstrate its utility in exposing key drivers of maternal-fetal transport. Author summaryIn our study, we explored how placental structure affects maternal blood flow and the delivery of oxygen to the fetus. The placenta is a complex and vital organ, but we do not fully understand how its morphology influences its functional efficiency. This is a critical gap in our knowledge as placental blood flow is implicated in serious pregnancy complications such as fetal growth restriction and pre-eclampsia. To investigate the effect of structure, we used detailed 2D and 3D models of the placenta. These models allowed us to simulate maternal blood flow and oxygen transport therein while changing various structural features, such as the number and location of placental veins and the density of the intervillous space. We found that the ratio of veins to arteries is a key factor in determining the maternal blood flow speed and the amount of oxygen the fetus receives. A higher number of veins relative to arteries helps create the slow-flow environment necessary for a healthy pregnancy, but an excessive ratio can reduce the efficiency of oxygen uptake. Our findings suggest that the vein-to-artery ratio could be an important indicator of placental health, offering a new perspective for understanding and enabling effective early intervention treatments of pregnancy complications.

physiology↗

bioRxiv2024

Virtual Clinical Trials of BMP4 Differentiation Therapy: Digital Twins to Aid Glioblastoma Trial Design

Glioma stem cells (GSCs) are considered a major driver of glioblastoma (GBM) progression and are highly resistant to standard cytotoxic treatments. BMP4 has been shown to drive differentiation of GSCs, increase sensitivity to radiotherapy, slow growth and increase survival times in animal models. To assess the potential of BMP4 as a differentiation therapy, we develop a mathematical model that describes the growth of a GBM tumor via a hierarchy of GSCs, progenitor cells and terminally differentiated cells. We parametrize our model using experimental data from twelve patient-derived GSC lines, on which we measured response to radiotherapy and population growth with and without exposure to BMP4. Cell lines were typically more sensitive to radiotherapy after two days of BMP4 treatment but population growth can either increase or decrease after seven days of exposure to BMP4. To identify key parameters that drive successful treatment we perform global sensitivity analysis which identifies key parameters for BMP4 efficacy including proliferation rate and self-renewal sensitivity of GSCs. We then compare two treatment schedules: a single dose of BMP4 at resection and continuous delivery of BMP4 from resection till the end of radiotherapy. Due to the short half-life of BMP4 and its synergy with radiotherapy, continuous delivery of BMP4 during radiotherapy is more effective than a single dose prior to radiotherapy. We then perform a series of virtual clinical trials, stratified by tumor proliferation rate and GSC self-renewal sensitivity, which allows us to estimate the probability of observing a successful early-phase clinical trial for various virtual patient cohorts. We find that trials that selected the subset of patients with more proliferative GBMs were more likely to lead to significant improvements in survival. SignificanceTargeting glioma stem cells with BMP4 provides a novel opportunity to shift the complex cellular ecosystem of gliomas to enhance treatment efficacy. Mathematical modelling can facilitate optimal patient tumor feature selection when designing successful clinical trials.

cancer biology↗

bioRxiv2020

Identifying the spatial and temporal dynamics of molecularly-distinct glioblastoma sub-populations

Glioblastomas (GBMs) are the most aggressive primary brain tumours and have no known cure. Each individual tumour comprises multiple sub-populations of genetically-distinct cells that may respond differently to targeted therapies and may contribute to disappointing clinical trial results. Image-localized biopsy techniques allow multiple biopsies to be taken during surgery and provide information that identifies regions where particular sub-populations occur within an individual GBM, thus providing insight into their regional genetic variability. These sub-populations may also interact with one another through a competitive or cooperative nature; it is important to ascertain the nature of these interactions, as they may have implications for responses to targeted therapies. We combine genetic information from biopsies with a mechanistic model of interacting GBM sub-populations to characterise the nature of interactions between two commonly occurring GBM sub-populations, those with EGFR and PDGFRA genes amplified. We study population levels found across image-localized biopsy data from a cohort of 25 patients and compare this to model outputs under competitive, cooperative and neutral interaction assumptions. We explore other factors affecting the observed simulated sub-populations, such as selection advantages and phylogenetic ordering of mutations, which may also contribute to the levels of EGFR and PDGFRA amplified populations observed in biopsy data.

cancer biology↗