The Importance of
Individual Patient
Response in Clinical
Trials
Group 8: Zhiyuan Xu, Yiwen Zhang, Chelsea Nichols, Lu
Wang, Nadir Demirel, Travis Myers
Introduction
• Trials should focus on the response of individual
patients taking into account their clinical
features instead of simply focusing on the
average response of all participants in the trial.
Introduction
• Focus on individual responses in clinical trials can
result in:
• Identification of enhanced treatment response for
certain subgroups of patients
• Avoidance of serious adverse effects for certain
subgroups and risk mitigation.
• Clinical trials that are shorter in duration, with
smaller sample sizes, and cost less.
Enhanced Treatment Response
• Personalized Medicine
• Goal: Reduce the burden of disease by
targeting treatment or prevention more
effectively.
• Classify individuals into subpopulations that
differ in their susceptibility to disease or
response to treatment.
Enhanced Treatment Response
• Subgroup Analysis:
• In clinical trials, subgroup analysis can be used
to determine heterogeneity of treatment
effects:
• Patients are broken into subgroups based on
certain baseline characteristics
• Quantitative or Qualitative heterogeneity
Enhanced Treatment Response
• The problem with using average response?
• Many cancer drugs are not approved because
on average, they don’t provide enough benefit
to patients.
Enhanced Treatment Response
• Exceptional Responders
• A response lasting at least 6 months in a
clinical trial for a drug that was not approved
for that cancer because too few patients
overall responded.
• Genetic or molecular peculiarities
• Identify new biological pathways or biomarkers
Avoid Adverse Effects
• Assess the efficacy of treatments in specific
subpopulations of patients.
• Example: Oncotype DX test in patients with
early stage breast cancer
Avoid Adverse Effects
• Identify patients who may suffer severe
adverse effects from a given treatment or
dosage
• Example: TPMT enzyme and bone marrow
toxicity
FDA and Personalized Medicine
• “Personalized medicines that may only be safe
and effective in particular sub-populations, or
must be administered in different doses in
different sub-populations, must be labeled
accordingly.”
• Labeling of more than 100 approved drugs
contain information on genomic biomarkers
(including gene variants, functional deficiencies,
expression changes, chromosomal
abnormalities, and others).
Resource Advantages
• “Moreover, the ability to stratify patients by
disease susceptibility or likely response to
treatment could also reduce the size, duration,
and cost of clinical trials, thus facilitating the
development of new treatments, diagnostics,
and prevention strategies."
Resource Advantages
• Generate new hypotheses for testing in future
clinical trials.
• Increase the proportion of responders or the
effect size in clinical trials.
• “Rescue” failed drug.
Conclusions
• Using only the average response of patients
when evaluating clinical trial data can hide
important effects in certain patient subgroups.
• Focusing on individual response can reveal
important information about which patients will
benefit from a treatment or which will be at
higher risk of adverse events.
• Inform physician treatment guidelines and drug
labeling.
Conclusions
• Focusing on individual response can also provide
insight on biological pathways and biomarkers,
which in turn can assist with generating new
hypotheses and result in clinical trials that
require less resources to conduct.
References
• Chang, D. K., Grimmond, S. M., Evans, T. J., & Biankin, A. V. (2014).
Mining the genomes of exceptional responders. Nature Reviews
Cancer.
• Goldberger, J. J., & Buxton, A. E. (2013). Personalized medicine vs
guideline-based medicine. Jama, 309(24), 2559-2560.
• Horwitz, R. I., Cullen, M. R., Abell, J., & Christian, J. B. (2013). (De)
Personalized Medicine. Science, 339(6124), 1155-1156.
• President's Council of Advisors - Recommendations on personalized
medicine www.whitehouse.gov/files/documents/ostp/PCAST/
pcast_report_v2.pdf
• Science & Research. (FDA). Personalized Medicine. Retrieved April 10, 2014,
from http://www.fda.gov/scienceresearch/specialtopics/
personalizedmedicine/default.htm
• Wang, R., Lagakos, S. W., Ware, J. H., Hunter, D. J., & Drazen, J. M. (2007).
Statistics in medicine—reporting of subgroup analyses in clinical trials.
New England Journal of Medicine, 357(21), 2189-2194.
• Varmus, H. (2013). A plan to find "exceptional responders" to drugs. Science,
April 19, 2013, 263.