Multiscalar cellular automaton simulates in-vivo tumour-stroma patterns calibrated from in-vitro assay data.

BACKGROUND: The tumour stroma -or tumour microenvironment- is an important constituent of solid cancers and it is thought to be one of the main obstacles to quantitative translation of drug activity between the preclinical and clinical phases of drug development. The tumour-stroma relationship has been described as being both pro- and antitumour in multiple studies. However, the causality of this complex biological relationship between the tumour and stroma has not yet been explored in a quantitative manner in complex tumour morphologies. METHODS: To understand how these stromal and microenvironmental factors contribute to tumour physiology and how oxygen distributes within them, we have developed a lattice-based multiscalar cellular automaton model. This model uses principles of cytokine and oxygen diffusion as well as cell motility and plasticity to describe tumour-stroma landscapes. Furthermore, to calibrate the model, we propose an innovative modelling platform to extract model parameters from multiple in-vitro assays. This platform provides a novel way to extract meta-data that can be used to complement in-vivo studies and can be further applied in other contexts. RESULTS: Here we show the necessity of the tumour-stroma opposing relationship for the model simulations to successfully describe the in-vivo stromal patterns of the human lung cancer cell lines Calu3 and Calu6, as models of clinical and preclinical tumour-stromal topologies. This is especially relevant to drugs that target the tumour microenvironment, such as antiangiogenics, compounds targeting the hedgehog pathway or immune checkpoint inhibitors, and is potentially a key platform to understand the mechanistic drivers for these drugs. CONCLUSION: The tumour-stroma automaton model presented here enables the interpretation of complex in-vitro data and uses it to parametrise a model for in-vivo tumour-stromal relationships.

Non-invasive 3D time-of-flight imaging technique for tumour volume assessment in subcutaneous models.

Subcutaneous tumour xenograft volumes are generally measured using callipers. This method is susceptible to inter- and intra-observer variability and systematic inaccuracies. Non-invasive 3D measurement using ultrasound and magnetic resonance imaging (MRI) have been considered, but require immobilization of the animal. An infrared-based 3D time-of-flight (3DToF) camera was used to acquire a depth map of tumour-bearing mice. A semi-automatic algorithm based on parametric surfaces was applied to estimate tumour volume. Four clay mouse models and 18 tumour-bearing mice were assessed using callipers (applying both prolate spheroid and ellipsoid models) and 3DToF methods, and validated using tumour weight. Inter-experimentalist variability could be up to 25% in the calliper method. Experimental results demonstrated good consistency and relatively low error rates for the 3DToF method, in contrast to biased overestimation using callipers. Accuracy is currently limited by camera performance; however, we anticipate the next generation 3DToF cameras will be able to support the development of a practical system. Here, we describe an initial proof of concept for a non-invasive, non-immobilized, morphology-independent, economical and potentially more precise tumour volume assessment technique. This affordable technique should maximize the datapoints per animal, by reducing the numbers required in experiments and reduce their distress.

A new general glucose homeostatic model using a proportional-integral-derivative controller.

The glucose-insulin system is a challenging process to model due to the feedback mechanisms present, hence the implementation of a model-based approach to the system is an on-going and challenging research area. A new approach is proposed here which provides an effective way of characterising glycaemic regulation. The resulting model is built on the premise that there are three phases of insulin secretion, similar to those seen in a proportional-integral-derivative (PID) type controller used in engineering control problems. The model relates these three phases to a biological understanding of the system, as well as the logical premise that the homeostatic mechanisms will maintain very tight control of the system. It includes states for insulin, glucose, insulin action and a state to simulate an integral function of glucose. Structural identifiability analysis was performed on the model to determine whether a unique set of parameter values could be identified from the available observations, which should permit meaningful conclusions to be drawn from parameter estimation. Although two parameters--glucose production rate and the proportional control coefficient--were found to be unidentifiable, the former is not a concern as this is known to be impossible to measure without a tracer experiment, and the latter can be easily estimated from other means. Subsequent parameter estimation using Intravenous Glucose Tolerance Test (IVGTT) and hyperglycaemic clamp data was performed and subsequent model simulations have shown good agreement with respect to these real data.

Oral and IV dosing: a method to determine the compartment of drug elimination for two-compartment models.

It has been shown previously that it is impossible to measure the volume of distribution at steady state conclusively for a multicompartment system from an iv bolus dose only. The problem lies in deciding from which compartment elimination of the drug occurs in the compartmental model. In this paper a new modelling strategy is examined whereby the compartment of elimination may be identified uniquely for the case of two-compartment models. The two models examined predict different profiles in the absorption phase of an oral profile. An in vivo data set is provided that favours a peripheral elimination explanation of its observed pharmacokinetics, based on the 'goodness of fit'.

Data reduction in headspace analysis of blood and urine samples for robust bacterial identification.

This paper demonstrates the application of chemical headspace analysis to the problem of classifying the presence of bacteria in biomedical samples by using computational tools. Blood and urine samples of disparate forms were analysed using a Cyrano Sciences C320 electronic nose together with an Agilent 4440 Chemosensor. The high dimensional data sets resulting from these devices present computational problems for parameter estimation of discriminant models. A variety of data reduction and pattern recognition techniques were employed in an attempt to optimise the classification process. A 100% successful classification rate for the blood data from the Agilent 4440 was achieved by combining a Sammon mapping with a radial basis function neural network. In comparison a successful classification rate of 80% was achieved for the urine data from the C320 which were analysed using a novel nonlinear time series model.