Development of a quantitative relationship between CAR-affinity, antigen abundance, tumor cell depletion and CAR-T cell expansion using a multiscale systems PK-PD model

DMPK perspective on quantitative model analysis for chimeric antigen receptor cell therapy: Advances and challenges

Chimeric antigen receptor (CAR) cells are genetically engineered immune cells that specifically target tumor-associated antigens and have revolutionized cancer treatment, particularly in hematological malignancies, with ongoing investigations into their potential applications in solid tumors. This review provides a comprehensive overview of the current status and challenges in drug metabolism and pharmacokinetics (DMPK) for CAR cell therapy, specifically emphasizing on quantitative modeling and simulation (M&S). Furthermore, the recent advances in quantitative model analysis have been reviewed, ranging from clinical data characterization to mechanism-based modeling that connects in vitro and in vivo nonclinical and clinical study data. Additionally, the future perspectives and areas for improvement in CAR cell therapy translation have been reviewed. This includes using formulation quality considerations, characterization of appropriate animal models, refinement of in vitro models for bottom-up approaches, and enhancement of quantitative bioanalytical methodology. Addressing these challenges within a DMPK framework is pivotal in facilitating the translation of CAR cell therapy, ultimately enhancing the patients' lives through efficient CAR cell therapies.

Mathematical modeling of endogenous and exogenously administered T cell recirculation in mouse and its application to pharmacokinetic studies of cell therapies

Introduction

In vivo T cell migration has been of interest to scientists for the past 60 years. T cell kinetics are important in the understanding of the immune response to infectious agents. More recently, adoptive T cell therapies have proven to be a most promising approach to treating a wide range of diseases, including autoimmune and cancer diseases, whereby the characterization of cellular kinetics represents an important step towards the prediction of therapeutic efficacy.

Methods

Here, we developed a physiologically-based pharmacokinetic (PBPK) model that describes endogenous T cell homeostasis and the kinetics of exogenously administered T cells in mouse. Parameter calibration was performed using a nonlinear fixed-effects modeling approach based on published data on T cell kinetics and steady-state levels in different tissues of mice. The Partial Rank Correlation Coefficient (PRCC) method was used to perform a global sensitivity assessment. To estimate the impact of kinetic parameters on exogenously administered T cell dynamics, a local sensitivity analysis was conducted.

Results

We simulated the model to analyze cellular kinetics following various T cell doses and frequencies of CCR7+ T cells in the population of infused lymphocytes. The model predicted the effects of T cell numbers and of population composition of infused T cells on the resultant concentration of T cells in various organs. For example, a higher percentage of CCR7+ T cells among exogenously administered T lymphocytes led to an augmented accumulation of T cells in the spleen. The model predicted a linear dependence of T cell dynamics on the dose of adoptively transferred T cells.

Discussion

The mathematical model of T cell migration presented here can be integrated into a multi-scale model of the immune system and be used in a preclinical setting for predicting the distribution of genetically modified T lymphocytes in various organs, following adoptive T cell therapies.

Making drugs from T cells: The quantitative pharmacology of engineered T cell therapeutics

Engineered T cells have emerged as highly effective treatments for hematological cancers. Hundreds of clinical programs are underway in efforts to expand the efficacy, safety, and applications of this immuno-therapeutic modality. A primary challenge in developing these "living drugs" is the complexity of their pharmacology, as the drug product proliferates, differentiates, traffics between tissues, and evolves through interactions with patient immune systems. Using publicly available clinical data from Chimeric Antigen Receptor (CAR) T cells, we demonstrate how mathematical models can be used to quantify the relationships between product characteristics, patient physiology, pharmacokinetics and clinical outcomes. As scientists work to develop next-generation cell therapy products, mathematical models will be integral for contextualizing data and facilitating the translation of product designs to clinical strategy.

A stochastic framework for evaluating CAR T cell therapy efficacy and variability

Based on a deterministic and stochastic process hybrid model, we use white noises to account for patient variabilities in treatment outcomes, use a hyperparameter to represent patient heterogeneity in a cohort, and construct a stochastic model in terms of Ito stochastic differential equations for testing the efficacy of three different treatment protocols in CAR T cell therapy. The stochastic model has three ergodic invariant measures which correspond to three unstable equilibrium solutions of the deterministic system, while the ergodic invariant measures are attractors under some conditions for tumor growth. As the stable dynamics of the stochastic system reflects long-term outcomes of the therapy, the transient dynamics provide chances of cure in short-term. Two stopping times, the time to cure and time to progress, allow us to conduct numerical simulations with three different protocols of CAR T cell treatment through the transient dynamics of the stochastic model. The probability distributions of the time to cure and time to progress present outcome details of different protocols, which are significant for current clinical study of CAR T cell therapy.

Antigen loss following CAR-T cell therapy: Mechanisms, implications, and potential solutions

Chimeric Antigen Receptor T-cell (CAR-T cell) therapy has emerged as a groundbreaking immunotherapeutic approach for treating various hematological malignancies. CAR-T cells are engineered to express synthetic receptors that target specific antigens on cancer cells, leading to their eradication. While the therapy has shown remarkable efficacy, a significant challenge that has been observed in 30%-70% of patients showing recurrent disease is antigen loss or downregulation. We searched PubMed/MEDLINE, EMBASE, and Google scholar for articles on antigen loss/escape following Chimeric antigen receptor T-cell therapy in malignancies. Antigen loss refers to the loss or reduction in the expression of the target antigen on cancer cells, rendering CAR-T cells ineffective. This phenomenon poses a significant clinical concern, as it can lead to disease relapse and limited treatment options. This review explores the mechanisms underlying antigen loss following CAR-T cell therapy, its implications on treatment outcomes, and potential strategies to overcome the problem.

Understanding the Interplay of CAR-NK Cells and Triple-Negative Breast Cancer: Insights from Computational Modeling

Chimeric antigen receptor (CAR)-engineered natural killer (NK) cells have recently emerged as a promising and safe alternative to CAR-T cells for targeting solid tumors. In the case of triple-negative breast cancer (TNBC), traditional cancer treatments and common immunotherapies have shown limited effectiveness. However, CAR-NK cells have been successfully employed to target epidermal growth factor receptor (EGFR) on TNBC cells, thereby enhancing the efficacy of immunotherapy. The effectiveness of CAR-NK-based immunotherapy is influenced by various factors, including the vaccination dose, vaccination pattern, and tumor immunosuppressive factors in the microenvironment. To gain insights into the dynamics and effects of CAR-NK-based immunotherapy, we propose a computational model based on experimental data and immunological theories. This model integrates an individual-based model that describes the interplay between the tumor and the immune system, along with an ordinary differential equation model that captures the variation of inflammatory cytokines. Computational results obtained from the proposed model shed light on the conditions necessary for initiating an effective anti-tumor response. Furthermore, global sensitivity analysis highlights the issue of low persistence of CAR-NK cells in vivo, which poses a significant challenge for the successful clinical application of these cells. Leveraging the model, we identify the optimal vaccination time, vaccination dose, and time interval between injections for maximizing therapeutic outcomes.

Developing a mechanistic translational PK/PD model for a trifunctional NK cell engager to predict the first-in-human dose for acute myeloid leukemia

Natural killer cell engagers (NKCEs), a treatment that stimulates innate immunity, have lately gained attention owing to their favorable safety profile, and their efficacy. Natural killer (NK) cell activation is driven by immune synapse formation between drugs, NK cells, and tumor cells. However, no clear translational modeling approach has been reported for first-in-human (FIH) dose estimation of humanized NKCEs. We developed the first translational mechanistic synapse-driven pharmacokinetic/pharmacodynamic (PK/PD) model for a trifunctional NKp46/CD16a-CD123 (CD123-NKCE) by integrating (i) in vitro target cell cytotoxicity in MOLM-13 tumor cell lines at varying effector-to-tumor cell ratios and incubation intervals; (ii) nonhuman primate PK and profiles of CD123+ cells and NKP46+ NK cells; and (iii) healthy human or patients with acute myeloid leukemia system-specific parameters. To depict direct tumor cell killing by the innate immunity, no transit compartment was included in PK/PD model structures. Model predictions suggested an intrapatient dose escalation of 10/30/100 μg/kg twice weekly to be selected as the starting dose in the FIH trial. However, sensitivity analyses revealed that CD123+ cell growth rate constant and maximal tumor killing rate constant were the key uncertainties to the recommended active dose. This novel translational model structure can be used as the basis to predict clinical PK/PD data for CD123-NKCE, and the translational strategy may serve as a foundation for future advancements of NKCEs.

Information-Theoretic Analysis of a Model of CAR-4-1BB-Mediated NFκB Activation

Systems biology utilizes computational approaches to examine an array of biological processes, such as cell signaling, metabolomics and pharmacology. This includes mathematical modeling of CAR T cells, a modality of cancer therapy by which genetically engineered immune cells recognize and combat a cancerous target. While successful against hematologic malignancies, CAR T cells have shown limited success against other cancer types. Thus, more research is needed to understand their mechanisms of action and leverage their full potential. In our work, we set out to apply information theory on a mathematical model of NFκB signaling initiated by the CAR following antigen encounter. First, we estimated channel capacity for CAR-4-1BB-mediated NFκB signal transduction. Next, we evaluated the pathway's ability to distinguish contrasting "low" and "high" antigen concentration levels, depending on the amount of variability in protein concentrations. Finally, we assessed the fidelity by which NFκB activation reflects the encountered antigen concentration, depending on the prevalence of antigen-positive targets in tumor population. We found that in most scenarios, fold change in the nuclear concentration of NFκB carries a higher channel capacity for the pathway than NFκB's absolute response. Additionally, we found that most errors in transducing the antigen signal through the pathway skew towards underestimating the concentration of encountered antigen. Finally, we found that disabling IKKβ deactivation could increase signaling fidelity against targets with antigen-negative cells. Our information-theoretic analysis of signal transduction can provide novel perspectives on biological signaling, as well as enable a more informed path to cell engineering.Kindly check and confirm whether the corresponding affiliation is correctly identified.this is correct.

Outsmarting trogocytosis to boost CAR NK/T cell therapy

Chimeric antigen receptor (CAR) NK and T cell therapy are promising immunotherapeutic approaches for the treatment of cancer. However, the efficacy of CAR NK/T cell therapy is often hindered by various factors, including the phenomenon of trogocytosis, which involves the bidirectional exchange of membrane fragments between cells. In this review, we explore the role of trogocytosis in CAR NK/T cell therapy and highlight potential strategies for its modulation to improve therapeutic efficacy. We provide an in-depth analysis of trogocytosis as it relates to the fate and function of NK and T cells, focusing on its effects on cell activation, cytotoxicity, and antigen presentation. We discuss how trogocytosis can mediate transient antigen loss on cancer cells, thereby negatively affecting the effector function of CAR NK/T cells. Additionally, we address the phenomenon of fratricide and trogocytosis-associated exhaustion, which can limit the persistence and effectiveness of CAR-expressing cells. Furthermore, we explore how trogocytosis can impact CAR NK/T cell functionality, including the acquisition of target molecules and the modulation of signaling pathways. To overcome the negative effects of trogocytosis on cellular immunotherapy, we propose innovative approaches to modulate trogocytosis and augment CAR NK/T cell therapy. These strategies encompass targeting trogocytosis-related molecules, engineering CAR NK/T cells to resist trogocytosis-induced exhaustion and leveraging trogocytosis to enhance the function of CAR-expressing cells. By overcoming the limitations imposed by trogocytosis, it may be possible to unleash the full potential of CAR NK/T therapy against cancer. The knowledge and strategies presented in this review will guide future research and development, leading to improved therapeutic outcomes in the field of immunotherapy.

Ligand-based targeting of c-kit using engineered γδ T cells as a strategy for treating acute myeloid leukemia

The application of immunotherapies such as chimeric antigen receptor (CAR) T therapy or bi-specific T cell engager (BiTE) therapy to manage myeloid malignancies has proven more challenging than for B-cell malignancies. This is attributed to a shortage of leukemia-specific cell-surface antigens that distinguish healthy from malignant myeloid populations, and the inability to manage myeloid depletion unlike B-cell aplasia. Therefore, the development of targeted therapeutics for myeloid malignancies, such as acute myeloid leukemia (AML), requires new approaches. Herein, we developed a ligand-based CAR and secreted bi-specific T cell engager (sBite) to target c-kit using its cognate ligand, stem cell factor (SCF). c-kit is highly expressed on AML blasts and correlates with resistance to chemotherapy and poor prognosis, making it an ideal candidate for which to develop targeted therapeutics. We utilize γδ T cells as a cytotoxic alternative to αβ T cells and a transient transfection system as both a safety precaution and switch to remove alloreactive modified cells that may hinder successful transplant. Additionally, the use of γδ T cells permits its use as an allogeneic, off-the-shelf therapeutic. To this end, we show mSCF CAR- and hSCF sBite-modified γδ T cells are proficient in killing c-kit+ AML cell lines and sca-1+ murine bone marrow cells in vitro. In vivo, hSCF sBite-modified γδ T cells moderately extend survival of NSG mice engrafted with disseminated AML, but therapeutic efficacy is limited by lack of γδ T-cell homing to murine bone marrow. Together, these data demonstrate preclinical efficacy and support further investigation of SCF-based γδ T-cell therapeutics for the treatment of myeloid malignancies.

Model-informed drug development of autologous CAR-T cell therapy: Strategies to optimize CAR-T cell exposure leveraging cell kinetic/dynamic modeling

Autologous Chimeric antigen receptor (CAR-T) cell therapy has been highly successful in the treatment of aggressive hematological malignancies and is also being evaluated for the treatment of solid tumors as well as other therapeutic areas. A challenge, however, is that up to 60% of patients do not sustain a long-term response. Low CAR-T cell exposure has been suggested as an underlying factor for a poor prognosis. CAR-T cell therapy is a novel therapeutic modality with unique kinetic and dynamic properties. Importantly, "clear" dose-exposure relationships do not seem to exist for any of the currently approved CAR-T cell products. In other words, dose increases have not led to a commensurate increase in the measurable in vivo frequency of transferred CAR-T cells. Therefore, alternative approaches beyond dose titration are needed to optimize CAR-T cell exposure. In this paper, we provide examples of actionable variables - design elements in CAR-T cell discovery, development, and clinical practice, which can be modified to optimize autologous CAR-T cell exposure. Most of these actionable variables can be assessed throughout the various stages of discovery and development as part of a well-informed research and development program. Model-informed drug development approaches can enable such study and program design choices from discovery through to clinical practice and can be an important contributor to cell therapy effectiveness and efficiency.

Deconvolution of clinical variance in CAR-T cell pharmacology and response

Chimeric antigen receptor T cell (CAR-T) expansion and persistence vary widely among patients and predict both efficacy and toxicity. However, the mechanisms underlying clinical outcomes and patient variability are poorly defined. In this study, we developed a mathematical description of T cell responses wherein transitions among memory, effector and exhausted T cell states are coordinately regulated by tumor antigen engagement. The model is trained using clinical data from CAR-T products in different hematological malignancies and identifies cell-intrinsic differences in the turnover rate of memory cells and cytotoxic potency of effectors as the primary determinants of clinical response. Using a machine learning workflow, we demonstrate that product-intrinsic differences can accurately predict patient outcomes based on pre-infusion transcriptomes, and additional pharmacological variance arises from cellular interactions with patient tumors. We found that transcriptional signatures outperform T cell immunophenotyping as predictive of clinical response for two CD19-targeted CAR-T products in three indications, enabling a new phase of predictive CAR-T product development.

Prospective approaches to gene therapy computational modeling - spotlight on viral gene therapy

Clinical studies have found there still exists a lack of gene therapy dose-toxicity and dose-efficacy data that causes gene therapy dose selection to remain elusive. Model informed drug development (MIDD) has become a standard tool implemented throughout the discovery, development, and approval of pharmaceutical therapies, and has the potential to inform dose-toxicity and dose-efficacy relationships to support gene therapy dose selection. Despite this potential, MIDD approaches for gene therapy remain immature and require standardization to be useful for gene therapy clinical programs. With the goal to advance MIDD approaches for gene therapy, in this review we first provide an overview of gene therapy types and how they differ from a bioanalytical, formulation, route of administration, and regulatory standpoint. With this biological and regulatory background, we propose how MIDD can be advanced for AAV-based gene therapies by utilizing physiological based pharmacokinetic modeling and quantitative systems pharmacology to holistically inform AAV and target protein dynamics following dosing. We discuss how this proposed model, allowing for in-depth exploration of AAV pharmacology, could be the key the field needs to treat these unmet disease populations.

PASCAR: a multiscale framework to explore the design space of constitutive and inducible CAR T cells

CAR T cells are engineered to bind and destroy tumor cells by targeting overexpressed surface antigens. However, healthy cells expressing lower abundances of these antigens can also be lysed by CAR T cells. Various CAR T cell designs increase tumor cell elimination, whereas reducing damage to healthy cells. However, these efforts are costly and labor-intensive, constraining systematic exploration of potential hypotheses. We develop a protein abundance structured population dynamic model for CAR T cells (PASCAR), a framework that combines multiscale population dynamic models and multi-objective optimization approaches with data from cytometry and cytotoxicity assays to systematically explore the design space of constitutive and tunable CAR T cells. PASCAR can quantitatively describe in vitro and in vivo results for constitutive and inducible CAR T cells and can successfully predict experiments outside the training data. Our exploration of the CAR design space reveals that optimal CAR affinities in the intermediate range of dissociation constants effectively reduce healthy cell lysis, whereas maintaining high tumor cell-killing rates. Furthermore, our modeling offers guidance for optimizing CAR expressions in synthetic notch CAR T cells. PASCAR can be extended to other CAR immune cells.

Translational Pharmacokinetic/Pharmacodynamic Modeling and Simulation of Oxaliplatin and Irinotecan in Colorectal Cancer

Despite the recent advances in this field, there are limited methods for translating organoid-based study results to clinical response. The goal of this study was to develop a pharmacokinetic/pharmacodynamic (PK/PD) model to facilitate the translation, using oxaliplatin and irinotecan treatments with colorectal cancer (CRC) as examples. The PK models were developed using qualified oxaliplatin and irinotecan PK data from the literature. The PD models were developed based on antitumor efficacy data of SN-38 and oxaliplatin evaluated in vitro using tumor organoids. To predict the clinical response, translational scaling of the models was established by incorporating predicted ultrafiltration platinum in plasma or SN-38 in tumors to PD models as the driver of efficacy. The final PK/PD model can predict PK profiles and responses following treatments with oxaliplatin or irinotecan. After generation of virtual patient cohorts, this model simulated their tumor shrinkages following treatments, which were used in analyzing the efficacies of the two treatments. Consistent with the published clinical trials, the model simulation suggested similar patient responses following the treatments of oxaliplatin and irinotecan with regards to the probabilities of progression-free survival (HR = 1.05, 95%CI [0.97;1.15]) and the objective response rate (OR = 1.15, 95%CI [1.00;1.32]). This proposed translational PK/PD modeling approach provides a significant tool for predicting clinical responses of different agents, which may help decision-making in drug development and guide clinical trial design.

Development of a multiscale mechanistic modeling framework integrating differential cellular kinetics of CAR T-cell subsets and immunophenotypes in cancer patients

Chimeric antigen receptor (CAR) T-cell subsets and immunophenotypic composition of the pre-infusion product, as well as their longitudinal changes following infusion, are expected to affect CAR T-cell expansion, persistence, and clinical outcomes. Herein, we sequentially evolved our previously described cellular kinetic-pharmacodynamic (CK-PD) model to incorporate CAR T-cell product-associated attributes by utilizing published preclinical and clinical datasets from two affinity variants (FMC63 and CAT19 scFv) anti-CD19 CAR T-cells. In step 1, a unified cell-level PD model was used to simultaneously characterize the in vitro killing datasets of two CAR T-cells against CD19+ cell lines at varying effector:target ratios. In step 2, an augmented CK-PD model for anti-CD19 CAR T-cells was developed, by integrating CK dataset(s) from two bioanalytical measurements (quantitative polymerase chain reaction and flow cytometry) in patients with cancer. The model described the differential in vivo expansion properties of CAR T-cell subsets. The estimated expansion rate constant was ~1.12-fold higher for CAR+CD8+ cells in comparison to CAR+CD4+ T-cells. In step 3, the model was extended to characterize the disposition of four immunophenotypic populations of CAR T-cells, including stem-cell memory, central memory, effector memory, and effector cells. The model adequately characterized the longitudinal changes in immunophenotypes post anti-CD19 CAR T-cell infusion in pediatric patients with acute lymphocytic leukemia. Polyclonality in the pre-infusion product was identified as a categorical covariate influencing differentiation of immunophenotypes. In the future, this model could be leveraged a priori toward optimizing the composition of CAR T-cell infusion product, and further understand the CK-PD relationship in patients.

Best Practices and Considerations for Clinical Pharmacology and Pharmacometric Aspects for Optimal Development of CAR-T and TCR-T Cell Therapies: An Industry Perspective

With the promise of a potentially "single dose curative" paradigm, CAR-T cell therapies have brought a paradigm shift in the treatment and management of hematological malignancies. Both CAR-T and TCR-T cell therapies have also made great progress toward the successful treatment of solid tumor indications. The field is rapidly evolving with recent advancements including the clinical development of "off-the-shelf" allogeneic CAR-T therapies that can overcome the long and difficult "vein-to-vein" wait time seen with autologous CAR-T therapies. There are unique clinical pharmacology, pharmacometric, bioanalytical, and immunogenicity considerations and challenges in the development of these CAR-T and TCR-T cell therapies. Hence, to help accelerate the development of these life-saving therapies for the patients with cancer, experts in this field came together under the umbrella of International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) to form a joint working group between the Clinical Pharmacology Leadership Group (CPLG) and the Translational and ADME Sciences Leadership Group (TALG). In this white paper, we present the IQ consortium perspective on the best practices and considerations for clinical pharmacology and pharmacometric aspects toward the optimal development of CAR-T and TCR-T cell therapies.

A Quantitative Systems Pharmacology Model Describing the Cellular Kinetic-Pharmacodynamic Relationship for a Live Biotherapeutic Product to Support Microbiome Drug Development

Live biotherapeutic products (LBPs) are human microbiome therapies showing promise in the clinic for a range of diseases and conditions. Describing the kinetics and behavior of LBPs poses a unique modeling challenge because, unlike traditional therapies, LBPs can expand, contract, and colonize the host digestive tract. Here, we present a novel cellular kinetic-pharmacodynamic quantitative systems pharmacology model of an LBP. The model describes bacterial growth and competition, vancomycin effects, binding and unbinding to the epithelial surface, and production and clearance of butyrate as a therapeutic metabolite. The model is calibrated and validated to published data from healthy volunteers. Using the model, we simulate the impact of treatment dose, frequency, and duration as well as vancomycin pretreatment on butyrate production. This model enables model-informed drug development and can be used for future microbiome therapies to inform decision making around antibiotic pretreatment, dose selection, loading dose, and dosing duration.

A systems and computational biology perspective on advancing CAR therapy

In the recent decades, chimeric antigen receptor (CAR) therapy signaled a new revolutionary approach to cancer treatment. This method seeks to engineer immune cells expressing an artificially designed receptor, which would endue those cells with the ability to recognize and eliminate tumor cells. While some CAR therapies received FDA approval and others are subject to clinical trials, many aspects of their workings remain elusive. Techniques of systems and computational biology have been frequently employed to explain the operating principles of CAR therapy and suggest further design improvements. In this review, we sought to provide a comprehensive account of those efforts. Specifically, we discuss various computational models of CAR therapy ranging in scale from organismal to molecular. Then, we describe the molecular and functional properties of costimulatory domains frequently incorporated in CAR structure. Finally, we describe the signaling cascades by which those costimulatory domains elicit cellular response against the target. We hope that this comprehensive summary of computational and experimental studies will further motivate the use of systems approaches in advancing CAR therapy.

Information-theoretic analysis of a model of CAR-4-1BB-mediated NFκB activation

Systems biology utilizes computational approaches to examine an array of biological processes, such as cell signaling, metabolomics and pharmacology. This includes mathematical modeling of CAR T cells, a modality of cancer therapy by which genetically engineered immune cells recognize and combat a cancerous target. While successful against hematologic malignancies, CAR T cells have shown limited success against other cancer types. Thus, more research is needed to understand their mechanisms of action and leverage their full potential. In our work, we set out to apply information theory on a mathematical model of cell signaling of CAR-mediated activation following antigen encounter. First, we estimated channel capacity for CAR-4-1BB-mediated NFκB signal transduction. Next, we evaluated the pathway's ability to distinguish contrasting "low" and "high" antigen concentration levels, depending on the amount of intrinsic noise. Finally, we assessed the fidelity by which NFκB activation reflects the encountered antigen concentration, depending on the prevalence of antigen-positive targets in tumor population. We found that in most scenarios, fold change in the nuclear concentration of NFκB carries a higher channel capacity for the pathway than NFκB's absolute response. Additionally, we found that most errors in transducing the antigen signal through the pathway skew towards underestimating the concentration of encountered antigen. Finally, we found that disabling IKKβ deactivation could increase signaling fidelity against targets with antigen-negative cells. Our information-theoretic analysis of signal transduction can provide novel perspectives on biological signaling, as well as enable a more informed path to cell engineering.

Activation priming and cytokine polyfunctionality modulate the enhanced functionality of low-affinity CD19 CAR T cells

We recently described a low-affinity second-generation CD19 chimeric antigen receptor (CAR) CAT that showed enhanced expansion, cytotoxicity, and antitumor efficacy compared with the high-affinity (FMC63-based) CAR used in tisagenlecleucel, in preclinical models. Furthermore, CAT demonstrated an excellent toxicity profile, enhanced in vivo expansion, and long-term persistence in a phase 1 clinical study. To understand the molecular mechanisms behind these properties of CAT CAR T cells, we performed a systematic in vitro characterization of the transcriptomic (RNA sequencing) and protein (cytometry by time of flight) changes occurring in T cells expressing low-affinity vs high-affinity CD19 CARs following stimulation with CD19-expressing cells. Our results show that CAT CAR T cells exhibit enhanced activation to CD19 stimulation and a distinct transcriptomic and protein profile, with increased activation and cytokine polyfunctionality compared with FMC63 CAR T cells. We demonstrate that the enhanced functionality of low-affinity CAT CAR T cells is a consequence of an antigen-dependent priming induced by residual CD19-expressing B cells present in the manufacture.

Towards systems immunology of critical illness at scale: from single cell 'omics to digital twins

Single-cell 'omics methodology has yielded unprecedented insights based largely on data-centric informatics for reducing, and thus interpreting, massive datasets. In parallel, parsimonious mathematical modeling based on abstractions of pathobiology has also yielded major insights into inflammation and immunity, with these models being extended to describe multi-organ disease pathophysiology as the basis of 'digital twins' and in silico clinical trials. The integration of these distinct methods at scale can drive both basic and translational advances, especially in the context of critical illness, including diseases such as COVID-19. Here, I explore achievements and argue the challenges that are inherent to the integration of data-driven and mechanistic modeling approaches, highlighting the potential of modeling-based strategies for rational immune system reprogramming.

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

Toward Establishing an Ideal Adjuvant for Non-Inflammatory Immune Enhancement

The vertebrate immune system functions to eliminate invading foreign nucleic acids and foreign proteins from infectious diseases and malignant tumors. Because pathogens and cancer cells have unique amino acid sequences and motifs (e.g., microbe-associated molecular patterns, MAMPs) that are recognized as "non-self" to the host, immune enhancement is one strategy to eliminate invading cells. MAMPs contain nucleic acids specific or characteristic of the microbe and are potential candidates for immunostimulants or adjuvants. Adjuvants are included in many vaccines and are a way to boost immunity by deliberately administering them along with antigens. Although adjuvants are an important component of vaccines, it is difficult to evaluate their efficacy ex vivo and in vivo on their own (without antigens). In addition, inflammation induced by currently candidate adjuvants may cause adverse events, which is a hurdle to their approval as drugs. In addition, the lack of guidelines for evaluating the safety and efficacy of adjuvants in drug discovery research also makes regulatory approval difficult. Viral double-stranded (ds) RNA mimics have been reported as potent adjuvants, but the safety barrier remains unresolved. Here we present ARNAX, a noninflammatory nucleic acid adjuvant that selectively targets Toll-like receptor 3 (TLR3) in antigen-presenting dendritic cells (APCs) to safely induce antigen cross-presentation and subsequently induce an acquired immune response independent of inflammation. This review discusses the challenges faced in the clinical development of novel adjuvants.

Computational model of CAR T-cell immunotherapy dissects and predicts leukemia patient responses at remission, resistance, and relapse

BACKGROUND

Adaptive CD19-targeted chimeric antigen receptor (CAR) T-cell transfer has become a promising treatment for leukemia. Although patient responses vary across different clinical trials, reliable methods to dissect and predict patient responses to novel therapies are currently lacking. Recently, the depiction of patient responses has been achieved using in silico computational models, with prediction application being limited.

METHODS

We established a computational model of CAR T-cell therapy to recapitulate key cellular mechanisms and dynamics during treatment with responses of continuous remission (CR), non-response (NR), and CD19-positive (CD19+) and CD19-negative (CD19-) relapse. Real-time CAR T-cell and tumor burden data of 209 patients were collected from clinical studies and standardized with unified units in bone marrow. Parameter estimation was conducted using the stochastic approximation expectation maximization algorithm for nonlinear mixed-effect modeling.

RESULTS

We revealed critical determinants related to patient responses at remission, resistance, and relapse. For CR, NR, and CD19+ relapse, the overall functionality of CAR T-cell led to various outcomes, whereas loss of the CD19+ antigen and the bystander killing effect of CAR T-cells may partly explain the progression of CD19- relapse. Furthermore, we predicted patient responses by combining the peak and accumulated values of CAR T-cells or by inputting early-stage CAR T-cell dynamics. A clinical trial simulation using virtual patient cohorts generated based on real clinical patient datasets was conducted to further validate the prediction.

CONCLUSIONS

Our model dissected the mechanism behind distinct responses of leukemia to CAR T-cell therapy. This patient-based computational immuno-oncology model can predict late responses and may be informative in clinical treatment and management.

Modeling Patient-Specific CAR-T Cell Dynamics: Multiphasic Kinetics via Phenotypic Differentiation

Chimeric Antigen Receptor (CAR)-T cell immunotherapy revolutionized cancer treatment and consists of the genetic modification of T lymphocytes with a CAR gene, aiming to increase their ability to recognize and kill antigen-specific tumor cells. The dynamics of CAR-T cell responses in patients present multiphasic kinetics with distribution, expansion, contraction, and persistence phases. The characteristics and duration of each phase depend on the tumor type, the infused product, and patient-specific characteristics. We present a mathematical model that describes the multiphasic CAR-T cell dynamics resulting from the interplay between CAR-T and tumor cells, considering patient and product heterogeneities. The CAR-T cell population is divided into functional (distributed and effector), memory, and exhausted CAR-T cell phenotypes. The model is able to describe the diversity of CAR-T cell dynamical behaviors in different patients and hematological cancers as well as their therapy outcomes. Our results indicate that the joint assessment of the area under the concentration-time curve in the first 28 days and the corresponding fraction of non-exhausted CAR-T cells may be considered a potential marker to classify therapy responses. Overall, the analysis of different CAR-T cell phenotypes can be a key aspect for a better understanding of the whole CAR-T cell dynamics.

Mechanistic Modeling of the Interplay Between Host Immune System, IL-7 and UCART19 Allogeneic CAR-T Cells in Adult B-cell Acute Lymphoblastic Leukemia

Chimeric antigen receptor (CAR)-T cell therapies have shown tremendous results against various hematologic cancers. Prior to cell infusion, a host preconditioning regimen is required to achieve lymphodepletion and improve CAR-T cell pharmacokinetic exposure, leading to greater chances of therapeutic success. To better understand and quantify the impact of the preconditioning regimen, we built a population-based mechanistic pharmacokinetic-pharmacodynamic model describing the complex interplay between lymphodepletion, host immune system, homeostatic cytokines, and pharmacokinetics of UCART19, an allogeneic product developed against CD19⁺ B cells. Data were collected from a phase I clinical trial in adult relapsed/refractory B-cell acute lymphoblastic leukemia and revealed three different UCART19 temporal patterns: (i) expansion and persistence, (ii) transient expansion with subsequent rapid decline, and (iii) absence of observed expansion. On the basis of translational assumptions, the final model was able to capture this variability through the incorporation of IL-7 kinetics, which are thought to be increased owing to lymphodepletion, and through an elimination of UCART19 by host T cells, which is specific to the allogeneic context. Simulations from the final model recapitulated UCART19 expansion rates in the clinical trial, confirmed the need for alemtuzumab to observe UCART19 expansion (along with fludarabine cyclophosphamide), quantified the importance of allogeneic elimination, and suggested a high impact of multipotent memory T-cell subpopulations on UCART19 expansion and persistence. In addition to supporting the role of host cytokines and lymphocytes in CAR-T cell therapy, such a model could help optimizing the preconditioning regimens in future clinical trials.

SIGNIFICANCE

A mathematical mechanistic pharmacokinetic/pharmacodynamic model supports and captures quantitatively the beneficial impact of lymphodepleting patients before the infusion of an allogeneic CAR-T cell product. Mediation through IL-7 increase and host T lymphocytes decrease is underlined, and the model can be further used to optimize CAR-T cell therapies lymphodepletion regimen.

Clinical Pharmacology Perspectives for Adoptive Cell Therapies in Oncology

Adoptive cell therapies (ACTs) have shown transformative efficacy in oncology with five US Food and Drug Administration (FDA) approvals for chimeric antigen receptor (CAR) T-cell therapies in hematological malignancies, and promising activity for T cell receptor T-cell therapies in both liquid and solid tumors. Clinical pharmacology can play a pivotal role in optimizing ACTs, aided by modeling and simulation toolboxes and deep understanding of the underlying biological and immunological processes. Close collaboration and multilevel data integration across functions, including chemistry, manufacturing, and control, biomarkers, bioanalytical, and clinical science and safety teams will be critical to ACT development. As ACT is comprised of alive, polyfunctional, and heterogeneous immune cells, its overall physicochemical and pharmacological property is vastly different from other platforms/modalities, such as small molecule and protein therapeutics. In this review, we first describe the unique kinetics of T cells and the appropriate bioanalytical strategies to characterize cellular kinetics. We then assess the distinct aspects of clinical pharmacology for ACTs in comparison to traditional small molecule and protein therapeutics. Additionally, we provide a review for the five FDA-approved CAR T-cell therapies and summarize their properties, cellular kinetic characteristics, dose-exposure-response relationship, and potential baseline factors/variables in product, patient, and regimen that may affect the safety and efficacy. Finally, we probe into existing empirical and mechanistic quantitative techniques to understand how various modeling and simulation approaches can support clinical pharmacology strategy and propose key considerations to be incorporated and explored in future models.

Application of physiologically-based pharmacokinetic models for therapeutic proteins and other novel modalities

The past two decades have seen diversification of drug development pipelines and approvals from traditional small molecule therapies to alternative modalities including monoclonal antibodies, engineered proteins, antibody drug conjugates (ADCs), oligonucleotides and gene therapies. At the same time, physiologically-based pharmacokinetic (PBPK) models for small molecules have seen increased industry and regulatory acceptance.This review focusses on the current status of the application of PBPK models to these newer modalities and give a perspective on the successes, challenges and future directions of this field.There is greatest experience in the development of PBPK models for therapeutic proteins, and PBPK models for ADCs benefit from prior experience for both therapeutic proteins and small molecules. For other modalities, the application of PBPK models is in its infancy.Challenges are discussed and a common theme is lack of availability of physiological and experimental data to characterise systems and drug parameters to enable a priori prediction of pharmacokinetics. Furthermore, sufficient clinical data are required to build confidence in developed models.The PBPK modelling approach provides a quantitative framework for integrating knowledge and data from multiple sources and can be built on as more data becomes available.

Combination of a Novel Fusion Protein CD3εζ28 and Bispecific T Cell Engager Enhances the Persistance and Anti-Cancer Effects of T Cells

Bi-specific T cell engager (BiTE), an artificial bi-functional fusion protein, has shown promising therapeutic potential in preclinical and clinical studies. However, T cells cannot be sufficiently activated by BiTE, most likely due to lacking co-stimulatory signal. We reasoned that incorporating co-stimulatory signal might have the potential to enhance the T cell activation mediated by BiTE. We, therefore, designed a chimeric fusion protein, named as CD3εζ28, which consists of the CD3ε extracellular region, the CD28 costimulatory signal and the intracellular region of CD3ζ in tandem. T cells genetically modified to express both CD3εζ28 and GFP (T-CD3εζ28-GFP) were generated by retroviral transduction. The results from in vitro experiments showed that T-CD3εζCD28-GFP cells had superior cytotoxic effects on tumor cells in presence of BiTE compared with control T cells, as evidenced by IL-2 and IFN-γ production, T cell proliferation and sequential killing assay. In vivo, T-CD3εζCD28-GFP cells showed superior anti-tumor effects in Hela-BiTE. EGFRvIII xenograft tumor model, as evaluated by tumor growth rate and T cell persistence in comparison with control T cells. In order to further confirm these findings, we generated T cells modified to express both CD3εζCD28 on cell surface and BiTE.CD19 by autocrine manner (T-CD3εζCD28-BiTE.19). The superior anti-tumor effects of T-CD3εζCD28-BiTE.19 cells could also be evidenced by the similar in vitro and in vivo experiments; thus, incorporating co-stimulatory signal may be an effective approach to improve the effector function of T cells mediated by BiTE.

Investigating Two Modes of Cancer-Associated Antigen Heterogeneity in an Agent-Based Model of Chimeric Antigen Receptor T-Cell Therapy

Simple Summary

Chimeric antigen receptor (CAR) T-cell therapy has shown much promise in liquid tumors but often fails in solid tumors. This work uses a computational model to examine under what conditions this therapy might fail or be successful. The model includes interactions between cancer cells, CAR T-cells (treatment), and vascular cells (that feed and support tumor growth). From our results, we determined specific tumor conditions in which CAR T-cell therapy is predicted to fail and suggest a combination treatment that might improve the efficacy of the treatment.

Abstract

Chimeric antigen receptor (CAR) T-cell therapy has been successful in treating liquid tumors but has had limited success in solid tumors. This work examines unanswered questions regarding CAR T-cell therapy using computational modeling, such as, what percentage of the tumor must express cancer-associated antigens for treatment to be successful? The model includes cancer cell and vascular and CAR T-cell modules that interact with each other. We compare two different models of antigen expression on tumor cells, binary (in which cancer cells are either susceptible or are immune to CAR T-cell therapy) and gradated (where each cancer cell has a probability of being killed by a CAR T-cell). We vary the antigen expression levels within the tumor and determine how effective each treatment is for the two models. The simulations show that the gradated antigen model eliminates the tumor under more parameter values than the binary model. Under both models, shielding, in which the low/non-antigen-expressing cells protect high antigen-expressing cells, reduced the efficacy of CAR T-cell therapy. One prediction is that a combination of CAR T-cell therapies that targets the general population of cells as well as one that specifically targets cancer stem cells should increase its efficacy.

Recent advances and clinical pharmacology aspects of Chimeric Antigen Receptor (CAR) T-cellular therapy development

Advances in immuno-oncology have provided a variety of novel therapeutics that harness the innate immune system to identify and destroy neoplastic cells. It is noteworthy that acceptable safety profiles accompany the development of these targeted therapies, which result in efficacious cancer treatment with higher survival rates and lower toxicities. Adoptive cellular therapy (ACT) has shown promising results in inducing sustainable remissions in patients suffering from refractory diseases. Two main types of ACT include engineered Chimeric Antigen Receptor (CAR) T cells and T cell receptor (TCR) T cells. The application of these immuno-therapies in the last few years has been successful and has demonstrated a safe and rapid treatment regimen for solid and non-solid tumors. The current review presents an insight into the clinical pharmacology aspects of immuno-therapies, especially CAR-T cells. Here, we summarize the current knowledge of TCR and CAR-T cell immunotherapy with particular focus on the structure of CAR-T cells, the effects and toxicities associated with these therapies in clinical trials, risk mitigation strategies, dose selection approaches, and cellular kinetics. Finally, the quantitative approaches and modeling techniques used in the development of CAR-T cell therapies are described.

Analysis of cellular kinetic models suggest that physiologically based model parameters may be inherently, practically unidentifiable

Physiologically-based pharmacokinetic and cellular kinetic models are used extensively to predict concentration profiles of drugs or adoptively transferred cells in patients and laboratory animals. Models are fit to data by the numerical optimisation of appropriate parameter values. When quantities such as the area under the curve are all that is desired, only a close qualitative fit to data is required. When the biological interpretation of the model that produced the fit is important, an assessment of uncertainties is often also warranted. Often, a goal of fitting PBPK models to data is to estimate parameter values, which can then be used to assess characteristics of the fit system or applied to inform new modelling efforts and extrapolation, to inform a prediction under new conditions. However, the parameters that yield a particular model output may not necessarily be unique, in which case the parameters are said to be unidentifiable. We show that the parameters in three published physiologically-based pharmacokinetic models are practically (deterministically) unidentifiable and that it is challenging to assess the associated parameter uncertainty with simple curve fitting techniques. This result could affect many physiologically-based pharmacokinetic models, and we advocate more widespread use of thorough techniques and analyses to address these issues, such as established Markov Chain Monte Carlo and Bayesian methodologies. Greater handling and reporting of uncertainty and identifiability of fit parameters would directly and positively impact interpretation and translation for physiologically-based model applications, enhancing their capacity to inform new model development efforts and extrapolation in support of future clinical decision-making.

Development of minimal physiologically-based pharmacokinetic-pharmacodynamic models for characterizing cellular kinetics of CAR T cells following local deliveries in mice

Chimeric antigen receptor (CAR) T cell therapies have revolutionized the treatment of hematologic malignancies and have potentials for solid tumor treatment. To overcome limited CAR T cell infiltration to solid tumors, local delivery of CAR T cells is a practical strategy that has shown promising therapeutic outcome and safety profile in the clinic. It is of great interest to understand the impact of dosing routes on CAR T cell distribution, subsequent proliferation and tumor killing in a quantitative manner to identify key factors that contribute to CAR T efficacy and safety. In this study, we established mouse minimal physiologically-based pharmacokinetic (mPBPK) models combined with pharmacodynamic (PD) components to delineate CAR T cell distribution, proliferation, tumor growth, and tumor cell killing in the cases of pleural and liver tumors. The pleural tumor model reasonably captured published CAR T cellular kinetic and tumor growth profiles in mice. The mPBPK-PD simulation of a liver tumor mouse model showed a substantial increase in initial tumor infiltration and earlier CAR T cell proliferation with local hepatic artery delivery compared to portal vein and intravenous (i.v.) injections whereas portal vein injection showed little difference from i.v. administration, suggesting the importance of having the injection site close to tumor for maximal effect of non-systemic administration. Blood flow rate in the liver tumor was found to be a sensitive parameter for cellular kinetics and efficacy, indicating a potential role of tumor vascularization in the efficacy of CAR T cell therapies.

Pharmacokinetic and Pharmacodynamic Modeling of siRNA Therapeutics - a Minireview

The approval of four small interfering RNA (siRNA) products in the past few years has demonstrated unequivocally the therapeutic potential of this novel modality. Three such products (givosiran, lumasiran and inclisiran) are liver-targeted, using tris N-acetylgalactosamine (GalNAc)₃ as the targeting ligand. Upon subcutaneous administration, GalNAc-conjugated siRNAs rapidly distribute into the liver via asialoglycoprotein receptor (ASGPR) mediated uptake in the hepatocytes, resulting in fast elimination from the systemic circulation. Patisiran, on the other hand, has been formulated in a lipid nanoparticle for optimal delivery to the liver. While several publications have described preclinical and clinical pharmacokinetic (PK) and pharmacodynamic (PD) results, including absorption, distribution, metabolism, and elimination (ADME) profiles in selected species as well as limited modeling efforts for siRNA therapeutics, there is no systematic review of the PK and PD models developed for these agents or work summarizing the utility and application(s) of such models in drug development and regulatory review. Here, we provide a mini-review of the current state of modeling efforts for siRNA therapeutics within the early preclinical, translational, and clinical stages of siRNA development. Diverse modeling methods including simple compartmental, mechanistic and systems PK/PD, physiologically-based PK (PBPK), population PK/PD, and dose-response-time models are introduced and reviewed. The utility of such models in development and regulatory review for siRNA therapeutics is also discussed with examples. Finally, the current knowledge gaps in mechanism of action of siRNA and resulting challenges in model development are summarized. The goal of this minireview is to trigger cross-functional discussion amongst all key stakeholders to generate key experimental datasets and align on current assumptions, model structures, and approaches to facilitate development and application of robust PK/PD models for siRNA therapeutics.

Cellular kinetics: A clinical and computational review of CAR-T cell pharmacology

To the extent that pharmacokinetics influence the effectiveness of nonliving therapeutics, so too do cellular kinetics influence the efficacy of Chimeric Antigen Receptor (CAR) -T cell therapy. Like conventional therapeutics, CAR-T cell therapies undergo a distribution phase upon administration. Unlike other therapeutics, however, this distribution phase is followed by subsequent phases of expansion, contraction, and persistence. The magnitude and duration of these phases unequivocally influence clinical outcomes. Furthermore, the "pharmacodynamics" of CAR-T cells is truly dynamic, as cells can rapidly become exhausted and lose their therapeutic efficacy. Mathematical models are among the translational tools commonly applied to assess, characterize, and predict the complex cellular kinetics and dynamics of CAR-T cells. Here, we provide a focused review of the cellular kinetics of CAR-T cells, the mechanisms underpinning their complexity, and the mathematical modeling approaches used to interrogate them.

B cell targeting in CAR T cell therapy: Side effect or driver of CAR T cell function?

Chimeric antigen receptor (CAR) T cell therapies targeting CD19 and CD22 have been successful for treating B cell cancers, but CAR T cells targeting non-B cell cancers remain unsuccessful. We propose that rather than being strictly a side effect of therapy, the large number of CAR interactions with normal B cells may be a key contributor to clinical CAR T cell responses.

Recent advances in the translation of drug metabolism and pharmacokinetics science for drug discovery and development

Drug metabolism and pharmacokinetics (DMPK) is an important branch of pharmaceutical sciences. The nature of ADME (absorption, distribution, metabolism, excretion) and PK (pharmacokinetics) inquiries during drug discovery and development has evolved in recent years from being largely descriptive to seeking a more quantitative and mechanistic understanding of the fate of drug candidates in biological systems. Tremendous progress has been made in the past decade, not only in the characterization of physiochemical properties of drugs that influence their ADME, target organ exposure, and toxicity, but also in the identification of design principles that can minimize drug-drug interaction (DDI) potentials and reduce the attritions. The importance of membrane transporters in drug disposition, efficacy, and safety, as well as the interplay with metabolic processes, has been increasingly recognized. Dramatic increases in investments on new modalities beyond traditional small and large molecule drugs, such as peptides, oligonucleotides, and antibody-drug conjugates, necessitated further innovations in bioanalytical and experimental tools for the characterization of their ADME properties. In this review, we highlight some of the most notable advances in the last decade, and provide future perspectives on potential major breakthroughs and innovations in the translation of DMPK science in various stages of drug discovery and development.

Evolution of Preclinical Characterization and Insights into Clinical Pharmacology of Checkpoint Inhibitors Approved for Cancer Immunotherapy

Cancer immunotherapy has significantly advanced the treatment paradigm in oncology, with approvals of immuno‐oncology agents for over 16 indications, many of them first line. Checkpoint inhibitors (CPIs) are recognized as an essential backbone for a successful anticancer therapy regimen. This review focuses on the US Food and Drug Administration (FDA) regulatory approvals of major CPIs and the evolution of translational advances since their first approval close to a decade ago. In addition, critical preclinical and clinical pharmacology considerations, an overview of the pharmacokinetic and dose/regimen aspects, and a discussion of the future of CPI translational and clinical pharmacology as combination therapy becomes a mainstay of industrial immunotherapy development and in clinical practice are also discussed.

Chimeric Antigen Receptor T-Cell Therapy in Lung Cancer: Potential and Challenges

Chimeric antigen receptor T (CAR-T) cell therapy has exhibited a substantial clinical response in hematological malignancies, including B-cell leukemia, lymphoma, and multiple myeloma. Therefore, the feasibility of using CAR-T cells to treat solid tumors is actively evaluated. Currently, multiple basic research projects and clinical trials are being conducted to treat lung cancer with CAR-T cell therapy. Although numerous advances in CAR-T cell therapy have been made in hematological tumors, the technology still entails considerable challenges in treating lung cancer, such as on−target, of−tumor toxicity, paucity of tumor-specific antigen targets, T cell exhaustion in the tumor microenvironment, and low infiltration level of immune cells into solid tumor niches, which are even more complicated than their application in hematological tumors. Thus, progress in the scientific understanding of tumor immunology and improvements in the manufacture of cell products are advancing the clinical translation of these important cellular immunotherapies. This review focused on the latest research progress of CAR-T cell therapy in lung cancer treatment and for the first time, demonstrated the underlying challenges and future engineering strategies for the clinical application of CAR-T cell therapy against lung cancer.

Prediction of cytochromes P450 3A and 2C19 modulation by both inflammation and drug interactions using physiologically based pharmacokinetics

Xenobiotics can interact with cytochromes P450 (CYPs), resulting in drug-drug interactions, but CYPs can also contribute to drug-disease interactions, especially in the case of inflammation, which downregulates CYP activities through pretranscriptional and posttranscriptional mechanisms. Interleukin-6 (IL-6), a key proinflammatory cytokine, is mainly responsible for this effect. The aim of our study was to develop a physiologically based pharmacokinetic (PBPK) model to foresee the impact of elevated IL-6 levels in combination with drug interactions with esomeprazole on CYP3A and CYP2C19. Data from a cohort of elective hip surgery patients whose CYP3A and CYP2C19 activities were measured before and after surgery were used to validate the accurate prediction of the developed models. Successive steps were to fit models for IL-6, esomeprazole, and omeprazole and its metabolite from the literature and to validate them. The models for midazolam and its metabolite were obtained from the literature. When appropriate, a correction factor was applied to convert drug concentrations from whole blood to plasma. Mean ratios between simulated and observed areas under the curve for omeprazole/5-hydroxy omeprazole, esomeprazole, and IL-6 were 1.53, 1.06, and 0.69, respectively, indicating an accurate prediction of the developed models. The impact of IL-6 and esomeprazole on the exposure to CYP3A and CYP2C19 probe substrates and respective metabolites were correctly predicted. Indeed, the ratio between predicted and observed mean concentrations were <2 for all observations (ranging from 0.51 to 1.7). The impact of IL-6 and esomeprazole on CYP3A and CYP2C19 activities after a hip surgery were correctly predicted with the developed PBPK models.

Chimeric Antigen Receptor T Cell Therapies: A Review of Cellular Kinetic-Pharmacodynamic Modeling Approaches

Chimeric antigen receptor T cell (CAR-T cell) therapies have shown significant efficacy in CD19+ leukemias and lymphomas. There remain many challenges and questions for improving next-generation CAR-T cell therapies, and mathematical modeling of CAR-T cells may play a role in supporting further development. In this review, we introduce a mathematical modeling taxonomy for a set of relatively simple cellular kinetic-pharmacodynamic models that describe the in vivo dynamics of CAR-T cell and their interactions with cancer cells. We then discuss potential extensions of this model to include target binding, tumor distribution, cytokine-release syndrome, immunophenotype differentiation, and genotypic heterogeneity.

Minimal Physiologically Based Pharmacokinetic-Pharmacodynamic (mPBPK-PD) Model of N-Acetylgalactosamine-Conjugated Small Interfering RNA Disposition and Gene Silencing in Preclinical Species and Humans

Conjugation of small interfering RNA (siRNA) to tris N-acetylgalactosamine [(GalNAc)₃] can enable highly selective, potent, and durable knockdown of targeted proteins in the liver. However, potential knowledge gaps between in vitro experiments, preclinical species, and clinical scenarios remain. A minimal physiologically based pharmacokinetic-pharmacodynamic model for GalNAc-conjugated siRNA (GalNAc-siRNA) was developed using published data for fitusiran (ALN-AT3), an investigational compound targeting liver antithrombin (AT), to delineate putative determinants governing the whole-body-to-cellular pharmacokinetic (PK) and pharmacodynamic (PD) properties of GalNAc-siRNA and facilitate preclinical-to-clinical translation. The model mathematically linked relevant mechanisms: 1) hepatic biodistribution, 2) tris-GalNAc binding to asialoglycoprotein receptors (ASGPRs) on hepatocytes, 3) ASGPR endocytosis and recycling, 4) endosomal transport and escape of siRNA, 5) cytoplasmic RNA-induced silencing complex (RISC) loading, 6) degradation of target mRNA by bound RISC, and 7) knockdown of protein. Physiologic values for 36 out of 48 model parameters were obtained from the literature. Kinetic parameters governing (GalNAc)₃-ASGPR binding and internalization were derived from published studies of uptake in hepatocytes. The proposed model well characterized reported pharmacokinetics, RISC dynamics, and knockdown of AT mRNA and protein by ALN-AT3 in mice. The model bridged multiple PK-PD data sets in preclinical species (mice, rat, monkey) and successfully captured reported plasma pharmacokinetics and AT knockdown in a phase I ascending-dose study. Estimates of in vivo potency were similar (∼2-fold) across species. Subcutaneous absorption and serum AT degradation rate constants scaled across species by body weight with allometric exponents of -0.29 and -0.22. The proposed mechanistic modeling framework characterizes the unique PK-PD properties of GalNAc-siRNA. SIGNIFICANCE STATEMENT: Tris N-acetylgalactosamine (GalNAc)₃-conjugated small interfering RNA (siRNA) therapeutics enable liver-targeted gene therapy and precision medicine. Using a translational and systems-based minimal physiologically based pharmacokinetic-pharmacodynamic (mPBPK-PD) modeling approach, putative determinants influencing GalNAc-conjugated siRNA (GalNAc-siRNA) functionality in three preclinical species and humans were investigated. The developed model successfully integrated and characterized relevant published in vitro-derived biomeasures, mechanistic PK-PD profiles in animals, and observed clinical PK-PD responses for an investigational GalNAc-siRNA (fitusiran). This modeling effort delineates the disposition and liver-targeted pharmacodynamics of GalNAc-siRNA.

Predicted limited redistribution of T cells to secondary lymphoid tissue correlates with increased risk of haematological malignancies in asplenic patients

The spleen, a secondary lymphoid tissue (SLT), has an important role in generation of adaptive immune responses. Although splenectomy remains a common procedure, recent studies reported poor prognosis and increased risk of haematological malignancies in asplenic patients. The high baseline trafficking of T lymphocytes to splenic tissue suggests splenectomy may lead to loss of blood-borne malignant immunosurveillance that is not compensated for by the remaining SLT. To date, no quantitative analysis of the impact of splenectomy on the human T cell trafficking dynamics and tissue localisation has been reported. We developed a quantitative computational model that describes organ distribution and trafficking of human lymphocytes to explore the likely impact of splenectomy on immune cell distributions. In silico splenectomy resulted in an average reduction of T cell numbers in SLT by 35% (95%CI 0.12–0.97) and a comparatively lower, 9% (95%CI 0.17–1.43), mean decrease of T cell concentration in SLT. These results suggest that the surveillance capacity of the remaining SLT insufficiently compensates for the absence of the spleen. This may, in part, explain haematological malignancy risk in asplenic patients and raises the question of whether splenectomy has a clinically meaningful impact on patient responses to immunotherapy.

Pharmacokinetic and pharmacodynamic studies of CD19 CAR T cell in human leukaemic xenograft models with dual-modality imaging

In recent years, chimeric antigen receptor T (CAR T)-cell therapy has shown great potential in treating haematologic disease, but no breakthrough has been achieved in solid tumours. In order to clarify the antitumour mechanism of CAR T cell in solid tumours, the pharmacokinetic (PK) and pharmacodynamic (PD) investigations of CD19 CAR T cell were performed in human leukaemic xenograft mouse models. For PK investigation, we radiolabelled CD19 CAR T cell with ⁸⁹ Zr and used PET imaging in the CD19-positive and the CD19-negative K562-luc animal models. For PD evaluation, optical imaging, tumour volume measurement and DNA copy-number detection were performed. Unfortunately, the qPCR results of the DNA copy number in the blood were below the detection limit. The tumour-specific uptake was higher in the CD19-positive model than in the CD19-negative model, and this was consistent with the PD results. The preliminary PK and PD studies of CD19 CAR T cell in solid tumours are instructive. Considering the less efficiency of CAR T-cell therapy of solid tumours with the limited number of CAR T cells entering the interior of solid tumours, this study is suggestive for the subsequent CAR T-cell design and evaluation of solid tumour therapy.

Profiling pharmacokinetics of double-negative T cells and cytokines via a single intravenous administration in NSG mice

This study was intended to delineate the profile of double-negative T cells (DNTs) in NOD.Cg-Prkdcscid Il2rgtm1wj /SzJ mice and cytokines released from DNTs in vivo and in vitro. Total 4 × 107 cells of RC1012 injection per mice were intravenously infused. IFN-γ, TNF-α, IL-1β, IL-2, IL-4, IL-6, IL-10 were measured in vivo and in vitro. A quantitative polymerase chain reaction (PCR) was employed to determine the gene copies of Notch2-NLA per DNT cell from collected organs. Cytokines were significantly increased in vitro (4 h) and in vivo (3 h). DNT cells were distributed into the lung, liver, heart, and kidney earlier, and redistributed to lymphocyte homing spleen and bone marrow, which seemed to frame a two-compartment pharmacokinetics (PK) model but more data are needed to confirm this, and the clearance of DNT cells fell into first-order kinetics.

Cytokine Release Syndrome By T-cell-Redirecting Therapies: Can We Predict and Modulate Patient Risk?

T-cell-redirecting therapies are promising new therapeutic options in the field of cancer immunotherapy, but the development of these modalities is challenging. A commonly observed adverse event in patients treated with T-cell-redirecting therapies is cytokine release syndrome (CRS). Its clinical manifestation is a burden on patients, and continues to be a big hurdle in the clinical development of this class of therapeutics. We review different T-cell-redirecting therapies, discuss key factors related to cytokine release and potentially leading to CRS, and present clinical mitigation strategies applied for those modalities. We propose to dissect those risk factors into drug-target-disease-related factors and individual patient risk factors. Aiming to optimize the therapeutic intervention of these modalities, we illustrate how the knowledge on drug-target-disease-related factors, such as target expression, binding affinity, and target accessibility, can be leveraged in a model-based framework and highlight with case examples how modeling and simulation is applied to guide drug discovery and development. We draw attention to the current gaps in predicting the individual patient's risk towards a high-grade CRS, which requires further considerations of risk factors related, but not limited to, the patient's demographics, genetics, underlying pathologies, treatment history, and environmental exposures. The drug-target-disease-related factors together with the individual patient's risk factors can be regarded as the patient's propensity for developing CRS in response to therapy. As an outlook, we suggest implementing a risk scoring system combined with mechanistic modeling to enable the prediction of an individual patient's risk of CRS for a given therapeutic intervention.

Early Survival Prediction Framework in CD19-Specific CAR-T Cell Immunotherapy Using a Quantitative Systems Pharmacology Model

Chimeric antigen receptor (CAR)-T cell therapy has revolutionized treatment of relapsed/refractory non-Hodgkin lymphoma (NHL). However, since 36-60% of patients relapse, early response prediction is crucial. We present a novel population quantitative systems pharmacology model, integrating literature knowledge on physiology, immunology, and adoptive cell therapy together with 133 CAR-T cell phenotype, 1943 cytokine, and 48 metabolic tumor measurements. The model well described post-infusion concentrations of four CAR-T cell phenotypes and CD19+ metabolic tumor volume over 3 months after CAR-T cell infusion. Leveraging the model, we identified a low expansion subpopulation with significantly lower CAR-T cell expansion capacities amongst 19 NHL patients. Together with two patient-/therapy-related factors (autologous stem cell transplantation, CD4+/CD8+ T cells), the low expansion subpopulation explained 2/3 of the interindividual variability in the CAR-T cell expansion capacities. Moreover, the low expansion subpopulation had poor prognosis as only 1/4 of the low expansion subpopulation compared to 2/3 of the reference population were still alive after 24 months. We translated the expansion capacities into a clinical composite score (CCS) of 'Maximum naïve CAR-T cell concentrations/Baseline tumor burden' ratio and propose a CCSTN-value > 0.00136 (cells·µL-1·mL-1 as predictor for survival. Once validated in a larger cohort, the model will foster refining survival prediction and solutions to enhance NHL CAR-T cell therapy response.