1
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Arsène S, Parès Y, Tixier E, Granjeon-Noriot S, Martin B, Bruezière L, Couty C, Courcelles E, Kahoul R, Pitrat J, Go N, Monteiro C, Kleine-Schultjann J, Jemai S, Pham E, Boissel JP, Kulesza A. In Silico Clinical Trials: Is It Possible? Methods Mol Biol 2024; 2716:51-99. [PMID: 37702936 DOI: 10.1007/978-1-0716-3449-3_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova's end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.
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2
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Mochan E, Sego TJ. Mathematical Modeling of the Lethal Synergism of Coinfecting Pathogens in Respiratory Viral Infections: A Review. Microorganisms 2023; 11:2974. [PMID: 38138118 PMCID: PMC10745501 DOI: 10.3390/microorganisms11122974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 12/05/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
Influenza A virus (IAV) infections represent a substantial global health challenge and are often accompanied by coinfections involving secondary viruses or bacteria, resulting in increased morbidity and mortality. The clinical impact of coinfections remains poorly understood, with conflicting findings regarding fatality. Isolating the impact of each pathogen and mechanisms of pathogen synergy during coinfections is challenging and further complicated by host and pathogen variability and experimental conditions. Factors such as cytokine dysregulation, immune cell function alterations, mucociliary dysfunction, and changes to the respiratory tract epithelium have been identified as contributors to increased lethality. The relative significance of these factors depends on variables such as pathogen types, infection timing, sequence, and inoculum size. Mathematical biological modeling can play a pivotal role in shedding light on the mechanisms of coinfections. Mathematical modeling enables the quantification of aspects of the intra-host immune response that are difficult to assess experimentally. In this narrative review, we highlight important mechanisms of IAV coinfection with bacterial and viral pathogens and survey mathematical models of coinfection and the insights gained from them. We discuss current challenges and limitations facing coinfection modeling, as well as current trends and future directions toward a complete understanding of coinfection using mathematical modeling and computer simulation.
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Affiliation(s)
- Ericka Mochan
- Department of Computational and Chemical Sciences, Carlow University, Pittsburgh, PA 15213, USA
| | - T. J. Sego
- Department of Medicine, University of Florida, Gainesville, FL 32611, USA;
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3
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Rao R, Musante CJ, Allen R. A quantitative systems pharmacology model of the pathophysiology and treatment of COVID-19 predicts optimal timing of pharmacological interventions. NPJ Syst Biol Appl 2023; 9:13. [PMID: 37059734 PMCID: PMC10102696 DOI: 10.1038/s41540-023-00269-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 02/09/2023] [Indexed: 04/16/2023] Open
Abstract
A quantitative systems pharmacology (QSP) model of the pathogenesis and treatment of SARS-CoV-2 infection can streamline and accelerate the development of novel medicines to treat COVID-19. Simulation of clinical trials allows in silico exploration of the uncertainties of clinical trial design and can rapidly inform their protocols. We previously published a preliminary model of the immune response to SARS-CoV-2 infection. To further our understanding of COVID-19 and treatment, we significantly updated the model by matching a curated dataset spanning viral load and immune responses in plasma and lung. We identified a population of parameter sets to generate heterogeneity in pathophysiology and treatment and tested this model against published reports from interventional SARS-CoV-2 targeting mAb and antiviral trials. Upon generation and selection of a virtual population, we match both the placebo and treated responses in viral load in these trials. We extended the model to predict the rate of hospitalization or death within a population. Via comparison of the in silico predictions with clinical data, we hypothesize that the immune response to virus is log-linear over a wide range of viral load. To validate this approach, we show the model matches a published subgroup analysis, sorted by baseline viral load, of patients treated with neutralizing Abs. By simulating intervention at different time points post infection, the model predicts efficacy is not sensitive to interventions within five days of symptom onset, but efficacy is dramatically reduced if more than five days pass post symptom onset prior to treatment.
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Affiliation(s)
- Rohit Rao
- Early Clinical Development, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA.
| | - Cynthia J Musante
- Early Clinical Development, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Richard Allen
- Early Clinical Development, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
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4
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Shen G, Moua KTY, Perkins K, Johnson D, Li A, Curtin P, Gao W, McCune JS. Precision sirolimus dosing in children: The potential for model-informed dosing and novel drug monitoring. Front Pharmacol 2023; 14:1126981. [PMID: 37021042 PMCID: PMC10069443 DOI: 10.3389/fphar.2023.1126981] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 02/14/2023] [Indexed: 04/07/2023] Open
Abstract
The mTOR inhibitor sirolimus is prescribed to treat children with varying diseases, ranging from vascular anomalies to sporadic lymphangioleiomyomatosis to transplantation (solid organ or hematopoietic cell). Precision dosing of sirolimus using therapeutic drug monitoring (TDM) of sirolimus concentrations in whole blood drawn at the trough (before the next dose) time-point is the current standard of care. For sirolimus, trough concentrations are only modestly correlated with the area under the curve, with R 2 values ranging from 0.52 to 0.84. Thus, it should not be surprising, even with the use of sirolimus TDM, that patients treated with sirolimus have variable pharmacokinetics, toxicity, and effectiveness. Model-informed precision dosing (MIPD) will be beneficial and should be implemented. The data do not suggest dried blood spots point-of-care sampling of sirolimus concentrations for precision dosing of sirolimus. Future research on precision dosing of sirolimus should focus on pharmacogenomic and pharmacometabolomic tools to predict sirolimus pharmacokinetics and wearables for point-of-care quantitation and MIPD.
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Affiliation(s)
- Guofang Shen
- Department of Hematologic Malignancies Translational Sciences, City of Hope, and Department of Hematopoietic Cell Transplantation, City of Hope Medical Center, Duarte, CA, United States
| | - Kao Tang Ying Moua
- Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, United States
| | - Kathryn Perkins
- Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, United States
| | - Deron Johnson
- Clinical Informatics, City of Hope Medical Center, Duarte, CA, United States
| | - Arthur Li
- Division of Biostatistics, City of Hope, Duarte, CA, United States
| | - Peter Curtin
- Department of Hematologic Malignancies Translational Sciences, City of Hope, and Department of Hematopoietic Cell Transplantation, City of Hope Medical Center, Duarte, CA, United States
| | - Wei Gao
- Division of Engineering and Applied Science, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, United States
| | - Jeannine S. McCune
- Department of Hematologic Malignancies Translational Sciences, City of Hope, and Department of Hematopoietic Cell Transplantation, City of Hope Medical Center, Duarte, CA, United States
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5
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Luque LM, Carlevaro CM, Llamoza Torres CJ, Lomba E. Physics-based tissue simulator to model multicellular systems: A study of liver regeneration and hepatocellular carcinoma recurrence. PLoS Comput Biol 2023; 19:e1010920. [PMID: 36877741 PMCID: PMC10019748 DOI: 10.1371/journal.pcbi.1010920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/16/2023] [Accepted: 02/03/2023] [Indexed: 03/07/2023] Open
Abstract
We present a multiagent-based model that captures the interactions between different types of cells with their microenvironment, and enables the analysis of the emergent global behavior during tissue regeneration and tumor development. Using this model, we are able to reproduce the temporal dynamics of regular healthy cells and cancer cells, as well as the evolution of their three-dimensional spatial distributions. By tuning the system with the characteristics of the individual patients, our model reproduces a variety of spatial patterns of tissue regeneration and tumor growth, resembling those found in clinical imaging or biopsies. In order to calibrate and validate our model we study the process of liver regeneration after surgical hepatectomy in different degrees. In the clinical context, our model is able to predict the recurrence of a hepatocellular carcinoma after a 70% partial hepatectomy. The outcomes of our simulations are in agreement with experimental and clinical observations. By fitting the model parameters to specific patient factors, it might well become a useful platform for hypotheses testing in treatments protocols.
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Affiliation(s)
- Luciana Melina Luque
- Instituto de Física de Líquidos y Sistemas Biológicos - CONICET. La Plata, Argentina
- * E-mail: (LML); (CMC)
| | - Carlos Manuel Carlevaro
- Instituto de Física de Líquidos y Sistemas Biológicos - CONICET. La Plata, Argentina
- Departamento de Ingeniería Mecánica, Universidad Tecnológica Nacional, Facultad Regional La Plata, La Plata, Argentina
- * E-mail: (LML); (CMC)
| | | | - Enrique Lomba
- Instituto de Química Física Rocasolano - CSIC. Madrid, España
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6
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Wang H, Zhao C, Santa-Maria CA, Emens LA, Popel AS. Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer. iScience 2022; 25:104702. [PMID: 35856032 PMCID: PMC9287616 DOI: 10.1016/j.isci.2022.104702] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/05/2022] [Accepted: 06/27/2022] [Indexed: 11/07/2022] Open
Abstract
Quantitative systems pharmacology (QSP) modeling is an emerging mechanistic computational approach that couples drug pharmacokinetics/pharmacodynamics and the course of disease progression. It has begun to play important roles in drug development for complex diseases such as cancer, including triple-negative breast cancer (TNBC). The combination of the anti-PD-L1 antibody atezolizumab and nab-paclitaxel has shown clinical activity in advanced TNBC with PD-L1-positive tumor-infiltrating immune cells. As tumor-associated macrophages (TAMs) serve as major contributors to the immuno-suppressive tumor microenvironment, we incorporated the dynamics of TAMs into our previously published QSP model to investigate their impact on cancer treatment. We show that through proper calibration, the model captures the macrophage heterogeneity in the tumor microenvironment while maintaining its predictive power of the trial results at the population level. Despite its high mechanistic complexity, the modularized QSP platform can be readily reproduced, expanded for new species of interest, and applied in clinical trial simulation. A mechanistic model of quantitative systems pharmacology in immuno-oncology Dynamics of tumor-associated macrophages are integrated into our previous work Conducting in silico clinical trials to predict clinical response to cancer therapy
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Affiliation(s)
- Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.,School of Pharmacy, Nanjing Medical University, Nanjing, Jiangsu211166, China
| | - Cesar A Santa-Maria
- Department of Oncology, the Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD21205, USA
| | - Leisha A Emens
- University of Pittsburgh Medical Center, Hillman Cancer Center, Pittsburgh, PA, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.,Department of Oncology, the Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD21205, USA
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7
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Laubenbacher R, Niarakis A, Helikar T, An G, Shapiro B, Malik-Sheriff RS, Sego TJ, Knapp A, Macklin P, Glazier JA. Building digital twins of the human immune system: toward a roadmap. NPJ Digit Med 2022; 5:64. [PMID: 35595830 PMCID: PMC9122990 DOI: 10.1038/s41746-022-00610-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 04/28/2022] [Indexed: 11/30/2022] Open
Abstract
Digital twins, customized simulation models pioneered in industry, are beginning to be deployed in medicine and healthcare, with some major successes, for instance in cardiovascular diagnostics and in insulin pump control. Personalized computational models are also assisting in applications ranging from drug development to treatment optimization. More advanced medical digital twins will be essential to making precision medicine a reality. Because the immune system plays an important role in such a wide range of diseases and health conditions, from fighting pathogens to autoimmune disorders, digital twins of the immune system will have an especially high impact. However, their development presents major challenges, stemming from the inherent complexity of the immune system and the difficulty of measuring many aspects of a patient’s immune state in vivo. This perspective outlines a roadmap for meeting these challenges and building a prototype of an immune digital twin. It is structured as a four-stage process that proceeds from a specification of a concrete use case to model constructions, personalization, and continued improvement.
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Affiliation(s)
- R Laubenbacher
- Department of Medicine, University of Florida, Gainesville, FL, USA.
| | - A Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde - Genhotel, Univ Evry, Evry, France.,Lifeware Group, Inria, Saclay-île de France, 91120, Palaiseau, France
| | - T Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - G An
- Department of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - B Shapiro
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - R S Malik-Sheriff
- European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Hinxton, Cambridge, UK
| | - T J Sego
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - A Knapp
- Department of Medicine, University of Florida, Gainesville, FL, USA
| | - P Macklin
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - J A Glazier
- Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
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8
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Data-driven learning how oncogenic gene expression locally alters heterocellular networks. Nat Commun 2022; 13:1986. [PMID: 35418177 PMCID: PMC9007999 DOI: 10.1038/s41467-022-29636-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 03/22/2022] [Indexed: 11/21/2022] Open
Abstract
Developing drugs increasingly relies on mechanistic modeling and simulation. Models that capture causal relations among genetic drivers of oncogenesis, functional plasticity, and host immunity complement wet experiments. Unfortunately, formulating such mechanistic cell-level models currently relies on hand curation, which can bias how data is interpreted or the priority of drug targets. In modeling molecular-level networks, rules and algorithms are employed to limit a priori biases in formulating mechanistic models. Here we combine digital cytometry with Bayesian network inference to generate causal models of cell-level networks linking an increase in gene expression associated with oncogenesis with alterations in stromal and immune cell subsets from bulk transcriptomic datasets. We predict how increased Cell Communication Network factor 4, a secreted matricellular protein, alters the tumor microenvironment using data from patients diagnosed with breast cancer and melanoma. Predictions are then tested using two immunocompetent mouse models for melanoma, which provide consistent experimental results. While mechanistic models play increasing roles in immuno-oncology, hand network curation is current practice. Here the authors use a Bayesian data-driven approach to infer how expression of a secreted oncogene alters the cellular landscape within the tumor.
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9
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Selvaggio G, Cristellon S, Marchetti L. A Novel Hybrid Logic-ODE Modeling Approach to Overcome Knowledge Gaps. Front Mol Biosci 2022; 8:760077. [PMID: 34988115 PMCID: PMC8721169 DOI: 10.3389/fmolb.2021.760077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/09/2021] [Indexed: 11/13/2022] Open
Abstract
Mathematical modeling allows using different formalisms to describe, investigate, and understand biological processes. However, despite the advent of high-throughput experimental techniques, quantitative information is still a challenge when looking for data to calibrate model parameters. Furthermore, quantitative formalisms must cope with stiffness and tractability problems, more so if used to describe multicellular systems. On the other hand, qualitative models may lack the proper granularity to describe the underlying kinetic processes. We propose a hybrid modeling approach that integrates ordinary differential equations and logical formalism to describe distinct biological layers and their communication. We focused on a multicellular system as a case study by applying the hybrid formalism to the well-known Delta-Notch signaling pathway. We used a differential equation model to describe the intracellular pathways while the cell-cell interactions were defined by logic rules. The hybrid approach herein employed allows us to combine the pros of different modeling techniques by overcoming the lack of quantitative information with a qualitative description that discretizes activation and inhibition processes, thus avoiding complexity.
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Affiliation(s)
- Gianluca Selvaggio
- Piazza Manifattura, Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy
| | - Serena Cristellon
- Piazza Manifattura, Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.,Department of Mathematics, University of Trento, Trento, Italy
| | - Luca Marchetti
- Piazza Manifattura, Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.,Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
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10
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Islam MA, Cook CV, Smith BJ, Ford Versypt AN. Mathematical Modeling of the Gut-Bone Axis and Implications of Butyrate Treatment on Osteoimmunology. Ind Eng Chem Res 2021; 60:17814-17825. [PMID: 34992331 PMCID: PMC8730472 DOI: 10.1021/acs.iecr.1c02949] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Butyrate, a short-chain fatty acid produced by the gut microbiota, has pivotal roles in the regulation of the immune system. Recent studies have revealed that butyrate increases the differentiation of peripheral regulatory T cells in the gut-bone axis and promotes osteoblasts' bone forming activity. However, the mechanism of the therapeutic benefit of butyrate in bone remodeling remains incompletely understood. Here, we develop a multicompartment mathematical model to quantitatively predict the contribution of butyrate on the expansion of regulatory T cells in the gut, blood, and bone compartments. We investigate the interplay between regulatory T cell-derived TGF-β and CD8+ T cell-derived Wnt-10b with changes in gut butyrate concentration. In addition, we connect our model to a detailed model of bone metabolism to study the impacts of butyrate and Wnt-10b on trabecular bone volume. Our results indicate both direct and indirect immune-mediated impacts of butyrate on bone metabolism.
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Affiliation(s)
- Mohammad Aminul Islam
- Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States; School of Chemical Engineering, Oklahoma State University, Stillwater, Oklahoma 74078, United States
| | - Carley V Cook
- Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States; School of Chemical Engineering, Oklahoma State University, Stillwater, Oklahoma 74078, United States
| | - Brenda J Smith
- Department of Nutritional Sciences, Oklahoma State University, Stillwater, Oklahoma 74078, United States
| | - Ashlee N Ford Versypt
- Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States; School of Chemical Engineering, Oklahoma State University, Stillwater, Oklahoma 74078, United States; Institute for Computational and Data Sciences, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States
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11
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Mehta K, Spaink HP, Ottenhoff THM, van der Graaf PH, van Hasselt JGC. Host-directed therapies for tuberculosis: quantitative systems pharmacology approaches. Trends Pharmacol Sci 2021; 43:293-304. [PMID: 34916092 DOI: 10.1016/j.tips.2021.11.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 10/26/2021] [Accepted: 11/18/2021] [Indexed: 12/26/2022]
Abstract
Host-directed therapies (HDTs) that modulate host-pathogen interactions offer an innovative strategy to combat Mycobacterium tuberculosis (Mtb) infections. When combined with tuberculosis (TB) antibiotics, HDTs could contribute to improving treatment outcomes, reducing treatment duration, and preventing resistance development. Translation of the interplay of host-pathogen interactions leveraged by HDTs towards therapeutic outcomes in patients is challenging. Quantitative understanding of the multifaceted nature of the host-pathogen interactions is vital to rationally design HDT strategies. Here, we (i) provide an overview of key Mtb host-pathogen interactions as basis for HDT strategies; and (ii) discuss the components and utility of quantitative systems pharmacology (QSP) models to inform HDT strategies. QSP models can be used to identify and optimize treatment targets, to facilitate preclinical to human translation, and to design combination treatment strategies.
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Affiliation(s)
| | | | - Tom H M Ottenhoff
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, The Netherlands
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12
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A quantitative systems pharmacology model for acute viral hepatitis B. Comput Struct Biotechnol J 2021; 19:4997-5007. [PMID: 34589180 PMCID: PMC8449028 DOI: 10.1016/j.csbj.2021.08.052] [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: 06/03/2021] [Revised: 08/31/2021] [Accepted: 08/31/2021] [Indexed: 12/25/2022] Open
Abstract
Mechanistic model characterizing acute immune response and HBV system interactions. Key role of the cellular and regulatory response triggering hepatitis B chronicity. Modelling framework to easily incorporate and explore additional biological mechanisms.
Hepatitis B liver infection is caused by hepatitis B virus (HBV) and represents a major global disease problem when it becomes chronic, as is the case for 80–90% of vertical or early life infections. However, in the vast majority (>95%) of adult exposures, the infected individuals are capable of mounting an effective immune response leading to infection resolution. A good understanding of HBV dynamics and the interaction between the virus and immune system during acute infection represents an essential step to characterize and understand the key biological processes involved in disease resolution, which may help to identify potential interventions to prevent chronic hepatitis B. In this work, a quantitative systems pharmacology model for acute hepatitis B characterizing viral dynamics and the main components of the innate, adaptive, and tolerant immune response has been successfully developed. To do so, information from multiple sources and across different organization levels has been integrated in a common mechanistic framework. The final model adequately describes the chronology and plausibility of an HBV-triggered immune response, as well as clinical data from acute patients reported in the literature. Given the holistic nature of the framework, the model can be used to illustrate the relevance of the different immune pathways and biological processes to ultimate response, observing the negligible contribution of the innate response and the key contribution of the cellular response on viral clearance. More specifically, moderate reductions of the proliferation of activated cytotoxic CD8+ lymphocytes or increased immunoregulatory effects can drive the system towards chronicity.
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Key Words
- AHB, acute hepatitis B
- ALT, alanine aminotransferase
- CHB, chronic hepatitis B
- CTL*, activated CTL
- CTL, antigen-specific cytotoxic T lymphocytes
- CTLm, memory CTL
- DC*, activated dendritic cells
- DC, dendritic cells
- HB, Hepatitis B
- HBV, hepatitis B virus, HBV DNA, circulating DNA levels of HBV
- HBsAg, hepatitis B surface antigen
- Hep, hepatocytes
- Hepatitis B
- Heptot, total hepatocytes
- IFN, interferon
- Immune system dynamics
- LN, lymph node
- LPC, long-lived plasma cells
- LV, liver
- MDSC, myeloid-derived suppressor cells
- Mechanistic modeling
- NK*, activated NK
- NK, natural killer cells
- ODE, ordinary differential equations
- PB, plasmablasts
- PC, plasma cells
- PL, plasma
- QSP, quantitative systems pharmacology
- Quantitative systems pharmacology
- SPC, short-lived plasma cells
- TRAIL, tumor necrosis factor–related apoptosis-inducing ligand
- Th0, naïve T cells
- Treg, regulatory T cells
- Viral dynamics
- anti-HBc, specific antibodies against core hepatitis B antigen
- anti-HBs, specific antibodies against surface hepatitis B antigen
- dHep, debris hepatocytes
- iHep, infected hepatocytes
- pDC, plasmacytoid DC
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13
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Tomasoni D, Paris A, Giampiccolo S, Reali F, Simoni G, Marchetti L, Kaddi C, Neves-Zaph S, Priami C, Azer K, Lombardo R. QSPcc reduces bottlenecks in computational model simulations. Commun Biol 2021; 4:1022. [PMID: 34471226 PMCID: PMC8410852 DOI: 10.1038/s42003-021-02553-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 08/09/2021] [Indexed: 01/13/2023] Open
Abstract
Mathematical models have grown in size and complexity becoming often computationally intractable. In sensitivity analysis and optimization phases, critical for tuning, validation and qualification, these models may be run thousands of times. Scientific programming languages popular for prototyping, such as MATLAB and R, can be a bottleneck in terms of performance. Here we show a compiler-based approach, designed to be universal at handling engineering and life sciences modeling styles, that automatically translates models into fast C code. At first QSPcc is demonstrated to be crucial in enabling the research on otherwise intractable Quantitative Systems Pharmacology models, such as in rare Lysosomal Storage Disorders. To demonstrate the full value in seamlessly accelerating, or enabling, the R&D efforts in natural sciences, we then benchmark QSPcc against 8 solutions on 24 real-world projects from different scientific fields. With speed-ups of 22000x peak, and 1605x arithmetic mean, our results show consistent superior performances. Lombardo and colleagues present QSPcc, a computational code compiler designed to convert code from popular scientific programming languages, such as MATLAB or R, into fast-running C code. This reduces the computational load required for complex modelling approaches and reduces user investment learning additional complex languages.
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Affiliation(s)
- Danilo Tomasoni
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Alessio Paris
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Stefano Giampiccolo
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Federico Reali
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Giulia Simoni
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Luca Marchetti
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
| | - Chanchala Kaddi
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, USA
| | - Susana Neves-Zaph
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, USA
| | - Corrado Priami
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy.,Department of Computer Science, University of Pisa, Pisa, Italy
| | - Karim Azer
- Data and Data Science - Translational Disease Modeling, Sanofi, Bridgewater, NJ, USA.,Axcella Health, Cambridge, MA, USA
| | - Rosario Lombardo
- Fondazione the Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy.
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14
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Wertheim KY, Puniya BL, La Fleur A, Shah AR, Barberis M, Helikar T. A multi-approach and multi-scale platform to model CD4+ T cells responding to infections. PLoS Comput Biol 2021; 17:e1009209. [PMID: 34343169 PMCID: PMC8376204 DOI: 10.1371/journal.pcbi.1009209] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 08/19/2021] [Accepted: 06/23/2021] [Indexed: 12/24/2022] Open
Abstract
Immune responses rely on a complex adaptive system in which the body and infections interact at multiple scales and in different compartments. We developed a modular model of CD4+ T cells, which uses four modeling approaches to integrate processes at three spatial scales in different tissues. In each cell, signal transduction and gene regulation are described by a logical model, metabolism by constraint-based models. Cell population dynamics are described by an agent-based model and systemic cytokine concentrations by ordinary differential equations. A Monte Carlo simulation algorithm allows information to flow efficiently between the four modules by separating the time scales. Such modularity improves computational performance and versatility and facilitates data integration. We validated our technology by reproducing known experimental results, including differentiation patterns of CD4+ T cells triggered by different combinations of cytokines, metabolic regulation by IL2 in these cells, and their response to influenza infection. In doing so, we added multi-scale insights to single-scale studies and demonstrated its predictive power by discovering switch-like and oscillatory behaviors of CD4+ T cells that arise from nonlinear dynamics interwoven across three scales. We identified the inflamed lymph node’s ability to retain naive CD4+ T cells as a key mechanism in generating these emergent behaviors. We envision our model and the generic framework encompassing it to serve as a tool for understanding cellular and molecular immunological problems through the lens of systems immunology. CD4+ T cells are a key part of the adaptive immune system. They differentiate into different phenotypes to carry out different functions. They do so by secreting molecules called cytokines to regulate other immune cells. Multi-scale modeling can potentially explain their emergent behaviors by integrating biological phenomena occurring at different spatial (intracellular, cellular, and systemic), temporal, and organizational scales (signal transduction, gene regulation, metabolism, cellular behaviors, and cytokine transport). We built a computational platform by combining disparate modeling frameworks (compartmental ordinary differential equations, agent-based modeling, Boolean network modeling, and constraint-based modeling). We validated the platform’s ability to predict CD4+ T cells’ emergent behaviors by reproducing their differentiation patterns, metabolic regulation, and population dynamics in response to influenza infection. We then used it to predict and explain novel switch-like and oscillatory behaviors for CD4+ T cells. On the basis of these results, we believe that our multi-approach and multi-scale platform will be a valuable addition to the systems immunology toolkit. In addition to its immediate relevance to CD4+ T cells, it also has the potential to become the foundation of a virtual immune system.
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Affiliation(s)
- Kenneth Y. Wertheim
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
- Department of Computer Science and Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Bhanwar Lal Puniya
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
| | - Alyssa La Fleur
- Department of Biochemistry, Department of Mathematics and Computer Science, Whitworth University, Spokane, Washington, United States of America
| | - Ab Rauf Shah
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
| | - Matteo Barberis
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
- Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Guildford, United Kingdom
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- * E-mail: , (MB); (TH)
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska–Lincoln, Lincoln, Nebraska, United States of America
- * E-mail: , (MB); (TH)
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15
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Aulin LB, de Lange DW, Saleh MA, van der Graaf PH, Völler S, van Hasselt JC. Biomarker-Guided Individualization of Antibiotic Therapy. Clin Pharmacol Ther 2021; 110:346-360. [PMID: 33559152 PMCID: PMC8359228 DOI: 10.1002/cpt.2194] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 02/02/2021] [Indexed: 12/11/2022]
Abstract
Treatment failure of antibiotic therapy due to insufficient efficacy or occurrence of toxicity is a major clinical challenge, and is expected to become even more urgent with the global rise of antibiotic resistance. Strategies to optimize treatment in individual patients are therefore of crucial importance. Currently, therapeutic drug monitoring plays an important role in optimizing antibiotic exposure to reduce treatment failure and toxicity. Biomarker-based strategies may be a powerful tool to further quantify and monitor antibiotic treatment response, and reduce variation in treatment response between patients. Host response biomarkers, such as CRP, procalcitonin, IL-6, and presepsin, could potentially carry significant information to be utilized for treatment individualization. To achieve this, the complex interactions among immune system, pathogen, drug, and biomarker need to be better understood and characterized. The purpose of this tutorial is to discuss the use and evidence of currently available biomarker-based approaches to inform antibiotic treatment. To this end, we also included a discussion on how treatment response biomarker data from preclinical, healthy volunteer, and patient-based studies can be further characterized using pharmacometric and system pharmacology based modeling approaches. As an illustrative example of how such modeling strategies can be used, we describe a case study in which we quantitatively characterize procalcitonin dynamics in relation to antibiotic treatments in patients with sepsis.
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Affiliation(s)
- Linda B.S. Aulin
- Division of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Dylan W. de Lange
- Department of Intensive Care MedicineUniversity Medical CenterUniversity UtrechtUtrechtThe Netherlands
| | - Mohammed A.A. Saleh
- Division of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - Piet H. van der Graaf
- Division of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
- CertaraCanterburyUK
| | - Swantje Völler
- Division of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
- Pharmacy, Leiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
| | - J.G. Coen van Hasselt
- Division of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityLeidenThe Netherlands
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16
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Friedrich T, Henthorn N, Durante M. Modeling Radioimmune Response-Current Status and Perspectives. Front Oncol 2021; 11:647272. [PMID: 33796470 PMCID: PMC8008061 DOI: 10.3389/fonc.2021.647272] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 02/25/2021] [Indexed: 12/13/2022] Open
Abstract
The combination of immune therapy with radiation offers an exciting and promising treatment modality in cancer therapy. It has been hypothesized that radiation induces damage signals within the tumor, making it more detectable for the immune system. In combination with inhibiting immune checkpoints an effective anti-tumor immune response may be established. This inversion from tumor immune evasion raises numerous questions to be solved to support an effective clinical implementation: These include the optimum immune drug and radiation dose time courses, the amount of damage and associated doses required to stimulate an immune response, and the impact of lymphocyte status and dynamics. Biophysical modeling can offer unique insights, providing quantitative information addressing these factors and highlighting mechanisms of action. In this work we review the existing modeling approaches of combined ‘radioimmune’ response, as well as associated fields of study. We propose modeling attempts that appear relevant for an effective and predictive model. We emphasize the importance of the time course of drug and dose delivery in view to the time course of the triggered biological processes. Special attention is also paid to the dose distribution to circulating blood lymphocytes and the effect this has on immune competence.
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Affiliation(s)
- Thomas Friedrich
- Biophysics Department, GSI Helmholtz Center for Heavy Ion Research, Darmstadt, Germany
| | - Nicholas Henthorn
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.,The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Marco Durante
- Biophysics Department, GSI Helmholtz Center for Heavy Ion Research, Darmstadt, Germany.,Institute for Solid State Physics, Technical University Darmstadt, Darmstadt, Germany
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17
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Dai W, Rao R, Sher A, Tania N, Musante CJ, Allen R. A Prototype QSP Model of the Immune Response to SARS-CoV-2 for Community Development. CPT Pharmacometrics Syst Pharmacol 2021; 10:18-29. [PMID: 33217169 PMCID: PMC7753647 DOI: 10.1002/psp4.12574] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 11/04/2020] [Indexed: 12/20/2022] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic requires the rapid development of efficacious treatments for patients with life-threatening coronavirus disease 2019 (COVID-19). Quantitative systems pharmacology (QSP) models are mathematical representations of pathophysiology for simulating and predicting the effects of existing or putative therapies. The application of model-based approaches, including QSP, have accelerated the development of some novel therapeutics. Nevertheless, the development of disease-scale mechanistic models can be a slow process, often taking years to be validated and considered mature. Furthermore, emerging data may make any QSP model quickly obsolete. We present a prototype QSP model to facilitate further development by the scientific community. The model accounts for the interactions between viral dynamics, the major host immune response mediators and tissue damage and regeneration. The immune response is determined by viral activation of innate and adaptive immune processes that regulate viral clearance and cell damage. The prototype model captures two physiologically relevant outcomes following infection: a "healthy" immune response that appropriately defends against the virus, and an uncontrolled alveolar inflammatory response that is characteristic of acute respiratory distress syndrome. We aim to significantly shorten the typical QSP model development and validation timeline by encouraging community use, testing, and refinement of this prototype model. It is our expectation that the model will be further advanced in an open science approach (i.e., by multiple contributions toward a validated quantitative platform in an open forum), with the ultimate goal of informing and accelerating the development of safe and effective treatment options for patients.
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Affiliation(s)
- Wei Dai
- Early Clinical DevelopmentPfizer Worldwide Research, Development and MedicalCambridgeMassachusettsUSA
| | - Rohit Rao
- Early Clinical DevelopmentPfizer Worldwide Research, Development and MedicalCambridgeMassachusettsUSA
| | - Anna Sher
- Early Clinical DevelopmentPfizer Worldwide Research, Development and MedicalCambridgeMassachusettsUSA
| | - Nessy Tania
- Early Clinical DevelopmentPfizer Worldwide Research, Development and MedicalCambridgeMassachusettsUSA
| | - Cynthia J. Musante
- Early Clinical DevelopmentPfizer Worldwide Research, Development and MedicalCambridgeMassachusettsUSA
| | - Richard Allen
- Early Clinical DevelopmentPfizer Worldwide Research, Development and MedicalCambridgeMassachusettsUSA
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18
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Formanowicz D, Rybarczyk A, Radom M, Tanaś K, Formanowicz P. A Stochastic Petri Net-Based Model of the Involvement of Interleukin 18 in Atherosclerosis. Int J Mol Sci 2020; 21:ijms21228574. [PMID: 33202974 PMCID: PMC7696504 DOI: 10.3390/ijms21228574] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/02/2020] [Accepted: 11/10/2020] [Indexed: 01/25/2023] Open
Abstract
Interleukin 18 (IL-18) is a proinflammatory and proatherogenic cytokine with pleiotropic properties, which is involved in T and NK cell maturation and the synthesis of other inflammatory cytokines and cell adhesion molecules. It plays a significant role in orchestrating the cytokine cascade, accelerates atherosclerosis and influences plaque vulnerability. To investigate the influence of IL-18 cytokine on atherosclerosis development, a stochastic Petri net model was built and then analyzed. First, MCT-sets and t-clusters were generated, then knockout and simulation-based analysis was conducted. The application of systems approach that was used in this research enabled an in-depth analysis of the studied phenomenon. Our results gave us better insight into the studied phenomenon and allow revealing that activation of macrophages by the classical pathway and IL-18-MyD88 signaling axis is crucial for the modeled process.
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Affiliation(s)
- Dorota Formanowicz
- Department of Clinical Biochemistry and Laboratory Medicine, Poznan University of Medical Sciences, 60-806 Poznan, Poland;
| | - Agnieszka Rybarczyk
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland; (A.R.); (M.R.); (K.T.)
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
- Faculty of Electrical Engineering, Gdynia Maritime University, 81-225 Gdynia, Poland
| | - Marcin Radom
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland; (A.R.); (M.R.); (K.T.)
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
| | - Krzysztof Tanaś
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland; (A.R.); (M.R.); (K.T.)
| | - Piotr Formanowicz
- Institute of Computing Science, Poznan University of Technology, 60-965 Poznan, Poland; (A.R.); (M.R.); (K.T.)
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland
- Correspondence:
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19
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Joslyn LR, Kirschner DE, Linderman JJ. CaliPro: A Calibration Protocol That Utilizes Parameter Density Estimation to Explore Parameter Space and Calibrate Complex Biological Models. Cell Mol Bioeng 2020; 14:31-47. [PMID: 33643465 DOI: 10.1007/s12195-020-00650-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 09/02/2020] [Indexed: 12/15/2022] Open
Abstract
Introduction Mathematical and computational modeling have a long history of uncovering mechanisms and making predictions for biological systems. However, to create a model that can provide relevant quantitative predictions, models must first be calibrated by recapitulating existing biological datasets from that system. Current calibration approaches may not be appropriate for complex biological models because: 1) many attempt to recapitulate only a single aspect of the experimental data (such as a median trend) or 2) Bayesian techniques require specification of parameter priors and likelihoods to experimental data that cannot always be confidently assigned. A new calibration protocol is needed to calibrate complex models when current approaches fall short. Methods Herein, we develop CaliPro, an iterative, model-agnostic calibration protocol that utilizes parameter density estimation to refine parameter space and calibrate to temporal biological datasets. An important aspect of CaliPro is the user-defined pass set definition, which specifies how the model might successfully recapitulate experimental data. We define the appropriate settings to use CaliPro. Results We illustrate the usefulness of CaliPro through four examples including predator-prey, infectious disease transmission, and immune response models. We show that CaliPro works well for both deterministic, continuous model structures as well as stochastic, discrete models and illustrate that CaliPro can work across diverse calibration goals. Conclusions We present CaliPro, a new method for calibrating complex biological models to a range of experimental outcomes. In addition to expediting calibration, CaliPro may be useful in already calibrated parameter spaces to target and isolate specific model behavior for further analysis.
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Affiliation(s)
- Louis R Joslyn
- Department of Chemical Engineering, University of Michigan, G045W NCRC B28, 2800 Plymouth Rd, Ann Arbor, MI 48109-2136 USA.,Department of Microbiology and Immunology, University of Michigan Medical School, 1150 W Medical Center Drive, 5641 Medical Science II, Ann Arbor, MI 48109-5620 USA
| | - Denise E Kirschner
- Department of Microbiology and Immunology, University of Michigan Medical School, 1150 W Medical Center Drive, 5641 Medical Science II, Ann Arbor, MI 48109-5620 USA
| | - Jennifer J Linderman
- Department of Chemical Engineering, University of Michigan, G045W NCRC B28, 2800 Plymouth Rd, Ann Arbor, MI 48109-2136 USA
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20
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Sové RJ, Jafarnejad M, Zhao C, Wang H, Ma H, Popel AS. QSP-IO: A Quantitative Systems Pharmacology Toolbox for Mechanistic Multiscale Modeling for Immuno-Oncology Applications. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2020; 9:484-497. [PMID: 32618119 PMCID: PMC7499194 DOI: 10.1002/psp4.12546] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 07/17/2020] [Indexed: 12/25/2022]
Abstract
Immunotherapy has shown great potential in the treatment of cancer; however, only a fraction of patients respond to treatment, and many experience autoimmune‐related side effects. The pharmaceutical industry has relied on mathematical models to study the behavior of candidate drugs and more recently, complex, whole‐body, quantitative systems pharmacology (QSP) models have become increasingly popular for discovery and development. QSP modeling has the potential to discover novel predictive biomarkers as well as test the efficacy of treatment plans and combination therapies through virtual clinical trials. In this work, we present a QSP modeling platform for immuno‐oncology (IO) that incorporates detailed mechanisms for important immune interactions. This modular platform allows for the construction of QSP models of IO with varying degrees of complexity based on the research questions. Finally, we demonstrate the use of the platform through two example applications of immune checkpoint therapy.
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Affiliation(s)
- Richard J Sové
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mohammad Jafarnejad
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Huilin Ma
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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21
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Rogers KV, Martin SW, Bhattacharya I, Singh RSP, Nayak S. A Dynamic Quantitative Systems Pharmacology Model of Inflammatory Bowel Disease: Part 1 - Model Framework. Clin Transl Sci 2020; 14:239-248. [PMID: 32822108 PMCID: PMC7877855 DOI: 10.1111/cts.12849] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Accepted: 06/14/2020] [Indexed: 12/14/2022] Open
Abstract
A mechanistic, multistate, mathematical model of inflammatory bowel disease (IBD) was developed by including key biological mechanisms in blood and gut, including cell differentiation, cytokine production, and clinical biomarkers. The model structure is consistent between healthy volunteers and IBD disease phenotype, with 24 parameters changed between diseases. Modular nature of the model allows for easy incorporation of new mechanisms or modification of existing interactions. Model simulations for steady-state levels of proteins and cells in the blood and gut using a population approach are consistent with published data. By simulating the response of two clinical biomarkers, C-reactive protein and fecal calprotectin, to parameter perturbations, the model explores hypotheses for possible treatment mechanisms. With additional experimental validation and addition of drug treatments, the model provides a platform to test hypothesis on treatment effects in IBD.
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Affiliation(s)
- Katharine V Rogers
- Biologics Development Sciences, Janssen Biotherapeutics, Janssen Research & Development, LLC, Raritan, New Jersey, USA
| | - Steven W Martin
- Pharmacometrics, Global Clinical Pharmacology, Pfizer Inc., Cambridge, Massachusetts, USA
| | | | | | - Satyaprakash Nayak
- Pharmacometrics, Global Clinical Pharmacology, Pfizer Inc., Cambridge, Massachusetts, USA
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22
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Ayyar VS, Jusko WJ. Transitioning from Basic toward Systems Pharmacodynamic Models: Lessons from Corticosteroids. Pharmacol Rev 2020; 72:414-438. [PMID: 32123034 DOI: 10.1124/pr.119.018101] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Technology in bioanalysis, -omics, and computation have evolved over the past half century to allow for comprehensive assessments of the molecular to whole body pharmacology of diverse corticosteroids. Such studies have advanced pharmacokinetic and pharmacodynamic (PK/PD) concepts and models that often generalize across various classes of drugs. These models encompass the "pillars" of pharmacology, namely PK and target drug exposure, the mass-law interactions of drugs with receptors/targets, and the consequent turnover and homeostatic control of genes, biomarkers, physiologic responses, and disease symptoms. Pharmacokinetic methodology utilizes noncompartmental, compartmental, reversible, physiologic [full physiologically based pharmacokinetic (PBPK) and minimal PBPK], and target-mediated drug disposition models using a growing array of pharmacometric considerations and software. Basic PK/PD models have emerged (simple direct, biophase, slow receptor binding, indirect response, irreversible, turnover with inactivation, and transduction models) that place emphasis on parsimony, are mechanistic in nature, and serve as highly useful "top-down" methods of quantitating the actions of diverse drugs. These are often components of more complex quantitative systems pharmacology (QSP) models that explain the array of responses to various drugs, including corticosteroids. Progressively deeper mechanistic appreciation of PBPK, drug-target interactions, and systems physiology from the molecular (genomic, proteomic, metabolomic) to cellular to whole body levels provides the foundation for enhanced PK/PD to comprehensive QSP models. Our research based on cell, animal, clinical, and theoretical studies with corticosteroids have provided ideas and quantitative methods that have broadly advanced the fields of PK/PD and QSP modeling and illustrates the transition toward a global, systems understanding of actions of diverse drugs. SIGNIFICANCE STATEMENT: Over the past half century, pharmacokinetics (PK) and pharmacokinetics/pharmacodynamics (PK/PD) have evolved to provide an array of mechanism-based models that help quantitate the disposition and actions of most drugs. We describe how many basic PK and PK/PD model components were identified and often applied to the diverse properties of corticosteroids (CS). The CS have complications in disposition and a wide array of simple receptor-to complex gene-mediated actions in multiple organs. Continued assessments of such complexities have offered opportunities to develop models ranging from simple PK to enhanced PK/PD to quantitative systems pharmacology (QSP) that help explain therapeutic and adverse CS effects. Concurrent development of state-of-the-art PK, PK/PD, and QSP models are described alongside experimental studies that revealed diverse CS actions.
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Affiliation(s)
- Vivaswath S Ayyar
- Department of Pharmaceutical Sciences University at Buffalo, School of Pharmacy and Pharmaceutical Sciences, Buffalo, New York
| | - William J Jusko
- Department of Pharmaceutical Sciences University at Buffalo, School of Pharmacy and Pharmaceutical Sciences, Buffalo, New York
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23
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Fribourg M. A case for the reuse and adaptation of mechanistic computational models to study transplant immunology. Am J Transplant 2020; 20:355-361. [PMID: 31562790 PMCID: PMC6984985 DOI: 10.1111/ajt.15623] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2019] [Revised: 09/19/2019] [Accepted: 09/20/2019] [Indexed: 02/06/2023]
Abstract
Computational mechanistic models constitute powerful tools for summarizing our knowledge in quantitative terms, providing mechanistic understanding, and generating new hypotheses. The present review emphasizes the advantages of reusing publicly available computational models as a way to capitalize on existing knowledge, reduce the number of parameters that need to be adjusted to experimental data, and facilitate hypothesis generation. Finally, it includes a step-by-step example of the reuse and adaptation of an existing model of immune responses to tuberculosis, tumor growth, and blood pathogens, to study donor-specific antibody (DSA) responses. This review aims to illustrate the benefit of leveraging the currently available computational models in immunology to accelerate the study of alloimmune responses, and to encourage modelers to share their models to further advance our understanding of transplant immunology.
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Affiliation(s)
- Miguel Fribourg
- Translational Transplant Research Center, Department of Medicine, and Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY
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24
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Griffiths JI, Cohen AL, Jones V, Salgia R, Chang JT, Bild AH. Opportunities for improving cancer treatment using systems biology. ACTA ACUST UNITED AC 2019; 17:41-50. [PMID: 32518857 DOI: 10.1016/j.coisb.2019.10.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Current cancer therapies target a limited set of tumor features, rather than considering the tumor as a whole. Systems biology aims to reveal therapeutic targets associated with a variety of facets in an individual's tumor, such as genetic heterogeneity and its evolution, cancer cell-autonomous phenotypes, and microenvironmental signaling. These disparate characteristics can be reconciled using mathematical modeling that incorporates concepts from ecology and evolution. This provides an opportunity to predict tumor growth and response to therapy, to tailor patient-specific approaches in real time or even prospectively. Importantly, as data regarding patient tumors is often available from only limited time points during treatment, systems-based approaches can address this limitation by interpolating longitudinal events within a principled framework. This review outlines areas in medicine that could benefit from systems biology approaches to deconvolve the complexity of cancer.
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Affiliation(s)
- Jason I Griffiths
- Department of Mathematics, University of Utah, Salt Lake City, UT 84112, USA
| | - Adam L Cohen
- Huntsman Cancer Institute, Department of Internal Medicine, University of Utah, Salt Lake City, UT 84112, USA
| | - Veronica Jones
- Department of Surgery, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Ravi Salgia
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA 91010, USA
| | - Jeffrey T Chang
- Department of Integrative Biology and Pharmacology, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Andrea H Bild
- Department of Medical Oncology, Division of Molecular Pharmacology, Beckman Research Institute of City of Hope, Duarte, CA 91010, USA
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25
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Milberg O, Gong C, Jafarnejad M, Bartelink IH, Wang B, Vicini P, Narwal R, Roskos L, Popel AS. A QSP Model for Predicting Clinical Responses to Monotherapy, Combination and Sequential Therapy Following CTLA-4, PD-1, and PD-L1 Checkpoint Blockade. Sci Rep 2019; 9:11286. [PMID: 31375756 PMCID: PMC6677731 DOI: 10.1038/s41598-019-47802-4] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 07/24/2019] [Indexed: 01/12/2023] Open
Abstract
Over the past decade, several immunotherapies have been approved for the treatment of melanoma. The most prominent of these are the immune checkpoint inhibitors, which are antibodies that block the inhibitory effects on the immune system by checkpoint receptors, such as CTLA-4, PD-1 and PD-L1. Preclinically, blocking these receptors has led to increased activation and proliferation of effector cells following stimulation and antigen recognition, and subsequently, more effective elimination of cancer cells. Translation from preclinical to clinical outcomes in solid tumors has shown the existence of a wide diversity of individual patient responses, linked to several patient-specific parameters. We developed a quantitative systems pharmacology (QSP) model that looks at the mentioned checkpoint blockade therapies administered as mono-, combo- and sequential therapies, to show how different combinations of specific patient parameters defined within physiological ranges distinguish different types of virtual patient responders to these therapies for melanoma. Further validation by fitting and subsequent simulations of virtual clinical trials mimicking actual patient trials demonstrated that the model can capture a wide variety of tumor dynamics that are observed in the clinic and can predict median clinical responses. Our aim here is to present a QSP model for combination immunotherapy specific to melanoma.
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Affiliation(s)
- Oleg Milberg
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
| | - Chang Gong
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mohammad Jafarnejad
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Imke H Bartelink
- Clinical Pharmacology, Pharmacometrics and DMPK (CPD), MedImmune, South San Francisco, California, USA.,Department of Clinical Pharmacology and Pharmacy, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bing Wang
- Clinical Pharmacology, Pharmacometrics and DMPK (CPD), MedImmune, South San Francisco, California, USA
| | - Paolo Vicini
- Clinical Pharmacology, Pharmacometrics and DMPK, MedImmune, Cambridge, United Kingdom
| | | | | | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Ward JP, Franks SJ, Tindall MJ, King JR, Curtis A, Evans GS. Mathematical modelling of contact dermatitis from nickel and chromium. J Math Biol 2019; 79:595-630. [PMID: 31197444 PMCID: PMC6647287 DOI: 10.1007/s00285-019-01371-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Revised: 04/08/2019] [Indexed: 01/21/2023]
Abstract
Dermal exposure to metal allergens can lead to irritant and allergic contact dermatitis (ACD). In this paper we present a mathematical model of the absorption of metal ions, hexavalent chromium and nickel, into the viable epidermis and compare the localised irritant and T-lymphocyte (T-cell) mediated immune responses. The model accounts for the spatial-temporal variation of skin health, extra and intracellular allergen concentrations, innate immune cells, T-cells, cytokine signalling and lymph node activity up to about 6 days after contact with these metals; repair processes associated with withdrawal of exposure to both metals is not considered in the current model, being assumed secondary during the initial phases of exposure. Simulations of the resulting system of PDEs are studied in one-dimension, i.e. across skin depth, and three-dimensional scenarios with the aim of comparing the responses to the two ions in the cases of first contact (no T-cells initially present) and second contact (T-cells initially present). The results show that on continuous contact, chromium ions elicit stronger skin inflammation, but for nickel, subsequent re-exposure stimulates stronger responses due to an accumulation of cytotoxic T-cell mediated responses which characterise ACD. Furthermore, the surface area of contact to these metals has little effect on the speed of response, whilst sensitivity is predicted to increase with the thickness of skin. The modelling approach is generic and should be applicable to describe contact dermatitis from a wide range of allergens.
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Affiliation(s)
- J P Ward
- Department of Mathematical Sciences, Loughborough University, Loughborough, LE11 3TU, UK.
| | - S J Franks
- School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK
| | - M J Tindall
- Department of Mathematics and Statistics, University of Reading, Reading, Berkshire, RG6 6AX, UK
- Institute for Cardiovascular and Metabolic Research, University of Reading, Reading, RG6 6AA, UK
| | - J R King
- School of Mathematical Sciences, University of Nottingham, Nottingham, NG7 2RD, UK
| | - A Curtis
- Health and Safety Laboratory, Harpur Hill, Buxton, Derbyshire, SK17 9JN, UK
| | - G S Evans
- Health and Safety Laboratory, Harpur Hill, Buxton, Derbyshire, SK17 9JN, UK
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27
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Asín-Prieto E, Parra-Guillen ZP, Mantilla JDG, Vandenbossche J, Stuyckens K, de Trixhe XW, Perez-Ruixo JJ, Troconiz IF. Immune network for viral hepatitis B: Topological representation. Eur J Pharm Sci 2019; 136:104939. [PMID: 31195071 DOI: 10.1016/j.ejps.2019.05.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 05/23/2019] [Indexed: 02/06/2023]
Abstract
The liver is a well-known immunotolerogenic environment, which provides the adequate setting for liver infectious pathogens persistence such as the hepatitis B virus (HBV). Consequently, HBV infection can derive in the development of chronic disease in a proportion of the patients. If this situation persists in time, chronic hepatitis B (CHB) would end in cirrhosis, hepatocellular carcinoma and eventually, the death of the patient. It is thought that this immunotolerogenic environment is the result of complex interactions between different elements of the immune system and the viral biology. Therefore, the purpose of this work is to unravel the mechanisms implied in the development of CHB and to design a tool able to help in the study of adequate therapies. Firstly, a conceptual framework with the main components of the immune system and viral dynamics was constructed providing an overall insight on the pathways and interactions implied in this disease. Secondly, a review of the literature was performed in a modular fashion: (i) viral dynamics, (ii) innate immune response, (iii) humoral and (iv) cellular adaptive immune responses and (v) tolerogenic aspects. Finally, the information collected was integrated into a single topological representation that could serve as the plan for the systems pharmacology model architecture. This representation can be considered as the previous unavoidable step to the construction of a quantitative model that could assist in biomarker and target identification, drug design and development, dosing optimization and disease progression analysis.
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Affiliation(s)
- Eduardo Asín-Prieto
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Zinnia P Parra-Guillen
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - José David Gómez Mantilla
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | | | - Kim Stuyckens
- Global Clinical Pharmacology, Janssen R&D, Beerse, Belgium
| | | | | | - Iñaki F Troconiz
- Pharmacometrics & Systems Pharmacology, Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain; IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.
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Vicini P, Standifer N, Hickling TP. Recruiting the Immune System Against Disease: Lessons for Clinical and Systems Pharmacology. CPT Pharmacometrics Syst Pharmacol 2019; 8:436-439. [PMID: 31004400 PMCID: PMC6656934 DOI: 10.1002/psp4.12416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Accepted: 03/16/2019] [Indexed: 12/03/2022] Open
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Abstract
Immunotherapy is now the fourth pillar of cancer therapy, with surgery, radiation, and traditional chemotherapy being the remaining pillars. Over the past decade, enthusiasm for immunotherapy has increased because of, in part, data showing that it consistently improves overall survival in select patients with historically refractory cancers. This issue covers various aspects of immunotherapy ranging from use of 1) chimeric antigen receptor (CAR) T cells to treat patients with B-cell acute lymphoblastic leukemia; 2) population pharmacokinetic/dynamic modeling to develop new immune checkpoint inhibitors; and 3) simulations of existing population pharmacokinetic models of immunotherapy to minimize waste without compromising exposure and efficacy.
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Novkovic M, Onder L, Cheng HW, Bocharov G, Ludewig B. Integrative Computational Modeling of the Lymph Node Stromal Cell Landscape. Front Immunol 2018; 9:2428. [PMID: 30405623 PMCID: PMC6206207 DOI: 10.3389/fimmu.2018.02428] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 10/02/2018] [Indexed: 11/13/2022] Open
Abstract
Adaptive immune responses develop in secondary lymphoid organs such as lymph nodes (LNs) in a well-coordinated series of interactions between migrating immune cells and resident stromal cells. Although many processes that occur in LNs are well understood from an immunological point of view, our understanding of the fundamental organization and mechanisms that drive these processes is still incomplete. The aim of systems biology approaches is to unravel the complexity of biological systems and describe emergent properties that arise from interactions between individual constituents of the system. The immune system is greater than the sum of its parts, as is the case with any sufficiently complex system. Here, we review recent work and developments of computational LN models with focus on the structure and organization of the stromal cells. We explore various mathematical studies of intranodal T cell motility and migration, their interactions with the LN-resident stromal cells, and computational models of functional chemokine gradient fields and lymph flow dynamics. Lastly, we discuss briefly the importance of hybrid and multi-scale modeling approaches in immunology and the technical challenges involved.
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Affiliation(s)
- Mario Novkovic
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Lucas Onder
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Hung-Wei Cheng
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - Gennady Bocharov
- Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia
| | - Burkhard Ludewig
- Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland
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Rahman A, Tiwari A, Narula J, Hickling T. Importance of Feedback and Feedforward Loops to Adaptive Immune Response Modeling. CPT Pharmacometrics Syst Pharmacol 2018; 7:621-628. [PMID: 30198637 PMCID: PMC6202469 DOI: 10.1002/psp4.12352] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 08/15/2018] [Indexed: 12/15/2022] Open
Abstract
The human adaptive immune system is a very complex network of different types of cells, cytokines, and signaling molecules. This complex network makes it difficult to understand the system level regulations. To properly explain the immune system, it is necessary to explicitly investigate the presence of different feedback and feedforward loops (FFLs) and their crosstalks. Considering that these loops increase the complexity of the system, the mathematical modeling has been proved to be an important tool to explain such complex biological systems. This review focuses on these regulatory loops and discusses their importance on systems modeling of the immune system.
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Liberman A, Kario D, Mussel M, Brill J, Buetow K, Efroni S, Nevo U. Cell studio: A platform for interactive, 3D graphical simulation of immunological processes. APL Bioeng 2018; 2:026107. [PMID: 31069304 PMCID: PMC6481718 DOI: 10.1063/1.5039473] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 05/04/2018] [Indexed: 12/27/2022] Open
Abstract
The field of computer modeling and simulation of biological systems is rapidly advancing, backed by significant progress in the fields of experimentation techniques, computer hardware, and programming software. The result of a simulation may be delivered in several ways, from numerical results, through graphs of the simulated run, to a visualization of the simulation. The vision of an in-silico experiment mimicking an in-vitro or in-vivo experiment as it is viewed under a microscope is appealing but technically demanding and computationally intensive. Here, we report “Cell Studio,” a generic, hybrid platform to simulate an immune microenvironment with biological and biophysical rules. We use game engines—generic programs for game creation which offer ready-made assets and tools—to create a visualized, interactive 3D simulation. We also utilize a scalable architecture that delegates the computational load to a server. The user may view the simulation, move the “camera” around, stop, fast-forward, and rewind it and inject soluble molecules into the extracellular medium at any point in time. During simulation, graphs are created in real time for a broad view of system-wide processes. The model is parametrized using a user-friendly Graphical User Interface (GUI). We show a simple validation simulation and compare its results with those from a “classical” simulation, validated against a “wet” experiment. We believe that interactive, real-time 3D visualization may aid in generating insights from the model and encourage intuition about the immunological scenario.
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Affiliation(s)
- Asaf Liberman
- The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv 6997801, Israel
| | | | - Matan Mussel
- Physics Department, TU Dortmund University, Dortmund 44227, Germany
| | - Jacob Brill
- Arizona State University, Tempe, Arizona 85281, USA
| | | | - Sol Efroni
- The Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan 52900, Israel
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Gong C, Milberg O, Wang B, Vicini P, Narwal R, Roskos L, Popel AS. A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition. J R Soc Interface 2018; 14:rsif.2017.0320. [PMID: 28931635 PMCID: PMC5636269 DOI: 10.1098/rsif.2017.0320] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 08/30/2017] [Indexed: 12/11/2022] Open
Abstract
When the immune system responds to tumour development, patterns of immune infiltrates emerge, highlighted by the expression of immune checkpoint-related molecules such as PDL1 on the surface of cancer cells. Such spatial heterogeneity carries information on intrinsic characteristics of the tumour lesion for individual patients, and thus is a potential source for biomarkers for anti-tumour therapeutics. We developed a systems biology multiscale agent-based model to capture the interactions between immune cells and cancer cells, and analysed the emergent global behaviour during tumour development and immunotherapy. Using this model, we are able to reproduce temporal dynamics of cytotoxic T cells and cancer cells during tumour progression, as well as three-dimensional spatial distributions of these cells. By varying the characteristics of the neoantigen profile of individual patients, such as mutational burden and antigen strength, a spectrum of pretreatment spatial patterns of PDL1 expression is generated in our simulations, resembling immuno-architectures obtained via immunohistochemistry from patient biopsies. By correlating these spatial characteristics with in silico treatment results using immune checkpoint inhibitors, the model provides a framework for use to predict treatment/biomarker combinations in different cancer types based on cancer-specific experimental data.
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Affiliation(s)
- Chang Gong
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Oleg Milberg
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | | | | | | | | | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.,Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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Abstract
Motivation The literature on complex diseases is abundant but not always quantitative. This is particularly so for Inflammatory Bowel Disease (IBD), where many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. We propose the elaboration and validation of a logic network for IBD able to capture the information available in the literature that will facilitate the identification/validation of therapeutic targets. Results In this article, we propose a logic model for Inflammatory Bowel Disease (IBD) which consists of 43 nodes and 298 qualitative interactions. The model presented is able to describe the pathogenic mechanisms of the disorder and qualitatively describes the characteristic chronic inflammation. A perturbation analysis performed on the IBD network indicates that the model is robust. Also, as described in clinical trials, a simulation of anti-TNFα, anti-IL2 and Granulocyte and Monocyte Apheresis showed a decrease in the Metalloproteinases node (MMPs), which means a decrease in tissue damage. In contrast, as clinical trials have demonstrated, a simulation of anti-IL17 and anti-IFNγ or IL10 overexpression therapy did not show any major change in MMPs expression, as corresponds to a failed therapy. The model proved to be a promising in silico tool for the evaluation of potential therapeutic targets, the identification of new IBD biomarkers, the integration of IBD polymorphisms to anticipate responders and non-responders and can be reduced and transformed in quantitative model/s.
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35
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Li C. Identifying the optimal anticancer targets from the landscape of a cancer-immunity interaction network. Phys Chem Chem Phys 2018; 19:7642-7651. [PMID: 28256642 DOI: 10.1039/c6cp07767f] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Cancer immunotherapy, an approach of targeting immune cells to attack tumor cells, has been suggested to be a promising way for cancer treatment recently. However, the successful application of this approach warrants a deeper understanding of the intricate interplay between cancer cells and the immune system. Especially, the mechanisms of immunotherapy remain elusive. In this work, we constructed a cancer-immunity interplay network by incorporating interactions among cancer cells and some representative immune cells, and uncovered the potential landscape of the cancer-immunity network. Three attractors emerge on the landscape, representing the cancer state, the immune state, and the hybrid state, which can correspond to escape, elimination, and equilibrium phases in the immunoediting theory, respectively. We quantified the transition processes between the cancer state and the immune state by calculating transition actions and identifying the corresponding minimum action paths (MAPs) between these two attractors. The transition actions, directly calculated from the high dimensional system, are correlated with the barrier heights from the landscape, but provide a more precise description of the dynamics of a system. By optimizing the transition actions from the cancer state to the immune state, we identified some optimal combinations of anticancer targets. Our combined approach of the landscape and optimization of transition actions offers a framework to study the stochastic dynamics and identify the optimal combination of targets for the cancer-immunity interplay, and can be applied to other cell communication networks or gene regulatory networks.
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Affiliation(s)
- Chunhe Li
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China. and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
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Tarca AL, Fitzgerald W, Chaemsaithong P, Xu Z, Hassan SS, Grivel J, Gomez‐Lopez N, Panaitescu B, Pacora P, Maymon E, Erez O, Margolis L, Romero R. The cytokine network in women with an asymptomatic short cervix and the risk of preterm delivery. Am J Reprod Immunol 2017; 78:e12686. [PMID: 28585708 PMCID: PMC5575567 DOI: 10.1111/aji.12686] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 03/20/2017] [Indexed: 01/06/2023] Open
Abstract
PROBLEM To characterize the amniotic fluid (AF) inflammatory-related protein (IRP) network in patients with a sonographic short cervix (SCx) and to determine its relation to early preterm delivery (ePTD). METHOD OF STUDY A retrospective cohort study included women with a SCx (≤25 mm; n=223) who had amniocentesis and were classified according to gestational age (GA) at diagnosis and delivery (ePTD <32 weeks of gestation). RESULTS (i) In women with a SCx ≤ 22 1/7 weeks, the concentration of most IRPs increased as the cervix shortened; those with ePTD had a higher rate of increase in MIP-1α, MCP-1, and IL-6 concentrations than those delivering later; and (ii) the concentration of most IRPs and the correlation between several IRP pairs were higher in the ePTD group than for those delivering later. CONCLUSION Women with a SCx at 16-22 1/7 weeks have a unique AF cytokine network that correlates with cervical length at diagnosis and GA at delivery. This network may aid in predicting ePTD.
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Affiliation(s)
- Adi L. Tarca
- Perinatology Research BranchProgram for Perinatal Research and ObstetricsDivision of Intramural ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthU.S. Department of Health and Human ServicesBethesdaMD, and Detroit, MIUSA
- Department of Obstetrics and GynecologyWayne State University School of MedicineDetroitMIUSA
| | - Wendy Fitzgerald
- Section on Intercellular InteractionsProgram on Physical BiologyEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthU.S. Department of Health and Human ServicesBethesdaMDUSA
| | - Piya Chaemsaithong
- Perinatology Research BranchProgram for Perinatal Research and ObstetricsDivision of Intramural ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthU.S. Department of Health and Human ServicesBethesdaMD, and Detroit, MIUSA
- Department of Obstetrics and GynecologyWayne State University School of MedicineDetroitMIUSA
| | - Zhonghui Xu
- Perinatology Research BranchProgram for Perinatal Research and ObstetricsDivision of Intramural ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthU.S. Department of Health and Human ServicesBethesdaMD, and Detroit, MIUSA
| | - Sonia S. Hassan
- Perinatology Research BranchProgram for Perinatal Research and ObstetricsDivision of Intramural ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthU.S. Department of Health and Human ServicesBethesdaMD, and Detroit, MIUSA
- Department of Obstetrics and GynecologyWayne State University School of MedicineDetroitMIUSA
| | - Jean‐Charles Grivel
- Division of Translational MedicineSidra Medical and Research CenterDohaQatar
| | - Nardhy Gomez‐Lopez
- Perinatology Research BranchProgram for Perinatal Research and ObstetricsDivision of Intramural ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthU.S. Department of Health and Human ServicesBethesdaMD, and Detroit, MIUSA
- Department of Obstetrics and GynecologyWayne State University School of MedicineDetroitMIUSA
- Department of ImmunologyMicrobiology and BiochemistryWayne State University School of MedicineDetroitMIUSA
| | - Bogdan Panaitescu
- Perinatology Research BranchProgram for Perinatal Research and ObstetricsDivision of Intramural ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthU.S. Department of Health and Human ServicesBethesdaMD, and Detroit, MIUSA
- Department of Obstetrics and GynecologyWayne State University School of MedicineDetroitMIUSA
| | - Percy Pacora
- Perinatology Research BranchProgram for Perinatal Research and ObstetricsDivision of Intramural ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthU.S. Department of Health and Human ServicesBethesdaMD, and Detroit, MIUSA
- Department of Obstetrics and GynecologyWayne State University School of MedicineDetroitMIUSA
| | - Eli Maymon
- Perinatology Research BranchProgram for Perinatal Research and ObstetricsDivision of Intramural ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthU.S. Department of Health and Human ServicesBethesdaMD, and Detroit, MIUSA
- Department of Obstetrics and GynecologyWayne State University School of MedicineDetroitMIUSA
| | - Offer Erez
- Perinatology Research BranchProgram for Perinatal Research and ObstetricsDivision of Intramural ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthU.S. Department of Health and Human ServicesBethesdaMD, and Detroit, MIUSA
- Department of Obstetrics and GynecologyWayne State University School of MedicineDetroitMIUSA
| | - Leonid Margolis
- Section on Intercellular InteractionsProgram on Physical BiologyEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthU.S. Department of Health and Human ServicesBethesdaMDUSA
| | - Roberto Romero
- Perinatology Research BranchProgram for Perinatal Research and ObstetricsDivision of Intramural ResearchEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthU.S. Department of Health and Human ServicesBethesdaMD, and Detroit, MIUSA
- Department of Obstetrics and GynecologyUniversity of MichiganAnn ArborMIUSA
- Department of Epidemiology and BiostatisticsMichigan State UniversityEast LansingMIUSA
- Center for Molecular Medicine and GeneticsWayne State UniversityDetroitMIUSA
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Abstract
BACKGROUND Moving from the molecular and cellular level to a multi-scale systems understanding of immune responses requires the development of novel approaches to integrate knowledge and data from different biological levels into mechanism-based integrative mathematical models. The aim of our study is to present a methodology for a hybrid modelling of immunological processes in their spatial context. METHODS A two-level hybrid mathematical model of immune cell migration and interaction integrating cellular and organ levels of regulation for a 2D spatial consideration of idealized secondary lymphoid organs is developed. It considers the population dynamics of antigen-presenting cells, CD4 + and CD8 + T lymphocytes in naive-, proliferation- and differentiated states. Cell division is assumed to be asymmetric and regulated by the extracellular concentration of interleukin-2 (IL-2) and type I interferon (IFN), together controlling the balance between proliferation and differentiation. The cytokine dynamics is described by reaction-diffusion PDEs whereas the intracellular regulation is modelled with a system of ODEs. RESULTS The mathematical model has been developed, calibrated and numerically implemented to study various scenarios in the regulation of T cell immune responses to infection, in particular the change in the diffusion coefficient of type I IFN as compared to IL-2. We have shown that a hybrid modelling approach provides an efficient tool to describe and analyze the interplay between spatio-temporal processes in the emergence of abnormal immune response dynamics. DISCUSSION Virus persistence in humans is often associated with an exhaustion of T lymphocytes. Many factors can contribute to the development of exhaustion. One of them is associated with a shift from a normal clonal expansion pathway to an altered one characterized by an early terminal differentiation of T cells. We propose that an altered T cell differentiation and proliferation sequence can naturally result from a spatial separation of the signaling events delivered via TCR, IL-2 and type I IFN receptors. Indeed, the spatial overlap of the concentration fields of extracellular IL-2 and IFN in lymph nodes changes dynamically due to different migration patterns of APCs and CD4 + T cells secreting them. CONCLUSIONS The proposed hybrid mathematical model of the immune response represents a novel analytical tool to examine challenging issues in the spatio-temporal regulation of cell growth and differentiation, in particular the effect of timing and location of activation signals.
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Affiliation(s)
- Anass Bouchnita
- Institut Camille Jordan, UMR 5208 CNRS, University Lyon 1, Villeurbanne, 69622 France
- Laboratoire de Biométrie et Biologie Evolutive (LBBE), UMR 5558 CNRS, University Lyon 1, Villeurbanne, 69622 France
- Mohammadia School of Engineering, Mohamed V University, Rabat, 10080 Morocco
| | - Gennady Bocharov
- Institute of Numerical Mathematics, Russian Academy of Sciences, Gubkina Street 8, Moscow, 119333 Russian Federation
| | - Andreas Meyerhans
- Institute of Numerical Mathematics, Russian Academy of Sciences, Gubkina Street 8, Moscow, 119333 Russian Federation
- Infection Biology Laboratory, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Doctor Aiguader, 88, Barcelona, 08003 Spain
- ICREA, Pg. Lluís Companys 23, Barcelona, 08010 Spain
| | - Vitaly Volpert
- Institut Camille Jordan, UMR 5208 CNRS, University Lyon 1, Villeurbanne, 69622 France
- Institute of Numerical Mathematics, Russian Academy of Sciences, Gubkina Street 8, Moscow, 119333 Russian Federation
- INRIA Team Dracula, INRIA Lyon La Doua, Villeurbanne, 69603 France
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38
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McCune JS. Immunotherapy to Treat Cancer. Clin Pharmacol Ther 2017; 100:198-203. [PMID: 27513619 DOI: 10.1002/cpt.404] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2016] [Accepted: 05/23/2016] [Indexed: 12/19/2022]
Abstract
This issue of Clinical Pharmacology & Therapeutics focuses on immunotherapy as an approach to treat cancer by generating or augmenting an immune response against it. The enthusiasm for immunotherapy has waxed and waned over the past century. Enthusiasm for immunotherapy has risen over the past decade due, in part, to data showing that cancer immunotherapy consistently improves overall survival in select patients with advanced-stage cancer. Antitumor immunotherapy has broad potential and could be used to treat many different types of advanced-stage cancer due to the durable and robust response that it elicits across a diverse spectrum of cancers. This issue covers various aspects of relevant therapeutic topics ranging from discovery of chimeric antigen receptor (CAR) T cells, development of novel immunotherapies using novel pharmacokinetic/dynamic modeling tools, to the utilization of immune checkpoint therapy. Regarding utilization, this issue addresses biomarker selection strategies for personalized treatment of non-small cell lung cancer (NSCLC) with immune checkpoint therapy and also the management of the unique immune response adverse events (irAEs).
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Affiliation(s)
- J S McCune
- Department of Pharmacy and Department of Pharmaceutics, University of Washington, Seattle, Washington, USA
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39
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Schleicher J, Conrad T, Gustafsson M, Cedersund G, Guthke R, Linde J. Facing the challenges of multiscale modelling of bacterial and fungal pathogen-host interactions. Brief Funct Genomics 2017; 16:57-69. [PMID: 26857943 PMCID: PMC5439285 DOI: 10.1093/bfgp/elv064] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Recent and rapidly evolving progress on high-throughput measurement techniques and computational performance has led to the emergence of new disciplines, such as systems medicine and translational systems biology. At the core of these disciplines lies the desire to produce multiscale models: mathematical models that integrate multiple scales of biological organization, ranging from molecular, cellular and tissue models to organ, whole-organism and population scale models. Using such models, hypotheses can systematically be tested. In this review, we present state-of-the-art multiscale modelling of bacterial and fungal infections, considering both the pathogen and host as well as their interaction. Multiscale modelling of the interactions of bacteria, especially Mycobacterium tuberculosis, with the human host is quite advanced. In contrast, models for fungal infections are still in their infancy, in particular regarding infections with the most important human pathogenic fungi, Candida albicans and Aspergillus fumigatus. We reflect on the current availability of computational approaches for multiscale modelling of host-pathogen interactions and point out current challenges. Finally, we provide an outlook for future requirements of multiscale modelling.
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Affiliation(s)
| | | | | | | | | | - Jörg Linde
- Corresponding author: Jörg Linde, Leibniz Institute for Natural Product Research and Infection Biology—Hans Knöll Institute, Jena, Germany. Tel.: +49-3641-532-1290; E-mail:
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Pharmacokinetics, Pharmacodynamics, and Pharmacogenomics of Immunosuppressants in Allogeneic Hematopoietic Cell Transplantation: Part II. Clin Pharmacokinet 2016; 55:551-93. [PMID: 26620047 DOI: 10.1007/s40262-015-0340-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Part I of this article included a pertinent review of allogeneic hematopoietic cell transplantation (alloHCT), the role of postgraft immunosuppression in alloHCT, and the pharmacokinetics, pharmacodynamics, and pharmacogenomics of the calcineurin inhibitors and methotrexate. In this article (Part II), we review the pharmacokinetics, pharmacodynamics, and pharmacogenomics of mycophenolic acid (MPA), sirolimus, and the antithymocyte globulins (ATG). We then discuss target concentration intervention (TCI) of these postgraft immunosuppressants in alloHCT patients, with a focus on current evidence for TCI and on how TCI may improve clinical management in these patients. Currently, TCI using trough concentrations is conducted for sirolimus in alloHCT patients. Several studies demonstrate that MPA plasma exposure is associated with clinical outcomes, with an increasing number of alloHCT patients needing TCI of MPA. Compared with MPA, there are fewer pharmacokinetic/dynamic studies of rabbit ATG and horse ATG in alloHCT patients. Future pharmacokinetic/dynamic research of postgraft immunosuppressants should include '-omics'-based tools: pharmacogenomics may be used to gain an improved understanding of the covariates influencing pharmacokinetics as well as proteomics and metabolomics as novel methods to elucidate pharmacodynamic responses.
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Morel PA, Lee REC, Faeder JR. Demystifying the cytokine network: Mathematical models point the way. Cytokine 2016; 98:115-123. [PMID: 27919524 DOI: 10.1016/j.cyto.2016.11.013] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 11/21/2016] [Indexed: 12/22/2022]
Abstract
Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed.
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Affiliation(s)
- Penelope A Morel
- Department of Immunology, University of Pittsburgh, Pittsburgh, USA.
| | - Robin E C Lee
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, USA
| | - James R Faeder
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, USA
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Knight-Schrijver V, Chelliah V, Cucurull-Sanchez L, Le Novère N. The promises of quantitative systems pharmacology modelling for drug development. Comput Struct Biotechnol J 2016; 14:363-370. [PMID: 27761201 PMCID: PMC5064996 DOI: 10.1016/j.csbj.2016.09.002] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 09/08/2016] [Accepted: 09/19/2016] [Indexed: 01/01/2023] Open
Abstract
Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.
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Affiliation(s)
| | - V. Chelliah
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | - N. Le Novère
- Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
- Corresponding author.
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Gao X, Arpin C, Marvel J, Prokopiou SA, Gandrillon O, Crauste F. IL-2 sensitivity and exogenous IL-2 concentration gradient tune the productive contact duration of CD8(+) T cell-APC: a multiscale modeling study. BMC SYSTEMS BIOLOGY 2016; 10:77. [PMID: 27535120 PMCID: PMC4989479 DOI: 10.1186/s12918-016-0323-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Accepted: 07/21/2016] [Indexed: 01/17/2023]
Abstract
Background The CD8+ T cell immune response fights acute infections by intracellular pathogens and, by generating an immune memory, enables immune responses against secondary infections. Activation of the CD8+ T cell immune response involves a succession of molecular events leading to modifications of CD8+ T cell population. To understand the endogenous and exogenous mechanisms controlling the activation of CD8+ T cells and to investigate the influence of early molecular events on the long-term cell population behavior, we developed a multiscale computational model. It integrates three levels of description: a Cellular Potts model describing the individual behavior of CD8+ T cells, a system of ordinary differential equations describing a decision-making molecular regulatory network at the intracellular level, and a partial differential equation describing the diffusion of IL-2 in the extracellular environment. Results We first calibrated the model parameters based on in vivo data and showed the model’s ability to reproduce early dynamics of CD8+ T cells in murine lymph nodes after influenza infection, both at the cell population and intracellular levels. We then showed the model’s ability to reproduce the proliferative responses of CD5hi and CD5lo CD8+ T cells to exogenous IL-2 under a weak TCR stimulation. This stressed the role of short-lasting molecular events and the relevance of explicitly describing both intracellular and cellular scale dynamics. Our results suggest that the productive contact duration of CD8+ T cell-APC is influenced by the sensitivity of individual CD8+ T cells to the activation signal and by the IL-2 concentration in the extracellular environment. Conclusions The multiscale nature of our model allows the reproduction and explanation of some acquired characteristics and functions of CD8+ T cells, and of their responses to multiple stimulation conditions, that would not be accessible in a classical description of cell population dynamics that would not consider intracellular dynamics. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0323-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xuefeng Gao
- Inria team Dracula, Inria Antenne Lyon la Doua, Bâtiment CEI-2, 56 Boulevard Niels Bohr, 69603, Villeurbanne cedex, France
| | - Christophe Arpin
- Inserm, U1111, Lyon, F-69007, France.,CNRS, UMR5308, Lyon, F-69007, France.,Centre International de Recherche en Infectiologie, Université Lyon 1, Lyon, F-69007, France.,Ecole Normale Supérieure de Lyon, Lyon, F-69007, France
| | - Jacqueline Marvel
- Inserm, U1111, Lyon, F-69007, France.,CNRS, UMR5308, Lyon, F-69007, France.,Centre International de Recherche en Infectiologie, Université Lyon 1, Lyon, F-69007, France.,Ecole Normale Supérieure de Lyon, Lyon, F-69007, France
| | - Sotiris A Prokopiou
- Inria team Dracula, Inria Antenne Lyon la Doua, Bâtiment CEI-2, 56 Boulevard Niels Bohr, 69603, Villeurbanne cedex, France
| | - Olivier Gandrillon
- Inria team Dracula, Inria Antenne Lyon la Doua, Bâtiment CEI-2, 56 Boulevard Niels Bohr, 69603, Villeurbanne cedex, France. .,Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d'Italie Site Jacques Monod, F-69007, Lyon, France.
| | - Fabien Crauste
- Inria team Dracula, Inria Antenne Lyon la Doua, Bâtiment CEI-2, 56 Boulevard Niels Bohr, 69603, Villeurbanne cedex, France. .,Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, 43 blvd. du 11 novembre 1918, F-69622, Villeurbanne cedex, France.
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Eberhardt M, Lai X, Tomar N, Gupta S, Schmeck B, Steinkasserer A, Schuler G, Vera J. Third-Kind Encounters in Biomedicine: Immunology Meets Mathematics and Informatics to Become Quantitative and Predictive. Methods Mol Biol 2016; 1386:135-179. [PMID: 26677184 DOI: 10.1007/978-1-4939-3283-2_9] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The understanding of the immune response is right now at the center of biomedical research. There are growing expectations that immune-based interventions will in the midterm provide new, personalized, and targeted therapeutic options for many severe and highly prevalent diseases, from aggressive cancers to infectious and autoimmune diseases. To this end, immunology should surpass its current descriptive and phenomenological nature, and become quantitative, and thereby predictive.Immunology is an ideal field for deploying the tools, methodologies, and philosophy of systems biology, an approach that combines quantitative experimental data, computational biology, and mathematical modeling. This is because, from an organism-wide perspective, the immunity is a biological system of systems, a paradigmatic instance of a multi-scale system. At the molecular scale, the critical phenotypic responses of immune cells are governed by large biochemical networks, enriched in nested regulatory motifs such as feedback and feedforward loops. This network complexity confers them the ability of highly nonlinear behavior, including remarkable examples of homeostasis, ultra-sensitivity, hysteresis, and bistability. Moving from the cellular level, different immune cell populations communicate with each other by direct physical contact or receiving and secreting signaling molecules such as cytokines. Moreover, the interaction of the immune system with its potential targets (e.g., pathogens or tumor cells) is far from simple, as it involves a number of attack and counterattack mechanisms that ultimately constitute a tightly regulated multi-feedback loop system. From a more practical perspective, this leads to the consequence that today's immunologists are facing an ever-increasing challenge of integrating massive quantities from multi-platforms.In this chapter, we support the idea that the analysis of the immune system demands the use of systems-level approaches to ensure the success in the search for more effective and personalized immune-based therapies.
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Affiliation(s)
- Martin Eberhardt
- Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Namrata Tomar
- Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Bernd Schmeck
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Marburg, Philipps University, Marburg, Germany
- Systems Biology Platform, Institute for Lung Research/iLung, German Center for Lung Research, Universities of Giessen and Marburg Lung Centre, Philipps University Marburg, Marburg, Germany
| | - Alexander Steinkasserer
- Department of Immune Modulation at the Department of Dermatology, University Hospital Erlangen, Erlangen, Germany
| | - Gerold Schuler
- Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
- Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
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The Role of Aggregates of Therapeutic Protein Products in Immunogenicity: An Evaluation by Mathematical Modeling. J Immunol Res 2015; 2015:401956. [PMID: 26682236 PMCID: PMC4670651 DOI: 10.1155/2015/401956] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Accepted: 10/07/2015] [Indexed: 01/12/2023] Open
Abstract
Therapeutic protein products (TPP) have been widely used to treat a variety of human diseases, including cancer, hemophilia, and autoimmune diseases. However, TPP can induce unwanted immune responses that can impact both drug efficacy and patient safety. The presence of aggregates is of particular concern as they have been implicated in inducing both T cell-independent and T cell-dependent immune responses. We used mathematical modeling to evaluate several mechanisms through which aggregates of TPP could contribute to the development of immunogenicity. Modeling interactions between aggregates and B cell receptors demonstrated that aggregates are unlikely to induce T cell-independent immune responses by cross-linking B cell receptors because the amount of signal transducing complex that can form under physiologically relevant conditions is limited. We systematically evaluate the role of aggregates in inducing T cell-dependent immune responses using a recently developed multiscale mechanistic mathematical model. Our analysis indicates that aggregates could contribute to T cell-dependent immune response by inducing high affinity epitopes which may not be present in the nonaggregated TPP and/or by enhancing danger signals to break tolerance. In summary, our computational analysis is suggestive of novel insights into the mechanisms underlying aggregate-induced immunogenicity, which could be used to develop mitigation strategies.
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McCune JS, Reynolds KS. Developing and Using Therapeutics for Emerging Infections. Clin Pharmacol Ther 2015; 98:346-51. [PMID: 26179402 PMCID: PMC7162320 DOI: 10.1002/cpt.183] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Accepted: 07/08/2015] [Indexed: 01/05/2023]
Abstract
This issue of Clinical Pharmacology & Therapeutics focuses on emerging infections. The outbreaks of the vaccine-preventable diseases (e.g., measles) and the emerging pathogens (e.g., Ebola) show us how small the world has become. These outbreaks also show the pressing need for effective public education and development of novel therapies. This issue covers various aspects of relevant therapeutic topics ranging from preclinical models, pharmacokinetics, pharmacodynamics, pharmacogenomics, and clinical trial results, to education efforts in this area. Pharmacokinetic/dynamic modeling had an appreciable role in reducing the morbidity and mortality associated with human immunodeficiency virus and hepatitis C virus, recent emerging infections. However, these gains could be lessened by poor adherence to therapies, which has contributed to the development of multidrug-resistant tuberculosis. We must not forget lessons from previous infections, or they may reemerge.
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Affiliation(s)
- Jeannine S McCune
- Department of Pharmacy, University of Washington, Seattle, Washington, USA
| | - Kellie S Reynolds
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA
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Stroh M, Carlile DJ, Li CC, Wagg J, Ribba B, Ramanujan S, Jin J, Xu J, Charoin JE, Xhu ZX, Morcos PN, Davis JD, Phipps A. Challenges and Opportunities for Quantitative Clinical Pharmacology in Cancer Immunotherapy: Something Old, Something New, Something Borrowed, and Something Blue. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2015; 4:495-7. [PMID: 26451328 PMCID: PMC4592528 DOI: 10.1002/psp4.12014] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Accepted: 07/13/2015] [Indexed: 01/13/2023]
Abstract
Cancer immunotherapy (CIT) initiates or enhances the host immune response against cancer. Following decades of development, patients with previously few therapeutic options may now benefit from CIT. Although the quantitative clinical pharmacology (qCP) of previous classes of anticancer drugs has matured during this time, application to CIT may not be straightforward since CIT acts via the immune system. Here we discuss where qCP approaches might best borrow or start anew for CIT.
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Affiliation(s)
- M Stroh
- Department of Clinical Pharmacology, Genentech South San Francisco, California, USA
| | - D J Carlile
- Department of Clinical Pharmacology, F. Hoffmann-La Roche, Roche Innovation Center Welwyn, UK
| | - C-C Li
- Department of Clinical Pharmacology, Genentech South San Francisco, California, USA
| | - J Wagg
- Roche Innovation Center Basel Switzerland
| | - B Ribba
- Roche Innovation Center Basel Switzerland
| | - S Ramanujan
- Department of Preclinical and Translational PKPD, Genentech South San Francisco, CA, USA
| | - J Jin
- Department of Clinical Pharmacology, Genentech South San Francisco, California, USA
| | - J Xu
- Department of Clinical Pharmacology, Genentech South San Francisco, California, USA
| | | | - Z-X Xhu
- Roche Innovation Center New York New York, USA
| | - P N Morcos
- Department of Preclinical and Translational PKPD, Genentech South San Francisco, CA, USA
| | - J D Davis
- Department of Clinical Pharmacology, Genentech South San Francisco, California, USA
| | - A Phipps
- Department of Clinical Pharmacology, F. Hoffmann-La Roche, Roche Innovation Center Welwyn, UK
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Benson N. Network-based discovery through mechanistic systems biology. Implications for applications--SMEs and drug discovery: where the action is. DRUG DISCOVERY TODAY. TECHNOLOGIES 2015; 15:41-8. [PMID: 26464089 DOI: 10.1016/j.ddtec.2015.07.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2014] [Revised: 06/30/2015] [Accepted: 07/14/2015] [Indexed: 01/10/2023]
Abstract
Phase II attrition remains the most important challenge for drug discovery. Tackling the problem requires improved understanding of the complexity of disease biology. Systems biology approaches to this problem can, in principle, deliver this. This article reviews the reports of the application of mechanistic systems models to drug discovery questions and discusses the added value. Although we are on the journey to the virtual human, the length, path and rate of learning from this remain an open question. Success will be dependent on the will to invest and make the most of the insight generated along the way.
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Affiliation(s)
- Neil Benson
- Xenologiq Ltd., Unit 43, Canterbury Innovation Centre, University Road, Canterbury CT2 7FG, UK.
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50
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Cappuccio A, Tieri P, Castiglione F. Multiscale modelling in immunology: a review. Brief Bioinform 2015; 17:408-18. [PMID: 25810307 DOI: 10.1093/bib/bbv012] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Accepted: 01/30/2015] [Indexed: 01/26/2023] Open
Abstract
One of the greatest challenges in biomedicine is to get a unified view of observations made from the molecular up to the organism scale. Towards this goal, multiscale models have been highly instrumental in contexts such as the cardiovascular field, angiogenesis, neurosciences and tumour biology. More recently, such models are becoming an increasingly important resource to address immunological questions as well. Systematic mining of the literature in multiscale modelling led us to identify three main fields of immunological applications: host-virus interactions, inflammatory diseases and their treatment and development of multiscale simulation platforms for immunological research and for educational purposes. Here, we review the current developments in these directions, which illustrate that multiscale models can consistently integrate immunological data generated at several scales, and can be used to describe and optimize therapeutic treatments of complex immune diseases.
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Affiliation(s)
- Antonio Cappuccio
- Laboratory of Integrative biology of human dendritic cells and T cells, U932 Immunity and cancer, Institut Curie, 26 Rue d`Ulm, 75005 Paris, France
| | - Paolo Tieri
- Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy
| | - Filippo Castiglione
- Institute for Applied Mathematics (IAC), National Research Council of Italy (CNR), Via dei Taurini 19, 00185 Rome, Italy
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