Sunghyun Hong
Kyemyung Park- 1Department of Biomedical Engineering, College of Information and Biotechnology, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
- 2Graduate School of Health Science and Technology, College of Information and Biotechnology, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
Immunotherapies using new modalities, including antibody-based drugs, nanoparticle-delivered drugs, and adoptive cell therapy, have become major treatment options for immune-related diseases such as cancer, autoimmune diseases, and infections. Although data characterizing individual patients’ pharmacological responses, immune statuses, and clinical outcomes become increasingly available, predicting individual patients’ immunotherapeutic responses for developing and deploying optimal immunotherapies remains challenging. Here, we propose “multi-physiology modeling” of the immune system that integrates omics-based and dynamic systems modeling-based systems immunology and pharmacometrics modeling on top of basic and clinical immunology. The multi-physiology modeling approach aims to integrate different physiological systems to realistically simulate the multi-scale and complex interactions of the immune system under intervention by immunotherapeutic agents for predictive immunotherapies tailored to individual patients. This will accelerate not only our understanding of basic immunology related to immune-related diseases but also the efficiency and accuracy of clinical immunotherapeutics in the era of precision immunotherapy.
1 Introduction
Throughout the last decade, new therapeutic modalities collectively called immunotherapies have emerged as major medical practices for curing or preventing immune-related diseases such as cancer, autoimmunity, and infection (1–3). Although immunotherapies have shown great potential to cure such diseases, the lack of reliable predictive ability for individual patients’ therapeutic responses still needs to be overcome (4). Personalized and precision medicine aims to predictive therapies that proactively adjust treatment plans by predicting individual patients’ responses or side effects to treatment before or during the treatment. This will enable the delivery of an optimal drug or a combination of drugs to appropriate patients at precise dosages and timings (5–7). Implementing this promising framework in immunotherapy requires the accurate characterization of the pharmacologic behaviors of immunotherapeutic agents, the baseline and therapy-induced changes of immune statuses, and the resultant clinical outcomes in detail for individual patients. Recent advances in new omics technologies, data science, and computational science have made it possible to work with biological and clinical data at a higher resolution than ever before. All this information should be transformed into prediction models of therapeutic responses tailored to individual patients’ personalized course of treatment (8). As personalized and precision medicine in immunotherapy becomes a near reality, more patients would likely benefit from immunotherapy (9).
To advance toward this promise, the prediction models should simultaneously describe quantitative pharmacometrics behaviors of immunotherapeutic agents and intricate immune behaviors while addressing inter-individual heterogeneities in such behaviors in a single framework. However, achieving such a framework has been staggering mainly due to separate pursuits for these aspects by experts from respective fields, needing more communication across those communities. For instance, immune behaviors are coordinated via sophisticated networks of interactions between numerous cellular and molecular components. These immune networks are intertwined with feedback and feedforward loops across scales spanning from intracellular and cellular to the organismal levels, resulting in nonlinear behavior that contributes to the lack of predictability (10–14). Although so-called systems biological approaches tackle such a complexity of the immune system, a considerable dichotomy between omics-based and dynamic systems modeling-based approaches hinders a realistic description of the immune system as prediction models. Omics data-driven analyses using statistical or machine learning approaches effectively uncover patterns directly from existing high-throughput datasets. However, purely data-driven predictions remain inherently limited by the availability and completeness of data, as they rely on interpolation within the bounds of observed clinical scenarios from which data are obtained. In contrast, dynamical systems modeling approaches integrate mechanistic immunological knowledge that potentially enables predictions even in clinically unexplored contexts through their capacity for extrapolation beyond existing data. However, mechanistic modeling is limited by its tendency to describe the system rather simplistically. From a different route, population pharmacometrics, including pharmacokinetics(PK) and pharmacodynamics(PD) modeling, utilizes mathematical modeling to provide quantitative information for dose-concentration-efficacy/toxicity relationships and, therefore, is instrumental in drug development, clinical trial design, and treatment strategies (15–17). Quantitative systems pharmacology (QSP) has been extending its boundary to integrate more biological pictures related to drug response (18–20). However, due to its origin in modeling the system as well-mixed compartments using ordinary differential equations (ODE), what QSP promises remains limited in capturing realistic immune behaviors, such as the heterogeneity of single cells along the spatial and phenotypic axes.
Here, to overcome existing limitations for establishing personalized and precision immunotherapy based on prediction models, we propose an overarching umbrella, “multi-physiology modeling” of the immune system as a common goal, toward which collective efforts are needed to concretize this conceptual framework. This framework should encompass population pharmacometrics and its extension to QSP, omics-driven and dynamical systems modeling-driven systems biology, and basic and clinical immunology on equal footing. We hope to overcome prejudices residing in each field via close communication across fields to identify impending problems to be solved to achieve the multi-physiology modeling of the immune system. In the following sections, we review each of the relevant fields and discuss their limitations. Then, we introduce a conceptual sketch of the multi-physiology modeling of the immune system, followed by a discussion on its promises.
2 Currently available immunotherapeutic modalities
Immunotherapeutic modalities directly targeting immune system components include antibody-based drugs, nanoparticle-delivered drugs such as mRNA vaccines, and adoptive cell therapies (1, 3, 21, 22). Antibody-based drugs utilize monoclonal antibodies designed to bind to target proteins on immune cells, allowing for precise control of the immune response. These antibodies can enhance antitumor activity against cancer or reduce excessive immune responses in autoimmune diseases. One significant application of monoclonal antibodies is as checkpoint inhibitors targeting immune checkpoints such as PD-1/PD-L1 and CTLA-4 to reinvigorate T cell cytotoxicity against cancer cells (23–25). Additionally, monoclonal antibodies treat chronic inflammatory diseases by targeting cytokines (26). Nanoparticle-based delivery systems can directly modulate immune system behavior by intracellular targeting (27). This approach has revolutionized vaccination, as demonstrated by the rapid development and high efficacy of COVID-19 vaccines (28, 29). These vaccines use lipid nanoparticles to deliver mRNA into cells, translating it into viral proteins that stimulate an immune response without causing disease. Nanoparticles protect mRNA from degradation and facilitate its delivery to target cells, ensuring efficient uptake and protein production (27–29). This technology holds promise for treating various diseases, including cancer and genetic disorders, by enabling precise delivery of therapeutic mRNA to specific tissues (30, 31). Adoptive cell therapy manipulates patients’ immune cells to improve the treatment of diseases. This therapy involves the isolation of immune cells, such as T-cells or natural killer (NK) cells, from a patient, engineering or multiplying them to boost their disease-fighting abilities, and reintroducing them into the patient (32–34). For example, in CAR-T cell therapy, T-cells are altered to express chimeric antigen receptors to target cancer cells (34–36). Adoptive cell therapy is not limited to cancer treatment. It is also being investigated for autoimmune diseases and infectious diseases. For instance, regulatory T-cells (Tregs) can be expanded to suppress excessive immune responses in autoimmune conditions (37, 38). These immunotherapies can treat previously intractable diseases such as cancer and autoimmune diseases and respond promptly to emerging pandemics. However, although various options for immunotherapies are available, they vary in efficacy between individuals, and it is difficult to prescribe the optimal dosage (39–41). In this regard, the related and optimized pharmacometrics modeling is essential.
3 PK/PD modeling in new emerging therapeutic modalities and its limitations
The PK/PD models have provided a robust quantitative basis for assessing the drug’s pharmacometric properties (15, 16). The PK model describes the drug’s absorption, distribution, metabolism, and excretion (ADME) and changes in drug concentration over time. The PD model explains the physiological or pharmacological responses induced by the drug concentration in the body. Furthermore, to capture inter-individual variabilities and their correlates, such as age, gender, or genetics, nonlinear mixed-effect modeling (NLME) is used. NLME includes fixed and random-effect parameters. Fixed-effects parameters represent the tendencies across the entire population. Random-effect parameters account for individual variations of fixed-effect parameters and are further modeled to be linked to covariates (17). The PK/PD models are constructed as simplistic representations, treating bodies or organs as homogeneous compartments analogous to well-mixed containers. These models could describe the quantitative pharmacologic behavior of antibody-based drugs (42–46), nanoparticle-delivery-based therapies (including mRNA vaccines) (47–50), and adoptive cell therapies (7, 51–55). However, such a simplistic way of describing the system is unsuitable for incorporating complex immunological processes, thereby rendering immunological complexity linked to modern immunotherapies not fully captured by existing PK/PD models.
A newly emerging field, quantitative systems pharmacology (QSP), has addressed some challenges by incorporating more mechanistic mathematical immune system models into pharmacometric models. Significant efforts have been made in compiling existing mathematical models of immune behaviors in various disease contexts, suggesting a new direction of incorporating newly uncovered immune features from new data types, and applying those models in accelerating drug development and target identification that grows with the vast combinatorial search space of combination therapies (56–58). For example, Arulraj et al. (59)demonstrated that a QSP model of triple-negative breast cancer augmented with bulk tumor data could be utilized to perform in silico (virtual) clinical trials and identify unrecognized biomarkers linked to therapeutic outcomes of anti-PD-1 therapy. There are also similar endeavors in adaptive cell therapy and mRNA vaccination in the QSP framework (49, 54, 60).
Although QSP foresees a future of model-informed drug development and personalized and precision immunotherapy, those employing the QSP framework still need more detailed descriptions of the immune system. For instance, the recent literature on immune diseases reveals highly heterogeneous single cells dispersed throughout the space with complex interactions among those (61). Moreover, immune behavior tends to be driven not by immune cells with major phenotypes but by the minorities of those heterogeneously dispersed in cellular phenotypic space (62, 63). Therefore, the inherent language of QSP, employing the picture of the immune system with merely “more” compartmentalization using ODE, may not be suitable. Hence, the continuing effort of the current practice of QSP may not achieve what it promises. To this end, we previously demonstrated that hybrid modeling capturing the multiscale nature with a continuum of phenotypic space among even the same cell type can give rise to non-intuitive immune behavior for establishing or breaking immune homeostasis (10, 62, 64).
Taken together, the difficulty of predictively modeling immunotherapeutic responses is a multifaceted problem rooted in the complex nature of the immune system and the insufficiency of reliable biomarkers due to the sparse characterization of the system (65–68). To address this, we need more comprehensive immune profiling together with methodologies to transform such profiling into prediction models that capture complex immune behaviors.
4 Dichotomic systems immunological approaches and their reconciliation needed
Systems immunology has emerged as a field that simultaneously considers many molecular and cellular constituents of the immune system quantitatively to provide holistic and predictive views of how the immune system operates (62, 69–71). Systems immunology possesses a dichotomy of being based on either high-throughput omics data or dynamical systems modeling.
Single-cell and spatial omics technologies have become a routine driven by technological advancements and the increasing need to comprehensively understand cellular heterogeneities and functions and their relations to immune regulation (72–74). Single-cell RNA sequencing (scRNA-seq) profiles transcriptomes at the single-cell level, which provides granular insights into cell types, states, and their roles in various immunological processes (75–78). In addition, to capture additional layers of cellular functions and regulatory mechanisms, researchers have developed methods to profile proteomes, epigenomes, and spatial information in single cells (73, 79–82). Multi-omics approaches provide a more holistic view of cellular phenotypes, combining the strengths of each modality to reveal more profound insights into cell biology. For example, CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by sequencing) allows simultaneous measurement of mRNA and surface protein expression in the same cells, bridging the gap between gene expression and functional protein data (83, 84). Moreover, integrating scRNA-seq with ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) has opened new avenues for understanding the upstream regulatory landscapes, such as enhancers and promoters that control gene expressions (85, 86). Spatial omics is another critical development in this field, preserving the spatial context of cells within tissues. This technology enables researchers to study how cells are organized and interact within their native microenvironments (87–89).
Dynamic systems modeling-based systems immunology utilizes many types of mathematical modeling to provide unique and nonintuitive insights into immune dynamics (70, 90–93). One primary type is the ordinary differential equation (ODE) model, which describes the interactions between immune cells, pathogens, and signaling molecules over time at cellular or molecular population levels. Such models can capture the time-dependent rates of changes in the quantities (the numbers or densities of cells or molecules) associated with each component (94–96). For example, ODE models can describe temporal changes in population sizes of immune cells and pathogens based on their growth, death rates, and interactions among them (97–99). Partial differential equations (PDE) are another essential modeling tool for the immune system, well suited to modeling spatial changes in the immune system over time. For example, a PDE model can describe the interaction of immune cells and pathogens as infection spreads within a tissue. The model can represent the spread of pathogens and the subsequent response of immune cells as the temporal evolution of spatial distributions of cells, pathogens, and/or signaling molecules’ concentrations (100, 101). Beyond these deterministic methods, there are also methods to capture the inherent uncertainty and variability of living phenomena. Stochastic models incorporate random elements for temporal fluctuations in immune cell counts, cytokine levels, or intracellular molecular copy numbers (102, 103). This approach helps to understand the dynamics of immunological/biological processes occurring with small cellular or molecular populations and helps predict the probabilistic outcome of immunological processes. Agent-based models (ABMs) are organized differently from the above approaches. Such models simulate the behavior and interactions of individual agents, such as cells. They can capture collective behavior shaped by interactions between individual agents by imposing migration patterns and interaction rules of immune cells (104, 105). For example, inflammatory responses at the site of infection can be modeled using agent-based modeling with the interactions of individual cells as simple discrete rules or ODEs (106, 107). More detailed reviews and analytical tools of various modeling approaches can be found in references (108–120).
Although both omics data-driven and dynamical systems modeling-driven approaches are legitimate in a quantitative and holistic understanding of the immune system, seamless integration of these is needed to accelerate capturing the genuinely complex and dynamic picture of the immune system. We identify two general challenges in this regard. First, although more comprehensive technologies to profile the immune systems are rapidly emerging, mainstream practice in the omics field is still in the descriptive cataloging of numerous cellular and molecular components. Transforming such comprehensive information about the immune system into predictive models of dynamic immune behavior is still in its infancy (69). Second, although various mathematical tools have been developed to model the immune system, each has its scope confined within particular biological layers simplistically. We need an overarching mathematical/computational framework for integrating each tool to describe immune behavior occurring across multiple biological layers. Given that we have successfully integrated the dichotomic systems immunological approaches, this should eventually be translated into therapy, seamlessly integrating the population PK/PD modeling framework and flexibly adapting to various immunotherapeutic modalities.
5 Toward multi-physiology models of the immune system: synergy of PK/PD modeling frameworks and systems immunological modeling beyond QSP
Thus far, we have dealt with the challenge in predictive immunotherapies arising due to the insufficiency of existing PK/PD modeling and the infancy of “genuine” systems immunological modeling. To achieve better predictions of the immunotherapeutic responses, therapeutic target identification, and designing therapeutic regimens to provide each individual patient with a better cure, we need to demonstrate the complex immune behavior realistically as in silico models. Here, “realistic” models should encompass cellular and molecular players that interact together across multiple layers of biological organizations. This multiscale nature of the immune system gives rise to non-intuitive and nonlinear behavior across space and time, in contrast with the models with oversimplification as the most existing mechanistic immune models.
Here, we propose an overarching umbrella, “multi-physiology modeling” of the immune system (Figure 1). The expression “multi-physiology” is analogous to “multi-physics” in engineering and earth science fields, where different aspects of systems are modeled simultaneously (121). We regard this as a central ground, treating all relevant fields equally rather than emphasizing one and extending to others. In this approach, we aim to realistically describe the immune system in silico exactly how it operates across multiple spatiotemporal scales with many constituent components interacting. In addition, we seamlessly integrate these immune models with pharmacometric frameworks that interface with immunotherapeutic agents and patient responses with inter-individual variability. This framework should be flexible enough to be continuously updated by newly accumulating knowledge in the relevant immune systems and diseases accelerated by quantitative omics data and be easily deployed in immunotherapy by accounting for unique pharmacological behaviors of novel and emerging immunotherapeutic agents.
Figure 1. Schematic of the multi-physiology modeling framework. The inter-individual heterogeneity of patients’ immune statuses and immunotherapeutic responses is represented as points within a high-dimensional parameter space captured by NLME. Parameters and variables derived from integrated multi-omics and clinical data and immunological knowledge are utilized to construct an integrated in silico model that combines PK/PD modeling and multiscale mathematical modeling. The model’s outputs can guide immunotherapy strategies at the individual patient level. Throughout treatment, continuous immune profiling of individual patients can update immunotherapy strategies in a model-informed manner, enabling personalized precision immunotherapy. NLME, Nonlinear mixed-effect modeling; ODE, ordinary differential equation; PDE, partial differential equation; SDE, stochastic differential equation; ABM, agent-based model; Created with BioRender.com.
To realize this, we should assess missing elements for methodological breakthroughs to establish multi-physiology models (122). An urgent need in mathematical modeling is to develop mathematical/computational frameworks to describe the multiscale spatio-temporal nature of the immune system. These frameworks need to seamlessly and flexibly integrate various modeling methods, such as ODE, PDE, SDE, or agent-based modeling, that tend to be independently used for their respective target layers of biological organizations. In addition, such frameworks should be able to faithfully encompass realistic immunologic pictures based on prior knowledge, experimental literature, and, nowadays, quantitative single-cell and spatial multi-omics data (Figure 2). We envision that this can be achieved, first, by extracting relevant multiscale and dynamic immunological features quantitatively from such dispersed sources. Quantitative features include cellular features such as cell-type annotations and spatial locations (if available through imaging-based data), and subcellular statuses such as molecular abundances and functional signatures. Then, we assemble those as networks of features interacting across scales, encompassing intercellular and intracellular connections. We can utilize tools such as CellPhoneDB (123), CellChat (124), and LIANA (125) to infer cell–cell communications through ligand-receptor pairs. OmniPath (126) can be used to reconstruct signaling networks. SCENIC+ (127) and CollecTRI (128) support the inference of gene regulatory networks, and NicheNet (129) provides multi-layered communication inferences. These tools operate based on curated knowledge-based databases, which are continuously expanded under various cell-type-specific perturbation conditions (72, 130, 131). Finally, we translate the network scaffolds into mathematical/computational (or dynamical) models by constructing reaction networks with propensity functions that define rates for each reaction. A major hurdle to be overcome is the general trade-off between data throughput and temporal resolution in available data. High-throughput data often sacrifices temporal details, making it difficult to extract dynamic patterns while maintaining high-dimensional biological complexity. For example, single-cell RNA sequencing or spatial transcriptomics data allow detailed snapshots of cellular states across thousands of cells but are typically limited to a single or a few time points due to cost and technical constraints. In contrast, blood-based biomarkers can be collected repeatedly, allowing immune responses to be tracked over time with lower throughput. Given that we have established all these, we should be able to intuitively interpret the multi-physiology models as we would analyze a much simpler model with a few variables and parameters by overcoming the difficulty in handling the inevitable high-dimensional parameters and variables in the models (132).
Figure 2. Construction of a multi-physiology model. Features are extracted from multi-omics data from patients, such as cellular features (such as abundances or locations) or intracellular features (such as molecular levels or signaling activation statuses), and assembled as multiscale networks using various computational tools. These networks form the basis for multiscale dynamical models with individualized parametrizations across a high-dimensional parameter space, using NLME and “pre-training”. A dynamic landscape is explored using a multi-physiology model across the high dimensional parameter space under immunotherapies, followed by patient-wise predictions.
Ultimately, the multi-physiology modeling should embrace inter-individual heterogeneities of immunological processes and immunotherapeutic responses through individualized model parametrizations and initializations (Figures 1 and 2). NLME has been crucial for parametrizing population PK/PD models accounting for inter-individual variabilities. Since NLME mainly deals with ODE-based compartmental models, further methodological developments are needed to apply it in multi-physiology modeling. One likely barrier to establishing NLME in the multi-physiology modeling of the immune system is the obsession with measuring and identifying (or fitting) high-dimensional model parameters all at once. Many of the model parameters cannot be reliably estimated from sparse data directly from patients, leading to identifiability issues (133). To overcome this, we may “pre-train” the models by gathering relevant parameter values and their reasonable ranges of variability from various experimental data and/or physical, biochemical, and biological reasoning. Pre-training can detour the difficulties in collecting all data modalities from every patient by utilizing partially matched multimodal datasets. We may obtain the correlation structures in the high-dimensional parameter space between parameters from model components describing different biological layers by aligning the corresponding partially paired data modalities. Recently emerging data linking genetic variations and cell-type- and/or condition-specific quantitative phenotypic variations can further help individualized model parameterizations (134–136). In most cases, the values can be constrained within a few orders of magnitude, within which the values can change either physically or pathologically. Then, we explore the plausible dynamical landscape of the multi-physiology models across the high-dimensional parameter space (Figure 2). Finally, we may calibrate the model to the patients undergoing immunotherapy of interest as an ensemble of parameter sets that recover observed or desired dynamical trajectories of immune behavior via approximate Bayesian computation (137). As a part of multi-physiology modeling, we should curate parameters for various biological and immunological processes and establish experimental platforms that facilitate accumulating parameter information (138).
To demonstrate how the multi-physiology modeling approach operates, we present a hypothetical scenario in which we treat a cold tumor to transform it into a hot tumor to make it more susceptible to T cell-targeting immunotherapies. First, single-cell and spatial transcriptomic data, along with clinical information, across tumors with varying immune phenotypes are collected. Multi-omics analysis enables the construction of a multiscale network representing intercellular and intracellular interactions in the tumor immune microenvironment. This network is then translated into mathematical equations via a reaction network scheme. We then form a multi-physiology model by implementing the equations into a multi-scale simulation framework combined with pharmacometric models for relevant immunotherapeutic agents. A tumor immune dynamical landscape is then constructed that maps high-dimensional model parameter space to tumor immune dynamics and clinical therapeutic outcomes by weaving existing data and massive simulations across the parameter space together. The landscape is then used to explore possible therapeutic outcomes under various therapeutic interventions to obtain insights into the transition between cold and hot tumors. Finally, by narrowing model parameters to reflect individual patient profiles, personalized strategies for precision immunotherapy are identified through individualized predictions.
6 Discussion
The advances in immunotherapeutics, such as antibody-based drugs, nanoparticle delivery vehicles, and adoptive cell therapies, are being accelerated in providing patients with new modes of treating immune-related diseases. At the same time, the exponential growth of multi-omics biological data, further accelerated by patient-derived experimental models, offers unprecedented insights into the immune system’s complexity (139). A number of studies have been conducted to relate the characteristics of patient-specific attributes to therapeutic outcomes in a data-driven manner (140, 141). However, we still lack a unified framework enabling predictive immunotherapies tailored to individual patients. In this article, we propose an overarching umbrella, “multi-physiology modeling” of the immune system. It quantitatively describes the immune system with its multiscale nonlinear dynamics of many interacting constituents and cellular phenotypic heterogeneities, together with PK/PD modeling that interfaces with individual patients. A major hurdle in achieving this is likely the lack of cross-disciplinary communications that resulted in discipline-oriented approaches, each limited.
With multi-physiology models of the immune system, what do we want to achieve eventually? First, we want to advance from mere statistical predictions of immunotherapeutic responses of predefined patient groups to quantitative and dynamic predictions of immunotherapeutic outcomes tailored to individual patients or at least more granular immune phenotypic groups. This will also allow more efficient drug target identification and virtual clinical trial platforms that perform combinatorial immunotherapeutic regimens. Clinicians who employ these platforms may collect a diseased tissue sample with relevant routine clinical data from the patient, which can then be transformed into more detailed immune profiling data. By conducting repeated simulations of the model under various immunotherapeutic scenarios, the clinician will be able to predict the outcomes of various treatment options and conclude the most suitable treatment method for the patient. Second, well-developed multi-physiology models will serve as integrative hubs to distill and accumulate vast amounts of immunological knowledge and data. This will accelerate not only our understanding of basic immunology related to immune-related diseases but also the efficiency and accuracy of clinical immunotherapeutics.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.
Author contributions
SH: Conceptualization, Investigation, Visualization, Writing – original draft, Writing – review & editing. KP: Conceptualization, Funding acquisition, Investigation, Supervision, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the 2022 Research Fund (1.220127.01) of UNIST (Ulsan National Institute of Science & Technology), the Institute for Basic Science, Republic of Korea, under project code IBS-R801-D9-2023-a08, a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHDI), funded by the Ministry of Health and Welfare, Republic of Korea (grant number: RS-2024-00408679), and the Bio&Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. RS-2024-00440930).
Acknowledgments
We thank Jihye Kim for feedback on the manuscript. We thank the members of the Systems ImmunoDynamics Lab for their active discussions throughout the development of the ideas in this manuscript.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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The author(s) declare that Generative AI was used in the creation of this manuscript. Proofreading sentences for better presentations.
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Keywords: multi-physiology modeling, systems immunology, precision immunotherapy, multi-omics data, multiscale modeling, quantitative systems pharmacology
Citation: Hong S and Park K (2025) Multi-physiology modeling of the immune system in the era of precision immunotherapy. Front. Immunol. 16:1548768. doi: 10.3389/fimmu.2025.1548768
Received: 20 December 2024; Accepted: 12 May 2025;
Published: 29 May 2025.
Edited by:
M. Cristina Vega, Spanish National Research Council (CSIC), SpainReviewed by:
Kevin Thurley, University Hospital Bonn, GermanyCopyright © 2025 Hong and Park. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Kyemyung Park, a3llbXl1bmcucGFya0B1bmlzdC5hYy5rcg==