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

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.

The Combination of Cell Cultured Technology and In Silico Model to Inform the Drug Development

Human-derived in vitro models can provide high-throughput efficacy and toxicity data without a species gap in drug development. Challenges are still encountered regarding the full utilisation of massive data in clinical settings. The lack of translated methods hinders the reliable prediction of clinical outcomes. Therefore, in this study, in silico models were proposed to tackle these obstacles from in vitro to in vivo translation, and the current major cell culture methods were introduced, such as human-induced pluripotent stem cells (hiPSCs), 3D cells, organoids, and microphysiological systems (MPS). Furthermore, the role and applications of several in silico models were summarised, including the physiologically based pharmacokinetic model (PBPK), pharmacokinetic/pharmacodynamic model (PK/PD), quantitative systems pharmacology model (QSP), and virtual clinical trials. These credible translation cases will provide templates for subsequent in vitro to in vivo translation. We believe that synergising high-quality in vitro data with existing models can better guide drug development and clinical use.

A Systematic Review of the Efforts and Hindrances of Modeling and Simulation of CAR T-cell Therapy

Chimeric antigen receptor (CAR) T-cell therapy is an immunotherapy that has recently become highly instrumental in the fight against life-threatening diseases. A variety of modeling and computational simulation efforts have addressed different aspects of CAR T-cell therapy, including T-cell activation, T- and malignant cell population dynamics, therapeutic cost-effectiveness strategies, and patient survival. In this article, we present a systematic review of those efforts, including mathematical, statistical, and stochastic models employing a wide range of algorithms, from differential equations to machine learning. To the best of our knowledge, this is the first review of all such models studying CAR T-cell therapy. In this review, we provide a detailed summary of the strengths, limitations, methodology, data used, and data gap in currently published models. This information may help in designing and building better models for enhanced prediction and assessment of the benefit-risk balance associated with novel CAR T-cell therapies, as well as with the data need for building such models.

Bench-to-bedside translation of chimeric antigen receptor (CAR) T cells using a multiscale systems pharmacokinetic-pharmacodynamic model: A case study with anti-BCMA CAR-T

Despite tremendous success of chimeric antigen receptor (CAR) T cell therapy in clinical oncology, the dose-exposure-response relationship of CAR-T cells in patients is poorly understood. Moreover, the key drug-specific and system-specific determinants leading to favorable clinical outcomes are also unknown. Here we have developed a multiscale mechanistic pharmacokinetic (PK)-pharmacodynamic (PD) model for anti-B-cell maturation antigen (BCMA) CAR-T cell therapy (bb2121) to characterize (i) in vitro target cell killing in multiple BCMA expressing tumor cell lines at varying effector to target cell ratios, (ii) preclinical in vivo tumor growth inhibition and blood CAR-T cell expansion in xenograft mice, and (iii) clinical PK and PD biomarkers in patients with multiple myeloma. Our translational PK-PD relationship was able to effectively describe the commonly observed multiphasic CAR-T cell PK profile in the clinic, consisting of the rapid distribution, expansion, contraction, and persistent phases, and accounted for the categorical individual responses in multiple myeloma to effectively calculate progression-free survival rates. Preclinical and clinical data analysis revealed comparable parameter estimates pertaining to CAR-T cell functionality and suggested that patient baseline tumor burden could be more sensitive than dose levels toward overall extent of exposure after CAR-T cell infusion. Virtual patient simulations also suggested a very steep dose-exposure-response relationship with CAR-T cell therapy and indicated the presence of a "threshold" dose, beyond which a flat dose-response curve could be observed. Our simulations were concordant with multiple clinical observations discussed in this article. Moving forward, this framework could be leveraged a priori to explore multiple infusions and support the preclinical/clinical development of future CAR-T cell therapies.

Pharmacokinetic-Pharmacodynamic Modeling of Tumor Targeted Drug Delivery Using Nano-Engineered Mesenchymal Stem Cells

Nano-engineered mesenchymal stem cells (nano-MSCs) are promising targeted drug delivery platforms for treating solid tumors. MSCs engineered with paclitaxel (PTX) loaded poly(lactide-co-glycolide) (PLGA) nanoparticles (NPs) are efficacious in treating lung and ovarian tumors in mouse models. The quantitative description of pharmacokinetics (PK) and pharmacodynamics (PD) of nano-MSCs is crucial for optimizing their therapeutic efficacy and clinical translatability. However, successful translation of nano-MSCs is challenging due to their complex composition and physiological mechanisms regulating their pharmacokinetic-pharmacodynamic relationship (PK-PD). Therefore, in this study, a mechanism-based preclinical PK-PD model was developed to characterize the PK-PD relationship of nano-MSCs in orthotopic A549 human lung tumors in SCID Beige mice. The developed model leveraged literature information on diffusivity and permeability of PTX and PLGA NPs, PTX release from PLGA NPs, exocytosis of NPs from MSCs as well as PK and PD profiles of nano-MSCs from previous in vitro and in vivo studies. The developed PK-PD model closely captured the reported tumor growth in animals receiving no treatment, PTX solution, PTX-PLGA NPs and nano-MSCs. Model simulations suggest that increasing the dosage of nano-MSCs and/or reducing the rate of PTX-PLGA NPs exocytosis from MSCs could result in improved anti-tumor efficacy in preclinical settings.

Computational modelling of modern cancer immunotherapy

Modern cancer immunotherapy has revolutionised oncology and carries the potential to radically change the approach to cancer treatment. However, numerous questions remain to be answered to understand immunotherapy response better and further improve the benefit for future cancer patients. Computational models are promising tools that can contribute to accelerated immunotherapy research by providing new clues and hypotheses that could be tested in future trials, based on preceding simulations in addition to the empirical rationale. In this topical review, we briefly summarise the history of cancer immunotherapy, including computational modelling of traditional cancer immunotherapy, and comprehensively review computational models of modern cancer immunotherapy, such as immune checkpoint inhibitors (as monotherapy and combination treatment), co-stimulatory agonistic antibodies, bispecific antibodies, and chimeric antigen receptor T cells. The modelling approaches are classified into one of the following categories: data-driven top-down vs mechanistic bottom-up, simplistic vs detailed, continuous vs discrete, and hybrid. Several common modelling approaches are summarised, such as pharmacokinetic/pharmacodynamic models, Lotka-Volterra models, evolutionary game theory models, quantitative systems pharmacology models, spatio-temporal models, agent-based models, and logic-based models. Pros and cons of each modelling approach are critically discussed, particularly with the focus on the potential for successful translation into immuno-oncology research and routine clinical practice. Specific attention is paid to calibration and validation of each model, which is a necessary prerequisite for any successful model, and at the same time, one of the main obstacles. Lastly, we provide guidelines and suggestions for the future development of the field.

Considerations in the development and validation of real-time quantitative polymerase chain reaction and its application in regulated bioanalysis to characterize the cellular kinetics of CAR-T products in clinical studies

Real-time quantitative polymerase chain reaction (qPCR) has become the standard method for monitoring cellular kinetics of CAR-T therapies with measurement of the CAR transgene copy numbers in peripheral blood mononuclear cells isolated from patients receiving the treatment. Unlike other biophysical and immunological methodologies for bioanalytical characterization of conventional small molecule drugs or protein biologics, there is no relevant regulatory guidance to date on the method development and validation for quantitative qPCR assays employed during clinical development of CAR-T products. This paper will provide an overview and considerations in the development and validation of a qPCR assay from sample extraction to assay parameters and its implementation in regulated bioanalysis for CAR-T or other types of cell therapies.

Informing Development of Bispecific Antibodies Using Physiologically Based Pharmacokinetic-Pharmacodynamic Models: Current Capabilities and Future Opportunities

Antibody therapeutics continue to represent a significant portion of the biotherapeutic pipeline, with growing promise for bispecific antibodies (BsAbs). BsAbs can target 2 different antigens at the same time, such as simultaneously binding tumor-cell receptors and recruiting cytotoxic immune cells. This simultaneous engagement of 2 targets can be potentially advantageous, as it may overcome disadvantages posed by a monotherapy approach, like the development of resistance to treatment. Combination therapy approaches that modulate 2 targets simultaneously offer similar advantages, but BsAbs are more efficient to develop. Unlike combination approaches, BsAbs can facilitate spatial proximity of targets that may be necessary to induce the desired effect. Successful development of BsAbs requires understanding antibody formatting and optimizing activity for both targets prior to clinical trials. To realize maximal efficacy, special attention is required to fully define pharmacokinetic (PK)/pharmacodynamic (PD) relationships enabling selection of dose and regimen. The application of physiologically based pharmacokinetics (PBPK) has been evolving to inform the development of novel treatment modalities such as bispecifics owing to the increase in our understanding of pharmacology, utility of multiscale models, and emerging clinical data. In this review, we discuss components of PBPK models to describe the PK characteristics of BsAbs and expand the discussion to integration of PBPK and PD models to inform development of BsAbs. A framework that can be adopted to build PBPK-PD models to inform the development of BsAbs is also proposed. We conclude with examples that highlight the application of PBPK-PD and share perspectives on future opportunities for this emerging quantitative tool.

Model-Based Cellular Kinetic Analysis of Chimeric Antigen Receptor-T Cells in Humans

Chimeric antigen receptor (CAR)-T cell therapy has achieved considerable success in treating B-cell hematologic malignancies. However, the challenges of extending CAR-T therapy to other tumor types, particularly solid tumors, remain appreciable. There are substantial variabilities in CAR-T cellular kinetics across CAR-designs, CAR-T products, dosing regimens, patient responses, disease types, tumor burdens, and lymphodepletion conditions. As a "living drug," CAR-T cellular kinetics typically exhibit four distinct phases: distribution, expansion, contraction, and persistence. The cellular kinetics of CAR-T may correlate with patient responses, but which factors determine CAR-T cellular kinetics remain poorly defined. Herein, we developed a cellular kinetic model to retrospectively characterize CAR-T kinetics in 217 patients from 7 trials and compared CAR-T kinetics across response status, patient populations, and tumor types. Based on our analysis results, CAR-T cells exhibited a significantly higher cell proliferation rate and capacity but a lower contraction rate in patients who responded to treatment. CAR-T cells proliferate to a higher degree in hematologic malignancies than in solid tumors. Within the assessed dose ranges (10⁷ -10⁹ cells), CAR-T doses were weakly correlated with CAR-T cellular kinetics and patient response status. In conclusion, the developed CAR-T cellular kinetic model adequately characterized the multiphasic CAR-T cellular kinetics and supported systematic evaluations of the potential influencing factors, which can have significant implications for the development of more effective CAR-T therapies.

Quantitative Clinical Pharmacology of T-Cell Engaging Bispecifics: Current Perspectives and Opportunities

T-cell directing/engaging bispecifics (TDBs) enable a powerful mode of action by activating T-cells through the creation of artificial immune synapses. Their pharmacological response involves the dynamic inter-relationships among T-cells, tumor cells, and TDBs. This results in complex and challenging issues in understanding pharmacokinetics, tissue distribution, target engagement, and exposure-response relationship. Dosing strategy plays a crucial role in determining the therapeutic window of TDBs because of the desire to maximize therapeutic efficacy in the context of known mechanism-related adverse events, such as cytokine release syndrome and neurological adverse events. Such adverse events are commonly reported as the most prominent events during the initial treatment cycles and dissipate over time. Therefore, the kinetic characterization of the inter-relationships between exposure/target engagement and safety/efficacy outcomes is crucial in designing the optimal dosing regimen to maximize the benefit/risk of TDB agents. In this review, we discuss the key clinical pharmacological considerations in drug discovery and development for TDBs and provide a summary of TDBs currently in clinical development. We also propose forward-looking perspectives and opportunities to derive insights through quantitative clinical pharmacology approaches.

Opportunities for Quantitative Translational Modeling in Oncology

A 2-day meeting was held by members of the UK Quantitative Systems Pharmacology Network () in November 2018 on the topic of Translational Challenges in Oncology. Participants from a wide range of backgrounds were invited to discuss current and emerging modeling applications in nonclinical and clinical drug development, and to identify areas for improvement. This resulting perspective explores opportunities for impactful quantitative pharmacology approaches. Four key themes arose from the presentations and discussions that were held, leading to the following recommendations: Evaluate the predictivity and reproducibility of animal cancer models through precompetitive collaboration. Apply mechanism of action (MoA) based mechanistic models derived from nonclinical data to clinical trial data. Apply MoA reflective models across trial data sets to more robustly quantify the natural history of disease and response to differing interventions. Quantify more robustly the dose and concentration dependence of adverse events through mathematical modelling techniques and modified trial design.

T-cell receptor repertoire analysis for the diagnosis and treatment of solid tumor: A methodology and clinical applications

T cells, which are involved in adaptive immunity, are essential in the elimination of tumor cells. Mature T cells can specifically recognize the antigen on the major histocompatibility complex (MHC) molecule through T‐cell receptors (TCR). The unique rearrangement mechanisms during T‐cell maturation provide great diversity to TCR, ensuring specific recognition between T cells and antigens. Thus, TCR repertoire analysis occupied an important position in T‐cell regarding research. Nowadays, next‐generation sequencing technology allows the simultaneous detection of TCR sequences with high throughput, and several evaluation indexes facilitate the measure of TCR repertoire. Based on this new methodology, discoveries are made across a range of tumor types. Results have shed light on the TCR repertoire differences between cancer patients and healthy control as well as between individual's lesions, paracancer, and peripheral blood samples. The potential of TCR repertoire as a biomarker for immunotherapy efficacy is also widely studied as TCR repertoire represents different baseline within individuals and shows dynamic change during treatment. Accurate delineation of the T‐cell repertoire can further the understanding of the immune system response to tumorigenesis. Still, existing researches are insufficient to clarify the specific clinical implications of TCR dynamic change and the definite role of TCR repertoire diversity during the treatment process. The results of some studies are even contrary. In this article, we reviewed TCR rearrangement mechanisms and analysis methods. Recent progress of TCR sequencing technology in tumor research is also discussed. In conclusion, intensive studies over an extended range of cancer types and a broadened group of subjects should be carried to solidify the TCR repertoire's position as an immunotherapy biomarker.