Capillary Electrophoresis (CE) versus quantitative Polymerase Chain Reaction (qPCR) for direct quantification of intact adenovirus particles

Key Learning Outcomes

• Understand the purpose and design of analytical method comparability studies when they are necessary, including the hypotheses they examine, and how to organise datasets from both controlled experiments and historical process data to support robust conclusions.
• Learn the strengths and limitations of key statistical methods used in method comparison, such as equivalence testing, tolerance and prediction interval approaches, Deming regression, Passing-Bablok regression, and Bland-Altman analysis, and how to choose the most suitable method for a specific dataset.
• Discover how correlated measurements and differences in method precision can challenge standard comparability methods, and how advanced models like weighted regression and correlated variance least squares regression overcome these challenges by utilising all available data without the need for pre-averaging.
• Explore how acceptance criteria for interchangeability are defined and applied in practice, illustrated through a real-world case study comparing qPCR and capillary electrophoresis methods for quantifying adenovirus particles.

Event Overview

This session by Dr Francisca Galindo Garre offers a practically grounded, technically rigorous exploration of analytical method comparability studies, the statistical challenge that occurs when a laboratory considers replacing an established method with a new one. Using a real case study comparing a qPCR method and a capillary electrophoresis method for quantifying adenovirus particles, the presentation progresses from first principles to published results, covering the entire analytical decision-making process.

The session begins by exploring why comparability studies are conducted to measure the level of agreement between methods and to assess whether one can replace the other, highlighting the reasons why this process is more complex than it initially seems. The case study immediately demonstrates this challenge: two methods based on different technologies, with notably different profiles of precision and bias, measured on log-transformed scales, using data from both controlled experiments and routine process monitoring.

From there, the presentation discusses the statistical toolkit available for method comparison. Equivalence testing is introduced as the current standard in regulation, with the key practical difficulty of setting acceptance criteria in advance. Tolerance interval and prediction interval methods are shown as alternatives, highlighting their sensitivity to sample size and data representativeness. Deming regression and Passing-Bablok regression are described as improvements over standard linear regression when both methods involve measurement error, a condition that always applies in practice, with the Bland-Altman plot offering additional graphical insight into bias and agreement across the measurement range.

The session then discusses two limitations that standard methods struggle with: differing precision between methods and the correlation structure inherent in process data collected by the same operator, on the same day, or at the same process step. Weighted regression and correlated variance least squares regression are introduced as solutions that address both issues, allowing analysis of the whole dataset without averaging, and providing more reliable prediction intervals for comparison against pre-defined acceptance criteria.

Who Should Attend?

Anyone engaged in analytical method development, transfer, or lifecycle management who needs to statistically compare methods and make defensible conclusions about their interchangeability or equivalence.

• Analytical Scientists and Method Development Chemists
• Validation and Compliance Scientists
• Statisticians and Data Scientists supporting analytical development
• Quality Control and Quality Assurance Professionals
• Regulatory Affairs and CMC Submission Specialists
• Biopharmaceutical Process Development and Analytical Teams
• R&D and Technical Operations Managers overseeing method change or transfer activities

Unlock Additional Educational Resources

Register today for Francisca’s presentation and gain access to exclusive bonus content, such as the insightful panel discussion on “Best Practices for Updating Analytical Procedures in Drug Development and Manufacturing”.

Brought to you by: