A modeling framework for uncertainty quantification in filter-plate-based high-throughput screening of the...

High‑throughput screening (HTS) of chromatography partition coefficients (K_p) is widely used in protein purification process development, spanning applications with markedly different precision requirements. Despite this widespread use, quantitative guidance on how experimental uncertainty propagates through filter-plate-based K_p assays and informs key design decisions has been lacking. We present an experimentally calibrated Monte Carlo (MC) framework for uncertainty propagation in filter-plate-based K_p HTS, which quantifies overall uncertainty and identifies the dominant experimental error sources across purification‑relevant equilibrium regimes. The model is parameterized by characterization of the major sources of experimental errors in a monoclonal antibody (mAb) monomer case study on POROS XS cation‑exchange media and accurately reproduces the magnitude, heteroscedasticity, and plate‑to‑plate variability observed. Variance decomposition identifies uncertainty in resin slurry distribution and supernatant concentration measurements as the dominant contributors, accounting for ≈ 97 % of total K_p variance over the investigated range of ideal partition coefficients (K_p,ideal). The framework further enables quantitative evaluation of design choices: analysis of the liquid‑to‑solid phase ratio (β) reveals a clear trade‑off between measurement precision and protein material demand, allowing derivation of application‑specific phase ratio recommendations. Overall, this work provides a quantitative basis for uncertainty‑aware HTS design and more reliable K_p data for purification process development decisions and modeling.