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

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.

Surrogacy and the Valuation of ATMPs: Taking Our Place in the Evidence Generation/Assessment Continuum

Medical technology is advancing rapidly, but established methods for health technology assessment are struggling to keep up. This challenge is particularly stark for the assessment of advanced therapy medicinal products-therapies often launched on the basis of single-arm studies powered to a surrogate primary endpoint. The most robust surrogacy methods investigate trial-level correlations between the treatment effect on the surrogate and the outcome of ultimate interest. However, these methods are often impossible with the evidence usually available for advanced therapy medicinal products at the time of the launch (randomized controlled trials are necessary for these advanced methods). Additionally, these surrogacy relationships are usually considered to be technology specific, adding uncertainty for any approach that primarily relies on historic data to estimate the surrogacy relationship for novel interventions such as advanced therapy medicinal products. The literature has already highlighted the need for early dialogue, staged assessment processes, and pricing arrangements that responsibly share the risk between the manufacturer and payer. However, it is our view that in addition to these critical developments, the modeling methods employed could also improve. Currently, health technology assessment practitioners typically either ignore the surrogate and simply extrapolate the endpoint of greatest patient relevance irrespective of the degree of maturity or assume historic surrogate relationships apply to the novel technology. In this opinion piece, we outline an additional avenue. By drawing on the understanding of the mechanism of action and insights generated earlier in the evidence generation/assessment continuum, cost-effectiveness modelers can make better use of the wider data available. These efforts are expected to reduce uncertainty at the time of the initial launch of pharmaceutical products and increase the value of subsequent data collection efforts.

"Digital twins elucidate critical role of Tscm in clinical persistence of TCR-engineered cell therapy"

Despite recent progress in adoptive T cell therapy for cancer, understanding and predicting the kinetics of infused T cells remains a challenge. Multiple factors can impact the distribution, expansion, and decay or persistence of infused T cells in patients. We have developed a novel quantitative systems pharmacology (QSP) model of TCR-transgenic T cell therapy in patients with solid tumors to describe the kinetics of endogenous T cells and multiple memory subsets of engineered T cells after infusion. These T cells undergo lymphodepletion, proliferation, trafficking, differentiation, and apoptosis in blood, lymph nodes, tumor site, and other peripheral tissues. Using the model, we generated patient-matched digital twins that recapitulate the circulating T cell kinetics reported from a clinical trial of TCR-engineered T cells targeting E7 in patients with metastatic HPV-associated epithelial cancers. Analyses of key parameters influencing cell kinetics and differences among digital twins identify stem cell-like memory T cells (Tscm) cells as an important determinant of both expansion and persistence and suggest that Tscm-related differences contribute significantly to the observed variability in cellular kinetics among patients. We simulated in silico clinical trials using digital twins and predict that Tscm enrichment in the infused product improves persistence of the engineered T cells and could enable administration of a lower dose. Finally, we verified the broader relevance of the QSP model, the digital twins, and findings on the importance of Tscm enrichment by predicting kinetics for two patients with pancreatic cancer treated with KRAS G12D targeting T cell therapy. This work offers insight into the key role of Tscm biology on T cell kinetics and provides a quantitative framework to evaluate cellular kinetics for future efforts in the development and clinical application of TCR-engineered T cell therapies.

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.

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 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.

Predictive short/long-term efficacy biomarkers and resistance mechanisms of CD19-directed CAR-T immunotherapy in relapsed/refractory B-cell lymphomas

Genetically modified T-cell immunotherapies are revolutionizing the therapeutic options for hematological malignancies, especially those of B-cell origin. Impressive efficacies of CD19-directed chimeric antigen receptor (CAR)-T therapy have been reported in refractory/relapsed (R/R) B-cell non-Hodgkin lymphoma (NHL) patients who were resistant to current standard therapies, with a complete remission (CR) rate of approximately 50%. At the same time, problems of resistance and relapse following CAR-T therapy have drawn growing attention. Recently, great efforts have been made to determine various factors that are connected to the responses and outcomes following CAR-T therapy, which may not only allow us to recognize those with a higher likelihood of responding and who could benefit most from the therapy but also identify those with a high risk of resistance and relapse and to whom further appropriate treatment should be administered following CAR-T therapy. Thus, we concentrate on the biomarkers that can predict responses and outcomes after CD19-directed CAR-T immunotherapy. Furthermore, the mechanisms that may lead to treatment failure are also discussed in this review.

Mathematical modeling of cancer immunotherapy for personalized clinical translation

Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients' lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies.

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.

Review: Role of Model-Informed Drug Development Approaches in the Lifecycle of Drug Development and Regulatory Decision-Making

Model-informed drug development (MIDD) is a powerful approach to support drug development and regulatory review. There is a rich history of MIDD applications at the U.S. Food and Drug Administration (FDA). MIDD applications span across the life cycle of the development of new drugs, generics, and biologic products. In new drug development, MIDD approaches are often applied to inform clinical trial design including dose selection/optimization, aid in the evaluation of critical regulatory review questions such as evidence of effectiveness, and development of policy. In the biopharmaceutics space, we see a trend for increasing role of computational modeling to inform formulation development and help strategize future in vivo studies or lifecycle plans in the post approval setting. As more information and knowledge becomes available pre-approval, quantitative mathematical models are becoming indispensable in supporting generic drug development and approval including complex generic drug products and are expected to help reduce overall time and cost. While the application of MIDD to inform the development of cell and gene therapy products is at an early stage, the potential for future application of MIDD include understanding and quantitative evaluation of information related to biological activity/pharmacodynamics, cell expansion/persistence, transgene expression, immune response, safety, and efficacy. With exciting innovations on the horizon, broader adoption of MIDD is poised to revolutionize drug development for greater patient and societal benefit.

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.

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.

A combination therapy of oncolytic viruses and chimeric antigen receptor T cells: a mathematical model proof-of-concept

Combining chimeric antigen receptor T (CAR-T) cells with oncolytic viruses (OVs) has recently emerged as a promising treatment approach in preclinical studies that aim to alleviate some of the barriers faced by CAR-T cell therapy. In this study, we address by means of mathematical modeling the main question of whether a single dose or multiple sequential doses of CAR-T cells during the OVs therapy can have a synergetic effect on tumor reduction. To that end, we propose an ordinary differential equations-based model with virus-induced synergism to investigate potential effects of different regimes that could result in efficacious combination therapy against tumor cell populations. Model simulations show that, while the treatment with a single dose of CAR-T cells is inadequate to eliminate all tumor cells, combining the same dose with a single dose of OVs can successfully eliminate the tumor in the absence of virus-induced synergism. However, in the presence of virus-induced synergism, the same combination therapy fails to eliminate the tumor. Furthermore, it is shown that if the intensity of virus-induced synergy and/or virus oncolytic potency is high, then the induced CAR-T cell response can inhibit virus oncolysis. Additionally, the simulations show a more robust synergistic effect on tumor cell reduction when OVs and CAR-T cells are administered simultaneously compared to the combination treatment where CAR-T cells are administered first or after OV injection. Our findings suggest that the combination therapy of CAR-T cells and OVs seems unlikely to be effective if the virus-induced synergistic effects are included when genetically engineering oncolytic viral vectors.

Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?

Health technology assessment (HTA) aims to be a systematic, transparent, unbiased synthesis of clinical efficacy, safety, and value of medical products (MPs) to help policymakers, payers, clinicians, and industry to make informed decisions. The evidence available for HTA has gaps—impeding timely prediction of the individual long-term effect in real clinical practice. Also, appraisal of an MP needs cross-stakeholder communication and engagement. Both aspects may benefit from extended use of modeling and simulation. Modeling is used in HTA for data-synthesis and health-economic projections. In parallel, regulatory consideration of model informed drug development (MIDD) has brought attention to mechanistic modeling techniques that could in fact be relevant for HTA. The ability to extrapolate and generate personalized predictions renders the mechanistic MIDD approaches suitable to support translation between clinical trial data into real-world evidence. In this perspective, we therefore discuss concrete examples of how mechanistic models could address HTA-related questions. We shed light on different stakeholder's contributions and needs in the appraisal phase and suggest how mechanistic modeling strategies and reporting can contribute to this effort. There are still barriers dissecting the HTA space and the clinical development space with regard to modeling: lack of an adapted model validation framework for decision-making process, inconsistent and unclear support by stakeholders, limited generalizable use cases, and absence of appropriate incentives. To address this challenge, we suggest to intensify the collaboration between competent authorities, drug developers and modelers with the aim to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders.