1
|
Stephan S, Galland S, Labbani Narsis O, Shoji K, Vachenc S, Gerart S, Nicolle C. Agent-based approaches for biological modeling in oncology: A literature review. Artif Intell Med 2024; 152:102884. [PMID: 38703466 DOI: 10.1016/j.artmed.2024.102884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/06/2024]
Abstract
CONTEXT Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions. METHODOLOGY This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023. RESULTS Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.
Collapse
Affiliation(s)
- Simon Stephan
- UTBM, CIAD UMR 7533, Belfort, F-90010, France; Université de Bourgogne, CIAD UMR 7533, Dijon, F-21000, France.
| | | | | | - Kenji Shoji
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
| | - Sébastien Vachenc
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
| | - Stéphane Gerart
- Oncodesign Precision Medicine (OPM), 18 Rue Jean Mazen, Dijon, F-21000, France
| | | |
Collapse
|
2
|
Dogra P, Schiavone C, Wang Z, Ruiz-Ramírez J, Caserta S, Staquicini DI, Markosian C, Wang J, Sostman HD, Pasqualini R, Arap W, Cristini V. A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection. JCI Insight 2023; 8:e169860. [PMID: 37227783 PMCID: PMC10371350 DOI: 10.1172/jci.insight.169860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 05/23/2023] [Indexed: 05/27/2023] Open
Abstract
While the development of different vaccines slowed the dissemination of SARS-CoV-2, the occurrence of breakthrough infections has continued to fuel the COVID-19 pandemic. To secure at least partial protection in the majority of the population through 1 dose of a COVID-19 vaccine, delayed administration of boosters has been implemented in many countries. However, waning immunity and emergence of new variants of SARS-CoV-2 suggest that such measures may induce breakthrough infections due to intermittent lapses in protection. Optimizing vaccine dosing schedules to ensure prolonged continuity in protection could thus help control the pandemic. We developed a mechanistic model of immune response to vaccines as an in silico tool for dosing schedule optimization. The model was calibrated with clinical data sets of acquired immunity to COVID-19 mRNA vaccines in healthy and immunocompromised participants and showed robust validation by accurately predicting neutralizing antibody kinetics in response to multiple doses of COVID-19 mRNA vaccines. Importantly, by estimating population vulnerability to breakthrough infections, we predicted tailored vaccination dosing schedules to minimize breakthrough infections, especially for immunocompromised individuals. We identified that the optimal vaccination schedules vary from CDC-recommended dosing, suggesting that the model is a valuable tool to optimize vaccine efficacy outcomes during future outbreaks.
Collapse
Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, Houston, Texas, USA
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, USA
| | - Carmine Schiavone
- Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, Naples, Italy
| | - Zhihui Wang
- Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, Houston, Texas, USA
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, Texas, USA
| | - Javier Ruiz-Ramírez
- Centro de Ciencias de la Salud, Universidad Autónoma de Aguascalientes, Aguascalientes, Mexico
| | - Sergio Caserta
- Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, Naples, Italy
- CEINGE Advanced Biotechnologies, Naples, Italy
| | - Daniela I. Staquicini
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Christopher Markosian
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Jin Wang
- Immunobiology and Transplant Science Center, Department of Surgery, Houston Methodist Research Institute, Houston, Texas, USA
- Department of Surgery, Weill Cornell Medical College, Cornell University, New York, New York, USA
| | - H. Dirk Sostman
- Weill Cornell Medicine, New York, New York, USA
- Houston Methodist Research Institute, Houston, Texas, USA
- Houston Methodist Academic Institute, Houston, Texas, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA
- Division of Hematology/Oncology, Department of Medicine, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, Houston, Texas, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, Texas, USA
- Department of Imaging Physics, 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
| |
Collapse
|
3
|
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. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 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] [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.
Collapse
|
4
|
Mechanistic modeling-guided optimization of microneedle-based skin patch for rapid transdermal delivery of naloxone for opioid overdose treatment. Drug Deliv Transl Res 2023; 13:320-338. [PMID: 35879533 DOI: 10.1007/s13346-022-01202-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/18/2022] [Indexed: 12/13/2022]
Abstract
Naloxone, an FDA-approved opioid inhibitor, used to reverse opioid overdose complications has up till date faced challenges associated with its delivery. Limitations include the use of invasive delivery forms and the need for frequent redosing due to its short half-life. The goal of the current study was to design a transdermal rapidly dissolving polymeric microneedle (MN) patch with delivery and pharmacokinetic properties comparable to that seen with the commercially available NAL products, eliminating their delivery limitations. Patches of varying dimensions (500 µm; 100 array,800 µm; 100array, and 600 µm; 225 array) were fabricated to evaluate the effect of increasing MN length, and density (no. of needles/unit area) on drug release. Drug dose in each of these patches was 17.89 ± 0.23 mg, 17.2 ± 0.77 mg, and 17.8 ± 1.01 mg, respectively. Furthermore, the insertion efficiency of each of the MN patches was 94 ± 4.8%, 90.6 ± 1.69%, and 96 ± 1.29%, respectively. Compared to passive permeation, a reduced lag time of about 5-15 min was observed with a significant drug flux of 15.09 ± 7.68 g[Formula: see text]/cm2/h seen in the first 1 h (p < 0.05) with the array of 100 needles (500 µm long). Over 24 h, a four and ten-fold increase in permeation was seen with the longer length and larger density MN patch, respectively, when compared to the 500 µm (100 array) patch. Model simulations and analyses revealed the significance of needle base diameter and needle count in improving systemic pharmacokinetics of NAL.
Collapse
|
5
|
Retzlaff J, Lai X, Berking C, Vera J. Integration of transcriptomics data into agent-based models of solid tumor metastasis. Comput Struct Biotechnol J 2023; 21:1930-1941. [PMID: 36942106 PMCID: PMC10024179 DOI: 10.1016/j.csbj.2023.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 02/06/2023] [Accepted: 02/06/2023] [Indexed: 03/06/2023] Open
Abstract
Recent progress in our understanding of cancer mostly relies on the systematic profiling of patient samples with high-throughput techniques like transcriptomics. With this approach, one can find gene signatures and networks underlying cancer aggressiveness and therapy resistance. However, omics data alone cannot generate insights into the spatiotemporal aspects of tumor progression. Here, multi-level computational modeling is a promising approach that would benefit from protocols to integrate the data generated by the high-throughput profiling of patient samples. We present a computational workflow to integrate transcriptomics data from tumor patients into hybrid, multi-scale cancer models. In the method, we conduct transcriptomics analysis to select key differentially regulated pathways in therapy responders and non-responders and link them to agent-based model parameters. We then determine global and local sensitivity through systematic model simulations that assess the relevance of parameter variations in triggering therapy resistance. We illustrate the methodology with a de novo generated agent-based model accounting for the interplay between tumor and immune cells in a melanoma micrometastasis. The application of the workflow identifies three distinct scenarios of therapy resistance.
Collapse
Affiliation(s)
- Jimmy Retzlaff
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Deutsches Zentrum Immuntherapie, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Deutsches Zentrum Immuntherapie, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
- BioMediTech, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Carola Berking
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Deutsches Zentrum Immuntherapie, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Deutsches Zentrum Immuntherapie, Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany
- Corresponding author at: Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
| |
Collapse
|
6
|
Dedifferentiation-mediated stem cell niche maintenance in early-stage ductal carcinoma in situ progression: insights from a multiscale modeling study. Cell Death Dis 2022; 13:485. [PMID: 35597788 PMCID: PMC9124196 DOI: 10.1038/s41419-022-04939-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 05/11/2022] [Accepted: 05/11/2022] [Indexed: 12/14/2022]
Abstract
We present a multiscale agent-based model of ductal carcinoma in situ (DCIS) to study how key phenotypic and signaling pathways are involved in the early stages of disease progression. The model includes a phenotypic hierarchy, and key endocrine and paracrine signaling pathways, and simulates cancer ductal growth in a 3D lattice-free domain. In particular, by considering stochastic cell dedifferentiation plasticity, the model allows for study of how dedifferentiation to a more stem-like phenotype plays key roles in the maintenance of cancer stem cell populations and disease progression. Through extensive parameter perturbation studies, we have quantified and ranked how DCIS is sensitive to perturbations in several key mechanisms that are instrumental to early disease development. Our studies reveal that long-term maintenance of multipotent stem-like cell niches within the tumor are dependent on cell dedifferentiation plasticity, and that disease progression will become arrested due to dilution of the multipotent stem-like population in the absence of dedifferentiation. We have identified dedifferentiation rates necessary to maintain biologically relevant multipotent cell populations, and also explored quantitative relationships between dedifferentiation rates and disease progression rates, which may potentially help to optimize the efficacy of emerging anti-cancer stem cell therapeutics.
Collapse
|
7
|
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] [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.
Collapse
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.
| |
Collapse
|
8
|
Gondal MN, Chaudhary SU. Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics. Front Oncol 2021; 11:712505. [PMID: 34900668 PMCID: PMC8652070 DOI: 10.3389/fonc.2021.712505] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/26/2021] [Indexed: 12/19/2022] Open
Abstract
Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.
Collapse
Affiliation(s)
- Mahnoor Naseer Gondal
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Safee Ullah Chaudhary
- Biomedical Informatics Research Laboratory, Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
| |
Collapse
|
9
|
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 . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 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] [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.
Collapse
|
10
|
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] [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.
Collapse
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
| |
Collapse
|
11
|
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 : THE PREPRINT SERVER FOR HEALTH SCIENCES 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] [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.
Collapse
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
| |
Collapse
|
12
|
Butner JD, Fuentes D, Ozpolat B, Calin GA, Zhou X, Lowengrub J, Cristini V, Wang Z. A Multiscale Agent-Based Model of Ductal Carcinoma In Situ. IEEE Trans Biomed Eng 2020; 67:1450-1461. [PMID: 31603768 PMCID: PMC8445608 DOI: 10.1109/tbme.2019.2938485] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
OBJECTIVE we present a multiscale agent-based model of Ductal Carcinoma in Situ (DCIS) in order to gain a detailed understanding of the cell-scale population dynamics, phenotypic distributions, and the associated interplay of important molecular signaling pathways that are involved in DCIS ductal invasion into the duct cavity (a process we refer to as duct advance rate here). METHODS DCIS is modeled mathematically through a hybridized discrete cell-scale model and a continuum molecular scale model, which are explicitly linked through a bidirectional feedback mechanism. RESULTS we find that duct advance rates occur in two distinct phases, characterized by an early exponential population expansion, followed by a long-term steady linear phase of population expansion, a result that is consistent with other modeling work. We further found that the rates were influenced most strongly by endocrine and paracrine signaling intensity, as well as by the effects of cell density induced quiescence within the DCIS population. CONCLUSION our model analysis identified a complex interplay between phenotypic diversity that may provide a tumor adaptation mechanism to overcome proliferation limiting conditions, allowing for dynamic shifts in phenotypic populations in response to variation in molecular signaling intensity. Further, sensitivity analysis determined DCIS axial advance rates and calcification rates were most sensitive to cell cycle time variation. SIGNIFICANCE this model may serve as a useful tool to study the cell-scale dynamics involved in DCIS initiation and intraductal invasion, and may provide insights into promising areas of future experimental research.
Collapse
|
13
|
Dogra P, Butner JD, Ruiz Ramírez J, Chuang YL, Noureddine A, Jeffrey Brinker C, Cristini V, Wang Z. A mathematical model to predict nanomedicine pharmacokinetics and tumor delivery. Comput Struct Biotechnol J 2020; 18:518-531. [PMID: 32206211 PMCID: PMC7078505 DOI: 10.1016/j.csbj.2020.02.014] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 02/14/2020] [Accepted: 02/22/2020] [Indexed: 02/07/2023] Open
Abstract
Towards clinical translation of cancer nanomedicine, it is important to systematically investigate the various parameters related to nanoparticle (NP) physicochemical properties, tumor characteristics, and inter-individual variability that affect the tumor delivery efficiency of therapeutic nanomaterials. Comprehensive investigation of these parameters using traditional experimental approaches is impractical due to the vast parameter space; mathematical models provide a more tractable approach to navigate through such a multidimensional space. To this end, we have developed a predictive mathematical model of whole-body NP pharmacokinetics and their tumor delivery in vivo, and have conducted local and global sensitivity analyses to identify the factors that result in low tumor delivery efficiency and high off-target accumulation of NPs. Our analyses reveal that NP degradation rate, tumor blood viscosity, NP size, tumor vascular fraction, and tumor vascular porosity are the key parameters in governing NP kinetics in the tumor interstitium. The impact of these parameters on tumor delivery efficiency of NPs is discussed, and optimal values for maximizing NP delivery are presented.
Collapse
Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Joseph D. Butner
- 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
| | - Yao-li Chuang
- Department of Mathematics, California State University, Northridge, CA 91330, USA
| | - Achraf Noureddine
- Department of Chemical and Biological Engineering, University of New Mexico, Albuquerque, NM 87106, USA
| | - C. Jeffrey Brinker
- Department of Chemical and Biological Engineering, University of New Mexico, Albuquerque, NM 87106, USA
- UNM Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87102, 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
- Corresponding author at: Mathematics in Medicine Program, The Houston Methodist Research Institute, HMRI R8-122, 6670 Bertner Ave, Houston, TX 77030, USA.
| |
Collapse
|
14
|
Brocato TA, Brown-Glaberman U, Wang Z, Selwyn RG, Wilson CM, Wyckoff EF, Lomo LC, Saline JL, Hooda-Nehra A, Pasqualini R, Arap W, Brinker CJ, Cristini V. Predicting breast cancer response to neoadjuvant chemotherapy based on tumor vascular features in needle biopsies. JCI Insight 2019; 5:126518. [PMID: 30835256 DOI: 10.1172/jci.insight.126518] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
In clinical breast cancer intervention, selection of the optimal treatment protocol based on predictive biomarkers remains an elusive goal. Here, we present a modeling tool to predict the likelihood of breast cancer response to neoadjuvant chemotherapy using patient specific tumor vasculature biomarkers. A semi-automated analysis was implemented and performed on 3990 histological images from 48 patients, with 10-208 images analyzed for each patient. We applied a histology-based model to resected primary breast cancer tumors (n = 30), and then evaluated a cohort of patients (n = 18) undergoing neoadjuvant chemotherapy, collecting pre- and post-treatment pathology specimens and MRI data. We found that core biopsy samples can be used with acceptable accuracy (r = 0.76) to determine histological parameters representative of the whole tissue region. Analysis of model histology parameters obtained from tumor vasculature measurements, specifically diffusion distance divided by radius of drug source (L/rb) and blood volume fraction (BVF), provides a statistically significant separation of patients obtaining a pathologic complete response (pCR) from those that do not (Student's t-test; P < 0.05). With this model, it is feasible to evaluate primary breast tumor vasculature biomarkers in a patient specific manner, thereby allowing a precision approach to breast cancer treatment.
Collapse
Affiliation(s)
- Terisse A Brocato
- Department of Chemical and Biological Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, New Mexico, USA
| | - Ursa Brown-Glaberman
- University of New Mexico Comprehensive Cancer Center, Albuquerque, New Mexico, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA.,Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Reed G Selwyn
- Center for Neuroscience and Regenerative Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA.,Department of Radiology, University of New Mexico, Albuquerque, New Mexico, USA
| | - Colin M Wilson
- Department of Radiology, University of New Mexico, Albuquerque, New Mexico, USA
| | - Edward F Wyckoff
- Center for Micro-Engineered Materials, University of New Mexico, Albuquerque, New Mexico, USA
| | - Lesley C Lomo
- Department of Pathology, University of Utah, Salt Lake City, Utah, USA
| | - Jennifer L Saline
- Department of Radiology, University of New Mexico, Albuquerque, New Mexico, USA
| | - Anupama Hooda-Nehra
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA.,Division of Hematology/Oncology, Department of Medicine, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA.,Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA.,Division of Hematology/Oncology, Department of Medicine, Rutgers New Jersey Medical School, Newark, New Jersey, USA
| | - C Jeffrey Brinker
- Department of Chemical and Biological Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, New Mexico, USA.,Center for Micro-Engineered Materials, University of New Mexico, Albuquerque, New Mexico, USA.,Department of Molecular Genetics and Microbiology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA.,Self-Assembled Materials Department, Sandia National Laboratories, Albuquerque, New Mexico, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA.,Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Nanomedicine, Methodist Hospital Research Institute, Houston, Texas, USA
| |
Collapse
|
15
|
Wang Z, Deisboeck TS. Dynamic Targeting in Cancer Treatment. Front Physiol 2019; 10:96. [PMID: 30890944 PMCID: PMC6413712 DOI: 10.3389/fphys.2019.00096] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 01/25/2019] [Indexed: 12/18/2022] Open
Abstract
With the advent of personalized medicine, design and development of anti-cancer drugs that are specifically targeted to individual or sets of genes or proteins has been an active research area in both academia and industry. The underlying motivation for this approach is to interfere with several pathological crosstalk pathways in order to inhibit or at the very least control the proliferation of cancer cells. However, after initially conferring beneficial effects, if sub-lethal, these artificial perturbations in cell function pathways can inadvertently activate drug-induced up- and down-regulation of feedback loops, resulting in dynamic changes over time in the molecular network structure and potentially causing drug resistance as seen in clinics. Hence, the targets or their combined signatures should also change in accordance with the evolution of the network (reflected by changes to the structure and/or functional output of the network) over the course of treatment. This suggests the need for a "dynamic targeting" strategy aimed at optimizing tumor control by interfering with different molecular targets, at varying stages. Understanding the dynamic changes of this complex network under various perturbed conditions due to drug treatment is extremely challenging under experimental conditions let alone in clinical settings. However, mathematical modeling can facilitate studying these effects at the network level and beyond, and also accelerate comparison of the impact of different dosage regimens and therapeutic modalities prior to sizeable investment in risky and expensive clinical trials. A dynamic targeting strategy based on the use of mathematical modeling can be a new, exciting research avenue in the discovery and development of therapeutic drugs.
Collapse
Affiliation(s)
- Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, United States.,Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Thomas S Deisboeck
- Department of Radiology, Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| |
Collapse
|
16
|
Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy. Proc Natl Acad Sci U S A 2017; 114:E6277-E6286. [PMID: 28716945 DOI: 10.1073/pnas.1703355114] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Cancer is a highly heterogeneous disease, exhibiting spatial and temporal variations that pose challenges for designing robust therapies. Here, we propose the VEPART (Virtual Expansion of Populations for Analyzing Robustness of Therapies) technique as a platform that integrates experimental data, mathematical modeling, and statistical analyses for identifying robust optimal treatment protocols. VEPART begins with time course experimental data for a sample population, and a mathematical model fit to aggregate data from that sample population. Using nonparametric statistics, the sample population is amplified and used to create a large number of virtual populations. At the final step of VEPART, robustness is assessed by identifying and analyzing the optimal therapy (perhaps restricted to a set of clinically realizable protocols) across each virtual population. As proof of concept, we have applied the VEPART method to study the robustness of treatment response in a mouse model of melanoma subject to treatment with immunostimulatory oncolytic viruses and dendritic cell vaccines. Our analysis (i) showed that every scheduling variant of the experimentally used treatment protocol is fragile (nonrobust) and (ii) discovered an alternative region of dosing space (lower oncolytic virus dose, higher dendritic cell dose) for which a robust optimal protocol exists.
Collapse
|
17
|
Wang Z, Deisboeck TS, Cristini V. Development of a sampling-based global sensitivity analysis workflow for multiscale computational cancer models. IET Syst Biol 2015; 8:191-7. [PMID: 25257020 DOI: 10.1049/iet-syb.2013.0026] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
There are two challenges that researchers face when performing global sensitivity analysis (GSA) on multiscale 'in silico' cancer models. The first is increased computational intensity, since a multiscale cancer model generally takes longer to run than does a scale-specific model. The second problem is the lack of a best GSA method that fits all types of models, which implies that multiple methods and their sequence need to be taken into account. In this study, the authors therefore propose a sampling-based GSA workflow consisting of three phases - pre-analysis, analysis and post-analysis - by integrating Monte Carlo and resampling methods with the repeated use of analysis of variance; they then exemplify this workflow using a two-dimensional multiscale lung cancer model. By accounting for all parameter rankings produced by multiple GSA methods, a summarised ranking is created at the end of the workflow based on the weighted mean of the rankings for each input parameter. For the cancer model investigated here, this analysis reveals that extracellular signal-regulated kinase, a downstream molecule of the epidermal growth factor receptor signalling pathway, has the most important impact on regulating both the tumour volume and expansion rate in the algorithm used.
Collapse
Affiliation(s)
- Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA.
| | - Thomas S Deisboeck
- Department of Radiology, Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Vittorio Cristini
- Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| |
Collapse
|
18
|
Wang Z, Butner JD, Cristini V, Deisboeck TS. Integrated PK-PD and agent-based modeling in oncology. J Pharmacokinet Pharmacodyn 2015; 42:179-89. [PMID: 25588379 DOI: 10.1007/s10928-015-9403-7] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2014] [Accepted: 01/08/2015] [Indexed: 01/11/2023]
Abstract
Mathematical modeling has become a valuable tool that strives to complement conventional biomedical research modalities in order to predict experimental outcome, generate new medical hypotheses, and optimize clinical therapies. Two specific approaches, pharmacokinetic-pharmacodynamic (PK-PD) modeling, and agent-based modeling (ABM), have been widely applied in cancer research. While they have made important contributions on their own (e.g., PK-PD in examining chemotherapy drug efficacy and resistance, and ABM in describing and predicting tumor growth and metastasis), only a few groups have started to combine both approaches together in an effort to gain more insights into the details of drug dynamics and the resulting impact on tumor growth. In this review, we focus our discussion on some of the most recent modeling studies building on a combined PK-PD and ABM approach that have generated experimentally testable hypotheses. Some future directions are also discussed.
Collapse
Affiliation(s)
- Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM, 87131, USA
| | | | | | | |
Collapse
|
19
|
Wang Z, Kerketta R, Chuang YL, Cristini V. Development of a diffusion-based mathematical model for predicting chemotherapy effects. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:2480-3. [PMID: 25570493 DOI: 10.1109/embc.2014.6944125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Mathematical modeling of drug transport can complement current experimental and clinical investigations to understand drug resistance mechanisms, which eventually will help to develop patient-specific chemotherapy treatments. In this paper, we present a general time- and space-dependent mathematical model based on diffusion theory for predicting chemotherapy outcome. This model has two important parameters: the blood volume fraction and radius of blood vessels divided by drug diffusion penetration length. Model analysis finds that a larger ratio of the radius of blood vessel to diffusion penetration length resulted in to a larger fraction of tumor killed, thereby leading to a better treatment outcome. Clinical translation of the model can help quantify and predict the optimal dosage size and frequency of chemotherapy for individual patients.
Collapse
|
20
|
Brocato T, Dogra P, Koay EJ, Day A, Chuang YL, Wang Z, Cristini V. Understanding Drug Resistance in Breast Cancer with Mathematical Oncology. CURRENT BREAST CANCER REPORTS 2014; 6:110-120. [PMID: 24891927 PMCID: PMC4039558 DOI: 10.1007/s12609-014-0143-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Chemotherapy is mainstay of treatment for the majority of patients with breast cancer, but results in only 26% of patients with distant metastasis living 5 years past treatment in the United States, largely due to drug resistance. The complexity of drug resistance calls for an integrated approach of mathematical modeling and experimental investigation to develop quantitative tools that reveal insights into drug resistance mechanisms, predict chemotherapy efficacy, and identify novel treatment approaches. This paper reviews recent modeling work for understanding cancer drug resistance through the use of computer simulations of molecular signaling networks and cancerous tissues, with a particular focus on breast cancer. These mathematical models are developed by drawing on current advances in molecular biology, physical characterization of tumors, and emerging drug delivery methods (e.g., nanotherapeutics). We focus our discussion on representative modeling works that have provided quantitative insight into chemotherapy resistance in breast cancer and how drug resistance can be overcome or minimized to optimize chemotherapy treatment. We also discuss future directions of mathematical modeling in understanding drug resistance.
Collapse
Affiliation(s)
- Terisse Brocato
- Department of Chemical and Nuclear Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131
| | - Prashant Dogra
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131
| | - Eugene J. Koay
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX 77030
| | - Armin Day
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131
| | - Yao-Li Chuang
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131
| | - Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131
| | - Vittorio Cristini
- Department of Chemical and Nuclear Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131
- Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| |
Collapse
|
21
|
Wang Z, Butner JD, Kerketta R, Cristini V, Deisboeck TS. Simulating cancer growth with multiscale agent-based modeling. Semin Cancer Biol 2014; 30:70-8. [PMID: 24793698 DOI: 10.1016/j.semcancer.2014.04.001] [Citation(s) in RCA: 132] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2014] [Revised: 03/18/2014] [Accepted: 04/04/2014] [Indexed: 01/01/2023]
Abstract
There have been many techniques developed in recent years to in silico model a variety of cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling approach that allows simulating the role of diversity in cell populations as well as within each individual cell; it has therefore become a powerful modeling method widely used by computational cancer researchers. Many aspects of tumor morphology including phenotype-changing mutations, the adaptation to microenvironment, the process of angiogenesis, the influence of extracellular matrix, reactions to chemotherapy or surgical intervention, the effects of oxygen and nutrient availability, and metastasis and invasion of healthy tissues have been incorporated and investigated in agent-based models. In this review, we introduce some of the most recent agent-based models that have provided insight into the understanding of cancer growth and invasion, spanning multiple biological scales in time and space, and we further describe several experimentally testable hypotheses generated by those models. We also discuss some of the current challenges of multiscale agent-based cancer models.
Collapse
Affiliation(s)
- Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA.
| | - Joseph D Butner
- Department of Chemical Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131, USA
| | - Romica Kerketta
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA
| | - Vittorio Cristini
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA; Department of Chemical Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, NM 87131, USA; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | | |
Collapse
|
22
|
Wang Z, Deisboeck TS. Mathematical modeling in cancer drug discovery. Drug Discov Today 2013; 19:145-50. [PMID: 23831857 DOI: 10.1016/j.drudis.2013.06.015] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Revised: 06/25/2013] [Accepted: 06/27/2013] [Indexed: 12/20/2022]
Abstract
Mathematical models have the potential to help discover new therapeutic targets and treatment strategies. In this review, we discuss how the latest developments in mathematical modeling can provide useful context for the rational design, validation and prioritization of novel cancer drug targets and their combinations. We give special attention to two modeling approaches: network-based modeling and multiscale modeling, because they have begun to show promise in facilitating the process of effective cancer drug discovery. Both modeling approaches are integrated with a variety of experimental methods to ensure proper parameterization and to maximize their predictive value. We also discuss several challenges faced in modeling-based drug discovery.
Collapse
Affiliation(s)
- Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, NM 87131, USA
| | | |
Collapse
|
23
|
Das H, Wang Z, Niazi MKK, Aggarwal R, Lu J, Kanji S, Das M, Joseph M, Gurcan M, Cristini V. Impact of diffusion barriers to small cytotoxic molecules on the efficacy of immunotherapy in breast cancer. PLoS One 2013; 8:e61398. [PMID: 23620747 PMCID: PMC3631240 DOI: 10.1371/journal.pone.0061398] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2012] [Accepted: 03/08/2013] [Indexed: 11/18/2022] Open
Abstract
Molecular-focused cancer therapies, e.g., molecularly targeted therapy and immunotherapy, so far demonstrate only limited efficacy in cancer patients. We hypothesize that underestimating the role of biophysical factors that impact the delivery of drugs or cytotoxic cells to the target sites (for associated preferential cytotoxicity or cell signaling modulation) may be responsible for the poor clinical outcome. Therefore, instead of focusing exclusively on the investigation of molecular mechanisms in cancer cells, convection-diffusion of cytotoxic molecules and migration of cancer-killing cells within tumor tissue should be taken into account to improve therapeutic effectiveness. To test this hypothesis, we have developed a mathematical model of the interstitial diffusion and uptake of small cytotoxic molecules secreted by T-cells, which is capable of predicting breast cancer growth inhibition as measured both in vitro and in vivo. Our analysis shows that diffusion barriers of cytotoxic molecules conspire with γδ T-cell scarcity in tissue to limit the inhibitory effects of γδ T-cells on cancer cells. This may increase the necessary ratios of γδ T-cells to cancer cells within tissue to unrealistic values for having an intended therapeutic effect, and decrease the effectiveness of the immunotherapeutic treatment.
Collapse
Affiliation(s)
- Hiranmoy Das
- Department of Medicine, Wexner Medical Center, The Ohio State University, Columbus, Ohio, United States of America
- * E-mail: (HD); (MG); (VC)
| | - Zhihui Wang
- Department of Pathology, University of New Mexico, Albuquerque, New Mexico, United States of America
| | - M. Khalid Khan Niazi
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States of America
| | - Reeva Aggarwal
- Department of Medicine, Wexner Medical Center, The Ohio State University, Columbus, Ohio, United States of America
| | - Jingwei Lu
- Department of Medicine, Wexner Medical Center, The Ohio State University, Columbus, Ohio, United States of America
| | - Suman Kanji
- Department of Medicine, Wexner Medical Center, The Ohio State University, Columbus, Ohio, United States of America
| | - Manjusri Das
- Department of Medicine, Wexner Medical Center, The Ohio State University, Columbus, Ohio, United States of America
| | - Matthew Joseph
- Department of Medicine, Wexner Medical Center, The Ohio State University, Columbus, Ohio, United States of America
| | - Metin Gurcan
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, United States of America
- * E-mail: (HD); (MG); (VC)
| | - Vittorio Cristini
- Department of Pathology, University of New Mexico, Albuquerque, New Mexico, United States of America
- Department of Chemical Engineering and Center for Biomedical Engineering, University of New Mexico, Albuquerque, New Mexico, United States of America
- * E-mail: (HD); (MG); (VC)
| |
Collapse
|