Batch Record Review and Product Release

Overview:

• Analytical validity
• Clinical validity
• Statistical process control

For analytical validity, scientific principles and regulatory guidelines that are related to assay validation will be overviewed. Then each of the assay performance characteristics (specificity, sensitivity, accuracy, precision, linearity, stability, reportable range, reference range) will be explained in terms of experimental design and statistical analysis.

Most assay validations are a compromise between scientific principles, regulatory requirements, and limited resources in reality. The webinar will use real life examples to demonstrate how to overcome some of the practical challenges when guidelines can not be exactly followed

There are many challenges in exploring and demonstrating clinical validity. These include statistical algorithm development, cross validation, independent validation.

Why should you Attend: Though it has been more than a decade since biomarker became a major topic in drug and diagnostic product development, and though there have been continuous efforts in developing guidelines (by FDA and other organizations such as CLSI, USP), the understanding and adoption of the guidelines still remain a challenge for the broad users.

This webinar will explain how analytical validity and clinical validity is demonstrated through statistics. Some most relevant guidelines will be introduced, including those by FDA, USP, and CLSI. The focuses are intuitive explanation of how statistics is used in experimental design and data analysis.

Biomarker clinical validity is the main reason for most biomarker failures. This webinar will touch on the major stumbling blocks in validating clinical validity.

Areas Covered in the Session:

• Overview of Biomarker Development
  • Statistics in Biomarker/Assay Development
  • Assay Life Cycle
• Assay validation guidelines overview
  • Sensitivity, accuracy, precision, specificity
• Biomarker clinical validation
  • Biomarker clinical trial designs
• Statistical Process Control (SPC)

Who Will Benefit:

• Preclinical R&D Scientists
• Clinical Labs
• Translational Research
• Diagnostic Labs
• Lab CRO
• CMC
• Quality Control