Best of the Week: From Cancer Diagnostics to Smarter LC Method Development

This past week’s content on ChromatographyOnline included AOA Dx Chief Scientific Officer Abby McElhinny explaining how high-resolution mass spectrometry-based multi-omics led to Omira OC, a targeted blood test for early ovarian cancer detection, and how the company’s lipid-based approach is showing promise for endometrial, cervical, and other cancers. In this month’s “LCGC Blog,” Hayley Brawley of the USAF 59th Medical Wing explores the wide range of career paths open to separation scientists. Two new two-part interviews focus on smarter LC method development: Sam Whitmarsh of CatSci presents a practical workflow for predicting reversed-phase LC retention times with ensemble machine learning and open chromatographic data, and discusses how such models cope with interlaboratory data, novel chemotypes, unusual phases, and ionizable compounds, while Leon Niezen and Gert Desmet of the Vrije Universiteit Brussel describe how predictive uncertainty can make automated LC method development more reliable. We also examine how Directive (EU) 2024/3019 makes drug manufacturers financially responsible for removing pharmaceutical residues from wastewater and tightens monitoring requirements, raising demand for trace-level analytical expertise. A new technical article shows how implementing the Separation Quality Factor (SQF) in chromatography data systems enables a more comprehensive, objective ranking of conditions for demanding oligonucleotide and lipid nanoparticle separations, and our coverage closes with a look at how advanced separations, inert hardware, orthogonal methods, and AI can address key challenges in GLP-1 peptide analysis.

This is the Best of the Week.

From 9000 Clues to a Clinical Test: Mass Spectrometry-Driven Lipidomics for Early Cancer Detection
AOA Dx Chief Scientific Officer Abby McElhinny discusses with LCGC International how the company used high-resolution mass spectrometry-based multi-omics to develop Omira OC, a targeted blood test for early ovarian cancer detection, and how its lipid-based approach is showing promise across endometrial, cervical, and other cancers.¹

LCGC Blog: Building a Career Series That Goes Beyond the Surface

In this month's “LCGC Blog,” Hayley Brawley, a senior research scientist with the USAF 59^th Medical Wing’s Clinical and Operational Space Medicine Consortium, explores the variety of career options that are open to separation scientists.²

Predicting Reversed-Phase LC Retention Using Ensemble Machine Learning and Open Chromatographic Data

In this two-part interview, Sam Whitmarsh from CatSci, UK discusses his presentation “Predicting Reversed-Phase LC Retention Using Ensemble Machine Learning and Open Chromatographic Data.” In Part 1,³ Whitmarsh describes a practical workflow for predicting reversed-phase LC retention times using ensemble machine learning and open chromatographic data. In Part 2,⁴ he explores how retention-time models handle interlaboratory data, novel chemotypes, unusual phases and ionizable compounds, and what laboratories need to do to build useful in-house models.

Part 1 of the Interview
Part 2 of the interview

New EU Wastewater Rules Put Pharmaceutical Micropollutant Monitoring in the Spotlight

Directive (EU) 2024/3019 makes drug manufacturers financially responsible for removing pharmaceutical residues from wastewater and introduces stricter, standardized monitoring requirements that are set to increase demand for trace-level analytical expertise.⁵

Implementation of the Separation Quality Factor (SQF) in Chromatography Data Systems for Automated Method Evaluation
The automated Separation Quality Factor (SQF) was applied to two demanding applications, the ion-pair reversed-phase LC separation of a complex oligonucleotide mixture and the reversed-phase LC analysis of lipid nanoparticle components under varied gradients and stationary phases, and in both it gave a more comprehensive assessment of chromatographic performance than conventional single-parameter descriptors while allowing objective ranking of conditions.⁶

Uncertainty-Driven Model-Based Search Methods for Method Development in Liquid Chromatography

In this two-part interview, Leon Niezen and Gert Desmet of the Vrije Universiteit Brussel describe how predictive uncertainty can make automated LC method development more reliable.^7,8

Part 1
Part 2

GLP-1 Peptide Analysis: Challenges and Solutions

How can advanced separations, inert hardware, orthogonal methods, and AI solve key challenges in reliable GLP-1 peptide analysis workflows?⁹

References

1. McElhinny, A.; Chasse, J. From 9000 Clues to a Clinical Test: Mass Spectrometry-Driven Lipidomics for Early Cancer Detection. Chromatography Online website. https://www.chromatographyonline.com/view/from-9000-clues-clinical-test-mass-spectrometry-driven-lipidomics-for-early-cancer-detection (accessed 2026-11-05)
2. Brawley, H. LCGC Blog: Building a Career Series That Goes Beyond the Surface. Chromatography Online website. https://www.chromatographyonline.com/view/building-a-career-series-that-goes-beyond-the-surface (accessed 2026-11-05)
3. Whitmarsh, S.; Matheson, A. AI/ML Now: Predicting Reversed-Phase LC Retention Using Ensemble Machine Learning and Open Chromatographic Data (Part One). Chromatography Online website. https://www.chromatographyonline.com/view/predicting-reversed-phase-lc-retention-using-ensemble-machine-learning-and-open-chromatographic-data (accessed 2026-11-05)
4. Whitmarsh, S.; Matheson, A.AI/ML Now: Predicting Reversed-Phase LC Retention Using Ensemble Machine Learning and Open Chromatographic Data (Part Two). Chromatography Online website. https://www.chromatographyonline.com/view/ai-ml-now-predicting-reversed-phase-lc-retention-using-ensemble-machine-learning-and-open-chromatographic-data-part-two- (accessed 2026-11-05)
5. Chasse, J. New EU Wastewater Rules Put Pharmaceutical Micropollutant Monitoring in the Spotlight. Chromatography Online website. https://www.chromatographyonline.com/view/new-eu-wastewater-rules-put-pharmaceutical-micropollutant-monitoring-in-the-spotlight (accessed 2026-11-05)
6. Fekete, S.; Buff, M.; Guillarme, D. et al. Implementation of the Separation Quality Factor (SQF) in Chromatography Data Systems for Automated Method Evaluation. Chromatography Online website.https://www.chromatographyonline.com/view/implementation-of-the-separation-quality-factor-sqf-in-chromatography-data-systems-for-automated-method-evaluation (accessed 2026-11-05)
7. Desmet, G.; Niezen, L.; Matheson, A.AI/ML Now: Uncertainty-Driven Model-Based Search Methods for Method Development in Liquid Chromatography (Part One). Chromatography Online website. https://www.chromatographyonline.com/view/ai-ml-now-uncertainty-driven-model-based-search-methods-for-method-development-in-liquid-chromatography-part-one (accessed 2026-11-05)
8. Desmet, G.; Niezen, L.; Matheson, A.AI/ML Now: Uncertainty-Driven Model-Based Search Methods for Method Development in Liquid Chromatography (Part Two). Chromatography Online website. https://www.chromatographyonline.com/view/ai-ml-now-uncertainty-driven-model-based-search-methods-for-method-development-in-liquid-chromatography-part-2 (accessed 2026-11-05)
9. Matheson, A.; Jones, K. GLP-1 Peptide Analysis: Challenges and Solutions. Chromatography Online website.https://www.chromatographyonline.com/view/glp-1-peptide-analysis-challenges-and-solutions (accessed 2026-11-05)