Target-Mediated Drug Disposition of Daratumumab Following Intravenous Infusion in Relapsed or Refractory Multiple Myeloma after Prior Proteasome Inhibitors and Immunomodulatory Drugs: A Population Pharmacokinetic Analysis
Authors: Yan Xiaoyu , Clemens Pamela L , Puchalski Thomas , Lonial Sagar , Lokhorst Henk M. , Orlowski Robert Z. , Losic Nedjad , Khan Imran , Jansson Richard , Ahmadi Tahamtan , Lantz Kristen , Perez Ruixo Juan Jose , Zhou Honghui , Xu Xu Steven 
Published: October 2019

Introduction: Daratumumab is a first-in-class human anti-CD38 IgG1 monoclonal antibody in clinical development across the multiple myeloma (MM) disease spectrum. A population pharmacokinetics (PK) analysis was conducted (1) to characterize the target-mediated drug disposition (TMDD) and the variability associated with daratumumab disposition, (2) to evaluate the effects of individual demographic characteristics and other factors (e.g., laboratory tests and baseline disease status) on the disposition of daratumumab, (3) to understand daratumumab exposure in special populations (e.g., patients with renal or hepatic impairment and elderly patients), and (4) to predict target saturation and its relationship with daratumumab exposure.

Methods: A total of 2,572 daratumumab concentration measurements from 223 patients enrolled in a Phase 1/2 study (GEN501 [NCT00574288]) and a Phase 2 study (MMY2002 [NCT01985126]) were included in the population PK modeling. Daratumumab doses ranged from 0.1 to 24 mg/kg, and 150 of these patients received 16 mg/kg. A mixed-effects 2-compartment pharmacokinetic model based on Michaelis-Menten approximation TMDD was developed. Model-based covariate analyses and simulations were conducted to evaluate the influence of individual characteristics/factors on exposure to daratumumab.

Results: The population PK model suggested that the disposition of daratumumab was concentration- and dose-dependent, i.e., lower clearance at higher daratumumab concentrations. The model further revealed that the nonlinear concentration-dependent clearance decreased over time, which could be described as a first-order process. The concentration- and time-dependency of daratumumab clearance suggested that the dynamics of target/tumor burden significantly influenced daratumumab disposition in MM patients. The estimated target binding affinity (ie, concentration at 50% target saturation) was 2.38 µg/mL. The population PK model predicted that a concentration of 21.4 µg/mL and 236 µg/mL would be needed to achieve 90% and 99% target saturation, respectively. The estimated linear clearance (0.17 L/day) was very close to the clearance of non-specific endogenous IgG described in the literature and the volume of distribution of central compartment (V1) approached plasma volume, confirming the robustness of the model.

The clearance and V1 of daratumumab significantly increased with increasing body weight. Consequently, exposure to daratumumab was relatively consistent across the range of body weights of MM patients after administration on a mg/kg basis. Age, race, renal impairment, and mild hepatic impairment had neither statistically significant nor clinically relevant effects on exposure to daratumumab. In addition, exposure was consistent across the different subgroups for performance status (ECOG), refractory status, and number of prior lines of therapy. Although baseline albumin levels, type of myeloma (IgG vs non-IgG), sex, and drug products had a statistically significant effect on daratumumab pharmacokinetics, further analyses demonstrated that these covariates had no clinically relevant impact on efficacy or safety profiles.

Conclusion: The population PK model revealed a concentration- and time-dependent clearance of daratumumab, suggesting the presence of TMDD. A serum daratumumab concentration of 236 μg/mL was predicted to yield 99% target (CD38) saturation. This analysis suggests that body weight-based dosing is a feasible strategy for MM patients. No clinically relevant demographic or clinical characteristics were identified. Therefore, no dose adjustment based on these factors is recommended.

Disclosures

Yan: Janssen: Employment. Clemens:Janssen: Employment. Puchalski:Janssen: Employment. Lonial:Novartis: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Onyx: Consultancy, Research Funding; Janssen: Consultancy, Research Funding. Lokhorst:Genmab: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Amgen: Honoraria. Orlowski:Millennium Pharaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Onyx Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Acetylon Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Array Biopharma: Membership on an entity's Board of Directors or advisory committees, Research Funding; Spectrum: Research Funding; Biotheryx: Membership on an entity's Board of Directors or advisory committees; Forma Therapeutics: Membership on an entity's Board of Directors or advisory committees; Genentech: Membership on an entity's Board of Directors or advisory committees. Losic:Genmab A/S: Employment. Khan:Janssen: Employment. Jansson:Janssen: Employment. Ahmadi:Janssen: Employment. Lantz:Janssen: Employment. Perez Ruixo:Janssen: Employment. Zhou:Janssen: Employment. Xu:Janssen: Employment.