In silico tools are emerging as a powerful means to overcome current experimental challenges and have the potential to elucidate the subtle, yet convoluted harmonization of vascular structure, haemodynamic, and exchange adaptations of the placenta. We present a computational fluid dynamics model of flow and diffusion, calibrated with a range of bio-fabricated three-dimensional (3D) networks (Fig 1A) and validated with microfluidic flow experiments. We applied established blood flow simulations methods to 3D geometries of rat feto-placental arterial casts, imaged with microCT, obtained from a model of growth restriction (chronic glucocorticoid exposure)(n = 3) and control (n = 3) pregnancies. A computational diffusion model was applied to idealised capillary models coupled to the outlets of the imaged vasculature (Fig 1B) to compute oxygen gain by each terminal vessel of the placenta. Modelling outcomes were validated against magnetic resonance imaging (MRI) of placental oxygenation (ΔT2*) in the same rat model. Capillary velocities were 47% higher in growth restricted placentas compared to controls and as a result, oxygenation was reduced by 74% in growth-restricted models (Fig 1C). Placental ΔT2* MRI imaging (Fig 1D) showed that responses were 70% lower in growth-restricted placentas, in good agreement with computer simulations (<10% error). Here, we show an in silico approach that elucidates the mechanisms of impairment of placental function in a rat model of growth restriction. Importantly, modelling of oxygen diffusion accurately recapitulates real-time MRI assessments, highlighting the potential of in silico approaches to predict and diagnose placental dysfunction.
Figure 1: in silico framework. Open-source bio-mimetric networks used for method development (A) before application to feto-placental arteries, coupled with idealised capillaries (B). Simulations reveal a gradual deceleration of blood and projections of outlet velocities and oxygenation show impairment in growth restriction (C). Representative 2D slices for normoxia and hyperoxia from MRI T2* images stacks (D).