Colorectal tumour simulation using agent based modelling and high performance computing

Kang, Guiyeom; Márquez, Claudio; Barat, Ana; Byrne, Annette T.; Prehn, Jochen H.M.; Sorribes, Joan; Eduardo, César

doi:10.1016/j.future.2016.03.026

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…Hence, we also extended the study to perform a comprehensive global fitting of the model using Bayesian parameter fitting techniques to two novel experimental data sets (producing normalised root squared mean errors of 0.38 and 0.24). This was made possible due to the economic computational cost of running our model, though we note that there have been recent advances in the simulation of more holistic cellular automaton models 23 . It may even be possible to calibrate such models with spatio-temporal data in the near future though it will require careful acquisition of three dimensional in vivo imaging data for effective calibration as discussed in 24 .…”

Section: Discussionmentioning

confidence: 99%

Anti-angiogenic drug scheduling optimisation with application to colorectal cancer

Sturrock

Miller

Kang

et al. 2018

Sci Rep

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Bevacizumab (bvz) is a first choice anti-angiogenic drug in oncology and is primarily administered in combination with chemotherapy. It has been hypothesized that anti-angiogenic drugs enhance efficacy of cytotoxic drugs by “normalizing” abnormal tumor vessels and improving drug penetration. Nevertheless, the clinical relevance of this phenomenon is still unclear with several studies over recent years suggesting an opposing relationship. Herein, we sought to develop a new computational tool to interrogate anti-angiogenic drug scheduling with particular application in the setting of colorectal cancer (CRC). Specifically, we have employed a mathematical model of vascular tumour growth which interrogates the impact of anti-angiogenic treatment and chemotherapeutic treatment on tumour volume. Model predictions were validated using CRC xenografts which underwent treatment with a clinically relevant combinatorial anti-angiogenic regimen. Bayesian model selection revealed the most appropriate term for capturing the effect of treatments on the tumour size, and provided insights into a switch-like dependence of FOLFOX delivery on the tumour vasculature. Our experimental data and mathematical model suggest that delivering chemotherapy prior to bvz may be optimal in the colorectal cancer setting.

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Section: Discussionmentioning

confidence: 99%

Anti-angiogenic drug scheduling optimisation with application to colorectal cancer

Sturrock

Miller

Kang

et al. 2018

Sci Rep

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“…The inclusion of elements that were not considered in other investigations [ 13 , 14 , 15 , 16 , 21 ] allowed us to obtain a more adjusted model in terms of the functionality conditions. Some of the results concurred with behaviors that have already been described in other studies, while others enabled us to identify values that did not correspond with the results reported by other authors due to their simplifications.…”

Section: Discussionmentioning

confidence: 99%

“…Regarding research in the field of computational simulation, most of the studies reviewed focused on the modeling of nanostructured biosensors [ 13 , 14 , 15 ], showing mathematical models and the results of simulations of the characteristics of the biosensor without specifically mentioning their application in the early detection of diseases. On the other hand, there were studies found on cancer evolution models based on agents and implementing high-performance computing (HPC) [ 16 , 17 ], as well as the progression of melanoma [ 18 ], evolution of metastatic processes, tumor angiogenesis [ 16 ], treatment response [ 19 , 20 ] and simulation of the adherence of therapeutic particles [ 21 ], evidencing efforts that are much more oriented toward the treatment and description of the evolution of CRC, which is oriented toward the early diagnosis of the description of the behavior of biomarkers in a dynamic domain.…”

Section: Introductionmentioning

confidence: 99%

Computational Simulation of Colorectal Cancer Biomarker Particle Mobility in a 3D Model

Morales

Guerrero²,

Palacio³

2023

Molecules

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Even though some methods for the detection of colorectal cancer have been used clinically, most of the techniques used do not consider the in situ detection of colorectal cancer (CRC) biomarkers, which would favor in vivo real-time monitoring of the carcinogenesis process and consequent studies of the disease. In order to give a scientific and computational framework ideal for the evaluation of diagnosis techniques based on the early detection of biomarker molecules modeled as spherical particles from the computational point of view, a computational representation of the rectum, stool and biomarker particles was developed. As consequence of the transport of stool, there was a displacement of CRC biomarker particles that entered the system as a result of the cellular apoptosis processes in polyps with a length lower than 1 cm, reaching a maximum velocity of 3.47×10−3 m/s. The biomarkers studied showed trajectories distant to regions of the polyp of origin in 1 min of simulation. The research results show that the biomarker particles for CRC respond to the variations in the movements of the stool with trajectories and speeds that depend on the location of the injury, which will allow locating the regions with the highest possibilities of catching particles through in situ measurement instruments in the future.

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“…Several other agent-based models have been described and used in related studies. An agent-based model focused on stem cell dynamics in the colon crypt has been developed ( 61 ) and an agent-based model has been developed and colorectal tumor growth stimulated on a high-performance computer with the intention, in the future, of optimizing therapeutic strategies ( 62 ). A multiscale agent-based model has been used to investigate the synergistic effects of a large space of dose combinations using local and global optimization algorithms ( 63 ).…”

Section: Discussionmentioning

confidence: 99%

Combination Chemotherapy of Multidrug-resistant Early-stage Colon Cancer: Determining Optimal Dose Schedules by High-performance Computer Simulation

Cockrell

Axelrod

2023

Cancer Research Communications

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The goal of this project was to utilize mechanistic simulation to demonstrate a methodology that could determine drug combination dose schedules and dose intensities that would be most effective in eliminating multidrug resistant cancer cells in early-stage colon cancer. An agent-based model of cell dynamics in human colon crypts was calibrated using measurements of human biopsy specimens. Mutant cancer cells were simulated as cells that were resistant to each of two drugs when the drugs were used separately. The drugs, 5-flurouracil and sulindac, have different mechanisms of action. An artificial neural network was used to generate nearly two hundred thousand two-drug dose schedules. A high-performance computer simulated each dose schedule as a in silico clinical trial and evaluated each dose schedule for its efficiency to cure (eliminate) multidrug resistant cancer cells and its toxicity to the host, as indicated by continued crypt function. Among the dose schedules that were generated, 2430 dose schedules were found to cure all multidrug resistant mutants in each of the 50 simulated trials and retained colon crypt function. One dose schedule was optimal; it eliminated multidrug resistant cancer cells with the minimum toxicity and had a time schedule that would be practical for implementation in the clinic. These results demonstrate a procedure to identify which combination drug dose schedules could be most effective in eliminating drug resistant cancer cells. This was accomplished using a calibrated agent-based model of a human tissue, and a high-performance computer simulation of clinical trials.

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Colorectal tumour simulation using agent based modelling and high performance computing

Cited by 12 publications

References 24 publications

Anti-angiogenic drug scheduling optimisation with application to colorectal cancer

Anti-angiogenic drug scheduling optimisation with application to colorectal cancer

Computational Simulation of Colorectal Cancer Biomarker Particle Mobility in a 3D Model

Combination Chemotherapy of Multidrug-resistant Early-stage Colon Cancer: Determining Optimal Dose Schedules by High-performance Computer Simulation

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