Abstract:One of the major sources of information on physiological and pathophysiological effects in pre-clinical oncology studies is the xenografted tumour animal model. However, measurement of tumour volume over time potentially masks a range of biological changes that the xenograft is undergoing. In this paper a mechanistic model of tumour growth in xenografts is presented that can be used to investigate the mode of drug action with respect to phenotypic changes. The model encapsulates key histological biomarkers and… Show more
“…4a and Ref. [27]); the oxygen levels are very low; and the viable rim is of almost constant thickness, which is consistent with numerous observations [28]. All these properties are congruent with the literature and our data; and are futher equivalent to those of an avascular tumour.Fig.…”
BackgroundThe 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.MethodsTo 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.ResultsHere 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.ConclusionThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-017-0461-1) contains supplementary material, which is available to authorized users.
“…4a and Ref. [27]); the oxygen levels are very low; and the viable rim is of almost constant thickness, which is consistent with numerous observations [28]. All these properties are congruent with the literature and our data; and are futher equivalent to those of an avascular tumour.Fig.…”
BackgroundThe 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.MethodsTo 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.ResultsHere 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.ConclusionThe 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-017-0461-1) contains supplementary material, which is available to authorized users.
“…In vitro cell culture is a significantly different environment from that of a solid tumor. To translate the in vitro calibrated model into a clinically relevant setting the model was combined with a model of xenograft tumor growth, describing transfer of cells between an actively growing outer shell and inert inner core (see supplementary material) 17 .…”
Section: Resultsmentioning
confidence: 99%
“…Incorporating biological mechanisms (described at a physiological level corresponding to the in vitro methods) enables the extension of empirical PK-PD tumor growth models with representation of the underlying cell population response. In our work this enables model prediction of the tumor cell population, incorporating DNA damage biomarker and cell growth kinetics to predict xenograft tumor response to treatment with AZD6738 as monotherapy and in combination with IR, which was not possible using the original published model 17 .…”
Section: Discussionmentioning
confidence: 99%
“…Mechanism-based PK-PD modeling has been shown to be informative when investigating non-linear dynamical systems 1 4 9 10 11 , and cell cycle models have been used to investigate macroscopic population dynamics when the microscopic underlying interactions cannot be fully characterized 12 13 14 15 . In this study, an existing PK-PD model of tumor growth published by Evans et al (2013) was extended with mechanistic modeling, supported by high content biology to develop an in vitro calibrated model for predicting tumor growth and biomarker kinetics in response to treatment with AZD6738 which targets the DNA damage response (DDR) pathway 16 17 .…”
Understanding the therapeutic effect of drug dose and scheduling is critical to inform the design and implementation of clinical trials. The increasing complexity of both mono, and particularly combination therapies presents a substantial challenge in the clinical stages of drug development for oncology. Using a systems pharmacology approach, we have extended an existing PK-PD model of tumor growth with a mechanistic model of the cell cycle, enabling simulation of mono and combination treatment with the ATR inhibitor AZD6738 and ionizing radiation. Using AZD6738, we have developed multi-parametric cell based assays measuring DNA damage and cell cycle transition, providing quantitative data suitable for model calibration. Our in vitro calibrated cell cycle model is predictive of tumor growth observed in in vivo mouse xenograft studies. The model is being used for phase I clinical trial designs for AZD6738, with the aim of improving patient care through quantitative dose and scheduling prediction.
“…One of the most commonly applied experimental models is the tumor growth inhibition (TGI) model (10)(11)(12)(13). The TGI model balances model complexity with data availability.…”
Abstract.Combination therapies are widely accepted as a cornerstone for treatment of different cancer types. A tumor growth inhibition (TGI) model is developed for combinations of cetuximab and cisplatin obtained from xenograft mice. Unlike traditional TGI models, both natural cell growth and cell death are considered explicitly. The growth rate was estimated to 0.006 h −1 and the natural cell death to 0.0039 h −1 resulting in a tumor doubling time of 14 days. The tumor static concentrations (TSC) are predicted for each individual compound. When the compounds are given as single-agents, the required concentrations were computed to be 506 μg · mL −1 and 56 ng · mL −1 for cetuximab and cisplatin, respectively. A TSC curve is constructed for different combinations of the two drugs, which separates concentration combinations into regions of tumor shrinkage and tumor growth. The more concave the TSC curve is, the lower is the total exposure to test compounds necessary to achieve tumor regression. The TSC curve for cetuximab and cisplatin showed weak concavity. TSC values and TSC curves were estimated that predict tumor regression for 95% of the population by taking between-subject variability into account. The TSC concept is further discussed for different concentration-effect relationships and for combinations of three or more compounds.
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