Students
Elsa Hélène Smith
MRes Clinical Drug Development / University College London (UCL), United Kingdom
PharMetrX Research+ Program
PhD student year: 2025
University of PhD: Freie Universität Berlin
Supervisor: Prof. Charlotte Kloft
Co-Supervisor: Prof. Wilhelm Huisinga
Mentoring I-Partner: SANOFI
PhD Project
Treatment optimisation of CAR-T cell therapy using mechanistic modelling
Chimeric antigen receptor (CAR)-T cell therapy is a novel cancer immunotherapy that is transforming the treatment landscape of haematological malignancies by using a patient’s own immune system to target and eliminate tumour cells. Currently, seven FDA-approved CAR-T cell products are available for the treatment of various haematological malignancies, including acute lymphoblastic leukaemia (ALL), diffuse large B-cell lymphoma (DLBCL), and multiple myeloma (MM). Although CAR-T cell therapies have shown the potential to induce deep and durable remissions, a substantial proportion of patients either fail to respond initially or relapse after treatment. Clinical data indicate that 40% to 60% of patients treated with anti-CD19 CAR-T cell therapy for DLBCL or ALL relapse within 12 months [1], with similar relapse rates observed for anti-BCMA (B-cell maturation antigen) CAR-T cell therapy [1]. However, the factors underlying these variable outcomes remain understudied, representing a critical knowledge gap. As CAR-T therapies gain wider clinical use, addressing this gap is crucial to enhance long-term response, identify patients at risk of early relapse, and improve outcomes.
My doctoral thesis focuses on optimising CAR-T cell therapy through mechanistic modelling approaches and support improved clinical decision-making. Pharmacometric approaches for modelling and simulation of dose-exposure and exposure-response relationships will be applied. Nonlinear mixed-effects modelling allows for the analysis of concentration-time profiles of drugs and CAR-T cell subsets at the level of a population of individuals, while capturing variability across patients [2,3]. In addition, individual demographic and clinical characteristics can be included as covariates to explain factors influencing variability from the typical population profile [2,3]. Altogether, modelling will form the basis for optimising CAR-T cell therapy throughout my doctoral studies.
Several factors influence CAR-T cell therapy outcomes, including patient-specific characteristics, the lymphodepleting conditioning regimen prior to the therapy and the CAR-T cell product itself [4]. In the context of my doctoral thesis, the improvement of lymphodepletion regimens and early survival prediction of CAR-T cell therapy is of special interest, especially given that these areas remain understudied, despite their critical role in treatment efficacy and the expanding clinical use of CAR-T cell therapies. Lymphodepletion chemotherapy is administered to patients 2-5 days prior to CAR-T cell infusion. This conditioning regimen, consisting of a combination of cyclophosphamide and fludarabine, aims at enhancing CAR-T cell engraftment, expansion, persistence, and overall therapeutic efficacy. Several mechanisms underly the benefits of lymphodepletion: (a) depletion of endogenous lymphocytes, (b) reduction of tumour cells, and (c) conditioning of the immune microenvironment to facilitate CAR-T cells homing [5]. Hence, unsuccessful lymphodepletion may lead to suboptimal CAR-T cell expansion and persistence and ultimately contribute to worse treatment outcomes. Understanding factors impacting the PK of lymphodepleting drugs, their link to successful lymphodepletion and to CAR-T cell expansion is thus of high interest.
In addition to lymphodepleting chemotherapy, several factors influence CAR-T cell expansion. These include individual clinical or biological characteristics, initial tumour burden and the immunophenotypic characteristics of the infused CAR-T cells [6,7]. Notably, T cell differentiation is associated with reduced proliferative capacity [6]. Despite growing insights into theses determinants, substantial variability in CAR-T cell expansion persists across patients. Predicting which individuals will benefit most from CAR-T cell therapy remains challenging. As a result, enhancing early prediction of treatment response and long-term survival remains a key area interest.
Project 1 will investigate the role of lymphodepletion in CAR-T cell therapy, with the aim of optimising lymphodepletion regimens. Pharmacokinetic (PK) models for the lymphodepleting agents fludarabine and cyclophosphamide will be developed to identify the factors impacting their PK profiles. The PK models will then be linked to the pharmacodynamic (PD) effects of the lymphodepleting drugs including changes in the number and composition of endogenous lymphocytes, reduction of tumour cells, and conditioning the immune microenvironment to facilitate CAR-T cells homing. For this purpose, biomarkers such as absolute lymphocyte count and concentrations of IL-7 and IL-15 will be used. Building on a mechanistic CAR-T cell model previously developed by our research group based on data of 19 Non-Hodgkin lymphoma patients [8], the PK/PD of fludarabine and cyclophosphamide will be linked to the expansion, therapeutic efficacy, and toxicity of CAR-T cells. Thus, this work aims to investigate the impact of lymphodepletion therapy on patient conditioning for CAR-T cell therapy, CAR-T cell expansion, and treatment outcomes, to ultimately optimise fludarabine and cyclophosphamide dosing and improve lymphodepleting regimens
Project 2 will focus on improving early response and survival prediction in CAR-T cell therapy. Clinical data is currently being collected through our network of clinical collaborators at CAR-T cell centres, including Charité and Ludwig-Maximilians-Universität, guided by an optimal sampling design developed by our research group [9]. These data will be pooled to validate and extend our previously developed CAR-T cell mechanistic model [8], leveraging information on the immune status before T cell collection and cell characteristics in the infusion product. By gaining a deeper understanding of CAR-T cell PK, the objective is to identify early biomarkers and model-derived predictors that relate with treatment outcomes.
References
[1] A. Suchiita, S.C. Sonkar. Revolutionizing immunotherapy: the next frontier in CAR T-cell engineering. Critical Reviews in Oncology/Hematology 211: 104751 (2025).
[2] D.R. Mould, R.N. Upton. Basic concepts in population modeling, simulation, and model-based drug development. CPT Pharmacometrics Syst Pharmacol 1: e6 (2012).
[3] D.R. Mould, R.N. Upton. Basic concepts in population modeling, simulation, and model-based drug development-part 2: introduction to pharmacokinetic modeling methods. CPT Pharmacometrics Syst Pharmacol 2: e38 (2013).
[4] A.M. Mc Laughlin, P.A. Milligan, C. Yee, M. Bergstrand. Model‐informed drug development of autologous CAR‐T cell therapy: Strategies to optimize CAR‐T cell exposure leveraging cell kinetic/dynamic modeling. CPT Pharmacom & Syst Pharma 12: 1577–1590 (2023).
[5] M. Canelo-Vilaseca, M. Sabbah, R. Di Blasi, C. Cristinelli, A. Sureda, S. Caillat-Zucman, C. Thieblemont. Lymphodepletion chemotherapy in chimeric antigen receptor-engineered T (CAR-T) cell therapy in lymphoma. Bone Marrow Transplant 60: 559–567 (2025).
[6] S. Guedan, M. Ruella, C.H. June. Emerging cellular therapies for cancer. Ann. Rev. Immunol. 37: 145–171 (2019).
[7] N. Dasyam, P. George, R. Weinkove. Chimeric antigen receptor T‐cell therapies: Optimising the dose. Br J Clin Pharmacol 86: 1678–1689 (2020).
[8] A. Mueller-Schoell, N. Puebla-Osorio, R. Michelet, M.R. Green, A. Künkele, W. Huisinga, P. Strati, B. Chasen, S.S. Neelapu, C. Yee, C. Kloft. Early survival prediction framework in CD19-specific CAR-T cell immunotherapy using a quantitative systems pharmacology model. Cancers 13: 2782 (2021).
[9] F. Klima, A.M. Mc Laughlin, R. Michelet, A. Winkler, W. Huisinga, K. Haussmann, A. Künkele, A.C. Hooker, C. Kloft. Development of efficient CAR-T cell clinical study designs: Towards understanding of novel anticancer therapies through optimal experimental design. Annual Meeting of the Population Approach Group in Europe (PAGE), Thessaloniki, Greece, 04-06 June 2025. www.page-meeting.org/default.asp?abstract=11783
Education
- 03/2025: Entering PharMetrX
- 09/2024-01/2025: Internship, Clinical Pharmacology and Therapeutics, University College London (UCL)
- 09/2023-09/2024: Master of Research (MRes) Clinical Drug Development, University College London (UCL)
- 06/2023-07/2023: Internship, Institute of Genetics and Cancer, The University of Edinburgh
- 07/2022-08/2022: Internship, Centre for Brain Sciences, The University of Edinburgh
- 06/2021-07/2021: Internship, Faculty of Sciences and Technology, Université de Lille
- 09/2019-05/2023: Bachelor of Science (BSc Hons) Pharmacology, The University of Edinburgh (UK)
- 06/2019: Scientific French Baccalaureate, Lycée international Montebello, Lille, France