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Kulesza A, Couty C, Lemarre P, Thalhauser CJ, Cao Y. Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice. J Pharmacokinet Pharmacodyn 2024:10.1007/s10928-024-09930-x. [PMID: 38904912 DOI: 10.1007/s10928-024-09930-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 06/07/2024] [Indexed: 06/22/2024]
Abstract
Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.
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Affiliation(s)
| | - Claire Couty
- Novadiscovery, 1 Place Giovanni Verrazzano, 69009, Lyon, France
| | - Paul Lemarre
- Novadiscovery, 1 Place Giovanni Verrazzano, 69009, Lyon, France
| | - Craig J Thalhauser
- Genmab US, Inc., 777 Scudders Mill Rd Bldg 2 4th Floor, Plainsboro, NJ, 08536, USA
| | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
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2
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Reyes-Aldasoro CC. Modelling the Tumour Microenvironment, but What Exactly Do We Mean by "Model"? Cancers (Basel) 2023; 15:3796. [PMID: 37568612 PMCID: PMC10416922 DOI: 10.3390/cancers15153796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/19/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
The Oxford English Dictionary includes 17 definitions for the word "model" as a noun and another 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For instance, "model railways" refer to replicas of railways and trains at a smaller scale and a "model student" refers to an exemplary individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning for model. Even if the context is narrowed, specifically, to research related to the tumour microenvironment, "model" can be understood in a wide variety of ways, from an animal model to a mathematical expression. This paper presents a review of different "models" of the tumour microenvironment, as grouped by different definitions of the word into four categories: model organisms, in vitro models, mathematical models and computational models. Then, the frequencies of different meanings of the word "model" related to the tumour microenvironment are measured from numbers of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequencies of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, particularly xenografts and mouse models, are the most commonly used "models", the number of these entries has been slowly decreasing. Mathematical models, as well as prognostic and risk models, follow in frequency, and these have been growing in use.
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3
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Wilson N, Drapaca CS, Enderling H, Caudell JJ, Wilkie KP. Modelling Radiation Cancer Treatment with a Death-Rate Term in Ordinary and Fractional Differential Equations. Bull Math Biol 2023; 85:47. [PMID: 37186175 PMCID: PMC10127975 DOI: 10.1007/s11538-023-01139-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 02/28/2023] [Indexed: 05/17/2023]
Abstract
Fractional calculus has recently been applied to the mathematical modelling of tumour growth, but its use introduces complexities that may not be warranted. Mathematical modelling with differential equations is a standard approach to study and predict treatment outcomes for population-level and patient-specific responses. Here, we use patient data of radiation-treated tumours to discuss the benefits and limitations of introducing fractional derivatives into three standard models of tumour growth. The fractional derivative introduces a history-dependence into the growth function, which requires a continuous death-rate term for radiation treatment. This newly proposed radiation-induced death-rate term improves computational efficiency in both ordinary and fractional derivative models. This computational speed-up will benefit common simulation tasks such as model parameterization and the construction and running of virtual clinical trials.
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Affiliation(s)
- Nicole Wilson
- Department of Mathematics, Toronto Metropolitan University, Toronto, Canada
| | - Corina S Drapaca
- Engineering Science and Mechanics, Pennsylvania State University, University Park, USA
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, USA
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | - Jimmy J Caudell
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | - Kathleen P Wilkie
- Department of Mathematics, Toronto Metropolitan University, Toronto, Canada.
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4
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Jørgensen ACS, Hill CS, Sturrock M, Tang W, Karamched SR, Gorup D, Lythgoe MF, Parrinello S, Marguerat S, Shahrezaei V. Data-driven spatio-temporal modelling of glioblastoma. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221444. [PMID: 36968241 PMCID: PMC10031411 DOI: 10.1098/rsos.221444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/23/2023] [Indexed: 06/18/2023]
Abstract
Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarize the scope, drawbacks and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. By providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks. Our article thus strikes a unique balance. On the one hand, we provide a comprehensive overview of the available modelling techniques and their applications, including key mathematical expressions. On the other hand, the content is accessible to mathematicians and biomedical scientists alike to accommodate the interdisciplinary nature of cancer research.
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Affiliation(s)
| | - Ciaran Scott Hill
- Department of Neurosurgery, The National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK
- Samantha Dickson Brain Cancer Unit, UCL Cancer Institute, London WC1E 6DD, UK
| | - Marc Sturrock
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, Dublin D02 YN77, Ireland
| | - Wenhao Tang
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London SW7 2AZ, UK
| | - Saketh R. Karamched
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Dunja Gorup
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Mark F. Lythgoe
- Division of Medicine, Centre for Advanced Biomedical Imaging, University College London (UCL), London WC1E 6BT, UK
| | - Simona Parrinello
- Samantha Dickson Brain Cancer Unit, UCL Cancer Institute, London WC1E 6DD, UK
| | - Samuel Marguerat
- Genomics Translational Technology Platform, UCL Cancer Institute, University College London, London WC1E 6DD, UK
| | - Vahid Shahrezaei
- Department of Mathematics, Faculty of Natural Sciences, Imperial College London, London SW7 2AZ, UK
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5
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Millar-Wilson A, Ward Ó, Duffy E, Hardiman G. Multiscale modeling in the framework of biological systems and its potential for spaceflight biology studies. iScience 2022; 25:105421. [DOI: 10.1016/j.isci.2022.105421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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6
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Kok HP, van Rhoon GC, Herrera TD, Overgaard J, Crezee J. Biological modeling in thermoradiotherapy: present status and ongoing developments toward routine clinical use. Int J Hyperthermia 2022; 39:1126-1140. [PMID: 35998930 DOI: 10.1080/02656736.2022.2113826] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022] Open
Abstract
Biological modeling for anti-cancer treatments using mathematical models can be very supportive in gaining more insight into dynamic processes responsible for cellular response to treatment, and predicting, evaluating and optimizing therapeutic effects of treatment. This review presents an overview of the current status of biological modeling for hyperthermia in combination with radiotherapy (thermoradiotherapy). Various distinct models have been proposed in the literature, with varying complexity; initially aiming to model the effect of hyperthermia alone, and later on to predict the effect of the combined thermoradiotherapy treatment. Most commonly used models are based on an extension of the linear-quadratic (LQ)-model enabling an easy translation to radiotherapy where the LQ model is widely used. Basic predictions of cell survival have further progressed toward 3 D equivalent dose predictions, i.e., the radiation dose that would be needed without hyperthermia to achieve the same biological effect as the combined thermoradiotherapy treatment. This approach, with the use of temperature-dependent model parameters, allows theoretical evaluation of the effectiveness of different treatment strategies in individual patients, as well as in patient cohorts. This review discusses the significant progress that has been made in biological modeling for hyperthermia combined with radiotherapy. In the future, when adequate temperature-dependent LQ-parameters will be available for a large number of tumor sites and normal tissues, biological modeling can be expected to be of great clinical importance to further optimize combined treatments, optimize clinical protocols and guide further clinical studies.
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Affiliation(s)
- H P Kok
- Amsterdam UMC Location University of Amsterdam, Radiation Oncology, Amsterdam, The Netherlands.,Cancer Center Amsterdam, Treatment and Quality of Life, Cancer Biology and Immunology, Amsterdam, The Netherlands
| | - G C van Rhoon
- Department of Radiation Oncology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.,Department of Radiation Science and Technology, Delft University of Technology, Delft, The Netherlands
| | - T D Herrera
- Amsterdam UMC Location University of Amsterdam, Radiation Oncology, Amsterdam, The Netherlands.,Cancer Center Amsterdam, Treatment and Quality of Life, Cancer Biology and Immunology, Amsterdam, The Netherlands
| | - J Overgaard
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - J Crezee
- Amsterdam UMC Location University of Amsterdam, Radiation Oncology, Amsterdam, The Netherlands.,Cancer Center Amsterdam, Treatment and Quality of Life, Cancer Biology and Immunology, Amsterdam, The Netherlands
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7
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Cells and material-based strategies for regenerative endodontics. Bioact Mater 2022; 14:234-249. [PMID: 35310358 PMCID: PMC8897646 DOI: 10.1016/j.bioactmat.2021.11.015] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 10/29/2021] [Accepted: 11/09/2021] [Indexed: 12/21/2022] Open
Abstract
<p class = "Abstract" style = "margin: 0 cm; line-height: 32px; font-size: 12 pt; font-family: "Times New Roman", serif; color: rgb(0, 0, 0); "><span lang = "EN-US">The carious process leads to inflammation of pulp tissue. Current care options include root canal treatment or apexification. These procedures, however, result in the loss of tooth vitality, sensitivity, and healing. Pulp capping and dental pulp regeneration are continually evolving techniques to regenerate pulp tissue, avoiding necrosis and loss of vitality. Many studies have successfully employed stem/progenitor cell populations, revascularization approaches, scaffolds or material-based strategies for pulp regeneration. Here we outline advantages and disadvantages of different methods and techniques which are currently being used in the field of regenerative endodontics. We also summarize recent findings on efficacious peptide-based materials which target the dental niche.<o:p></o:p></span></p> Pulp infection necessitates removal of necrotic, inflamed and infected tissue. Materials used clinically are inert (such as gutta percha, mineral trioxide aggregate). Recent developments in materials (angiogenic hydrogels, stem cell composites) have tuneable bioactivity. Dental pulp regeneration may now be possible through the use of bioactive systems, that guide regeneration.
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8
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. Deep Neural Networks for Neuro-oncology: Towards Patient Individualized Design of Chemo-Radiation Therapy for Glioblastoma Patients. J Biomed Inform 2022; 127:104006. [DOI: 10.1016/j.jbi.2022.104006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/04/2022] [Accepted: 01/25/2022] [Indexed: 11/28/2022]
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9
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Gondal MN, Chaudhary SU. Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics. Front Oncol 2021; 11:712505. [PMID: 34900668 PMCID: PMC8652070 DOI: 10.3389/fonc.2021.712505] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/26/2021] [Indexed: 12/19/2022] Open
Abstract
Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.
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Affiliation(s)
- Mahnoor Naseer Gondal
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Safee Ullah Chaudhary
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
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10
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Targeting Cellular DNA Damage Responses in Cancer: An In Vitro-Calibrated Agent-Based Model Simulating Monolayer and Spheroid Treatment Responses to ATR-Inhibiting Drugs. Bull Math Biol 2021; 83:103. [PMID: 34459993 PMCID: PMC8405495 DOI: 10.1007/s11538-021-00935-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 08/10/2021] [Indexed: 11/26/2022]
Abstract
We combine a systems pharmacology approach with an agent-based modelling approach to simulate LoVo cells subjected to AZD6738, an ATR (ataxia–telangiectasia-mutated and rad3-related kinase) inhibiting anti-cancer drug that can hinder tumour proliferation by targeting cellular DNA damage responses. The agent-based model used in this study is governed by a set of empirically observable rules. By adjusting only the rules when moving between monolayer and multi-cellular tumour spheroid simulations, whilst keeping the fundamental mathematical model and parameters intact, the agent-based model is first parameterised by monolayer in vitro data and is thereafter used to simulate treatment responses in in vitro tumour spheroids subjected to dynamic drug delivery. Spheroid simulations are subsequently compared to in vivo data from xenografts in mice. The spheroid simulations are able to capture the dynamics of in vivo tumour growth and regression for approximately 8 days post-tumour injection. Translating quantitative information between in vitro and in vivo research remains a scientifically and financially challenging step in preclinical drug development processes. However, well-developed in silico tools can be used to facilitate this in vitro to in vivo translation, and in this article, we exemplify how data-driven, agent-based models can be used to bridge the gap between in vitro and in vivo research. We further highlight how agent-based models, that are currently underutilised in pharmaceutical contexts, can be used in preclinical drug development.
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11
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Bridging cell-scale simulations and radiologic images to explain short-time intratumoral oxygen fluctuations. PLoS Comput Biol 2021; 17:e1009206. [PMID: 34310608 PMCID: PMC8341701 DOI: 10.1371/journal.pcbi.1009206] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 08/05/2021] [Accepted: 06/22/2021] [Indexed: 11/19/2022] Open
Abstract
Radiologic images provide a way to monitor tumor development and its response to therapies in a longitudinal and minimally invasive fashion. However, they operate on a macroscopic scale (average value per voxel) and are not able to capture microscopic scale (cell-level) phenomena. Nevertheless, to examine the causes of frequent fast fluctuations in tissue oxygenation, models simulating individual cells’ behavior are needed. Here, we provide a link between the average data values recorded for radiologic images and the cellular and vascular architecture of the corresponding tissues. Using hybrid agent-based modeling, we generate a set of tissue morphologies capable of reproducing oxygenation levels observed in radiologic images. We then use these in silico tissues to investigate whether oxygen fluctuations can be explained by changes in vascular oxygen supply or by modulations in cellular oxygen absorption. Our studies show that intravascular changes in oxygen supply reproduce the observed fluctuations in tissue oxygenation in all considered regions of interest. However, larger-magnitude fluctuations cannot be recreated by modifications in cellular absorption of oxygen in a biologically feasible manner. Additionally, we develop a procedure to identify plausible tissue morphologies for a given temporal series of average data from radiology images. In future applications, this approach can be used to generate a set of tissues comparable with radiology images and to simulate tumor responses to various anti-cancer treatments at the tissue-scale level. Low levels of oxygen, called hypoxia, are observable in many solid tumors. They are associated with more aggressive malignant cells that are resistant to chemo-, radio-, and immunotherapies. Recently developed imaging techniques provide a way to measure the magnitude of frequent short-term oxygen fluctuations, but they operate on a macro-scale voxel level. To examine the possible causes of rapid oxygen fluctuations at the cell level, we developed a hybrid agent-based mathematical model. We tested two different mechanisms that may be responsible for these cyclic effects on tissue oxygenation: temporal variations in vascular influx of oxygen and modulations in cellular oxygen absorption. Additionally, we developed a procedure to identify plausible tissue morphologies from data collected from radiological images. This can provide a bridge between the micro-scale simulations with individual cells and the longitudinal medical images containing average values. In future applications, this approach can be used to generate a set of tissues compatible with radiology images and to simulate tumor responses to various anticancer treatments at the cell-scale level.
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12
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Possible roadmap to advancing the knowledge system and tackling challenges from complexity. Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2021.116548] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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13
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Zheng F, Xiao Y, Liu H, Fan Y, Dao M. Patient-Specific Organoid and Organ-on-a-Chip: 3D Cell-Culture Meets 3D Printing and Numerical Simulation. Adv Biol (Weinh) 2021; 5:e2000024. [PMID: 33856745 PMCID: PMC8243895 DOI: 10.1002/adbi.202000024] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 02/13/2021] [Indexed: 12/11/2022]
Abstract
The last few decades have witnessed diversified in vitro models to recapitulate the architecture and function of living organs or tissues and contribute immensely to advances in life science. Two novel 3D cell culture models: 1) Organoid, promoted mainly by the developments of stem cell biology and 2) Organ-on-a-chip, enhanced primarily due to microfluidic technology, have emerged as two promising approaches to advance the understanding of basic biological principles and clinical treatments. This review describes the comparable distinct differences between these two models and provides more insights into their complementarity and integration to recognize their merits and limitations for applicable fields. The convergence of the two approaches to produce multi-organoid-on-a-chip or human organoid-on-a-chip is emerging as a new approach for building 3D models with higher physiological relevance. Furthermore, rapid advancements in 3D printing and numerical simulations, which facilitate the design, manufacture, and results-translation of 3D cell culture models, can also serve as novel tools to promote the development and propagation of organoid and organ-on-a-chip systems. Current technological challenges and limitations, as well as expert recommendations and future solutions to address the promising combinations by incorporating organoids, organ-on-a-chip, 3D printing, and numerical simulation, are also summarized.
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Affiliation(s)
- Fuyin Zheng
- Key Laboratory for Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- School of Biological Sciences, Nanyang Technological University, Singapore, 639798, Singapore
| | - Yuminghao Xiao
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Hui Liu
- Key Laboratory for Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Yubo Fan
- Key Laboratory for Biomechanics and Mechanobiology, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Ming Dao
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- School of Biological Sciences, Nanyang Technological University, Singapore, 639798, Singapore
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14
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Goldberg E, Soba A, Gandía D, Fernández ML, Suárez C. Coupled mathematical modeling of cisplatin electroporation. Bioelectrochemistry 2021; 140:107788. [PMID: 33838515 DOI: 10.1016/j.bioelechem.2021.107788] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 02/09/2021] [Accepted: 02/10/2021] [Indexed: 12/11/2022]
Abstract
The use of electrochemotherapy (ECT) is a well-established technique to increase the cellular uptake of cytotoxic agents within certain cancer treatment strategies. The study of the mechanisms that take part in this complex process is of high interest to gain a deeper knowledge of it, enabling the improvement of these strategies. In this work, we present a coupled multi-physics electroporation model based on a related previous one, to describe the effect of a set of electric pulses on cisplatin transport across the plasma membrane. The model applies a system of partial differential equations that includes Poisson's equation for the electric field, Nernst-Planck's equation for species transport, Maxwell's tensor and mechanical equilibrium equation for membrane deformation and Smoluchowski's equation for pore creation dynamics. Our numerical results were compared with previous numerical and experimental published data with good qualitative and quantitative agreement. These results indicate that pore aperture is favored at the cell poles by the electric field and mechanical stress forces, giving support to the dominant hypothesis of hydrophilic pore creation as the main mechanism of drug entry during an ECT treatment.
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Affiliation(s)
- Ezequiel Goldberg
- Centro Atómico Constituyentes, Comisión Nacional de Energía Atómica, Buenos Aires, Argentina
| | - Alejandro Soba
- Centro Atómico Constituyentes, Comisión Nacional de Energía Atómica, Buenos Aires, Argentina
| | - Daniel Gandía
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad de Buenos Aires, Instituto de Fısica del Plasma (INFIP), Buenos Aires, Argentina
| | - María Laura Fernández
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad de Buenos Aires, Instituto de Fısica del Plasma (INFIP), Buenos Aires, Argentina; Universidad de Buenos Aires (UBA), Facultad de Ciencias Exactas y Naturales, Departamento de Física, Buenos Aires, Argentina
| | - Cecilia Suárez
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad de Buenos Aires, Instituto de Fısica del Plasma (INFIP), Buenos Aires, Argentina; Universidad de Buenos Aires (UBA), Facultad de Ciencias Exactas y Naturales, Departamento de Física, Buenos Aires, Argentina.
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15
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Ebrahimi Zade A, Shahabi Haghighi S, Soltani M. A neuro evolutionary algorithm for patient calibrated prediction of survival in Glioblastoma patients. J Biomed Inform 2021; 115:103694. [PMID: 33545332 DOI: 10.1016/j.jbi.2021.103694] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 01/28/2021] [Accepted: 01/29/2021] [Indexed: 01/30/2023]
Abstract
BACKGROUND AND OBJECTIVES Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumors. Radiation therapy (RT) plus concomitant and adjuvant Temozolomide (TMZ) constitute standard treatment of GBM. Existing models for GBM growth do not consider the effect of different schedules on tumor growth and patient survival. However, clinical trials show that treatment schedule and drug dosage significantly affect patient survival. The goal is to provide a patient calibrated model for predicting survival according to the treatment schedule. METHODS We propose a top-down method based on artificial neural networks (ANN) and genetic algorithm (GA) to predict survival of GBM patients. A feed forward undercomplete Autoencoder network is integrated with the neuro-evolutionary (NE) algorithm in order to extract a compressed representation of input clinical data. The proposed NE algorithm uses GA to obtain optimal architecture of a multi-layer perceptron (MLP). Taguchi L16 orthogonal design of experiments is used to tune parameters of the proposed NE algorithm. Finally, the optimal MLP is used to predict survival of GBM patients. RESULTS Data from 8 related clinical trials have been collected and integrated to train the model. From 847 evaluable cases, 719 were used for train and validation and the remaining 128 cases were used to test the model. Mean absolute error of the predictions on the test data is 0.087 months which shows excellent performance of the proposed model in predicting survival of the patients. Also, the results show that the proposed NE algorithm is superior to other existing models in both the mean and variability of the prediction error.
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Affiliation(s)
- Amir Ebrahimi Zade
- Faculty of Industrial Engineering and Systems Management, Amirkabir University of Technology, Tehran, Iran
| | | | - M Soltani
- Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, Iran; Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran; Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
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16
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Hernández-Lemus E, Martínez-García M. Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics. Front Oncol 2021; 10:605680. [PMID: 33520715 PMCID: PMC7841291 DOI: 10.3389/fonc.2020.605680] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 11/24/2020] [Indexed: 12/11/2022] Open
Abstract
Cancer is a set of complex pathologies that has been recognized as a major public health problem worldwide for decades. A myriad of therapeutic strategies is indeed available. However, the wide variability in tumor physiology, response to therapy, added to multi-drug resistance poses enormous challenges in clinical oncology. The last years have witnessed a fast-paced development of novel experimental and translational approaches to therapeutics, that supplemented with computational and theoretical advances are opening promising avenues to cope with cancer defiances. At the core of these advances, there is a strong conceptual shift from gene-centric emphasis on driver mutations in specific oncogenes and tumor suppressors-let us call that the silver bullet approach to cancer therapeutics-to a systemic, semi-mechanistic approach based on pathway perturbations and global molecular and physiological regulatory patterns-we will call this the shrapnel approach. The silver bullet approach is still the best one to follow when clonal mutations in driver genes are present in the patient, and when there are targeted therapies to tackle those. Unfortunately, due to the heterogeneous nature of tumors this is not the common case. The wide molecular variability in the mutational level often is reduced to a much smaller set of pathway-based dysfunctions as evidenced by the well-known hallmarks of cancer. In such cases "shrapnel gunshots" may become more effective than "silver bullets". Here, we will briefly present both approaches and will abound on the discussion on the state of the art of pathway-based therapeutic designs from a translational bioinformatics and computational oncology perspective. Further development of these approaches depends on building collaborative, multidisciplinary teams to resort to the expertise of clinical oncologists, oncological surgeons, and molecular oncologists, but also of cancer cell biologists and pharmacologists, as well as bioinformaticians, computational biologists and data scientists. These teams will be capable of engaging on a cycle of analyzing high-throughput experiments, mining databases, researching on clinical data, validating the findings, and improving clinical outcomes for the benefits of the oncological patients.
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Affiliation(s)
- Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Mireya Martínez-García
- Sociomedical Research Unit, National Institute of Cardiology “Ignacio Chávez”, Mexico City, Mexico
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17
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Hamis S, Kohandel M, Dubois LJ, Yaromina A, Lambin P, Powathil GG. Combining hypoxia-activated prodrugs and radiotherapy in silico: Impact of treatment scheduling and the intra-tumoural oxygen landscape. PLoS Comput Biol 2020; 16:e1008041. [PMID: 32745136 PMCID: PMC7425994 DOI: 10.1371/journal.pcbi.1008041] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 08/13/2020] [Accepted: 06/11/2020] [Indexed: 12/30/2022] Open
Abstract
Hypoxia-activated prodrugs (HAPs) present a conceptually elegant approach to not only overcome, but better yet, exploit intra-tumoural hypoxia. Despite being successful in vitro and in vivo, HAPs are yet to achieve successful results in clinical settings. It has been hypothesised that this lack of clinical success can, in part, be explained by the insufficiently stringent clinical screening selection of determining which tumours are suitable for HAP treatments. Taking a mathematical modelling approach, we investigate how tumour properties and HAP-radiation scheduling influence treatment outcomes in simulated tumours. The following key results are demonstrated in silico: (i) HAP and ionising radiation (IR) monotherapies may attack tumours in dissimilar, and complementary, ways. (ii) HAP-IR scheduling may impact treatment efficacy. (iii) HAPs may function as IR treatment intensifiers. (iv) The spatio-temporal intra-tumoural oxygen landscape may impact HAP efficacy. Our in silico framework is based on an on-lattice, hybrid, multiscale cellular automaton spanning three spatial dimensions. The mathematical model for tumour spheroid growth is parameterised by multicellular tumour spheroid (MCTS) data.
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Affiliation(s)
- Sara Hamis
- School of Mathematics and Statistics, University of St Andrews, St Andrews, Scotland
- Department of Mathematics, College of Science, Swansea University, Swansea, Wales, United Kingdom
| | - Mohammad Kohandel
- Department of Applied Mathematics, University of Waterloo, Waterloo, Canada
| | - Ludwig J. Dubois
- The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Ala Yaromina
- The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Philippe Lambin
- The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Gibin G. Powathil
- Department of Mathematics, College of Science, Swansea University, Swansea, Wales, United Kingdom
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18
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Mothersill CE, Oughton DH, Schofield PN, Abend M, Adam-Guillermin C, Ariyoshi K, Beresford NA, Bonisoli-Alquati A, Cohen J, Dubrova Y, Geras’kin SA, Hevrøy TH, Higley KA, Horemans N, Jha AN, Kapustka LA, Kiang JG, Madas BG, Powathil G, Sarapultseva EI, Seymour CB, Vo NTK, Wood MD. From tangled banks to toxic bunnies; a reflection on the issues involved in developing an ecosystem approach for environmental radiation protection. Int J Radiat Biol 2020; 98:1185-1200. [DOI: 10.1080/09553002.2020.1793022] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
| | | | - Paul N. Schofield
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Michael Abend
- Bundeswehr Institute of Radiobiology, Munich, Germany
| | | | - Kentaro Ariyoshi
- Integrated Center for Science and Humanities, Fukushima Medical University, Fukushima City, Japan
| | | | | | - Jason Cohen
- Department of Biology and Department of Physics and Astronomy, McMaster University, Hamilton, Canada
| | - Yuri Dubrova
- Department of Genetics, University of Leicester, Leicester, UK
| | | | | | - Kathryn A. Higley
- School of Nuclear Science and Engineering, Oregon State University, Corvallis, OR, USA
| | - Nele Horemans
- Belgian Nuclear Research Centre (SCK CEN), Mol, Belgium
| | - Awadhesh N. Jha
- School of Biological and Marine Sciences, University of Plymouth, Plymouth, UK
| | | | - Juliann G. Kiang
- Armed Forces Radiobiology Research Institute, Uniformed services University of the Health Sciences, Bethesda, MD, USA
| | - Balázs G. Madas
- Environmental Physics Department, Centre for Energy Research, Budapest, Hungary
| | - Gibin Powathil
- Department of Mathematics, Computational Foundry, Swansea University, Swansea, UK
| | | | | | - Nguyen T. K. Vo
- Department of Biology and Department of Physics and Astronomy, McMaster University, Hamilton, Canada
| | - Michael D. Wood
- School of Science, Engineering & Environment, University of Salford, Salford, UK
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19
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Hamis S, Powathil GG, Chaplain MAJ. Blackboard to Bedside: A Mathematical Modeling Bottom-Up Approach Toward Personalized Cancer Treatments. JCO Clin Cancer Inform 2020; 3:1-11. [PMID: 30742485 DOI: 10.1200/cci.18.00068] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Cancers present with high variability across patients and tumors; thus, cancer care, in terms of disease prevention, detection, and control, can highly benefit from a personalized approach. For a comprehensive personalized oncology practice, this personalization should ideally consider data gathered from various information levels, which range from the macroscale population level down to the microscale tumor level, without omission of the central patient level. Appropriate data mined from each of these levels can significantly contribute in devising personalized treatment plans tailored to the individual patient and tumor. Mathematical models of solid tumors, combined with patient-specific tumor profiles, present a unique opportunity to personalize cancer treatments after detection using a bottom-up approach. Here, we discuss how information harvested from mathematical models and from corresponding in silico experiments can be implemented in preclinical and clinical applications. To conceptually illustrate the power of these models, one such model is presented, and various pertinent tumor and treatment scenarios are demonstrated in silico. The presented model, specifically a multiscale, hybrid cellular automaton, has been fully validated in vitro using multiple cell-line-specific data. We discuss various insights provided by this model and other models like it and their role in designing predictive tools that are both patient, and tumor specific. After refinement and parametrization with appropriate data, such in silico tools have the potential to be used in a clinical setting to aid in treatment protocols and decision making.
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Affiliation(s)
- Sara Hamis
- Swansea University, Swansea, Wales, United Kingdom
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20
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Optimising Hydrogel Release Profiles for Viro-Immunotherapy Using Oncolytic Adenovirus Expressing IL-12 and GM-CSF with Immature Dendritic Cells. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10082872] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Sustained-release delivery systems, such as hydrogels, significantly improve cancer therapies by extending the treatment efficacy and avoiding excess wash-out. Combined virotherapy and immunotherapy (viro-immunotherapy) is naturally improved by these sustained-release systems, as it relies on the continual stimulation of the antitumour immune response. In this article, we consider a previously developed viro-immunotherapy treatment where oncolytic viruses that are genetically engineered to infect and lyse cancer cells are loaded onto hydrogels with immature dendritic cells (DCs). The time-dependent release of virus and immune cells results in a prolonged cancer cell killing from both the virus and activated immune cells. Although effective, a major challenge is optimising the release profile of the virus and immature DCs from the gel so as to obtain a minimum tumour size. Using a system of ordinary differential equations calibrated to experimental results, we undertake a novel numerical investigation of different gel-release profiles to determine the optimal release profile for this viro-immunotherapy. Using a data-calibrated mathematical model, we show that if the virus is released rapidly within the first few days and the DCs are released for two weeks, the tumour burden can be significantly decreased. We then find the true optimal gel-release kinetics using a genetic algorithm and suggest that complex profiles present unnecessary risk and that a simple linear-release model is optimal. In this work, insight is provided into a fundamental problem in the growing field of sustained-delivery systems using mathematical modelling and analysis.
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21
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From tumour perfusion to drug delivery and clinical translation of in silico cancer models. Methods 2020; 185:82-93. [PMID: 32147442 DOI: 10.1016/j.ymeth.2020.02.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 02/13/2020] [Accepted: 02/24/2020] [Indexed: 12/14/2022] Open
Abstract
In silico cancer models have demonstrated great potential as a tool to improve drug design, optimise the delivery of drugs to target sites in the host tissue and, hence, improve therapeutic efficacy and patient outcome. However, there are significant barriers to the successful translation of in silico technology from bench to bedside. More precisely, the specification of unknown model parameters, the necessity for models to adequately reflect in vivo conditions, and the limited amount of pertinent validation data to evaluate models' accuracy and assess their reliability, pose major obstacles in the path towards their clinical translation. This review aims to capture the state-of-the-art in in silico cancer modelling of vascularised solid tumour growth, and identify the important advances and barriers to success of these models in clinical oncology. Particular emphasis has been put on continuum-based models of cancer since they - amongst the class of mechanistic spatio-temporal modelling approaches - are well-established in simulating transport phenomena and the biomechanics of tissues, and have demonstrated potential for clinical translation. Three important avenues in in silico modelling are considered in this contribution: first, since systemic therapy is a major cancer treatment approach, we start with an overview of the tumour perfusion and angiogenesis in silico models. Next, we present the state-of-the-art in silico work encompassing the delivery of chemotherapeutic agents to cancer nanomedicines through the bloodstream, and then review continuum-based modelling approaches that demonstrate great promise for successful clinical translation. We conclude with a discussion of what we view to be the key challenges and opportunities for in silico modelling in personalised and precision medicine.
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22
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Macfarlane FR, Chaplain MAJ, Eftimie R. Quantitative Predictive Modelling Approaches to Understanding Rheumatoid Arthritis: A Brief Review. Cells 2019; 9:E74. [PMID: 31892234 PMCID: PMC7016994 DOI: 10.3390/cells9010074] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 12/19/2019] [Accepted: 12/24/2019] [Indexed: 02/07/2023] Open
Abstract
Rheumatoid arthritis is a chronic autoimmune disease that is a major public health challenge. The disease is characterised by inflammation of synovial joints and cartilage erosion, which lead to chronic pain, poor life quality and, in some cases, mortality. Understanding the biological mechanisms behind the progression of the disease, as well as developing new methods for quantitative predictions of disease progression in the presence/absence of various therapies is important for the success of therapeutic approaches. The aim of this study is to review various quantitative predictive modelling approaches for understanding rheumatoid arthritis. To this end, we start by briefly discussing the biology of this disease and some current treatment approaches, as well as emphasising some of the open problems in the field. Then, we review various mathematical mechanistic models derived to address some of these open problems. We discuss models that investigate the biological mechanisms behind the progression of the disease, as well as pharmacokinetic and pharmacodynamic models for various drug therapies. Furthermore, we highlight models aimed at optimising the costs of the treatments while taking into consideration the evolution of the disease and potential complications.
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Affiliation(s)
- Fiona R. Macfarlane
- School of Mathematics and Statistics, University of St Andrews, St Andrews KY16 9RJ, UK;
| | - Mark A. J. Chaplain
- School of Mathematics and Statistics, University of St Andrews, St Andrews KY16 9RJ, UK;
| | - Raluca Eftimie
- Department of Mathematics, University of Dundee, Dundee DD1 4HN, UK;
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23
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Schulthess P, Rottschäfer V, Yates JWT, van der Graaf PH. Optimization of Cancer Treatment in the Frequency Domain. AAPS JOURNAL 2019; 21:106. [PMID: 31512089 PMCID: PMC6739279 DOI: 10.1208/s12248-019-0372-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 08/14/2019] [Indexed: 01/23/2023]
Abstract
Thorough exploration of alternative dosing frequencies is often not performed in conventional pharmacometrics approaches. Quantitative systems pharmacology (QSP) can provide novel insights into optimal dosing regimen and drug behaviors which could add a new dimension to the design of novel treatments. However, methods for such an approach are currently lacking. Recently, we illustrated the utility of frequency-domain response analysis (FdRA), an analytical method used in control engineering, using several generic pharmacokinetic-pharmacodynamic case studies. While FdRA is not applicable to models harboring ever increasing variables such as those describing tumor growth, studying such models in the frequency domain provides valuable insight into optimal dosing frequencies. Through the analysis of three distinct tumor growth models (cell cycle-specific, metronomic, and acquired resistance), we demonstrate the application of a simulation-based analysis in the frequency domain to optimize cancer treatments. We study the response of tumor growth to dosing frequencies while simultaneously examining treatment safety, and found for all three models that above a certain dosing frequency, tumor size is insensitive to an increase in dosing frequency, e.g., for the cell cycle-specific model, one dose per 3 days, and an hourly dose yield the same reduction of tumor size to 3% of the initial size after 1 year of treatment. Additionally, we explore the effect of drug elimination rate changes on the tumor growth response. In summary, we show that the frequency-domain view of three models of tumor growth dynamics can help in optimizing drug dosing regimen to improve treatment success.
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Affiliation(s)
- Pascal Schulthess
- LYO-X GmbH, Basel, Switzerland.,Systems Biomedicine & Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, 2333 CC, Leiden, The Netherlands
| | - Vivi Rottschäfer
- Mathematical Institute, Leiden University, Leiden, The Netherlands
| | - James W T Yates
- DMPK, Oncology R&D, AstraZeneca, Chesterford Research Park, Cambridge, UK
| | - Piet H van der Graaf
- Systems Biomedicine & Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, 2333 CC, Leiden, The Netherlands. .,Certara QSP, Canterbury Innovation Centre, Canterbury, UK.
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24
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Krause AL, Beliaev D, Van Gorder RA, Waters SL. Lattice and continuum modelling of a bioactive porous tissue scaffold. MATHEMATICAL MEDICINE AND BIOLOGY-A JOURNAL OF THE IMA 2019; 36:325-360. [PMID: 30107530 DOI: 10.1093/imammb/dqy012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 01/18/2018] [Accepted: 07/16/2018] [Indexed: 12/29/2022]
Abstract
A contemporary procedure to grow artificial tissue is to seed cells onto a porous biomaterial scaffold and culture it within a perfusion bioreactor to facilitate the transport of nutrients to growing cells. Typical models of cell growth for tissue engineering applications make use of spatially homogeneous or spatially continuous equations to model cell growth, flow of culture medium, nutrient transport and their interactions. The network structure of the physical porous scaffold is often incorporated through parameters in these models, either phenomenologically or through techniques like mathematical homogenization. We derive a model on a square grid lattice to demonstrate the importance of explicitly modelling the network structure of the porous scaffold and compare results from this model with those from a modified continuum model from the literature. We capture two-way coupling between cell growth and fluid flow by allowing cells to block pores, and by allowing the shear stress of the fluid to affect cell growth and death. We explore a range of parameters for both models and demonstrate quantitative and qualitative differences between predictions from each of these approaches, including spatial pattern formation and local oscillations in cell density present only in the lattice model. These differences suggest that for some parameter regimes, corresponding to specific cell types and scaffold geometries, the lattice model gives qualitatively different model predictions than typical continuum models. Our results inform model selection for bioactive porous tissue scaffolds, aiding in the development of successful tissue engineering experiments and eventually clinically successful technologies.
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Affiliation(s)
- Andrew L Krause
- Mathematical Institute, Andrew Wiles Building, University of Oxford, Radcliffe Observatory Quarter, Woodstock Rd, UK
| | - Dmitry Beliaev
- Mathematical Institute, Andrew Wiles Building, University of Oxford, Radcliffe Observatory Quarter, Woodstock Rd, UK
| | - Robert A Van Gorder
- Mathematical Institute, Andrew Wiles Building, University of Oxford, Radcliffe Observatory Quarter, Woodstock Rd, UK
| | - Sarah L Waters
- Mathematical Institute, Andrew Wiles Building, University of Oxford, Radcliffe Observatory Quarter, Woodstock Rd, UK
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25
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Chamseddine IM, Rejniak KA. Hybrid modeling frameworks of tumor development and treatment. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2019; 12:e1461. [PMID: 31313504 PMCID: PMC6898741 DOI: 10.1002/wsbm.1461] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 06/13/2019] [Accepted: 06/13/2019] [Indexed: 12/15/2022]
Abstract
Tumors are complex multicellular heterogeneous systems comprised of components that interact with and modify one another. Tumor development depends on multiple factors: intrinsic, such as genetic mutations, altered signaling pathways, or variable receptor expression; and extrinsic, such as differences in nutrient supply, crosstalk with stromal or immune cells, or variable composition of the surrounding extracellular matrix. Tumors are also characterized by high cellular heterogeneity and dynamically changing tumor microenvironments. The complexity increases when this multiscale, multicomponent system is perturbed by anticancer treatments. Modeling such complex systems and predicting how tumors will respond to therapies require mathematical models that can handle various types of information and combine diverse theoretical methods on multiple temporal and spatial scales, that is, hybrid models. In this update, we discuss the progress that has been achieved during the last 10 years in the area of the hybrid modeling of tumors. The classical definition of hybrid models refers to the coupling of discrete descriptions of cells with continuous descriptions of microenvironmental factors. To reflect on the direction that the modeling field has taken, we propose extending the definition of hybrid models to include of coupling two or more different mathematical frameworks. Thus, in addition to discussing recent advances in discrete/continuous modeling, we also discuss how these two mathematical descriptions can be coupled with theoretical frameworks of optimal control, optimization, fluid dynamics, game theory, and machine learning. All these methods will be illustrated with applications to tumor development and various anticancer treatments. This article is characterized under:Analytical and Computational Methods > Computational Methods Translational, Genomic, and Systems Medicine > Therapeutic Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models
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Affiliation(s)
- Ibrahim M. Chamseddine
- Department of Integrated Mathematical OncologyH. Lee Moffitt Cancer Center and Research InstituteTampaFlorida
| | - Katarzyna A. Rejniak
- Department of Integrated Mathematical OncologyH. Lee Moffitt Cancer Center and Research InstituteTampaFlorida
- Department of Oncologic Sciences, Morsani College of MedicineUniversity of South FloridaTampaFlorida
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26
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Karolak A, Markov DA, McCawley LJ, Rejniak KA. Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues. J R Soc Interface 2019; 15:rsif.2017.0703. [PMID: 29367239 DOI: 10.1098/rsif.2017.0703] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 01/02/2018] [Indexed: 02/06/2023] Open
Abstract
A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.
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Affiliation(s)
- Aleksandra Karolak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Dmitry A Markov
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Lisa J McCawley
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Katarzyna A Rejniak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA .,Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
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27
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Stéphanou A, Ballesta A. pH as a potential therapeutic target to improve temozolomide antitumor efficacy : A mechanistic modeling study. Pharmacol Res Perspect 2019; 7:e00454. [PMID: 30705757 PMCID: PMC6349072 DOI: 10.1002/prp2.454] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 11/15/2018] [Accepted: 11/28/2018] [Indexed: 12/13/2022] Open
Abstract
Despite intensive treatments including temozolomide (TMZ) administration, glioblastoma patient prognosis remains dismal and innovative therapeutic strategies are urgently needed. A systems pharmacology approach was undertaken to investigate TMZ pharmacokinetics-pharmacodynamics (PK-PD) incorporating the effect of local pH, tumor spatial configuration and micro-environment. A hybrid mathematical framework was designed coupling ordinary differential equations describing the intracellular reactions, with a spatial cellular automaton to individualize the cells. A differential drug impact on tumor and healthy cells at constant extracellular pH was computationally demonstrated as TMZ-induced DNA damage was larger in tumor cells as compared to normal cells due to less acidic intracellular pH in cancer cells. Optimality of TMZ efficacy defined as maximum difference between damage in tumor and healthy cells was reached for extracellular pH between 6.8 and 7.5. Next, TMZ PK-PD in a solid tumor was demonstrated to highly depend on its spatial configuration as spread cancer cells or fragmented tumors presented higher TMZ-induced damage as compared to compact tumor spheroid. Simulations highlighted that smaller tumors were less acidic than bigger ones allowing for faster TMZ activation and their closer distance to blood capillaries allowed for better drug penetration. For model parameters corresponding to U87 glioma cells, inter-cell variability in TMZ uptake play no role regarding the mean drug-induced damage in the whole cell population whereas this quantity was increased by inter-cell variability in TMZ efflux which was thus a disadvantage in terms of drug resistance. Overall, this study revealed pH as a new potential target to significantly improve TMZ antitumor efficacy.
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Affiliation(s)
| | - Annabelle Ballesta
- INSERM and Paris Sud universityUMRS 935Team “Cancer Chronotherapy and Postoperative Liver Functions”VillejuifFrance
- University of WarwickCoventryUK
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28
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Groß A, Kracher B, Kraus JM, Kühlwein SD, Pfister AS, Wiese S, Luckert K, Pötz O, Joos T, Van Daele D, De Raedt L, Kühl M, Kestler HA. Representing dynamic biological networks with multi-scale probabilistic models. Commun Biol 2019; 2:21. [PMID: 30675519 PMCID: PMC6336720 DOI: 10.1038/s42003-018-0268-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 12/07/2018] [Indexed: 12/26/2022] Open
Abstract
Dynamic models analyzing gene regulation and metabolism face challenges when adapted to modeling signal transduction networks. During signal transduction, molecular reactions and mechanisms occur in different spatial and temporal frames and involve feedbacks. This impedes the straight-forward use of methods based on Boolean networks, Bayesian approaches, and differential equations. We propose a new approach, ProbRules, that combines probabilities and logical rules to represent the dynamics of a system across multiple scales. We demonstrate that ProbRules models can represent various network motifs of biological systems. As an example of a comprehensive model of signal transduction, we provide a Wnt network that shows remarkable robustness under a range of phenotypical and pathological conditions. Its simulation allows the clarification of controversially discussed molecular mechanisms of Wnt signaling by predicting wet-lab measurements. ProbRules provides an avenue in current computational modeling by enabling systems biologists to integrate vast amounts of available data on different scales.
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Affiliation(s)
- Alexander Groß
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany
| | - Barbara Kracher
- Institute of Biochemistry and Molecular Biology, Ulm University, 89081 Ulm, Germany
| | - Johann M. Kraus
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany
| | - Silke D. Kühlwein
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany
| | - Astrid S. Pfister
- Institute of Biochemistry and Molecular Biology, Ulm University, 89081 Ulm, Germany
| | - Sebastian Wiese
- Core Unit Mass Spectrometry and Proteomics, Ulm University, 89081 Ulm, Germany
| | - Katrin Luckert
- NMI Natural and Medical Sciences Institute at the University of Tübingen, 72770 Reutlingen, Germany
| | - Oliver Pötz
- NMI Natural and Medical Sciences Institute at the University of Tübingen, 72770 Reutlingen, Germany
| | - Thomas Joos
- NMI Natural and Medical Sciences Institute at the University of Tübingen, 72770 Reutlingen, Germany
| | - Dries Van Daele
- Department of Computer Science, Katholieke Universiteit Leuven, 3001 Heverlee, Belgium
| | - Luc De Raedt
- Department of Computer Science, Katholieke Universiteit Leuven, 3001 Heverlee, Belgium
| | - Michael Kühl
- Institute of Biochemistry and Molecular Biology, Ulm University, 89081 Ulm, Germany
| | - Hans A. Kestler
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany
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Victori P, Buffa FM. The many faces of mathematical modelling in oncology. Br J Radiol 2019; 92:20180856. [PMID: 30485129 PMCID: PMC6435080 DOI: 10.1259/bjr.20180856] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 11/21/2018] [Accepted: 11/22/2018] [Indexed: 11/05/2022] Open
Abstract
The application of modelling to solve problems in biology and medicine, and specifically in oncology and radiation therapy, is increasingly established and holds big promise. We provide an overview of the basic concepts of the field and its current state, along with new tools available and future directions for research. We will outline radiobiology models, examples of other anticancer therapy models, multiscale modelling, and we will discuss mechanistic and phenomenological approaches to modelling.
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Affiliation(s)
- Pedro Victori
- CRUK/MRC Oxford Institute, Department of Oncology, Medical Science Division, University of Oxford, Oxford, United Kingdom
| | - Francesca M Buffa
- CRUK/MRC Oxford Institute, Department of Oncology, Medical Science Division, University of Oxford, Oxford, United Kingdom
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Mao X, McManaway S, Jaiswal JK, Patel PB, Wilson WR, Hicks KO, Bogle G. An agent-based model for drug-radiation interactions in the tumour microenvironment: Hypoxia-activated prodrug SN30000 in multicellular tumour spheroids. PLoS Comput Biol 2018; 14:e1006469. [PMID: 30356233 PMCID: PMC6218095 DOI: 10.1371/journal.pcbi.1006469] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Revised: 11/05/2018] [Accepted: 08/27/2018] [Indexed: 02/07/2023] Open
Abstract
Multicellular tumour spheroids capture many characteristics of human tumour microenvironments, including hypoxia, and represent an experimentally tractable in vitro model for studying interactions between radiotherapy and anticancer drugs. However, interpreting spheroid data is challenging because of limited ability to observe cell fate within spheroids dynamically. To overcome this limitation, we have developed a hybrid continuum/agent-based model (ABM) for HCT116 tumour spheroids, parameterised using experimental models (monolayers and multilayers) in which reaction and diffusion can be measured directly. In the ABM, cell fate is simulated as a function of local oxygen, glucose and drug concentrations, determined by solving diffusion equations and intracellular reactions. The model is lattice-based, with cells occupying discrete locations on a 3D grid embedded within a coarser grid that encompasses the culture medium; separate solvers are employed for each grid. The generated concentration fields account for depletion in the medium and specify concentration-time profiles within the spheroid. Cell growth and survival are determined by intracellular oxygen and glucose concentrations, the latter based on direct measurement of glucose diffusion/reaction (in multilayers) for the first time. The ABM reproduces known features of spheroids including overall growth rate, its oxygen and glucose dependence, peripheral cell proliferation, central hypoxia and necrosis. We extended the ABM to describe in detail the hypoxia-dependent interaction between ionising radiation and a hypoxia-activated prodrug (SN30000), again using experimentally determined parameters; the model accurately simulated clonogenic cell killing in spheroids, while inclusion of reversible cell cycle delay was required to account for the marked spheroid growth delay after combined radiation and SN30000. This ABM of spheroid growth and response exemplifies the utility of integrating computational and experimental tools for investigating radiation/drug interactions, and highlights the critical importance of understanding oxygen, glucose and drug concentration gradients in interpreting activity of therapeutic agents in spheroid models. Studies in 3D cultures, notably multicellular tumour spheroids that mimic many features of solid tumours, have great potential for speeding up anticancer drug development. However the increased complexity of 3D cultures makes interpretation of experiments more difficult. We have developed a hybrid continuum/agent-based mathematical model, validated by experiments, to aid interpretation of spheroid experiments in developing drugs designed to eliminate radiation-resistant hypoxic cells. This model includes key features of the tumour microenvironment including oxygen and glucose transport and regions of hypoxia where the cells are resistant to radiation, but sensitive to hypoxia-activated prodrugs such as SN30000. This enables us to predict the growth and cell response in untreated spheroids and compare the results to spheroids treated with radiation and SN30000. We demonstrate good prediction of cellular responses in spheroids treated with radiation and SN30000 and good agreement with spheroid regrowth after treatment when additional effects of cellular growth delay are added. This demonstrates that the modelling approach has potential to improve interpretation of experimental investigations of drug and radiation combinations.
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Affiliation(s)
- Xinjian Mao
- Auckland Cancer Society Research Centre, School of Medical Sciences, University of Auckland, Auckland, New Zealand
| | - Sarah McManaway
- Auckland Cancer Society Research Centre, School of Medical Sciences, University of Auckland, Auckland, New Zealand
| | - Jagdish K. Jaiswal
- Auckland Cancer Society Research Centre, School of Medical Sciences, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand
| | - Priyanka B. Patel
- Auckland Cancer Society Research Centre, School of Medical Sciences, University of Auckland, Auckland, New Zealand
| | - William R. Wilson
- Auckland Cancer Society Research Centre, School of Medical Sciences, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand
| | - Kevin O. Hicks
- Auckland Cancer Society Research Centre, School of Medical Sciences, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand
- * E-mail:
| | - Gib Bogle
- Maurice Wilkins Centre for Molecular Biodiscovery, University of Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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Hamis S, Nithiarasu P, Powathil GG. What does not kill a tumour may make it stronger: In silico insights into chemotherapeutic drug resistance. J Theor Biol 2018; 454:253-267. [DOI: 10.1016/j.jtbi.2018.06.014] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 06/04/2018] [Accepted: 06/12/2018] [Indexed: 12/01/2022]
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Mothersill C, Abend M, Bréchignac F, Iliakis G, Impens N, Kadhim M, Møller AP, Oughton D, Powathil G, Saenen E, Seymour C, Sutcliffe J, Tang FR, Schofield PN. When a duck is not a duck; a new interdisciplinary synthesis for environmental radiation protection. ENVIRONMENTAL RESEARCH 2018; 162:318-324. [PMID: 29407763 DOI: 10.1016/j.envres.2018.01.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 01/18/2018] [Accepted: 01/19/2018] [Indexed: 06/07/2023]
Abstract
This consensus paper presents the results of a workshop held in Essen, Germany in September 2017, called to examine critically the current approach to radiological environmental protection. The meeting brought together participants from the field of low dose radiobiology and those working in radioecology. Both groups have a common aim of identifying radiation exposures and protecting populations and individuals from harmful effects of ionising radiation exposure, but rarely work closely together. A key question in radiobiology is to understand mechanisms triggered by low doses or dose rates, leading to adverse outcomes of individuals while in radioecology a key objective is to recognise when harm is occurring at the level of the ecosystem. The discussion provided a total of six strategic recommendations which would help to address these questions.
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Affiliation(s)
- Carmel Mothersill
- Department of Biology, McMaster University, Hamilton, Ontario, Canada L8S 4K1.
| | - Michael Abend
- Bundeswehr Institute of Radiobiology, Neuherbergstr. 11, 80937 Munich, Germany.
| | - François Bréchignac
- Institute for Radioprotection and Nuclear Safety (IRSN) & International Union of Radioecology (IUR), Centre du Cadarache, Bldg 229, St Paul-lez-Durance, France.
| | - George Iliakis
- Institute of Medical Radiation Biology, University of Duisburg-Essen, Medical School, Hufeland Str. 55, 45122 Essen, Germany.
| | - Nathalie Impens
- Institute of Environment, Health and Safety, Biosphere Impact Studies, SCK•CEN, Boeretang 200, 2400 Mol, Belgium.
| | - Munira Kadhim
- Department of Biological and Medical Sciences, Oxford Brookes University, Oxford, UK.
| | - Anders Pape Møller
- Ecologie Systématique Evolution, Equipe Diversité, Ecologie et Evolution Microbiennes Université Paris-Sud, CNRS, and AgroParisTech, Université Paris-Saclay, F-91405 Orsay Cedex, France.
| | - Deborah Oughton
- Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Campus Ås, Universitetstunet 3, 1432 Ås, Norway.
| | - Gibin Powathil
- Department of Mathematics, College of Science, Swansea University, Singleton Park, Swansea Wales SA2 8PP, UK.
| | - Eline Saenen
- Institute of Environment, Health and Safety, Biosphere Impact Studies, SCK•CEN, Boeretang 200, 2400 Mol, Belgium.
| | - Colin Seymour
- Department of Biology, McMaster University, Hamilton, Ontario, Canada L8S 4K1.
| | - Jill Sutcliffe
- Low Level Radiation and Health Group, Ingrams Farm Fittleworth Road, Wisborough Green RH14 0JA, West Sussex, UK.
| | - Fen-Ru Tang
- National University of Singapore, Radiobiology Research Laboratory, Singapore Nuclear, Research and Safety Initiative, Singapore.
| | - Paul N Schofield
- Dept of Physiology Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3EG, UK.
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Karolak A, Rejniak KA. Micropharmacology: An In Silico Approach for Assessing Drug Efficacy Within a Tumor Tissue. Bull Math Biol 2018; 81:3623-3641. [PMID: 29423880 DOI: 10.1007/s11538-018-0402-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Accepted: 01/30/2018] [Indexed: 12/25/2022]
Abstract
Systemic chemotherapy is one of the main anticancer treatments used for most kinds of clinically diagnosed tumors. However, the efficacy of these drugs can be hampered by the physical attributes of the tumor tissue, such as tortuous vasculature, dense and fibrous extracellular matrix, irregular cellular architecture, tumor metabolic gradients, and non-uniform expression of the cell membrane receptors. This can impede the transport of therapeutic agents to tumor cells in sufficient quantities. In addition, tumor microenvironments undergo dynamic spatio-temporal changes during tumor progression and treatment, which can also obstruct drug efficacy. To examine ways to improve drug delivery on a cell-to-tissue scale (single-cell pharmacology), we developed the microscale pharmacokinetics/pharmacodynamics (microPKPD) modeling framework. Our model is modular and can be adjusted to include only the mathematical equations that are crucial for a biological problem under consideration. This modularity makes the model applicable to a broad range of pharmacological cases. As an illustration, we present two specific applications of the microPKPD methodology that help to identify optimal drug properties. The hypoxia-activated drugs example uses continuous drug concentrations, diffusive-advective transport through the tumor interstitium, and passive transmembrane drug uptake. The targeted therapy example represents drug molecules as discrete particles that move by diffusion and actively bind to cell receptors. The proposed modeling approach takes into account the explicit tumor tissue morphology, its metabolic landscape and/or specific receptor distribution. All these tumor attributes can be assessed from patients' diagnostic biopsies; thus, the proposed methodology can be developed into a tool suitable for personalized medicine, such as neoadjuvant chemotherapy.
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Affiliation(s)
- Aleksandra Karolak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Katarzyna A Rejniak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA. .,Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, USA.
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Brüningk S, Powathil G, Ziegenhein P, Ijaz J, Rivens I, Nill S, Chaplain M, Oelfke U, Ter Haar G. Combining radiation with hyperthermia: a multiscale model informed by in vitro experiments. J R Soc Interface 2018; 15:20170681. [PMID: 29343635 PMCID: PMC5805969 DOI: 10.1098/rsif.2017.0681] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2017] [Accepted: 12/18/2017] [Indexed: 12/23/2022] Open
Abstract
Combined radiotherapy and hyperthermia offer great potential for the successful treatment of radio-resistant tumours through thermo-radiosensitization. Tumour response heterogeneity, due to intrinsic, or micro-environmentally induced factors, may greatly influence treatment outcome, but is difficult to account for using traditional treatment planning approaches. Systems oncology simulation, using mathematical models designed to predict tumour growth and treatment response, provides a powerful tool for analysis and optimization of combined treatments. We present a framework that simulates such combination treatments on a cellular level. This multiscale hybrid cellular automaton simulates large cell populations (up to 107 cells) in vitro, while allowing individual cell-cycle progression, and treatment response by modelling radiation-induced mitotic cell death, and immediate cell kill in response to heating. Based on a calibration using a number of experimental growth, cell cycle and survival datasets for HCT116 cells, model predictions agreed well (R2 > 0.95) with experimental data within the range of (thermal and radiation) doses tested (0-40 CEM43, 0-5 Gy). The proposed framework offers flexibility for modelling multimodality treatment combinations in different scenarios. It may therefore provide an important step towards the modelling of personalized therapies using a virtual patient tumour.
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Affiliation(s)
- S Brüningk
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
| | - G Powathil
- Department of Mathematics, College of Science, Swansea University, Swansea,, UK
| | - P Ziegenhein
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
| | - J Ijaz
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
| | - I Rivens
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
| | - S Nill
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
| | - M Chaplain
- School of Mathematics and Statistics, University of St Andrews, St Andrews, UK
| | - U Oelfke
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
| | - G Ter Haar
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
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The proportion of cancer-related entries in PubMed has increased considerably; is cancer truly "The Emperor of All Maladies"? PLoS One 2017; 12:e0173671. [PMID: 28282418 PMCID: PMC5345838 DOI: 10.1371/journal.pone.0173671] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 02/26/2017] [Indexed: 02/07/2023] Open
Abstract
In this work, the public database of biomedical literature PubMed was mined using queries with combinations of keywords and year restrictions. It was found that the proportion of Cancer-related entries per year in PubMed has risen from around 6% in 1950 to more than 16% in 2016. This increase is not shared by other conditions such as AIDS, Malaria, Tuberculosis, Diabetes, Cardiovascular, Stroke and Infection some of which have, on the contrary, decreased as a proportion of the total entries per year. Organ-related queries were performed to analyse the variation of some specific cancers. A series of queries related to incidence, funding, and relationship with DNA, Computing and Mathematics, were performed to test correlation between the keywords, with the hope of elucidating the cause behind the rise of Cancer in PubMed. Interestingly, the proportion of Cancer-related entries that contain “DNA”, “Computational” or “Mathematical” have increased, which suggests that the impact of these scientific advances on Cancer has been stronger than in other conditions. It is important to highlight that the results obtained with the data mining approach here presented are limited to the presence or absence of the keywords on a single, yet extensive, database. Therefore, results should be observed with caution. All the data used for this work is publicly available through PubMed and the UK’s Office for National Statistics. All queries and figures were generated with the software platform Matlab and the files are available as supplementary material.
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36
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Schmitz A, Fischer SC, Mattheyer C, Pampaloni F, Stelzer EHK. Multiscale image analysis reveals structural heterogeneity of the cell microenvironment in homotypic spheroids. Sci Rep 2017; 7:43693. [PMID: 28255161 PMCID: PMC5334646 DOI: 10.1038/srep43693] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Accepted: 01/30/2017] [Indexed: 12/31/2022] Open
Abstract
Three-dimensional multicellular aggregates such as spheroids provide reliable in vitro substitutes for tissues. Quantitative characterization of spheroids at the cellular level is fundamental. We present the first pipeline that provides three-dimensional, high-quality images of intact spheroids at cellular resolution and a comprehensive image analysis that completes traditional image segmentation by algorithms from other fields. The pipeline combines light sheet-based fluorescence microscopy of optically cleared spheroids with automated nuclei segmentation (F score: 0.88) and concepts from graph analysis and computational topology. Incorporating cell graphs and alpha shapes provided more than 30 features of individual nuclei, the cellular neighborhood and the spheroid morphology. The application of our pipeline to a set of breast carcinoma spheroids revealed two concentric layers of different cell density for more than 30,000 cells. The thickness of the outer cell layer depends on a spheroid’s size and varies between 50% and 75% of its radius. In differently-sized spheroids, we detected patches of different cell densities ranging from 5 × 105 to 1 × 106 cells/mm3. Since cell density affects cell behavior in tissues, structural heterogeneities need to be incorporated into existing models. Our image analysis pipeline provides a multiscale approach to obtain the relevant data for a system-level understanding of tissue architecture.
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Affiliation(s)
- Alexander Schmitz
- Physical Biology/Physikalische Biologie (IZN, FB 15), Buchmann Institute for Molecular Life Sciences (BMLS), Cluster of Excellence Frankfurt - Macromolecular Complexes (CEF - MC), Goethe Universität - Frankfurt am Main (Campus Riedberg), Max-von-Laue-Straße 15 - D-60348 Frankfurt am Main, Germany
| | - Sabine C Fischer
- Physical Biology/Physikalische Biologie (IZN, FB 15), Buchmann Institute for Molecular Life Sciences (BMLS), Cluster of Excellence Frankfurt - Macromolecular Complexes (CEF - MC), Goethe Universität - Frankfurt am Main (Campus Riedberg), Max-von-Laue-Straße 15 - D-60348 Frankfurt am Main, Germany
| | - Christian Mattheyer
- Physical Biology/Physikalische Biologie (IZN, FB 15), Buchmann Institute for Molecular Life Sciences (BMLS), Cluster of Excellence Frankfurt - Macromolecular Complexes (CEF - MC), Goethe Universität - Frankfurt am Main (Campus Riedberg), Max-von-Laue-Straße 15 - D-60348 Frankfurt am Main, Germany
| | - Francesco Pampaloni
- Physical Biology/Physikalische Biologie (IZN, FB 15), Buchmann Institute for Molecular Life Sciences (BMLS), Cluster of Excellence Frankfurt - Macromolecular Complexes (CEF - MC), Goethe Universität - Frankfurt am Main (Campus Riedberg), Max-von-Laue-Straße 15 - D-60348 Frankfurt am Main, Germany
| | - Ernst H K Stelzer
- Physical Biology/Physikalische Biologie (IZN, FB 15), Buchmann Institute for Molecular Life Sciences (BMLS), Cluster of Excellence Frankfurt - Macromolecular Complexes (CEF - MC), Goethe Universität - Frankfurt am Main (Campus Riedberg), Max-von-Laue-Straße 15 - D-60348 Frankfurt am Main, Germany
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Kanigel Winner KR, Costello JC. A SPATIOTEMPORAL MODEL TO SIMULATE CHEMOTHERAPY REGIMENS FOR HETEROGENEOUS BLADDER CANCER METASTASES TO THE LUNG. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017; 22:611-622. [PMID: 27897011 PMCID: PMC5154750 DOI: 10.1142/9789813207813_0056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Tumors are composed of heterogeneous populations of cells. Somatic genetic aberrations are one form of heterogeneity that allows clonal cells to adapt to chemotherapeutic stress, thus providing a path for resistance to arise. In silico modeling of tumors provides a platform for rapid, quantitative experiments to inexpensively study how compositional heterogeneity contributes to drug resistance. Accordingly, we have built a spatiotemporal model of a lung metastasis originating from a primary bladder tumor, incorporating in vivo drug concentrations of first-line chemotherapy, resistance data from bladder cancer cell lines, vascular density of lung metastases, and gains in resistance in cells that survive chemotherapy. In metastatic bladder cancer, a first-line drug regimen includes six cycles of gemcitabine plus cisplatin (GC) delivered simultaneously on day 1, and gemcitabine on day 8 in each 21-day cycle. The interaction between gemcitabine and cisplatin has been shown to be synergistic in vitro, and results in better outcomes in patients. Our model shows that during simulated treatment with this regimen, GC synergy does begin to kill cells that are more resistant to cisplatin, but repopulation by resistant cells occurs. Post-regimen populations are mixtures of the original, seeded resistant clones, and/or new clones that have gained resistance to cisplatin, gemcitabine, or both drugs. The emergence of a tumor with increased resistance is qualitatively consistent with the five-year survival of 6.8% for patients with metastatic transitional cell carcinoma of the urinary bladder treated with a GC regimen. The model can be further used to explore the parameter space for clinically relevant variables, including the timing of drug delivery to optimize cell death, and patient-specific data such as vascular density, rates of resistance gain, disease progression, and molecular profiles, and can be expanded for data on toxicity. The model is specific to bladder cancer, which has not previously been modeled in this context, but can be adapted to represent other cancers.
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Affiliation(s)
- Kimberly R Kanigel Winner
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus 12801 E. 17th Ave. MailStop 8303, Aurora, CO 80045, USA2Department of Pharmacology, University of Colorado Anschutz Medical Campus 12801 E. 17th Ave. MailStop 8303, Aurora, CO 80045, USA,
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Lorenzi T, Chisholm RH, Clairambault J. Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations. Biol Direct 2016; 11:43. [PMID: 27550042 PMCID: PMC4994266 DOI: 10.1186/s13062-016-0143-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Accepted: 07/20/2016] [Indexed: 02/06/2023] Open
Abstract
Background A thorough understanding of the ecological and evolutionary mechanisms that drive the phenotypic evolution of neoplastic cells is a timely and key challenge for the cancer research community. In this respect, mathematical modelling can complement experimental cancer research by offering alternative means of understanding the results of in vitro and in vivo experiments, and by allowing for a quick and easy exploration of a variety of biological scenarios through in silico studies. Results To elucidate the roles of phenotypic plasticity and selection pressures in tumour relapse, we present here a phenotype-structured model of evolutionary dynamics in a cancer cell population which is exposed to the action of a cytotoxic drug. The analytical tractability of our model allows us to investigate how the phenotype distribution, the level of phenotypic heterogeneity, and the size of the cell population are shaped by the strength of natural selection, the rate of random epimutations, the intensity of the competition for limited resources between cells, and the drug dose in use. Conclusions Our analytical results clarify the conditions for the successful adaptation of cancer cells faced with environmental changes. Furthermore, the results of our analyses demonstrate that the same cell population exposed to different concentrations of the same cytotoxic drug can take different evolutionary trajectories, which culminate in the selection of phenotypic variants characterised by different levels of drug tolerance. This suggests that the response of cancer cells to cytotoxic agents is more complex than a simple binary outcome, i.e., extinction of sensitive cells and selection of highly resistant cells. Also, our mathematical results formalise the idea that the use of cytotoxic agents at high doses can act as a double-edged sword by promoting the outgrowth of drug resistant cellular clones. Overall, our theoretical work offers a formal basis for the development of anti-cancer therapeutic protocols that go beyond the ‘maximum-tolerated-dose paradigm’, as they may be more effective than traditional protocols at keeping the size of cancer cell populations under control while avoiding the expansion of drug tolerant clones. Reviewers This article was reviewed by Angela Pisco, Sébastien Benzekry and Heiko Enderling. Electronic supplementary material The online version of this article (doi:10.1186/s13062-016-0143-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tommaso Lorenzi
- School of Mathematics and Statistics, University of St Andrews, North Haugh, St Andrews, KY16 9SS, UK.
| | - Rebecca H Chisholm
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW, Sydney, 2052, Australia
| | - Jean Clairambault
- INRIA Paris Research Centre, MAMBA team, 2, rue Simone Iff, CS 42112, Paris Cedex 12, 75589, France.,Sorbonne Universités, UPMC Univ. Paris 6, UMR 7598, Laboratoire Jacques-Louis Lions, Boîte courrier 187, 4 Place Jussieu, Paris Cedex 05, 75252, France
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Luján E, Guerra LN, Soba A, Visacovsky N, Gandía D, Calvo JC, Suárez C. Mathematical modelling of microtumour infiltration based on in vitro experiments. Integr Biol (Camb) 2016; 8:879-85. [PMID: 27466056 DOI: 10.1039/c6ib00110f] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The present mathematical models of microtumours consider, in general, volumetric growth and spherical tumour invasion shapes. Nevertheless in many cases, such as in gliomas, a need for more accurate delineation of tumour infiltration areas in a patient-specific manner has arisen. The objective of this study was to build a mathematical model able to describe in a case-specific way as well as to predict in a probabilistic way the growth and the real invasion pattern of multicellular tumour spheroids (in vitro model of an avascular microtumour) immersed in a collagen matrix. The two-dimensional theoretical model was represented by a reaction-convection-diffusion equation that considers logistic proliferation, volumetric growth, a rim with proliferative cells at the tumour surface and invasion with diffusive and convective components. Population parameter values of the model were extracted from the experimental dataset and a shape function that describes the invasion area was derived from each experimental case by image processing. New possible and aleatory shape functions were generated by data mining and Monte Carlo tools by means of a satellite EGARCH model, which were fed with all the shape functions of the dataset. Then the main model is used in two different ways: to reproduce the growth and invasion of a given experimental tumour in a case-specific manner when fed with the corresponding shape function (descriptive simulations) or to generate new possible tumour cases that respond to the general population pattern when fed with an aleatory-generated shape function (predictive simulations). Both types of simulations are in good agreement with empirical data, as it was revealed by area quantification and Bland-Altman analysis. This kind of experimental-numerical interaction has wide application potential in designing new strategies able to predict as much as possible the invasive behaviour of a tumour based on its particular characteristics and microenvironment.
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Affiliation(s)
- Emmanuel Luján
- Laboratorio de Sistemas Complejos, Departamento de Computación/Instituto de Física del Plasma, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, CONICET, Buenos Aires, Argentina.
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Powathil GG, Munro AJ, Chaplain MAJ, Swat M. Bystander effects and their implications for clinical radiation therapy: Insights from multiscale in silico experiments. J Theor Biol 2016; 401:1-14. [PMID: 27084360 DOI: 10.1016/j.jtbi.2016.04.010] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Revised: 03/14/2016] [Accepted: 04/10/2016] [Indexed: 12/19/2022]
Abstract
Radiotherapy is a commonly used treatment for cancer and is usually given in varying doses. At low radiation doses relatively few cells die as a direct response to radiation but secondary radiation effects, such as DNA mutation or bystander phenomena, may affect many cells. Consequently it is at low radiation levels where an understanding of bystander effects is essential in designing novel therapies with superior clinical outcomes. In this paper, we use a hybrid multiscale mathematical model to study the direct effects of radiation as well as radiation-induced bystander effects on both tumour cells and normal cells. We show that bystander responses play a major role in mediating radiation damage to cells at low-doses of radiotherapy, doing more damage than that due to direct radiation. The survival curves derived from our computational simulations showed an area of hyper-radiosensitivity at low-doses that are not obtained using a traditional radiobiological model.
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Affiliation(s)
- Gibin G Powathil
- Department of Mathematics, Swansea University, Swansea SA2 8PP, UK.
| | - Alastair J Munro
- Radiation Oncology, Division of Cancer Research, University of Dundee, Ninewells Hospital and Medical School, Dundee DD1 9SY, UK
| | - Mark A J Chaplain
- School of Mathematics and Statistics, University of St Andrews, St Andrews KY16 9SS, UK
| | - Maciej Swat
- The Biocomplexity Institute and Department of Physics, Indiana University Bloomington, Bloomington, Indiana, USA
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Pérez-Velázquez J, Gevertz JL, Karolak A, Rejniak KA. Microenvironmental Niches and Sanctuaries: A Route to Acquired Resistance. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 936:149-164. [PMID: 27739047 DOI: 10.1007/978-3-319-42023-3_8] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
A tumor vasculature that is functionally abnormal results in irregular gradients of metabolites and drugs within the tumor tissue. Recently, significant efforts have been committed to experimentally examine how cellular response to anti-cancer treatments varies based on the environment in which the cells are grown. In vitro studies point to specific conditions in which tumor cells can remain dormant and survive the treatment. In vivo results suggest that cells can escape the effects of drug therapy in tissue regions that are poorly penetrated by the drugs. Better understanding how the tumor microenvironments influence the emergence of drug resistance in both primary and metastatic tumors may improve drug development and the design of more effective therapeutic protocols. This chapter presents a hybrid agent-based model of the growth of tumor micrometastases and explores how microenvironmental factors can contribute to the development of acquired resistance in response to a DNA damaging drug. The specific microenvironments of interest in this work are tumor hypoxic niches and tumor normoxic sanctuaries with poor drug penetration. We aim to quantify how spatial constraints of limited drug transport and quiescent cell survival contribute to the development of drug resistant tumors.
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Affiliation(s)
- Judith Pérez-Velázquez
- Mathematical Modeling of Biological Systems, Centre for Mathematical Science, Technical University of Munich, Garching, Germany.
| | - Jana L Gevertz
- Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA
| | - Aleksandra Karolak
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Katarzyna A Rejniak
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.,Department of Oncologic Sciences, College of Medicine, University of South Florida, Tampa, FL, USA
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Loizides C, Iacovides D, Hadjiandreou MM, Rizki G, Achilleos A, Strati K, Mitsis GD. Model-Based Tumor Growth Dynamics and Therapy Response in a Mouse Model of De Novo Carcinogenesis. PLoS One 2015; 10:e0143840. [PMID: 26649886 PMCID: PMC4674149 DOI: 10.1371/journal.pone.0143840] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Accepted: 11/10/2015] [Indexed: 12/17/2022] Open
Abstract
Tumorigenesis is a complex, multistep process that depends on numerous alterations within the cell and contribution from the surrounding stroma. The ability to model macroscopic tumor evolution with high fidelity may contribute to better predictive tools for designing tumor therapy in the clinic. However, attempts to model tumor growth have mainly been developed and validated using data from xenograft mouse models, which fail to capture important aspects of tumorigenesis including tumor-initiating events and interactions with the immune system. In the present study, we investigate tumor growth and therapy dynamics in a mouse model of de novo carcinogenesis that closely recapitulates tumor initiation, progression and maintenance in vivo. We show that the rate of tumor growth and the effects of therapy are highly variable and mouse specific using a Gompertz model to describe tumor growth and a two-compartment pharmacokinetic/ pharmacodynamic model to describe the effects of therapy in mice treated with 5-FU. We show that inter-mouse growth variability is considerably larger than intra-mouse variability and that there is a correlation between tumor growth and drug kill rates. Our results show that in vivo tumor growth and regression in a double transgenic mouse model are highly variable both within and between subjects and that mathematical models can be used to capture the overall characteristics of this variability. In order for these models to become useful tools in the design of optimal therapy strategies and ultimately in clinical practice, a subject-specific modelling strategy is necessary, rather than approaches that are based on the average behavior of a given subject population which could provide erroneous results.
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Affiliation(s)
- Charalambos Loizides
- Department of Electrical & Electronic Engineering & KIOS Research Center for Intelligent Systems & Networks, University of Cyprus, Nicosia, Cyprus
| | - Demetris Iacovides
- Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
| | - Marios M. Hadjiandreou
- Department of Electrical & Electronic Engineering & KIOS Research Center for Intelligent Systems & Networks, University of Cyprus, Nicosia, Cyprus
| | - Gizem Rizki
- Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Achilleas Achilleos
- Department of Electrical & Electronic Engineering & KIOS Research Center for Intelligent Systems & Networks, University of Cyprus, Nicosia, Cyprus
| | - Katerina Strati
- Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
- * E-mail: (KS); (GM)
| | - Georgios D. Mitsis
- Department of Electrical & Electronic Engineering & KIOS Research Center for Intelligent Systems & Networks, University of Cyprus, Nicosia, Cyprus
- Department of Bioengineering, McGill University, Montreal QC, Canada
- * E-mail: (KS); (GM)
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Barbolosi D, Ciccolini J, Lacarelle B, Barlési F, André N. Computational oncology — mathematical modelling of drug regimens for precision medicine. Nat Rev Clin Oncol 2015; 13:242-54. [DOI: 10.1038/nrclinonc.2015.204] [Citation(s) in RCA: 144] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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Rejniak KA, Lloyd MC, Reed DR, Bui MM. Diagnostic assessment of osteosarcoma chemoresistance based on Virtual Clinical Trials. Med Hypotheses 2015; 85:348-54. [PMID: 26130106 PMCID: PMC4549200 DOI: 10.1016/j.mehy.2015.06.015] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Revised: 05/28/2015] [Accepted: 06/17/2015] [Indexed: 01/03/2023]
Abstract
Osteosarcoma is the most common primary bone tumor in pediatric and young adult patients. Successful treatment of osteosarcomas requires a combination of surgical resection and systemic chemotherapy, both neoadjuvant (prior to surgery) and adjuvant (after surgery). The degree of necrosis following neoadjuvant chemotherapy correlates with the subsequent probability of disease-free survival. Tumors with less than 10% of viable cells after treatment represent patients with a more favorable prognosis. However, being able to predict early, such as at the time of the pre-treatment tumor biopsy, how the patient will respond to the standard chemotherapy would provide an opportunity for more personalized patient care. Patients with unfavorable predictions could be studied in a protocol, rather than a standard setting, towards improving therapeutic success. The onset of necrotic cells in osteosarcomas treated with chemotherapeutic agents is a measure of tumor sensitivity to the drugs. We hypothesize that the remaining viable cells, i.e., cells that have not responded to the treatment, are chemoresistant, and that the pathological characteristics of these chemoresistant tumor cells within the osteosarcoma pre-treatment biopsy can predict tumor response to the standard-of-care chemotherapeutic treatment. This hypothesis can be tested by comparing patient histopathology samples before, as well as after treatment to identify both morphological and immunochemical cellular features that are characteristic of chemoresistant cells, i.e., cells that survived treatment. Consequently, using computational simulations of dynamic changes in tumor pathology under the simulated standard of care chemotherapeutic treatment, one can couple the pre- and post-treatment morphological and spatial patterns of chemoresistant cells, and correlate them with patient clinical diagnoses. This procedure, that we named 'Virtual Clinical Trials', can serve as a potential predictive biomarker providing a novel value-added decision support tool for oncologists.
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Affiliation(s)
- K A Rejniak
- Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Center of Excellence in Cancer Imaging and Technology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Oncologic Sciences Department, School of Medicine, University of South Florida, Tampa, FL, United States.
| | - M C Lloyd
- Analytic Microscopy Core, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Department of Biological Sciences, University of Illinois in Chicago, Chicago, IL, United States
| | - D R Reed
- Sarcoma Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Adolescent and Young Adult Program, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Chemical Biology and Molecular Medicine Program, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Oncologic Sciences Department, School of Medicine, University of South Florida, Tampa, FL, United States
| | - M M Bui
- Sarcoma Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Chemical Biology and Molecular Medicine Program, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Department of Anatomic Pathology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Analytic Microscopy Core, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, United States; Oncologic Sciences Department, School of Medicine, University of South Florida, Tampa, FL, United States
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Grizzi F, Mirandola L, Qehajaj D, Cobos E, Figueroa JA, Chiriva-Internati M. Cancer-Testis Antigens and Immunotherapy in the Light of Cancer Complexity. Int Rev Immunol 2015; 34:143-53. [PMID: 25901859 DOI: 10.3109/08830185.2015.1018418] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Masoudi-Nejad A, Wang E. Cancer modeling and network biology: Accelerating toward personalized medicine. Semin Cancer Biol 2015; 30:1-3. [DOI: 10.1016/j.semcancer.2014.06.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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