1
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Kulesza A, Couty C, Lemarre P, Thalhauser CJ, Cao Y. Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice. J Pharmacokinet Pharmacodyn 2024:10.1007/s10928-024-09930-x. [PMID: 38904912 DOI: 10.1007/s10928-024-09930-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 06/07/2024] [Indexed: 06/22/2024]
Abstract
Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.
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Affiliation(s)
| | - Claire Couty
- Novadiscovery, 1 Place Giovanni Verrazzano, 69009, Lyon, France
| | - Paul Lemarre
- Novadiscovery, 1 Place Giovanni Verrazzano, 69009, Lyon, France
| | - Craig J Thalhauser
- Genmab US, Inc., 777 Scudders Mill Rd Bldg 2 4th Floor, Plainsboro, NJ, 08536, USA
| | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
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2
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Lin MR, Guo X, Azizi A, Fewell JH, Milner F. Mechanistic modeling of alarm signaling in seed-harvester ants. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:5536-5555. [PMID: 38872547 DOI: 10.3934/mbe.2024244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Ant colonies demonstrate a finely tuned alarm response to potential threats, offering a uniquely manageable empirical setting for exploring adaptive information diffusion within groups. To effectively address potential dangers, a social group must swiftly communicate the threat throughout the collective while conserving energy in the event that the threat is unfounded. Through a combination of modeling, simulation, and empirical observations of alarm spread and damping patterns, we identified the behavioral rules governing this adaptive response. Experimental trials involving alarmed ant workers (Pogonomyrmex californicus) released into a tranquil group of nestmates revealed a consistent pattern of rapid alarm propagation followed by a comparatively extended decay period [1]. The experiments in [1] showed that individual ants exhibiting alarm behavior increased their movement speed, with variations in response to alarm stimuli, particularly during the peak of the reaction. We used the data in [1] to investigate whether these observed characteristics alone could account for the swift mobility increase and gradual decay of alarm excitement. Our self-propelled particle model incorporated a switch-like mechanism for ants' response to alarm signals and individual variations in the intensity of speed increased after encountering these signals. This study aligned with the established hypothesis that individual ants possess cognitive abilities to process and disseminate information, contributing to collective cognition within the colony (see [2] and the references therein). The elements examined in this research support this hypothesis by reproducing statistical features of the empirical speed distribution across various parameter values.
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Affiliation(s)
- Michael R Lin
- Simon A. Levin Mathematical, Computational and Modeling Sciences Center, Arizona State University, Tempe 85281, USA
| | - Xiaohui Guo
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 7632706, Israel
| | - Asma Azizi
- Department of Mathematics, Kennesaw State University, Marietta 30062, USA
| | | | - Fabio Milner
- Simon A. Levin Mathematical, Computational and Modeling Sciences Center, Arizona State University, Tempe 85281, USA
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe 85287, USA
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3
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Valega-Mackenzie W, Rodriguez Messan M, Yogurtcu ON, Nukala U, Sauna ZE, Yang H. Dose optimization of an adjuvanted peptide-based personalized neoantigen melanoma vaccine. PLoS Comput Biol 2024; 20:e1011247. [PMID: 38427689 PMCID: PMC10936818 DOI: 10.1371/journal.pcbi.1011247] [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: 06/07/2023] [Revised: 03/13/2024] [Accepted: 01/03/2024] [Indexed: 03/03/2024] Open
Abstract
The advancements in next-generation sequencing have made it possible to effectively detect somatic mutations, which has led to the development of personalized neoantigen cancer vaccines that are tailored to the unique variants found in a patient's cancer. These vaccines can provide significant clinical benefit by leveraging the patient's immune response to eliminate malignant cells. However, determining the optimal vaccine dose for each patient is a challenge due to the heterogeneity of tumors. To address this challenge, we formulate a mathematical dose optimization problem based on a previous mathematical model that encompasses the immune response cascade produced by the vaccine in a patient. We propose an optimization approach to identify the optimal personalized vaccine doses, considering a fixed vaccination schedule, while simultaneously minimizing the overall number of tumor and activated T cells. To validate our approach, we perform in silico experiments on six real-world clinical trial patients with advanced melanoma. We compare the results of applying an optimal vaccine dose to those of a suboptimal dose (the dose used in the clinical trial and its deviations). Our simulations reveal that an optimal vaccine regimen of higher initial doses and lower final doses may lead to a reduction in tumor size for certain patients. Our mathematical dose optimization offers a promising approach to determining an optimal vaccine dose for each patient and improving clinical outcomes.
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Affiliation(s)
- Wencel Valega-Mackenzie
- Office of Biostatistics and Pharmacovigilance, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Marisabel Rodriguez Messan
- Office of Biostatistics and Pharmacovigilance, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Osman N. Yogurtcu
- Office of Biostatistics and Pharmacovigilance, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Ujwani Nukala
- Office of Biostatistics and Pharmacovigilance, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Zuben E. Sauna
- Office of Therapeutic Products, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Hong Yang
- Office of Biostatistics and Pharmacovigilance, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, United States of America
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4
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Strobl MAR, Gallaher J, Robertson-Tessi M, West J, Anderson ARA. Treatment of evolving cancers will require dynamic decision support. Ann Oncol 2023; 34:867-884. [PMID: 37777307 PMCID: PMC10688269 DOI: 10.1016/j.annonc.2023.08.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 08/01/2023] [Accepted: 08/21/2023] [Indexed: 10/02/2023] Open
Abstract
Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.
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Affiliation(s)
- M A R Strobl
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa; Translational Hematology and Oncology Research, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, USA
| | - J Gallaher
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - M Robertson-Tessi
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - J West
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa
| | - A R A Anderson
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa.
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5
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Creemers JHA, Ankan A, Roes KCB, Schröder G, Mehra N, Figdor CG, de Vries IJM, Textor J. In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome. Nat Commun 2023; 14:2348. [PMID: 37095077 PMCID: PMC10125995 DOI: 10.1038/s41467-023-37933-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 04/06/2023] [Indexed: 04/26/2023] Open
Abstract
Late-stage cancer immunotherapy trials often lead to unusual survival curve shapes, like delayed curve separation or a plateauing curve in the treatment arm. It is critical for trial success to anticipate such effects in advance and adjust the design accordingly. Here, we use in silico cancer immunotherapy trials - simulated trials based on three different mathematical models - to assemble virtual patient cohorts undergoing late-stage immunotherapy, chemotherapy, or combination therapies. We find that all three simulation models predict the distinctive survival curve shapes commonly associated with immunotherapies. Considering four aspects of clinical trial design - sample size, endpoint, randomization rate, and interim analyses - we demonstrate how, by simulating various possible scenarios, the robustness of trial design choices can be scrutinized, and possible pitfalls can be identified in advance. We provide readily usable, web-based implementations of our three trial simulation models to facilitate their use by biomedical researchers, doctors, and trialists.
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Affiliation(s)
- Jeroen H A Creemers
- Medical BioSciences, Radboud university medical center, Nijmegen, The Netherlands
- Oncode Institute, Nijmegen, The Netherlands
| | - Ankur Ankan
- Data Science group, Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands
| | - Kit C B Roes
- Department of Health Evidence, Section Biostatistics, Radboud university medical center, Nijmegen, The Netherlands
| | - Gijs Schröder
- Data Science group, Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands
| | - Niven Mehra
- Department of Medical Oncology, Radboud university medical center, Nijmegen, The Netherlands
| | - Carl G Figdor
- Medical BioSciences, Radboud university medical center, Nijmegen, The Netherlands
- Oncode Institute, Nijmegen, The Netherlands
| | - I Jolanda M de Vries
- Medical BioSciences, Radboud university medical center, Nijmegen, The Netherlands
| | - Johannes Textor
- Medical BioSciences, Radboud university medical center, Nijmegen, The Netherlands.
- Data Science group, Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands.
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6
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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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7
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Han J, Sheng T, Zhang Y, Cheng H, Gao J, Yu J, Gu Z. Bioresponsive Immunotherapeutic Materials. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023:e2209778. [PMID: 36639983 DOI: 10.1002/adma.202209778] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 12/31/2022] [Indexed: 06/17/2023]
Abstract
The human immune system is an interaction network of biological processes, and its dysfunction is closely associated with a wide array of diseases, such as cancer, infectious diseases, tissue damage, and autoimmune diseases. Manipulation of the immune response network in a desired and controlled fashion has been regarded as a promising strategy for maximizing immunotherapeutic efficacy and minimizing side effects. Integration of "smart" bioresponsive materials with immunoactive agents including small molecules, biomacromolecules, and cells can achieve on-demand release of agents at targeted sites to reduce overdose-related toxicity and alleviate off-target effects. This review highlights the design principles of bioresponsive immunotherapeutic materials and discusses the critical roles of controlled release of immunoactive agents from bioresponsive materials in recruiting, housing, and manipulating immune cells for evoking desired immune responses. Challenges and future directions from the perspective of clinical translation are also discussed.
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Affiliation(s)
- Jinpeng Han
- Zhejiang Provincial Key Laboratory for Advanced Drug Delivery Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Tao Sheng
- Zhejiang Provincial Key Laboratory for Advanced Drug Delivery Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yuqi Zhang
- Zhejiang Provincial Key Laboratory for Advanced Drug Delivery Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- Department of Burns and Wound Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China
| | - Hao Cheng
- Department of Materials Science and Engineering, Drexel University, Philadelphia, PA, 19104, USA
| | - Jianqing Gao
- Zhejiang Provincial Key Laboratory for Advanced Drug Delivery Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, China
- Jinhua Institute of Zhejiang University, Jinhua, 321299, China
| | - Jicheng Yu
- Zhejiang Provincial Key Laboratory for Advanced Drug Delivery Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- Jinhua Institute of Zhejiang University, Jinhua, 321299, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, 311121, China
- Department of General Surgery, Sir Run Run Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, China
| | - Zhen Gu
- Zhejiang Provincial Key Laboratory for Advanced Drug Delivery Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- Jinhua Institute of Zhejiang University, Jinhua, 321299, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, 311121, China
- Department of General Surgery, Sir Run Run Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, China
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, 310027, China
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8
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Butner JD, Dogra P, Chung C, Pasqualini R, Arap W, Lowengrub J, Cristini V, Wang Z. Mathematical modeling of cancer immunotherapy for personalized clinical translation. NATURE COMPUTATIONAL SCIENCE 2022; 2:785-796. [PMID: 38126024 PMCID: PMC10732566 DOI: 10.1038/s43588-022-00377-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2023]
Abstract
Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.
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Affiliation(s)
- Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, NJ, USA
- Department of Radiation Oncology, Division of Cancer Biology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey, Newark, NJ, USA
- Department of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - John Lowengrub
- Department of Mathematics, University of California at Irvine, Irvine, CA, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
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9
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Cancerous Tumor Controlled Treatment Using Search Heuristic (GA)-Based Sliding Mode and Synergetic Controller. Cancers (Basel) 2022; 14:cancers14174191. [PMID: 36077727 PMCID: PMC9454425 DOI: 10.3390/cancers14174191] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/11/2022] [Accepted: 08/25/2022] [Indexed: 11/27/2022] Open
Abstract
Simple Summary Cancer is basically a tough condition on a patient’s body where cell grows uncontrollably. Normal cells are affected, which destroys the health of the patient. The main problem in cancer is spreading from one part to another. Therefore, the mathematical modeling of cancerous tumors integrates to check overall stability. A novel approach is introduced such as Bernstein polynomial with combination of genetic algorithm, sliding mode controller, and synergetic control. The proposed solution has easily eliminated cancerous cells within five days using synergetic control. In addition, five cases are incorporated to evaluate error function. In addition, a brief comparative study is added to contrast the simulation results with theoretical modeling. Abstract Cancerous tumor cells divide uncontrollably, which results in either tumor or harm to the immune system of the body. Due to the destructive effects of chemotherapy, optimal medications are needed. Therefore, possible treatment methods should be controlled to maintain the constant/continuous dose for affecting the spreading of cancerous tumor cells. Rapid growth of cells is classified into primary and secondary types. In giving a proper response, the immune system plays an important role. This is considered a natural process while fighting against tumors. In recent days, achieving a better method to treat tumors is the prime focus of researchers. Mathematical modeling of tumors uses combined immune, vaccine, and chemotherapies to check performance stability. In this research paper, mathematical modeling is utilized with reference to cancerous tumor growth, the immune system, and normal cells, which are directly affected by the process of chemotherapy. This paper presents novel techniques, which include Bernstein polynomial (BSP) with genetic algorithm (GA), sliding mode controller (SMC), and synergetic control (SC), for giving a possible solution to the cancerous tumor cells (CCs) model. Through GA, random population is generated to evaluate fitness. SMC is used for the continuous exponential dose of chemotherapy to reduce CCs in about forty-five days. In addition, error function consists of five cases that include normal cells (NCs), immune cells (ICs), CCs, and chemotherapy. Furthermore, the drug control process is explained in all the cases. In simulation results, utilizing SC has completely eliminated CCs in nearly five days. The proposed approach reduces CCs as early as possible.
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10
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Lam I, Pilla Reddy V, Ball K, Arends RH, Mac Gabhann F. Development of and insights from systems pharmacology models of
antibody‐drug
conjugates. CPT Pharmacometrics Syst Pharmacol 2022; 11:967-990. [PMID: 35712824 PMCID: PMC9381915 DOI: 10.1002/psp4.12833] [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: 03/29/2022] [Revised: 05/26/2022] [Accepted: 06/02/2022] [Indexed: 01/02/2023] Open
Abstract
Antibody‐drug conjugates (ADCs) have gained traction in the oncology space in the past few decades, with significant progress being made in recent years. Although the use of pharmacometric modeling is well‐established in the drug development process, there is an increasing need for a better quantitative biological understanding of the pharmacokinetic and pharmacodynamic relationships of these complex molecules. Quantitative systems pharmacology (QSP) approaches can assist in this endeavor; recent computational QSP models incorporate ADC‐specific mechanisms and use data‐driven simulations to predict experimental outcomes. Various modeling approaches and platforms have been developed at the in vitro, in vivo, and clinical scales, and can be further integrated to facilitate preclinical to clinical translation. These new tools can help researchers better understand the nature and mechanisms of these targeted therapies to help achieve a more favorable therapeutic window. This review delves into the world of systems pharmacology modeling of ADCs, discussing various modeling efforts in the field thus far.
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Affiliation(s)
- Inez Lam
- Institute for Computational Medicine and Department of Biomedical Engineering Johns Hopkins University Baltimore Maryland USA
| | - Venkatesh Pilla Reddy
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D AstraZeneca Cambridge UK
| | - Kathryn Ball
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D AstraZeneca Cambridge UK
| | - Rosalinda H. Arends
- Clinical Pharmacology and Quantitative Pharmacology, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D AstraZeneca Gaithersburg Maryland USA
| | - Feilim Mac Gabhann
- Institute for Computational Medicine and Department of Biomedical Engineering Johns Hopkins University Baltimore Maryland USA
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11
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Mahasa KJ, Ouifki R, Eladdadi A, Pillis LD. A combination therapy of oncolytic viruses and chimeric antigen receptor T cells: a mathematical model proof-of-concept. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:4429-4457. [PMID: 35430822 DOI: 10.3934/mbe.2022205] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Combining chimeric antigen receptor T (CAR-T) cells with oncolytic viruses (OVs) has recently emerged as a promising treatment approach in preclinical studies that aim to alleviate some of the barriers faced by CAR-T cell therapy. In this study, we address by means of mathematical modeling the main question of whether a single dose or multiple sequential doses of CAR-T cells during the OVs therapy can have a synergetic effect on tumor reduction. To that end, we propose an ordinary differential equations-based model with virus-induced synergism to investigate potential effects of different regimes that could result in efficacious combination therapy against tumor cell populations. Model simulations show that, while the treatment with a single dose of CAR-T cells is inadequate to eliminate all tumor cells, combining the same dose with a single dose of OVs can successfully eliminate the tumor in the absence of virus-induced synergism. However, in the presence of virus-induced synergism, the same combination therapy fails to eliminate the tumor. Furthermore, it is shown that if the intensity of virus-induced synergy and/or virus oncolytic potency is high, then the induced CAR-T cell response can inhibit virus oncolysis. Additionally, the simulations show a more robust synergistic effect on tumor cell reduction when OVs and CAR-T cells are administered simultaneously compared to the combination treatment where CAR-T cells are administered first or after OV injection. Our findings suggest that the combination therapy of CAR-T cells and OVs seems unlikely to be effective if the virus-induced synergistic effects are included when genetically engineering oncolytic viral vectors.
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Affiliation(s)
- Khaphetsi Joseph Mahasa
- Department of Mathematics and Computer Science, National University of Lesotho, Roma 180, Maseru, Lesotho
| | - Rachid Ouifki
- Department of Mathematics and Applied Mathematics, North-West University, Mafikeng campus, Private Bag X2046, Mmabatho 2735, South Africa
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12
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Classical mathematical models for prediction of response to chemotherapy and immunotherapy. PLoS Comput Biol 2022; 18:e1009822. [PMID: 35120124 PMCID: PMC8903251 DOI: 10.1371/journal.pcbi.1009822] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 03/08/2022] [Accepted: 01/10/2022] [Indexed: 01/02/2023] Open
Abstract
Classical mathematical models of tumor growth have shaped our understanding of cancer and have broad practical implications for treatment scheduling and dosage. However, even the simplest textbook models have been barely validated in real world-data of human patients. In this study, we fitted a range of differential equation models to tumor volume measurements of patients undergoing chemotherapy or cancer immunotherapy for solid tumors. We used a large dataset of 1472 patients with three or more measurements per target lesion, of which 652 patients had six or more data points. We show that the early treatment response shows only moderate correlation with the final treatment response, demonstrating the need for nuanced models. We then perform a head-to-head comparison of six classical models which are widely used in the field: the Exponential, Logistic, Classic Bertalanffy, General Bertalanffy, Classic Gompertz and General Gompertz model. Several models provide a good fit to tumor volume measurements, with the Gompertz model providing the best balance between goodness of fit and number of parameters. Similarly, when fitting to early treatment data, the general Bertalanffy and Gompertz models yield the lowest mean absolute error to forecasted data, indicating that these models could potentially be effective at predicting treatment outcome. In summary, we provide a quantitative benchmark for classical textbook models and state-of-the art models of human tumor growth. We publicly release an anonymized version of our original data, providing the first benchmark set of human tumor growth data for evaluation of mathematical models. Mathematical oncology uses quantitative models for prediction of tumor growth and treatment response. The theoretical foundation of mathematical oncology is provided by six classical mathematical models: the Exponential, Logistic, Classic Bertalanffy, General Bertalanffy, Classic Gompertz and General Gompertz model. These models have been introduced decades ago, have been used in thousands of scientific articles and are part of textbooks and curricula in mathematical oncology. However, these models have not been systematically tested in clinical data from actual patients. In this study, we have collected quantitative tumor volume measurements from thousands of patients in five large clinical trials of cancer immunotherapy. We use this dataset to systematically investigate how accurately mathematical models can describe tumor growth, showing that there are pronounced differences between models. In addition, we show that two of these models can predict tumor response to immunotherapy and chemotherapy at later time points when trained on early tumor growth dynamics. Thus, our article closes a conceptual gap in the literature and at the same time provides a simple tool to predict response to chemotherapy and immunotherapy on the level of individual patients.
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Brummer AB, Yang X, Ma E, Gutova M, Brown CE, Rockne RC. Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy. PLoS Comput Biol 2022; 18:e1009504. [PMID: 35081104 PMCID: PMC8820647 DOI: 10.1371/journal.pcbi.1009504] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 02/07/2022] [Accepted: 01/12/2022] [Indexed: 12/14/2022] Open
Abstract
Chimeric antigen receptor (CAR) T-cell therapy is potentially an effective targeted immunotherapy for glioblastoma, yet there is presently little known about the efficacy of CAR T-cell treatment when combined with the widely used anti-inflammatory and immunosuppressant glucocorticoid, dexamethasone. Here we present a mathematical model-based analysis of three patient-derived glioblastoma cell lines treated in vitro with CAR T-cells and dexamethasone. Advanced in vitro experimental cell killing assay technologies allow for highly resolved temporal dynamics of tumor cells treated with CAR T-cells and dexamethasone, making this a valuable model system for studying the rich dynamics of nonlinear biological processes with translational applications. We model the system as a nonautonomous, two-species predator-prey interaction of tumor cells and CAR T-cells, with explicit time-dependence in the clearance rate of dexamethasone. Using time as a bifurcation parameter, we show that (1) dexamethasone destabilizes coexistence equilibria between CAR T-cells and tumor cells in a dose-dependent manner and (2) as dexamethasone is cleared from the system, a stable coexistence equilibrium returns in the form of a Hopf bifurcation. With the model fit to experimental data, we demonstrate that high concentrations of dexamethasone antagonizes CAR T-cell efficacy by exhausting, or reducing the activity of CAR T-cells, and by promoting tumor cell growth. Finally, we identify a critical threshold in the ratio of CAR T-cell death to CAR T-cell proliferation rates that predicts eventual treatment success or failure that may be used to guide the dose and timing of CAR T-cell therapy in the presence of dexamethasone in patients. Bioengineering and gene-editing technologies have paved the way for advance immunotherapies that can target patient-specific tumor cells. One of these therapies, chimeric antigen receptor (CAR) T-cell therapy has recently shown promise in treating glioblastoma, an aggressive brain cancer often with poor patient prognosis. Dexamethasone is a commonly prescribed anti-inflammatory medication due to the health complications of tumor associated swelling in the brain. However, the immunosuppressant effects of dexamethasone on the immunotherapeutic CAR T-cells are not well understood. To address this issue, we use mathematical modeling to study in vitro dynamics of dexamethasone and CAR T-cells in three patient-derived glioblastoma cell lines. We find that in each cell line studied there is a threshold of tolerable dexamethasone concentration. Below this threshold, CAR T-cells are successful at eliminating the cancer cells, while above this threshold, dexamethasone critically inhibits CAR T-cell efficacy. Our modeling suggests that in the presence of high dexamethasone reduced CAR T-cell efficacy, or increased exhaustion, can occur and result in CAR T-cell treatment failure.
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Affiliation(s)
- Alexander B. Brummer
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
- * E-mail: (ABB); (CEB); (RCR)
| | - Xin Yang
- Department of Hematology and Hematopoietic Cell Translation and Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
| | - Eric Ma
- Department of Hematology and Hematopoietic Cell Translation and Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
| | - Margarita Gutova
- Department of Stem Cell Biology and Regenerative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
| | - Christine E. Brown
- Department of Hematology and Hematopoietic Cell Translation and Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
- * E-mail: (ABB); (CEB); (RCR)
| | - Russell C. Rockne
- Department of Computational and Quantitative Medicine, Division of Mathematical Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, United States of America
- * E-mail: (ABB); (CEB); (RCR)
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Rodriguez Messan M, Yogurtcu ON, McGill JR, Nukala U, Sauna ZE, Yang H. Mathematical model of a personalized neoantigen cancer vaccine and the human immune system. PLoS Comput Biol 2021; 17:e1009318. [PMID: 34559809 PMCID: PMC8462726 DOI: 10.1371/journal.pcbi.1009318] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 08/02/2021] [Indexed: 12/30/2022] Open
Abstract
Cancer vaccines are an important component of the cancer immunotherapy toolkit enhancing immune response to malignant cells by activating CD4+ and CD8+ T cells. Multiple successful clinical applications of cancer vaccines have shown good safety and efficacy. Despite the notable progress, significant challenges remain in obtaining consistent immune responses across heterogeneous patient populations, as well as various cancers. We present a mechanistic mathematical model describing key interactions of a personalized neoantigen cancer vaccine with an individual patient’s immune system. Specifically, the model considers the vaccine concentration of tumor-specific antigen peptides and adjuvant, the patient’s major histocompatibility complexes I and II copy numbers, tumor size, T cells, and antigen presenting cells. We parametrized the model using patient-specific data from a clinical study in which individualized cancer vaccines were used to treat six melanoma patients. Model simulations predicted both immune responses, represented by T cell counts, to the vaccine as well as clinical outcome (determined as change of tumor size). This model, although complex, can be used to describe, simulate, and predict the behavior of the human immune system to a personalized cancer vaccine. Personalized cancer vaccines have gained attention in recent years due to the advances in sequencing techniques that have facilitated the identification of multiple tumor-specific mutations. This type of individualized immunotherapy has the potential to be specific, efficacious, and safe since it induces an immune response to protein targets not found on normal cells. This work focuses on understanding and analyzing important mechanisms involved in the activity of personalized cancer vaccines using a mechanistic mathematical model. This model describes the interactions of a personalized neoantigen peptide cancer vaccine, the human immune system and tumor cells operating at the molecular and cellular level. The molecular level captures the processing and presentation of neoantigens by dendritic cells to the T cells using cell surface proteins. The cellular level describes the differentiation of dendritic cells due to peptides and adjuvant concentrations in the vaccine, activation, and proliferation of T cells in response to treatment, and tumor growth. The model captures immune response behavior to a vaccine associated with patient-specific factors (e.g., different initial tumor burdens). This model enables the simulation of a complex biological system, the human immune system, by performing in silico experiments that may become the input for further analysis such as the identification of key parameters or mechanisms and/or interpretation of data.
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Affiliation(s)
- Marisabel Rodriguez Messan
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Osman N. Yogurtcu
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Joseph R. McGill
- Office of Tissues and Advanced Therapies, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Ujwani Nukala
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Zuben E. Sauna
- Office of Tissues and Advanced Therapies, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Hong Yang
- Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, Maryland, United States of America
- * E-mail:
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Saleem M, Baba MY, Raheem A, Noman M. A caution for oncologists: chemotherapy can cause chaotic dynamics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105865. [PMID: 33257112 DOI: 10.1016/j.cmpb.2020.105865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 11/13/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE The effect of chemotherapy in cancer models is mostly handled by using a separate equation for chemotherapeutic agent. In this study, we do not consider a separate equation for drug but rather introduce its effect in terms of a parameter m representing the fraction of tumor cells killed by chemotherapeutic drug module. The main objective of this study is to provide conditions on model parameters which when fulfilled the grave consequences of cancer can be avoided. This study also shows that chemotherapy at times can produce unexpected results. METHODS Linearization method to study the stability of model equilibria. RESULTS The results obtained in this study are governed by the trichotomy law on the number 1-a12-d1, where a12 represents the negative effect on the growth of cancer cells due to their competition with host cells for resources and d1 is rate of annihilation of cancer cells due to chemotherapy. It is seen that in case of under-dose drug module when d1<1-a12, the complete eradication of cancer is not possible. When d1=1-a12, the model suggests occurrence of chaotic dynamics. When the drug dose is properly adjusted so that d1>1-a12, the complete eradication of cancer is guaranteed. CONCLUSION The results of the model of this paper given for the post vascular stages of tumor suggest criteria to select a particular drug module (a single drug or a combination of drugs) that the chemotherapy procedure should adapt to eradicate cancer. This study injects a note of caution for oncologists that chemotherapy as cancer treatment can also cause chaotic dynamics in certain situations. This study also presents a plausible explanation to the question why sometimes a tumor grows in the body and then gets cured without any medical intervention.
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Affiliation(s)
- M Saleem
- Department of Applied Mathematics, Z.H. College of Engineering and Technology, AMU, Aligarh 202002 India.
| | - M Younus Baba
- Department of Applied Mathematics, Z.H. College of Engineering and Technology, AMU, Aligarh 202002 India.
| | - Abdur Raheem
- Department of Mathematics, Faculty of Science, AMU, Aligarh 202002 India.
| | - M Noman
- Department of Physics, B.N. College, Patna 800004, India.
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Tang Q, Zhang G. Stability and Hopf bifurcations in a competitive tumour-immune system with intrinsic recruitment delay and chemotherapy. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:1941-1965. [PMID: 33892531 DOI: 10.3934/mbe.2021101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In this paper, a three-dimensional nonlinear delay differential system including Tumour cells, cytotoxic-T lymphocytes, T-helper cells is constructed to investigate the effects of intrinsic recruitment delay and chemotherapy. It is found that the introduction of chemotherapy and time delay can generate richer dynamics in tumor-immune system. In particular, there exists bistable phenomenon and the tumour cells would be cleared if the effect of chemotherapy on depletion of the tumour cells is strong enough or the side effect of chemotherapy on the hunting predator cells is under a threshold. It is also shown that a branch of stable periodic solutions bifurcates from the coexistence equilibrium when the intrinsic recruitment delay of tumor crosses the threshold which is new mechanism, which can help understand the short-term oscillations in tumour sizes as well as long-term tumour relapse. Numerical simulations are presented to illustrate that larger intrinsic recruitment delay of tumor leads to larger amplitude and longer period of the bifurcated periodic solution, which indicates that there exists longer relapse time and then contributes to the control of tumour growth.
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Affiliation(s)
- Qingfeng Tang
- School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
| | - Guohong Zhang
- School of Mathematics and Statistics, Southwest University, Chongqing 400715, China
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Dual-Target CAR-Ts with On- and Off-Tumour Activity May Override Immune Suppression in Solid Cancers: A Mathematical Proof of Concept. Cancers (Basel) 2021; 13:cancers13040703. [PMID: 33572301 PMCID: PMC7916125 DOI: 10.3390/cancers13040703] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 01/27/2021] [Accepted: 02/05/2021] [Indexed: 02/06/2023] Open
Abstract
Simple Summary (CAR)-T cell-based therapies have achieved substantial success against different haematological malignancies. However, results for solid tumours have been limited up to now, in part due to the fact that the immunosuppressive tumour microenvironment inactivates CAR-T cell clones. In this paper we study mathematically the competition of CAR-T and tumour cells, taking into account their immunosuppressive capacity. Using computer simulations, we show that the use of large numbers of CAR-T cells targetting the solid tumour antigens could overcome the immunosuppressive potential of cancer. To achieve such high levels of CAR-T cells we propose, and study in silico, the manufacture and injection of CAR-T cells targetting two antigens: CD19 and a tumour-associated antigen. This strategy lead in our simulations to the expansion of the CAR-T cells injected and the production of a massive army of CAR-T cells targetting the solid tumour, and potentially overcoming its immune suppression capabilities. Thus, our proposed strategy could provide a way to develop successful CAR-T cell therapies against solid tumours. Abstract Chimeric antigen receptor (CAR)-T cell-based therapies have achieved substantial success against B-cell malignancies, which has led to a growing scientific and clinical interest in extending their use to solid cancers. However, results for solid tumours have been limited up to now, in part due to the immunosuppressive tumour microenvironment, which is able to inactivate CAR-T cell clones. In this paper we put forward a mathematical model describing the competition of CAR-T and tumour cells, taking into account their immunosuppressive capacity. Using the mathematical model, we show that the use of large numbers of CAR-T cells targetting the solid tumour antigens could overcome the immunosuppressive potential of cancer. To achieve such high levels of CAR-T cells we propose, and study computationally, the manufacture and injection of CAR-T cells targetting two antigens: CD19 and a tumour-associated antigen. We study in silico the resulting dynamics of the disease after the injection of this product and find that the expansion of the CAR-T cell population in the blood and lymphopoietic organs could lead to the massive production of an army of CAR-T cells targetting the solid tumour, and potentially overcoming its immune suppression capabilities. This strategy could benefit from the combination with PD-1 inhibitors and low tumour loads. Our computational results provide theoretical support for the treatment of different types of solid tumours using T cells engineered with combination treatments of dual CARs with on- and off-tumour activity and anti-PD-1 drugs after completion of classical cytoreductive treatments.
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Balti A, Zugaj D, Fenneteau F, Tremblay PO, Nekka F. Dynamical systems analysis as an additional tool to inform treatment outcomes: The case study of a quantitative systems pharmacology model of immuno-oncology. CHAOS (WOODBURY, N.Y.) 2021; 31:023124. [PMID: 33653032 DOI: 10.1063/5.0022238] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 01/22/2021] [Indexed: 06/12/2023]
Abstract
Quantitative systems pharmacology (QSP) proved to be a powerful tool to elucidate the underlying pathophysiological complexity that is intensified by the biological variability and overlapped by the level of sophistication of drug dosing regimens. Therapies combining immunotherapy with more traditional therapeutic approaches, including chemotherapy and radiation, are increasingly being used. These combinations are purposed to amplify the immune response against the tumor cells and modulate the suppressive tumor microenvironment (TME). In order to get the best performance from these combinatorial approaches and derive rational regimen strategies, a better understanding of the interaction of the tumor with the host immune system is needed. The objective of the current work is to provide new insights into the dynamics of immune-mediated TME and immune-oncology treatment. As a case study, we will use a recent QSP model by Kosinsky et al. [J. Immunother. Cancer 6, 17 (2018)] that aimed to reproduce the dynamics of interaction between tumor and immune system upon administration of radiation therapy and immunotherapy. Adopting a dynamical systems approach, we here investigate the qualitative behavior of the representative components of this QSP model around its key parameters. The ability of T cells to infiltrate tumor tissue, originally identified as responsible for individual therapeutic inter-variability [Y. Kosinsky et al., J. Immunother. Cancer 6, 17 (2018)], is shown here to be a saddle-node bifurcation point for which the dynamical system oscillates between two states: tumor-free or maximum tumor volume. By performing a bifurcation analysis of the physiological system, we identified equilibrium points and assessed their nature. We then used the traditional concept of basin of attraction to assess the performance of therapy. We showed that considering the therapy as input to the dynamical system translates into the changes of the trajectory shapes of the solutions when approaching equilibrium points and thus providing information on the issue of therapy.
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Affiliation(s)
- Aymen Balti
- Faculty of pharmacy, Université de Montréal, Montreal, Quebec H3C 3J7, Canada
| | - Didier Zugaj
- Syneos Health, Clinical Pharmacology, Quebec, Quebec G1P 0A2, Canada
| | | | | | - Fahima Nekka
- Faculty of pharmacy, Université de Montréal, Montreal, Quebec H3C 3J7, Canada
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Abstract
Modern cancer immunotherapy has revolutionised oncology and carries the potential to radically change the approach to cancer treatment. However, numerous questions remain to be answered to understand immunotherapy response better and further improve the benefit for future cancer patients. Computational models are promising tools that can contribute to accelerated immunotherapy research by providing new clues and hypotheses that could be tested in future trials, based on preceding simulations in addition to the empirical rationale. In this topical review, we briefly summarise the history of cancer immunotherapy, including computational modelling of traditional cancer immunotherapy, and comprehensively review computational models of modern cancer immunotherapy, such as immune checkpoint inhibitors (as monotherapy and combination treatment), co-stimulatory agonistic antibodies, bispecific antibodies, and chimeric antigen receptor T cells. The modelling approaches are classified into one of the following categories: data-driven top-down vs mechanistic bottom-up, simplistic vs detailed, continuous vs discrete, and hybrid. Several common modelling approaches are summarised, such as pharmacokinetic/pharmacodynamic models, Lotka-Volterra models, evolutionary game theory models, quantitative systems pharmacology models, spatio-temporal models, agent-based models, and logic-based models. Pros and cons of each modelling approach are critically discussed, particularly with the focus on the potential for successful translation into immuno-oncology research and routine clinical practice. Specific attention is paid to calibration and validation of each model, which is a necessary prerequisite for any successful model, and at the same time, one of the main obstacles. Lastly, we provide guidelines and suggestions for the future development of the field.
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Affiliation(s)
- Damijan Valentinuzzi
- Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia. Faculty of Mathematics and Physics, University of Ljubljana, Jadranska ulica 19, 1111 Ljubljana, Slovenia
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Byun JH, Yoon IS, Jeong YD, Kim S, Jung IH. A Tumor-Immune Interaction Model for Synergistic Combinations of Anti PD-L1 and Ionizing Irradiation Treatment. Pharmaceutics 2020; 12:pharmaceutics12090830. [PMID: 32878065 PMCID: PMC7558639 DOI: 10.3390/pharmaceutics12090830] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 08/25/2020] [Accepted: 08/29/2020] [Indexed: 11/30/2022] Open
Abstract
Combination therapy with immune checkpoint blockade and ionizing irradiation therapy (IR) generates a synergistic effect to inhibit tumor growth better than either therapy does alone. We modeled the tumor-immune interactions occurring during combined IT and IR based on the published data from Deng et al. The mathematical model considered programmed cell death protein 1 and programmed death ligand 1, to quantify data fitting and global sensitivity of critical parameters. Fitting of data from control, IR and IT samples was conducted to verify the synergistic effect of a combination therapy consisting of IR and IT. Our approach using the model showed that an increase in the expression level of PD-1 and PD-L1 was proportional to tumor growth before therapy, but not after initiating therapy. The high expression level of PD-L1 in T cells may inhibit IT efficacy. After combination therapy begins, the tumor size was also influenced by the ratio of PD-1 to PD-L1. These results highlight that the ratio of PD-1 to PD-L1 in T cells could be considered in combination therapy.
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Affiliation(s)
- Jong Hyuk Byun
- Department of Mathematics, Pusan National University, Busan 46241, Korea; (J.H.B.); (Y.D.J.); (S.K.)
- Institute of Mathematical Sciences, Pusan National University, Busan 46241, Korea
| | - In-Soo Yoon
- College of Pharmacy, Pusan National University, Busan 46241, Korea;
| | - Yong Dam Jeong
- Department of Mathematics, Pusan National University, Busan 46241, Korea; (J.H.B.); (Y.D.J.); (S.K.)
| | - Sungchan Kim
- Department of Mathematics, Pusan National University, Busan 46241, Korea; (J.H.B.); (Y.D.J.); (S.K.)
- Finance Fishery Manufacture Industrial Mathematics Center on Big Data, Pusan National University, Busan 46241, Korea
| | - Il Hyo Jung
- Department of Mathematics, Pusan National University, Busan 46241, Korea; (J.H.B.); (Y.D.J.); (S.K.)
- Finance Fishery Manufacture Industrial Mathematics Center on Big Data, Pusan National University, Busan 46241, Korea
- Correspondence:
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Wang Q, Wang Z, Wu Y, Klinke DJ. An in silico exploration of combining Interleukin-12 with Oxaliplatin to treat liver-metastatic colorectal cancer. BMC Cancer 2020; 20:26. [PMID: 31914948 PMCID: PMC6950805 DOI: 10.1186/s12885-019-6500-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Accepted: 12/24/2019] [Indexed: 11/10/2022] Open
Abstract
Background Combining anti-cancer therapies with orthogonal modes of action, such as direct cytotoxicity and immunostimulatory, hold promise for expanding clinical benefit to patients with metastatic disease. For instance, a chemotherapy agent Oxaliplatin (OXP) in combination with Interleukin-12 (IL-12) can eliminate pre-existing liver metastatic colorectal cancer and protect from relapse in a murine model. However, the underlying dynamics associated with the targeted biology and the combinatorial space consisting of possible dosage and timing of each therapy present challenges for optimizing treatment regimens. To address some of these challenges, we developed a predictive simulation platform for optimizing dose and timing of the combination therapy involving Mifepristone-induced IL-12 and chemotherapy agent OXP. Methods A multi-scale mathematical model comprised of impulsive ordinary differential equations was developed to describe the interaction between the immune system and tumor cells in response to the combined IL-12 and OXP therapy. An ensemble of model parameters were calibrated to published experimental data using a genetic algorithm and used to represent three different phenotypes: responders, partial-responders, and non-responders. Results The multi-scale model captures tumor growth patterns of the three phenotypic responses observed in mice in response to the combination therapy against a tumor re-challenge and was used to explore the impacts of changing the dose and timing of the mixed immune-chemotherapy on tumor growth subjected to a tumor re-challenge in mice. An increased ratio of CD8 + T effectors to regulatory T cells during and after treatment was key to improve tumor control in the responder cohort. Sensitivity analysis indicates that combined OXP and IL-12 therapy worked more efficiently in responders by increased priming of T cells, enhanced CD8 + T cell-mediated killing, and functional inhibition of regulatory T cells. In a virtual cohort that mimics non-responders and partial-responders, simulations show that an increased dose of OXP alone would improve the response. In addition, enhanced IL-12 expression alone or an increased number of treatment cycles of the mixed immune-chemotherapy can barely improve tumor control for non-responders and partial responders. Conclusions Overall, this study illustrates how mechanistic models can be used for in silico screening of the optimal therapeutic dose and timing in combined cancer treatment strategies.
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Affiliation(s)
- Qing Wang
- Department of Computer Sciences, Mathematics, and Engineering, Shepherd University, Shepherdstown, 25443, WV, USA
| | - Zhijun Wang
- Department of Computer Sciences, Mathematics, and Engineering, Shepherd University, Shepherdstown, 25443, WV, USA
| | - Yan Wu
- Department of Mathematical Sciences, Georgia Southern University, Statesboro, 30458, GA, USA
| | - David J Klinke
- Department of Chemical and Biomedical Engineering and WVU Cancer Institute, West Virginia University, Morgantown, 25606, WV, USA. .,Department of Microbiology, Immunology, & Cell Biology, West Virginia University, Morgantown, 25606, WV, USA.
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22
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Tsur N, Kogan Y, Rehm M, Agur Z. Response of patients with melanoma to immune checkpoint blockade – insights gleaned from analysis of a new mathematical mechanistic model. J Theor Biol 2020; 485:110033. [DOI: 10.1016/j.jtbi.2019.110033] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 07/05/2019] [Accepted: 09/26/2019] [Indexed: 12/30/2022]
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Piretto E, Delitala M, Kim PS, Frascoli F. Effects of mutations and immunogenicity on outcomes of anti-cancer therapies for secondary lesions. Math Biosci 2019; 315:108238. [PMID: 31401294 DOI: 10.1016/j.mbs.2019.108238] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 08/02/2019] [Accepted: 08/03/2019] [Indexed: 12/30/2022]
Abstract
Cancer development is driven by mutations and selective forces, including the action of the immune system and interspecific competition. When administered to patients, anti-cancer therapies affect the development and dynamics of tumours, possibly with various degrees of resistance due to immunoediting and microenvironment. Tumours are able to express a variety of competing phenotypes with different attributes and thus respond differently to various anti-cancer therapies. In this paper, a mathematical framework incorporating a system of delay differential equations for the immune system activation cycle and an agent-based approach for tumour-immune interaction is presented. The focus is on those metastatic, secondary solid lesions that are still undetected and non-vascularised. By using available experimental data, we analyse the effects of combination therapies on these lesions and investigate the role of mutations on the rates of success of common treatments. Findings show that mutations, growth properties and immunoediting influence therapies' outcomes in nonlinear and complex ways, affecting cancer lesion morphologies, phenotypical compositions and overall proliferation patterns. Cascade effects on final outcomes for secondary lesions are also investigated, showing that actions on primary lesions could sometimes result in unexpected clearances of secondary tumours. This outcome is strongly dependent on the clonal composition of the primary and secondary masses and is shown to allow, in some cases, the control of the disease for years.
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Affiliation(s)
- Elena Piretto
- Department of Mathematical Sciences, Politecnico di Torino, Turin, Italy; Department of Mathematics, Universitá di Torino, Turin, Italy; Department of Mathematics, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Marcello Delitala
- Department of Mathematical Sciences, Politecnico di Torino, Turin, Italy
| | - Peter S Kim
- School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia
| | - Federico Frascoli
- Department of Mathematics, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, Victoria, Australia.
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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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25
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Peskov K, Azarov I, Chu L, Voronova V, Kosinsky Y, Helmlinger G. Quantitative Mechanistic Modeling in Support of Pharmacological Therapeutics Development in Immuno-Oncology. Front Immunol 2019; 10:924. [PMID: 31134058 PMCID: PMC6524731 DOI: 10.3389/fimmu.2019.00924] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 04/10/2019] [Indexed: 12/15/2022] Open
Abstract
Following the approval, in recent years, of the first immune checkpoint inhibitor, there has been an explosion in the development of immuno-modulating pharmacological modalities for the treatment of various cancers. From the discovery phase to late-stage clinical testing and regulatory approval, challenges in the development of immuno-oncology (IO) drugs are multi-fold and complex. In the preclinical setting, the multiplicity of potential drug targets around immune checkpoints, the growing list of immuno-modulatory molecular and cellular forces in the tumor microenvironment-with additional opportunities for IO drug targets, the emergence of exploratory biomarkers, and the unleashed potential of modality combinations all have necessitated the development of quantitative, mechanistically-oriented systems models which incorporate key biology and patho-physiology aspects of immuno-oncology and the pharmacokinetics of IO-modulating agents. In the clinical setting, the qualification of surrogate biomarkers predictive of IO treatment efficacy or outcome, and the corresponding optimization of IO trial design have become major challenges. This mini-review focuses on the evolution and state-of-the-art of quantitative systems models describing the tumor vs. immune system interplay, and their merging with quantitative pharmacology models of IO-modulating agents, as companion tools to support the addressing of these challenges.
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Affiliation(s)
- Kirill Peskov
- M&S Decisions, Moscow, Russia.,Computational Oncology Group, I.M. Sechenov First Moscow State Medical University of the Russian Ministry of Health, Moscow, Russia
| | | | - Lulu Chu
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, MA, United States
| | | | | | - Gabriel Helmlinger
- Quantitative Clinical Pharmacology, Early Clinical Development, IMED Biotech Unit, AstraZeneca Pharmaceuticals, Boston, MA, United States
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26
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Mahlbacher GE, Reihmer KC, Frieboes HB. Mathematical modeling of tumor-immune cell interactions. J Theor Biol 2019; 469:47-60. [PMID: 30836073 DOI: 10.1016/j.jtbi.2019.03.002] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 02/14/2019] [Accepted: 03/01/2019] [Indexed: 12/22/2022]
Abstract
The anti-tumor activity of the immune system is increasingly recognized as critical for the mounting of a prolonged and effective response to cancer growth and invasion, and for preventing recurrence following resection or treatment. As the knowledge of tumor-immune cell interactions has advanced, experimental investigation has been complemented by mathematical modeling with the goal to quantify and predict these interactions. This succinct review offers an overview of recent tumor-immune continuum modeling approaches, highlighting spatial models. The focus is on work published in the past decade, incorporating one or more immune cell types and evaluating immune cell effects on tumor progression. Due to their relevance to cancer, the following immune cells and their combinations are described: macrophages, Cytotoxic T Lymphocytes, Natural Killer cells, dendritic cells, T regulatory cells, and CD4+ T helper cells. Although important insight has been gained from a mathematical modeling perspective, the development of models incorporating patient-specific data remains an important goal yet to be realized for potential clinical benefit.
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Affiliation(s)
| | - Kara C Reihmer
- Department of Bioengineering, University of Louisville, KY, USA
| | - Hermann B Frieboes
- Department of Bioengineering, University of Louisville, KY, USA; James Graham Brown Cancer Center, University of Louisville, KY, USA; Department of Pharmacology & Toxicology, University of Louisville, KY, USA.
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27
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Valentinuzzi D, Simončič U, Uršič K, Vrankar M, Turk M, Jeraj R. Predicting tumour response to anti-PD-1 immunotherapy with computational modelling. ACTA ACUST UNITED AC 2019; 64:025017. [DOI: 10.1088/1361-6560/aaf96c] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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28
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Optimization of combination therapy for chronic myeloid leukemia with dosing constraints. J Math Biol 2018; 77:1533-1561. [PMID: 29992481 DOI: 10.1007/s00285-018-1262-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 06/05/2018] [Indexed: 10/28/2022]
Abstract
In this work, we demonstrate a mathematical technique for optimizing combination regimens with constraints. We apply the technique to a mathematical model for treatment of patients with chronic myeloid leukemia. The in-host model includes leukemic cell and immune system dynamics during treatment with tyrosine kinase inhibitors and immunomodulatory compounds. The model is minimal (semi-mechanistic) with just enough detail that all relevant therapeutic effects can be represented. The regimens are optimized to yield the highest possible reduction in disease burden, taking into account dosing constraints and side effect risks due to drug exposure. We compare the following three types of regimens: (1) regimens that are restricted to certain discrete dose levels, which can only change every three months; (2) optimal regimens determined using optimal control; and (3) regimens that are piecewise-constant like the first type of regimen, but are obtained as approximations to the optimal control regimens. All three types of regimens result in similar outcomes, but the last one is easy to compute in addition to being clinically feasible.
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29
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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: 93] [Impact Index Per Article: 15.5] [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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30
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Piretto E, Delitala M, Ferraro M. Combination therapies and intra-tumoral competition: Insights from mathematical modeling. J Theor Biol 2018; 446:149-159. [PMID: 29548736 DOI: 10.1016/j.jtbi.2018.03.014] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2017] [Revised: 01/31/2018] [Accepted: 03/12/2018] [Indexed: 12/18/2022]
Abstract
Drug resistance is one of the major obstacles to a successful treatment of cancer and, in turn, has been recognized to be linked to intratumoral heterogeneity, which increases the probability of the emergence of cancer clones refractory to treatment. Combination therapies have been introduced to overcome resistance, but the design of successful combined protocols is still an open problem. In order to provide some indications on the effectiveness of medical treatments, a mathematical model is proposed, comprising two cancer populations competing for resources and with different susceptibilities to the action of immune system cells and therapies: the focus is on the effects of chemotherapy and immunotherapy, used singularly or in combination. First, numerical predictions of the model have been tested with experimental data from the literature and next therapeutic protocols with different doses and temporal order have been simulated. Finally the role of competitive interactions has been also investigated, to provide some insights on the role of competitive interactions among cancer clones in determining treatment outcomes.
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Affiliation(s)
- Elena Piretto
- Department of Mathematics, Università di Torino, via Carlo Alberto, 10, Torino 10123, Italy; Politecnico di Torino, Department of Mathematical Sciences, corso Duca degli Abruzzi 24, Torino 10129, Italy
| | - Marcello Delitala
- Politecnico di Torino, Department of Mathematical Sciences, corso Duca degli Abruzzi 24, Torino 10129, Italy.
| | - Mario Ferraro
- Department of Physics, Università di Torino, via P. Giuria 1, Torino 10125, Italy
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31
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Santiago DN, Heidbuechel JPW, Kandell WM, Walker R, Djeu J, Engeland CE, Abate-Daga D, Enderling H. Fighting Cancer with Mathematics and Viruses. Viruses 2017; 9:E239. [PMID: 28832539 PMCID: PMC5618005 DOI: 10.3390/v9090239] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 08/18/2017] [Accepted: 08/18/2017] [Indexed: 12/19/2022] Open
Abstract
After decades of research, oncolytic virotherapy has recently advanced to clinical application, and currently a multitude of novel agents and combination treatments are being evaluated for cancer therapy. Oncolytic agents preferentially replicate in tumor cells, inducing tumor cell lysis and complex antitumor effects, such as innate and adaptive immune responses and the destruction of tumor vasculature. With the availability of different vector platforms and the potential of both genetic engineering and combination regimens to enhance particular aspects of safety and efficacy, the identification of optimal treatments for patient subpopulations or even individual patients becomes a top priority. Mathematical modeling can provide support in this arena by making use of experimental and clinical data to generate hypotheses about the mechanisms underlying complex biology and, ultimately, predict optimal treatment protocols. Increasingly complex models can be applied to account for therapeutically relevant parameters such as components of the immune system. In this review, we describe current developments in oncolytic virotherapy and mathematical modeling to discuss the benefit of integrating different modeling approaches into biological and clinical experimentation. Conclusively, we propose a mutual combination of these research fields to increase the value of the preclinical development and the therapeutic efficacy of the resulting treatments.
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Affiliation(s)
- Daniel N Santiago
- Department of Immunology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
| | | | - Wendy M Kandell
- Department of Immunology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
- Cancer Biology PhD Program, University of South Florida, Tampa, FL 33612, USA.
| | - Rachel Walker
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
| | - Julie Djeu
- Department of Immunology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
| | - Christine E Engeland
- German Cancer Research Center, Heidelberg University, 69120 Heidelberg, Germany.
- National Center for Tumor Diseases Heidelberg, Department of Translational Oncology, Department of Medical Oncology, 69120 Heidelberg, Germany.
| | - Daniel Abate-Daga
- Department of Immunology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
- Department of Cutaneous Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
- Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
| | - Heiko Enderling
- Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.
- Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL 33612, USA.
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32
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Migali C, Milano M, Trapani D, Criscitiello C, Esposito A, Locatelli M, Minchella I, Curigliano G. Strategies to modulate the immune system in breast cancer: checkpoint inhibitors and beyond. Ther Adv Med Oncol 2016; 8:360-74. [PMID: 27583028 DOI: 10.1177/1758834016658423] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Is breast cancer (BC) immunogenic? Many data suggest that it is. Many observations demonstrated the prognostic role of tumor-infiltrating lymphocytes (TILs) in triple negative (TN) and human epidermal growth factor receptor 2 (HER-2)-positive BC. TNBCs are poorly differentiated tumors with high genetic instability and very high heterogeneity. This heterogeneity enhances the 'danger signals' and select clone variants that could be more antigenic or, in other words, that could more strongly stimulate a host immune antitumor response. The response to chemotherapy is at least partly dependent on an immunological reaction against those tumor cells that are dying during the chemotherapy. One of the mechanisms whereby chemotherapy can stimulate the immune system to recognize and destroy malignant cells is commonly known as immunogenic cell death (ICD). ICD elicits an adaptive immune response. Which are the clinical implications of all 'immunome' data produced in the last years? First, validate prognostic or predictive role of TILs. Second, validate immune genomic signatures that may be predictive and prognostic in patients with TN disease. Third, incorporate an 'immunoscore' into traditional classification of BC, thus providing an essential prognostic and potentially predictive tool in the pathology report. Fourth, implement clinical trials for BC in the metastatic setting with drugs that target immune-cell-intrinsic checkpoints. Blockade of one of these checkpoints, cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) or the programmed cell death 1 (PD-1) receptor may provide proof of concepts for the activity of an immune-modulation approach in the treatment of a BC.
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Affiliation(s)
- Cristina Migali
- Division of Experimental Therapeutics, Istituto Europeo di Oncologia, Milano, Italy
| | - Monica Milano
- Division of Experimental Therapeutics, Istituto Europeo di Oncologia, Milano, Italy
| | - Dario Trapani
- Division of Experimental Therapeutics, Istituto Europeo di Oncologia, Milano, Italy
| | - Carmen Criscitiello
- Division of Experimental Therapeutics, Istituto Europeo di Oncologia, Milano, Italy
| | - Angela Esposito
- Division of Experimental Therapeutics, Istituto Europeo di Oncologia, Milano, Italy
| | - Marzia Locatelli
- Division of Experimental Therapeutics, Istituto Europeo di Oncologia, Milano, Italy
| | - Ida Minchella
- Division of Experimental Therapeutics, Istituto Europeo di Oncologia, Milano, Italy
| | - Giuseppe Curigliano
- Division of Experimental Therapeutics, Istituto Europeo di Oncologia, Via Ripamonti 435, 20141 Milano, Italy
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33
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Mahasa KJ, Ouifki R, Eladdadi A, Pillis LD. Mathematical model of tumor-immune surveillance. J Theor Biol 2016; 404:312-330. [PMID: 27317864 DOI: 10.1016/j.jtbi.2016.06.012] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Revised: 06/09/2016] [Accepted: 06/09/2016] [Indexed: 12/26/2022]
Abstract
We present a novel mathematical model involving various immune cell populations and tumor cell populations. The model describes how tumor cells evolve and survive the brief encounter with the immune system mediated by natural killer (NK) cells and the activated CD8(+) cytotoxic T lymphocytes (CTLs). The model is composed of ordinary differential equations describing the interactions between these important immune lymphocytes and various tumor cell populations. Based on up-to-date knowledge of immune evasion and rational considerations, the model is designed to illustrate how tumors evade both arms of host immunity (i.e. innate and adaptive immunity). The model predicts that (a) an influx of an external source of NK cells might play a crucial role in enhancing NK-cell immune surveillance; (b) the host immune system alone is not fully effective against progression of tumor cells; (c) the development of immunoresistance by tumor cells is inevitable in tumor immune surveillance. Our model also supports the importance of infiltrating NK cells in tumor immune surveillance, which can be enhanced by NK cell-based immunotherapeutic approaches.
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Affiliation(s)
- Khaphetsi Joseph Mahasa
- DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), University of Stellenbosch, Stellenbosch, South Africa.
| | - Rachid Ouifki
- DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), University of Stellenbosch, Stellenbosch, South Africa
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34
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Foryś U, Bodnar M, Kogan Y. Asymptotic dynamics of some t-periodic one-dimensional model with application to prostate cancer immunotherapy. J Math Biol 2016; 73:867-83. [PMID: 26897354 PMCID: PMC5018042 DOI: 10.1007/s00285-016-0978-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Revised: 12/02/2015] [Indexed: 01/21/2023]
Abstract
In the case of some specific cancers, immunotherapy is one of the possible treatments that can be considered. Our study is based on a mathematical model of patient-specific immunotherapy proposed in Kronik et al. (PLoS One 5(12):e15,482, 2010). This model was validated for clinical trials presented in Michael et al. (Clin Cancer Res 11(12):4469–4478, 2005). It consists of seven ordinary differential equations and its asymptotic dynamics can be described by some t-periodic one-dimensional dynamical system. In this paper we propose a generalised version of this t-periodic system and study the dynamics of the proposed model. We show that there are three possible types of the model behaviour: the solution either converges to zero, or diverges to infinity, or it is periodic. Moreover, the periodic solution is unique, and it divides the phase space into two sub-regions. The general results are applied to the PC specific case, which allow to derive conditions guaranteeing successful as well as unsuccessful treatment. The results indicate that a single vaccination is not sufficient to cure the cancer.
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Affiliation(s)
- U Foryś
- Faculty of Mathematics, Informatics and Mechanics, Institute of Applied Mathematics and Mechanics, Warsaw University, Warsaw, Poland
| | - M Bodnar
- Faculty of Mathematics, Informatics and Mechanics, Institute of Applied Mathematics and Mechanics, Warsaw University, Warsaw, Poland.
| | - Y Kogan
- Institute for Medical BioMathematics, Bene Ataroth, Israel
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