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Audio conference highlights
With the emphasis on biomarkers to improve drug development, the question arises as how to interpret the data.
The traditional clinical laboratory methodology for interpreting data involves the identification of the expected values (the normal range), and from this range, defining the probability of disease or no disease (sensitivity and specificity). The challenge in drug development is in monitoring the patient over time while identifying if important changes have occurred in the biomarker values that indicate inappropriate toxicity or demonstrate appropriate efficacy.
The use of the reference interval as a means of identifying toxicity or efficacy will be challenged and an alternative approach embracing biological variation will be proposed.
This audio conference covers:
- The role biological variation plays in data analysis and clinical results
- The importance of data interpretation when evaluating biomarker performance
- Tools to improve on traditional methods for identifying toxicity or efficacy
- The role of data interpretation in a biomarker strategy
About the speakers:
Gordon F. Kapke, Ph.D. joined the central laboratory SciCor (now Covance)
as Laboratory Technical Director in 1991 and designed the Covance CLS operating requirements
for global combinable data. Dr. Kapke earned the distinction of 2008 R&D Directions' Most Notable People and specializes in the areas of clinical chemistry, physiological chemistry, biomarker methods, analyte biological variability and enzyme mechanisms.
Dr. Kapke has a Bachelor's Degree in Chemistry from the University of Wisconsin and earned his Ph.D. in chemistry from the University of Iowa. He is a Fellow with the Academy of Clinical Biochemistry and a Diplomat with the American Board for Clinical Chemistry.
Who should attend?
- Manager/ Director/ VP/ Biomarker/ R&D Services
- Manager/ Director/ VP Translational Medicine
- Manager/ Director/ VP Experimental Medicine
- Manager/ Director/ VP/ Discovery Medicine
- Manager/ Director/ VP/ Clinical Data Analysis