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Wang R, Huang X, Chen X, Zhang Y. Nanoparticle-Mediated Immunotherapy in Triple-Negative Breast Cancer. ACS Biomater Sci Eng 2024; 10:3568-3598. [PMID: 38815129 PMCID: PMC11167598 DOI: 10.1021/acsbiomaterials.4c00108] [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: 01/18/2024] [Revised: 05/05/2024] [Accepted: 05/07/2024] [Indexed: 06/01/2024]
Abstract
Triple-negative breast cancer (TNBC) is an aggressive subtype with the worst prognosis and highest recurrence rates. The treatment choices are limited due to the scarcity of endocrine and HER2 targets, except for chemotherapy. However, the side effects of chemotherapy restrict its long-term usage. Immunotherapy shows potential as a promising therapeutic strategy, such as inducing immunogenic cell death, immune checkpoint therapy, and immune adjuvant therapy. Nanotechnology offers unique advantages in the field of immunotherapy, such as improved delivery and targeted release of immunotherapeutic agents and enhanced bioavailability of immunomodulators. As well as the potential for combination therapy synergistically enhanced by nanocarriers. Nanoparticles-based combined application of multiple immunotherapies is designed to take the tactics of enhancing immunogenicity and reversing immunosuppression. Moreover, the increasing abundance of biomedical materials holds more promise for the development of this field. This review summarizes the advances in the field of nanoparticle-mediated immunotherapy in terms of both immune strategies for treatment and the development of biomaterials and presents challenges and hopes for the future.
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Affiliation(s)
- Ruoyi Wang
- Department of Breast
Surgery, The Second Norman Bethune Hospital
of Jilin University, Changchun 130021, P.R.C
| | - Xu Huang
- Department of Breast
Surgery, The Second Norman Bethune Hospital
of Jilin University, Changchun 130021, P.R.C
| | - Xiaoxi Chen
- Department of Breast
Surgery, The Second Norman Bethune Hospital
of Jilin University, Changchun 130021, P.R.C
| | - Yingchao Zhang
- Department of Breast
Surgery, The Second Norman Bethune Hospital
of Jilin University, Changchun 130021, P.R.C
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2
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Dogra P, Shinglot V, Ruiz-Ramírez J, Cave J, Butner JD, Schiavone C, Duda DG, Kaseb AO, Chung C, Koay EJ, Cristini V, Ozpolat B, Calin GA, Wang Z. Translational modeling-based evidence for enhanced efficacy of standard-of-care drugs in combination with anti-microRNA-155 in non-small-cell lung cancer. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.14.24304306. [PMID: 38559070 PMCID: PMC10980136 DOI: 10.1101/2024.03.14.24304306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Elevated microRNA-155 (miR-155) expression in non-small-cell lung cancer (NSCLC) promotes cisplatin resistance and negatively impacts treatment outcomes. However, miR-155 can also boost anti-tumor immunity by suppressing PD-L1 expression. We developed a multiscale mechanistic model, calibrated with in vivo data and then extrapolated to humans, to investigate the therapeutic effects of nanoparticle-delivered anti-miR-155 in NSCLC, alone or in combination with standard-of-care drugs. Model simulations and analyses of the clinical scenario revealed that monotherapy with anti-miR-155 at a dose of 2.5 mg/kg administered once every three weeks has substantial anti-cancer activity. It led to a median progression-free survival (PFS) of 6.7 months, which compared favorably to cisplatin and immune checkpoint inhibitors. Further, we explored the combinations of anti-miR-155 with standard-of-care drugs, and found strongly synergistic two- and three-drug combinations. A three-drug combination of anti-miR-155, cisplatin, and pembrolizumab resulted in a median PFS of 13.1 months, while a two-drug combination of anti-miR-155 and cisplatin resulted in a median PFS of 11.3 months, which emerged as a more practical option due to its simple design and cost-effectiveness. Our analyses also provided valuable insights into unfavorable dose ratios for drug combinations, highlighting the need for optimizing dose regimen to prevent antagonistic effects. Thus, this work bridges the gap between preclinical development and clinical translation of anti-miR-155 and unravels the potential of anti-miR-155 combination therapies in NSCLC.
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Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, Houston, TX, USA
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, USA
| | - Vrushaly Shinglot
- Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, Houston, TX, USA
| | | | - Joseph Cave
- Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, Houston, TX, USA
- Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Joseph D. Butner
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Carmine Schiavone
- Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, Houston, TX, USA
- Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, Naples, Italy
| | - Dan G. Duda
- Edwin. L. Steele Laboratories for Tumor Biology, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ahmed O. Kaseb
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Eugene J. Koay
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, Houston, TX, USA
- Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
- Department of Imaging Physics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Bulent Ozpolat
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX, USA
| | - George A. Calin
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, Houston, TX, USA
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
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3
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L'Hostis A, Palgen JL, Perrillat-Mercerot A, Peyronnet E, Jacob E, Bosley J, Duruisseaux M, Toueg R, Lefèvre L, Kahoul R, Ceres N, Monteiro C. Knowledge-based mechanistic modeling accurately predicts disease progression with gefitinib in EGFR-mutant lung adenocarcinoma. NPJ Syst Biol Appl 2023; 9:37. [PMID: 37524705 PMCID: PMC10390488 DOI: 10.1038/s41540-023-00292-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 06/21/2023] [Indexed: 08/02/2023] Open
Abstract
Lung adenocarcinoma (LUAD) is associated with a low survival rate at advanced stages. Although the development of targeted therapies has improved outcomes in LUAD patients with identified and specific genetic alterations, such as activating mutations on the epidermal growth factor receptor gene (EGFR), the emergence of tumor resistance eventually occurs in all patients and this is driving the development of new therapies. In this paper, we present the In Silico EGFR-mutant LUAD (ISELA) model that links LUAD patients' individual characteristics, including tumor genetic heterogeneity, to tumor size evolution and tumor progression over time under first generation EGFR tyrosine kinase inhibitor gefitinib. This translational mechanistic model gathers extensive knowledge on LUAD and was calibrated on multiple scales, including in vitro, human tumor xenograft mouse and human, reproducing more than 90% of the experimental data identified. Moreover, with 98.5% coverage and 99.4% negative logrank tests, the model accurately reproduced the time to progression from the Lux-Lung 7 clinical trial, which was unused in calibration, thus supporting the model high predictive value. This knowledge-based mechanistic model could be a valuable tool in the development of new therapies targeting EGFR-mutant LUAD as a foundation for the generation of synthetic control arms.
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Affiliation(s)
- Adèle L'Hostis
- Novadiscovery SA, Pl. Giovanni da Verrazzano, Lyon, 69009, Rhône, France
| | - Jean-Louis Palgen
- Novadiscovery SA, Pl. Giovanni da Verrazzano, Lyon, 69009, Rhône, France
| | | | - Emmanuel Peyronnet
- Novadiscovery SA, Pl. Giovanni da Verrazzano, Lyon, 69009, Rhône, France
| | - Evgueni Jacob
- Novadiscovery SA, Pl. Giovanni da Verrazzano, Lyon, 69009, Rhône, France
| | - James Bosley
- Novadiscovery SA, Pl. Giovanni da Verrazzano, Lyon, 69009, Rhône, France
| | - Michaël Duruisseaux
- Respiratory Department and Early Phase, Louis Pradel Hospital, Hospices Civils de Lyon Cancer Institute, Lyon, 69100, France
- Cancer Research Center of Lyon, UMR INSERM 1052 CNRS 5286, Lyon, France
- Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France
| | - Raphaël Toueg
- Janssen-Cilag, France, 1, rue Camille Desmoulins - TSA 60009, Issy-Les-Moulineaux Cedex 9, Issy-Les-Moulineaux, 92787, France
| | - Lucile Lefèvre
- Janssen-Cilag, France, 1, rue Camille Desmoulins - TSA 60009, Issy-Les-Moulineaux Cedex 9, Issy-Les-Moulineaux, 92787, France
| | - Riad Kahoul
- Novadiscovery SA, Pl. Giovanni da Verrazzano, Lyon, 69009, Rhône, France
| | - Nicoletta Ceres
- Novadiscovery SA, Pl. Giovanni da Verrazzano, Lyon, 69009, Rhône, France
| | - Claudio Monteiro
- Novadiscovery SA, Pl. Giovanni da Verrazzano, Lyon, 69009, Rhône, France.
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4
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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.
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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
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5
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Pelaez MJ, Ruiz-Ramirez J, Shen Y, Birur RM, Schiavone C, Cristini V, Puri A, Wang Z, Dogra P. Mechanistic modeling for optimal design of dissolvable microneedle-based patches for transdermal drug delivery. 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: 38082979 DOI: 10.1109/embc40787.2023.10341093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Polymeric microneedle (MN)-based patches are an efficient, non-invasive, and painless means of drug delivery through the skin to systemic circulation. The design of these MN-based patches can be customized for various drug delivery applications, particularly modified release of drugs. In this study, we developed a mathematical model of drug delivery via MN-based patches to study the effect of patch design properties on drug delivery kinetics and systemic pharmacokinetics (PK). We calibrated the model against two representative formulations: a rapid release patch of naloxone and a sustained-release patch of levonorgestrel. The model was then applied to assess the relative significance of model parameters in governing systemic PK of drugs and obtain optimal design parameters to achieve therapeutically meaningful drug levels in a clinical setting. We identified the importance of drug loading fraction, MN base radius, and MN height as the key control parameters responsible for drug PK.Clinical Relevance- Through the application of modeling and simulation, we can improve drug delivery from MN-based patches by identifying optimal design parameters to support the clinical translation of these novel drug delivery systems.
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6
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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. 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.
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7
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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 INTERDISCIPLINARY REVIEWS. NANOMEDICINE AND NANOBIOTECHNOLOGY 2023; 15:e1855. [PMID: 36148978 DOI: 10.1002/wnan.1855] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [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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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. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2022.09.14.22279959. [PMID: 36415468 PMCID: PMC9681049 DOI: 10.1101/2022.09.14.22279959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
While the development of different vaccines has slowed the dissemination of SARS-CoV-2, the occurrence of breakthrough infections continues to fuel the pandemic. As a strategy to secure at least partial protection, with a single dose of a given COVID-19 vaccine to maximum possible fraction of the population, delayed administration of subsequent doses (or boosters) has been implemented in many countries. However, waning immunity and emergence of new variants of SARS-CoV-2 suggest that such measures may jeopardize the attainment of herd immunity due to intermittent lapses in protection. Optimizing vaccine dosing schedules could thus make the difference between periodic occurrence of breakthrough infections or effective control of the pandemic. To this end, we have developed a mechanistic mathematical model of adaptive immune response to vaccines and demonstrated its applicability to COVID-19 mRNA vaccines as a proof-of-concept for future outbreaks. The model was thoroughly calibrated against multiple clinical datasets involving immune response to SARS-CoV-2 infection and mRNA vaccines in healthy and immunocompromised subjects (cancer patients undergoing therapy); the model showed robust clinical validation by accurately predicting neutralizing antibody kinetics, a correlate of vaccine-induced protection, in response to multiple doses of mRNA vaccines. Importantly, we estimated population vulnerability to breakthrough infections and predicted tailored vaccination dosing schedules to maximize protection and thus minimize breakthrough infections, based on the immune status of a sub-population. We have identified a critical waiting window for cancer patients (or, immunocompromised subjects) to allow recovery of the immune system (particularly CD4+ T-cells) for effective differentiation of B-cells to produce neutralizing antibodies and thus achieve optimal vaccine efficacy against variants of concern, especially between the first and second doses. Also, we have obtained optimized dosing schedules for subsequent doses in healthy and immunocompromised subjects, which vary from the CDC-recommended schedules, to minimize breakthrough infections. The developed modeling tool is based on generalized adaptive immune response to antigens and can thus be leveraged to guide vaccine dosing schedules during future outbreaks.
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Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, Houston, TX, USA
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, 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, TX, USA
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, 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, NJ, USA
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Christopher Markosian
- Rutgers Cancer Institute of New Jersey, Newark, NJ, USA
- Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Jin Wang
- Immunobiology and Transplant Science Center, Department of Surgery, Houston Methodist Research Institute, Houston, TX, USA
- Department of Surgery, Weill Cornell Medical College, Cornell University, New York, NY, USA
| | - H. Dirk Sostman
- Weill Cornell Medicine, New York, NY, USA
- Houston Methodist Research Institute, Houston, TX, USA
- Houston Methodist Academic Institute, Houston, TX, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, NJ, USA
- Division of Cancer Biology, Department of Radiation Oncology, 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
| | - Vittorio Cristini
- Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, Houston, TX, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
- Department of Imaging Physics, University of Texas M.D. 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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9
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Multiphysics Simulation in Drug Development and Delivery. Pharm Res 2023; 40:611-613. [PMID: 35794396 PMCID: PMC9944723 DOI: 10.1007/s11095-022-03330-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 06/30/2022] [Indexed: 10/17/2022]
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10
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Butner JD, Dogra P, Chung C, Pasqualini R, Arap W, Lowengrub J, Cristini V, Wang Z. Mathematical modeling of cancer immunotherapy for personalized clinical translation. NATURE COMPUTATIONAL SCIENCE 2022; 2:785-796. [PMID: 38126024 PMCID: PMC10732566 DOI: 10.1038/s43588-022-00377-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2023]
Abstract
Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.
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Affiliation(s)
- Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
| | - Caroline Chung
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey, Newark, NJ, USA
- Department of Radiation Oncology, Division of Cancer Biology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey, Newark, NJ, USA
- Department of Medicine, Division of Hematology/Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - John Lowengrub
- Department of Mathematics, University of California at Irvine, Irvine, CA, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, USA
- Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
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11
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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] [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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Physiologically Based Pharmacokinetic Modeling of Nanoparticle Biodistribution: A Review of Existing Models, Simulation Software, and Data Analysis Tools. Int J Mol Sci 2022; 23:ijms232012560. [PMID: 36293410 PMCID: PMC9604366 DOI: 10.3390/ijms232012560] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/07/2022] [Accepted: 10/14/2022] [Indexed: 11/30/2022] Open
Abstract
Cancer treatment and pharmaceutical development require targeted treatment and less toxic therapeutic intervention to achieve real progress against this disease. In this scenario, nanomedicine emerged as a reliable tool to improve drug pharmacokinetics and to translate to the clinical biologics based on large molecules. However, the ability of our body to recognize foreign objects together with carrier transport heterogeneity derived from the combination of particle physical and chemical properties, payload and surface modification, make the designing of effective carriers very difficult. In this scenario, physiologically based pharmacokinetic modeling can help to design the particles and eventually predict their ability to reach the target and treat the tumor. This effort is performed by scientists with specific expertise and skills and familiarity with artificial intelligence tools such as advanced software that are not usually in the “cords” of traditional medical or material researchers. The goal of this review was to highlight the advantages that computational modeling could provide to nanomedicine and bring together scientists with different background by portraying in the most simple way the work of computational developers through the description of the tools that they use to predict nanoparticle transport and tumor targeting in our body.
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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.
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