Ken Kurosawa 1, Lijuan Kang 2, Yue Guo 2, Maria Rubin 3, Arpan Patel 2, Tzu-min Yeh 2, Deborah Briseno-Toomey 2, Lisa O’Rourke 2, Anne Seifert 4, Kristen Lantz 2, Weili Sun 5, Robin Carson 2, Indrajeet Singh 2, Nahor Haddish-Berhane 2, Mahesh N. Samtani 2, Yaming Su 3, Carlos Perez-Ruixo 6
1 Johnson & Johnson Innovative Medicine (Tokyo, Japan), 2 Johnson & Johnson Innovative Medicine (Spring House, USA), 3 Johnson & Johnson Innovative Medicine (Raritan, USA), 4 Johnson & Johnson (High Wycombe, UK), 5 Johnson & Johnson (Los Angeles, USA), 6 Johnson & Johnson Innovative Medicine (Madrid, Spain)
Objectives:
Teclistamab, a bispecific antibody targeting B-cell maturation antigen (BCMA) and cluster of differentiation 3 (CD3), is approved as monotherapy for relapsed or refractory multiple myeloma (RRMM) based on MajesTEC-1 study [1]. The objectives of this analysis are to: (1) characterize the population pharmacokinetics (popPK) of teclistamab for RRMM patients who have received at least one prior line of therapy and were treated with teclistamab SC in combination with daratumumab SC (Tec-Dara) [2] based on an existing popPK model for teclistamab monotherapy [3]; (2) evaluate the impact of potential covariates on teclistamab PK and (3) explore the relationship between teclistamab exposure and efficacy and safety endpoints.
Methods:
The popPK analysis pooled data from MajesTEC-3 and TriMM-2 studies. MajesTEC-3 is a Phase 3, randomized, open label study comparing Tec-Dara with daratumumab plus pomalidomide/dexamethasone (DPd) or daratumumab plus bortezomib/dexamethasone (DVd) and TriMM-2, a Phase 1b, multicohort study evaluating daratumumab subcutaneous (SC) regimens combined with teclistamab or talquetamab. Serum concentration-time data of teclistamab from Tec-Dara SC cohorts together with demographic and baseline disease-status covariates, were included in the analysis including a total of 2,515 PK samples from 385 participants (275 participants from MajesTEC-3 and 110 participants from TriMM-2, respectively). The existing popPK model comprised a two-compartment model with first-order absorption and time-independent and time-dependent elimination pathways [3]. The existing model was parameterized in terms of CL1, CL2, V1, KDES, Q, V2, Ka, and F, and incorporated covariates for body weight, International Staging System (ISS) staging, and type of multiple myeloma (IgG vs non-IgG). External model validation was based on the maximum a posteriori approach (NONMEM version 7.5) [4] gauged by goodness-of-fit (GOF) plots and prediction-corrected visual predictive checks (pcVPC) [5]. For MajesTec-3, early exposure (Cave,1stdose) and long-term treatment exposure (Ctrough,Q2W and Ctrough Q4W) were evaluated across demographic, clinical, and disease related subgroups using forest plots. Exposure-response (E-R) analyses evaluated the exposure–efficacy relationship using Kaplan-Meier plots and Cox proportional hazards models for progression-free survival (PFS) and the exposure–safety relationships by comparing rates of Grade ≥3 treatment-emergent adverse events (TEAEs) across exposure quartiles in MajesTEC-3.
Results:
The existing popPK model adequately described teclistamab PK under the combination therapy, indicating no impact of daratumumab. The popPK model was considered suitable for simulating and deriving the hereinbefore described exposure metrics. None of the investigated covariates (age, sex, race, ethnicity, country, region, body weight, albumin, hepatic and renal function, and baseline disease status such as Eastern Cooperative Oncology Group [ECOG], number of prior therapy lines, ISS stage, cytogenetic risk, soluble B cell maturation antigen [sBCMA], bone marrow percentage of plasma cells, and presence of plasmacytomas) had clinically meaningful effects on teclistamab PK exposure with all geometric mean ratios within 80-125%, except for type of myeloma (IgG vs non-IgG). The IgG type myeloma patient had ~20% lower exposure with E-R analysis showing no impact on efficacy, hence no dose adjustment in IgG type myeloma is warranted. The E-R analyses included 273 Tec-Dara participants and 290 DPd/DVd participants in MajesTEC-3. No statistically significant exposure-PFS relationship was observed for teclistamab, when analyzed by quartiles of exposure or as a continuous variable in the univariate Cox regression model with exposure metric Cave,1stdose. This result was consistent with long-term exposure metrics Ctrough,Q2W and Ctrough Q4W. The exposure-safety analysis evidenced that there was no increase in TEAE rates with increasing teclistamab exposure for assessed AEs: anemia, neutropenia, thrombocytopenia, and infections. No association between teclistamab exposure and Grade 5 fatal infections was observed in the Tec-Dara arm.
Conclusions:
Teclistamab PK in combination with daratumumab is well characterized by the existing teclistamab monotherapy popPK model, confirming no impact of daratumumab on teclistamab PK. None of the evaluated covariates had clinically meaningful effects on exposure, therefore, no teclistamab dose adjustment is warranted. Overall, findings from the E–R analysis indicate that the evaluated Tec Dara dosing regimen provides adequate teclistamab exposure and demonstrates a consistent safety profile across the exposure range, thereby supporting its recommendation for adult patients with RRMM who have received at least one prior line of therapy.
References:
[1] Moreau P, Garfall AL, van de Donk NWCJ, et al. Teclistamab in Relapsed or Refractory Multiple Myeloma. N Engl J Med. 2022;387(6):495-505.
[2] Costa LJ, Bahlis NJ, Perrot A, et al. Teclistamab plus Daratumumab in Relapsed or Refractory Multiple Myeloma. N Engl J Med. Published online December 9, 2025. doi:10.1056/NEJMoa2514663.
[3] Miao X, Wu LS, Lin SXW, et al. Population pharmacokinetics and exposure-response with teclistamab in patients with relapsed/refractory multiple myeloma: Results from MajesTEC-1. Target Oncol. 2023;18(5):667-684.
[4] NONMEM 7.5.0 Users Guides (1989-2009). Beal SL, Sheiner LB, Boeckmann AJ, and Bauer RJ (eds). Icon Development Solutions, Ellicott City, MD.
[5] Bergstrand M, Hooker AC, Wallin JE, et al. Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models. AAPS J. 2011;13(2):143-151.
Reference: PAGE 34 (2026) Abstr 11967 [www.page-meeting.org/?abstract=11967]
Poster: Drug/Disease Modelling - Oncology