Streamlined Ultrafast Chromatography for High-Throughput Discovery and Development Chemistry via Predictive Analytical Modeling
- 1Pharmaceutical Analysis and Digital Technologies
- 2Merck & Co., Inc.
- 3Process Research and Development
Abstract
Modern high-throughput experimentation can generate complex chemical data at an unprecedented pace taxing existing analytical capabilities. Although recent advances in ultrafast liquid chromatography (LC) enable rapid analysis of reaction profiles, enantiopurity, and multicomponent mixtures, the experimental effort required to identify suitable chromatographic resolution often limits streamlined implementation. Herein, a predictive in silico framework that transforms ultrafast LC assay development from empirical screening into a model-driven design workflow is introduced. Experimental retention data are used to model chromatographic selectivity across temperature, gradient slope, flow rate, and column geometry, enabling rapid identification of the fastest separation conditions that preserve decision-critical information. The approach is demonstrated across closely related isomers, chiral analyses, and high-throughput reaction screening, where model-predicted separations translated directly to reliable experimentation while reducing analysis cycle times substantially with minimal manual intervention. Retention modeling is further combined with fit-for-purpose resolution criteria and in silico peak clustering to selectively resolve critical components while intentionally grouping analytes to maximize throughput. Together, this framework establishes a general strategy for the systematic deployment of reliable ultrafast chromatographic assays, reducing method-development burden and accelerating analytical decision-making across applications in biocatalysis, natural products, metabolomics, high-throughput experimentation, and modern synthesis.
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BibTeX
@misc{makey2026streamlined,
title = {{Streamlined Ultrafast Chromatography for High-Throughput Discovery and Development Chemistry via Predictive Analytical Modeling}},
author = {Devin M. Makey and Rodell C. Barrientos and Andrew McGrath and Dan Lehnherr and Andrew N. Singh and Eli J. Larson and Emmanuel Appiah-Amponsah and Erik L. Regalado},
howpublished = {ChemRxiv},
year = {2026},
month = aug,
doi = {10.26434/chemrxiv.15008098/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008098/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Streamlined Ultrafast Chromatography for High-Throughput Discovery and Development Chemistry via Predictive Analytical Modeling
AU - Devin M. Makey
AU - Rodell C. Barrientos
AU - Andrew McGrath
AU - Dan Lehnherr
AU - Andrew N. Singh
AU - Eli J. Larson
AU - Emmanuel Appiah-Amponsah
AU - Erik L. Regalado
PY - 2026
DA - 2026/08/31
PB - ChemRxiv
DO - 10.26434/chemrxiv.15008098/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008098/v1
AB - Modern high-throughput experimentation can generate complex chemical data at an unprecedented pace taxing existing analytical capabilities. Although recent advances in ultrafast liquid chromatography (LC) enable rapid analysis of reaction profiles, enantiopurity, and multicomponent mixtures, the experimental effort required to identify suitable chromatographic resolution often limits streamlined implementation. Herein, a predictive in silico framework that transforms ultrafast LC assay development from empirical screening into a model-driven design workflow is introduced. Experimental retention data are used to model chromatographic selectivity across temperature, gradient slope, flow rate, and column geometry, enabling rapid identification of the fastest separation conditions that preserve decision-critical information. The approach is demonstrated across closely related isomers, chiral analyses, and high-throughput reaction screening, where model-predicted separations translated directly to reliable experimentation while reducing analysis cycle times substantially with minimal manual intervention. Retention modeling is further combined with fit-for-purpose resolution criteria and in silico peak clustering to selectively resolve critical components while intentionally grouping analytes to maximize throughput. Together, this framework establishes a general strategy for the systematic deployment of reliable ultrafast chromatographic assays, reducing method-development burden and accelerating analytical decision-making across applications in biocatalysis, natural products, metabolomics, high-throughput experimentation, and modern synthesis.
ER -