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Yuan Y, Li J, Chen J, Han L, Wang L, Yue Y, Liu J, Zhang B, Yuan Y, Wu M, Bian Y, Xie Y, Zhu J. Characterization of a novel T cell-engaging bispecific antibody for elimination of L1CAM-positive tumors. Biomed Pharmacother 2024; 174:116565. [PMID: 38603888 DOI: 10.1016/j.biopha.2024.116565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 03/09/2024] [Accepted: 04/04/2024] [Indexed: 04/13/2024] Open
Abstract
Neural cell adhesion molecule L1 (L1CAM) is a cell-surface glycoprotein involved in cancer occurrence and migration. Up to today, L1CAM-targeted therapy appeared limited efficacy in clinical trials although quite a few attempts by monoclonal antibody (mAb) or chimeric antigen receptor T-cell therapy (CAR-T) have been reported. Therefore, the development of new effective therapies targeting L1CAM is highly desirable. It has been demonstrated that T cell-engaging bispecific antibody (TCE) plays an effective role in cancer immunotherapy by redirecting the cytotoxic activity of CD3+ T cells to tumor cells, resulting in tumor cell death. In this study, we designed and characterized a novel bispecific antibody (CE7-TCE) based on the IgG-(L)-ScFv format, which targets L1CAM and CD3 simultaneously. In vitro, CE7-TCE induced specific killing of L1CAM-positive tumor cells through T cells. In vivo, CE7-TCE inhibited tumor growth in human peripheral blood mononuclear cell/tumor cell co-grafting models. To overcome the adaptive immune resistance (AIR) that impairs the efficacy of TCEs, we conducted a combination therapy of CE7-TCE with Pembrolizumab (anti-PD1 mAb), which enhanced the anti-tumor activity of CE7-TCE. Our results confirmed the feasibility of using L1CAM as a TCE target for the treatment of solid tumors and revealed the therapeutic potential of CE7-TCE combined with immune checkpoint inhibitors.
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Affiliation(s)
- Yuan Yuan
- Engineering Research Center of Cell & Therapeutical Antibody, Ministry of Education, China, School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Junyan Li
- Engineering Research Center of Cell & Therapeutical Antibody, Ministry of Education, China, School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jie Chen
- Engineering Research Center of Cell & Therapeutical Antibody, Ministry of Education, China, School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lei Han
- Jecho Institute, Co. Ltd, Shanghai 200241, China
| | - Lei Wang
- Engineering Research Center of Cell & Therapeutical Antibody, Ministry of Education, China, School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yali Yue
- Engineering Research Center of Cell & Therapeutical Antibody, Ministry of Education, China, School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Junjun Liu
- Engineering Research Center of Cell & Therapeutical Antibody, Ministry of Education, China, School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Baohong Zhang
- Engineering Research Center of Cell & Therapeutical Antibody, Ministry of Education, China, School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yunsheng Yuan
- Engineering Research Center of Cell & Therapeutical Antibody, Ministry of Education, China, School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Mingyuan Wu
- Engineering Research Center of Cell & Therapeutical Antibody, Ministry of Education, China, School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yanlin Bian
- Engineering Research Center of Cell & Therapeutical Antibody, Ministry of Education, China, School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yueqing Xie
- Jecho Institute, Co. Ltd, Shanghai 200241, China
| | - Jianwei Zhu
- Engineering Research Center of Cell & Therapeutical Antibody, Ministry of Education, China, School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China; Jecho Institute, Co. Ltd, Shanghai 200241, China.
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Logghe T, van Zwol E, Immordino B, Van den Cruys K, Peeters M, Giovannetti E, Bogers J. Hyperthermia in Combination with Emerging Targeted and Immunotherapies as a New Approach in Cancer Treatment. Cancers (Basel) 2024; 16:505. [PMID: 38339258 PMCID: PMC10854776 DOI: 10.3390/cancers16030505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/10/2024] [Accepted: 01/19/2024] [Indexed: 02/12/2024] Open
Abstract
Despite significant advancements in the development of novel therapies, cancer continues to stand as a prominent global cause of death. In many cases, the cornerstone of standard-of-care therapy consists of chemotherapy (CT), radiotherapy (RT), or a combination of both. Notably, hyperthermia (HT), which has been in clinical use in the last four decades, has proven to enhance the effectiveness of CT and RT, owing to its recognized potency as a sensitizer. Furthermore, HT exerts effects on all steps of the cancer-immunity cycle and exerts a significant impact on key oncogenic pathways. Most recently, there has been a noticeable expansion of cancer research related to treatment options involving immunotherapy (IT) and targeted therapy (TT), a trend also visible in the research and development pipelines of pharmaceutical companies. However, the potential results arising from the combination of these innovative therapeutic approaches with HT remain largely unexplored. Therefore, this review aims to explore the oncology pipelines of major pharmaceutical companies, with the primary objective of identifying the principal targets of forthcoming therapies that have the potential to be advantageous for patients by specifically targeting molecular pathways involved in HT. The ultimate goal of this review is to pave the way for future research initiatives and clinical trials that harness the synergy between emerging IT and TT medications when used in conjunction with HT.
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Affiliation(s)
- Tine Logghe
- Elmedix NV, Dellingstraat 34/1, 2800 Mechelen, Belgium
| | - Eke van Zwol
- Elmedix NV, Dellingstraat 34/1, 2800 Mechelen, Belgium
| | - Benoît Immordino
- Cancer Pharmacology Lab, Fondazione Pisana per la Scienza, San Giuliano, 56017 Pisa, Italy
- Institute of Life Sciences, Sant’Anna School of Advanced Studies, 56127 Pisa, Italy
| | | | - Marc Peeters
- Department of Oncology, Antwerp University Hospital, 2650 Edegem, Belgium
| | - Elisa Giovannetti
- Cancer Pharmacology Lab, Fondazione Pisana per la Scienza, San Giuliano, 56017 Pisa, Italy
- Department of Medical Oncology, Amsterdam UMC, Location Vrije Universiteit, Cancer Center Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Johannes Bogers
- Elmedix NV, Dellingstraat 34/1, 2800 Mechelen, Belgium
- Laboratory of Cell Biology and Histology, Faculty of Medicine and Health Sciences, University of Antwerp, 2610 Antwerp, Belgium
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Qi T, Liao X, Cao Y. Development of bispecific T cell engagers: harnessing quantitative systems pharmacology. Trends Pharmacol Sci 2023; 44:880-890. [PMID: 37852906 PMCID: PMC10843027 DOI: 10.1016/j.tips.2023.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 09/22/2023] [Accepted: 09/25/2023] [Indexed: 10/20/2023]
Abstract
Bispecific T cell engagers (bsTCEs) have emerged as a promising class of cancer immunotherapy. Several bsTCEs have achieved marketing approval; dozens more are under clinical investigation. However, the clinical development of bsTCEs remains rife with challenges, including nuanced pharmacology, limited translatability of preclinical findings, frequent on-target toxicity, and convoluted dosing regimens. In this opinion article we present a distinct perspective on how quantitative systems pharmacology (QSP) can serve as a powerful tool for overcoming these obstacles. Recent advances in QSP modeling have empowered developers of bsTCEs to gain a deeper understanding of their context-dependent pharmacology, bridge gaps in experimental data, guide first-in-human (FIH) dose selection, design dosing regimens with expanded therapeutic windows, and improve long-term treatment outcomes. We use recent case studies to exemplify the potential of QSP techniques to support future bsTCE development.
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Affiliation(s)
- Timothy Qi
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xiaozhi Liao
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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4
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Weddell J. Mechanistically modeling peripheral cytokine dynamics following bispecific dosing in solid tumors. CPT Pharmacometrics Syst Pharmacol 2023; 12:1726-1737. [PMID: 36710368 PMCID: PMC10681545 DOI: 10.1002/psp4.12928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/11/2023] [Accepted: 01/18/2023] [Indexed: 01/31/2023] Open
Abstract
Bispecific antibodies exhibit proven clinical benefit, and many bispecifics are currently in clinical development for oncology. Cytokine release syndrome (CRS) is a common clinical adverse effect observed following CD3-based bispecific dosing. However, the pathophysiology of CRS is not fully understood, and no computational model mechanistically describing clinical cytokine dynamics following bispecific dosing in solid tumors exists. Here, a quantitative systems pharmacology (QSP) model describing peripheral clinical cytokine dynamics following bispecific dosing in solid tumors is presented. Using tebentafusp as a case study, a CD3-bispecific approved for uveal melanoma, the model successfully captures the dynamics of five cytokines. The QSP model was shown to predict observed phenomena, such as cytokine maximum concentration suppression using step-up dosing regimens and the importance of on-target off-tumor binding toward CRS and toxicity. Furthermore, the QSP model provides rationale for these biological phenomena based on dynamics of immune cell activation and desensitization in tumors and healthy tissues. Overall, the QSP model structure presented here serves as a basis to infer cytokine dynamics for other CD3-based bispecifics or tumor types by altering model parameters to capture the scenario of interest, supporting applications including dose selection, candidate nomination, and disease area selection.
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Affiliation(s)
- Jared Weddell
- Clinical Pharmacology and Exploratory DevelopmentAstellas Pharma Global Development Inc.NorthbrookIllinoisUSA
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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] [What about the content of this article? (0)] [Affiliation(s)] [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. Sci Adv 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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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9
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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 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] [What about the content of this article? (0)] [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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10
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Flowers D, Bassen D, Kapitanov GI, Marcantonio D, Burke JM, Apgar JF, Betts A, Hua F. A next generation mathematical model for the in vitro to clinical translation of T-cell engagers. J Pharmacokinet Pharmacodyn 2023; 50:215-227. [PMID: 36790614 DOI: 10.1007/s10928-023-09846-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 02/01/2023] [Indexed: 02/16/2023]
Abstract
T-cell engager (TCE) molecules activate the immune system and direct it to kill tumor cells. The key mechanism of action of TCEs is to crosslink CD3 on T cells and tumor associated antigens (TAAs) on tumor cells. The formation of this trimolecular complex (i.e. trimer) mimics the immune synapse, leading to therapeutic-dependent T-cell activation and killing of tumor cells. Computational models supporting TCE development must predict trimer formation accurately. Here, we present a next-generation two-step binding mathematical model for TCEs to describe trimer formation. Specifically, we propose to model the second binding step with trans-avidity and as a two-dimensional (2D) process where the reactants are modeled as the cell-surface density. Compared to the 3D binding model where the reactants are described in terms of concentration, the 2D model predicts less sensitivity of trimer formation to varying cell densities, which better matches changes in EC50 from in vitro cytotoxicity assay data with varying E:T ratios. In addition, when translating in vitro cytotoxicity data to predict in vivo active clinical dose for blinatumomab, the choice of model leads to a notable difference in dose prediction. The dose predicted by the 2D model aligns better with the approved clinical dose and the prediction is robust under variations in the in vitro to in vivo translation assumptions. In conclusion, the 2D model with trans-avidity to describe trimer formation is an improved approach for TCEs and is likely to produce more accurate predictions to support TCE development.
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Affiliation(s)
| | | | | | | | | | | | | | - Fei Hua
- Applied BioMath, Concord, MA, USA.
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11
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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12
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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: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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13
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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: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [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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14
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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15
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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: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [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
- *Correspondence: Haoyang Mi, ; Mark Yarchoan,
| | - 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
- *Correspondence: Haoyang Mi, ; Mark Yarchoan,
| | - 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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16
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Sharifi E, Bigham A, Yousefiasl S, Trovato M, Ghomi M, Esmaeili Y, Samadi P, Zarrabi A, Ashrafizadeh M, Sharifi S, Sartorius R, Dabbagh Moghaddam F, Maleki A, Song H, Agarwal T, Maiti TK, Nikfarjam N, Burvill C, Mattoli V, Raucci MG, Zheng K, Boccaccini AR, Ambrosio L, Makvandi P. Mesoporous Bioactive Glasses in Cancer Diagnosis and Therapy: Stimuli-Responsive, Toxicity, Immunogenicity, and Clinical Translation. Adv Sci (Weinh) 2022; 9:e2102678. [PMID: 34796680 PMCID: PMC8805580 DOI: 10.1002/advs.202102678] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 10/03/2021] [Indexed: 05/10/2023]
Abstract
Cancer is one of the top life-threatening dangers to the human survival, accounting for over 10 million deaths per year. Bioactive glasses have developed dramatically since their discovery 50 years ago, with applications that include therapeutics as well as diagnostics. A new system within the bioactive glass family, mesoporous bioactive glasses (MBGs), has evolved into a multifunctional platform, thanks to MBGs easy-to-functionalize nature and tailorable textural properties-surface area, pore size, and pore volume. Although MBGs have yet to meet their potential in tumor treatment and imaging in practice, recently research has shed light on the distinguished MBGs capabilities as promising theranostic systems for cancer imaging and therapy. This review presents research progress in the field of MBG applications in cancer diagnosis and therapy, including synthesis of MBGs, mechanistic overview of MBGs application in tumor diagnosis and drug monitoring, applications of MBGs in cancer therapy ( particularly, targeted delivery and stimuli-responsive nanoplatforms), and immunological profile of MBG-based nanodevices in reference to the development of novel cancer therapeutics.
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Affiliation(s)
- Esmaeel Sharifi
- Department of Tissue Engineering and BiomaterialsSchool of Advanced Medical Sciences and TechnologiesHamadan University of Medical SciencesHamadan6517838736Iran
- Institute of PolymersComposites and BiomaterialsNational Research Council (IPCB‐CNR)Naples80125Italy
| | - Ashkan Bigham
- Institute of PolymersComposites and BiomaterialsNational Research Council (IPCB‐CNR)Naples80125Italy
| | - Satar Yousefiasl
- School of DentistryHamadan University of Medical SciencesHamadan6517838736Iran
| | - Maria Trovato
- Institute of Biochemistry and Cell Biology (IBBC)National Research Council (CNR)Naples80131Italy
| | - Matineh Ghomi
- Chemistry DepartmentFaculty of ScienceShahid Chamran University of AhvazAhvaz61537‐53843Iran
- School of ChemistryDamghan UniversityDamghan36716‐41167Iran
| | - Yasaman Esmaeili
- Biosensor Research CenterSchool of Advanced Technologies in MedicineIsfahan University of Medical SciencesIsfahan8174673461Iran
| | - Pouria Samadi
- Research Center for Molecular MedicineHamadan University of Medical SciencesHamadan6517838736Iran
| | - Ali Zarrabi
- Sabanci University Nanotechnology Research and Application Center (SUNUM)TuzlaIstanbul34956Turkey
- Department of Biomedical EngineeringFaculty of Engineering and Natural SciencesIstinye UniversitySariyerIstanbul34396Turkey
| | - Milad Ashrafizadeh
- Faculty of Engineering and Natural SciencesSabanci UniversityOrta Mahalle, Üniversite Caddesi No. 27, OrhanlıTuzlaIstanbul34956Turkey
| | - Shokrollah Sharifi
- Department of Mechanical EngineeringUniversity of MelbourneMelbourne3010Australia
| | - Rossella Sartorius
- Institute of Biochemistry and Cell Biology (IBBC)National Research Council (CNR)Naples80131Italy
| | | | - Aziz Maleki
- Department of Pharmaceutical NanotechnologySchool of PharmacyZanjan University of Medical SciencesZanjan45139‐56184Iran
| | - Hao Song
- Australian Institute for Bioengineering and NanotechnologyThe University of QueenslandBrisbane4072Australia
| | - Tarun Agarwal
- Department of BiotechnologyIndian Institute of TechnologyKharagpur721302India
| | - Tapas Kumar Maiti
- Department of BiotechnologyIndian Institute of TechnologyKharagpur721302India
| | - Nasser Nikfarjam
- Department of ChemistryInstitute for Advanced Studies in Basic Sciences (IASBS)Zanjan45137‐66731Iran
| | - Colin Burvill
- Department of Mechanical EngineeringUniversity of MelbourneMelbourne3010Australia
| | - Virgilio Mattoli
- Istituto Italiano di TecnologiaCentre for Materials InterfacePontederaPisa56025Italy
| | - Maria Grazia Raucci
- Institute of PolymersComposites and BiomaterialsNational Research Council (IPCB‐CNR)Naples80125Italy
| | - Kai Zheng
- Istituto Italiano di TecnologiaCentre for Materials InterfacePontederaPisa56025Italy
| | - Aldo R. Boccaccini
- Institute of BiomaterialsUniversity of Erlangen‐NurembergErlangen91058Germany
| | - Luigi Ambrosio
- Institute of PolymersComposites and BiomaterialsNational Research Council (IPCB‐CNR)Naples80125Italy
| | - Pooyan Makvandi
- Chemistry DepartmentFaculty of ScienceShahid Chamran University of AhvazAhvaz6153753843Iran
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17
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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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18
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Ma H, Wang H, Sové RJ, Wang J, Giragossian C, Popel AS. Combination therapy with T cell engager and PD-L1 blockade enhances the antitumor potency of T cells as predicted by a QSP model. J Immunother Cancer 2021; 8:jitc-2020-001141. [PMID: 32859743 PMCID: PMC7454244 DOI: 10.1136/jitc-2020-001141] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2020] [Indexed: 12/12/2022] Open
Abstract
Background T cells have been recognized as core effectors for cancer immunotherapy. How to restore the anti-tumor ability of suppressed T cells or improve the lethality of cytotoxic T cells has become the main focus in immunotherapy. Bispecific antibodies, especially bispecific T cell engagers (TCEs), have shown their unique ability to enhance the patient’s immune response to tumors by stimulating T cell activation and cytokine production in an MHC-independent manner. Antibodies targeting the checkpoint inhibitory molecules such as programmed cell death protein 1 (PD-1), PD-ligand 1 (PD-L1) and cytotoxic lymphocyte activated antigen 4 are able to restore the cytotoxic effect of immune suppressed T cells and have also shown durable responses in patients with malignancies. However, both types have their own limitations in treating certain cancers. Preclinical and clinical results have emphasized the potential of combining these two antibodies to improve tumor response and patients’ survival. However, the selection and evaluation of combination partners clinically is a costly endeavor. In addition, despite advances made in immunotherapy, there are subsets of patients who are non-responders, and reliable biomarkers for different immunotherapies are urgently needed to improve the ability to prospectively predict patients’ response and improve clinical study design. Therefore, mathematical and computational models are essential to optimize patient benefit, and guide combination approaches with lower cost and in a faster manner. Method In this study, we continued to extend the quantitative systems pharmacology (QSP) model we developed for a bispecific TCE to explore efficacy of combination therapy with an anti-PD-L1 monoclonal antibody in patients with colorectal cancer. Results Patient-specific response to TCE monotherapy, anti-PD-L1 monotherapy and the combination therapy were predicted using this model according to each patient’s individual characteristics. Conclusions Individual biomarkers for TCE monotherapy, anti-PD-L1 monotherapy and their combination have been determined based on the QSP model. Best treatment options for specific patients could be suggested based on their own characteristics to improve clinical trial efficiency. The model can be further used to assess plausible combination strategies for different TCEs and immune checkpoint inhibitors in different types of cancer.
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Affiliation(s)
- Huilin Ma
- 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
| | - Richard J Sové
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jun Wang
- Biotherapeutics Discovery Research, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA
| | - Craig Giragossian
- Biotherapeutics Discovery Research, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Oncology and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, Maryland, USA
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19
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Belmontes B, Sawant DV, Zhong W, Tan H, Kaul A, Aeffner F, O'Brien SA, Chun M, Noubade R, Eng J, Ma H, Muenz M, Li P, Alba BM, Thomas M, Cook K, Wang X, DeVoss J, Egen JG, Nolan-Stevaux O. Immunotherapy combinations overcome resistance to bispecific T cell engager treatment in T cell-cold solid tumors. Sci Transl Med 2021; 13:13/608/eabd1524. [PMID: 34433637 DOI: 10.1126/scitranslmed.abd1524] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 07/28/2021] [Indexed: 12/15/2022]
Abstract
Therapeutic approaches are needed to promote T cell-mediated destruction of poorly immunogenic, "cold" tumors typically associated with minimal response to immune checkpoint blockade (ICB) therapy. Bispecific T cell engager (BiTE) molecules induce redirected lysis of cancer cells by polyclonal T cells and have demonstrated promising clinical activity against solid tumors in some patients. However, little is understood about the key factors that govern clinical responses to these therapies. Using an immunocompetent mouse model expressing a humanized CD3ε chain (huCD3e mice) and BiTE molecules directed against mouse CD19, mouse CLDN18.2, or human EPCAM antigens, we investigated the pharmacokinetic and pharmacodynamic parameters and immune correlates associated with BiTE efficacy across multiple syngeneic solid-tumor models. These studies demonstrated that pretreatment tumor-associated T cell density is a critical determinant of response to BiTE therapy, identified CD8+ T cells as important targets and mediators of BiTE activity, and revealed an antagonistic role for CD4+ T cells in BiTE efficacy. We also identified therapeutic combinations, including ICB and 4-1BB agonism, that synergized with BiTE treatment in poorly T cell-infiltrated, immunotherapy-refractory tumors. In these models, BiTE efficacy was dependent on local expansion of tumor-associated CD8+ T cells, rather than their recruitment from circulation. Our findings highlight the relative contributions of baseline T cell infiltration, local T cell proliferation, and peripheral T cell trafficking for BiTE molecule-mediated efficacy, identify combination strategies capable of overcoming resistance to BiTE therapy, and have clinical relevance for the development of BiTE and other T cell engager therapies.
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Affiliation(s)
- Brian Belmontes
- Amgen Research, Thousand Oaks, CA 91320, USA.,Inflammation and Oncology Therapeutic Area, Amgen, Thousand Oaks, CA 91320, USA
| | - Deepali V Sawant
- Amgen Research, Thousand Oaks, CA 91320, USA.,Inflammation and Oncology Therapeutic Area, Amgen, South San Francisco, CA 94080, USA
| | - Wendy Zhong
- Amgen Research, Thousand Oaks, CA 91320, USA.,Inflammation and Oncology Therapeutic Area, Amgen, South San Francisco, CA 94080, USA
| | - Hong Tan
- Amgen Research, Thousand Oaks, CA 91320, USA.,Inflammation and Oncology Therapeutic Area, Amgen, Thousand Oaks, CA 91320, USA
| | - Anupurna Kaul
- Amgen Research, Thousand Oaks, CA 91320, USA.,Inflammation and Oncology Therapeutic Area, Amgen, South San Francisco, CA 94080, USA
| | - Famke Aeffner
- Amgen Research, Thousand Oaks, CA 91320, USA.,Translational Safety and Bioanalytical Sciences, Amgen, South San Francisco, CA 94080, USA
| | - Sarah A O'Brien
- Amgen Research, Thousand Oaks, CA 91320, USA.,Inflammation and Oncology Therapeutic Area, Amgen, South San Francisco, CA 94080, USA
| | - Matthew Chun
- Amgen Research, Thousand Oaks, CA 91320, USA.,Inflammation and Oncology Therapeutic Area, Amgen, South San Francisco, CA 94080, USA
| | - Rajkumar Noubade
- Amgen Research, Thousand Oaks, CA 91320, USA.,Inflammation and Oncology Therapeutic Area, Amgen, South San Francisco, CA 94080, USA
| | - Jason Eng
- Amgen Research, Thousand Oaks, CA 91320, USA.,Inflammation and Oncology Therapeutic Area, Amgen, South San Francisco, CA 94080, USA
| | - Hayley Ma
- Amgen Research, Thousand Oaks, CA 91320, USA.,Inflammation and Oncology Therapeutic Area, Amgen, Thousand Oaks, CA 91320, USA
| | - Markus Muenz
- Amgen Research, Thousand Oaks, CA 91320, USA.,Amgen Research GmbH, Munich 81477, Germany
| | - Peng Li
- Amgen Research, Thousand Oaks, CA 91320, USA.,Therapeutic Discovery, Amgen, South San Francisco, CA 94080, USA
| | - Benjamin M Alba
- Amgen Research, Thousand Oaks, CA 91320, USA.,Therapeutic Discovery, Amgen, South San Francisco, CA 94080, USA
| | - Melissa Thomas
- Amgen Research, Thousand Oaks, CA 91320, USA.,Therapeutic Discovery, Amgen, South San Francisco, CA 94080, USA
| | - Kevin Cook
- Amgen Research, Thousand Oaks, CA 91320, USA.,Pharmacokinetics and Drug Metabolism, Amgen, South San Francisco, CA 94080, USA
| | - Xiaoting Wang
- Amgen Research, Thousand Oaks, CA 91320, USA.,Translational Safety and Bioanalytical Sciences, Amgen, South San Francisco, CA 94080, USA
| | - Jason DeVoss
- Amgen Research, Thousand Oaks, CA 91320, USA.,Inflammation and Oncology Therapeutic Area, Amgen, South San Francisco, CA 94080, USA
| | - Jackson G Egen
- Amgen Research, Thousand Oaks, CA 91320, USA. .,Inflammation and Oncology Therapeutic Area, Amgen, South San Francisco, CA 94080, USA
| | - Olivier Nolan-Stevaux
- Amgen Research, Thousand Oaks, CA 91320, USA. .,Inflammation and Oncology Therapeutic Area, Amgen, South San Francisco, CA 94080, USA
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20
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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 DOI: 10.3390/cancers13153751] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [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
Simple Summary Mathematical and computational models, such as quantitative systems pharmacology (QSP) models, are becoming a popular tool in drug discovery. The advancement of imaging techniques creates a unique opportunity to further expand these models and enhance their predictive power by incorporating characteristics of individual patients embedded in the image data obtained from tissue samples. The aim of this study is to develop a platform which combines the strength of QSP models and spatially resolved agent-based models (ABM), and create a model of cancer development in the context of anti-cancer immunity and immune checkpoint inhibition therapy. The model can be applied in virtual clinical trials and biomarker discovery to help refine trial design. 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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21
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Haber L, Olson K, Kelly MP, Crawford A, DiLillo DJ, Tavaré R, Ullman E, Mao S, Canova L, Sineshchekova O, Finney J, Pawashe A, Patel S, McKay R, Rizvi S, Damko E, Chiu D, Vazzana K, Ram P, Mohrs K, D'Orvilliers A, Xiao J, Makonnen S, Hickey C, Arnold C, Giurleo J, Chen YP, Thwaites C, Dudgeon D, Bray K, Rafique A, Huang T, Delfino F, Hermann A, Kirshner JR, Retter MW, Babb R, MacDonald D, Chen G, Olson WC, Thurston G, Davis S, Lin JC, Smith E. Generation of T-cell-redirecting bispecific antibodies with differentiated profiles of cytokine release and biodistribution by CD3 affinity tuning. Sci Rep 2021; 11:14397. [PMID: 34257348 DOI: 10.1038/s41598-021-93842-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 06/30/2021] [Indexed: 01/07/2023] Open
Abstract
T-cell-redirecting bispecific antibodies have emerged as a new class of therapeutic agents designed to simultaneously bind to T cells via CD3 and to tumor cells via tumor-cell-specific antigens (TSA), inducing T-cell-mediated killing of tumor cells. The promising preclinical and clinical efficacy of TSAxCD3 antibodies is often accompanied by toxicities such as cytokine release syndrome due to T-cell activation. How the efficacy and toxicity profile of the TSAxCD3 bispecific antibodies depends on the binding affinity to CD3 remains unclear. Here, we evaluate bispecific antibodies that were engineered to have a range of CD3 affinities, while retaining the same binding affinity for the selected tumor antigen. These agents were tested for their ability to kill tumor cells in vitro, and their biodistribution, serum half-life, and anti-tumor activity in vivo. Remarkably, by altering the binding affinity for CD3 alone, we can generate bispecific antibodies that maintain potent killing of TSA + tumor cells but display differential patterns of cytokine release, pharmacokinetics, and biodistribution. Therefore, tuning CD3 affinity is a promising method to improve the therapeutic index of T-cell-engaging bispecific antibodies.
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22
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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23
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Mi H, Gong C, Sulam J, Fertig EJ, Szalay AS, Jaffee EM, Stearns V, Emens LA, Cimino-Mathews AM, Popel AS. Digital Pathology Analysis Quantifies Spatial Heterogeneity of CD3, CD4, CD8, CD20, and FoxP3 Immune Markers in Triple-Negative Breast Cancer. Front Physiol 2020; 11:583333. [PMID: 33192595 PMCID: PMC7604437 DOI: 10.3389/fphys.2020.583333] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 09/24/2020] [Indexed: 12/17/2022] Open
Abstract
Overwhelming evidence has shown the significant role of the tumor microenvironment (TME) in governing the triple-negative breast cancer (TNBC) progression. Digital pathology can provide key information about the spatial heterogeneity within the TME using image analysis and spatial statistics. These analyses have been applied to CD8+ T cells, but quantitative analyses of other important markers and their correlations are limited. In this study, a digital pathology computational workflow is formulated for characterizing the spatial distributions of five immune markers (CD3, CD4, CD8, CD20, and FoxP3) and then the functionality is tested on whole slide images from patients with TNBC. The workflow is initiated by digital image processing to extract and colocalize immune marker-labeled cells and then convert this information to point patterns. Afterward invasive front (IF), central tumor (CT), and normal tissue (N) are characterized. For each region, we examine the intra-tumoral heterogeneity. The workflow is then repeated for all specimens to capture inter-tumoral heterogeneity. In this study, both intra- and inter-tumoral heterogeneities are observed for all five markers across all specimens. Among all regions, IF tends to have higher densities of immune cells and overall larger variations in spatial model fitting parameters and higher density in cell clusters and hotspots compared to CT and N. Results suggest a distinct role of IF in the tumor immuno-architecture. Though the sample size is limited in the study, the computational workflow could be readily reproduced and scaled due to its automatic nature. Importantly, the value of the workflow also lies in its potential to be linked to treatment outcomes and identification of predictive biomarkers for responders/non-responders, and its application to parameterization and validation of computational immuno-oncology models.
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Affiliation(s)
- Haoyang Mi
- 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
| | - Jeremias Sulam
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States.,Johns Hopkins Mathematical Institute for Data Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Elana J Fertig
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States.,Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United States
| | - Alexander S Szalay
- Henry A. Rowland Department of Physics and Astronomy, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, United States.,Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Elizabeth M Jaffee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United States.,The Bloomberg∼Kimmel Institute for Cancer Immunotherapy, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Vered Stearns
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United States
| | - Leisha A Emens
- Department of Medicine/Hematology-Oncology, Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Ashley M Cimino-Mathews
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United States.,Department of Pathology, 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, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United States
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24
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Sové RJ, Jafarnejad M, Zhao C, Wang H, Ma H, Popel AS. QSP-IO: A Quantitative Systems Pharmacology Toolbox for Mechanistic Multiscale Modeling for Immuno-Oncology Applications. CPT Pharmacometrics Syst Pharmacol 2020; 9:484-497. [PMID: 32618119 PMCID: PMC7499194 DOI: 10.1002/psp4.12546] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 07/17/2020] [Indexed: 12/25/2022]
Abstract
Immunotherapy has shown great potential in the treatment of cancer; however, only a fraction of patients respond to treatment, and many experience autoimmune‐related side effects. The pharmaceutical industry has relied on mathematical models to study the behavior of candidate drugs and more recently, complex, whole‐body, quantitative systems pharmacology (QSP) models have become increasingly popular for discovery and development. QSP modeling has the potential to discover novel predictive biomarkers as well as test the efficacy of treatment plans and combination therapies through virtual clinical trials. In this work, we present a QSP modeling platform for immuno‐oncology (IO) that incorporates detailed mechanisms for important immune interactions. This modular platform allows for the construction of QSP models of IO with varying degrees of complexity based on the research questions. Finally, we demonstrate the use of the platform through two example applications of immune checkpoint therapy.
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Affiliation(s)
- Richard J Sové
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mohammad Jafarnejad
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Chen Zhao
- 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
| | - Huilin Ma
- Department of Biomedical Engineering, 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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