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Stribbling SM, Beach C, Ryan AJ. Orthotopic and metastatic tumour models in preclinical cancer research. Pharmacol Ther 2024; 257:108631. [PMID: 38467308 DOI: 10.1016/j.pharmthera.2024.108631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 02/27/2024] [Accepted: 03/08/2024] [Indexed: 03/13/2024]
Abstract
Mouse models of disease play a pivotal role at all stages of cancer drug development. Cell-line derived subcutaneous tumour models are predominant in early drug discovery, but there is growing recognition of the importance of the more complex orthotopic and metastatic tumour models for understanding both target biology in the correct tissue context, and the impact of the tumour microenvironment and the immune system in responses to treatment. The aim of this review is to highlight the value that orthotopic and metastatic models bring to the study of tumour biology and drug development while pointing out those models that are most likely to be encountered in the literature. Important developments in orthotopic models, such as the increasing use of early passage patient material (PDXs, organoids) and humanised mouse models are discussed, as these approaches have the potential to increase the predictive value of preclinical studies, and ultimately improve the success rate of anticancer drugs in clinical trials.
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Affiliation(s)
- Stephen M Stribbling
- Department of Chemistry, University College London, Gower Street, London WC1E 6BT, UK.
| | - Callum Beach
- Department of Oncology, University of Oxford, ORCRB, Roosevelt Drive, Oxford OX3 7DQ, UK
| | - Anderson J Ryan
- Department of Oncology, University of Oxford, ORCRB, Roosevelt Drive, Oxford OX3 7DQ, UK; Fast Biopharma, Aston Rowant, Oxfordshire, OX49 5SW, UK.
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2
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Liu Z, Duan X, Yun Y, Li S, Feng Z, Zhan J, Liu R, Li Y, Zhang J. Photoactivatable Aptamer-CRISPR Nanodevice Enables Precise Profiling of Interferon-Gamma Release in Humanized Mice. ACS NANO 2024; 18:3826-3838. [PMID: 38241471 DOI: 10.1021/acsnano.3c12499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2024]
Abstract
Real-time dynamic imaging of immunoactivation-related cytokines is crucial for evaluating the efficacy of immune checkpoint blockade therapy and optimizing the treatment regimen. We introduce herein a spatiotemporally controlled nanodevice that allows in situ photoactivated imaging of interferon-gamma (IFN-γ) secretion from T cells in vitro and in vivo. The nanodevice is constructed by rational engineering of an aptamer-embedded, UV-cleavable PC-DNA probe and further integration with upconversion nanoparticles- and CRISPR-Cas12a-enhanced fluorescence systems. Using human peripheral blood mononuclear cells (PBMC)-engrafted mouse models, this nanodevice allows for the quantitative imaging of endogenous IFN-γ and its intratumoral dynamics responding to antiprogrammed cell death receptor 1 (anti-PD-1) therapy. This study thus provides a toolbox for boosting the sensitivity and precision of cytokine imaging during immune checkpoint blockade therapy, enlightening research toward imaging-guided tumor therapy.
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Affiliation(s)
- Zheng Liu
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing 210023, China
| | - Xiang Duan
- State Key Laboratory of Pharmaceutical Biotechnology, National Resource Center for Mutant Mice, MOE Key Laboratory of Model Animals for Disease Study, MOE Engineering Research Center of Protein and Peptide Medicine, Chemistry and Biomedicine Innovation Center, Jiangsu Key Laboratory of Molecular Medicine, Model Animal Research Center, Medical School of Nanjing University, Nanjing 210061, China
| | - Yangfang Yun
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing 210023, China
| | - Siqi Li
- State Key Laboratory of Pharmaceutical Biotechnology, National Resource Center for Mutant Mice, MOE Key Laboratory of Model Animals for Disease Study, MOE Engineering Research Center of Protein and Peptide Medicine, Chemistry and Biomedicine Innovation Center, Jiangsu Key Laboratory of Molecular Medicine, Model Animal Research Center, Medical School of Nanjing University, Nanjing 210061, China
| | - Zhiyuan Feng
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing 210023, China
| | - Jiayin Zhan
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing 210023, China
| | - Ran Liu
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing 210023, China
| | - Yan Li
- State Key Laboratory of Pharmaceutical Biotechnology, National Resource Center for Mutant Mice, MOE Key Laboratory of Model Animals for Disease Study, MOE Engineering Research Center of Protein and Peptide Medicine, Chemistry and Biomedicine Innovation Center, Jiangsu Key Laboratory of Molecular Medicine, Model Animal Research Center, Medical School of Nanjing University, Nanjing 210061, China
| | - Jingjing Zhang
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing 210023, China
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Singh FA, Afzal N, Smithline SJ, Thalhauser CJ. Assessing the performance of QSP models: biology as the driver for validation. J Pharmacokinet Pharmacodyn 2023:10.1007/s10928-023-09871-x. [PMID: 37386340 DOI: 10.1007/s10928-023-09871-x] [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: 11/28/2022] [Accepted: 06/15/2023] [Indexed: 07/01/2023]
Abstract
Validation of a quantitative model is a critical step in establishing confidence in the model's suitability for whatever analysis it was designed. While processes for validation are well-established in the statistical sciences, the field of quantitative systems pharmacology (QSP) has taken a more piecemeal approach to defining and demonstrating validation. Although classical statistical methods can be used in a QSP context, proper validation of a mechanistic systems model requires a more nuanced approach to what precisely is being validated, and what role said validation plays in the larger context of the analysis. In this review, we summarize current thoughts of QSP validation in the scientific community, contrast the aims of statistical validation from several contexts (including inference, pharmacometrics analysis, and machine learning) with the challenges faced in QSP analysis, and use examples from published QSP models to define different stages or levels of validation, any of which may be sufficient depending on the context at hand.
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Affiliation(s)
- Fulya Akpinar Singh
- Genmab US, Inc., 777 Scudders Mill Rd Bldg 2 4th Floor, Plainsboro, NJ, 08536, USA
| | - Nasrin Afzal
- Genmab US, Inc., 777 Scudders Mill Rd Bldg 2 4th Floor, Plainsboro, NJ, 08536, USA
| | - Shepard J Smithline
- Genmab US, Inc., 777 Scudders Mill Rd Bldg 2 4th Floor, Plainsboro, NJ, 08536, USA
| | - Craig J Thalhauser
- Genmab US, Inc., 777 Scudders Mill Rd Bldg 2 4th Floor, Plainsboro, NJ, 08536, USA.
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4
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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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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] [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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Yoneyama T, Kim MS, Piatkov K, Wang H, Zhu AZX. Leveraging a physiologically-based quantitative translational modeling platform for designing B cell maturation antigen-targeting bispecific T cell engagers for treatment of multiple myeloma. PLoS Comput Biol 2022; 18:e1009715. [PMID: 35839267 PMCID: PMC9328551 DOI: 10.1371/journal.pcbi.1009715] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 07/27/2022] [Accepted: 06/24/2022] [Indexed: 11/18/2022] Open
Abstract
Bispecific T cell engagers (TCEs) are an emerging anti-cancer modality that redirects cytotoxic T cells to tumor cells expressing tumor-associated antigens (TAAs), thereby forming immune synapses to exert anti-tumor effects. Designing pharmacokinetically acceptable TCEs and optimizing their size presents a considerable protein engineering challenge, particularly given the complexity of intercellular bridging between T cells and tumor cells. Therefore, a physiologically-relevant and clinically-verified computational modeling framework is of crucial importance to understand the protein engineering trade-offs. In this study, we developed a quantitative, physiologically-based computational framework to predict immune synapse formation for a variety of molecular formats of TCEs in tumor tissues. Our model incorporates a molecular size-dependent biodistribution using the two-pore theory, extravasation of T cells and hematologic cancer cells, mechanistic bispecific intercellular binding of TCEs, and competitive inhibitory interactions by shed targets. The biodistribution of TCEs was verified by positron emission tomography imaging of [89Zr]AMG211 (a carcinoembryonic antigen-targeting TCE) in patients. Parameter sensitivity analyses indicated that immune synapse formation was highly sensitive to TAA expression, degree of target shedding, and binding selectivity to tumor cell surface TAAs over shed targets. Notably, the model suggested a “sweet spot” for TCEs’ CD3 binding affinity, which balanced the trapping of TCEs in T-cell-rich organs. The final model simulations indicated that the number of immune synapses is similar (~55/tumor cell) between two distinct clinical stage B cell maturation antigen (BCMA)-targeting TCEs, PF-06863135 in an IgG format and AMG420 in a BiTE format, at their respective efficacious doses in multiple myeloma patients. This result demonstrates the applicability of the developed computational modeling framework to molecular design optimization and clinical benchmarking for TCEs, thus suggesting that this framework can be applied to other targets to provide a quantitative means to facilitate model-informed best-in-class TCE discovery and development.
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Affiliation(s)
- Tomoki Yoneyama
- Quantitative Solutions, Takeda Pharmaceuticals International Co., Cambridge, Massachusetts, United States of America
- * E-mail:
| | - Mi-Sook Kim
- Global Drug Metabolism and Pharmacokinetics, Takeda Pharmaceuticals International Co., Cambridge, Massachusetts, United States of America
| | - Konstantin Piatkov
- Global Drug Metabolism and Pharmacokinetics, Takeda Pharmaceuticals International Co., Cambridge, Massachusetts, United States of America
| | - Haiqing Wang
- Global Drug Metabolism and Pharmacokinetics, Takeda Pharmaceuticals International Co., Cambridge, Massachusetts, United States of America
| | - Andy Z. X. Zhu
- Quantitative Solutions, Takeda Pharmaceuticals International Co., Cambridge, Massachusetts, United States of America
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Qiao W, Lin L, Young C, Narula J, Hua F, Matteson A, Hooper A, Gruenbaum L, Betts A. Quantitative systems pharmacology modeling provides insight into inter‐mouse variability of
Anti‐CTLA4
response. CPT Pharmacometrics Syst Pharmacol 2022; 11:880-893. [PMID: 35439371 PMCID: PMC9286718 DOI: 10.1002/psp4.12800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 03/03/2022] [Accepted: 03/30/2022] [Indexed: 11/12/2022] Open
Abstract
Clinical responses of immuno‐oncology therapies are highly variable among patients. Similar response variability has been observed in syngeneic mouse models. Understanding of the variability in the mouse models may shed light on patient variability. Using a murine anti‐CTLA4 antibody as a case study, we developed a quantitative systems pharmacology model to capture the molecular interactions of the antibody and relevant cellular interactions that lead to tumor cell killing. Nonlinear mixed effect modeling was incorporated to capture the inter‐animal variability of tumor growth profiles in response to anti‐CTLA4 treatment. The results suggested that intratumoral CD8+ T cell kinetics and tumor proliferation rate were the main drivers of the variability. In addition, simulations indicated that nonresponsive mice to anti‐CTLA4 treatment could be converted to responders by increasing the number of intratumoral CD8+ T cells. The model provides a mechanistic starting point for translation of CTLA4 inhibitors from syngeneic mice to the clinic.
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Affiliation(s)
- Wenlian Qiao
- BioMedicine Design, World Research, Development and Medical Pfizer, Inc. Cambridge Massachusetts USA
| | - Lin Lin
- Formerly, Applied BioMath, Inc. Concord Massachusetts USA
| | - Carissa Young
- Formerly, Applied BioMath, Inc. Concord Massachusetts USA
| | - Jatin Narula
- BioMedicine Design, World Research, Development and Medical Pfizer, Inc. Cambridge Massachusetts USA
| | - Fei Hua
- Applied BioMath, Inc. Concord Massachusetts USA
| | | | - Andrea Hooper
- Formerly, Oncology Research Unit, World Research, Development and Medical, Pfizer, Inc. Pearl River New York USA
| | - Lore Gruenbaum
- Formerly, Applied BioMath, Inc. Concord Massachusetts USA
| | - Alison Betts
- Applied BioMath, Inc. Concord Massachusetts USA
- Formerly, BioMedicine Design, World Research, Development and Medical Pfizer, Inc. Cambridge Massachusetts USA
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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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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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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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Wang H, Ma H, Sové RJ, Emens LA, Popel AS. Quantitative systems pharmacology model predictions for efficacy of atezolizumab and nab-paclitaxel in triple-negative breast cancer. J Immunother Cancer 2021; 9:jitc-2020-002100. [PMID: 33579739 PMCID: PMC7883871 DOI: 10.1136/jitc-2020-002100] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/03/2021] [Indexed: 12/18/2022] Open
Abstract
Background Immune checkpoint blockade therapy has clearly shown clinical activity in patients with triple-negative breast cancer, but less than half of the patients benefit from the treatments. While a number of ongoing clinical trials are investigating different combinations of checkpoint inhibitors and chemotherapeutic agents, predictive biomarkers that identify patients most likely to benefit remains one of the major challenges. Here we present a modular quantitative systems pharmacology (QSP) platform for immuno-oncology that incorporates detailed mechanisms of immune–cancer cell interactions to make efficacy predictions and identify predictive biomarkers for treatments using atezolizumab and nab-paclitaxel. Methods A QSP model was developed based on published data of triple-negative breast cancer. With the model, we generated a virtual patient cohort to conduct in silico virtual clinical trials and make retrospective analyses of the pivotal IMpassion130 trial that led to the accelerated approval of atezolizumab and nab-paclitaxel for patients with programmed death-ligand 1 (PD-L1) positive triple-negative breast cancer. Available data from clinical trials were used for model calibration and validation. Results With the calibrated virtual patient cohort based on clinical data from the placebo comparator arm of the IMpassion130 trial, we made efficacy predictions and identified potential predictive biomarkers for the experimental arm of the trial using the proposed QSP model. The model predictions are consistent with clinically reported efficacy endpoints and correlated immune biomarkers. We further performed a series of virtual clinical trials to compare different doses and schedules of the two drugs for simulated therapeutic optimization. Conclusions This study provides a QSP platform, which can be used to generate virtual patient cohorts and conduct virtual clinical trials. Our findings demonstrate its potential for making efficacy predictions for immunotherapies and chemotherapies, identifying predictive biomarkers, and guiding future clinical trial designs.
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Affiliation(s)
- Hanwen Wang
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Huilin Ma
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Richard J Sové
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Leisha A Emens
- Department of Medicine, University of Pittsburgh Medical Center, Hillman Cancer Center, Pittsburgh, Pennsylvania, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.,Department of Oncology, Johns Hopkins Medicine Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, USA
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