When running multiple independent but parallel experiments, include well-characterized compounds in every group. These "anchor compounds" serve as internal calibration references, creating a baseline that allows for robust and reliable comparison of results across the otherwise separate experimental sets.
To optimize a complex biosimilar profile with many correlated attributes like glycoforms, use Mahalanobis distance. It calculates a single multivariate distance to the target profile, correctly accounting for inter-glycoform correlations, providing an objective, data-driven method for ranking experimental outcomes.
Instead of one massive experiment, split numerous factors into smaller, biologically-themed groups. Running these focused experiments in parallel is superior to both one-factor-at-a-time and large DOE approaches, as it maintains the breadth of a large screen while providing the high-quality signal of a small one.
A single, massive Design of Experiments (DOE) for screening many compounds is flawed. Adding numerous stock solutions causes dilution, untested combinations can be toxic to cells, and the strong effect of one compound can mask the subtler, yet crucial, effects of others, leading to poor data quality.