Myrianthi Hadjicharalambous

The effectiveness of chemotherapy in cancer cell regression is often limited by drug resistance, toxicity, and neoplasia heterogeneity. However, due to the significant complexities entailed by the many cancer growth processes, predicting the impact of interference and symmetry-breaking mechanisms is a difficult problem. To quantify and understand more about cancer drug pharmacodynamics, we combine in vitro with in silico cancer models. The anti-proliferative action of selected cytostatics is interrogated on human colorectal and breast adenocarcinoma cells, while an agent-based computational model is employed to reproduce experiments and shed light on the main therapeutic mechanisms of each chemotherapeutic agent. Multiple drug administration scenarios on each cancer cell line are simulated by varying the drug concentration, while a Bayesian-based method for model parameter optimisation is employed. Our proposed procedure of combining in vitro cancer drug screening with an in silico agent-based model successfully reproduces the impact of chemotherapeutic drugs in cancer growth behaviour, while the mechanisms of action of each drug are characterised through model-derived probabilities of cell apoptosis and division. We suggest that our approach could form the basis for the prospective generation of experimentally-derived and model-optimised pharmacological variables towards personalised cancer therapy.

We developed an in silico approach to model B16F10 melanoma cell response to a helium atmospheric pressure plasma jet (APPJ) or/and doxorubicin drug (DOX). The in silico model is informed by relevant data from previously published in vitro experiments (cancer cell viability), providing detailed information on (i) cell population number (N_cell) development during incubation and (ii) probability values for apoptosis (%P_Apoptosis) and mitosis (%P_Mitosis) following cell subjection to the plasma-conditioned RPMI-1640 medium (PC-RPMI), DOX, and DOX combined with APPJ. When treating cancer cells with PC-RPMI and DOX separately, at the smallest plasma duration (d_Plasma= 15 s) and DOX concentration (c_DOX= 0.05 μM), only a small decline in N_cell, an increase in %P_Apoptosis, or/and a decrease in %P_Mitosis are measured with respect to the control conditions (non-treated cancer cells). However, cell cytotoxicity is increasingly enhanced with increasing d_Plasma and c_DOX up to 120 s and 0.5 μM, respectively. At those highest values studied in in silico, simulated %P_Apoptosis are significantly larger than %P_Mitosis, resulting in a severe decrease in N_cell compared to control in agreement with the corresponding in vitro experiments. Furthermore, cell treatments combining the smallest two c_DOX (0.05 and 0.1 μM) with d_Plasma= 15 s result in smaller N_cell, larger %P_Apoptosis, and lower %P_Mitosis compared to PC-RPMI and DOX effects alone. The present in silico model is particularly useful in the plasma (cancer) medicine field since it can effectively simulate and quantify responses of various cancers to APPJ or/and cancer drugs being strongly complementary to in vitro experiments.

A major concern in personalised models of heart mechanics is the unknown zero-pressure domain, a prerequisite for accurately predicting cardiac biomechanics. As the reference configuration cannot be captured by clinical data, studies often employ in-vivo frames which are unlikely to correspond to unloaded geometries. Alternatively, zero-pressure domain is approximated through inverse methodologies, which, however, entail assumptions pertaining to boundary conditions and material parameters. Both approaches are likely to introduce biases in estimated biomechanical properties; nevertheless, quantification of these effects is unattainable without ground-truth data. In this work, we assess the unloaded state influence on model-derived biomechanics, by employing an in-silico modelling framework relying on experimental data on porcine hearts. In-vivo images are used for model personalisation, while in-situ experiments provide a reliable approximation of the reference domain, creating a unique opportunity for a validation study. Personalised whole-cycle cardiac models are developed which employ different reference domains (image-derived, inversely estimated) and are compared against ground-truth model outcomes. Simulations are conducted with varying boundary conditions, to investigate the effect of data-derived constraints on model accuracy. Attention is given to modelling the influence of the ribcage on the epicardium, due to its close proximity to the heart in the porcine anatomy. Our results find merit in both approaches for dealing with the unknown reference domain, but also demonstrate differences in estimated biomechanical quantities such as material parameters, strains and stresses. Notably, they highlight the importance of a boundary condition accounting for the constraining influence of the ribcage, in forward and inverse biomechanical models.

Understanding the biomechanics of the heart in health and disease plays an important role in the diagnosis and treatment of heart failure. The use of computational biomechanical models for therapy assessment is paving the way for personalized treatment, and relies on accurate constitutive equations mapping strain to stress. Current state-of-the art constitutive equations account for the nonlinear anisotropic stress-strain response of cardiac muscle using hyperelasticity theory. While providing a solid foundation for understanding the biomechanics of heart tissue, most current laws neglect viscoelastic phenomena observed experimentally. Utilizing experimental data from human myocardium and knowledge of the hierarchical structure of heart muscle, we present a fractional nonlinear anisotropic viscoelastic constitutive model. The model is shown to replicate biaxial stretch, triaxial cyclic shear and triaxial stress relaxation experiments (mean error ~7.5%), comparing well with its hyperelastic (mean error ~25%) counterparts. Model sensitivity, fidelity and parameter uniqueness are demonstrated. The model is also compared to rate-dependent biaxial stretch as well as different modes of biaxial stretch, illustrating extensibility of the model to a range of loading phenomena.