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The Importance of Individual Patient Response in Clinical Trials

The Importance of Individual Patient Response in Clinical Trials. Group 8: Zhiyuan Xu , Yiwen Zhang, Chelsea Nichols, Lu Wang, Nadir Demirel , Travis Myers. Introduction.

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The Importance of Individual Patient Response in Clinical Trials

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  1. The Importance of Individual Patient Response in Clinical Trials Group 8: ZhiyuanXu, Yiwen Zhang, Chelsea Nichols, Lu Wang, Nadir Demirel, Travis Myers

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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

  8. Avoid Adverse Effects • Assess the efficacy of treatments in specific subpopulations of patients. • Example: Oncotype DX test in patients with early stage breast cancer

  9. Avoid Adverse Effects • Identify patients who may suffer severe adverse effects from a given treatment or dosage • Example: TPMT enzyme and bone marrow toxicity

  10. 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).

  11. 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."

  12. 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.

  13. 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.

  14. 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.

  15. 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.

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