Franziska Kluwe (1), Alejandro Pérez-Pitarch (1)*, Eva Germovsek (1), Ronald Niebecker (1) and Lukas Kovar (1)
(1) Boehringer Ingelheim Pharma GmbH & Co. KG, Germany; *at time of project involvement, now: Regeneron Pharmaceuticals, Inc., Tarrytown, NY
Objectives: T cell engager therapies can direct T cells to attack tumor cells, thereby creating an immunological synapse that results in the elimination of tumor cells [1]. The downstream signaling of the T cell receptor in these therapies triggers the release of pro-inflammatory cytokines, potentially causing Cytokine Release Syndrome (CRS) [2]. The predictability and management of CRS incidence pose significant challenges associated with the on-target activity of T cell engager therapies [3]. This highlights the importance to gain a better understanding of CRS and the need for tools to optimize T cell engager drug therapies. Therefore, the aim of this work was to develop a population pharmacokinetic/pharmacodynamic model for the T cell engager BI 764532 that can describe the incidence of different CRS event grades over the time course of T cell engager treatment.
Methods: The ordered categorical CRS event data used for model development originated from a Phase I dose escalation trial (NCT04429087) that investigates anti-tumor efficacy of BI 764532 in patients with relapsed/refractory small-cell lung cancer (SCLC) or neuroendocrine carcinomas (NECs) [4]. The data was investigated applying a proportional odds model for the probabilities of observing CRS grades ‘1’, ‘2 or higher’ (according to Common Terminology Criteria for Adverse Events v5.0) and ‘no CRS event’ after repeated dosing. The model estimates the cumulative probabilities of the different CRS event grades which are affected by drug exposure. Random effects implemented as additive distribution on baseline parameters or exponential distribution for drug effect-related parameters were investigated. Model parameter estimates were obtained by maximum likelihood as well as with the use of the Laplacian estimation method as implemented for the analysis of ordered categorical data in NONMEM. Goodness of fit was assessed based on the objective function value and using simulated-based diagnostics, including visual predictive checks.
Results: CRS event data was available from 107 patients. Overall, 89 CRS events of grade 1 and 20 CRS events of grade 2 or higher were integrated for model development. Various models describing the drug effect on the probability of CRS events at different doses and dosing regimens were evaluated. In the current best model, for predicting the probabilities of a CRS event of different grades, a drug effect was implemented which depended both on the drug concentration in the central compartment as well as on a hypothetical CRS event capacity (CRSC). The CRSC was implemented as a turnover model where the CRSC decrease is driven by drug exposure. The drug concentration, an interindividual variability and the CRSC were linked to the probability of observing CRS events grade ≥1. Visual predictive checks showed that the model was able to successfully describe the decreasing risk of CRS events over time in the Phase I dose escalation trial setting.
Conclusions: The integration of exposure-response with a proportional odds modelling approach led to successful description of the incidence of ordered categorical CRS event data from 107 patients. The model could be used to further investigate predictors of CRS events as well as to simulate CRS event rates in different settings. As a result, this model may help to support the clinical development program of BI 764532 and other T cell engagers.
References:
[1] Carrasco-Padilla C, Hernaiz-Esteban A, Álvarez-Vallina L, Aguilar-Sopeña O, Roda-Navarro P. Bispecific Antibody Format and the Organization of Immunological Synapses in T Cell-Redirecting Strategies for Cancer Immunotherapy. Pharmaceutics 2023; 15(1):132.
[2] Khadka RH, Sakemura R, Kenderian SS, Johnson AJ. Management of cytokine release syndrome: an update on emerging antigen-specific T cell engaging immunotherapies. Immunotherapy 2019; 11:10, 851-857.
[3] Leclercq G, Steinhoff N, Haegel H, De Marco D, Bacac M, Klein C. Novel strategies for the mitigation of cytokine release syndrome induced by T cell engaging therapies with a focus on the use of kinase inhibitors. OncoImmunology 2022; 11:1.
[4] Wermke M, Felip E, Gambardella V, Kuboki Y, Morgensztern D, Hamed ZO, Liu M, Studeny M, Owonikoko TK. Phase I trial of the DLL3/CD3 bispecific T-cell engager BI 764532 in DLL3-positive small-cell lung cancer and neuroendocrine carcinomas. Future Oncol; 2022; 18(24); 2639-2649.
Reference: PAGE 32 (2024) Abstr 10883 [www.page-meeting.org/?abstract=10883]
Poster: Drug/Disease Modelling - Oncology