1. INTRODUCTION
While contemporary drug development in oncology strives to deliver novel
therapies to patients rapidly, it is also important to optimize dosing
regimens to improve patient-centered care. Doses selected for pivotal
trials may be efficacious doses, but not necessarily optimal to
minimizing toxicity and maximizing clinical efficacy for all patients.
Exposure-response (E-R) analysis is an approach that is used to support
dose selection by characterizing the relationship between drug
concentrations, efficacy, and safety. A variety of E-R analyses have
supported dose labeling of many approved oncology
drugs.1 Among the oncology therapies, however,
additional complexity has been observed in characterizing E-R
relationships for monoclonal antibodies. Specifically, prognostic
factors can impact both pharmacokinetics (PK) and efficacy. This may
result in a correlation between exposure and outcome that does not
represent a causal E-R relationship and therefore, may not provide a
useful basis for dose recommendations. This was exemplified by the
HELOISE trial (NCT01450696) of trastuzumab, which was conducted as part
of a post-marketing requirement. Following the phase 3 trial ToGA
(NCT01041404), trastuzumab was approved in combination with chemotherapy
for first-line treatment of HER2-positive advanced gastric cancer. An
E-R analysis, however, found that the patients in the lowest exposure
quartile had an overall survival (OS) approximately 8 months shorter
than those with higher exposures.2 This suggested that
trastuzumab exposure in this low-exposure subgroup may survival benefit,
and supported the requirement of conducting a post-marketing trial for a
higher dose. For this requirement, in the HELOISE trial, a higher
trastuzumab dose was compared with the labeled dose in a population with
similar prognostic factors as the low-exposure subgroup of the ToGA
trial. Despite reliably increasing exposure, the higher dose did not
improve OS in patients.3 This discrepancy between the
results of the E-R analysis and the HELOISE trial indicates confounding
in E-R analyses of monoclonal antibodies at a single dose level in
oncology.
In addition to the potential confounding factors for E-R analyses in
oncology, there have been reports of time-dependent changes in the PK
that require additional considerations. Monoclonal antibodies that
target B-cell receptors, such as rituximab, have been reported to
exhibit time-dependent decrease in clearance (CL) owing to target
mediated drug disposition (TMDD).4-6 Time-dependent PK
has also been observed for checkpoint inhibitors nivolumab,
pembrolizumab, durvalumab, and avelumab. Across these molecules the
range of CL decrease over time was 17% to 32%.7-10Best overall response was included as a covariate on CL in the
time-dependent population PK models of nivolumab and
pembrolizumab.7, 9 The time-dependent population PK
model of durvalumab included time-varying albumin and tumor size as
covariates on CL.8 While the model for avelumab did
not incorporate response or time-varying biomarkers as covariates on CL,
visual inspection of change in CL over time demonstrated a larger
reduction in CL in responders than in nonresponders.10Overall, the decrease in CL over time in these molecules corresponded to
changes in patients’ prognoses based on their responses to treatments
over time. This observation may be attributed to changes in catabolic
degradation of the monoclonal antibodies as a result of changing disease
status.11 The changing drug CL and patient prognostic
factors over time could potentially confound E-R analyses.
In this review we will discuss key considerations in interpreting E-R
relationships and mitigation strategies to address the confounding
effects in E-R analyses in oncology.
2. EXPOSURE-RESPONSE (E-R) ANALYSIS CONSIDERATIONS IN
ONCOLOGY
In order to address confounding factors in
E-R analyses for monoclonal
antibodies in oncology several key determinants need to be considered.
In this review we have described the importance of selecting the
appropriate drug exposure metric for E-R analyses and have summarized
three main approaches in oncology to address confounding in E-R
analyses: CPH and case-matching analysis, tumor growth inhibition
overall survival (TGI-OS) modeling, and clinical studies with multiple
dose levels (Figure 1, Table 1 ). In addition to describing
these approaches we will discuss their strengths and limitations. The
current review will focus on E-R analyses for exposure-survival
relationships.