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Cogno N, Axenie C, Bauer R, Vavourakis V. Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation. Cancer Biol Ther 2024; 25:2344600. [PMID: 38678381 PMCID: PMC11057625 DOI: 10.1080/15384047.2024.2344600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 04/15/2024] [Indexed: 04/29/2024] Open
Abstract
Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.
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Affiliation(s)
- Nicolò Cogno
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Institute for Condensed Matter Physics, Technische Universit¨at Darmstadt, Darmstadt, Germany
| | - Cristian Axenie
- Computer Science Department and Center for Artificial Intelligence, Technische Hochschule Nürnberg Georg Simon Ohm, Nuremberg, Germany
| | - Roman Bauer
- Nature Inspired Computing and Engineering Research Group, Computer Science Research Centre, University of Surrey, Guildford, UK
| | - Vasileios Vavourakis
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
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2
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Lorenzo G, Ahmed SR, Hormuth DA, Vaughn B, Kalpathy-Cramer J, Solorio L, Yankeelov TE, Gomez H. Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data. Annu Rev Biomed Eng 2024; 26:529-560. [PMID: 38594947 DOI: 10.1146/annurev-bioeng-081623-025834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Despite the remarkable advances in cancer diagnosis, treatment, and management over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires the integration of patient-specific information with an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous yet practical mathematical theory of tumor initiation, development, invasion, and response to therapy. We begin this review with an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on big data and artificial intelligence. We then present illustrative examples of mathematical models manifesting their utility and discuss the limitations of stand-alone mechanistic and data-driven models. We then discuss the potential of mechanistic models for not only predicting but also optimizing response to therapy on a patient-specific basis. We describe current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.
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Affiliation(s)
- Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Syed Rakin Ahmed
- Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
- Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard University, Cambridge, Massachusetts, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - David A Hormuth
- Livestrong Cancer Institutes, University of Texas, Austin, Texas, USA
- Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA
| | - Brenna Vaughn
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | | | - Luis Solorio
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | - Thomas E Yankeelov
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, Texas, USA
- Department of Biomedical Engineering, Department of Oncology, and Department of Diagnostic Medicine, University of Texas, Austin, Texas, USA
- Livestrong Cancer Institutes, University of Texas, Austin, Texas, USA
- Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA
| | - Hector Gomez
- School of Mechanical Engineering and Purdue Center for Cancer Research, Purdue University, West Lafayette, Indiana, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
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3
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Sweeney PW, Walsh C, Walker-Samuel S, Shipley RJ. A three-dimensional, discrete-continuum model of blood pressure in microvascular networks. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2024:e3832. [PMID: 38770788 DOI: 10.1002/cnm.3832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/28/2024] [Accepted: 04/22/2024] [Indexed: 05/22/2024]
Abstract
We present a 3D discrete-continuum model to simulate blood pressure in large microvascular tissues in the absence of known capillary network architecture. Our hybrid approach combines a 1D Poiseuille flow description for large, discrete arteriolar and venular networks coupled to a continuum-based Darcy model, point sources of flux, for transport in the capillary bed. We evaluate our hybrid approach using a vascular network imaged from the mouse brain medulla/pons using multi-fluorescence high-resolution episcopic microscopy (MF-HREM). We use the fully-resolved vascular network to predict the hydraulic conductivity of the capillary network and generate a fully-discrete pressure solution to benchmark against. Our results demonstrate that the discrete-continuum methodology is a computationally feasible and effective tool for predicting blood pressure in real-world microvascular tissues when capillary microvessels are poorly defined.
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Affiliation(s)
- Paul W Sweeney
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Mechanical Engineering, University College London, London, UK
| | - Claire Walsh
- Department of Mechanical Engineering, University College London, London, UK
- Centre for Computational Medicine, University College London, London, UK
| | | | - Rebecca J Shipley
- Department of Mechanical Engineering, University College London, London, UK
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4
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Budhwani KI, Patel ZH, Guenter RE, Charania AA. A hitchhiker's guide to cancer models. Trends Biotechnol 2022; 40:1361-1373. [PMID: 35534320 PMCID: PMC9588514 DOI: 10.1016/j.tibtech.2022.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/31/2022] [Accepted: 04/08/2022] [Indexed: 01/21/2023]
Abstract
Cancer is a complex and uniquely personal disease. More than 1.7 million people in the United States are diagnosed with cancer every year. As the burden of cancer grows, so does the need for new, more effective therapeutics and for predictive tools to identify optimal, personalized treatment options for every patient. Cancer models that recapitulate various aspects of the disease are fundamental to making advances along the continuum of cancer treatment from benchside discoveries to bedside delivery. In this review, we use a thought experiment as a vehicle to arrive at four broad categories of cancer models and explore the strengths, weaknesses, opportunities, and threats for each category in advancing our understanding of the disease and improving treatment strategies.
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Affiliation(s)
- Karim I Budhwani
- CerFlux, Inc., Birmingham, AL, USA; Department of Radiation Oncology, Heersink School of Medicine, University of Alabama at Birmingham (UAB), Birmingham, AL, USA; Department of Physics, Coe College, Cedar Rapids, IA, USA.
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5
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Hadjicharalambous M, Ioannou E, Aristokleous N, Gazeli K, Anastassiou C, Vavourakis V. Combined anti-angiogenic and cytotoxic treatment of a solid tumour: In silico investigation of a xenograft animal model's digital twin. J Theor Biol 2022; 553:111246. [PMID: 36007551 DOI: 10.1016/j.jtbi.2022.111246] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/05/2022] [Accepted: 08/11/2022] [Indexed: 10/31/2022]
Abstract
Anti-angiogenic (AA) treatments have received significant research interest due to the key role of angiogenesis in cancer progression. AA agents can have a strong effect on cancer regression, by blocking new vessels and reducing the density of the existing vasculature. Moreover, in a process termed vascular normalisation, AA drugs can improve the abnormal structure and function of the tumour vasculature, enhancing the delivery of chemotherapeutics to the tumour site. Despite their promising potential, an improved understanding of AA treatments is necessary to optimise their administration as a monotherapy or in combination with other cancer treatments. In this work we present an in silico multiscale cancer model which is used to systematically interrogate the role of individual mechanisms of action of AA drugs in tumour regression. Focus is placed on the reduction of vascular density and on vascular normalisation through a parametric study, which are considered either as monotherapies or in combination with conventional/metronomic chemotherapy. The model is specified to data from a mammary carcinoma xenograft in immunodeficient mice, to enhance the physiological relevance of model predictions. Our results suggest that conventional chemotherapy might be more beneficial when combined with AA treatments, hindering tumour growth without causing excessive damage on healthy tissue. Notably, metronomic chemotherapy has shown significant potential in stopping tumour growth with minimal toxicity, even as a monotherapy. Our findings underpin the potential of our in silico framework for non-invasive and cost-effective evaluation of treatment strategies, which can enhance our understanding of combined therapeutic strategies and contribute towards improving cancer treatment management.
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Affiliation(s)
- Myrianthi Hadjicharalambous
- Department of Mechanical & Manufacturing Engineering, University of Cyprus, 75, Kallipoleos Av., Nicosia, 1678, Cyprus.
| | - Eleftherios Ioannou
- Department of Mechanical & Manufacturing Engineering, University of Cyprus, 75, Kallipoleos Av., Nicosia, 1678, Cyprus.
| | - Nicolas Aristokleous
- Department of Mechanical & Manufacturing Engineering, University of Cyprus, 75, Kallipoleos Av., Nicosia, 1678, Cyprus.
| | - Kristaq Gazeli
- ENAL Electromagnetics and Novel Applications Lab, Department of Electrical and Computer Engineering, University of Cyprus, 75, Kallipoleos Av., Nicosia, 1678, Cyprus; FOSS Research Centre for Sustainable Energy, Department of Electrical and Computer Engineering, University of Cyprus, 75, Kallipoleos Av., Nicosia, 1678, Cyprus; Université Sorbonne Paris Nord, Laboratoire des Sciences des Procédés et des Matériaux, LSPM, CNRS, UPR 3407, 99 av. Jean-Baptiste, Villetaneuse, F-93430, France.
| | - Charalambos Anastassiou
- ENAL Electromagnetics and Novel Applications Lab, Department of Electrical and Computer Engineering, University of Cyprus, 75, Kallipoleos Av., Nicosia, 1678, Cyprus; FOSS Research Centre for Sustainable Energy, Department of Electrical and Computer Engineering, University of Cyprus, 75, Kallipoleos Av., Nicosia, 1678, Cyprus.
| | - Vasileios Vavourakis
- Department of Mechanical & Manufacturing Engineering, University of Cyprus, 75, Kallipoleos Av., Nicosia, 1678, Cyprus; Department of Medical Physics & Biomedical Engineering, University College London, Gower Street, London, WC1E 6BT, UK.
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6
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Lambride C, Vavourakis V, Stylianopoulos T. Convection-Enhanced Delivery In Silico Study for Brain Cancer Treatment. Front Bioeng Biotechnol 2022; 10:867552. [PMID: 35694227 PMCID: PMC9177080 DOI: 10.3389/fbioe.2022.867552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 05/02/2022] [Indexed: 12/02/2022] Open
Abstract
Brain cancer therapy remains a formidable challenge in oncology. Convection-enhanced delivery (CED) is an innovative and promising local drug delivery method for the treatment of brain cancer, overcoming the challenges of the systemic delivery of drugs to the brain. To improve our understanding about CED efficacy and drug transport, we present an in silico methodology for brain cancer CED treatment simulation. To achieve this, a three-dimensional finite element formulation is utilized which employs a brain model representation from clinical imaging data and is used to predict the drug deposition in CED regimes. The model encompasses biofluid dynamics and the transport of drugs in the brain parenchyma. Drug distribution is studied under various patho-physiological conditions of the tumor, in terms of tumor vessel wall pore size and tumor tissue hydraulic conductivity as well as for drugs of various sizes, spanning from small molecules to nanoparticles. Through a parametric study, our contribution reports the impact of the size of the vascular wall pores and that of the therapeutic agent on drug distribution during and after CED. The in silico findings provide useful insights of the spatio-temporal distribution and average drug concentration in the tumor towards an effective treatment of brain cancer.
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Affiliation(s)
- Chryso Lambride
- Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
| | - Vasileios Vavourakis
- Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
- *Correspondence: Vasileios Vavourakis, ; Triantafyllos Stylianopoulos,
| | - Triantafyllos Stylianopoulos
- Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
- *Correspondence: Vasileios Vavourakis, ; Triantafyllos Stylianopoulos,
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7
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Mahesh N, Singh N, Talukdar P. A mathematical model for understanding nanoparticle biodistribution after intratumoral injection in cancer tumors. J Drug Deliv Sci Technol 2022. [DOI: 10.1016/j.jddst.2021.103048] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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8
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Zhang Y, Wang H, Oliveira RHM, Zhao C, Popel AS. Systems biology of angiogenesis signaling: Computational models and omics. WIREs Mech Dis 2021; 14:e1550. [PMID: 34970866 PMCID: PMC9243197 DOI: 10.1002/wsbm.1550] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/03/2021] [Accepted: 12/06/2021] [Indexed: 01/10/2023]
Abstract
Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases. This article is categorized under:Cardiovascular Diseases > Computational Models Cancer > Computational Models
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Affiliation(s)
- Yu Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Rebeca Hannah M Oliveira
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,School of Pharmacy, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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9
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Hormuth DA, Phillips CM, Wu C, Lima EABF, Lorenzo G, Jha PK, Jarrett AM, Oden JT, Yankeelov TE. Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data. Cancers (Basel) 2021; 13:3008. [PMID: 34208448 PMCID: PMC8234316 DOI: 10.3390/cancers13123008] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/07/2021] [Accepted: 06/13/2021] [Indexed: 01/03/2023] Open
Abstract
Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2-3 mm3. Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions.
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Affiliation(s)
- David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Caleb M. Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Ernesto A. B. F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78758, USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy
| | - Prashant K. Jha
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Angela M. Jarrett
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
| | - J. Tinsley Oden
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Mathematics, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, USA
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Nezhadi S, Saadat E, Handali S, Dorkoosh F. Nanomedicine and chemotherapeutics drug delivery: challenges and opportunities. J Drug Target 2020; 29:185-198. [PMID: 32772739 DOI: 10.1080/1061186x.2020.1808000] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Cancer is considered as one of the biggest threats to humans worldwide. Researchers suggest that tumour is not just a single mass, it comprises cancerous cells surrounded by noncancerous cells such as immune cells, adipocytes and cancer stem cells (CSCs) in the extracellular matrix (ECM) containing distinct components such as proteins, glycoproteins and enzymes; thus tumour microenvironment (TME) is partially complex. Multiple interactions happen in the dynamic microenvironment (ME) lead to an acidic, hypoxic and stiff ME that is considered as one of the major contributors to cancer progression and metastasis. Furthermore, TME involves in drug resistance mechanisms and affects enhanced permeability and retention (EPR) in tumours. In such a scenario, the first step to accomplish satisfying results is the identification and recognition of this ME. Then designing proper drug delivery systems can perform selectively towards cancerous cells. In this way, several targeting and stimuli/enzyme responsive drug delivery systems have been designed. More importantly, it is necessary to design a drug delivery system that can penetrate deeper into the tumours, efficiently and selectively. Various drug delivery systems such as exosomes and size-switchable nanocarriers (NCs) could decrease side effects and increase tumour treatment results by selective accumulation in tumours. In this review, TME features, current drug delivery approaches, challenges and promising strategies towards cancer treatment are discussed.
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Affiliation(s)
- Sepideh Nezhadi
- Department of Pharmaceutics, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Ir an
| | | | - Somayeh Handali
- Medical Biomaterial Research Center (MBRC), Tehran University of Medical Sciences, Tehran, Iran
| | - Farid Dorkoosh
- Department of Pharmaceutics, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Ir an.,Medical Biomaterial Research Center (MBRC), Tehran University of Medical Sciences, Tehran, Iran
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11
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Drug delivery: Experiments, mathematical modelling and machine learning. Comput Biol Med 2020; 123:103820. [PMID: 32658778 DOI: 10.1016/j.compbiomed.2020.103820] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 04/22/2020] [Accepted: 05/10/2020] [Indexed: 01/28/2023]
Abstract
We address the problem of determining from laboratory experiments the data necessary for a proper modeling of drug delivery and efficacy in anticancer therapy. There is an inherent difficulty in extracting the necessary parameters, because the experiments often yield an insufficient quantity of information. To overcome this difficulty, we propose to combine real experiments, numerical simulation, and Machine Learning (ML) based on Artificial Neural Networks (ANN), aiming at a reliable identification of the physical model factors, e.g. the killing action of the drug. To this purpose, we exploit the employed mathematical-numerical model for tumor growth and drug delivery, together with the ANN - ML procedure, to integrate the results of the experimental tests and feed back the model itself, thus obtaining a reliable predictive tool. The procedure represents a hybrid data-driven, physics-informed approach to machine learning. The physical and mathematical model employed for the numerical simulations is without extracellular matrix (ECM) and healthy cells because of the experimental conditions we reproduce.
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12
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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: 11] [Impact Index Per Article: 2.8] [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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13
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Wirthl B, Kremheller J, Schrefler BA, Wall WA. Extension of a multiphase tumour growth model to study nanoparticle delivery to solid tumours. PLoS One 2020; 15:e0228443. [PMID: 32023318 PMCID: PMC7001947 DOI: 10.1371/journal.pone.0228443] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 01/15/2020] [Indexed: 01/04/2023] Open
Abstract
One of the main challenges in increasing the efficacy of conventional chemotherapeutics is the fact that they do not reach cancerous cells at a sufficiently high dosage. In order to remedy this deficiency, nanoparticle-based drugs have evolved as a promising novel approach to more specific tumour targeting. Nevertheless, several biophysical phenomena prevent the sufficient penetration of nanoparticles in order to target the entire tumour. We therefore extend our vascular multiphase tumour growth model, enabling it to investigate the influence of different biophysical factors on the distribution of nanoparticles in the tumour microenvironment. The novel model permits the examination of the interplay between the size of vessel-wall pores, the permeability of the blood-vessel endothelium and the lymphatic drainage on the delivery of particles of different sizes. Solid tumours develop a non-perfused core and increased interstitial pressure. Our model confirms that those two typical features of solid tumours limit nanoparticle delivery. Only in case of small nanoparticles is the transport dominated by diffusion, and particles can reach the entire tumour. The size of the vessel-wall pores and the permeability of the blood-vessel endothelium have a major impact on the amount of delivered nanoparticles. This extended in-silico tumour growth model permits the examination of the characteristics and of the limitations of nanoparticle delivery to solid tumours, which currently complicate the translation of nanoparticle therapy to a clinical stage.
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Affiliation(s)
- Barbara Wirthl
- Institute for Computational Mechanics, Technical University of Munich, Garching b. München, Germany
| | - Johannes Kremheller
- Institute for Computational Mechanics, Technical University of Munich, Garching b. München, Germany
| | - Bernhard A. Schrefler
- Institute for Advanced Study, Technical University of Munich, Garching b. München, Germany
- Department of Civil, Environmental and Architectural Engineering, University of Padova, Padova, Italy
| | - Wolfgang A. Wall
- Institute for Computational Mechanics, Technical University of Munich, Garching b. München, Germany
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14
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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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15
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Wijeratne PA, Vavourakis V. A quantitative in silico platform for simulating cytotoxic and nanoparticle drug delivery to solid tumours. Interface Focus 2019; 9:20180063. [PMID: 31065337 DOI: 10.1098/rsfs.2018.0063] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/11/2019] [Indexed: 12/20/2022] Open
Abstract
The role of tumour-host mechano-biology and the mechanisms involved in the delivery of anti-cancer drugs have been extensively studied using in vitro and in vivo models. A complementary approach is offered by in silico models, which can also potentially identify the main factors affecting the transport of tumour-targeting molecules. Here, we present a generalized three-dimensional in silico modelling framework of dynamic solid tumour growth, angiogenesis and drug delivery. Crucially, the model allows for drug properties-such as size and binding affinity-to be explicitly defined, hence facilitating investigation into the interaction between the changing tumour-host microenvironment and cytotoxic and nanoparticle drugs. We use the model to qualitatively recapitulate experimental evidence of delivery efficacy of cytotoxic and nanoparticle drugs on matrix density (and hence porosity). Furthermore, we predict a highly heterogeneous distribution of nanoparticles after delivery; that nanoparticles require a high porosity extracellular matrix to cause tumour regression; and that post-injection transvascular fluid velocity depends on matrix porosity, and implicitly on the size of the drug used to treat the tumour. These results highlight the utility of predictive in silico modelling in better understanding the factors governing efficient cytotoxic and nanoparticle drug delivery.
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Affiliation(s)
- Peter A Wijeratne
- Centre for Medical Imaging Computing, Department of Computer Science, University College London, London, UK
| | - Vasileios Vavourakis
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK.,Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
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16
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Vavourakis V, Stylianopoulos T, Wijeratne PA. Correction: In-silico dynamic analysis of cytotoxic drug administration to solid tumours: Effect of binding affinity and vessel permeability. PLoS Comput Biol 2019; 15:e1006880. [PMID: 30830900 PMCID: PMC6398825 DOI: 10.1371/journal.pcbi.1006880] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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