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Boutin L, Barjon C, Chauvet M, Lafrance L, Senechal E, Bourges D, Vigne E, Scotet E. Camelid-derived Tcell engagers harnessing human γδ T cells as promising antitumor immunotherapeutic agents. Eur J Immunol 2024; 54:e2350773. [PMID: 38804118 DOI: 10.1002/eji.202350773] [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: 09/13/2023] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 05/29/2024]
Abstract
In the last decade, there has been a surge in developing immunotherapies to enhance the immune system's ability to eliminate tumor cells. Bispecific antibodies known as T cell engagers (TCEs) present an attractive strategy in this pursuit. TCEs aim to guide cytotoxic T cells toward tumor cells, thereby inducing a strong activation and subsequent tumor cell lysis. In this study, we investigated the activity of different TCEs on both conventional alpha-beta (αβ) T cells and unconventional gamma delta (γδ) T cells. TCEs were built using camelid single-domain antibodies (VHHs) targeting the tumor-associated antigen CEACAM5 (CEA), together with T cell receptor chains or a CD3 domain. We show that Vγ9Vδ2 T cells display stronger in vitro antitumor activity than αβ T cells when stimulated with a CD3xCEA TCE. Furthermore, restricting the activation of fresh human peripheral T cells to Vγ9Vδ2 T cells limited the production of protumor factors and proinflammatory cytokines, commonly associated with toxicity in patients. Taken together, our findings provide further insights that γδ T cell-specific TCEs hold promise as specific, effective, and potentially safe molecules to improve antitumor immunotherapies.
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Affiliation(s)
- Lola Boutin
- Nantes Université, Inserm UMR 1307, CNRS UMR 6075, Université d'Angers, CRCI2NA, Nantes, France
- LabEx IGO "Immunotherapy, Graft, Oncology", Nantes, France
- Sanofi, Large Molecule Research, Vitry-sur-Seine, France
| | | | - Morgane Chauvet
- Nantes Université, Inserm UMR 1307, CNRS UMR 6075, Université d'Angers, CRCI2NA, Nantes, France
- LabEx IGO "Immunotherapy, Graft, Oncology", Nantes, France
- Sanofi, Oncology, Vitry-sur-Seine, France
| | - Laura Lafrance
- Nantes Université, Inserm UMR 1307, CNRS UMR 6075, Université d'Angers, CRCI2NA, Nantes, France
- LabEx IGO "Immunotherapy, Graft, Oncology", Nantes, France
| | - Eric Senechal
- Sanofi, Large Molecule Research, Vitry-sur-Seine, France
| | | | | | - Emmanuel Scotet
- Nantes Université, Inserm UMR 1307, CNRS UMR 6075, Université d'Angers, CRCI2NA, Nantes, France
- LabEx IGO "Immunotherapy, Graft, Oncology", Nantes, France
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2
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Wang H, Arulraj T, Ippolito A, Popel AS. From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling. NPJ Digit Med 2024; 7:189. [PMID: 39014005 PMCID: PMC11252162 DOI: 10.1038/s41746-024-01188-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 07/03/2024] [Indexed: 07/18/2024] Open
Abstract
Virtual patients and digital patients/twins are two similar concepts gaining increasing attention in health care with goals to accelerate drug development and improve patients' survival, but with their own limitations. Although methods have been proposed to generate virtual patient populations using mechanistic models, there are limited number of applications in immuno-oncology research. Furthermore, due to the stricter requirements of digital twins, they are often generated in a study-specific manner with models customized to particular clinical settings (e.g., treatment, cancer, and data types). Here, we discuss the challenges for virtual patient generation in immuno-oncology with our most recent experiences, initiatives to develop digital twins, and how research on these two concepts can inform each other.
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Affiliation(s)
- Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Theinmozhi Arulraj
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Alberto Ippolito
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Departments of Medicine and Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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3
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Wang Y, Liu F, Du X, Shi J, Yu R, Li S, Na R, Zhao Y, Zhou M, Guo Y, Cheng L, Wang G, Zheng T. Combination of Anti-PD-1 and Electroacupuncture Induces a Potent Antitumor Immune Response in Microsatellite-Stable Colorectal Cancer. Cancer Immunol Res 2024; 12:26-35. [PMID: 37956404 DOI: 10.1158/2326-6066.cir-23-0309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 08/22/2023] [Accepted: 11/07/2023] [Indexed: 11/15/2023]
Abstract
Programmed death receptor-1 (PD-1) inhibitors are ineffective against microsatellite-stable (MSS) colorectal cancer. Electroacupuncture (EA) has oncosuppressive and immunomodulatory properties. Here, we investigated the antitumor effects of EA and explored the feasibility of EA combined with anti-PD-1 in MSS colorectal cancer. Results showed that EA exerted its antitumor effect in an intensity-specific manner, and moderate-intensity EA (1.0 mA) induced maximal tumor inhibition. EA enhanced antitumor immune responses by increasing lymphocytes and granzyme B (GzmB) levels, as well as activating the stimulator of IFN genes (STING) pathway. EA combined with anti-PD-1 showed superior efficacy compared with either monotherapy in multiple MSS colorectal cancer mouse models. Single-cell RNA sequencing revealed that cotreatment reprogrammed the tumor immune microenvironment (TIME), as characterized by enhancement of cytotoxic functions. Mechanically, we found that the potentiated effect of EA was dependent upon the STING pathway. Collectively, EA reshapes the TIME of MSS colorectal cancer and sensitizes tumors to anti-PD-1 in a STING pathway-dependent manner. These results provide a mechanistic rationale for using EA as an immunomodulatory strategy to improve the clinical efficacy of anti-PD-1 in MSS colorectal cancer. EA is safe, well-tolerated, and feasible for clinical translation as a promising strategy for treating MSS colorectal cancer.
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Affiliation(s)
- Yuan Wang
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, P. R. China
- Department of Phase 1 Trials Center, Harbin Medical University Cancer Hospital, Harbin, P. R. China
- Heilongjiang Province Key Laboratory of Molecular Oncology, Harbin, P. R. China
| | - Fengyi Liu
- Department of Integrated Traditional Chinese and Western Medicine, First Affiliated Hospital of Harbin Medical University, Harbin, P. R. China
- Heilongjiang University of Chinese Medicine, Harbin, P. R. China
| | - Xiaoxue Du
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, P. R. China
- Department of Phase 1 Trials Center, Harbin Medical University Cancer Hospital, Harbin, P. R. China
- Heilongjiang Province Key Laboratory of Molecular Oncology, Harbin, P. R. China
| | - Jiaqi Shi
- Department of Phase 1 Trials Center, Harbin Medical University Cancer Hospital, Harbin, P. R. China
- Heilongjiang Province Key Laboratory of Molecular Oncology, Harbin, P. R. China
| | - Rui Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Shuang Li
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, P. R. China
- Heilongjiang Province Key Laboratory of Molecular Oncology, Harbin, P. R. China
| | - Ruisi Na
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, P. R. China
- Department of Phase 1 Trials Center, Harbin Medical University Cancer Hospital, Harbin, P. R. China
- Heilongjiang Province Key Laboratory of Molecular Oncology, Harbin, P. R. China
| | - Ying Zhao
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, P. R. China
- Department of Phase 1 Trials Center, Harbin Medical University Cancer Hospital, Harbin, P. R. China
- Heilongjiang Province Key Laboratory of Molecular Oncology, Harbin, P. R. China
| | - Meng Zhou
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, P. R. China
- Department of Phase 1 Trials Center, Harbin Medical University Cancer Hospital, Harbin, P. R. China
- Heilongjiang Province Key Laboratory of Molecular Oncology, Harbin, P. R. China
| | - Ying Guo
- Heilongjiang Province Key Laboratory of Molecular Oncology, Harbin, P. R. China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, NHC Key Laboratory of Molecular Probe and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, P. R. China
| | - Guangyu Wang
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, P. R. China
- Department of Phase 1 Trials Center, Harbin Medical University Cancer Hospital, Harbin, P. R. China
| | - Tongsen Zheng
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, P. R. China
- Department of Phase 1 Trials Center, Harbin Medical University Cancer Hospital, Harbin, P. R. China
- Heilongjiang Province Key Laboratory of Molecular Oncology, Harbin, P. R. China
- Heilongjiang Cancer Institute, Harbin, P. R. China
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4
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Niu J, Wang W, Ouellet D. Mechanism-based pharmacokinetic and pharmacodynamic modeling for bispecific antibodies: challenges and opportunities. Expert Rev Clin Pharmacol 2023; 16:977-990. [PMID: 37743720 DOI: 10.1080/17512433.2023.2257136] [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: 06/15/2023] [Accepted: 09/06/2023] [Indexed: 09/26/2023]
Abstract
INTRODUCTION Unlike conventional antibodies, bispecific antibodies (bsAbs) are engineered antibody- or antibody fragment-based molecules that can simultaneously recognize two different epitopes or antigens. Over the past decade, there has been an explosion of bsAbs being developed across therapeutic areas. Development of bsAbs presents unique challenges and mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) modeling has served as a powerful tool to optimize their development and realize their clinical utility. AREAS COVERED In this review, the guiding principles and case examples of how fit-for-purpose, mechanism-based PK/PD models have been applied to answer questions commonly encountered in bsAb development are presented. Such models characterize the key pharmacological elements of bsAbs, and they can be utilized for model-informed drug development. We also include the discussion of challenges, knowledge gaps and future direction for such models. EXPERT OPINION Mechanistic PK/PD modeling is a powerful tool to support the development of bsAbs. These models can be extrapolated to predict treatment outcomes based on mechanisms of action (MoA) and clinical observations to form positive learn-and-confirm cycles during drug development, due to their abilities to differentiate system- and drug-specific parameters. Meanwhile, the models should keep being adapted according to novel drug design and MoA, providing continuous opportunities for model-informed drug development.
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Affiliation(s)
- Jin Niu
- Clinical Pharmacology and Pharmacometrics, Janssen Research & Development, Spring House, PA, USA
| | - Weirong Wang
- Clinical Pharmacology and Pharmacometrics, Janssen Research & Development, Spring House, PA, USA
| | - Daniele Ouellet
- Clinical Pharmacology and Pharmacometrics, Janssen Research & Development, Spring House, PA, USA
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Arulraj T, Wang H, Emens LA, Santa-Maria CA, Popel AS. A transcriptome-informed QSP model of metastatic triple-negative breast cancer identifies predictive biomarkers for PD-1 inhibition. SCIENCE ADVANCES 2023; 9:eadg0289. [PMID: 37390206 PMCID: PMC10313177 DOI: 10.1126/sciadv.adg0289] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 05/26/2023] [Indexed: 07/02/2023]
Abstract
Triple-negative breast cancer (TNBC), a highly metastatic breast cancer subtype, has limited treatment options. While a small number of patients attain clinical benefit with single-agent checkpoint inhibitors, identifying these patients before the therapy remains challenging. Here, we developed a transcriptome-informed quantitative systems pharmacology model of metastatic TNBC by integrating heterogenous metastatic tumors. In silico clinical trial with an anti-PD-1 drug, pembrolizumab, predicted that several features, such as the density of antigen-presenting cells, the fraction of cytotoxic T cells in lymph nodes, and the richness of cancer clones in tumors, could serve individually as biomarkers but had a higher predictive power as combinations of two biomarkers. We showed that PD-1 inhibition neither consistently enhanced all antitumorigenic factors nor suppressed all protumorigenic factors but ultimately reduced the tumor carrying capacity. Collectively, our predictions suggest several candidate biomarkers that might effectively predict the response to pembrolizumab monotherapy and potential therapeutic targets to develop treatment strategies for metastatic TNBC.
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Affiliation(s)
- Theinmozhi Arulraj
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Leisha A. Emens
- University of Pittsburgh Medical Center, Hillman Cancer Center, Pittsburgh, PA, 15213, USA
| | - Cesar A. Santa-Maria
- Department of Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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6
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Anbari S, Wang H, Zhang Y, Wang J, Pilvankar M, Nickaeen M, Hansel S, Popel AS. Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager. Front Pharmacol 2023; 14:1163432. [PMID: 37408756 PMCID: PMC10318535 DOI: 10.3389/fphar.2023.1163432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/06/2023] [Indexed: 07/07/2023] Open
Abstract
Although immune checkpoint blockade therapies have shown evidence of clinical effectiveness in many types of cancer, the outcome of clinical trials shows that very few patients with colorectal cancer benefit from treatments with checkpoint inhibitors. Bispecific T cell engagers (TCEs) are gaining popularity because they can improve patients' immunological responses by promoting T cell activation. The possibility of combining TCEs with checkpoint inhibitors to increase tumor response and patient survival has been highlighted by preclinical and clinical outcomes. However, identifying predictive biomarkers and optimal dose regimens for individual patients to benefit from combination therapy remains one of the main challenges. In this article, we describe a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that includes specific processes of immune-cancer cell interactions and was created based on published data on colorectal cancer. We generated a virtual patient cohort with the model to conduct in silico virtual clinical trials for combination therapy of a PD-L1 checkpoint inhibitor (atezolizumab) and a bispecific T cell engager (cibisatamab). Using the model calibrated against the clinical trials, we conducted several virtual clinical trials to compare various doses and schedules of administration for two drugs with the goal of therapy optimization. Moreover, we quantified the score of drug synergy for these two drugs to further study the role of the combination therapy.
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Affiliation(s)
- Samira Anbari
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Yu Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jun Wang
- Biotherapeutics Discovery Research, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Minu Pilvankar
- Biotherapeutics Discovery Research, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Masoud Nickaeen
- Biotherapeutics Discovery Research, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Steven Hansel
- Biotherapeutics Discovery Research, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Oncology, Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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7
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Wang H, Arulraj T, Kimko H, Popel AS. Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition. NPJ Precis Oncol 2023; 7:55. [PMID: 37291190 DOI: 10.1038/s41698-023-00405-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 05/25/2023] [Indexed: 06/10/2023] Open
Abstract
Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models.
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Affiliation(s)
- Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
| | - Theinmozhi Arulraj
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Holly Kimko
- Clinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Gaithersburg, MD, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Oncology, and the Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
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8
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Wang H, Arulraj T, Kimko H, Popel AS. Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.25.538191. [PMID: 37162938 PMCID: PMC10168221 DOI: 10.1101/2023.04.25.538191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Generating realistic virtual patients from a limited amount of patient data is one of the major challenges for quantitative systems pharmacology modeling in immuno-oncology. Quantitative systems pharmacology (QSP) is a mathematical modeling methodology that integrates mechanistic knowledge of biological systems to investigate dynamics in a whole system during disease progression and drug treatment. In the present analysis, we parameterized our previously published QSP model of the cancer-immunity cycle to non-small cell lung cancer (NSCLC) and generated a virtual patient cohort to predict clinical response to PD-L1 inhibition in NSCLC. The virtual patient generation was guided by immunogenomic data from iAtlas portal and population pharmacokinetic data of durvalumab, a PD-L1 inhibitor. With virtual patients generated following the immunogenomic data distribution, our model predicted a response rate of 18.6% (95% bootstrap confidence interval: 13.3-24.2%) and identified CD8/Treg ratio as a potential predictive biomarker in addition to PD-L1 expression and tumor mutational burden. We demonstrated that omics data served as a reliable resource for virtual patient generation techniques in immuno-oncology using QSP models.
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9
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Ball K, Dovedi SJ, Vajjah P, Phipps A. Strategies for clinical dose optimization of T cell-engaging therapies in oncology. MAbs 2023; 15:2181016. [PMID: 36823042 PMCID: PMC9980545 DOI: 10.1080/19420862.2023.2181016] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 02/07/2023] [Accepted: 02/13/2023] [Indexed: 02/25/2023] Open
Abstract
Innovative approaches in the design of T cell-engaging (TCE) molecules are ushering in a new wave of promising immunotherapies for the treatment of cancer. Their mechanism of action, which generates an in trans interaction to create a synthetic immune synapse, leads to complex and interconnected relationships between the exposure, efficacy, and toxicity of these drugs. Challenges thus arise when designing optimal clinical dose regimens for TCEs with narrow therapeutic windows, with a variety of dosing strategies being evaluated to mitigate key side effects such as cytokine release syndrome, neurotoxicity, and on-target off-tumor toxicities. This review evaluates the current approaches to dose optimization throughout the preclinical and clinical development of TCEs, along with perspectives for improvement of these strategies. Quantitative approaches used to aid the understanding of dose-exposure-response relationships are highlighted, along with opportunities to guide the rational design of next-generation TCE molecules, and optimize their dose regimens in patients.
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Affiliation(s)
- Kathryn Ball
- Clinical Pharmacology and Quantitative Pharmacology, Biopharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | | | - Pavan Vajjah
- Clinical Pharmacology and Quantitative Pharmacology, Biopharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Alex Phipps
- Clinical Pharmacology and Quantitative Pharmacology, Biopharmaceuticals R&D, AstraZeneca, Cambridge, UK
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10
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Zhang Y, Li Y, Fu Q, Han Z, Wang D, Umar Shinge SA, Muluh TA, Lu X. Combined Immunotherapy and Targeted Therapies for Cancer Treatment: Recent Advances and Future Perspectives. Curr Cancer Drug Targets 2023; 23:251-264. [PMID: 36278447 DOI: 10.2174/1568009623666221020104603] [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: 05/11/2022] [Revised: 08/30/2022] [Accepted: 09/01/2022] [Indexed: 11/22/2022]
Abstract
The previous year's worldview for cancer treatment has advanced from general to more precise therapeutic approaches. Chemotherapies were first distinguished as the most reliable and brief therapy with promising outcomes in cancer patients. However, patients could also suffer from severe toxicities resulting from chemotherapeutic drug usage. An improved comprehension of cancer pathogenesis has led to new treatment choices, including tumor-targeted therapy and immunotherapy. Subsequently, cancer immunotherapy and targeted therapy give more hope to patients since their combination has tremendous therapeutic efficacy. The immune system responses are also initiated and modulated by targeted therapies and cytotoxic agents, which create the principal basis that when targeted therapies are combined with immunotherapy, the clinical outcomes are of excellent efficacy, as presented in this review. This review focuses on how immunotherapy and targeted therapy are applicable in cancer management and treatment. Also, it depicts promising therapeutic results with more extensive immunotherapy applications with targeted therapy. Further elaborate that immune system responses are also initiated and modulated by targeted therapies and cytotoxic agents, which create the principal basis that this combination therapy with immunotherapy can be of great outcome clinically.
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Affiliation(s)
- Yan Zhang
- Department of Oncology, The People's Hospital of Luzhou, 646000 Luzhou, Sichuan, P.R. China
| | - Yafei Li
- Department of Oncology, The People's Hospital of Luzhou, 646000 Luzhou, Sichuan, P.R. China
| | - Qiuxia Fu
- Department of Oncology, The People's Hospital of Luzhou, 646000 Luzhou, Sichuan, P.R. China
| | - Zhiqiang Han
- Department of Oncology, The People's Hospital of Luzhou, 646000 Luzhou, Sichuan, P.R. China
| | - Daijie Wang
- Department of Oncology, The People's Hospital of Luzhou, 646000 Luzhou, Sichuan, P.R. China
| | - Shafiu A Umar Shinge
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Sichuan, P.R. China
| | - Tobias Achu Muluh
- Department of Cardiovascular Surgery, The Affiliated Hospital of Southwest Medical University, Sichuan, P.R. China.,School of Medicine, Health Science Center, Shenzhen University, Shenzhen 518060, P.R. China
| | - Xiaohong Lu
- Department of Oncology, The People's Hospital of Luzhou, 646000 Luzhou, Sichuan, P.R. China
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11
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Mi H, Sivagnanam S, Betts CB, Liudahl SM, Jaffee EM, Coussens LM, Popel AS. Quantitative Spatial Profiling of Immune Populations in Pancreatic Ductal Adenocarcinoma Reveals Tumor Microenvironment Heterogeneity and Prognostic Biomarkers. Cancer Res 2022; 82:4359-4372. [PMID: 36112643 PMCID: PMC9716253 DOI: 10.1158/0008-5472.can-22-1190] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 08/04/2022] [Accepted: 09/12/2022] [Indexed: 01/24/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive disease with poor 5-year survival rates, necessitating identification of novel therapeutic targets. Elucidating the biology of the tumor immune microenvironment (TiME) can provide vital insights into mechanisms of tumor progression. In this study, we developed a quantitative image processing platform to analyze sequential multiplexed IHC data from archival PDAC tissue resection specimens. A 27-plex marker panel was employed to simultaneously phenotype cell populations and their functional states, followed by a computational workflow to interrogate the immune contextures of the TiME in search of potential biomarkers. The PDAC TiME reflected a low-immunogenic ecosystem with both high intratumoral and intertumoral heterogeneity. Spatial analysis revealed that the relative distance between IL10+ myelomonocytes, PD-1+ CD4+ T cells, and granzyme B+ CD8+ T cells correlated significantly with survival, from which a spatial proximity signature termed imRS was derived that correlated with PDAC patient survival. Furthermore, spatial enrichment of CD8+ T cells in lymphoid aggregates was also linked to improved survival. Altogether, these findings indicate that the PDAC TiME, generally considered immuno-dormant or immunosuppressive, is a spatially nuanced ecosystem orchestrated by ordered immune hierarchies. This new understanding of spatial complexity may guide novel treatment strategies for PDAC. SIGNIFICANCE Quantitative image analysis of PDAC specimens reveals intertumoral and intratumoral heterogeneity of immune populations and identifies spatial immune architectures that are significantly associated with disease prognosis.
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Affiliation(s)
- Haoyang Mi
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.,Corresponding Authors: Haoyang Mi, Johns Hopkins University, Baltimore, MD 21205. Phone: 410-528-3768; E-mail: ; and Lisa M. Coussens,
| | | | - Courtney B. Betts
- Department of Cell, Development, and Cancer Biology, Oregon Health and Science University, Portland, Oregon
| | - Shannon M. Liudahl
- Department of Cell, Development, and Cancer Biology, Oregon Health and Science University, Portland, Oregon
| | - Elizabeth M. Jaffee
- Skip Viragh Center for Pancreatic Cancer, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Johns Hopkins Medicine, Baltimore, Maryland.,Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Lisa M. Coussens
- Department of Cell, Development, and Cancer Biology, Oregon Health and Science University, Portland, Oregon.,Brenden-Colson Center for Pancreatic Care, Oregon Health and Science University, Portland, Oregon.,Knight Cancer Institute, Portland, Oregon.,Corresponding Authors: Haoyang Mi, Johns Hopkins University, Baltimore, MD 21205. Phone: 410-528-3768; E-mail: ; and Lisa M. Coussens,
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.,Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
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12
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Farhangnia P, Akbarpour M, Yazdanifar M, Aref AR, Delbandi AA, Rezaei N. Advances in therapeutic targeting of immune checkpoints receptors within the CD96-TIGIT axis: clinical implications and future perspectives. Expert Rev Clin Immunol 2022; 18:1217-1237. [PMID: 36154551 DOI: 10.1080/1744666x.2022.2128107] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
INTRODUCTION The development of therapeutic antibodies targeting immune checkpoint molecules (ICMs) that induce long-term remissions in cancer patients has revolutionized cancer immunotherapy. However, a major drawback is that relapse after an initial response may be attributed to innate and acquired resistance. Additionally, these treatments are not beneficial to all patients. Therefore, the discovery and targeting of novel ICMs and their combination with other immunotherapeutics are urgently needed. AREAS COVERED There has been increasing evidence of the CD96-TIGIT axis as ICMs in cancer immunotherapy in the last five years. This review will highlight and discuss the current knowledge about the role of CD96 and TIGIT in hematological and solid tumor immunotherapy in the context of empirical studies and clinical trials, and provide a comprehensive list of ongoing cancer clinical trials on the blockade of these ICMs, as well as the rationale behind combinational therapies with anti-PD-1/PD-L1 agents, chemotherapy drugs, and radiotherapy. Moreover, we share our perspectives on anti-CD96/TIGIT-related combination therapies. EXPERT OPINION CD96-TIGIT axis regulates anti-tumor immune responses. Thus, the receptors within this axis are the potential candidates for cancer immunotherapy. Combining the inhibition of CD96-TIGIT with anti-PD-1/PD-L1 mAbs and chemotherapy drugs has shown relatively effective results in the context of preclinical studies and tumor models.
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Affiliation(s)
- Pooya Farhangnia
- Department of Immunology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.,Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran.,Immunology Board for Transplantation and Cell-Based Therapeutics (ImmunoTACT), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Mahzad Akbarpour
- Immunology Board for Transplantation and Cell-Based Therapeutics (ImmunoTACT), Universal Scientific Education and Research Network (USERN), Tehran, Iran.,Advanced Cellular Therapeutics Facility (ACTF), Hematopoietic Cellular Therapy Program, Section of Hematology & Oncology, Department of Medicine, University of Chicago Medical Center, Chicago, IL, USA
| | - Mahboubeh Yazdanifar
- Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Amir Reza Aref
- Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Ali-Akbar Delbandi
- Department of Immunology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.,Immunology Research Center, Institute of Immunology and Infectious Disease, Iran University of Medical Sciences, Tehran, Iran
| | - Nima Rezaei
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran.,Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran.,Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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13
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Sové RJ, Verma BK, Wang H, Ho WJ, Yarchoan M, Popel AS. Virtual clinical trials of anti-PD-1 and anti-CTLA-4 immunotherapy in advanced hepatocellular carcinoma using a quantitative systems pharmacology model. J Immunother Cancer 2022; 10:e005414. [PMID: 36323435 PMCID: PMC9639136 DOI: 10.1136/jitc-2022-005414] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/05/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer and is the third-leading cause of cancer-related death worldwide. Most patients with HCC are diagnosed at an advanced stage, and the median survival for patients with advanced HCC treated with modern systemic therapy is less than 2 years. This leaves the advanced stage patients with limited treatment options. Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 (PD-1) or its ligand, are widely used in the treatment of HCC and are associated with durable responses in a subset of patients. ICIs targeting cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) also have clinical activity in HCC. Combination therapy of nivolumab (anti-PD-1) and ipilimumab (anti-CTLA-4) is the first treatment option for HCC to be approved by Food and Drug Administration that targets more than one immune checkpoints. METHODS In this study, we used the framework of quantitative systems pharmacology (QSP) to perform a virtual clinical trial for nivolumab and ipilimumab in HCC patients. Our model incorporates detailed biological mechanisms of interactions of immune cells and cancer cells leading to antitumor response. To conduct virtual clinical trial, we generate virtual patient from a cohort of 5,000 proposed patients by extending recent algorithms from literature. The model was calibrated using the data of the clinical trial CheckMate 040 (ClinicalTrials.gov number, NCT01658878). RESULTS Retrospective analyses were performed for different immune checkpoint therapies as performed in CheckMate 040. Using machine learning approach, we predict the importance of potential biomarkers for immune blockade therapies. CONCLUSIONS This is the first QSP model for HCC with ICIs and the predictions are consistent with clinically observed outcomes. This study demonstrates that using a mechanistic understanding of the underlying pathophysiology, QSP models can facilitate patient selection and design clinical trials with improved success.
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Affiliation(s)
- Richard J Sové
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Babita K Verma
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Won Jin Ho
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mark Yarchoan
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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14
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Passariello M, Yoshioka A, Takahashi K, Hashimoto SI, Inoue T, Nakamura K, De Lorenzo C. Novel tri-specific tribodies induce strong T cell activation and anti-tumor effects in vitro and in vivo. J Exp Clin Cancer Res 2022; 41:269. [PMID: 36071464 PMCID: PMC9450414 DOI: 10.1186/s13046-022-02474-3] [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: 07/21/2022] [Accepted: 08/22/2022] [Indexed: 11/10/2022] Open
Abstract
Background Immunotherapy based on Bi-specific T Cell Engagers (TCE) represents one of the most attractive strategy to treat cancers resistant to conventional therapies. TCE are antibody-like proteins that simultaneously bind with one arm to a Tumor Associated Antigen (TAA) on cancer cells and with the other one to CD3 complex on a T-cell to form a TCR-independent immune synapse and circumvent Human Leucocyte Antigen restriction. Among them, the tribodies, such as Tb535H, a bi-specific molecule, made up of a Fab and a scFv domain both targeting 5T4 and another scFv targeting CD3, have demonstrated anti-tumor efficacy in preclinical studies. Methods Here, we generated five novel tri-specific and multi-functional tribodies, called 53X tribodies, composed of a 5T4 binding Fab arm and a CD3 binding scFv, but differently from the parental Tb535H, they contain an additional scFv derived from an antibody specific for an immune checkpoint, such as PD-1, PD-L1 or LAG-3. Results Compared with the parental Tb535H bi-specific T cell engager targeting 5T4, the novel 53X tribodies retained similar binding properties of Tb535H tribody, but showed enhanced anti-tumor potency due to the incorporation of the checkpoint inhibitory moiety. In particular, one of them, called 53L10, a tri-specific T cell engager targeting 5T4, CD3 and PD-L1, showed the most promising anti-tumor efficacy in vitro and led to complete tumor regression in vivo. Conclusions The novel tribodies have the potential to become strong and safe therapeutic drugs, allowing to reduce also the cost of production as one single molecule contains three different specificities including the anti-TAA, anti-CD3 and anti-IC binding arms. Supplementary Information The online version contains supplementary material available at 10.1186/s13046-022-02474-3.
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15
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Wang H, Zhao C, Santa-Maria CA, Emens LA, Popel AS. Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer. iScience 2022; 25:104702. [PMID: 35856032 PMCID: PMC9287616 DOI: 10.1016/j.isci.2022.104702] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/05/2022] [Accepted: 06/27/2022] [Indexed: 11/07/2022] Open
Abstract
Quantitative systems pharmacology (QSP) modeling is an emerging mechanistic computational approach that couples drug pharmacokinetics/pharmacodynamics and the course of disease progression. It has begun to play important roles in drug development for complex diseases such as cancer, including triple-negative breast cancer (TNBC). The combination of the anti-PD-L1 antibody atezolizumab and nab-paclitaxel has shown clinical activity in advanced TNBC with PD-L1-positive tumor-infiltrating immune cells. As tumor-associated macrophages (TAMs) serve as major contributors to the immuno-suppressive tumor microenvironment, we incorporated the dynamics of TAMs into our previously published QSP model to investigate their impact on cancer treatment. We show that through proper calibration, the model captures the macrophage heterogeneity in the tumor microenvironment while maintaining its predictive power of the trial results at the population level. Despite its high mechanistic complexity, the modularized QSP platform can be readily reproduced, expanded for new species of interest, and applied in clinical trial simulation. A mechanistic model of quantitative systems pharmacology in immuno-oncology Dynamics of tumor-associated macrophages are integrated into our previous work Conducting in silico clinical trials to predict clinical response to cancer therapy
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Affiliation(s)
- Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.,School of Pharmacy, Nanjing Medical University, Nanjing, Jiangsu211166, China
| | - Cesar A Santa-Maria
- Department of Oncology, the Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD21205, USA
| | - Leisha A Emens
- University of Pittsburgh Medical Center, Hillman Cancer Center, Pittsburgh, PA, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.,Department of Oncology, the Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD21205, USA
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16
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Chan JR, Allen R, Boras B, Cabal A, Damian V, Gibbons FD, Gulati A, Hosseini I, Kearns JD, Saito R, Cucurull-Sanchez L, Selimkhanov J, Stein AM, Umehara K, Wang G, Wang W, Neves-Zaph S. Current practices for QSP model assessment: an IQ consortium survey. J Pharmacokinet Pharmacodyn 2022:10.1007/s10928-022-09811-1. [PMID: 35953664 PMCID: PMC9371373 DOI: 10.1007/s10928-022-09811-1] [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: 04/28/2022] [Accepted: 06/03/2022] [Indexed: 12/02/2022]
Abstract
Quantitative Systems Pharmacology (QSP) modeling is increasingly applied in the pharmaceutical industry to influence decision making across a wide range of stages from early discovery to clinical development to post-marketing activities. Development of standards for how these models are constructed, assessed, and communicated is of active interest to the modeling community and regulators but is complicated by the wide variability in the structures and intended uses of the underlying models and the diverse expertise of QSP modelers. With this in mind, the IQ Consortium conducted a survey across the pharmaceutical/biotech industry to understand current practices for QSP modeling. This article presents the survey results and provides insights into current practices and methods used by QSP practitioners based on model type and the intended use at various stages of drug development. The survey also highlights key areas for future development including better integration with statistical methods, standardization of approaches towards virtual populations, and increased use of QSP models for late-stage clinical development and regulatory submissions.
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Affiliation(s)
- Jason R Chan
- Global PKPD and Pharmacometrics, Eli Lilly and Company, Indianapolis, IN, 46285, USA.
| | - Richard Allen
- Worldwide Research, Development and Medical, Pfizer Inc. Kendall Square, Cambridge, MA, 02139, USA
| | - Britton Boras
- Worldwide Research, Development and Medical, Pfizer Inc.,, La Jolla, CA, 92121, USA
| | | | | | | | | | | | - Jeffrey D Kearns
- Novartis Institutes for BioMedical Research, Cambridge, MA, 02139, USA
| | - Ryuta Saito
- Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Yokohama, Japan
| | | | | | - Andrew M Stein
- Pharmacometrics, Novartis Institutes of BioMedical Research, Cambridge, MA, USA
| | - Kenichi Umehara
- Roche Pharmaceutical Research and Early Development, Basel, Switzerland
| | - Guanyu Wang
- Drug Metabolism and Pharmacokinetics, Vertex Pharmaceuticals, Abingdon, Oxfordshire, UK
| | - Weirong Wang
- Clinical Pharmacology and Pharmacometrics, Janssen Research and Development, LLC, Spring House, PA, 19477, USA
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17
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Ruiz-Martinez A, Gong C, Wang H, Sové RJ, Mi H, Kimko H, Popel AS. Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model. PLoS Comput Biol 2022; 18:e1010254. [PMID: 35867773 PMCID: PMC9348712 DOI: 10.1371/journal.pcbi.1010254] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 08/03/2022] [Accepted: 05/26/2022] [Indexed: 12/23/2022] Open
Abstract
Quantitative systems pharmacology (QSP) models and spatial agent-based models (ABM) are powerful and efficient approaches for the analysis of biological systems and for clinical applications. Although QSP models are becoming essential in discovering predictive biomarkers and developing combination therapies through in silico virtual trials, they are inadequate to capture the spatial heterogeneity and randomness that characterize complex biological systems, and specifically the tumor microenvironment. Here, we extend our recently developed spatial QSP (spQSP) model to analyze tumor growth dynamics and its response to immunotherapy at different spatio-temporal scales. In the model, the tumor spatial dynamics is governed by the ABM, coupled to the QSP model, which includes the following compartments: central (blood system), tumor, tumor-draining lymph node, and peripheral (the rest of the organs and tissues). A dynamic recruitment of T cells and myeloid-derived suppressor cells (MDSC) from the QSP central compartment has been implemented as a function of the spatial distribution of cancer cells. The proposed QSP-ABM coupling methodology enables the spQSP model to perform as a coarse-grained model at the whole-tumor scale and as an agent-based model at the regions of interest (ROIs) scale. Thus, we exploit the spQSP model potential to characterize tumor growth, identify T cell hotspots, and perform qualitative and quantitative descriptions of cell density profiles at the invasive front of the tumor. Additionally, we analyze the effects of immunotherapy at both whole-tumor and ROI scales under different tumor growth and immune response conditions. A digital pathology computational analysis of triple-negative breast cancer specimens is used as a guide for modeling the immuno-architecture of the invasive front.
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Affiliation(s)
- Alvaro Ruiz-Martinez
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
| | - Chang Gong
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
| | - Richard J. Sové
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
| | - Haoyang Mi
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
| | - Holly Kimko
- Clinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Gaithersburg, Maryland, United States of America
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins, University School of Medicine, Baltimore, Maryland, United States of America
- Department of Oncology and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, United States of America
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18
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Abrams RE, Pierre K, El-Murr N, Seung E, Wu L, Luna E, Mehta R, Li J, Larabi K, Ahmed M, Pelekanou V, Yang ZY, van de Velde H, Stamatelos SK. Quantitative systems pharmacology modeling sheds light into the dose response relationship of a trispecific T cell engager in multiple myeloma. Sci Rep 2022; 12:10976. [PMID: 35768621 PMCID: PMC9243109 DOI: 10.1038/s41598-022-14726-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 06/10/2022] [Indexed: 02/08/2023] Open
Abstract
In relapsed and refractory multiple myeloma (RRMM), there are few treatment options once patients progress from the established standard of care. Several bispecific T-cell engagers (TCE) are in clinical development for multiple myeloma (MM), designed to promote T-cell activation and tumor killing by binding a T-cell receptor and a myeloma target. In this study we employ both computational and experimental tools to investigate how a novel trispecific TCE improves activation, proliferation, and cytolytic activity of T-cells against MM cells. In addition to binding CD3 on T-cells and CD38 on tumor cells, the trispecific binds CD28, which serves as both co-stimulation for T-cell activation and an additional tumor target. We have established a robust rule-based quantitative systems pharmacology (QSP) model trained against T-cell activation, cytotoxicity, and cytokine data, and used it to gain insight into the complex dose response of this drug. We predict that CD3-CD28-CD38 killing capacity increases rapidly in low dose levels, and with higher doses, killing plateaus rather than following the bell-shaped curve typical of bispecific TCEs. We further predict that dose–response curves are driven by the ability of tumor cells to form synapses with activated T-cells. When competition between cells limits tumor engagement with active T-cells, response to therapy may be diminished. We finally suggest a metric related to drug efficacy in our analysis—“effective” receptor occupancy, or the proportion of receptors engaged in synapses. Overall, this study predicts that the CD28 arm on the trispecific antibody improves efficacy, and identifies metrics to inform potency of novel TCEs.
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Affiliation(s)
- R E Abrams
- Sanofi, 55 Corporate Dr, Bridgewater, NJ, 08807, USA.,Daichi Sankyo, 211 Mt. Airy Rd., Basking Ridge, NJ, 07920, USA
| | - K Pierre
- Sanofi, 55 Corporate Dr, Bridgewater, NJ, 08807, USA.
| | - N El-Murr
- Sanofi, 13 quai Jules Guesde 94403 Cedex, VITRY-SUR-SEINE, Vitry/Alfortville, France
| | - E Seung
- Sanofi, 270 Albany St., Cambridge, MA, 02139, USA.,Modex Therapeutics, 22 Strathmore Road, Natick, MA, 01760, USA
| | - L Wu
- Sanofi, 270 Albany St., Cambridge, MA, 02139, USA.,Modex Therapeutics, 22 Strathmore Road, Natick, MA, 01760, USA
| | | | | | - J Li
- Sanofi, 55 Corporate Dr, Bridgewater, NJ, 08807, USA
| | - K Larabi
- Sanofi, 13 quai Jules Guesde 94403 Cedex, VITRY-SUR-SEINE, Vitry/Alfortville, France
| | - M Ahmed
- Sanofi, 50 Binney St., Cambridge, MA, 02142, USA
| | - V Pelekanou
- Sanofi, 50 Binney St., Cambridge, MA, 02142, USA.,Bayer Pharmaceuticals, Cambridge, MA, 02142, USA
| | - Z-Y Yang
- Sanofi, 270 Albany St., Cambridge, MA, 02139, USA.,Modex Therapeutics, 22 Strathmore Road, Natick, MA, 01760, USA
| | | | - S K Stamatelos
- Sanofi, 55 Corporate Dr, Bridgewater, NJ, 08807, USA. .,Bayer Pharmaceuticals, PH100 Bayer Boulevard, Whippany, NJ, 07981, USA.
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19
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Mi H, Ho WJ, Yarchoan M, Popel AS. Multi-Scale Spatial Analysis of the Tumor Microenvironment Reveals Features of Cabozantinib and Nivolumab Efficacy in Hepatocellular Carcinoma. Front Immunol 2022; 13:892250. [PMID: 35634309 PMCID: PMC9136005 DOI: 10.3389/fimmu.2022.892250] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 04/05/2022] [Indexed: 11/21/2022] Open
Abstract
Background Concomitant inhibition of vascular endothelial growth factor (VEGF) and programmed cell death protein 1 (PD-1) or its ligand PD-L1 is a standard of care for patients with advanced hepatocellular carcinoma (HCC), but only a minority of patients respond, and responses are usually transient. Understanding the effects of therapies on the tumor microenvironment (TME) can provide insights into mechanisms of therapeutic resistance. Methods 14 patients with HCC were treated with the combination of cabozantinib and nivolumab through the Johns Hopkins Sidney Kimmel Comprehensive Cancer Center. Among them, 12 patients (5 responders + 7 non-responders) underwent successful margin negative resection and are subjects to tissue microarray (TMA) construction containing 37 representative tumor region cores. Using the TMAs, we performed imaging mass cytometry (IMC) with a panel of 27-cell lineage and functional markers. All multiplexed images were then segmented to generate a single-cell dataset that enables (1) tumor-immune compartment analysis and (2) cell community analysis based on graph-embedding methodology. Results from these hierarchies are merged into response-associated biological process patterns. Results Image processing on 37 multiplexed-images discriminated 59,453 cells and was then clustered into 17 cell types. Compartment analysis showed that at immune-tumor boundaries from NR, PD-L1 level on tumor cells is significantly higher than remote regions; however, Granzyme B expression shows the opposite pattern. We also identify that the close proximity of CD8+ T cells to arginase 1hi (Arg1hi) macrophages, rather than CD4+ T cells, is a salient feature of the TME in non-responders. Furthermore, cell community analysis extracted 8 types of cell-cell interaction networks termed cellular communities (CCs). We observed that in non-responders, macrophage-enriched CC (MCC) and lymphocyte-enriched CC (LCC) strongly communicate with tumor CC, whereas in responders, such communications were undermined by the engagement between MCC and LCC. Conclusion These results demonstrate the feasibility of a novel application of multiplexed image analysis that is broadly applicable to quantitative analysis of pathology specimens in immuno-oncology and provides further evidence that CD163-Arg1hi macrophages may be a therapeutic target in HCC. The results also provide critical information for the development of mechanistic quantitative systems pharmacology models aimed at predicting outcomes of clinical trials.
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Affiliation(s)
- Haoyang Mi
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Won Jin Ho
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Mark Yarchoan
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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20
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Hu LF, Lan HR, Huang D, Li XM, Jin KT. Personalized Immunotherapy in Colorectal Cancers: Where Do We Stand? Front Oncol 2021; 11:769305. [PMID: 34888246 PMCID: PMC8649954 DOI: 10.3389/fonc.2021.769305] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 10/26/2021] [Indexed: 12/17/2022] Open
Abstract
Colorectal cancer (CRC) is the second leading cause of cancer death in the world. Immunotherapy using monoclonal antibodies, immune-checkpoint inhibitors, adoptive cell therapy, and cancer vaccines has raised great hopes for treating poor prognosis metastatic CRCs that are resistant to the conventional therapies. However, high inter-tumor and intra-tumor heterogeneity hinder the success of immunotherapy in CRC. Patients with a similar tumor phenotype respond differently to the same immunotherapy regimen. Mutation-based classification, molecular subtyping, and immunoscoring of CRCs facilitated the multi-aspect grouping of CRC patients and improved immunotherapy. Personalized immunotherapy using tumor-specific neoantigens provides the opportunity to consider each patient as an independent group deserving of individualized immunotherapy. In the recent decade, the development of sequencing and multi-omics techniques has helped us classify patients more precisely. The expansion of such advanced techniques along with the neoantigen-based immunotherapy could herald a new era in treating heterogeneous tumors such as CRC. In this review article, we provided the latest findings in immunotherapy of CRC. We elaborated on the heterogeneity of CRC patients as a bottleneck of CRC immunotherapy and reviewed the latest advances in personalized immunotherapy to overcome CRC heterogeneity.
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Affiliation(s)
- Li-Feng Hu
- Department of Colorectal Surgery, Shaoxing People’s Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, China
| | - Huan-Rong Lan
- Department of Breast and Thyroid Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Dong Huang
- Department of Colorectal Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Xue-Min Li
- Department of Hepatobiliary Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
| | - Ke-Tao Jin
- Department of Colorectal Surgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China
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21
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Zhang S, Gong C, Ruiz-Martinez A, Wang H, Davis-Marcisak E, Deshpande A, Popel AS, Fertig EJ. Integrating single cell sequencing with a spatial quantitative systems pharmacology model spQSP for personalized prediction of triple-negative breast cancer immunotherapy response. ACTA ACUST UNITED AC 2021; 1-2. [PMID: 34708216 PMCID: PMC8547770 DOI: 10.1016/j.immuno.2021.100002] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Response to cancer immunotherapies depends on the complex and dynamic interactions between T cell recognition and killing of cancer cells that are counteracted through immunosuppressive pathways in the tumor microenvironment. Therefore, while measurements such as tumor mutational burden provide biomarkers to select patients for immunotherapy, they neither universally predict patient response nor implicate the mechanisms that underlie immunotherapy resistance. Recent advances in single-cell RNA sequencing technology measure cellular heterogeneity within cells of an individual tumor but have yet to realize the promise of predictive oncology. In addition to data, mechanistic multiscale computational models are developed to predict treatment response. Incorporating single-cell data from tumors to parameterize these computational models provides deeper insights into prediction of clinical outcome in individual patients. Here, we integrate whole-exome sequencing and scRNA-seq data from Triple-Negative Breast Cancer patients to model neoantigen burden in tumor cells as input to a spatial Quantitative System Pharmacology model. The model comprises a four-compartmental Quantitative System Pharmacology sub-model to represent a whole patient and a spatial agent-based sub-model to represent tumor volumes at the cellular scale. We use the high-throughput single-cell data to model the role of antigen burden and heterogeneity relative to the tumor microenvironment composition on predicted immunotherapy response. We demonstrate how this integrated modeling and single-cell analysis framework can be used to relate neoantigen heterogeneity to immunotherapy treatment outcomes. Our results demonstrate feasibility of merging single-cell data to initialize cell states in multiscale computational models such as the spQSP for personalized prediction of clinical outcomes to immunotherapy.
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Affiliation(s)
- Shuming Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Chang Gong
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Alvaro Ruiz-Martinez
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Emily Davis-Marcisak
- Department of Oncology and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States.,Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Atul Deshpande
- Department of Oncology and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States.,Department of Oncology and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Elana J Fertig
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States.,Department of Oncology and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States.,Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, United States
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22
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Aghamiri SS, Amin R, Helikar T. Recent applications of quantitative systems pharmacology and machine learning models across diseases. J Pharmacokinet Pharmacodyn 2021; 49:19-37. [PMID: 34671863 PMCID: PMC8528185 DOI: 10.1007/s10928-021-09790-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/07/2021] [Indexed: 12/29/2022]
Abstract
Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.
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Affiliation(s)
- Sara Sadat Aghamiri
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Rada Amin
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.
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23
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Sancho-Araiz A, Zalba S, Garrido MJ, Berraondo P, Topp B, de Alwis D, Parra-Guillen ZP, Mangas-Sanjuan V, Trocóniz IF. Semi-Mechanistic Model for the Antitumor Response of a Combination Cocktail of Immuno-Modulators in Non-Inflamed (Cold) Tumors. Cancers (Basel) 2021; 13:cancers13205049. [PMID: 34680196 PMCID: PMC8534053 DOI: 10.3390/cancers13205049] [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: 09/14/2021] [Accepted: 10/05/2021] [Indexed: 11/30/2022] Open
Abstract
Simple Summary The clinical efficacy of immunotherapies when treating cold tumors is still low, and different treatment combinations are needed when dealing with this challenging scenario. In this work, a middle-out strategy was followed to develop a model describing the antitumor efficacy of different immune-modulator combinations, including an antigen, a toll-like receptor-3 agonist, and an immune checkpoint inhibitor in mice treated with non-inflamed tumor cells. Our results support that clinical response requires antigen-presenting cell activation and also relies on the amount of CD8 T cells and tumor resistance mechanisms present. This mathematical model is a very useful platform to evaluate different immuno-oncology combinations in both preclinical and clinical settings. Abstract Immune checkpoint inhibitors, administered as single agents, have demonstrated clinical efficacy. However, when treating cold tumors, different combination strategies are needed. This work aims to develop a semi-mechanistic model describing the antitumor efficacy of immunotherapy combinations in cold tumors. Tumor size of mice treated with TC-1/A9 non-inflamed tumors and the drug effects of an antigen, a toll-like receptor-3 agonist (PIC), and an immune checkpoint inhibitor (anti-programmed cell death 1 antibody) were modeled using Monolix and following a middle-out strategy. Tumor growth was best characterized by an exponential model with an estimated initial tumor size of 19.5 mm3 and a doubling time of 3.6 days. In the treatment groups, contrary to the lack of response observed in monotherapy, combinations including the antigen were able to induce an antitumor response. The final model successfully captured the 23% increase in the probability of cure from bi-therapy to triple-therapy. Moreover, our work supports that CD8+ T lymphocytes and resistance mechanisms are strongly related to the clinical outcome. The activation of antigen-presenting cells might be needed to achieve an antitumor response in reduced immunogenic tumors when combined with other immunotherapies. These models can be used as a platform to evaluate different immuno-oncology combinations in preclinical and clinical scenarios.
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Affiliation(s)
- Aymara Sancho-Araiz
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (A.S.-A.); (S.Z.); (M.J.G.); (Z.P.P.-G.)
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain;
| | - Sara Zalba
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (A.S.-A.); (S.Z.); (M.J.G.); (Z.P.P.-G.)
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain;
| | - María J. Garrido
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (A.S.-A.); (S.Z.); (M.J.G.); (Z.P.P.-G.)
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain;
| | - Pedro Berraondo
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain;
- Program of Immunology and Immunotherapy, CIMA Universidad de Navarra, 31008 Pamplona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), 28029 Madrid, Spain
| | - Brian Topp
- Quantitative Pharmacology and Pharmacometrics, Merck & Co., Inc., Kenilworth, NJ 07033, USA; (B.T.); (D.d.A.)
| | - Dinesh de Alwis
- Quantitative Pharmacology and Pharmacometrics, Merck & Co., Inc., Kenilworth, NJ 07033, USA; (B.T.); (D.d.A.)
| | - Zinnia P. Parra-Guillen
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (A.S.-A.); (S.Z.); (M.J.G.); (Z.P.P.-G.)
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain;
| | - Víctor Mangas-Sanjuan
- Department of Pharmacy Technology and Parasitology, Faculty of Pharmacy, University of Valencia, 46100 Valencia, Spain;
- Interuniversity Institute of Recognition Research Molecular and Technological Development, Polytechnic University of Valencia-University of Valencia, 46100 Valencia, Spain
| | - Iñaki F. Trocóniz
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, 31008 Pamplona, Spain; (A.S.-A.); (S.Z.); (M.J.G.); (Z.P.P.-G.)
- Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain;
- Correspondence:
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24
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Gong C, Ruiz-Martinez A, Kimko H, Popel AS. A Spatial Quantitative Systems Pharmacology Platform spQSP-IO for Simulations of Tumor-Immune Interactions and Effects of Checkpoint Inhibitor Immunotherapy. Cancers (Basel) 2021; 13:3751. [PMID: 34359653 PMCID: PMC8345161 DOI: 10.3390/cancers13153751] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 07/09/2021] [Accepted: 07/20/2021] [Indexed: 12/15/2022] Open
Abstract
Quantitative systems pharmacology (QSP) models have become increasingly common in fundamental mechanistic studies and drug discovery in both academic and industrial environments. With imaging techniques widely adopted and other spatial quantification of tumor such as spatial transcriptomics gaining traction, it is crucial that these data reflecting tumor spatial heterogeneity be utilized to inform the QSP models to enhance their predictive power. We developed a hybrid computational model platform, spQSP-IO, to extend QSP models of immuno-oncology with spatially resolved agent-based models (ABM), combining their powers to track whole patient-scale dynamics and recapitulate the emergent spatial heterogeneity in the tumor. Using a model of non-small-cell lung cancer developed based on this platform, we studied the role of the tumor microenvironment and cancer-immune cell interactions in tumor development and applied anti-PD-1 treatment to virtual patients and studied how the spatial distribution of cells changes during tumor growth in response to the immune checkpoint inhibition treatment. Using parameter sensitivity analysis and biomarker analysis, we are able to identify mechanisms and pretreatment measurements correlated with treatment efficacy. By incorporating spatial data that highlight both heterogeneity in tumors and variability among individual patients, spQSP-IO models can extend the QSP framework and further advance virtual clinical trials.
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Affiliation(s)
- Chang Gong
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (A.R.-M.); (A.S.P.)
| | - Alvaro Ruiz-Martinez
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (A.R.-M.); (A.S.P.)
| | - Holly Kimko
- Clinical Pharmacology & Quantitative Pharmacology, AstraZeneca, Gaithersburg, MD 20878, USA;
| | - Aleksander S. Popel
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (A.R.-M.); (A.S.P.)
- Sidney Kimmel Comprehensive Cancer Center, Department of Oncology, Johns Hopkins University, Baltimore, MD 21231, USA
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25
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Sancho-Araiz A, Mangas-Sanjuan V, Trocóniz IF. The Role of Mathematical Models in Immuno-Oncology: Challenges and Future Perspectives. Pharmaceutics 2021; 13:pharmaceutics13071016. [PMID: 34371708 PMCID: PMC8309057 DOI: 10.3390/pharmaceutics13071016] [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: 05/11/2021] [Revised: 06/24/2021] [Accepted: 06/29/2021] [Indexed: 12/12/2022] Open
Abstract
Immuno-oncology (IO) focuses on the ability of the immune system to detect and eliminate cancer cells. Since the approval of the first immune checkpoint inhibitor, immunotherapies have become a major player in oncology treatment and, in 2021, represented the highest number of approved drugs in the field. In spite of this, there is still a fraction of patients that do not respond to these therapies and develop resistance mechanisms. In this sense, mathematical models offer an opportunity to identify predictive biomarkers, optimal dosing schedules and rational combinations to maximize clinical response. This work aims to outline the main therapeutic targets in IO and to provide a description of the different mathematical approaches (top-down, middle-out, and bottom-up) integrating the cancer immunity cycle with immunotherapeutic agents in clinical scenarios. Among the different strategies, middle-out models, which combine both theoretical and evidence-based description of tumor growth and immunological cell-type dynamics, represent an optimal framework to evaluate new IO strategies.
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Affiliation(s)
- Aymara Sancho-Araiz
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, 31009 Pamplona, Spain; (A.S.-A.); (I.F.T.)
- Navarra Institute for Health Research (IdiSNA), 31009 Pamplona, Spain
| | - Victor Mangas-Sanjuan
- Department of Pharmacy and Pharmaceutical Technology and Parasitology, University of Valencia, 46100 Valencia, Spain
- Interuniversity Research Institute for Molecular Recognition and Technological Development, 46100 Valencia, Spain
- Correspondence: ; Tel.: +34-96354-3351
| | - Iñaki F. Trocóniz
- Department of Pharmaceutical Technology and Chemistry, School of Pharmacy and Nutrition, University of Navarra, 31009 Pamplona, Spain; (A.S.-A.); (I.F.T.)
- Navarra Institute for Health Research (IdiSNA), 31009 Pamplona, Spain
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26
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Bazzazi H, Shahraz A. A mechanistic systems pharmacology modeling platform to investigate the effect of PD-L1 expression heterogeneity and dynamics on the efficacy of PD-1 and PD-L1 blocking antibodies in cancer. J Theor Biol 2021; 522:110697. [PMID: 33794288 DOI: 10.1016/j.jtbi.2021.110697] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 12/14/2020] [Accepted: 03/22/2021] [Indexed: 11/19/2022]
Abstract
Tumors have developed multitude of ways to evade immune response and suppress cytotoxic T cells. Programed cell death protein 1 (PD-1) and programed cell death ligand 1 (PD-L1) are immune checkpoints that when activated, rapidly inactivate the cytolytic activity of T cells. Expression heterogeneity of PD-L1 and the surface receptor dynamics of both PD-1 and PD-L1 may be important parameters in modulating the immune response. PD-L1 is expressed on both tumor and non-tumor immune cells and this differential expression reflects different aspects of anti-tumor immunity. Here, we developed a mechanistic computational model to investigate the role of PD-1 and PD-L1 dynamics in modulating the efficacy of PD-1 and PD-L1 blocking antibodies. Our model incorporates immunological synapse restricted interaction of PD-1 and PD-L1, basal parameters for receptor dynamics, and T cell interaction with tumor and non-tumor immune cells. Simulations predict the existence of a threshold in PD-1 expression above which there is no efficacy for both anti-PD-1 and anti-PD-L1. Model also predicts that anti-tumor response is more sensitive to PD-L1 expression on non-tumor immune cells than tumor cells. New combination strategies are suggested that may enhance efficacy in resistant cases such as combining anti-PD-1 with a low dose of anti-PD-L1 or with inhibitors of PD-L1 recycling and synthesis. Another combination strategy suggested by the model is the combination of anti-PD-1 and anti-PD-L1 with enhancers of PD-L1 degradation rate. Virtual patients are then generated to test specific biomarkers of response. Intriguing predictions that emerge from the virtual patient simulations are that PD-1 blocking antibody results in higher response rate than PD-L1 blockade and that PD-L1 expression density on non-tumor immune cells rather than tumor cells is a predictor of response.
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Affiliation(s)
- Hojjat Bazzazi
- Millenium Pharmaceuticals, a wholly-owned subsidiary of Takeda Pharmaceuticals, Cambridge, MA, United States.
| | - Azar Shahraz
- Simulations Plus Inc., Lancaster, CA, United States
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27
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Ma H, Pilvankar M, Wang J, Giragossian C, Popel AS. Quantitative Systems Pharmacology Modeling of PBMC-Humanized Mouse to Facilitate Preclinical Immuno-oncology Drug Development. ACS Pharmacol Transl Sci 2020; 4:213-225. [PMID: 33615174 DOI: 10.1021/acsptsci.0c00178] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Indexed: 12/12/2022]
Abstract
Progress in immunotherapy has resulted in explosively increased new therapeutic interventions and they have shown promising results in the treatment of cancer. Animal testing is performed to provide preliminary efficacy and safety data for drugs under development prior to clinical trials. However, translational challenges remain for preclinical studies such as study design and the relevance of animal models to humans. Hence, only a small fraction of cancer patients showed response. The explosion of drug candidates and therapies makes preclinical assessment of every plausible option impossible, but it can be easily tested using Quantitative System Pharmacology (QSP) models. Here, we developed a QSP model for humanized mice. Tumor growth dynamics, T cell dynamics, cytokine release, immune checkpoint expression, and drug administration were modeled and calibrated using experimental data. Tumor growth inhibition data were used for model validation. Pharmacokinetics of T cell engager (TCE), tumor growth profile, T cell expansion in the blood and infiltration into tumor, T cell dissemination from primary tumor, cytokine release profile, and expression of additional PD-L1 induced by IFN-γ were modeled and calibrated using a variety of experimental data and showed good consistency. Mouse-specific response to T cell engager monotherapy also showed the key features of in vivo efficacy of TCE. This novel QSP model, designed for human peripheral blood mononuclear cells (PBMC) engrafted xenograft mice, incorporating the most critical components of the mouse model with key cancer and immune cells, can become an integral part of preclinical drug development.
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Affiliation(s)
- Huilin Ma
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Minu Pilvankar
- Biotherapeutics Discovery Research, Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, Connecticut 06877, United States
| | - Jun Wang
- Biotherapeutics Discovery Research, Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, Connecticut 06877, United States
| | - Craig Giragossian
- Biotherapeutics Discovery Research, Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, Connecticut 06877, United States
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States.,Department of Oncology and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland 21231, United States
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28
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Alfonso S, Jenner AL, Craig M. Translational approaches to treating dynamical diseases through in silico clinical trials. CHAOS (WOODBURY, N.Y.) 2020; 30:123128. [PMID: 33380031 DOI: 10.1063/5.0019556] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 11/20/2020] [Indexed: 06/12/2023]
Abstract
The primary goal of drug developers is to establish efficient and effective therapeutic protocols. Multifactorial pathologies, including dynamical diseases and complex disorders, can be difficult to treat, given the high degree of inter- and intra-patient variability and nonlinear physiological relationships. Quantitative approaches combining mechanistic disease modeling and computational strategies are increasingly leveraged to rationalize pre-clinical and clinical studies and to establish effective treatment strategies. The development of clinical trials has led to new computational methods that allow for large clinical data sets to be combined with pharmacokinetic and pharmacodynamic models of diseases. Here, we discuss recent progress using in silico clinical trials to explore treatments for a variety of complex diseases, ultimately demonstrating the immense utility of quantitative methods in drug development and medicine.
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Affiliation(s)
- Sofia Alfonso
- Department of Physiology, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Adrianne L Jenner
- Department of Mathematics and Statistics, Université de Montréal, Montreal, Quebec H3C 3J7, Canada
| | - Morgan Craig
- Department of Physiology, McGill University, Montreal, Quebec H3A 0G4, Canada
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