Keynote Speakers

When conventional high-performance liquid chromatography (HPLC) fails to provide sufficient separation efficiency, two-dimensional liquid chromatography (2D-LC) offers a powerful alternative by substantially increasing peak capacity. Coupled with mass spectrometry, multidimensional separation techniques are particularly well-suited for analyzing highly complex proteomic samples. Despite their advantages, 2D-LC methods are notoriously difficult and timeconsuming to optimize. 

The interplay between column performance, particularly separation speed and resolution, and the macrostructure of randomly packed beds of spherical particles has been a defining theme in chromatography research since the pioneering work of Horvath, Giddings, and Knox in the 1960s. The pursuit of faster, higher-resolution separations has inspired successive innovations: elevated operating temperatures, more ordered packing architectures (e.g., Regnier’s and Desmet’s pillar array columns), reduced flow resistance through highly porous monoliths (Tanaka, Svec), and the leap from conventional HPLC (400 bar) to UHPLC (1000–1500 bar) with sub-2 μm particles at extreme pressures (Jorgenson; Waters Corporation, 2004).

Lipidomics and metabolomics are analytical approaches widely adopted in biomedical research with the aim to obtain mechanistic insights in biochemical processes, to find diagnostic and prognostic biomarkers, and generate new hypothesis about biological processes or validate concepts from other experimentation. Although common untargeted methodologies allow simultaneous analysis of hundreds of metabolites, they often fall short in full coverage of pathways in which isomeric species play a major role, e.g. in oxylipidomics, the phosphoinositide network, glycolysis and pentosephosphate pathways. 

We report on a computational study to gain insight in the potential advantage of spiky particles in packed bed columns for liquid chromatography. Using the recently adopted Two-Zone Moment Analysis method, a comprehensive set of band broadening data has been generated over an exhaustive range of particle/spike combinations and shapes, velocities, diƯusion coeƯicients and retention factors.

Numerous studies have investigated changes in protein expression at the system level using proteomic mass spectrometry, but only recently have studies explored the structure of proteins at the proteome level. We developed covalent protein painting (CPP), a protein footprinting method that quantitatively labels exposed lysine, and have now extended the method to whole intact animals to measure surface accessibility as a surrogate of in vivo protein conformations. These types of methods will allow the interrogation of the in vivo 3-Dimensional proteome to study different aspects of biology and disease.

Sample preparation constitutes the initial and often most critical stage in the analysis of complex sample, fundamentally determines the overall performance of analytical method. However, by guiding substances from mess to order, sample preparation cannot occur automatically and spontaneously, frequently constitutes the rate-limiting step in complex analytical workflows. 

In recent years, the pharmaceutical drug landscape has rapidly evolved beyond traditional small molecules and therapeutic antibodies, with an increasing importance from therapeutic peptides and oligonucleotides (“TIDES”). Concurrently, the fast development of cell and gene therapy (CGT) approaches led to the introduction of complex genome editing materials such as guide RNAs (gRNAs), mRNAs, Cas enzyme and recombinant adeno-associated viruses (rAAVs). Several of these emerging drug modalities and CGT approaches have demonstrated remarkable clinical success and are now marketed. 
 

Metabolomics, the systematic analysis of low-molecular-weight metabolites, is being revolutionized through the integration of artificial intelligence (AI) with advanced liquid chromatography-mass spectrometry (LC-MS) platforms. This presentation explores how AI transforms every aspect of the metabolomics workflow: from LC-MS-based data acquisition and feature extraction to metabolite annotation and biological interpretation

3D printing allows the custom creation of miniaturized structures with three-dimensional complexity. We have developed stereolithographic 3D printers that can make devices containing components such as pumps and valves to control fluid transport in microfluidic channels [1], enabling a broad range of bioassays. Recently, we have focused on 3D printing of chromatographic columns within these microfluidic systems, for improved sample preparation capabilities compared to forming columns in devices after their fabrication.

Biological tissues are highly heterogeneous, consisting of a variety of cell types, states and subpopulations, and understanding heterogeneity at the single cell level is of great interest for biomedical research. We have developed approaches to minimize sample losses normally incurred during sample processing. In combination ultra-low-flow separations and latest-generation mass spectrometry instrumentation, we now achieve in-depth proteome coverage for low-input samples including single cells. 

Comprehensive lipidomic quantitation remains analytically demanding due to the large structural diversity of lipids, their broad polarity range, and concentration differences spanning several orders of magnitude. Although UHPLC/MS is widely used, limitations in lipidome coverage and analysis time remain. Ultrahigh-performance supercritical fluid chromatography–mass spectrometry (UHPSFC/MS) offers a high-throughput alternative, but its broader application to highly polar and ionic lipid classes has been restricted by undesired interactions with metal surfaces, leading to peak tailing and sensitivity loss. Here, we present a UHPSFC/MS method using a bioinert column, which enables fast, robust, and comprehensive quantitative lipidomic analysis for nonpolar, polar, and ionic lipid classes.

Synthetic oligonucleotides are a novel class of drugs with the potential to treat a wide spectrum of indications, including cancer, cardiovascular and metabolic conditions, neurological disorders, and ophthalmic diseases. Several oligonucleotide drugs have recently been approved, and a greater number are in clinical trials. Discovery, development, and manufacturing of synthetic oligonucleotides necessitate analysis of the main drug component and its impurities by appropriate analytical techniques.

Monoclonal antibodies (mAbs) leverage the immune system’s natural precision to target disease pathways with high specificity and have become a major force in the pharmaceutical industry. Their success has spurred the development of various next-generation formats such as antibody-drug conjugates (ADCs), bispecific antibodies (bsAbs), antibody fragments, Fc fusion proteins, etc. With vast therapeutic potential comes an immense structural and functional intricacy highly demanding towards analytics.

The development of chromatography material plays a central role in the advancement of separation science and purification technology, and have significant impact on life science discovery and pharmaceutical R&D and productions. Over the past 20 years, new generations of particulate materials, such as sub-2 micron particles, core-shell particles, and colloidal crystals, have remarkably expanded the kinetic performance limits. Additionally, the development of novel stationary phases in HILIC, mixed-mode chromatography, and chiral chromatography has significantly broadened HPLC's capabilities. 
 

Oligonucleotide-based therapeutics market is experiencing rapid expansion, projected to reach over USD 17-51 billion by the early 2030s with a high CAGR of nearly 20%. Driven by advancements in RNA-targeted therapies, such as siRNA and antisense oligonucleotides (ASOs), this market is shifting from niche, rare disease applications to broader indications like oncology and chronic diseases. Nonetheless, this new class of therapeutics poses new challenges to the biopharma industry as it requires the support of robust and sensitive analytical methods.

The clinical success of glucagon-like peptide-1 (GLP-1)–based therapeutics has renewed interest in peptide drugs for metabolic diseases, particularly obesity. Despite this momentum, analytical strategies for the comprehensive characterization of therapeutic peptides remain limited. This gap is notable given the structural and physicochemical diversity of therapeutic peptides, which include linear, macrocyclic, and disulfideconstrained formats and frequently incorporate unnatural amino acids.

Most of the amino acids have enantiomers (D and L forms) due to the chiral center at the α-position. Although these D and L-enantiomers are formed in the racemic 50/50 mixtures by the chemical synthesis, L-enantiomers are predominant in living beings especially in higher animals like mammals. The antipodes (D-enantiomers) are believed to be not present in higher animals for a long time, however, several D-amino acids were found in various tissues and physiological fluids of mammals including humans along with the progress of analytical technologies.

As greener approaches to chemical analysis continue to grow in popularity, the significant decrease in mobile phase flow rates makes capillary LC an appealing choice for liquid chromatographic separations. In this presentation, recent advances in capillary LC column design will be described. The use of a “tube-in-manifold” design to enable circular cross-section columns within microfluidic column formats improves column loading and performance for integrated column chips. Integrated column cartridges where a column, heater, and detector are combined within a single unit promote ease-of-use for capillary LC, especially within miniaturized systems. Descriptions of various stationary phases used in these column formats will also be presented.

Advanced drug formulations, such as polymer-encapsulated drugs for extended release or lipid nanoparticles for delivery of labile RNA treatments (RNA-LNP), require extensive characterization. A multimodal liquid chromatography system (multiLC) was designed for multiattribute characterization. The multiLC features an on-line trap column with a follow-on column to separate compounds not captured by the trap column. 

Central to advancing neuroscience is characterizing and monitoring the chemical composition of the brain extracellular space. Neurotransmitters, neuromodulators, and other metabolites within this microenvironment reflect several physiological functions occurring simultaneously on different spatiotemporal scales. Elucidating the role that these metabolites play in normal and diseased brains can improve our understanding of neurological disorders, thereby providing potential new drug targets and therapies.

LC-MS methods have been critical for advancing metabolomics. A persistent finding has been that while many features, i.e. signals at a given m/z and retention time, are detected in complex samples using untargeted LC-MS, a relatively small number of metabolites are identified.

Oligonucleotides (ONs) are synthetic nucleic acid polymers that are transforming modern therapeutics by enabling sequence-specific recognition of nucleic acids, most commonly RNA, enabling modulation of gene and protein expression. An increasing number of ON-based drugs is currently in development to address diseases that were previously difficult or impossible to treat with conventional medicines based on small molecules. ON manufacturing typically generates numerous structurally related impurities that must be carefully monitored and controlled to ensure product quality and safety. High-Performance Liquid Chromatography (HPLC) remains the primary analytical technique for this purpose. Despite recent advances in column technology and stationary phases, HPLC analysis of ONs continues to be highly challenging due to the intrinsic complexity of these samples and the limited understanding of how specific column parameters influence ON separation.

New drug substances pose challenging demands on discovery and process chemistry which requires adequate and fast advancement in analytical tools to avoid bottlenecks in drug development. Challenges encountered in analyzing new medicines go beyond the complexities encountered during the manufacturing process, especially for biopharmaceuticals and multicombination drugs.

The development of advanced biopolymer drugs necessitates the development of analytical tools with precise selectivity. Affinity liquid chromatography (aLC) is such technique, useful for specific isolation of target analytes from complex media. We describe development of high-performance aLC columns based on covalently immobilized ligands, or immobilization of biotinylated ligands on streptavidin modified sorbent. In the latter case the biotinylated affinity ligands were loaded on column and characterized in chromatographic experimental setup. Both proteins and nucleic acids were immobilized on the aLC sorbent and used as affinity selectors.

Chemical modifications of ribonucleic acid (RNA) are increasingly recognized to play central roles in therapeutic and endogenous RNAs. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is the gold standard for directly detecting and sequencing massaltering RNA modifications with single-nucleotide resolution, but this powerful technique suffers from limited sample throughput and quantitative accuracy.

The color of young red wines is primarily determined by the anthocyanins extracted from black grape skins during maceration. Anthocyanins are however highly reactive, and from the moment they are extracted become involved in a range of reactions with a host of wine constituents to produce more stable anthocyanin-derived pigments. As a consequence, the levels of grapederived anthocyanins rapidly decrease during the first few years of ageing, and their contribution to the color of aged wines is limited and overshadowed by the derived pigments. Significant progress has been made in elucidating the reactions involved in the production of several derived pigment classes through model solution studies. A complete understanding of wine color evolution during ageing, however, remains elusive because of the challenges associated with analyzing the large number of derived pigments often present at low levels in the complex wine medium.

Therapeutic oligonucleotides continue to offer hope for the treatment and prevention of uncurable diseases. High performance liquid chromatography is an essential tool for separating impurities and identifying quality attributes of these complex therapeutic modalities; however, current methodologies are insufficient for full characterization.

Proteomics experiments using liquid chromatography with tandem mass spectrometry (LCMS/MS) have become valuable for identifying changes in protein abundance and posttranslational modifications (PTMs) during disease progression. In these experiments, proteins are extracted from cells and/or tissues in the presence of chemical additives that maintain protein solubility. However, these additives can suppress protein digestion, negatively impact LC separations of peptides, and reduce peptide ionization.

Liquid chromatography underpins pharmaceutical development from early discovery through commercial manufacturing; however, chromatographic method development remains largely empirical, resource-intensive, and fragmented across development stages and scales. Advances in machine learning (ML) and in-silico modeling offer a unique opportunity to transform chromatographic method development into a more robust, scalable, and lifecycle-aware process suitable for industrial and regulated environments.

Antibody drug conjugates (ADCs) represent one of the fastest-growing therapeutic modalities, yet their analytical characterization and process control remain fundamentally constrained by limitations in chromatographic–mass spectrometric integration. Hydrophobic interaction chromatography (HIC), the gold standard for drug-to-antibody ratio (DAR) analysis, has historically been incompatible with MS due to nonvolatile salts, while process monitoring tools lack the speed and dimensionality needed for modern ADC manufacturing.

In drug development, precise characterization of material composition and purity is essential. Conventional liquid chromatography (LC) detectors like UV/vis (200–800 nm) spectrophotometers are limited in scope, as they cannot detect analytes lacking chromophores. Alternative detection methods—including refractive index, charged aerosol, light-scattering, and mass spectrometry—address some gaps, but each has distinct limitations.

Over the past two-and-a-half decades numerous strides have been made in both the theory and practice of multi-dimensional liquid phase separations. Theoretical frameworks have been developed that enable the analyst to effectively manage compromises that must be made between analysis time, peak capacity, and analyte dilution. Very powerful and robust commercial instrumentation has been introduced that enables a wider range of users to effectively deploy the technology on a routine basis for analysis of both complex and difficult-to-resolve samples in fields ranging from polymer characterization to analysis of foods and beverages. Through this lens, we see that the evolution of multi-dimensional liquid chromatography during this century has been very impressive.

The analysis of complex environmental and biological matrices remains one of the principal challenges in liquid-phase separation science, where matrix interferences can significantly compromise chromatographic performance, column stability, and mass spectrometric detection. In many conventional workflows, insufficient sample cleanup leads to the introduction of co-extracted matrix components that contribute to column fouling, reduced separation efficiency, and ionization suppression in electrospray ionization (ESI).

Conventional C18 reversed-phase columns remain the default choice for many HPLC separations, yet their widespread use can obscure opportunities for improved performance through alternative selectivity. This work examines retention mechanisms associated with nontraditional stationary phases, with particular emphasis on embedded polar group and fluorinated reversed-phase materials. In addition, the use of hydrophilic interaction liquid chromatography (HILIC) for alternative selectivity is explored. By dissecting the molecular interactions governing retention and selectivity, this contribution highlights how changes in stationary-phase chemistry can produce substantial gains in resolution, robustness, and method simplicity. 
 

Biotherapeutics such as monoclonal antibodies and antibody–drug conjugates (ADCs) play a vital role in modern medicine by providing highly targeted treatments for complex diseases. Isolating and characterizing product variants, degradation products, and other trace impurities within complex matrices, such as cell‑culture harvests or formulated drug products, is essential for ensuring biotherapeutic efficacy and safety. However, low‑level species present in biotherapeutics remain difficult to isolate due to challenges in sensitivity, recovery, and sample stability. We previously demonstrated an automated trapping‑enrichment multidimensional liquid chromatography (TE‑mDLC) strategy to isolate, enrich, and collect small‑molecule targets for detailed characterization.

Peter Carr was a pioneer in separation science, specifically in liquid chromatography (LC), where he contributed so many pivotal insights. These include optimization theory, the use of the hydrophobic subtraction model, two-dimensional liquid chromatography (2DLC) and the thermodynamics of reversed phase liquid chromatography (RPLC), amongst other studies highlighting the most basic aspects of LC.

Despite the popularity of reversed phase liquid chromatographic separations to address a wide range of analytical problems, there has yet to be a comprehensive model developed for the accurate prediction of separation selectivity over a broad range of potential analytes and chromatographic conditions. However, significant steps towards a comprehensive model have been the development of linear solvation energy relationships and the hydrophobic subtraction model (HSM), pioneered by Pete Carr.

Comprehensive characterization of organic pollutants in real-world samples remains challenging due to matrix complexity, chemical diversity, and the prevalence of isomers. Efficient chromatographic separation is therefore critical for reliable analysis of emerging contaminants in complex environmental and biological matrices. This presentation addresses these needs through (i) chromatography–mass spectrometry workflows that enhance chemical coverage and identification confidence, and (ii) automation-oriented chromatographic platforms that translate high-selectivity separations into scalable practice.