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Derippe T, Fouliard S, Decleves X, Mager DE. Quantitative systems pharmacology modeling of tumor heterogeneity in response to BH3-mimetics using virtual tumors calibrated with cell viability assays. CPT Pharmacometrics Syst Pharmacol 2024; 13:1252-1263. [PMID: 38747730 PMCID: PMC11247121 DOI: 10.1002/psp4.13158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/17/2024] [Accepted: 04/24/2024] [Indexed: 07/16/2024] Open
Abstract
Both primary and acquired resistance mechanisms that involve intra-tumoral cell heterogeneity limit the use of BH3-mimetics to trigger tumor cell apoptosis. This article proposes a new quantitative systems pharmacology (QSP)-based methodology in which cell viability assays are used to calibrate virtual tumors (VTs) made of virtual cells whose fate is determined by simulations from an apoptosis QSP model. VTs representing SU-DHL-4 and KARPAS-422 cell lines were calibrated using in vitro data involving venetoclax (anti-BCL2), A-1155463 (anti-BCLXL), and/or A-1210477 (anti-MCL1). The calibrated VTs provide insights into the combination of several BH3-mimetics, such as the distinction between cells eliminated by at least one of the drugs (monotherapies) from the cells eliminated by a pharmacological combination only. Calibrated VTs can also be used as initial conditions in an agent-based model (ABM) framework, and a minimal ABM was developed to bridge in vitro SU-DHL-4 cell viability results to tumor growth inhibition experiments in mice.
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Affiliation(s)
- Thibaud Derippe
- Institut de Recherches Internationales Servier, Suresnes, France
- Inserm, UMRS-1144, Optimisation Thérapeutique en Neuropsychopharmacologie, Université Paris Cité, Paris, France
- Department of Pharmaceutical Sciences, University at Buffalo, SUNY, Buffalo, New York, USA
| | - Sylvain Fouliard
- Institut de Recherches Internationales Servier, Suresnes, France
| | - Xavier Decleves
- Inserm, UMRS-1144, Optimisation Thérapeutique en Neuropsychopharmacologie, Université Paris Cité, Paris, France
| | - Donald E Mager
- Department of Pharmaceutical Sciences, University at Buffalo, SUNY, Buffalo, New York, USA
- Enhanced Pharmacodynamics, LLC, Buffalo, New York, USA
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2
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Weathered C, Bardehle S, Yoon C, Kumar N, Leyns CEG, Kennedy ME, Bloomingdale P, Pienaar E. Microglial roles in Alzheimer's disease: An agent-based model to elucidate microglial spatiotemporal response to beta-amyloid. CPT Pharmacometrics Syst Pharmacol 2024; 13:449-463. [PMID: 38078626 PMCID: PMC10941569 DOI: 10.1002/psp4.13095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 11/16/2023] [Accepted: 11/20/2023] [Indexed: 02/03/2024] Open
Abstract
Alzheimer's disease (AD) is characterized by beta-amyloid (Aβ) plaques in the brain and widespread neuronal damage. Because of the high drug attrition rates in AD, there is increased interest in characterizing neuroimmune responses to Aβ plaques. In response to AD pathology, microglia are innate phagocytotic immune cells that transition into a neuroprotective state and form barriers around plaques. We seek to understand the role of microglia in modifying Aβ dynamics and barrier formation. To quantify the influence of individual microglia behaviors (activation, chemotaxis, phagocytosis, and proliferation) on plaque size and barrier coverage, we developed an agent-based model to characterize the spatiotemporal interactions between microglia and Aβ. Our model qualitatively reproduces mouse data trends where the fraction of microglia coverage decreases as plaques become larger. In our model, the time to microglial arrival at the plaque boundary is significantly negatively correlated (p < 0.0001) with plaque size, indicating the importance of the time to microglial activation for regulating plaque size. In addition, in silico behavioral knockout simulations show that phagocytosis knockouts have the strongest impact on plaque size, but modest impacts on microglial coverage and activation. In contrast, the chemotaxis knockouts had a strong impact on microglial coverage with a more modest impact on plaque volume and microglial activation. These simulations suggest that phagocytosis, chemotaxis, and replication of activated microglia have complex impacts on plaque volume and coverage, whereas microglial activation remains fairly robust to perturbations of these functions. Thus, our work provides insights into the potential and limitations of targeting microglial activation as a pharmacological strategy for the treatment of AD.
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Affiliation(s)
- Catherine Weathered
- Weldon School of Biomedical EngineeringPurdue UniversityWest LafayetteIndianaUSA
| | - Sophia Bardehle
- NeuroimmunologyMerck & Co., Inc.RahwayNew JerseyUSA
- Present address:
Cerevel TherapeuticsCambridgeMassachusettsUSA
| | - Choya Yoon
- NeuroimmunologyMerck & Co., Inc.RahwayNew JerseyUSA
| | - Niyanta Kumar
- Pharmacokinetics and PharmacodynamicsMerck & Co., Inc.RahwayNew JerseyUSA
- Present address:
Mersana TherapeuticsCambridgeMassachusettsUSA
| | | | | | - Peter Bloomingdale
- Quantitative Pharmacology and PharmacometricsMerck & Co., Inc.RahwayNew JerseyUSA
- Present address:
Boehringer IngelheimIngelheim am RheinGermany
| | - Elsje Pienaar
- Weldon School of Biomedical EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Regenstrief Center for Healthcare EngineeringWest LafayetteIndianaUSA
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3
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Gazo Hanna E, Younes K, Roufayel R, Khazaal M, Fajloun Z. Engineering innovations in medicine and biology: Revolutionizing patient care through mechanical solutions. Heliyon 2024; 10:e26154. [PMID: 38390063 PMCID: PMC10882044 DOI: 10.1016/j.heliyon.2024.e26154] [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: 06/02/2023] [Revised: 01/24/2024] [Accepted: 02/08/2024] [Indexed: 02/24/2024] Open
Abstract
The overlap between mechanical engineering and medicine is expanding more and more over the years. Engineers are now using their expertise to design and create functional biomaterials and are continually collaborating with physicians to improve patient health. In this review, we explore the state of scientific knowledge in the areas of biomaterials, biomechanics, nanomechanics, and computational fluid dynamics (CFD) in relation to the pharmaceutical and medical industry. Focusing on current research and breakthroughs, we provide an overview of how these fields are being used to create new technologies for medical treatments of human patients. Barriers and constraints in these fields, as well as ways to overcome them, are also described in this review. Finally, the potential for future advances in biomaterials to fundamentally change the current approach to medicine and biology is also discussed.
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Affiliation(s)
- Eddie Gazo Hanna
- College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait
| | - Khaled Younes
- College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait
| | - Rabih Roufayel
- College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait
| | - Mickael Khazaal
- École Supérieure des Techniques Aéronautiques et de Construction Automobile, ISAE-ESTACA, France
| | - Ziad Fajloun
- Faculty of Sciences 3, Department of Biology, Lebanese University, Campus Michel Slayman Ras Maska, 1352, Tripoli, Lebanon
- Laboratory of Applied Biotechnology (LBA3B), Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, 1300, Tripoli, Lebanon
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4
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Uatay A, Gall L, Irons L, Tewari SG, Zhu XS, Gibbs M, Kimko H. Physiological Indirect Response Model to Omics-Powered Quantitative Systems Pharmacology Model. J Pharm Sci 2024; 113:11-21. [PMID: 37898164 DOI: 10.1016/j.xphs.2023.10.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/21/2023] [Accepted: 10/21/2023] [Indexed: 10/30/2023]
Abstract
Over the past several decades, mathematical modeling has been applied to increasingly wider scopes of questions in drug development. Accordingly, the range of modeling tools has also been evolving, as showcased by contributions of Jusko and colleagues: from basic pharmacokinetics/pharmacodynamics (PK/PD) modeling to today's platform-based approach of quantitative systems pharmacology (QSP) modeling. Aimed at understanding the mechanism of action of investigational drugs, QSP models characterize systemic effects by incorporating information about cellular signaling networks, which is often represented by omics data. In this perspective, we share a few examples illustrating approaches for the integration of omics into mechanistic QSP modeling. We briefly overview how the evolution of PK/PD modeling into QSP has been accompanied by an increase in available data and the complexity of mathematical methods that integrate it. We discuss current gaps and challenges of integrating omics data into QSP models and propose several potential areas where integrated QSP and omics modeling may benefit drug development.
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Affiliation(s)
- Aydar Uatay
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Cambridge, United Kingdom.
| | - Louis Gall
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Cambridge, United Kingdom
| | - Linda Irons
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Waltham, MA, United States
| | - Shivendra G Tewari
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Gaithersburg, MD, United States
| | - Xu Sue Zhu
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Waltham, MA, United States
| | - Megan Gibbs
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Waltham, MA, United States
| | - Holly Kimko
- Clinical Pharmacology & Quantitative Pharmacology, R&D Biopharmaceuticals, Gaithersburg, MD, United States.
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5
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Johnson JA, Stein-O’Brien GL, Booth M, Heiland R, Kurtoglu F, Bergman DR, Bucher E, Deshpande A, Forjaz A, Getz M, Godet I, Lyman M, Metzcar J, Mitchell J, Raddatz A, Rocha H, Solorzano J, Sundus A, Wang Y, Gilkes D, Kagohara LT, Kiemen AL, Thompson ED, Wirtz D, Wu PH, Zaidi N, Zheng L, Zimmerman JW, Jaffee EM, Hwan Chang Y, Coussens LM, Gray JW, Heiser LM, Fertig EJ, Macklin P. Digitize your Biology! Modeling multicellular systems through interpretable cell behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.17.557982. [PMID: 37745323 PMCID: PMC10516032 DOI: 10.1101/2023.09.17.557982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.
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Affiliation(s)
- Jeanette A.I. Johnson
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Genevieve L. Stein-O’Brien
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Neuroscience, Johns Hopkins University. Baltimore, MD USA
| | - Max Booth
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
| | - Randy Heiland
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Furkan Kurtoglu
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Daniel R. Bergman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Elmar Bucher
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Atul Deshpande
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - André Forjaz
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Michael Getz
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Ines Godet
- Memorial Sloan Kettering Cancer Center. New York, NY USA
| | - Melissa Lyman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - John Metzcar
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
- Department of Informatics, Indiana University. Bloomington, IN USA
| | - Jacob Mitchell
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Human Genetics, Johns Hopkins University. Baltimore, MD USA
| | - Andrew Raddatz
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University. Atlanta, GA USA
| | - Heber Rocha
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Jacobo Solorzano
- Centre de Recherches en Cancerologie de Toulouse. Toulouse, France
| | - Aneequa Sundus
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Yafei Wang
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Danielle Gilkes
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
| | - Luciane T. Kagohara
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Ashley L. Kiemen
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Pathology, Johns Hopkins University. Baltimore, MD USA
| | | | - Denis Wirtz
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Baltimore, MD USA
- Department of Pathology, Johns Hopkins University. Baltimore, MD USA
- Department of Materials Science and Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Pei-Hsun Wu
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Neeha Zaidi
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Lei Zheng
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Jacquelyn W. Zimmerman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Elizabeth M. Jaffee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health & Science University. Portland, OR USA
| | - Lisa M. Coussens
- Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University. Portland, OR USA
| | - Joe W. Gray
- Department of Biomedical Engineering, Oregon Health & Science University. Portland, OR USA
| | - Laura M. Heiser
- Department of Biomedical Engineering, Oregon Health & Science University. Portland, OR USA
| | - Elana J. Fertig
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
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6
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Tindall MJ, Cucurull-Sanchez L, Mistry H, Yates JWT. Quantitative Systems Pharmacology and Machine Learning: A Match Made in Heaven or Hell? J Pharmacol Exp Ther 2023; 387:92-99. [PMID: 37652709 DOI: 10.1124/jpet.122.001551] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 07/24/2023] [Accepted: 07/26/2023] [Indexed: 09/02/2023] Open
Abstract
As pharmaceutical development moves from early-stage in vitro experimentation to later in vivo and subsequent clinical trials, data and knowledge are acquired across multiple time and length scales, from the subcellular to whole patient cohort scale. Realizing the potential of this data for informing decision making in pharmaceutical development requires the individual and combined application of machine learning (ML) and mechanistic multiscale mathematical modeling approaches. Here we outline how these two approaches, both individually and in tandem, can be applied at different stages of the drug discovery and development pipeline to inform decision making compound development. The importance of discerning between knowledge and data are highlighted in informing the initial use of ML or mechanistic quantitative systems pharmacology (QSP) models. We discuss the application of sensitivity and structural identifiability analyses of QSP models in informing future experimental studies to which ML may be applied, as well as how ML approaches can be used to inform mechanistic model development. Relevant literature studies are highlighted and we close by discussing caveats regarding the application of each approach in an age of constant data acquisition. SIGNIFICANCE STATEMENT: We consider when best to apply machine learning (ML) and mechanistic quantitative systems pharmacology (QSP) approaches in the context of the drug discovery and development pipeline. We discuss the importance of prior knowledge and data available for the system of interest and how this informs the individual and combined application of ML and QSP approaches at each stage of the pipeline.
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Affiliation(s)
- Marcus John Tindall
- Department of Mathematics and Statistics and Institute of Cardiovascular and Metabolic Research, University of Reading, Whiteknights, Reading, United Kingdom (M.J.T.); GSK Medicines Research Centre, Stevenage, United Kingdom (L.C.-S., J.W.T.Y.); and Pharmacy, Division of Pharmacy and Optometry, University of Manchester, Oxford Road, Manchester, United Kingdom (H.M.)
| | - Lourdes Cucurull-Sanchez
- Department of Mathematics and Statistics and Institute of Cardiovascular and Metabolic Research, University of Reading, Whiteknights, Reading, United Kingdom (M.J.T.); GSK Medicines Research Centre, Stevenage, United Kingdom (L.C.-S., J.W.T.Y.); and Pharmacy, Division of Pharmacy and Optometry, University of Manchester, Oxford Road, Manchester, United Kingdom (H.M.)
| | - Hitesh Mistry
- Department of Mathematics and Statistics and Institute of Cardiovascular and Metabolic Research, University of Reading, Whiteknights, Reading, United Kingdom (M.J.T.); GSK Medicines Research Centre, Stevenage, United Kingdom (L.C.-S., J.W.T.Y.); and Pharmacy, Division of Pharmacy and Optometry, University of Manchester, Oxford Road, Manchester, United Kingdom (H.M.)
| | - James W T Yates
- Department of Mathematics and Statistics and Institute of Cardiovascular and Metabolic Research, University of Reading, Whiteknights, Reading, United Kingdom (M.J.T.); GSK Medicines Research Centre, Stevenage, United Kingdom (L.C.-S., J.W.T.Y.); and Pharmacy, Division of Pharmacy and Optometry, University of Manchester, Oxford Road, Manchester, United Kingdom (H.M.)
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7
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Benson J, Bessonov M, Burke K, Cassani S, Ciocanel MV, Cooney DB, Volkening A. How do classroom-turnover times depend on lecture-hall size? MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:9179-9207. [PMID: 37161239 DOI: 10.3934/mbe.2023403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Academic spaces in colleges and universities span classrooms for 10 students to lecture halls that hold over 600 people. During the break between consecutive classes, students from the first class must leave and the new class must find their desks, regardless of whether the room holds 10 or 600 people. Here we address the question of how the size of large lecture halls affects classroom-turnover times, focusing on non-emergency settings. By adapting the established social-force model, we treat students as individuals who interact and move through classrooms to reach their destinations. We find that social interactions and the separation time between consecutive classes strongly influence how long it takes entering students to reach their desks, and that these effects are more pronounced in larger lecture halls. While the median time that individual students must travel increases with decreased separation time, we find that shorter separation times lead to shorter classroom-turnover times overall. This suggests that the effects of scheduling gaps and lecture-hall size on classroom dynamics depends on the perspective-individual student or whole class-that one chooses to take.
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Affiliation(s)
- Joseph Benson
- Mathematics, Statistics, and Computer Science, Macalester College, Saint Paul, MN 55105, USA
| | - Mariya Bessonov
- Department of Mathematics, NYC College of Technology, Brooklyn, NY 11201
| | - Korana Burke
- Department of Mathematics, University of California Davis, Davis, CA 95616
| | - Simone Cassani
- Department of Mathematics, University at Buffalo, Buffalo, NY 14260
| | | | - Daniel B Cooney
- Department of Mathematics and Center for Mathematical Biology, University of Pennsylvania, Philadelphia, PA 19104
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8
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Shafiekhani S, Jafari A, Jafarzadeh L, Sadeghi V, Gheibi N. Predicting efficacy of 5-fluorouracil therapy via a mathematical model with fuzzy uncertain parameters. JOURNAL OF MEDICAL SIGNALS & SENSORS 2022; 12:202-218. [PMID: 36120402 PMCID: PMC9480509 DOI: 10.4103/jmss.jmss_92_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 11/12/2021] [Accepted: 01/21/2022] [Indexed: 11/08/2022]
Abstract
Background: Due to imprecise/missing data used for parameterization of ordinary differential equations (ODEs), model parameters are uncertain. Uncertainty of parameters has hindered the application of ODEs that require accurate parameters. Methods: We extended an available ODE model of tumor-immune system interactions via fuzzy logic to illustrate the fuzzification procedure of an ODE model. The fuzzy ODE (FODE) model assigns a fuzzy number to the parameters, to capture parametric uncertainty. We used the FODE model to predict tumor and immune cell dynamics and to assess the efficacy of 5-fluorouracil (5-FU) chemotherapy. Result: FODE model investigates how parametric uncertainty affects the uncertainty band of cell dynamics in the presence and absence of 5-FU treatment. In silico experiments revealed that the frequent 5-FU injection created a beneficial tumor microenvironment that exerted detrimental effects on tumor cells by enhancing the infiltration of CD8+ T cells, and natural killer cells, and decreasing that of myeloid-derived suppressor cells. The global sensitivity analysis was proved model robustness against random perturbation to parameters. Conclusion: ODE models with fuzzy uncertain kinetic parameters cope with insufficient/imprecise experimental data in the field of mathematical oncology and can predict cell dynamics uncertainty band.
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9
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Cosgrove J, Alden K, Stein JV, Coles MC, Timmis J. Simulating CXCR5 Dynamics in Complex Tissue Microenvironments. Front Immunol 2021; 12:703088. [PMID: 34557191 PMCID: PMC8452942 DOI: 10.3389/fimmu.2021.703088] [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: 04/30/2021] [Accepted: 08/16/2021] [Indexed: 11/25/2022] Open
Abstract
To effectively navigate complex tissue microenvironments, immune cells sense molecular concentration gradients using G-protein coupled receptors. However, due to the complexity of receptor activity, and the multimodal nature of chemokine gradients in vivo, chemokine receptor activity in situ is poorly understood. To address this issue, we apply a modelling and simulation approach that permits analysis of the spatiotemporal dynamics of CXCR5 expression within an in silico B-follicle with single-cell resolution. Using this approach, we show that that in silico B-cell scanning is robust to changes in receptor numbers and changes in individual kinetic rates of receptor activity, but sensitive to global perturbations where multiple parameters are altered simultaneously. Through multi-objective optimization analysis we find that the rapid modulation of CXCR5 activity through receptor binding, desensitization and recycling is required for optimal antigen scanning rates. From these analyses we predict that chemokine receptor signaling dynamics regulate migration in complex tissue microenvironments to a greater extent than the total numbers of receptors on the cell surface.
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Affiliation(s)
- Jason Cosgrove
- Department of Electronic Engineering, University of York, York, United Kingdom.,Institut Curie, Université PSL, Sorbonne Université, CNRS UMR168, Laboratoire Physico Chimie Curie, Paris, France
| | - Kieran Alden
- Department of Electronic Engineering, University of York, York, United Kingdom
| | - Jens V Stein
- Department of Oncology, Microbiology and Immunology, University of Fribourg, Fribourg, Switzerland
| | - Mark C Coles
- Kennedy Institute of Rheumatology at the University of Oxford, Oxford, United Kingdom
| | - Jon Timmis
- School of Computer Science, University of Sunderland, Sunderland, United Kingdom
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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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Truong VT, Baverel PG, Lythe GD, Vicini P, Yates JWT, Dubois VFS. Step-by-step comparison of ordinary differential equation and agent-based approaches to pharmacokinetic-pharmacodynamic models. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 11:133-148. [PMID: 34399036 PMCID: PMC8846629 DOI: 10.1002/psp4.12703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 04/28/2021] [Accepted: 05/14/2021] [Indexed: 12/03/2022]
Abstract
Mathematical models in oncology aid in the design of drugs and understanding of their mechanisms of action by simulation of drug biodistribution, drug effects, and interaction between tumor and healthy cells. The traditional approach in pharmacometrics is to develop and validate ordinary differential equation models to quantify trends at the population level. In this approach, time‐course of biological measurements is modeled continuously, assuming a homogenous population. Another approach, agent‐based models, focuses on the behavior and fate of biological entities at the individual level, which subsequently could be summarized to reflect the population level. Heterogeneous cell populations and discrete events are simulated, and spatial distribution can be incorporated. In this tutorial, an agent‐based model is presented and compared to an ordinary differential equation model for a tumor efficacy model inhibiting the pERK pathway. We highlight strengths, weaknesses, and opportunities of each approach.
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Affiliation(s)
- Van Thuy Truong
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, AstraZeneca, Aaron Klug Building, Granta Park, Cambridge, CB21 6GH, UK.,Department of Applied Mathematics, University of Leeds, Leeds, United Kingdom
| | - Paul G Baverel
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, AstraZeneca, Aaron Klug Building, Granta Park, Cambridge, CB21 6GH, UK.,Roche Pharma Research and Early Development, Clinical Pharmacology, Pharmaceutical Sciences, Roche Innovation Center Basel F. Hoffmann-La Roche Ltd, Switzerland
| | - Grant D Lythe
- Department of Applied Mathematics, University of Leeds, Leeds, United Kingdom
| | - Paolo Vicini
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, AstraZeneca, Aaron Klug Building, Granta Park, Cambridge, CB21 6GH, UK.,Confo Therapeutics, Technologiepark 94, 9052, Ghent (Zwijnaarde), Belgium
| | | | - Vincent F S Dubois
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, AstraZeneca, Aaron Klug Building, Granta Park, Cambridge, CB21 6GH, UK
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12
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Belenchia M, Rocchetti G, Maestri S, Cimadamore A, Montironi R, Santoni M, Merelli E. Agent-Based Learning Model for the Obesity Paradox in RCC. Front Bioeng Biotechnol 2021; 9:642760. [PMID: 33996779 PMCID: PMC8116955 DOI: 10.3389/fbioe.2021.642760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 03/05/2021] [Indexed: 12/17/2022] Open
Abstract
A recent study on the immunotherapy treatment of renal cell carcinoma reveals better outcomes in obese patients compared to lean subjects. This enigmatic contradiction has been explained, in the context of the debated obesity paradox, as the effect produced by the cell-cell interaction network on the tumor microenvironment during the immune response. To better understand this hypothesis, we provide a computational framework for the in silico study of the tumor behavior. The starting model of the tumor, based on the cell-cell interaction network, has been described as a multiagent system, whose simulation generates the hypothesized effects on the tumor microenvironment. The medical needs in the immunotherapy design meet the capabilities of a multiagent simulator to reproduce the dynamics of the cell-cell interaction network, meaning a reaction to environmental changes introduced through the experimental data.
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Affiliation(s)
- Matteo Belenchia
- Laboratory of Data Science and Bioshape, School of Science and Technology, University of Camerino, Camerino, Italy
| | - Giacomo Rocchetti
- Laboratory of Data Science and Bioshape, School of Science and Technology, University of Camerino, Camerino, Italy
| | - Stefano Maestri
- Laboratory of Data Science and Bioshape, School of Science and Technology, University of Camerino, Camerino, Italy.,Centre de Physique Théorique, Aix-Marseille University, Marseilles, France
| | - Alessia Cimadamore
- Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Ancona, Italy
| | - Rodolfo Montironi
- Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Ancona, Italy
| | - Matteo Santoni
- Department of Oncology, Macerata Hospital, Macerata, Italy
| | - Emanuela Merelli
- Laboratory of Data Science and Bioshape, School of Science and Technology, University of Camerino, Camerino, Italy
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13
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Azer K, Kaddi CD, Barrett JS, Bai JPF, McQuade ST, Merrill NJ, Piccoli B, Neves-Zaph S, Marchetti L, Lombardo R, Parolo S, Immanuel SRC, Baliga NS. History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications. Front Physiol 2021; 12:637999. [PMID: 33841175 PMCID: PMC8027332 DOI: 10.3389/fphys.2021.637999] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 01/25/2021] [Indexed: 12/24/2022] Open
Abstract
Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges.
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Affiliation(s)
- Karim Azer
- Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States
| | - Chanchala D. Kaddi
- Quantitative Sciences, Bill and Melinda Gates Medical Research Institute, Cambridge, MA, United States
| | | | - Jane P. F. Bai
- Office of Clinical Pharmacology, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, United States
| | - Sean T. McQuade
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Nathaniel J. Merrill
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Benedetto Piccoli
- Department of Mathematical Sciences and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Susana Neves-Zaph
- Translational Disease Modeling, Data and Data Science, Sanofi, Bridgewater, NJ, United States
| | - Luca Marchetti
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Rosario Lombardo
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Silvia Parolo
- Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
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14
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Bloomingdale P, Karelina T, Cirit M, Muldoon SF, Baker J, McCarty WJ, Geerts H, Macha S. Quantitative systems pharmacology in neuroscience: Novel methodologies and technologies. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:412-419. [PMID: 33719204 PMCID: PMC8129713 DOI: 10.1002/psp4.12607] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 12/02/2020] [Accepted: 02/03/2021] [Indexed: 11/12/2022]
Abstract
The development and application of quantitative systems pharmacology models in neuroscience have been modest relative to other fields, such as oncology and immunology, which may reflect the complexity of the brain. Technological and methodological advancements have enhanced the quantitative understanding of brain physiology and pathophysiology and the effects of pharmacological interventions. To maximize the knowledge gained from these novel data types, pharmacometrics modelers may need to expand their toolbox to include additional mathematical and statistical frameworks. A session was held at the 10th annual American Conference on Pharmacometrics (ACoP10) to highlight several recent advancements in quantitative and systems neuroscience. In this mini‐review, we provide a brief overview of technological and methodological advancements in the neuroscience therapeutic area that were discussed during the session and how these can be leveraged with quantitative systems pharmacology modeling to enhance our understanding of neurological diseases. Microphysiological systems using human induced pluripotent stem cells (IPSCs), digital biomarkers, and large‐scale imaging offer more clinically relevant experimental datasets, enhanced granularity, and a plethora of data to potentially improve the preclinical‐to‐clinical translation of therapeutics. Network neuroscience methodologies combined with quantitative systems models of neurodegenerative disease could help bridge the gap between cellular and molecular alterations and clinical end points through the integration of information on neural connectomics. Additional topics, such as the neuroimmune system, microbiome, single‐cell transcriptomic technologies, and digital device biomarkers, are discussed in brief.
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Affiliation(s)
- Peter Bloomingdale
- Quantitative Pharmacology and Pharmacometrics, Merck & Co. Inc, Kenilworth, New Jersey, USA
| | | | - Murat Cirit
- Javelin Biotech, Inc, Woburn, Massachusetts, USA
| | - Sarah F Muldoon
- Mathematics Department, CDSE Program, Neuroscience Program, University at Buffalo, SUNY, Buffalo, New York, USA
| | - Justin Baker
- Harvard Medical School, Boston, Massachusetts, USA
| | | | | | - Sreeraj Macha
- Quantitative Pharmacology, Sanofi, Bridgewater, New Jersey, USA
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15
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Nardini JT, Baker RE, Simpson MJ, Flores KB. Learning differential equation models from stochastic agent-based model simulations. J R Soc Interface 2021; 18:20200987. [PMID: 33726540 PMCID: PMC8086865 DOI: 10.1098/rsif.2020.0987] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 02/22/2021] [Indexed: 12/15/2022] Open
Abstract
Agent-based models provide a flexible framework that is frequently used for modelling many biological systems, including cell migration, molecular dynamics, ecology and epidemiology. Analysis of the model dynamics can be challenging due to their inherent stochasticity and heavy computational requirements. Common approaches to the analysis of agent-based models include extensive Monte Carlo simulation of the model or the derivation of coarse-grained differential equation models to predict the expected or averaged output from the agent-based model. Both of these approaches have limitations, however, as extensive computation of complex agent-based models may be infeasible, and coarse-grained differential equation models can fail to accurately describe model dynamics in certain parameter regimes. We propose that methods from the equation learning field provide a promising, novel and unifying approach for agent-based model analysis. Equation learning is a recent field of research from data science that aims to infer differential equation models directly from data. We use this tutorial to review how methods from equation learning can be used to learn differential equation models from agent-based model simulations. We demonstrate that this framework is easy to use, requires few model simulations, and accurately predicts model dynamics in parameter regions where coarse-grained differential equation models fail to do so. We highlight these advantages through several case studies involving two agent-based models that are broadly applicable to biological phenomena: a birth-death-migration model commonly used to explore cell biology experiments and a susceptible-infected-recovered model of infectious disease spread.
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Affiliation(s)
- John T. Nardini
- North Carolina State University, Mathematics, Raleigh, NC, USA
| | - Ruth E. Baker
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Matthew J. Simpson
- School of Mathematical Sciences, Queensland University of Technology, Brisbane 4001, Australia
| | - Kevin B. Flores
- North Carolina State University, Mathematics, Raleigh, NC, USA
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16
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McComb M, Bies R, Ramanathan M. Machine learning in pharmacometrics: Opportunities and challenges. Br J Clin Pharmacol 2021; 88:1482-1499. [PMID: 33634893 DOI: 10.1111/bcp.14801] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 02/08/2021] [Accepted: 02/12/2021] [Indexed: 12/13/2022] Open
Abstract
The explosive growth in medical devices, imaging and diagnostics, computing, and communication and information technologies in drug development and healthcare has created an ever-expanding data landscape that the pharmacometrics (PMX) research community must now traverse. The tools of machine learning (ML) have emerged as a powerful computational approach in other data-rich disciplines but its effective utilization in the pharmaceutical sciences and PMX modelling is in its infancy. ML-based methods can complement PMX modelling by enabling the information in diverse sources of big data, e.g. population-based public databases and disease-specific clinical registries, to be harnessed because they are capable of efficiently identifying salient variables associated with outcomes and delineating their interdependencies. ML algorithms are computationally efficient, have strong predictive capabilities and can enable learning in the big data setting. ML algorithms can be viewed as providing a computational bridge from big data to complement PMX modelling. This review provides an overview of the strengths and weaknesses of ML approaches vis-à-vis population methods, assesses current research into ML applications in the pharmaceutical sciences and provides perspective for potential opportunities and strategies for the successful integration and utilization of ML in PMX.
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Affiliation(s)
- Mason McComb
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Robert Bies
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA.,Institute for Computational Data Science, University at Buffalo, NY, USA
| | - Murali Ramanathan
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA.,Department of Neurology, University at Buffalo, State University of New York, Buffalo, NY, USA
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17
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Molina Ortiz JP, McClure DD, Shanahan ER, Dehghani F, Holmes AJ, Read MN. Enabling rational gut microbiome manipulations by understanding gut ecology through experimentally-evidenced in silico models. Gut Microbes 2021; 13:1965698. [PMID: 34455914 PMCID: PMC8432618 DOI: 10.1080/19490976.2021.1965698] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 07/01/2021] [Accepted: 07/27/2021] [Indexed: 02/04/2023] Open
Abstract
The gut microbiome has emerged as a contributing factor in non-communicable disease, rendering it a target of health-promoting interventions. Yet current understanding of the host-microbiome dynamic is insufficient to predict the variation in intervention outcomes across individuals. We explore the mechanisms that underpin the gut bacterial ecosystem and highlight how a more complete understanding of this ecology will enable improved intervention outcomes. This ecology varies within the gut over space and time. Interventions disrupt these processes, with cascading consequences throughout the ecosystem. In vivo studies cannot isolate and probe these processes at the required spatiotemporal resolutions, and in vitro studies lack the representative complexity required. However, we highlight that, together, both approaches can inform in silico models that integrate cellular-level dynamics, can extrapolate to explain bacterial community outcomes, permit experimentation and observation over ecological processes at high spatiotemporal resolution, and can serve as predictive platforms on which to prototype interventions. Thus, it is a concerted integration of these techniques that will enable rational targeted manipulations of the gut ecosystem.
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Affiliation(s)
- Juan P. Molina Ortiz
- School of Chemical and Biomolecular Engineering, Faculty of Engineering, The University of Sydney, Sydney, Australia
- Faculty of Engineering, Centre for Advanced Food Engineering, The University of Sydney, Sydney, Australia
| | - Dale D. McClure
- School of Chemical and Biomolecular Engineering, Faculty of Engineering, The University of Sydney, Sydney, Australia
- Faculty of Engineering, Centre for Advanced Food Engineering, The University of Sydney, Sydney, Australia
| | - Erin R. Shanahan
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, Australia
| | - Fariba Dehghani
- School of Chemical and Biomolecular Engineering, Faculty of Engineering, The University of Sydney, Sydney, Australia
- Faculty of Engineering, Centre for Advanced Food Engineering, The University of Sydney, Sydney, Australia
| | - Andrew J. Holmes
- Faculty of Engineering, Centre for Advanced Food Engineering, The University of Sydney, Sydney, Australia
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, Australia
| | - Mark N. Read
- Faculty of Engineering, Centre for Advanced Food Engineering, The University of Sydney, Sydney, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, Australia
- School of Computer Science, Faculty of Engineering, The University of Sydney, Sydney, Australia
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18
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Russo G, Pennisi M, Fichera E, Motta S, Raciti G, Viceconti M, Pappalardo F. In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform. BMC Bioinformatics 2020; 21:527. [PMID: 33308153 PMCID: PMC7733700 DOI: 10.1186/s12859-020-03872-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 11/09/2020] [Indexed: 02/07/2023] Open
Abstract
Background SARS-CoV-2 is a severe respiratory infection that infects humans. Its outburst entitled it as a pandemic emergence. To get a grip on this outbreak, specific preventive and therapeutic interventions are urgently needed. It must be said that, until now, there are no existing vaccines for coronaviruses. To promptly and rapidly respond to pandemic events, the application of in silico trials can be used for designing and testing medicines against SARS-CoV-2 and speed-up the vaccine discovery pipeline, predicting any therapeutic failure and minimizing undesired effects. Results We present an in silico platform that showed to be in very good agreement with the latest literature in predicting SARS-CoV-2 dynamics and related immune system host response. Moreover, it has been used to predict the outcome of one of the latest suggested approach to design an effective vaccine, based on monoclonal antibody. Universal Immune System Simulator (UISS) in silico platform is potentially ready to be used as an in silico trial platform to predict the outcome of vaccination strategy against SARS-CoV-2. Conclusions In silico trials are showing to be powerful weapons in predicting immune responses of potential candidate vaccines. Here, UISS has been extended to be used as an in silico trial platform to speed-up and drive the discovery pipeline of vaccine against SARS-CoV-2.
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Affiliation(s)
- Giulia Russo
- Department of Drug Sciences, University of Catania, 95125, Catania, Italy
| | - Marzio Pennisi
- Computer Science Institute, DiSIT, University of Eastern Piedmont, 15125, Alessandria, Italy
| | | | - Santo Motta
- National Research Council of Italy, 00185, Rome, Italy
| | - Giuseppina Raciti
- Department of Drug Sciences, University of Catania, 95125, Catania, Italy.
| | - Marco Viceconti
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, 40136, Bologna, Italy
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19
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Bao K. An elementary mathematical modeling of drug resistance in cancer. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2020; 18:339-353. [PMID: 33525095 DOI: 10.3934/mbe.2021018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Targeted therapy is one of the promising strategies for the treatment of cancer. However, resistance to anticancer drug strongly limits the long-term effectiveness of treatment, which is a major obstacle for successfully treating cancer. In this paper, we analyze a linear system of ordinary differential equations for cancer multi-drug resistance induced mainly by random genetic point mutation. We investigate that the resistance generated before the beginning of the treatment is greater than that developed during-treatment. This result depends on the concentration of the drug, which holds only when the concentration of the drug reaches a lower limit. Moreover, no matter how many drugs are used in the treatment, the amount of resistance (generated at the beginning of the treatment and within a certain period of time after the treatment) always depends on the turnover rate. Using numerical simulations, we also evaluate the response of the mutant cancer cell population as a function of time under different treatment strategies. At appropriate dosages, combination therapy produces significant effects for the treatment with low-turnover rate cancer. For cancer with very high-turnover rate (close to 1), combination therapy can not significantly reduce the amount of resistant mutants compared to monotherapy, so in this case, combination therapy would not have advantage over monotherapy.
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Affiliation(s)
- Kangbo Bao
- School of Mathematics and Statistics, Central China Normal University, Wuhan 430079, China
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20
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Goliaei A, Woods HA, Tron AE, Belmonte MA, Secrist JP, Ferguson D, Drew L, Fretland AJ, Aldridge BB, Gibbons FD. Multiscale Model Identifies Improved Schedule for Treatment of Acute Myeloid Leukemia In Vitro With the Mcl-1 Inhibitor AZD5991. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 9:561-570. [PMID: 32860732 PMCID: PMC7577016 DOI: 10.1002/psp4.12552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 04/20/2020] [Indexed: 11/06/2022]
Abstract
Anticancer efficacy is driven not only by dose but also by frequency and duration of treatment. We describe a multiscale model combining cell cycle, cellular heterogeneity of B‐cell lymphoma 2 family proteins, and pharmacology of AZD5991, a potent small‐molecule inhibitor of myeloid cell leukemia 1 (Mcl‐1). The model was calibrated using in vitro viability data for the MV‐4‐11 acute myeloid leukemia cell line under continuous incubation for 72 hours at concentrations of 0.03–30 μM. Using a virtual screen, we identified two schedules as having significantly different predicted efficacy and showed experimentally that a “short” schedule (treating cells for 6 of 24 hours) is significantly better able to maintain the rate of cell kill during treatment than a “long” schedule (18 of 24 hours). This work suggests that resistance can be driven by heterogeneity in protein expression of Mcl‐1 alone without requiring mutation or resistant subclones and demonstrates the utility of mathematical models in efficiently identifying regimens for experimental exploration.
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Affiliation(s)
- Ardeshir Goliaei
- Drug Metabolism and Pharmacokinetics (DMPK), Oncology R&D, AstraZeneca, Waltham, Massachusetts, USA.,Novartis Institutes for Biomedical Research, Cambridge, Massachusetts, USA
| | - Haley A Woods
- Bioscience, Oncology R&D, AstraZeneca, Waltham, Massachusetts, USA
| | - Adriana E Tron
- Bioscience, Oncology R&D, AstraZeneca, Waltham, Massachusetts, USA.,Agios Pharmaceuticals, Cambridge, Massachusetts, USA
| | | | - J Paul Secrist
- Bioscience, Oncology R&D, AstraZeneca, Waltham, Massachusetts, USA
| | - Douglas Ferguson
- Drug Metabolism and Pharmacokinetics (DMPK), Oncology R&D, AstraZeneca, Waltham, Massachusetts, USA
| | - Lisa Drew
- Bioscience, Oncology R&D, AstraZeneca, Waltham, Massachusetts, USA
| | - Adrian J Fretland
- Drug Metabolism and Pharmacokinetics (DMPK), Oncology R&D, AstraZeneca, Waltham, Massachusetts, USA
| | - Bree B Aldridge
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, Massachusetts, USA.,Department of Biomedical Engineering, Tufts University, Medford, Massachusetts, USA
| | - Francis D Gibbons
- Drug Metabolism and Pharmacokinetics (DMPK), Oncology R&D, AstraZeneca, Waltham, Massachusetts, USA
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21
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Bodine EN, Panoff RM, Voit EO, Weisstein AE. Agent-Based Modeling and Simulation in Mathematics and Biology Education. Bull Math Biol 2020; 82:101. [PMID: 32725363 PMCID: PMC7385329 DOI: 10.1007/s11538-020-00778-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 07/11/2020] [Indexed: 12/20/2022]
Abstract
With advances in computing, agent-based models (ABMs) have become a feasible and appealing tool to study biological systems. ABMs are seeing increased incorporation into both the biology and mathematics classrooms as powerful modeling tools to study processes involving substantial amounts of stochasticity, nonlinear interactions, and/or heterogeneous spatial structures. Here we present a brief synopsis of the agent-based modeling approach with an emphasis on its use to simulate biological systems, and provide a discussion of its role and limitations in both the biology and mathematics classrooms.
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Affiliation(s)
- Erin N. Bodine
- Department of Mathematics and Computer Science, Rhodes College, 2000 N. Parkway, Memphis, TN 38112 USA
| | - Robert M. Panoff
- Shodor Education Foundation and Wofford College, 701 William Vickers Avenue, Durham, NC 27701 USA
| | - Eberhard O. Voit
- Department of Biomedical Engineering, Georgia Institute of Technology, 2115 EBB, 950 Atlantic Drive, Atlanta, GA 30332-2000 USA
| | - Anton E. Weisstein
- Department of Biology, Truman State University, 100 E. Normal Street, Kirksville, MO 63501 USA
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22
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Cosgrove J, Novkovic M, Albrecht S, Pikor NB, Zhou Z, Onder L, Mörbe U, Cupovic J, Miller H, Alden K, Thuery A, O'Toole P, Pinter R, Jarrett S, Taylor E, Venetz D, Heller M, Uguccioni M, Legler DF, Lacey CJ, Coatesworth A, Polak WG, Cupedo T, Manoury B, Thelen M, Stein JV, Wolf M, Leake MC, Timmis J, Ludewig B, Coles MC. B cell zone reticular cell microenvironments shape CXCL13 gradient formation. Nat Commun 2020; 11:3677. [PMID: 32699279 PMCID: PMC7376062 DOI: 10.1038/s41467-020-17135-2] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Accepted: 03/12/2020] [Indexed: 02/07/2023] Open
Abstract
Through the formation of concentration gradients, morphogens drive graded responses to extracellular signals, thereby fine-tuning cell behaviors in complex tissues. Here we show that the chemokine CXCL13 forms both soluble and immobilized gradients. Specifically, CXCL13+ follicular reticular cells form a small-world network of guidance structures, with computer simulations and optimization analysis predicting that immobilized gradients created by this network promote B cell trafficking. Consistent with this prediction, imaging analysis show that CXCL13 binds to extracellular matrix components in situ, constraining its diffusion. CXCL13 solubilization requires the protease cathepsin B that cleaves CXCL13 into a stable product. Mice lacking cathepsin B display aberrant follicular architecture, a phenotype associated with effective B cell homing to but not within lymph nodes. Our data thus suggest that reticular cells of the B cell zone generate microenvironments that shape both immobilized and soluble CXCL13 gradients.
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Affiliation(s)
- Jason Cosgrove
- York Computational Immunology Lab, University of York, York, UK
- Centre for Immunology and Infection, Department of Biology and Hull York Medical School, University of York, York, UK
- Department of Electronic Engineering, University of York, York, UK
| | - Mario Novkovic
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Stefan Albrecht
- Theodor Kocher Institute, University of Bern, Bern, Switzerland
| | - Natalia B Pikor
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Zhaoukun Zhou
- Department of Biology, University of York, York, UK
- Biological Physical Sciences Institute (BPSI), University of York, York, UK
- Department of Physics, University of York, York, UK
| | - Lucas Onder
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Urs Mörbe
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Jovana Cupovic
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Helen Miller
- Department of Biology, University of York, York, UK
- Biological Physical Sciences Institute (BPSI), University of York, York, UK
- Department of Physics, University of York, York, UK
| | - Kieran Alden
- York Computational Immunology Lab, University of York, York, UK
- Department of Electronic Engineering, University of York, York, UK
| | - Anne Thuery
- Centre for Immunology and Infection, Department of Biology and Hull York Medical School, University of York, York, UK
| | | | - Rita Pinter
- Kennedy Institute of Rheumatology at the University of Oxford, Oxford, UK
| | - Simon Jarrett
- Kennedy Institute of Rheumatology at the University of Oxford, Oxford, UK
| | - Emily Taylor
- Centre for Immunology and Infection, Department of Biology and Hull York Medical School, University of York, York, UK
| | - Daniel Venetz
- Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland
| | - Manfred Heller
- Department of Clinical Research, University of Bern, Bern, Switzerland
| | - Mariagrazia Uguccioni
- Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland
| | - Daniel F Legler
- Biotechnology Institute Thurgau (BITg) at the University of Konstanz, Kreuzlingen, Switzerland
| | - Charles J Lacey
- York Computational Immunology Lab, University of York, York, UK
| | | | - Wojciech G Polak
- Department of Surgery, Erasmus University Medical Centre, Rotterdam, Netherlands
| | - Tom Cupedo
- Department of Hematology, Erasmus University Medical Centre, Rotterdam, Netherlands
| | - Bénedicte Manoury
- Institut Necker Enfants Malades, INSERM U1151- CNRS UMR 8253, 149 rue de Sèvres 75015 Paris, France Université René Descartes, 75005, Paris, France
- Université Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Marcus Thelen
- Institute for Research in Biomedicine, Università della Svizzera italiana, Bellinzona, Switzerland
| | - Jens V Stein
- Department of Oncology, Microbiology and Immunology, University of Fribourg, Fribourg, Switzerland
| | - Marlene Wolf
- Theodor Kocher Institute, University of Bern, Bern, Switzerland
| | - Mark C Leake
- Department of Biology, University of York, York, UK.
- Biological Physical Sciences Institute (BPSI), University of York, York, UK.
- Department of Physics, University of York, York, UK.
| | - Jon Timmis
- York Computational Immunology Lab, University of York, York, UK.
- Department of Electronic Engineering, University of York, York, UK.
| | - Burkhard Ludewig
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland.
| | - Mark C Coles
- York Computational Immunology Lab, University of York, York, UK.
- Kennedy Institute of Rheumatology at the University of Oxford, Oxford, UK.
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23
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Cassani S, Olson SD. A Hybrid Model of Cartilage Regeneration Capturing the Interactions Between Cellular Dynamics and Porosity. Bull Math Biol 2020; 82:18. [PMID: 31970523 DOI: 10.1007/s11538-020-00695-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Accepted: 12/27/2019] [Indexed: 12/31/2022]
Abstract
To accelerate the development of strategies for cartilage tissue engineering, models are necessary to investigate the interactions between cellular dynamics and the local microenvironment. We use a discrete framework to capture the individual behavior of cells, modeling experiments where cells are seeded in a porous scaffold or hydrogel and over the time course of a month, the scaffold slowly degrades while cells divide and synthesize extracellular matrix constituents. The movement of cells and the ability to proliferate is a function of the local porosity, defined as the volume fraction of fluid in the surrounding region. A phenomenological approach is used to capture a continuous profile for the degrading scaffold and accumulating matrix, which will then change the local porosity throughout the construct. We parameterize the model by first matching total cell counts in the construct to chondrocytes seeded in a polyglycolic acid scaffold (Freed et al. in Biotechnol Bioeng 43:597-604, 1994). We investigate the influence of initial scaffold porosity on the total cell count and spatial profiles of cell and ECM in the construct. Cell counts were higher at day 30 in scaffolds of lower initial porosity, and similar cell counts were obtained using different models of scaffold degradation and matrix accumulation (either uniform or cell-specific). Using this modeling framework, we study the interplay between a phenomenological representation of scaffold architecture and porosity as well as the potential continuous application of growth factors. We determine parameter regimes where large cellular aggregates occur, which can hinder matrix accumulation and cellular proliferation. The developed modeling framework can easily be extended and can be used to identify optimal scaffolds and culture conditions that lead to a desired distribution of extracellular matrix and cell counts throughout the construct.
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Affiliation(s)
- Simone Cassani
- Department of Mathematics, University at Buffalo, The State University of New York, 244 Mathematics Building, Buffalo, NY, 14260, USA
| | - Sarah D Olson
- Department of Mathematical Sciences, Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA.
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24
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Buckley PR, Alden K, Coccia M, Chalon A, Collignon C, Temmerman ST, Didierlaurent AM, van der Most R, Timmis J, Andersen CA, Coles MC. Application of Modeling Approaches to Explore Vaccine Adjuvant Mode-of-Action. Front Immunol 2019; 10:2150. [PMID: 31572370 PMCID: PMC6751289 DOI: 10.3389/fimmu.2019.02150] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 08/27/2019] [Indexed: 01/12/2023] Open
Abstract
Novel adjuvant technologies have a key role in the development of next-generation vaccines, due to their capacity to modulate the duration, strength and quality of the immune response. The AS01 adjuvant is used in the malaria vaccine RTS,S/AS01 and in the licensed herpes-zoster vaccine (Shingrix) where the vaccine has proven its ability to generate protective responses with both robust humoral and T-cell responses. For many years, animal models have provided insights into adjuvant mode-of-action (MoA), generally through investigating individual genes or proteins. Furthermore, modeling and simulation techniques can be utilized to integrate a variety of different data types; ranging from serum biomarkers to large scale “omics” datasets. In this perspective we present a framework to create a holistic integration of pre-clinical datasets and immunological literature in order to develop an evidence-based hypothesis of AS01 adjuvant MoA, creating a unified view of multiple experiments. Furthermore, we highlight how holistic systems-knowledge can serve as a basis for the construction of models and simulations supporting exploration of key questions surrounding adjuvant MoA. Using the Systems-Biology-Graphical-Notation, a tool for graphical representation of biological processes, we have captured high-level cellular behaviors and interactions, and cytokine dynamics during the early immune response, which are substantiated by a series of diagrams detailing cellular dynamics. Through explicitly describing AS01 MoA we have built a consensus of understanding across multiple experiments, and so we present a framework to integrate modeling approaches into exploring adjuvant MoA, in order to guide experimental design, interpret results and inform rational design of vaccines.
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Affiliation(s)
- Paul R Buckley
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, United Kingdom.,Department of Electronic Engineering, University of York, York, United Kingdom
| | - Kieran Alden
- Department of Electronic Engineering, University of York, York, United Kingdom
| | | | | | | | | | | | | | - Jon Timmis
- Department of Electronic Engineering, University of York, York, United Kingdom.,Faculty of Technology, University of Sunderland, Sunderland, United Kingdom
| | | | - Mark C Coles
- Kennedy Institute of Rheumatology, University of Oxford, Oxford, United Kingdom
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25
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Sun X, Hu B. Mathematical modeling and computational prediction of cancer drug resistance. Brief Bioinform 2019; 19:1382-1399. [PMID: 28981626 PMCID: PMC6402530 DOI: 10.1093/bib/bbx065] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Indexed: 12/23/2022] Open
Abstract
Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic–pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of computational methods for studying drug resistance, including inferring drug-induced signaling networks, multiscale modeling, drug combinations and precision medicine.
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Affiliation(s)
- Xiaoqiang Sun
- Zhong-shan School of Medicine, Sun Yat-Sen University
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University
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26
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Helmlinger G, Sokolov V, Peskov K, Hallow KM, Kosinsky Y, Voronova V, Chu L, Yakovleva T, Azarov I, Kaschek D, Dolgun A, Schmidt H, Boulton DW, Penland RC. Quantitative Systems Pharmacology: An Exemplar Model-Building Workflow With Applications in Cardiovascular, Metabolic, and Oncology Drug Development. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 8:380-395. [PMID: 31087533 PMCID: PMC6617832 DOI: 10.1002/psp4.12426] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 05/03/2019] [Indexed: 12/13/2022]
Abstract
Quantitative systems pharmacology (QSP), a mechanistically oriented form of drug and disease modeling, seeks to address a diverse set of problems in the discovery and development of therapies. These problems bring a considerable amount of variability and uncertainty inherent in the nonclinical and clinical data. Likewise, the available modeling techniques and related software tools are manifold. Appropriately, the development, qualification, application, and impact of QSP models have been similarly varied. In this review, we describe the progressive maturation of a QSP modeling workflow: a necessary step for the efficient, reproducible development and qualification of QSP models, which themselves are highly iterative and evolutive. Furthermore, we describe three applications of QSP to impact drug development; one supporting new indications for an approved antidiabetic clinical asset through mechanistic hypothesis generation, one highlighting efficacy and safety differentiation within the sodium‐glucose cotransporter‐2 inhibitor drug class, and one enabling rational selection of immuno‐oncology drug combinations.
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Affiliation(s)
- Gabriel Helmlinger
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, Massachusetts, USA
| | | | - Kirill Peskov
- M&S Decisions LLC, Moscow, Russia.,Computational Oncology Group, I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health, Moscow, Russia
| | - Karen M Hallow
- School of Chemical, Materials, and Biomedical Engineering, University of Georgia, Athens, Georgia, USA.,Department of Epidemiology and Biostatistics, University of Georgia, Athens, Georgia, USA
| | | | | | - Lulu Chu
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, Massachusetts, USA
| | | | | | | | | | | | - David W Boulton
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Gaithersburg, Maryland, USA
| | - Robert C Penland
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, Massachusetts, USA
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27
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Liang W, Zheng Y, Zhang J, Sun X. Multiscale modeling reveals angiogenesis-induced drug resistance in brain tumors and predicts a synergistic drug combination targeting EGFR and VEGFR pathways. BMC Bioinformatics 2019; 20:203. [PMID: 31074391 PMCID: PMC6509865 DOI: 10.1186/s12859-019-2737-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Experimental studies have demonstrated that both the extracellular vasculature or microenvironment and intracellular molecular network (e.g., epidermal growth factor receptor (EGFR) signaling pathway) are important for brain tumor growth. Additionally, some drugs have been developed to inhibit EGFR signaling pathways. However, how angiogenesis affects the response of tumor cells to drug treatment has rarely been mechanistically studied. Therefore, a multiscale model is required to investigate such complex biological systems that contain interactions and feedback among multiple levels. RESULTS In this study, we developed a single cell-based multiscale spatiotemporal model to simulate vascular tumor growth and the drug response based on the vascular endothelial growth factor receptor (VEGFR) signaling pathway, the EGFR signaling pathway and the cell cycle as well as several microenvironmental factors that determine cell fate switches in a temporal and spatial context. By incorporating the EGFRI treatment effect, the model showed an interesting phenomenon in which the survival rate of tumor cells decreased in the early stage but rebounded in a later stage, revealing the emergence of drug resistance. Moreover, we revealed the critical role of angiogenesis in acquired drug resistance, since inhibiting blood vessel growth using a VEGFR inhibitor prevented the recovery of the survival rate of tumor cells in the later stage. We further investigated the optimal timing of combining VEGFR inhibition with EGFR inhibition and predicted that the drug combination targeting both the EGFR pathway and VEGFR pathway has a synergistic effect. The experimental data validated the prediction of drug synergy, confirming the effectiveness of our model. In addition, the combination of EGFR and VEGFR genes showed clinical relevance in glioma patients. CONCLUSIONS The developed multiscale model revealed angiogenesis-induced drug resistance mechanisms of brain tumors to EGFRI treatment and predicted a synergistic drug combination targeting both EGFR and VEGFR pathways with optimal combination timing. This study explored the mechanistic and functional mechanisms of the angiogenesis underlying tumor growth and drug resistance, which advances our understanding of novel mechanisms of drug resistance and provides implications for designing more effective cancer therapies.
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Affiliation(s)
- Weishan Liang
- Zhong-shan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China.,Key Laboratory of Tropical Disease Control (Sun Yat-Sen University), Chinese Ministry of Education, Guangzhou, 510080, China.,School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, China
| | - Yongjiang Zheng
- Department of Hematology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ji Zhang
- Department of Neurosurgery, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510275, China
| | - Xiaoqiang Sun
- Zhong-shan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China. .,Key Laboratory of Tropical Disease Control (Sun Yat-Sen University), Chinese Ministry of Education, Guangzhou, 510080, China. .,School of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, China.
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28
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Norton KA, Gong C, Jamalian S, Popel AS. Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment. Processes (Basel) 2019; 7:37. [PMID: 30701168 PMCID: PMC6349239 DOI: 10.3390/pr7010037] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Multiscale systems biology and systems pharmacology are powerful methodologies that are playing increasingly important roles in understanding the fundamental mechanisms of biological phenomena and in clinical applications. In this review, we summarize the state of the art in the applications of agent-based models (ABM) and hybrid modeling to the tumor immune microenvironment and cancer immune response, including immunotherapy. Heterogeneity is a hallmark of cancer; tumor heterogeneity at the molecular, cellular, and tissue scales is a major determinant of metastasis, drug resistance, and low response rate to molecular targeted therapies and immunotherapies. Agent-based modeling is an effective methodology to obtain and understand quantitative characteristics of these processes and to propose clinical solutions aimed at overcoming the current obstacles in cancer treatment. We review models focusing on intra-tumor heterogeneity, particularly on interactions between cancer cells and stromal cells, including immune cells, the role of tumor-associated vasculature in the immune response, immune-related tumor mechanobiology, and cancer immunotherapy. We discuss the role of digital pathology in parameterizing and validating spatial computational models and potential applications to therapeutics.
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Affiliation(s)
- Kerri-Ann Norton
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
- Computer Science Program, Department of Science, Mathematics, and Computing, Bard College, Annandale-on-Hudson, NY 12504, USA
| | - Chang Gong
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Samira Jamalian
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Aleksander S. Popel
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
- Department of Oncology and the Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
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29
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Read MN, Alden K, Timmis J, Andrews PS. Strategies for calibrating models of biology. Brief Bioinform 2018; 21:24-35. [PMID: 30239570 DOI: 10.1093/bib/bby092] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 08/10/2018] [Accepted: 08/27/2018] [Indexed: 11/14/2022] Open
Abstract
Computational and mathematical modelling has become a valuable tool for investigating biological systems. Modelling enables prediction of how biological components interact to deliver system-level properties and extrapolation of biological system performance to contexts and experimental conditions where this is unknown. A model's value hinges on knowing that it faithfully represents the biology under the contexts of use, or clearly ascertaining otherwise and thus motivating further model refinement. These qualities are evaluated through calibration, typically formulated as identifying model parameter values that align model and biological behaviours as measured through a metric applied to both. Calibration is critical to modelling but is often underappreciated. A failure to appropriately calibrate risks unrepresentative models that generate erroneous insights. Here, we review a suite of strategies to more rigorously challenge a model's representation of a biological system. All are motivated by features of biological systems, and illustrative examples are drawn from the modelling literature. We examine the calibration of a model against distributions of biological behaviours or outcomes, not only average values. We argue for calibration even where model parameter values are experimentally ascertained. We explore how single metrics can be non-distinguishing for complex systems, with multiple-component dynamic and interaction configurations giving rise to the same metric output. Under these conditions, calibration is insufficiently constraining and the model non-identifiable: multiple solutions to the calibration problem exist. We draw an analogy to curve fitting and argue that calibrating a biological model against a single experiment or context is akin to curve fitting against a single data point. Though useful for communicating model results, we explore how metrics that quantify heavily emergent properties may not be suitable for use in calibration. Lastly, we consider the role of sensitivity and uncertainty analysis in calibration and the interpretation of model results. Our goal in this manuscript is to encourage a deeper consideration of calibration, and how to increase its capacity to either deliver faithful models or demonstrate them otherwise.
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Affiliation(s)
| | | | | | - Paul S Andrews
- SimOmics Ltd, Suite 10 IT Centre, Innovation Way, York, UK
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30
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Brown LV, Gaffney EA, Wagg J, Coles MC. Applications of mechanistic modelling to clinical and experimental immunology: an emerging technology to accelerate immunotherapeutic discovery and development. Clin Exp Immunol 2018; 193:284-292. [PMID: 30240512 PMCID: PMC6150250 DOI: 10.1111/cei.13182] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/31/2018] [Indexed: 12/15/2022] Open
Abstract
The application of in-silico modelling is beginning to emerge as a key methodology to advance our understanding of mechanisms of disease pathophysiology and related drug action, and in the design of experimental medicine and clinical studies. From this perspective, we will present a non-technical discussion of a small number of recent and historical applications of mathematical, statistical and computational modelling to clinical and experimental immunology. We focus specifically upon mechanistic questions relating to human viral infection, tumour growth and metastasis and T cell activation. These exemplar applications highlight the potential of this approach to impact upon human immunology informed by ever-expanding experimental, clinical and 'omics' data. Despite the capacity of mechanistic modelling to accelerate therapeutic discovery and development and to de-risk clinical trial design, it is not utilized widely across the field. We outline ongoing challenges facing the integration of mechanistic modelling with experimental and clinical immunology, and suggest how these may be overcome. Advances in key technologies, including multi-scale modelling, machine learning and the wealth of 'omics' data sets, coupled with advancements in computational capacity, are providing the basis for mechanistic modelling to impact on immunotherapeutic discovery and development during the next decade.
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Affiliation(s)
- L. V. Brown
- Wolfson Centre for Mathematical BiologyMathematical InstituteUniversity of OxfordOxfordUK
| | - E. A. Gaffney
- Wolfson Centre for Mathematical BiologyMathematical InstituteUniversity of OxfordOxfordUK
| | - J. Wagg
- Pharmaceutical Sciences, Clinical PharmacologyRoche Innovation CenterBaselSwitzerland
| | - M. C. Coles
- Kennedy Institute of RheumatologyUniversity of OxfordOxfordUK
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31
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Deng J, Jhandey A, Zhu X, Yang Z, Yik KFP, Zuo Z, Lam TN. In silico drug absorption tract: An agent-based biomimetic model for human oral drug absorption. PLoS One 2018; 13:e0203361. [PMID: 30169515 PMCID: PMC6118387 DOI: 10.1371/journal.pone.0203361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 08/20/2018] [Indexed: 11/26/2022] Open
Abstract
Background An agent-based modeling approach has been suggested as an alternative to traditional, equation-based modeling methods for describing oral drug absorption. It enables researchers to gain a better understanding of the pharmacokinetic (PK) mechanisms of a drug. This project demonstrates that a biomimetic agent-based model can adequately describe the absorption and disposition kinetics both of midazolam and clonazepam. Methods An agent-based biomimetic model, in silico drug absorption tract (ISDAT), was built to mimic oral drug absorption in humans. The model consisted of distinct spaces, membranes, and metabolic enzymes, and it was altogether representative of human physiology relating to oral drug absorption. Simulated experiments were run with the model, and the results were compared to the referent data from clinical equivalence trials. Acceptable similarity was verified by pre-specified criteria, which included 1) qualitative visual matching between the clinical and simulated concentration-time profiles, 2) quantitative similarity indices, namely, weighted root mean squared error (RMSE), and weighted mean absolute percentage error (MAPE) and 3) descriptive similarity which requires less than 25% difference between key PK parameters calculated by the clinical and the simulated concentration-time profiles. The model and its parameters were iteratively refined until all similarity criteria were met. Furthermore, simulated PK experiments were conducted to predict bioavailability (F). For better visualization, a graphical user interface for the model was developed and a video is available in Supporting Information. Results Simulation results satisfied all three levels of similarity criteria for both drugs. The weighted RMSE was 0.51 and 0.92, and the weighted MAPE was 5.99% and 8.43% for midazolam and clonazepam, respectively. Calculated PK parameter values, including area under the curve (AUC), peak plasma drug concentration (Cmax), time to reach Cmax (Tmax), terminal elimination rate constant (Kel), terminal elimination half life (T1/2), apparent oral clearance (CL/F), and apparent volume of distribution (V/F), were reasonable compared to the referent values. The predicted absolute oral bioavailability (F) was 44% for midazolam (literature reported value, 31–72%) and 93% (literature reported value, ≥ 90%) for clonazepam. Conclusion The ISDAT met all the pre-specified similarity criteria for both midazolam and clonazepam, and demonstrated its ability to describe absorption kinetics of both drugs. Therefore, the validated ISDAT can be a promising platform for further research into the use of similar in silico models for drug absorption kinetics.
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Affiliation(s)
- Jianyuan Deng
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Anika Jhandey
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
- School of Pharmacy, University of Nottingham, Nottingham, United Kingdom
| | - Xiao Zhu
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Zhibo Yang
- Department of Computer Science, Stony Brook University, Stony Brook, NY, United States of America
| | - Kin Fu Patrick Yik
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Zhong Zuo
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Tai Ning Lam
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong
- * E-mail:
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32
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Abstract
New technologies to generate, store and retrieve medical and research data are inducing a rapid change in clinical and translational research and health care. Systems medicine is the interdisciplinary approach wherein physicians and clinical investigators team up with experts from biology, biostatistics, informatics, mathematics and computational modeling to develop methods to use new and stored data to the benefit of the patient. We here provide a critical assessment of the opportunities and challenges arising out of systems approaches in medicine and from this provide a definition of what systems medicine entails. Based on our analysis of current developments in medicine and healthcare and associated research needs, we emphasize the role of systems medicine as a multilevel and multidisciplinary methodological framework for informed data acquisition and interdisciplinary data analysis to extract previously inaccessible knowledge for the benefit of patients.
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33
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Read MN, Alden K, Rose LM, Timmis J. Automated multi-objective calibration of biological agent-based simulations. J R Soc Interface 2017; 13:rsif.2016.0543. [PMID: 27628175 DOI: 10.1098/rsif.2016.0543] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Accepted: 08/22/2016] [Indexed: 12/27/2022] Open
Abstract
Computational agent-based simulation (ABS) is increasingly used to complement laboratory techniques in advancing our understanding of biological systems. Calibration, the identification of parameter values that align simulation with biological behaviours, becomes challenging as increasingly complex biological domains are simulated. Complex domains cannot be characterized by single metrics alone, rendering simulation calibration a fundamentally multi-metric optimization problem that typical calibration techniques cannot handle. Yet calibration is an essential activity in simulation-based science; the baseline calibration forms a control for subsequent experimentation and hence is fundamental in the interpretation of results. Here, we develop and showcase a method, built around multi-objective optimization, for calibrating ABSs against complex target behaviours requiring several metrics (termed objectives) to characterize. Multi-objective calibration (MOC) delivers those sets of parameter values representing optimal trade-offs in simulation performance against each metric, in the form of a Pareto front. We use MOC to calibrate a well-understood immunological simulation against both established a priori and previously unestablished target behaviours. Furthermore, we show that simulation-borne conclusions are broadly, but not entirely, robust to adopting baseline parameter values from different extremes of the Pareto front, highlighting the importance of MOC's identification of numerous calibration solutions. We devise a method for detecting overfitting in a multi-objective context, not previously possible, used to save computational effort by terminating MOC when no improved solutions will be found. MOC can significantly impact biological simulation, adding rigour to and speeding up an otherwise time-consuming calibration process and highlighting inappropriate biological capture by simulations that cannot be well calibrated. As such, it produces more accurate simulations that generate more informative biological predictions.
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Affiliation(s)
- Mark N Read
- School of Life and Environmental Sciences, The University of Sydney, Camperdown, New South Wales, Australia Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia
| | - Kieran Alden
- Department of Electronics, University of York, York, UK
| | - Louis M Rose
- Department of Computer Science, University of York, York, UK
| | - Jon Timmis
- Department of Electronics, University of York, York, UK
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Abstract
Introduction Tuberculosis (TB), one of the most common infectious diseases, requires treatment with multiple antibiotics taken over at least 6 months. This long treatment often results in poor patient-adherence, which can lead to the emergence of multi-drug resistant TB. New antibiotic treatment strategies are sorely needed. New antibiotics are being developed or repurposed to treat TB, but as there are numerous potential antibiotics, dosing sizes and potential schedules, the regimen design space for new treatments is too large to search exhaustively. Here we propose a method that combines an agent-based multi-scale model capturing TB granuloma formation with algorithms for mathematical optimization to identify optimal TB treatment regimens. Methods We define two different single-antibiotic treatments to compare the efficiency and accuracy in predicting optimal treatment regimens of two optimization algorithms: genetic algorithms (GA) and surrogate-assisted optimization through radial basis function (RBF) networks. We also illustrate the use of RBF networks to optimize double-antibiotic treatments. Results We found that while GAs can locate optimal treatment regimens more accurately, RBF networks provide a more practical strategy to TB treatment optimization with fewer simulations, and successfully estimated optimal double-antibiotic treatment regimens. Conclusions Our results indicate surrogate-assisted optimization can locate optimal TB treatment regimens from a larger set of antibiotics, doses and schedules, and could be applied to solve optimization problems in other areas of research using systems biology approaches. Our findings have important implications for the treatment of diseases like TB that have lengthy protocols or for any disease that requires multiple drugs.
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35
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Read MN, Holmes AJ. Towards an Integrative Understanding of Diet-Host-Gut Microbiome Interactions. Front Immunol 2017; 8:538. [PMID: 28533782 PMCID: PMC5421151 DOI: 10.3389/fimmu.2017.00538] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2017] [Accepted: 04/21/2017] [Indexed: 12/11/2022] Open
Abstract
Over the last 20 years, a sizeable body of research has linked the microbiome and host diet to a remarkable diversity of diseases. Yet, unifying principles of microbiome assembly or function, at levels required to rationally manipulate a specific individual's microbiome to their benefit, have not emerged. A key driver of both community composition and activity is the host diet, but diet-microbiome interactions cannot be characterized without consideration of host-diet interactions such as appetite and digestion. This becomes even more complex if health outcomes are to be explored, as microbes engage in multiple interactions and feedback pathways with the host. Here, we review these interactions and set forth the need to build conceptual models of the diet-microbiome-host axes that draw out the key principles governing this system's dynamics. We highlight how "units of response," characterizations of similarly behaving microbes, do not correlate consistently with microbial sequence relatedness, raising a challenge for relating high-throughput data sets to conceptual models. Furthermore, they are question-specific; responses to resource environment may be captured at higher taxonomic levels, but capturing microbial products that depend on networks of different interacting populations, such as short-chain fatty acid production through anaerobic fermentation, can require consideration of the entire community. We posit that integrative approaches to teasing apart diet-microbe-host interactions will help bridge between experimental data sets and conceptual models and will be of value in formulating predictive models.
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Affiliation(s)
- Mark N. Read
- The School of Environmental and Life Sciences, The Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
| | - Andrew J. Holmes
- The School of Environmental and Life Sciences, The Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
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Butler JA, Cosgrove J, Alden K, Timmis J, Coles MC. Model-Driven Experimentation: A New Approach to Understand Mechanisms of Tertiary Lymphoid Tissue Formation, Function, and Therapeutic Resolution. Front Immunol 2017; 7:658. [PMID: 28421068 PMCID: PMC5378811 DOI: 10.3389/fimmu.2016.00658] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Accepted: 12/16/2016] [Indexed: 11/13/2022] Open
Abstract
The molecular and cellular processes driving the formation of secondary lymphoid tissues have been extensively studied using a combination of mouse knockouts, lineage-specific reporter mice, gene expression analysis, immunohistochemistry, and flow cytometry. However, the mechanisms driving the formation and function of tertiary lymphoid tissue (TLT) experimental techniques have proven to be more enigmatic and controversial due to differences between experimental models and human disease pathology. Systems-based approaches including data-driven biological network analysis (gene interaction network, metabolic pathway network, cell-cell signaling, and cascade networks) and mechanistic modeling afford a novel perspective from which to understand TLT formation and identify mechanisms that may lead to the resolution of tissue pathology. In this perspective, we make the case for applying model-driven experimentation using two case studies, which combined simulations with experiments to identify mechanisms driving lymphoid tissue formation and function, and then discuss potential applications of this experimental paradigm to identify novel therapeutic targets for TLT pathology.
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Affiliation(s)
- James A. Butler
- Centre for Immunology and Infection, Department of Biology, Hull York Medical School, York, UK
- Department of Electronics, University of York, York, UK
- York Computational Immunology Laboratory, University of York, York, UK
| | - Jason Cosgrove
- Centre for Immunology and Infection, Department of Biology, Hull York Medical School, York, UK
- Department of Electronics, University of York, York, UK
- York Computational Immunology Laboratory, University of York, York, UK
| | - Kieran Alden
- Department of Electronics, University of York, York, UK
- York Computational Immunology Laboratory, University of York, York, UK
| | - Jon Timmis
- Department of Electronics, University of York, York, UK
- York Computational Immunology Laboratory, University of York, York, UK
| | - Mark Christopher Coles
- Centre for Immunology and Infection, Department of Biology, Hull York Medical School, York, UK
- York Computational Immunology Laboratory, University of York, York, UK
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Zhu X, Deng J, Zuo Z, Lam TN. An Agent-Based Approach to Dynamically Represent the Pharmacokinetic Properties of Baicalein. AAPS JOURNAL 2016; 18:1475-1488. [PMID: 27480317 DOI: 10.1208/s12248-016-9955-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Accepted: 07/06/2016] [Indexed: 11/30/2022]
Abstract
Baicalein, a typical flavonoid presented in Scutellariae radix, exhibits a unique metabolic profile during first-pass metabolism: parallel glucuronidation and sulfation pathways, with possible substrate inhibition in both pathways. In this project, we aimed to construct an agent-based model to dynamically represent baicalein pharmacokinetics and to verify the substrate inhibition hypothesis. The model consisted of three 3D spaces and two membranes: apical space (S1), intracellular space (S2), basolateral space (S3), apical membrane (M1), and basolateral membrane (M2). In silico enzymes (UDP-glucuronosyltransferases (UGTs) and sulfotransferases (SULTs)) and binder components were placed in S2. The model was then executed to simulate one-pass metabolism experiments of baicalein. With the implementation of a two-site enzyme design, the simulated results captured the preset qualitative and quantitative features of the wet-lab observations. The feasible parameter set showed that substrate inhibition happened in both conjugation pathways of baicalein. The simulation results suggested that the sulfation pathway was dominant at low concentrations and that SULT was more inclined to substrate inhibition than UGT. Cross-model validation was satisfactory. Our findings were consistent with a previously reported catenary model. We conclude that the mechanisms represented by our model are plausible. Our novel modeling approach could dynamically represent the metabolic pathways of baicalein in a Caco-2 system.
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Affiliation(s)
- Xiao Zhu
- School of Pharmacy, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
| | - Jianyuan Deng
- School of Pharmacy, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
| | - Zhong Zuo
- School of Pharmacy, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
| | - Tai Ning Lam
- School of Pharmacy, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong.
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Mathematical Modeling of Therapy-induced Cancer Drug Resistance: Connecting Cancer Mechanisms to Population Survival Rates. Sci Rep 2016; 6:22498. [PMID: 26928089 PMCID: PMC4772546 DOI: 10.1038/srep22498] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 02/16/2016] [Indexed: 12/21/2022] Open
Abstract
Drug resistance significantly limits the long-term effectiveness of targeted therapeutics for cancer patients. Recent experimental studies have demonstrated that cancer cell heterogeneity and microenvironment adaptations to targeted therapy play important roles in promoting the rapid acquisition of drug resistance and in increasing cancer metastasis. The systematic development of effective therapeutics to overcome drug resistance mechanisms poses a major challenge. In this study, we used a modeling approach to connect cellular mechanisms underlying cancer drug resistance to population-level patient survival. To predict progression-free survival in cancer patients with metastatic melanoma, we developed a set of stochastic differential equations to describe the dynamics of heterogeneous cell populations while taking into account micro-environment adaptations. Clinical data on survival and circulating tumor cell DNA (ctDNA) concentrations were used to confirm the effectiveness of our model. Moreover, our model predicted distinct patterns of dose-dependent synergy when evaluating a combination of BRAF and MEK inhibitors versus a combination of BRAF and PI3K inhibitors. These predictions were consistent with the findings in previously reported studies. The impact of the drug metabolism rate on patient survival was also discussed. The proposed model might facilitate the quantitative evaluation and optimization of combination therapeutics and cancer clinical trial design.
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