Population Pharmacokinetics of an Anti-PD-L1 Antibody, Durvalumab in Patients with Hematologic Malignancies

The programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) pathway plays a critical role in maintaining an immunosuppressive tumor microenvironment and the blockade of PD-1/PD-L1 pathway has become the key component of cancer immunotherapy [1]. Durvalumab (MEDI4736) is a human immunoglobulin G1 (IgG1) kappa monoclonal antibody that binds to PD-L1, blocking the ability to bind to PD-1 or a cluster of differentiation 80 on activated T cells, leading to immune-mediated killing [2]. Durvalumab has been approved for the treatment of patients with urothelial carcinoma and stage III non-small cell lung cancer by the US Food and Drug Administration [3], and is currently being evaluated in various solid tumors and hematologic malignancies, including non-Hodgkin lymphoma (NHL), multiple myeloma (MM), myelodysplastic syndromes (MDS), and acute myeloid leukemia (AML). Anti-PD-1 antibodies (nivolumab and pembrolizumab) and other anti-PD-L1 antibodies (atezolizumab and avelumab) have also been approved for the indication of various solid tumors and some hematologic malignancies such as classical Hodgkin lymphoma and primary mediastinal large B-cell lymphoma [3].

The pharmacokinetics of these PD-1/PD-L1 inhibitors is largely similar to that of endogenous IgG, except the time-varying clearance (CL) [4]. Clearance of PD-1/PD-L1 inhibitors decreases over time, which appears to be associated with response to treatment [4]. Population-pharmacokinetic (PK) models of anti-PD-1/PD-L1 antibodies in solid tumors have been reported [5‐11], and the PK profiles of anti-PD-1/PD-L1 antibodies were typically characterized by a two-compartment model with linear elimination [4]. A time-dependent decrease in CL of nivolumab and pembrolizumab was described with empirical time-varying CL models [6, 8, 9]. Baverel et al. [7] recently reported the population-PK analysis of durvalumab in solid tumors, where the change in CL over time was well explained by a semi-mechanistic time-varying CL model with longitudinal covariates related to disease status. For hematologic malignancies, one population-PK model has been reported for nivolumab in patients with classical Hodgkin lymphoma, indicating consistent PK properties with solid tumors except for a lower baseline CL by 28% [12]. However, population-PK analyses of PD-1/PD-L1 inhibitors in other common hematologic malignancies such as NHL and MM have not been reported.

In this study, a population-PK model of durvalumab was developed using pooled data from four clinical trials of hematologic malignancies (NHL, MM, MDS, and AML), and the model structure and covariate effects were compared with those in solid tumors. In addition, differences in durvalumab pharmacokinetics and the covariate effects among hematologic malignancies were explored.

This is the first study to report a population-PK model of anti-PD-1/PL-1 antibodies in subjects with common hematologic malignancies such as NHL, MM, MDS, and AML. In this population-PK model, durvalumab pharmacokinetics was well described by a two-compartment model with first-order CL. Serum albumin, IgG, sPD-L1, LDH, weight, sex, and some malignancy type (MDS/AML and MM) were incorporated in the final model as covariates on CL and V_c. Change in albumin (in all patients) and IgG (in patients with MM) over time adequately accounts for the time-dependent decrease in durvalumab CL. For MM, patients with IgG of ≥ 20 g/L showed 30% lower AUC compared with patients with IgG of < 20 g/L.

The population-PK model of durvalumab in hematologic malignancies was generally consistent with that in solid tumors [7]. The structural model of durvalumab in this analysis was a two-compartment model with first-order CL, which is the most common base model of monoclonal antibodies [13] including other PD-1/PD-L1 inhibitors [4]. Most of the covariates in the final model (albumin on CL, sex on CL and V_c, and weight on CL and V_c) were also observed in the population-PK model of durvalumab in solid tumors [7]. The sPD-L1 level was a covariate on CL and V_max in hematologic and solid tumors, respectively, and a high sPD-L1 level was associated with higher total CL in both models. Additionally, a time-dependent change in albumin significantly improved the fitting of both models.

Several differences were observed in the base model structure between the two durvalumab population-PK models in patients with hematologic malignancies and solid tumors. Nonlinear CL was not included in our population-PK model of durvalumab in hematologic malignancies, while a previous population-PK model of durvalumab in solid tumors contained nonlinear Michaelis–Menten CL [7]. In addition, we failed to obtain robust parameter estimates of the empirical time-varying CL model. The discrepancy in nonlinearity may in part be explained by the difference in dose levels of durvalumab. Durvalumab showed nonlinear pharmacokinetics with saturable target-mediated CL at doses < 3 mg/kg, and the solid tumor model included data from doses ranging from 0.1 to 20 mg/kg [7]. However, our four clinical trials in subjects with hematologic malignancies tested only one dose and at a relatively high dose level, 1500 mg every 4 weeks (equivalent to 10 mg/kg every 2 weeks or 20 mg/kg every 4 weeks), where the linearity of exposure with doses was approached. Failure to add the empirical time-varying CL model into the current model could be explained in part by the shorter follow-up period of PK sampling. In the previously reported durvalumab model [7], population mean value of T₅₀ was 173 days (about 5 months). In our analysis, 85% and 95% of concentrations were obtained within 2 and 4 months after the first dose, indicating the difficulty of estimating robust empirical time-varying CL model parameters in our model. The goodness-of-fit plot (conditional weighted residual vs. time after first dose, Fig. 1) of the final model did not show any under-prediction of durvalumab concentrations at later timepoints, justifying the final model with time-varying covariates.

The covariate with the largest impact on durvalumab exposure was sex, and female patients showed 27% higher exposure than male patients (Fig. 4). In the final population-PK model, lower body weight and being female were independently associated with lower durvalumab CL, which is often observed in other monoclonal antibodies [13]. In this study, median body weight of female patients (63.5 kg) was 20% lower than that of male patients (79.0 kg). Effect of sex on durvalumab exposure was higher in hematologic malignancies (27%) compared with solid tumors (17%) [7], which would be in part owing to larger actual doses in female patients in studies of hematologic malignancies, caused by the switch from a weight-based dosing regimen in solid tumor studies (10 mg/kg every 2 weeks) to a fixed dosing regimen in hematologic malignancy studies (1500 mg every 4 weeks). Simulation results indicated the similar durvalumab concentration–time profile between 1500 mg and 20 mg/kg (which is comparable to 1500 mg). The slightly higher 95 percentile concentration in 1500 mg would be explained by the larger actual dose in female patients. Nevertheless, the impact of sex on durvalumab pharmacokinetics would not be clinically significant because the impact of sex was relatively small and no exposure–efficacy or exposure–safety relationship for durvalumab was observed in patients with solid tumors receiving the recommended dosing regimen (10 mg/kg every 2 weeks) [14]. Overall, the impact of any baseline covariates on durvalumab pharmacokinetics did not appear to be clinically meaningful.

Incorporation of albumin and IgG as time-varying covariates in the final model significantly improved the statistical fit (∆OFV: − 239 and − 153, respectively). An apparent time-dependent increase in albumin and a decrease in IgG were observed in patients with NHL and MM, respectively (Fig. 2 of the ESM). In the time-invariant model, baseline albumin and IgG reduced OFV by 35 and 62, respectively (data not shown), indicating the appropriateness of time-varying covariates in the final model. Soluble PD-L1 improved the statistical fit as a time-varying covariate (∆OFV: − 18), however, it was not retained in the final time-invariant model (data not shown). The sPD-L1 level in most patients with available sPD-L1 data at a later time was lower than the limit of detection (Fig. 2 of the ESM), accounting for a 16% decrease in CL from the baseline at cycle 2 and beyond. The decrease in OFV by including time-varying LDH was 15, which was comparable to that of the time-invariant model (∆OFV: −27, data not shown), indicating the impact on CL would be mostly accounted for by the LDH at baseline. As shown in Fig. 4, the effect of LDH on durvalumab exposure was minimal.

The neonatal Fc receptor plays a critical role in salvaging both IgG and albumin from lysosomal degradation [15]. Albumin level was positively correlated with IgG at below 20 g/L of IgG, which is the upper range of normal IgG levels (Fig. 5a). This suggests that at normal IgG levels, a lower albumin level could be an indicator of higher CL of both albumin and IgG (including externally administered monoclonal antibodies) [13]. However, the albumin level decreased with an increasing IgG level above 20 g/L (Fig. 5a), where the salvage of IgG and albumin by the neonatal Fc receptor appeared to be saturated. Hypergammaglobulinemia is commonly seen in patients with MM, and the higher CL of monoclonal antibodies in this populations has been suggested [16, 17]. The current study demonstrated that albumin and IgG were independent covariates of the durvalumab population-PK model and the impact of albumin on durvalumab CL was different at IgG of below and above 20 g/L (Fig. 5b). These findings suggest that both albumin and IgG are independent determinants of CL of monoclonal antibodies in patients with MM with hypergammaglobulinemia, while albumin could be enough to explain the CL of monoclonal antibodies at the normal range of IgG levels. A time-dependent decrease in durvalumab CL would be explained by the change in IgG over time in patients with MM, which is consistent with a time-dependent decrease in CL of daratumumab in patients with MM [18].

The type of hematologic malignancy had a relatively small impact on durvalumab pharmacokinetics. The differences in durvalumab exposure (median and 90% CI) were within 20% among MDS/AML, MM, and NHL/Hodgkin lymphoma (Fig. 4). In addition, there was no apparent difference in durvalumab exposure among various NHL subtypes (Fig. 6b). For MM, hypergammaglobulinemia was a key determinant of durvalumab exposure (Fig. 6a).

Our study was limited by a single-dose level and a relatively shorter collection duration of PK data. Because only one dose level of durvalumab (1500 mg every 4 weeks) was included in this population-PK analysis, it is difficult to capture nonlinear CL of durvalumab that was observed over a wide dose range in solid tumors [7]. In addition, the PK sampling period from the first dose in our analysis was shorter than that in the previous analyses of durvalumab and other PD-1/PD-L1 inhibitors where empirical time-varying CL was implemented [7‐9]. Therefore, we could not draw an unequivocal conclusion that these differences between the current and previous analyses is due to the difference in tumor type (solid tumors vs. hematologic malignancies). Rather, durvalumab pharmacokinetics in hematologic malignancies was mostly consistent with that in solid tumors. Second, some time-varying covariates were not available from all patients (IgG) or available at less frequent timepoints (sPD-L1). Time-varying IgG data were available only from patients with MM. More complete time-varying data of IgG and sPD-L1 would improve the statistical fit of the population-PK model; however, the impact would be minimal because of the following reasons: (1) sPD-L1 level is considered to affect nonlinear CL; however, the contribution of nonlinear CL to the total CL of durvalumab is small and (2) hypergammaglobulinemia in non-MM hematologic malignancies is not as common as MM, and a significant time-dependent change in IgG is not expected in non-MM hematologic malignancies.

The pharmacokinetics of durvalumab in hematologic malignancies was generally consistent with that in solid tumors, and these results support the same dosing regimen (1500 mg every 4 weeks) for both solid tumors and hematologic malignancies. In addition, IgG level was indicated to be a critical covariate of monoclonal antibodies in patients with MM.