Ensuring Success in Early Phase Oncology Clinical Trials | Page 4

Clinically meaningful endpoints such as progression-free survival take a long
time to measure and are rarely used in Phase 1 studies.
Shorter-term endpoints can include surrogate parameters of efficacy, such as
activity of a kinase or amount of drug binding to excised tumor cells. Other
diagnostic variables may indicate something about the activity of the drug
before clinical effects are seen.
For example, in the case of tumor vaccines, immune responses deemed to be a
mandatory response for any later clinical response can be detected as a valid
surrogate endpoint far before an effect on a tumor can be discerned.
Although later phase trials must have strong power analysis to scientifically
prove the value of a therapy, early phase trials, especially phase 1 studies, do
not have such a consistent need for power analysis or justification of sample
size.
Dosing is an important consideration in study design. The goal is to start at
lowest therapeutic dose possible to avoid having cohorts that will not yield
useful information. Furthermore, because of regulatory and scientific
considerations it is useful to determine the minimal effective dose of a new
agent. If possible, preclinical data should be used to determine whether dosing
should be daily or twice daily. Multiple dosing schedules within various cohorts
may be needed. Dose escalation procedures should be clearly detailed in the
protocol to avoid errors in treatment assignment. If patients are not assigned to
correct cohorts and doses, they will be unenrolled from the study, which
increases the cost of the trial and is disappointing to patients and the enrolling
sites.
If the experimental therapy is a targeted one, diagnostic tools will be needed to
identify patients with specific disease characteristics. For example, if a therapy
targets tumor cells carrying a mutation in a tyrosine kinase receptor protein,
such as EGFR in lung cancer or B-Raf kinase in skin cancer, patient tumors will
need to be screened for those that carry the mutation. Using biomarkers (Box 1,
Table 1) for inclusion criteria in a study can slow enrollment and add costs. Also,
having complex study designs and novel biomarkers can slow the process of
regulatory approval.
It can be challenging to write a clear protocol, especially when a treatment is
being tested for the first time in humans. Getting feedback from key opinion
leaders and principal investigators can be especially helpful in crafting inclusion
and exclusion criteria to maximize the population of enrollable patients.