1
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Richfield O, Piotrowski-Daspit AS, Shin K, Saltzman WM. Rational nanoparticle design: Optimization using insights from experiments and mathematical models. J Control Release 2023; 360:772-783. [PMID: 37442201 PMCID: PMC10529591 DOI: 10.1016/j.jconrel.2023.07.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 06/22/2023] [Accepted: 07/08/2023] [Indexed: 07/15/2023]
Abstract
Polymeric nanoparticles are highly tunable drug delivery systems that show promise in targeting therapeutics to specific sites within the body. Rational nanoparticle design can make use of mathematical models to organize and extend experimental data, allowing for optimization of nanoparticles for particular drug delivery applications. While rational nanoparticle design is attractive from the standpoint of improving therapy and reducing unnecessary experiments, it has yet to be fully realized. The difficulty lies in the complexity of nanoparticle structure and behavior, which is added to the complexity of the physiological mechanisms involved in nanoparticle distribution throughout the body. In this review, we discuss the most important aspects of rational design of polymeric nanoparticles. Ultimately, we conclude that many experimental datasets are required to fully model polymeric nanoparticle behavior at multiple scales. Further, we suggest ways to consider the limitations and uncertainty of experimental data in creating nanoparticle design optimization schema, which we call quantitative nanoparticle design frameworks.
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Affiliation(s)
- Owen Richfield
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
| | | | - Kwangsoo Shin
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
| | - W Mark Saltzman
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA; Department of Cellular & Molecular Physiology, Yale University, New Haven, CT 06511, USA; Department of Chemical & Environmental Engineering, Yale University, New Haven, CT 06511, USA; Department of Dermatology, Yale University, New Haven, CT 06511, USA.
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2
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Cave J, Shinglot V, Butner JD, Cristini V, Ozpolat B, Calin GA, Dogra P, Wang Z. Mechanistic modeling of anti-microRNA-155 therapy combinations in lung cancer. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-4. [PMID: 38083518 DOI: 10.1109/embc40787.2023.10341114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
To improve treatment outcomes in non-small cell lung cancer (NSCLC), it is crucial to identify treatment strategies with the potential to exhibit drug synergism. This can lower the required effective dose, reducing exposure to drugs and associated toxicities, while improving treatment efficacy. In previous studies, drugs targeting the microRNA-155 or PD-L1 have been promising in restraining NSCLC tumor growth. We have developed a mathematical model that simulates the in vivo pharmacokinetics and pharmacodynamics of the novel nanoparticle-delivered anti-microRNA-155 for potential use with standard-of-care drug atezolizumab for NSCLC. Through modeling and simulation, we identified possible drug synergism between the two drugs that holds promise to improve tumor response at reduced drug exposure.Clinical Relevance-Identifying the possibility of drug synergism for an anti-microRNA-155 based nanotherapeutic with standard-of-care immunotherapy to improve lung cancer treatment outcomes.
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3
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Syed M, Cagely M, Dogra P, Hollmer L, Butner JD, Cristini V, Koay EJ. Immune-checkpoint inhibitor therapy response evaluation using oncophysics-based mathematical models. Wiley Interdiscip Rev Nanomed Nanobiotechnol 2023; 15:e1855. [PMID: 36148978 DOI: 10.1002/wnan.1855] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 06/10/2022] [Accepted: 08/23/2022] [Indexed: 11/08/2022]
Abstract
The field of oncology has transformed with the advent of immunotherapies. The standard of care for multiple cancers now includes novel drugs that target key checkpoints that function to modulate immune responses, enabling the patient's immune system to elicit an effective anti-tumor response. While these immune-based approaches can have dramatic effects in terms of significantly reducing tumor burden and prolonging survival for patients, the therapeutic approach remains active only in a minority of patients and is often not durable. Multiple biological investigations have identified key markers that predict response to the most common form of immunotherapy-immune checkpoint inhibitors (ICI). These biomarkers help enrich patients for ICI but are not 100% predictive. Understanding the complex interactions of these biomarkers with other pathways and factors that lead to ICI resistance remains a major goal. Principles of oncophysics-the idea that cancer can be described as a multiscale physical aberration-have shown promise in recent years in terms of capturing the essence of the complexities of ICI interactions. Here, we review the biological knowledge of mechanisms of ICI action and how these are incorporated into modern oncophysics-based mathematical models. Building on the success of oncophysics-based mathematical models may help to discover new, rational methods to engineer immunotherapy for patients in the future. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.
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Affiliation(s)
- Mustafa Syed
- Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Matthew Cagely
- Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA.,Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, USA
| | - Lauren Hollmer
- Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Joseph D Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA.,Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Eugene J Koay
- Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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4
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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. Nat Comput Sci 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] [What about the content of this article? (0)] [Affiliation(s)] [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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5
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Butner JD, Farhat M, Cristini V, Chung C, Wang Z. Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy. STAR Protoc 2022; 3:101886. [PMID: 36595890 PMCID: PMC9719106 DOI: 10.1016/j.xpro.2022.101886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/03/2022] [Accepted: 11/03/2022] [Indexed: 12/03/2022] Open
Abstract
This protocol describes the application of a mechanistic mathematical model of immune checkpoint inhibitor (ICI) immunotherapy to patient tumor imaging data for predicting solid tumor response and patient survival under ICI intervention. We describe steps for data collection and processing, data pipelines, and approaches to increase precision. The protocol is highly predictive as early as the first restaging after treatment start and can be used with standard-of-care imaging measures. For complete details on the use and execution of this protocol, please refer to Butner et al. (2020)1 and Butner et al. (2021).2.
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Affiliation(s)
- Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA,Corresponding author
| | - Maguy Farhat
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA,Neal Cancer Center, Houston Methodist Research Institute, Houston, TX 77030, USA,Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77230, USA,Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY 10065, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA,Corresponding author
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA,Neal Cancer Center, Houston Methodist Research Institute, Houston, TX 77030, USA,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA,Department of Medical Education, Texas A&M University School of Medicine, Bryan, TX 77807, USA,Corresponding author
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6
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Terracciano R, Carcamo-Bahena Y, Royal ALR, Messina L, Delk J, Butler EB, Demarchi D, Grattoni A, Wang Z, Cristini V, Dogra P, Filgueira CS. Zonal Intratumoral Delivery of Nanoparticles Guided by Surface Functionalization. Langmuir 2022; 38:13983-13994. [PMID: 36318182 PMCID: PMC9671122 DOI: 10.1021/acs.langmuir.2c02319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/13/2022] [Indexed: 06/12/2023]
Abstract
Delivery of small molecules and anticancer agents to malignant cells or specific regions within a tumor is limited by penetration depth and poor spatial drug distribution, hindering anticancer efficacy. Herein, we demonstrate control over gold nanoparticle (GNP) penetration and spatial distribution across solid tumors by administering GNPs with different surface chemistries at a constant injection rate via syringe pump. A key finding in this study is the discovery of different zone-specific accumulation patterns of intratumorally injected nanoparticles dependent on surface functionalization. Computed tomography (CT) imaging performed in vivo of C57BL/6 mice harboring Lewis lung carcinoma (LLC) tumors on their flank and gross visualization of excised tumors consistently revealed that intratumorally administered citrate-GNPs accumulate in particle clusters in central areas of the tumor, while GNPs functionalized with thiolated phosphothioethanol (PTE-GNPs) and thiolated polyethylene glycol (PEG-GNPs) regularly accumulate in the tumor periphery. Further, PEG functionalization resulted in larger tumoral surface coverage than PTE, reaching beyond the outer zone of the tumor mass and into the surrounding stroma. To understand the dissimilarities in spatiotemporal evolution across the different GNP surface chemistries, we modeled their intratumoral transport with reaction-diffusion equations. Our results suggest that GNP surface passivation affects nanoparticle reactivity with the tumor microenvironment, leading to differential transport behavior across tumor zones. The present study provides a mechanistic understanding of the factors affecting spatiotemporal distribution of nanoparticles in the tumor. Our proof of concept of zonal delivery within the tumor may prove useful for directing anticancer therapies to regions of biomarker overexpression.
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Affiliation(s)
- Rossana Terracciano
- Department
of Nanomedicine, Houston Methodist Research
Institute, Houston, Texas77030, United States
- Department
of Electronics and Telecommunications, Politecnico
di Torino, Torino10129, Italy
| | - Yareli Carcamo-Bahena
- Department
of Nanomedicine, Houston Methodist Research
Institute, Houston, Texas77030, United States
| | - Amber Lee R. Royal
- Department
of Nanomedicine, Houston Methodist Research
Institute, Houston, Texas77030, United States
| | - Luca Messina
- Univestià
degli Studi di Napoli Federico II, Naples80138, Italy
| | - Jack Delk
- Texas
A&M University, College
Station, Texas77843, United States
| | - E. Brian Butler
- Department
of Radiation Oncology, Houston Methodist
Research Institute, Houston, Texas77030, United States
| | - Danilo Demarchi
- Department
of Electronics and Telecommunications, Politecnico
di Torino, Torino10129, Italy
| | - Alessandro Grattoni
- Department
of Nanomedicine, Houston Methodist Research
Institute, Houston, Texas77030, United States
- Department
of Radiation Oncology, Houston Methodist
Research Institute, Houston, Texas77030, United States
- Department
of Surgery, Houston Methodist Research Institute, Houston, Texas77030, United States
| | - Zhihui Wang
- Mathematics
in Medicine Program, Houston Methodist Research
Institute, Houston, Texas77030, United
States
- Department
of Imaging Physics, University of Texas
MD Anderson Cancer Center, Houston, Texas77030, United States
- Department
of Physiology and Biophysics, Weill Cornell
Medical College, New York, New York10022, United States
| | - Vittorio Cristini
- Mathematics
in Medicine Program, Houston Methodist Research
Institute, Houston, Texas77030, United
States
- Department
of Imaging Physics, University of Texas
MD Anderson Cancer Center, Houston, Texas77030, United States
- Physiology,
Biophysics, and Systems Biology Program, Graduate School of Medical
Sciences, Weill Cornell Medicine, New York, New York10022, United States
| | - Prashant Dogra
- Mathematics
in Medicine Program, Houston Methodist Research
Institute, Houston, Texas77030, United
States
- Department
of Physiology and Biophysics, Weill Cornell
Medical College, New York, New York10022, United States
| | - Carly S. Filgueira
- Department
of Nanomedicine, Houston Methodist Research
Institute, Houston, Texas77030, United States
- Department
of Cardiovascular Surgery, Houston Methodist
Research Institute, Houston, Texas77030, United States
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7
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Hormuth DA, Farhat M, Christenson C, Curl B, Chad Quarles C, Chung C, Yankeelov TE. Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy. Adv Drug Deliv Rev 2022; 187:114367. [PMID: 35654212 DOI: 10.1016/j.addr.2022.114367] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/25/2022] [Accepted: 05/25/2022] [Indexed: 11/01/2022]
Abstract
Immunotherapy has become a fourth pillar in the treatment of brain tumors and, when combined with radiation therapy, may improve patient outcomes and reduce the neurotoxicity. As with other combination therapies, the identification of a treatment schedule that maximizes the synergistic effect of radiation- and immune-therapy is a fundamental challenge. Mechanism-based mathematical modeling is one promising approach to systematically investigate therapeutic combinations to maximize positive outcomes within a rigorous framework. However, successful clinical translation of model-generated combinations of treatment requires patient-specific data to allow the models to be meaningfully initialized and parameterized. Quantitative imaging techniques have emerged as a promising source of high quality, spatially and temporally resolved data for the development and validation of mathematical models. In this review, we will present approaches to personalize mechanism-based modeling frameworks with patient data, and then discuss how these techniques could be leveraged to improve brain cancer outcomes through patient-specific modeling and optimization of treatment strategies.
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Affiliation(s)
- David A Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Maguy Farhat
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Chase Christenson
- Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Brandon Curl
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - C Chad Quarles
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Caroline Chung
- Departments of Radiation Oncology, MD Anderson Cancer Center, Houston, TX 77230, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Oncology, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA; Departments of Imaging Physics, MD Anderson Cancer Center, Houston, TX 77230, USA
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Cai N, Lai AC, Liao K, Corridon PR, Graves DJ, Chan V. Recent Advances in Fluorescence Recovery after Photobleaching for Decoupling Transport and Kinetics of Biomacromolecules in Cellular Physiology. Polymers (Basel) 2022; 14:1913. [PMID: 35567083 PMCID: PMC9105003 DOI: 10.3390/polym14091913] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/27/2022] [Accepted: 04/29/2022] [Indexed: 12/16/2022] Open
Abstract
Among the new molecular tools available to scientists and engineers, some of the most useful include fluorescently tagged biomolecules. Tools, such as green fluorescence protein (GFP), have been applied to perform semi-quantitative studies on biological signal transduction and cellular structural dynamics involved in the physiology of healthy and disease states. Such studies focus on drug pharmacokinetics, receptor-mediated endocytosis, nuclear mechanobiology, viral infections, and cancer metastasis. In 1976, fluorescence recovery after photobleaching (FRAP), which involves the monitoring of fluorescence emission recovery within a photobleached spot, was developed. FRAP allowed investigators to probe two-dimensional (2D) diffusion of fluorescently-labelled biomolecules. Since then, FRAP has been refined through the advancements of optics, charged-coupled-device (CCD) cameras, confocal microscopes, and molecular probes. FRAP is now a highly quantitative tool used for transport and kinetic studies in the cytosol, organelles, and membrane of a cell. In this work, the authors intend to provide a review of recent advances in FRAP. The authors include epifluorescence spot FRAP, total internal reflection (TIR)/FRAP, and confocal microscope-based FRAP. The underlying mathematical models are also described. Finally, our understanding of coupled transport and kinetics as determined by FRAP will be discussed and the potential for future advances suggested.
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Le A, Wearing HJ, Li D. Streamlining physiologically‐based pharmacokinetic model design for intravenous delivery of nanoparticle drugs. CPT Pharmacometrics Syst Pharmacol 2022; 11:409-424. [PMID: 35045205 PMCID: PMC9007599 DOI: 10.1002/psp4.12762] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 11/19/2021] [Accepted: 01/11/2022] [Indexed: 12/13/2022] Open
Abstract
Physiologically‐based pharmacokinetic (PBPK) modeling for nanoparticles elucidates the nanoparticle drug’s disposition in the body and serves a vital role in drug development and clinical studies. This paper offers a systematic and tutorial‐like approach to developing a model structure and writing distribution ordinary differential equations based on asking binary questions involving the physicochemical nature of the drug in question. Further, by synthesizing existing knowledge, we summarize pertinent aspects in PBPK modeling and create a guide for building model structure and distribution equations, optimizing nanoparticle and non‐nanoparticle specific parameters, and performing sensitivity analysis and model validation. The purpose of this paper is to facilitate a streamlined model development process for students and practitioners in the field.
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Affiliation(s)
- Anh‐Dung Le
- Nanoscience & Microsystems Engineering University of New Mexico Albuquerque New Mexico USA
| | - Helen J. Wearing
- Department of Biology Department of Mathematics & Statistics University of New Mexico Albuquerque New Mexico USA
| | - Dingsheng Li
- School of Community Health Sciences University of Nevada Reno Nevada USA
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10
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Dogra P, Ramírez JR, Butner JD, Peláez MJ, Chung C, Hooda-Nehra A, Pasqualini R, Arap W, Cristini V, Calin GA, Ozpolat B, Wang Z. Translational Modeling Identifies Synergy between Nanoparticle-Delivered miRNA-22 and Standard-of-Care Drugs in Triple-Negative Breast Cancer. Pharm Res 2022; 39:511-528. [PMID: 35294699 PMCID: PMC8986735 DOI: 10.1007/s11095-022-03176-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 01/21/2022] [Indexed: 12/29/2022]
Abstract
Purpose Downregulation of miRNA-22 in triple-negative breast cancer (TNBC) is associated with upregulation of eukaryotic elongation 2 factor kinase (eEF2K) protein, which regulates tumor growth, chemoresistance, and tumor immunosurveillance. Moreover, exogenous administration of miRNA-22, loaded in nanoparticles to prevent degradation and improve tumor delivery (termed miRNA-22 nanotherapy), to suppress eEF2K production has shown potential as an investigational therapeutic agent in vivo. Methods To evaluate the translational potential of miRNA-22 nanotherapy, we developed a multiscale mechanistic model, calibrated to published in vivo data and extrapolated to the human scale, to describe and quantify the pharmacokinetics and pharmacodynamics of miRNA-22 in virtual patient populations. Results Our analysis revealed the dose-response relationship, suggested optimal treatment frequency for miRNA-22 nanotherapy, and highlighted key determinants of therapy response, from which combination with immune checkpoint inhibitors was identified as a candidate strategy for improving treatment outcomes. More importantly, drug synergy was identified between miRNA-22 and standard-of-care drugs against TNBC, providing a basis for rational therapeutic combinations for improved response Conclusions The present study highlights the translational potential of miRNA-22 nanotherapy for TNBC in combination with standard-of-care drugs. Supplementary Information The online version contains supplementary material available at 10.1007/s11095-022-03176-3.
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Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, 10065, USA
| | - Javier Ruiz Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA
| | - Joseph D Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA
| | - Maria J Peláez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, 77030, USA
| | - Anupama Hooda-Nehra
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, 07101, USA
- Department of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, 07103, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, 07101, USA
- Department of Radiation Oncology, Division of Cancer Biology, Rutgers New Jersey Medical School, Newark, New Jersey, 07103, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, 07101, USA
- Department of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, 07103, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA
- Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, 77230, USA
- Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, New York, 10065, USA
| | - George A Calin
- Department of Translational Molecular Pathology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, 77030, USA
| | - Bulent Ozpolat
- Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas, 77030, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, 77030, USA.
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, 10065, USA.
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11
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Miller CT, Gray WG, Schrefler BA. A continuum mechanical framework for modeling tumor growth and treatment in two- and three-phase systems. Arch Appl Mech 2022; 92:461-489. [PMID: 35811645 PMCID: PMC9269988 DOI: 10.1007/s00419-021-01891-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The growth and treatment of tumors is an important problem to society that involves the manifestation of cellular phenomena at length scales on the order of centimeters. Continuum mechanical approaches are being increasingly used to model tumors at the largest length scales of concern. The issue of how to best connect such descriptions to smaller-scale descriptions remains open. We formulate a framework to derive macroscale models of tumor behavior using the thermodynamically constrained averaging theory (TCAT), which provides a firm connection with the microscale and constraints on permissible forms of closure relations. We build on developments in the porous medium mechanics literature to formulate fundamental entropy inequality expressions for a general class of three-phase, compositional models at the macroscale. We use the general framework derived to formulate two classes of models, a two-phase model and a three-phase model. The general TCAT framework derived forms the basis for a wide range of potential models of varying sophistication, which can be derived, approximated, and applied to understand not only tumor growth but also the effectiveness of various treatment modalities.
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Affiliation(s)
- Cass T Miller
- Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC, USA
| | - William G Gray
- Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC, USA
| | - Bernhard A Schrefler
- Department of Civil, Environmental and Architectural Engineering, University of Padua, Padua, Italy
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12
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Butner JD, Martin GV, Wang Z, Corradetti B, Ferrari M, Esnaola N, Chung C, Hong DS, Welsh JW, Hasegawa N, Mittendorf EA, Curley SA, Chen SH, Pan PY, Libutti SK, Ganesan S, Sidman RL, Pasqualini R, Arap W, Koay EJ, Cristini V. Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling. eLife 2021; 10:70130. [PMID: 34749885 PMCID: PMC8629426 DOI: 10.7554/elife.70130] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 10/25/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. We have developed a mechanistic mathematical model for better understanding these factors and their relations in order to predict treatment outcome and optimize personal treatment strategies. Methods: Here, we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (α), tumor-immune infiltration (Λ), and immunotherapy-mediated amplification of anti-tumor response (µ). The model was calibrated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials. Results: The derived parameters Λ and µ were both significantly different between responding versus nonresponding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within 2 months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology. Conclusions: These results have demonstrated reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis. Funding: We gratefully acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center. We have also received Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey). This research has also been supported in part by grants from the National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC). BC acknowledges support through the SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie Skłodowska-Curie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF). EK has also received support from the Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). MF was supported through NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute. RP and WA received serial research awards from AngelWorks, the Gillson-Longenbaugh Foundation, and the Marcus Foundation. This work was also supported in part by grants from the National Cancer Institute to SHC (R01CA109322, R01CA127483, R01CA208703, and U54CA210181 CITO pilot grant) and to PYP (R01CA140243, R01CA188610, and U54CA210181 CITO pilot grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Affiliation(s)
- Joseph D Butner
- The Houston Methodist Research Institute, Houston, United States
| | - Geoffrey V Martin
- The University of Texas MD Anderson Cancer Center, Houston, United States
| | - Zhihui Wang
- The Houston Methodist Research Institute, Houston, United States
| | - Bruna Corradetti
- The Houston Methodist Research Institute, Houston, United States
| | - Mauro Ferrari
- The Houston Methodist Research Institute, Houston, United States
| | - Nestor Esnaola
- The Houston Methodist Research Institute, Houston, United States
| | - Caroline Chung
- The University of Texas MD Anderson Cancer Center, Houston, United States
| | - David S Hong
- The University of Texas MD Anderson Cancer Center, Houston, United States
| | - James W Welsh
- The Houston Methodist Research Institute, Houston, United States
| | - Naomi Hasegawa
- University of Texas Health Science Center, Houston, United States
| | | | | | - Shu-Hsia Chen
- The Houston Methodist Research Institute, Houston, United States
| | - Ping-Ying Pan
- The Houston Methodist Research Institute, Houston, United States
| | | | | | - Richard L Sidman
- Department of Neurology, Harvard Medical School, Boston, United States
| | | | - Wadih Arap
- Hematology and Oncology, Rutgers Cancer Institute of New Jersey, Newark, United States
| | - Eugene J Koay
- University of Texas MD Anderson Cancer Center, Houston, United States
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13
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Dogra P, Ramirez JR, Butner JD, Pelaez MJ, Cristini V, Wang Z. A Multiscale Model to Identify Limiting Factors in Nanoparticle-Based miRNA Delivery for Tumor Inhibition . Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:4230-4233. [PMID: 34892157 PMCID: PMC8712117 DOI: 10.1109/embc46164.2021.9630862] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
MicroRNA-based gene therapy for cancer treatment via nanoparticles (NPs) requires navigation of multiple physical and physiological barriers in order to efficiently deliver the miRNAs to the cancer cell cytoplasm. We here present a mathematical model to investigate the variability associated with tumor, NP, and miRNA characteristics, and identify the limiting factors in miRNA delivery to tumors. Through global parameter analysis, the miRNA release rate from NPs and NP degradability were found to have the most significant impact on cytosolic accumulation of miRNAs. These NP properties can be fine-tuned in order to optimize the delivery system for enhancing the efficacy of miRNA-based therapy.Clinical Relevance-Understanding the effect of nanoparticle, tumor, and miRNA characteristics in governing the efficacy of miRNA-based cancer therapy will support its clinical translation.
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14
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Bernal A, Calcagno C, Mulder WJM, Pérez-Medina C. Imaging-guided nanomedicine development. Curr Opin Chem Biol 2021; 63:78-85. [PMID: 33735814 DOI: 10.1016/j.cbpa.2021.01.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 01/18/2021] [Accepted: 01/22/2021] [Indexed: 12/14/2022]
Abstract
Nanomedicine research is an active field that produces thousands of studies every year. However, translation of nanotherapeutics to the clinic has yet to catch up with such a vast output. In recent years, the need to better understand nanomedicines' in vivo behavior has been identified as one of the major challenges for efficient clinical translation. In this context, noninvasive imaging offers attractive solutions to provide valuable information about nanomedicine biodistribution, pharmacokinetics, stability, or therapeutic efficacy. Here, we review the latest imaging approaches used in the development of therapeutic nanomedicines, discuss why these strategies bring added value along the translational pipeline, and give a perspective on future advances in the field.
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Affiliation(s)
- Aurora Bernal
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| | - Claudia Calcagno
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Willem J M Mulder
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Chemical Biology, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Carlos Pérez-Medina
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain; BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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15
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Affiliation(s)
- Marianne B. Ashford
- Advanced Drug Delivery Pharmaceutical Sciences, R&D, AstraZeneca Macclesfield SK10 2NA UK
| | - Richard M. England
- Advanced Drug Delivery Pharmaceutical Sciences, R&D, AstraZeneca Macclesfield SK10 2NA UK
| | - Nadim Akhtar
- New Modalities & Parenteral Development Pharmaceutical Technology & Development, Operations, AstraZeneca Macclesfield SK10 2NA UK
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16
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Dogra P, Ruiz-Ramírez J, Sinha K, Butner JD, Peláez MJ, Rawat M, Yellepeddi VK, Pasqualini R, Arap W, Sostman HD, Cristini V, Wang Z. Innate Immunity Plays a Key Role in Controlling Viral Load in COVID-19: Mechanistic Insights from a Whole-Body Infection Dynamics Model. ACS Pharmacol Transl Sci 2021; 4:248-265. [PMID: 33615177 PMCID: PMC7805603 DOI: 10.1021/acsptsci.0c00183] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Indexed: 12/18/2022]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a pathogen of immense public health concern. Efforts to control the disease have only proven mildly successful, and the disease will likely continue to cause excessive fatalities until effective preventative measures (such as a vaccine) are developed. To develop disease management strategies, a better understanding of SARS-CoV-2 pathogenesis and population susceptibility to infection are needed. To this end, mathematical modeling can provide a robust in silico tool to understand COVID-19 pathophysiology and the in vivo dynamics of SARS-CoV-2. Guided by ACE2-tropism (ACE2 receptor dependency for infection) of the virus and by incorporating cellular-scale viral dynamics and innate and adaptive immune responses, we have developed a multiscale mechanistic model for simulating the time-dependent evolution of viral load distribution in susceptible organs of the body (respiratory tract, gut, liver, spleen, heart, kidneys, and brain). Following parameter quantification with in vivo and clinical data, we used the model to simulate viral load progression in a virtual patient with varying degrees of compromised immune status. Further, we ranked model parameters through sensitivity analysis for their significance in governing clearance of viral load to understand the effects of physiological factors and underlying conditions on viral load dynamics. Antiviral drug therapy, interferon therapy, and their combination were simulated to study the effects on viral load kinetics of SARS-CoV-2. The model revealed the dominant role of innate immunity (specifically interferons and resident macrophages) in controlling viral load, and the importance of timing when initiating therapy after infection.
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Affiliation(s)
- Prashant Dogra
- Mathematics
in Medicine Program, Houston Methodist Research
Institute, Houston, Texas 77030, United States
| | - Javier Ruiz-Ramírez
- Mathematics
in Medicine Program, Houston Methodist Research
Institute, Houston, Texas 77030, United States
| | - Kavya Sinha
- DeBakey
Heart and Vascular Center, Houston Methodist
Hospital, Houston, Texas 77030, United States
| | - Joseph D. Butner
- Mathematics
in Medicine Program, Houston Methodist Research
Institute, Houston, Texas 77030, United States
| | - Maria J. Peláez
- Mathematics
in Medicine Program, Houston Methodist Research
Institute, Houston, Texas 77030, United States
| | - Manmeet Rawat
- Department
of Internal Medicine, University of New
Mexico School of Medicine, Albuquerque, New Mexico 87131, United States
| | - Venkata K. Yellepeddi
- Division
of Clinical Pharmacology, Department of Pediatrics, School of Medicine, University of Utah, Salt Lake City, Utah 84132, United States
- Department
of Pharmaceutics and Pharmaceutical Chemistry, College of Pharmacy, University of Utah, Salt Lake City, Utah 84112, United States
| | - Renata Pasqualini
- Rutgers
Cancer Institute of New Jersey, Newark, New Jersey 07101, United States
- Department
of Radiation Oncology, Division of Cancer Biology, Rutgers New Jersey Medical School, Newark, New Jersey 07103, United States
| | - Wadih Arap
- Rutgers
Cancer Institute of New Jersey, Newark, New Jersey 07101, United States
- Department
of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, New Jersey 07103, United States
| | - H. Dirk Sostman
- Weill
Cornell Medicine, New York, New York 10065, United States
- Houston
Methodist Research Institute, Houston, Texas 77030, United States
- Houston
Methodist Academic Institute, Houston, Texas 77030, United States
| | - Vittorio Cristini
- Mathematics
in Medicine Program, Houston Methodist Research
Institute, Houston, Texas 77030, United States
- Weill
Cornell Medicine, New York, New York 10065, United States
| | - Zhihui Wang
- Mathematics
in Medicine Program, Houston Methodist Research
Institute, Houston, Texas 77030, United States
- Weill
Cornell Medicine, New York, New York 10065, United States
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17
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Anaya DA, Dogra P, Wang Z, Haider M, Ehab J, Jeong DK, Ghayouri M, Lauwers GY, Thomas K, Kim R, Butner JD, Nizzero S, Ramírez JR, Plodinec M, Sidman RL, Cavenee WK, Pasqualini R, Arap W, Fleming JB, Cristini V. A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases. Cancers (Basel) 2021; 13:cancers13030444. [PMID: 33503971 PMCID: PMC7866038 DOI: 10.3390/cancers13030444] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 01/21/2021] [Indexed: 12/22/2022] Open
Abstract
Simple Summary It is known that drug transport barriers in the tumor determine drug concentration at the tumor site, causing disparity from the systemic (plasma) drug concentration. However, current clinical standard of care still bases dosage and treatment optimization on the systemic concentration of drugs. Here, we present a proof of concept observational cohort study to accurately estimate drug concentration at the tumor site from mathematical modeling using biologic, clinical, and imaging/perfusion data, and correlate it with outcome in colorectal cancer liver metastases. We demonstrate that drug concentration at the tumor site, not in systemic circulation, can be used as a credible biomarker for predicting chemotherapy outcome, and thus our mathematical modeling approach can be applied prospectively in the clinic to personalize treatment design to optimize outcome. Abstract Chemotherapy remains a primary treatment for metastatic cancer, with tumor response being the benchmark outcome marker. However, therapeutic response in cancer is unpredictable due to heterogeneity in drug delivery from systemic circulation to solid tumors. In this proof-of-concept study, we evaluated chemotherapy concentration at the tumor-site and its association with therapy response by applying a mathematical model. By using pre-treatment imaging, clinical and biologic variables, and chemotherapy regimen to inform the model, we estimated tumor-site chemotherapy concentration in patients with colorectal cancer liver metastases, who received treatment prior to surgical hepatic resection with curative-intent. The differential response to therapy in resected specimens, measured with the gold-standard Tumor Regression Grade (TRG; from 1, complete response to 5, no response) was examined, relative to the model predicted systemic and tumor-site chemotherapy concentrations. We found that the average calculated plasma concentration of the cytotoxic drug was essentially equivalent across patients exhibiting different TRGs, while the estimated tumor-site chemotherapeutic concentration (eTSCC) showed a quadratic decline from TRG = 1 to TRG = 5 (p < 0.001). The eTSCC was significantly lower than the observed plasma concentration and dropped by a factor of ~5 between patients with complete response (TRG = 1) and those with no response (TRG = 5), while the plasma concentration remained stable across TRG groups. TRG variations were driven and predicted by differences in tumor perfusion and eTSCC. If confirmed in carefully planned prospective studies, these findings will form the basis of a paradigm shift in the care of patients with potentially curable colorectal cancer and liver metastases.
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Affiliation(s)
- Daniel A. Anaya
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (M.H.); (J.E.); (R.K.); (J.B.F.)
- Correspondence: (D.A.A.); (V.C.); Tel.: +1-813-745-1432 (D.A.A.); +1-505-934-1813 (V.C.); Fax: +1-813-745-7229 (D.A.A.)
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
| | - Mintallah Haider
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (M.H.); (J.E.); (R.K.); (J.B.F.)
| | - Jasmina Ehab
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (M.H.); (J.E.); (R.K.); (J.B.F.)
| | - Daniel K. Jeong
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (D.K.J.); (M.G.); (G.Y.L.); (K.T.)
| | - Masoumeh Ghayouri
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (D.K.J.); (M.G.); (G.Y.L.); (K.T.)
| | - Gregory Y. Lauwers
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (D.K.J.); (M.G.); (G.Y.L.); (K.T.)
| | - Kerry Thomas
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (D.K.J.); (M.G.); (G.Y.L.); (K.T.)
| | - Richard Kim
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (M.H.); (J.E.); (R.K.); (J.B.F.)
| | - Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
| | - Sara Nizzero
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
| | - Javier Ruiz Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
| | - Marija Plodinec
- Biozentrum and the Swiss Nanoscience Institute & ARTIDIS AG, University of Basel, 4056 Basel, Switzerland;
| | - Richard L. Sidman
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA;
| | - Webster K. Cavenee
- Ludwig Institute for Cancer Research, University of California-San Diego, La Jolla, CA 92093, USA;
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey & Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, NJ 07103, USA;
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey & Division of Hematology/Oncology, Department of Medicine Rutgers New Jersey Medical School, Newark, NJ 07103, USA;
| | - Jason B. Fleming
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (M.H.); (J.E.); (R.K.); (J.B.F.)
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
- Correspondence: (D.A.A.); (V.C.); Tel.: +1-813-745-1432 (D.A.A.); +1-505-934-1813 (V.C.); Fax: +1-813-745-7229 (D.A.A.)
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18
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Patterson CM, Balachander SB, Grant I, Pop-Damkov P, Kelly B, McCoull W, Parker J, Giannis M, Hill KJ, Gibbons FD, Hennessy EJ, Kemmitt P, Harmer AR, Gales S, Purbrick S, Redmond S, Skinner M, Graham L, Secrist JP, Schuller AG, Wen S, Adam A, Reimer C, Cidado J, Wild M, Gangl E, Fawell SE, Saeh J, Davies BR, Owen DJ, Ashford MB. Design and optimisation of dendrimer-conjugated Bcl-2/x L inhibitor, AZD0466, with improved therapeutic index for cancer therapy. Commun Biol 2021; 4:112. [PMID: 33495510 DOI: 10.1038/s42003-020-01631-8] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 12/21/2020] [Indexed: 02/08/2023] Open
Abstract
Dual Bcl-2/Bcl-xL inhibitors are expected to deliver therapeutic benefit in many haematological and solid malignancies, however, their use is limited by tolerability issues. AZD4320, a potent dual Bcl-2/Bcl-xL inhibitor, has shown good efficacy however had dose limiting cardiovascular toxicity in preclinical species, coupled with challenging physicochemical properties, which prevented its clinical development. Here, we describe the design and development of AZD0466, a drug-dendrimer conjugate, where AZD4320 is chemically conjugated to a PEGylated poly-lysine dendrimer. Mathematical modelling was employed to determine the optimal release rate of the drug from the dendrimer for maximal therapeutic index in terms of preclinical anti-tumour efficacy and cardiovascular tolerability. The optimised candidate is shown to be efficacious and better tolerated in preclinical models compared with AZD4320 alone. The AZD4320-dendrimer conjugate (AZD0466) identified, through mathematical modelling, has resulted in an improved therapeutic index and thus enabled progression of this promising dual Bcl-2/Bcl-xL inhibitor into clinical development. Claire Patterson et al. present the design and development of AZD0466, a drug-dendrimer conjugate, and use preclinical and mathematical models to determine the optimal release rate of the drug from the dendrimer carrier for maximal therapeutic index in terms of anti-tumour efficacy and cardiovascular tolerability. This study identifies this promising dual Bcl-2/Bcl-xL inhibitor for progression to clinical development.
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19
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Butner JD, Wang Z, Elganainy D, Al Feghali KA, Plodinec M, Calin GA, Dogra P, Nizzero S, Ruiz-Ramírez J, Martin GV, Tawbi HA, Chung C, Koay EJ, Welsh JW, Hong DS, Cristini V. A mathematical model for the quantification of a patient's sensitivity to checkpoint inhibitors and long-term tumour burden. Nat Biomed Eng 2021; 5:297-308. [PMID: 33398132 PMCID: PMC8669771 DOI: 10.1038/s41551-020-00662-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 11/14/2020] [Indexed: 02/06/2023]
Abstract
A large proportion of patients with cancer are unresponsive to treatment with immune checkpoint blockade and other immunotherapies. Here, we report a mathematical model of the time-course of tumour responses to immune-checkpoint inhibitors. The model takes into account intrinsic tumour-growth rates, the rates of immune activation and of tumour–immune-cell interactions, and the efficacy of immune-mediated tumour killing. For 124 patients, four cancer types and two immunotherapy agents, the model reliably described the immune responses and final tumour burden across all different cancers and drug combinations examined. In validation cohorts from four clinical trials of checkpoint inhibitors (with a total of 177 patients), the model accurately stratified the patients according to reduced or increased long-term tumour burden. We also provide model-derived quantitative measures of treatment sensitivity for specific drug–cancer combinations. The model can be used to predict responses to therapy and to quantify specific drug–cancer sensitivities in individual patients.
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Affiliation(s)
- Joseph D Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA. .,Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Dalia Elganainy
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Karine A Al Feghali
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Marija Plodinec
- Biozentrum and the Swiss Nanoscience Institute, University of Basel, Basel, Switzerland
| | - George A Calin
- Department of Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Sara Nizzero
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Javier Ruiz-Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Geoffrey V Martin
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Hussein A Tawbi
- Department of Melanoma Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Eugene J Koay
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - James W Welsh
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - David S Hong
- Department of Investigational Cancer Therapeutics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, 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.
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20
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Dogra P, Ruiz-Ramírez J, Sinha K, Butner JD, Peláez MJ, Rawat M, Yellepeddi VK, Pasqualini R, Arap W, Sostman HD, Cristini V, Wang Z. Innate immunity plays a key role in controlling viral load in COVID-19: mechanistic insights from a whole-body infection dynamics model. medRxiv 2020:2020.10.30.20215335. [PMID: 33173913 PMCID: PMC7654909 DOI: 10.1101/2020.10.30.20215335] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a pathogen of immense public health concern. Efforts to control the disease have only proven mildly successful, and the disease will likely continue to cause excessive fatalities until effective preventative measures (such as a vaccine) are developed. To develop disease management strategies, a better understanding of SARS-CoV-2 pathogenesis and population susceptibility to infection are needed. To this end, physiologically-relevant mathematical modeling can provide a robust in silico tool to understand COVID-19 pathophysiology and the in vivo dynamics of SARS-CoV-2. Guided by ACE2-tropism (ACE2 receptor dependency for infection) of the virus, and by incorporating cellular-scale viral dynamics and innate and adaptive immune responses, we have developed a multiscale mechanistic model for simulating the time-dependent evolution of viral load distribution in susceptible organs of the body (respiratory tract, gut, liver, spleen, heart, kidneys, and brain). Following calibration with in vivo and clinical data, we used the model to simulate viral load progression in a virtual patient with varying degrees of compromised immune status. Further, we conducted global sensitivity analysis of model parameters and ranked them for their significance in governing clearance of viral load to understand the effects of physiological factors and underlying conditions on viral load dynamics. Antiviral drug therapy, interferon therapy, and their combination was simulated to study the effects on viral load kinetics of SARS-CoV-2. The model revealed the dominant role of innate immunity (specifically interferons and resident macrophages) in controlling viral load, and the importance of timing when initiating therapy following infection.
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Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Javier Ruiz-Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Kavya Sinha
- DeBakey Heart and Vascular Center, Houston Methodist Hospital, Houston, TX 77030, USA
| | - Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Maria J Peláez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Manmeet Rawat
- Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, USA
| | - Venkata K. Yellepeddi
- Division of Clinical Pharmacology, Department of Pediatrics, School of Medicine, University of Utah, Salt Lake City, UT 84132, USA
- Department of Pharmaceutics and Pharmaceutical Chemistry, College of Pharmacy, University of Utah, Salt Lake City, UT 84112, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, NJ, 07101, USA
- Department of Radiation Oncology, Division of Cancer Biology, Rutgers Cancer Institute of New Jersey, Rutgers New Jersey Medical School, Newark, NJ, 07103, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey, Newark, NJ, 07101, USA
- Department of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, NJ, 07103, USA
| | - H. Dirk Sostman
- Weill Cornell Medicine, New York, NY 10065, USA
- Houston Methodist Research Institute, Houston, TX 77030, USA
- Houston Methodist Academic Institute, Houston, TX 77030, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
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21
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Dogra P, Butner JD, Ramirez JR, Cristini V, Wang Z. Investigating the Effect of Aging on the Pharmacokinetics and Tumor Delivery of Nanomaterials using Mathematical Modeling. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2447-2450. [PMID: 33018501 DOI: 10.1109/embc44109.2020.9175322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The application of nanomedicine for diagnosis and treatment of cancer has immense potential, but has witnessed only limited clinical success, in part due to insufficient understanding of the role of nanomaterial properties and physiological variables in governing nanoparticle (NP) pharmacology. Here, we present a multiscale mathematical model to examine the effects of physiological changes associated with patient age on the pharmacokinetics and tumor delivery efficiency of NPs. We show that physiological changes due to aging prolong the residence of NPs in the systemic circulation, thereby improving passive accumulation of NPs in tumors.Clinical Relevance - Understanding the effect of inter-individual variability on the pharmacological behavior of nanomaterials will improve their clinical translatability.
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22
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Dogra P, Butner JD, Nizzero S, Ruiz Ramírez J, Noureddine A, Peláez MJ, Elganainy D, Yang Z, Le AD, Goel S, Leong HS, Koay EJ, Brinker CJ, Cristini V, Wang Z. Image-guided mathematical modeling for pharmacological evaluation of nanomaterials and monoclonal antibodies. Wiley Interdiscip Rev Nanomed Nanobiotechnol 2020; 12:e1628. [PMID: 32314552 PMCID: PMC7507140 DOI: 10.1002/wnan.1628] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 02/06/2020] [Accepted: 02/15/2020] [Indexed: 12/13/2022]
Abstract
While plasma concentration kinetics has traditionally been the predictor of drug pharmacological effects, it can occasionally fail to represent kinetics at the site of action, particularly for solid tumors. This is especially true in the case of delivery of therapeutic macromolecules (drug-loaded nanomaterials or monoclonal antibodies), which can experience challenges to effective delivery due to particle size-dependent diffusion barriers at the target site. As a result, disparity between therapeutic plasma kinetics and kinetics at the site of action may exist, highlighting the importance of target site concentration kinetics in determining the pharmacodynamic effects of macromolecular therapeutic agents. Assessment of concentration kinetics at the target site has been facilitated by non-invasive in vivo imaging modalities. This allows for visualization and quantification of the whole-body disposition behavior of therapeutics that is essential for a comprehensive understanding of their pharmacokinetics and pharmacodynamics. Quantitative non-invasive imaging can also help guide the development and parameterization of mathematical models for descriptive and predictive purposes. Here, we present a review of the application of state-of-the-art imaging modalities for quantitative pharmacological evaluation of therapeutic nanoparticles and monoclonal antibodies, with a focus on their integration with mathematical models, and identify challenges and opportunities. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Diagnostic Tools > in vivo Nanodiagnostics and Imaging Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.
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Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Joseph D Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Sara Nizzero
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Javier Ruiz Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Achraf Noureddine
- Department of Chemical and Biological Engineering, University of New Mexico, Albuquerque, New Mexico, USA
| | - María J Peláez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA.,Applied Physics Graduate Program, Rice University, Houston, Texas, USA
| | - Dalia Elganainy
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Zhen Yang
- Center for Bioenergetics, Houston Methodist Research Institute, Houston, Texas, USA
| | - Anh-Dung Le
- Nanoscience and Microsystems Engineering, University of New Mexico, Albuquerque, New Mexico, USA
| | - Shreya Goel
- Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Hon S Leong
- Biological Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Eugene J Koay
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - C Jeffrey Brinker
- Department of Chemical and Biological Engineering and UNM Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
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