Model-Based Characterization of the Bidirectional Interaction Between Pharmacokinetics and Tumor Growth Dynamics in Patients with Metastatic Merkel Cell Carcinoma Treated with Avelumab

Abstract

Purpose: Empirical time-varying clearance models have been reported for several immune checkpoint inhibitors, including avelumab (anti–programmed death ligand 1). To investigate the exposure-response relationship for avelumab, we explored semimechanistic pharmacokinetic (PK)–tumor growth dynamics (TGD) models. Patients and Methods: Plasma PK data were pooled from three phase I and II trials (JAVELIN Merkel 200, JAVELIN Solid Tumor, and JAVELIN Solid Tumor JPN); tumor size (TS) data were collected from patients with metastatic Merkel cell carcinoma (mMCC) enrolled in JAVELIN Merkel 200. A PK model was developed first, followed by TGD modeling to investigate interactions between avelumab exposure and TGD. A PK-TGD feedback loop was evaluated with simultaneous fitting of the PK and TGD models. Results: In total, 1,835 PK observations and 338 TS observations were collected from 147 patients. In the final PK-TGD model, which included the bidirectional relationship between PK and TGD, avelumab PK was described by a two-compartment model with a positive association between clearance and longitudinal TS, with no additional empirical time-varying clearance identified. TGD was described by first-order tumor growth/shrinkage rates, with the tumor shrinkage rate decreasing exponentially over time; the exponential time-decay constant decreased with increasing drug concentration, representing the treatment effect through tumor shrinkage inhibition. Conclusions: We developed a TGD model that mechanistically captures the prevention of loss of antitumor immunity (i.e., T-cell suppression in the tumor microenvironment) by avelumab, and a bidirectional interaction between PK and TGD in patients with mMCC treated with avelumab, thus mechanistically describing previously reported time variance of avelumab elimination.

Time‐dependent clearance can confound exposure–response analysis of therapeutic antibodies: A comprehensive review of the current literature

“…Because these models include a mechanistic hypothesis, they can be applied to understanding the underlying causes and influential factors of time‐dependent clearance. For the purpose of this review, any model that explicitly characterized the trajectory of longitudinal PD markers, such as the target receptor concentration, tumor size, or target cell burden, in addition to the drug concentration were considered mechanistic, and only five models met this definition 54,62,68,71,72 . The mechanistic approach is less common than empirical models due to the difficulty of identifying a secondary process or biomarker that accurately describes the time‐dependent clearance and the challenge of fitting more complex models to the available data.…”

“…While time‐stationary models are fundamentally limited in addressing time‐dependent clearance effects in ER analysis, longitudinal PK/PD models hold promise to capture the bidirectional relationship between PK and PD over time more directly. A mechanistic PK/PD model of the anti‐PD‐L1 antibody avelumab described the bidirectional relationship between PK and tumor growth dynamics 54 . Parameters in the model estimated the influence of drug concentration on the tumor shrinkage rate and the simultaneous effect of tumor size on the drug clearance over time.…”

“…For the purpose of this review, any model that explicitly characterized the trajectory of longitudinal PD markers, such as the target receptor concentration, tumor size, or target cell burden, in addition to the drug concentration were considered mechanistic, and only five models met this definition. 54 , 62 , 68 , 71 , 72 The mechanistic approach is less common than empirical models due to the difficulty of identifying a secondary process or biomarker that accurately describes the time‐dependent clearance and the challenge of fitting more complex models to the available data. Additionally, development of biomarker assays and collection of additional data from all patients increases the cost and logistical burden of clinical trials.…”

“…A mechanistic PK/PD model of the anti‐PD‐L1 antibody avelumab described the bidirectional relationship between PK and tumor growth dynamics. 54 Parameters in the model estimated the influence of drug concentration on the tumor shrinkage rate and the simultaneous effect of tumor size on the drug clearance over time. The clearance decreased as the tumor shrunk in size, inherently describing the effect of response on exposure.…”