From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling

Wong, C. H., Siah, K. W. & Lo, A. W. Estimation of clinical trial success rates and related parameters. Biostatistics 20, 273–286 (2019).Article 
PubMed 

Google Scholar 
Madabushi, R., Seo, P., Zhao, L., Tegenge, M. & Zhu, H. Review: role of model-informed drug development approaches in the lifecycle of drug development and regulatory decision-making. Pharm. Res. 39, 1669–1680 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Azer, K. et al. History and future perspectives on the discipline of quantitative systems pharmacology modeling and its applications. Front. Physiol. 12, 637999 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Bai, J. P. F. et al. Quantitative systems pharmacology: landscape analysis of regulatory submissions to the US Food and Drug Administration. CPT Pharm. Syst. Pharma 10, 1479–1484 (2021).Article 
CAS 

Google Scholar 
Holford, N. H. G., Kimko, H. C., Monteleone, J. P. R. & Peck, C. C. Simulation of clinical trials. Annu. Rev. Pharmacol. Toxicol. 40, 209–234 (2000).Article 
CAS 
PubMed 

Google Scholar 
Brown, L. V., Gaffney, E. A., Wagg, J. & Coles, M. C. Applications of mechanistic modelling to clinical and experimental immunology: an emerging technology to accelerate immunotherapeutic discovery and development. Clin. Exp. Immunol. 193, 284–292 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sorger, P. K. et al. Quantitative and systems pharmacology in the post‐genomic era: new approaches to discovering drugs and understanding therapeutic mechanisms. An NIH White Paper by the QSP Workshop Group (2011).Michelson, S. The impact of systems biology and biosimulation on drug discovery and development. Mol. BioSyst. 2, 288 (2006).Article 
CAS 
PubMed 

Google Scholar 
Chelliah, V. et al. Quantitative systems pharmacology approaches for immuno‐oncology: adding virtual patients to the development paradigm. Clin. Pharma Therapeutics 109, 605–618 (2021).Article 

Google Scholar 
Surendran, A. et al. Approaches to generating virtual patient cohorts with applications in oncology. in Personalized Medicine Meets Artificial Intelligence (eds. Cesario, A., D’Oria, M., Auffray, C. & Scambia, G.) 97–119 (Springer International Publishing, Cham, 2023). https://doi.org/10.1007/978-3-031-32614-1_8.Craig, M., Gevertz, J. L., Kareva, I. & Wilkie, K. P. A practical guide for the generation of model-based virtual clinical trials. Front. Syst. Biol. 3, 1174647 (2023).Article 

Google Scholar 
Hormuth, D. A. et al. Mechanism-based modeling of tumor growth and treatment response constrained by multiparametric imaging data. JCO Clin. Cancer Inf. 1–10 https://doi.org/10.1200/CCI.18.00055 (2019).Lazarou, G. et al. Integration of omics data sources to inform mechanistic modeling of immune-oncology therapies: a tutorial for clinical pharmacologists. Clin. Pharm. Ther. 107, 858–870 (2020).Article 

Google Scholar 
Arulraj, T. et al. Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology. Brief. Bioinf. 25, bbae131 (2024).Article 

Google Scholar 
Stahlberg, E. A. et al. Exploring approaches for predictive cancer patient digital twins: opportunities for collaboration and innovation. Front. Digit. Health 4, 1007784 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Cheng, Y. et al. Virtual populations for quantitative systems pharmacology models. Methods Mol. Biol. 2486, 129–179 (2022).Article 
CAS 
PubMed 

Google Scholar 
Mellman, I., Chen, D. S., Powles, T. & Turley, S. J. The cancer-immunity cycle: Indication, genotype, and immunotype. Immunity 56, 2188–2205 (2023).Article 
CAS 
PubMed 

Google Scholar 
Chen, D. S. & Mellman, I. Oncology meets immunology: the cancer-immunity cycle. Immunity 39, 1–10 (2013).Article 
PubMed 

Google Scholar 
Niederer, S. A. et al. Creation and application of virtual patient cohorts of heart models. Philos. Trans. A Math. Phys. Eng. Sci. 378, 20190558 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sové, R. J. et al. QSP‐IO: a quantitative systems pharmacology toolbox for mechanistic multiscale modeling for immuno‐oncology applications. Clin. Pharmacol. Ther. 9, 484–497 (2020).Article 

Google Scholar 
Jafarnejad, M. et al. A computational model of neoadjuvant PD-1 inhibition in non-small cell lung cancer. AAPS J. 21, 79 (2019).Article 
PubMed 

Google Scholar 
Ma, H. et al. A quantitative systems pharmacology model of T cell engager applied to solid tumor. AAPS J. 22, 85 (2020).Article 
CAS 
PubMed 

Google Scholar 
Ma, H. et al. 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 8, e001141 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, H. et al. Conducting a virtual clinical trial in HER2-negative breast cancer using a quantitative systems pharmacology model with an epigenetic modulator and immune checkpoint inhibitors. Front. Bioeng. Biotechnol. 8, 141 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, H., Ma, H., Sové, R. J., Emens, L. A. & Popel, A. S. Quantitative systems pharmacology model predictions for efficacy of atezolizumab and nab-paclitaxel in triple-negative breast cancer. J. Immunother. Cancer 9, e002100 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Anbari, S. et al. Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager. Front. Pharmacol. 14, 1163432 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, H., Zhao, C., Santa-Maria, C. A., Emens, L. A. & Popel, A. S. Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer. iScience 25, 104702 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, H., Arulraj, T., Kimko, H. & Popel, A. S. Generating immunogenomic data-guided virtual patients using a QSP model to predict response of advanced NSCLC to PD-L1 inhibition. npj Precis. Onc. 7, 55 (2023).Article 
CAS 

Google Scholar 
Ippolito, A. et al. Eliciting the antitumor immune response with a conditionally activated PD‐L1 targeting antibody analyzed with a quantitative systems pharmacology model. CPT Pharmacom & Syst. Pharma psp4.13060 https://doi.org/10.1002/psp4.13060 (2023).Arulraj, T., Wang, H., Emens, L. A., Santa-Maria, C. A. & Popel, A. S. A transcriptome-informed QSP model of metastatic triple-negative breast cancer identifies predictive biomarkers for PD-1 inhibition. Sci. Adv. 9, eadg0289 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gong, C., Ruiz-Martinez, A., Kimko, H. & Popel, A. S. A spatial quantitative systems pharmacology platform spQSP-IO for simulations of tumor-immune interactions and effects of checkpoint Inhibitor Immunotherapy. Cancers (Basel) 13, 3751 (2021).Article 
CAS 
PubMed 

Google Scholar 
Ruiz-Martinez, A. et al. 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. 18, e1010254 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nikfar, M., Mi, H., Gong, C., Kimko, H. & Popel, A. S. Quantifying intratumoral heterogeneity and immunoarchitecture generated in-silico by a spatial quantitative systems pharmacology model. Cancers 15, 2750 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, S. et al. Integration of clinical trial spatial multi-omics analysis and virtual clinical trials enables immunotherapy response prediction and biomarker discovery. Cancer Res. https://doi.org/10.1158/0008-5472.CAN-24-0943 (2024).Allen, R. J., Rieger, T. R. & Musante, C. J. Efficient generation and selection of virtual populations in quantitative systems pharmacology models. CPT Pharmacomet. Syst. Pharm. 5, 140–146 (2016).Article 
CAS 

Google Scholar 
Rieger, T. R. et al. Improving the generation and selection of virtual populations in quantitative systems pharmacology models. Prog. Biophys. Mol. Biol. 139, 15–22 (2018).Article 
CAS 
PubMed 

Google Scholar 
Mi, H. et al. Spatial and compositional biomarkers in tumor microenvironment predicts clinical outcomes in triple-negative breast cancer. bioRxiv 2023.12.18.572234 https://doi.org/10.1101/2023.12.18.572234 (2023).Cimino-Mathews, A. et al. PD-L1 (B7-H1) expression and the immune tumor microenvironment in primary and metastatic breast carcinomas. Hum. Pathol. 47, 52–63 (2016).Article 
CAS 
PubMed 

Google Scholar 
Shiao, S. L. et al. Single-cell and spatial profiling identify three response trajectories to pembrolizumab and radiation therapy in triple negative breast cancer. Cancer Cell 42, 70–84.e8 (2024).Article 
CAS 
PubMed 

Google Scholar 
Jenner, A. L., Cassidy, T., Belaid, K., Bourgeois-Daigneault, M.-C. & Craig, M. In silico trials predict that combination strategies for enhancing vesicular stomatitis oncolytic virus are determined by tumor aggressivity. J. Immunother. Cancer 9, e001387 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Cardinal, O. et al. Establishing combination PAC‐1 and TRAIL regimens for treating ovarian cancer based on patient‐specific pharmacokinetic profiles using in silico clinical trials. Comp. Sys Onco 2, e1035 (2022).Article 

Google Scholar 
Limpert, E., Stahel, W. A. & Abbt, M. Log-normal distributions across the sciences: keys and clues. BioScience 51, 341 (2001).Article 

Google Scholar 
Sender, R. et al. The total mass, number, and distribution of immune cells in the human body. Proc. Natl Acad. Sci. USA 120, e2308511120 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Autissier, P., Soulas, C., Burdo, T. H. & Williams, K. C. Evaluation of a 12-color flow cytometry panel to study lymphocyte, monocyte, and dendritic cell subsets in humans. Cytom. A 77, 410–419 (2010).Article 

Google Scholar 
Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830.e14 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Garcia-Recio, S. et al. Multiomics in primary and metastatic breast tumors from the AURORA US network finds microenvironment and epigenetic drivers of metastasis. Nat. Cancer 4, 128–147 (2023).CAS 
PubMed 

Google Scholar 
Rozenblatt-Rosen, O. et al. The Human Tumor Atlas Network: charting tumor transitions across space and time at single-cell resolution. Cell 181, 236–249 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Eddy, J. A. et al. CRI iAtlas: an interactive portal for immuno-oncology research. F1000Res 9, 1028 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Siegel, M. B. et al. Integrated RNA and DNA sequencing reveals early drivers of metastatic breast cancer. J. Clin. Investig. 128, 1371–1383 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Racle, J., De Jonge, K., Baumgaertner, P., Speiser, D. E. & Gfeller, D. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. eLife 6, e26476 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Finotello, F. et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Med. 11, 34 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Venkatesh, K. P., Raza, M. M. & Kvedar, J. C. Health digital twins as tools for precision medicine: considerations for computation, implementation, and regulation. npj Digit. Med. 5, 150 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Laubenbacher, R., Mehrad, B., Shmulevich, I. & Trayanova, N. Digital twins in medicine. Nat. Comput Sci. 4, 184–191 (2024).Article 
CAS 
PubMed 

Google Scholar 
Katsoulakis, E. et al. Digital twins for health: a scoping review. npj Digit. Med. 7, 77 (2024).Article 
PubMed 
PubMed Central 

Google Scholar 
Moingeon, P., Chenel, M., Rousseau, C., Voisin, E. & Guedj, M. Virtual patients, digital twins and causal disease models: Paving the ground for in silico clinical trials. Drug Discov. Today 28, 103605 (2023).Article 
CAS 
PubMed 

Google Scholar 
Vallée, A. Digital twin for healthcare systems. Front. Digit. Health 5, 1253050 (2023).Article 
PubMed 
PubMed Central 

Google Scholar 
Wright, L. & Davidson, S. How to tell the difference between a model and a digital twin. Adv. Model. Simul. Eng. Sci. 7, 13 (2020).Article 

Google Scholar 
An, G. & Cockrell, C. Drug development digital twins for drug discovery, testing and repurposing: a schema for requirements and development. Front. Syst. Biol. 2, 928387 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Laubenbacher, R. et al. Building digital twins of the human immune system: toward a roadmap. npj Digit. Med. 5, 64 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, C. et al. MRI-based digital models forecast patient-specific treatment responses to neoadjuvant chemotherapy in triple-negative breast cancer. Cancer Res. 82, 3394–3404 (2022).Article 
CAS 
PubMed 

Google Scholar 
Board on Mathematical Sciences and Analytics et al. Opportunities and Challenges for Digital Twins in Biomedical Research: Proceedings of a Workshop-in Brief. 26922 (National Academies Press, Washington, D.C, 2023). https://doi.org/10.17226/26922.Lorenzo, G. et al. Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data. Annu. Rev. Biomed. Eng. https://doi.org/10.1146/annurev-bioeng-081623-025834 (2024).Jarrett, A. M. et al. Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nat. Protoc. 16, 5309–5338 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Committee on Foundational Research Gaps and Future Directions for Digital Twins et al. Foundational Research Gaps and Future Directions for Digital Twins. 26894 (National Academies Press, Washington, D.C, 2024). https://doi.org/10.17226/26894.Alber, M. et al. Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. npj Digit. Med. 2, 115 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Susilo, M. E. et al. Systems‐based digital twins to help characterize clinical dose–response and propose predictive biomarkers in a Phase I study of bispecific antibody, mosunetuzumab, in NHL. Clinical Translational Sci cts. 13501 https://doi.org/10.1111/cts.13501 (2023).Tivay, A., Kramer, G. C. & Hahn, J.-O. Virtual patient generation using physiological models through a compressed latent parameterization. in 2020 American Control Conference (ACC) 1335–1340 (IEEE, Denver, CO, USA, 2020). https://doi.org/10.23919/ACC45564.2020.9147298.Sun, T., He, X. & Li, Z. Digital twin in healthcare: Recent updates and challenges. Digital Health 9, 205520762211496 (2023).Article 

Google Scholar 

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