Shaun Kumar (1), Shamshad Ali (2), Gloria Tao (2), Himal Thakar (2), David McDougall (1), Bruce Green (1)
(1) Parexel International, Australia, (2) bluebird bio, USA
Introduction: β-thalassemia is a rare hereditary genetic blood disease caused by mutations in the β-globin gene, resulting in reduced or absent production of functional hemoglobin. The standard care for patients with transfusion-dependent beta-thalassemia (TDT) requires regular (lifelong) transfusions of packed red blood cells. Recently the FDA approved an autologous transplant/ex-vivo gene therapy beti-cel (betibeglogene autotemcel; commercial brand name: Zynteglo), beti-cel is a gene therapy designed to add functional copies of a modified β-globin gene (via a lentiviral vector [LVV]encoding the βA-T87Q-globin gene) into TDT patients own hematopoietic stem cells (HSCs, specifically CD34+ cells). A lentiviral vector encoding the βA-T87Q-globin gene (producing hemoglobin containing βA-T87Q-globin, HbAT87Q) was transduced into the patients HSCs. Peripheral blood vector copy number (PB VCN, expressed as average number of vector copies per diploid genome; c/dg) is a measure of drug product (DP) vector copies that is used to monitor transgene integration. Over the course of clinical development, the manufacturing process was optimized from DP process 1 to DP process 2 (the commercial DP process), specifically the transduction step to increase the percentage of vector-positive transduced cells (DP %LVV+ cells) and the total VCN.
Objectives: To develop a pharmacodynamic (PD) model to describe the time course PB VCN and HbAT87Q post beti-cel infusion and specifically:
- Identify and quantify both patient and DP specific covariates that impacted PB VCN and HbAT87Q time to approach steady-state and thereby understand the dose-exposure-response relationship for beti-cel.
Methods: As of 09Mar2021, data (n=59) were available from three clinical studies (HGB-204, HGB-207, HGB-212) and the long-term follow-up study (LTF-303). Patients had either the non-β0/β0 genotype (n=39) or the β0/β0 genotype (n=20). A total of 566 PB VCN and 560 HbAT87Q observations were analyzed using a non-linear mixed effects modeling approach in NONMEM (Version 7.4, ICON, Hanover, MD), specifically using $PRED. A non-parametric bootstrap was performed to determine the 95% confidence interval (CI) for each model parameter. Simulations were conducted using the final model to assess the impact of covariates on the time to approach 90% of the steady-state values for both PB VCN and HbAT87Q.
Results: PB VCN data was best described by an exponential asymptotic growth model in terms of maximum VCN (VCNMAX, 0.831 c/dg, 95%CI 0.597 – 1.07) and rate of transgene appearance (KTA, 5.87/month, 95%CI 0.0081 – 0.0159). The statistically significant covariates identified were DP manufacturing process and DP %LVV+ cells. HbAT87Q data was described by an Emax model with a Hill slope. The parameters estimates were maximum HbAT87Q (HbMAX, 9.4 g/dL, 95%CI 8.4 – 12.3), time to 50% maximal HbAT87Q (ET50, 2.42/month, 95%CI 2.24 – 2.66), and Hill slope (γ, 3.85, 95%CI 3.28 – 4.39) The statistically significant covariates were the individual maximum PB VCN (derived from the PB VCN model) and DP manufacturing process.
Patients receiving DP Process 1 compared to patients that received DP Process 2 were had a lower median steady-state maximum PB VCN (0.336 c/dg vs 1.50 c/dg) and HbAT87Q (5.66 g/dL vs 8.85 g/dL) and a longer median time to achieve 90% steady-state maximum PB VCN (1.66 months vs 0.393 months) and HbAT87Q (6.44 months vs 3.91 months). Age, weight, sex, race, or genotype did not impact PB VCN or HBAT87Q time-course profiles.
Conclusions: PB VCN and HbAT87Q were fit to empirical time-based models, similar approaches have been used for other cell or gene-based therapies such as CAR-T cell therapy. Utilizing empirical models in this setting provided a fast and robust method for identifying important covariates as well as extrapolating steady-state values compared to more complex methods such as quantitative systems pharmacology (QSP).
Reference: PAGE 31 (2023) Abstr 10380 [www.page-meeting.org/?abstract=10380]
Poster: Drug/Disease Modelling - Other Topics