3. DISCUSSION
The inability to select an appropriate dose in pivotal trials has been
shown to contribute to the declining success rates of drug development
programs.41, 42 A study examining FDA approval
packages between 2015 and 2017 found that in a third of development
programs no E-R analysis was reported.43 The expanded
use of E-R analysis in more drug development programs may serve as a
solution for declining success rates. E-R analysis is particularly
useful in early clinical trials where multiple doses are administered to
inform dose selection and optimization. It is often repeated in phase 3
trials given the meaningful sample size for efficacy and safety
interpretation. Rather than assuming one dose fits all patients this
approach identifies whether specific patient subgroups would benefit
from alternative doses. A successful example of E-R application was
shown for the exposure-survival analysis of ipilimumab. Ipilimumab was
originally approved in several countries at a dose of 3 mg/kg for the
treatment of advanced melanoma. A phase 2 dose-ranging study, however,
suggested improvement in OS with the 10 mg/kg dose.44While this study was not statistically powered to detect differences in
survival an E-R analysis pooling data from four phase 2 trials
demonstrated that OS improved with increasing exposure. In the CPH model
results patients in the 5th and 95thpercentiles of steady state trough concentration
(Cminss) had an OS HR of 1.52 and 0.552, respectively,
relative to patients with median
Cminss.45 This suggested that OS
improved with increased ipilimumab doses. In the post-marketing trial
conducted with the 3 mg/kg and 10 mg/kg doses this relationship was
confirmed. Median OS was 15.7 months for the 10 mg/kg dose group, and
11.5 months for the 3 mg/kg dose group (HR 0.84,
p=0.04).46 The results of this phase 3 trial are
included in the ipilimumab label, and demonstrate that E-R analyses
could identify potential survival benefits gained from increased doses
and exposures. While the 10 mg/kg dose provided a survival benefit it
was also associated with increased treatment-related adverse events. The
3 mg/kg dose was selected as the labeled dose after accounting for
efficacy benefit and safety risk.
While the utility of E-R analyses applies across a variety of
therapeutic areas additional considerations are needed for oncology due
to the impact of prognostic factors on both exposure and outcome.
Performance status, clinical symptoms (dyspnea, appetite loss, cognitive
function), primary tumor site, and c-reactive protein (CRP)
concentration are examples of prognostic factors used to predict outcome
in clinical settings.47, 48 In oncology, E-R is more
than just considering the unidirectional relationship where the dose
affects exposure which subsequently affects response. OS is often the
primary response endpoint for oncology trials, and its relationship with
exposure is confounded by prognostic factors. Recognizing and accounting
for the impact of time-varying clinical response and prognostic factors
on exposure are critical for accurate E-R
interpretations.49 This relationship is illustrated by
the findings for nivolumab, avelumab, durvalumab, and pembrolizumab
where patients with improved post-treatment disease status showed
greater time-dependent decreases in drug CL.7-10 The
mechanism is not fully understood, but there is an interaction between
clinical response, prognostic factors, and exposure. When patients
respond to treatment their prognostic factors improve, which in turn
decrease the drug CL and increase drug exposure (Figure 2) . In
an E-R analysis this could lead to incorrect conclusions that higher
drug exposure caused clinical response when in fact the E-R relationship
is confounded by the effect of changing prognostic factors on exposure.
This may be caused by the unique nature of disease progression in
oncology. As a patient’s disease status declines clinical changes such
as cachexia and inflammation can increase the catabolism and clearance
of both endogenous and therapeutic proteins.50, 51This is supported by the significance of tumor burden and albumin as a
covariate on CL in the population PK analyses for nivolumab, avelumab,
durvalumab, and pembrolizumab.8-10, 12 Clearance
increased with higher tumor burden and lower albumin concentrations. In
addition, time-dependent PK was observed for nivolumab in advanced
malignancies, but not in patients with resected
melanoma.52 The latter had tumors surgically resected
prior to adjuvant treatment with nivolumab and were overall healthier
than patients with advanced malignancies. This further supports the
impact of disease status and prognostic factors on exposure. Looking
prospectively, these collective observations also suggest that the
presence of time-dependent PK, and the significance of albumin as a
covariate on CL would indicate the risk of a confounded E-R analysis.
A confounded E-R analysis may result in false positive E-R relationships
which may lead to the wrong conclusion that the dose for patients with
lower exposure is suboptimal. As seen in the ToGA/HELOISE example it may
lead to the initiation of a new trial in an attempt to rescue patients
who failed treatment. Considering these risks three mitigation
strategies have been in this review: CPH modeling and case matching
analysis, TGI-OS modeling, and multiple dose study design. Studying
multiple dose levels in randomized, balanced groups appears to be an
effective approach that can distinguish with certainty between a true
positive E-R relationship versus a false positive relationship with
hidden confounders. This strategy, however, is impractical in most
oncology indications, and may offer limited value for monoclonal
antibodies with a wide therapeutic window. TGI-OS modeling explicitly
separates the drug-specific and disease-specific effect on OS when
evaluating the E-R relationship. It also incorporates an estimate of
tumor dynamics which serves as an informative biomarker of disease
status. CPH modeling and case-matching analysis lack this separation
between drug and disease-specific effects. This makes it more
challenging to consistently distinguish between exposure- and prognostic
factor-driven changes in OS. Case-matching may be preferred over CPH
modeling due to the assumptions involved in CPH modeling regarding the
relationship between predictors and outcome. While a suggestion of the
relative utility of each approach is made here the unique limitations of
each methodology should be considered.
While the effect of changing prognostic factors on exposure and OS can
confound exposure-efficacy relationships it does not appear to
significantly impact exposure-safety relationships. Among molecules
discussed here it appears that exposure-safety analyses for
pembrolizumab, nivolumab, durvalumab, and T-DM1 have not faced the same
confounding issues as exposure-efficacy analyses.15,
20, 53-58 If patients with worsening disease status and prognostic
factors are more likely to experience adverse events, and have a
decreased drug exposure it could be thought that differences in
prognostic factors can confound the exposure-safety relationship. The
confounding, however, would contribute to an inverse E-R relationship
rather than a positive relationship. In addition, because safety
endpoints in E-R analyses are usually drug-related adverse events rather
than disease-related adverse events exposure-safety relationships may be
less likely to be influenced by differences in prognostic factors.
Current oncology drug development is rapid and aggressive. Many recent
development programs bypass a dose-ranging phase 2 trial and go directly
from phase 1 to phase 3 trials with a single dose level. In some
programs, a phase 2 trial is done, but only with a single dose level or
a limited efficacy endpoint. This severely limits the range of exposure
data available for an exposure-survival analysis and increases the risk
of a confounded E-R analysis. The FDA’s E-R Guidance has previously
described the risk of characterizing E-R relationships based on data
from single dose levels.59 Sponsors should consider
conducting expanded dose-ranging trials early in development programs to
better inform dose selection and potentially avoid the need to study
multiple dose levels in late phase trials. Despite the current
limitations of E-R analyses they are required to be included as part of
a filing package. The HELOISE trial is an example of the potential risks
of confounded E-R analyses. The E-R analysis performed using data from
the ToGA trial supported the conduct of the HELOISE trial. No
dose-response relationship was observed, however, and patients did not
benefit from higher doses of trastuzumab. If the E-R analysis is limited
by the range of available exposure data, and could be confounded any
observed E-R relationship should be interpreted with great caution.
Drug development must not only focus on developing novel treatment
modalities, but also on selecting the optimal dose for patients. E-R
analysis is a useful tool for dose optimization in a variety of
therapeutic areas, and also has many applications to support modern drug
development. Despite its wide utility E-R analysis in oncology faces
unique challenges when applied to monoclonal antibodies tested at a
single dose level. E-R analyses in oncology are susceptible to
confounding from unique, disease-related factors. Mitigation strategies
presented in the current paper can be employed to account for
confounding factors and elucidate the true E-R relationship. In a
broader scope, the design of oncology drug development programs may be
structured to more effectively inform dose-response and E-R
relationships for dose optimization. Once an E-R analysis is performed
its application in decision-making must be carefully considered based on
the methodology and the data used in the analysis. The improvement and
effective use of E-R analysis is an effort that must be addressed on
multiple fronts of oncology drug development with the common goal of
maximizing benefit to the patient and minimizing toxicities.