Strategic Assay Selection for analytics in high-throughput process development: case studies for downstream...

Area of Science:

• Biotechnology and Bioprocess Engineering
• Analytical Chemistry
• Pharmaceutical Development

Background:

• Bioprocess development requires measuring numerous analytes, often leading to analytical bottlenecks.
• Current methods for selecting analytical assays rely on heuristics, lacking a systematic approach.
• Efficient analytical method selection is critical for high-throughput process development.

Purpose of the Study:

• To introduce a generic methodology, Strategic Assay Selection (SAS), for screening and selecting optimal analytical methods.
• To provide a systematic approach for reducing the number of analytical assays in bioprocess development.
• To minimize the impact of analytical requirements on high-throughput studies.

Main Methods:

• Developed a stochastic ranking approach to evaluate and rank analytical methods based on holistic performance criteria.
• Defined key performance criteria for assessing analytical methods in bioprocessing.
• Applied the SAS methodology to a case study involving downstream purification of monoclonal antibodies.

Main Results:

• The Strategic Assay Selection methodology successfully screened a large set of analytical methods.
• The approach identified a subset of analytics that best suited high-throughput studies for monoclonal antibody purification.
• Demonstrated the selection of analytical methods with the most favorable features based on defined criteria.

Conclusions:

• Strategic Assay Selection offers a robust, data-driven approach to analytical method selection in bioprocess development.
• The methodology effectively reduces analytical bottlenecks, enhancing efficiency in high-throughput studies.
• SAS provides a valuable tool for optimizing analytical strategies in pharmaceutical and bioprocess research.