Topics to be covered: 

• Statistics: definition and its application to assay development 
• The arithmetic mean, the standard deviation, and how to prove differences 
• Assay intended use, hypothesis testing, and evaluating fitness-for-purpose 
• Inferential statistics: terminology and symbols explained 
• Standard error of the mean, t values, and p values 
• Reported mean values, margin of error, and confidence interval of a mean 
• Statistical Significance: difference vs. equivalence testing
• Type I and Type II errors and statistical power
• Dose response curve-model selection, system and sample suitability evaluation
• Log transformation of response readouts and relative potency values
• Calculating relative potency values: parallel line vs. 4 PL
• Quality-by-Design (QbD): assays as processes to be optimized and evaluated against target performance requirements
• Overview of available guidance: FDA, USP, and ICH documents

Participants will leave the course with:  

• A strong foundation in statistical terminology and practices
• The ability to design and evaluate experiments effectively
• Practical insights into optimizing assays and improving quality
• Confidence in applying statistical tools to real-world scenarios.

Topics to be covered:

• Introduction to Bioassays
• Guidance Documents on Bioassays
• Design & Development 
• Robustness 
• Validation & Post Validation
• Statistical Analysis Models
• Examples/Case Studies​
  
  

Who should attend? 

• Bioassay Development Scientists
• QA/QC Personnel
• Stability Scientist
• Statisticians
• CMC Scientists
• Assay Development Scientists​
  
  

Key takeaways: 

• Recognize applicable terminology
• Explain concepts of relative potency
• Understand the bioassay life-cycle
• Recognize bioassay best practices
• Explain bioassay design and development: fitness for use/potential challenges, screening and optimization, data and assumptions, variance heterogeneity, goodness for fit, and measurement of uncertainty, normality, transformation and weighting, validity/assay/system/sample suitable criteria, and acceptance criteria
• Understand robustness (experimental design concepts)
• Explain bioassay validation and post-validation: Identifying and measuring significant sources of error, experimental design, and acceptance criteria and  the statistical considerations involved​