Tag: Bob Pirok

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Conferences

CAST group participates in full force at HPLC 2025 with posters and lectures

CAST Group 2025

It is a tradition within our CAST group to attend one major scientific conference each year as a unified team. This provides an opportunity to present our work, strengthen internal cohesion, and engage with the broader research community. With liquid-phase separations as a common thread across our projects, all scientists from the CAST group, as well as those involved in related projects embedded within the Centre for Analytical Sciences Amsterdam, were therefore present at the HPLC 2025 symposium in Bruges, Belgium.

The full CAST group with fellow CASA members at the conference dinner party on Wednesday at HPLC2025 in Bruges, Belgium.

Four poster nominations for the Gargano team

The CAST group contributed to the symposium with a large number of posters. On Monday and Tuesday, our young scientists presented and discussed their work with the delegates. All four posters of the team of Andrea Gargano were nominated for the Best Poster Award.

Improving IgG Glycoform Analysis with Advanced HILIC-MS

Thomas Holmark presented his latest advances in improving the sensitivity and reliability of IgG glycoform analysis. Their work focuses on developing capillary-based HILIC-MS methods to better characterize glycosylation patterns in immunoglobulin G (IgG), which are important biomarkers for disease progression. By optimizing monolithic column synthesis and implementing self-packed trap columns, the team significantly reduced sample carryover and enhanced detection sensitivity—paving the way for more accurate, low-input analysis of IgGs secreted by in-vitro stimulated B-cells.

Masa Sawisawa, Thomas Holmark, Annika van der Zon and Ziran Zhai were nominated for a Best Poster Award.

Annika van der Zon receiving her best poster pitch prize.

Third poster-pitch prize for monolithic HILIC-MS for Monoclonal Antibody Glycoform Profiling

Annika van der Zon introduced an acrylamide-based monolithic HILIC stationary phase, specifically designed to overcome the limitations of conventional silica-based materials. This new column enables high-resolution separation of intact mAb glycoforms, significantly outperforming traditional LC-MS methods such as RPLC. Using trastuzumab as a model, the team achieved chromatographic separation and mass spectrometric identification of up to 17 N-glycoforms—demonstrating enhanced sensitivity to minor variants. This development represents a major step forward in therapeutic antibody characterization, offering a powerful tool for quality control and drug development

NanoSEC-nMS Enhances Native Protein Complex Characterization

Ziran Zhai was also nominated for the poster-pitch prize and presented an innovative nanoflow size-exclusion chromatography-native mass spectrometry (nanoSEC-nMS) method at HPLC 2025, pushing the boundaries of intact protein and protein complex analysis. By operating at ultra-low flow rates (500 nL/min) and using capillary-format columns, the method drastically reduces sample requirements while preserving native protein structures and non-covalent interactions. This approach enables sensitive, high-resolution analysis of complex biomolecular assemblies, even from limited or biologically relevant samples like urine. With enhanced ionization stability and compatibility with high-salt buffers, nanoSEC-nMS represents a powerful advancement for native top-down proteomics and structural biology workflows.

Pharmaceutical Analysis Poster Award for Masa Serizawa

Masa Serizawa showcased a major advance in the analysis of biodegradable polymers, focusing on poly(lactic-co-glycolic acid) (PLGA)—a cornerstone material in drug delivery systems. The team developed optimized SEC-MS and normal-phase LC methods to achieve precise characterization of PLGA microstructure, including molecular weight distribution (MWD), chemical composition distribution (CCD), and functionality-type distribution (FTD). By minimizing fragmentation during electrospray ionization with cesium iodide, they enabled reliable identification of PLGA isomers and block structures. Furthermore, their new NPLC approach extends analysis to high-molecular-weight PLGAs (up to 185 kDa), offering detailed insight into both end-group functionality and lactic/glycolic acid ratios. These innovations pave the way for the design of more effective, tailored PLGA-based drug delivery platforms. He was awarded the Best Pharmaceutical Analysis Poster Award for his great work.

Masa Serizawa receiving the Best Pharmaceutical Analysis Poster Award.

Other poster contributions from PhD candidates included those of Sanne Boot, who showcased her efforts to simulate LC×LC-MS data for optimizing oligonucleotide separations, enabling the exploration of separation conditions within complex chemical spaces. PhD candidate Gerben van Henten showed his work on the evaluation of chromatographic response functions. His poster explained how some optimization methods struggle with chromatographic response functions that fundamentally were believed to be suitable.

Several CAST graduate students were also present at HPLC2025. Lonneke van Dalen presented her poster on developing a new peak integration method using neural networks in collaboration with Unilever.

Merel Konings demonstrated the optimization of light degradation reactors to conduct degradation studies in liquid-phase separations, and Rebecca Gibkes presented her work on plate-height modeling and peak parking as part of a collaboration between the University of Amsterdam, the Vrije Universiteit Brussel and Gustavus Adolphus College.

Rick van den Hurk as Csaba Horvath Finalist

Rick van den Hurk presenting his latest work on the effect of a lack of radial mixing in systems where flows are combined.

Rick van den Hurk was nominated as Csaba-Horváth finalist and addressed a key obstacle in expanding two-dimensional liquid chromatography (2D-LC): the solvent mismatch between coupled separation dimensions. His work focused on active modulation strategies, such as SPAM, ACD, and ASM, that dilute the first-dimension effluent with a weaker solvent to improve retention and peak shape in the second dimension. Using experimental setups and computational fluid dynamics simulations, the team investigated how radial mixing at T-junction interfaces impacts analyte retention. Van den Hurk explained how these findings provide valuable insights into optimizing flow conditions and mixer design, paving the way for broader and more robust application of 2D-LC in industrial and research settings. This work is conducted in collaboration with the group of Dwight Stoll at Gustavus Adolphus College.

Bob Pirok was invited to present a lecture on the state of machine learning in chromatography, and explained during his presentation the importance of equiping machine-learning workflows with suitable data processing strategies. A key part of the lecture focused on the work by CAST researcher Nino Milano and his efforts to simulate realistic 2D chromatographic data for the evaluation of peak detection algorithms.

Andrea Gargano presented a novel HILIC-MS approach using custom-designed acrylamide-based monolithic columns to improve the analysis of intact biomacromolecules. These columns offer enhanced selectivity and reduced secondary interactions, enabling the separation of closely related variants that are challenging for traditional RPLC-MS. Gargano demonstrated applications which included the resolution of oligonucleotide impurities, glycated protein isomers, and monoclonal antibody glycoforms, advancing analytical capabilities in therapeutic development and quality control. 

Luca Tutis’s contribution on the development of an ion-pair HILIC method for oligonucleotide impurity analysis was selected for a talk. This method significantly alters HILIC selectivity for ONs by emphasizing nucleobases and conjugated groups (GalNAc) over highly polar phosphate groups. This enabled the resolution of deamination, non-conjugated, and PS-PO converted impurities. Importantly, this IP-HILIC approach is fully compatible with MS, crucial for accurate impurity identification.

Honorary CAST member and emeritus professor Peter Schoenmakers lectured on the sense and nonsense of artificial intelligence in chromatography and gave a historical overview in his lecture along with applications that he placed into context using fundamental principles known to chromatographers.

Special events

Advancing Education in Separation Science

Andrea Gargano, Simone Dimartino and Martina Catani organized the Education session workshop, introducing in the HPLC program a new space to discuss innovation in education. Many delegates joined discussing innovations in separation science education (including teaching materials, software based tools, project based learning and a session discussing perspectives in skills and knowledge that the analytical scientist of the future should be educated on) including Bob Pirok.

Second prize in HPLC Tube

Dissemination of scientific work is incredible important to inform society about the latest progress. In this context, the symposium annually hosts the HPLC Tube where researchers can make brief videos to explain their work. CAST researchers Andrea Gargano, Annika van der Zon, Ziran Zhai and Thomas Holmark won the third prize in this category on their video entitled: “Stay Intact: AcryHILIC’ Glycoform Impact.”

Scientists discussed the state of current education practice of chromatography and how to improve this further.

Bob dedicating the first official copy to CASA leader and co-teacher Prof. Govert Somsen.

Launch of educational textbook Analytical Separation Science

Bob Pirok and Peter Schoenmakers launched their textbook Analytical Separation Science. The book is designed to support students, educators, and practitioners by offering a comprehensive and structured overview of modern separation techniques. With clear explanations, didactic modules, and academic exercises, it serves as a valuable resource for classroom teaching and self-study alike. The book is accompanied by an interactive website (https://ass-ets.org), featuring figures, lectures, and supporting materials to bridge the gap between theory and practice.

Short course in AI in Chromatatography

Bob Pirok and Tijmen Bos delivered a well-attended short course on Artificial Intelligence in Chromatography, introducing participants to the growing role of AI in analytical method development and data processing. The course covered foundational concepts in machine learning, modern optimization techniques, and practical applications across the chromatographic workflow, from peak detection to intelligent gradient design. Through interactive examples and real-world case studies, the session provided researchers and practitioners with the tools to understand and begin applying AI in their own laboratory environments.

Scientists discussed the state of current education practice of chromatography and how to improve this further.

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Publications

Educational textbook Analytical Separation Science launched by Pirok and Schoenmakers

Bookframe

At the renowned HPLC2025 conference, a milestone in the field of separation science was celebrated with the official launch of Analytical Separation Science, a comprehensive new textbook authored by Dr. Bob Pirok and Prof. Peter Schoenmakers. The launch event, held on June 16 at the Historium Bruges and organized by the Royal Society of Chemistry (RSC), brought together leading scientists and educators in the field.

The first official copy was presented to Prof. Govert Somsen of the Vrije Universiteit Amsterdam, a long-time colleague and co-educator in analytical chemistry.

Structured around Basic, Master, and Advanced modules, the book serves both as a teaching tool and as a professional reference. It introduces fundamental concepts, offers in-depth treatments for graduate-level study, and explores cutting-edge developments in chromatographic and electrophoretic techniques.

“This book reflects our shared commitment to educating the next generation of analytical scientists,” said Prof. Peter Schoenmakers. “By combining foundational theory with real-world case studies and emerging methods, we aim to make separation science engaging and relevant across career stages.”

Figure 1. Cover of the book.

An interactive companion website (https://ass-ets.org) extends the book’s reach. It offers additional resources including a literature repository, academic lectures with interactive figures, and exercises based on decades of teaching experience at the University of Amsterdam and Vrije Universiteit Amsterdam.

The website is a community based effort with universities to be supporting their expertise to complete the analytical portfolio as much as possible.

Figure 2. Prof. Wolfgang Lindner (University of Vienna) and Prof. Peter Schoenmakers (University of Amsterdam) draw the winners of the book competition.

“Our goal was to make learning separation science both accessible and inspiring,” added Dr. Bob Pirok. “This project combines our classroom experience with insights from industry collaborations, bridging education and practice.”

Figure 3. Photograph from the launch event.

Conference participants were able to win a copy of the book by solving a series of puzzles and Prof. Wolfgang Lindner (University of Vienna) and Prof. Schoenmakers (University of Amsterdam) drew the five winners from the entries.

The book is now available at the Royal Society of Chemistry or any other book vendor.

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Conferences

Short Course on AI in Chromatography

Screenshot

Machine learning is considered increasingly important in analytical separation science because of its potential to enable faster and more accurate interpretation of complex, high-dimensional data from techniques like chromatography and mass spectrometry.  Additionally, machine learning accelerates method development and improves reproducibility, leading to more efficient and reliable analytical workflows.

Introduction to Artificial Intelligence

To help both newcomers and experienced practitioners in the field, Bob Pirok and Tijmen Bos were invited to give a short course entitled Introduction to Artificial Intelligence in Chromatography at the 54th International Symposium on High Performance Liquid Phase Separations and Related Techniques (HPLC2025) in Bruges, Belgium.

The course, designed for both academic and industry scientists, was structured in four parts. It began with a clear introduction to the foundations of artificial intelligence, including the historical context and core concepts such as regression, optimization, and pattern recognition. In the second part, the presenters expanded into modern machine learning techniques — from support vector machines to neural networks and reinforcement learning approaches such as Q-learning and Proximal Policy Optimization (PPO).

The third part showcased real-world applications in chromatography, including predictive modeling for retention time, peak detection using neural networks, and data-driven optimization of method parameters. The session concluded with hands-on exercises, challenging participants to apply what they had learned to realistic chromatographic problems.

“AI is not a replacement for analytical expertise, it’s an extension of it,” said Dr. Pirok. “With the right understanding, these tools can help us interpret complex data faster and develop better methods with fewer experiments.”

Our goal is to demystify AI for chromatographers. These technologies are no longer futuristic — they’re ready to be applied, provided we know how to ask the right questions.

Dr. Tijmen Bos

Dr. Bos added, “Our goal is to demystify AI for chromatographers. These technologies are no longer futuristic — they’re ready to be applied, provided we know how to ask the right questions.”

The short course also served as a platform to emphasize responsible and informed application of machine learning in laboratory settings. The presenters stressed the importance of data quality, domain knowledge, and understanding the assumptions behind different models.

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Recognition

Bob Pirok wins HTC Innovation Award

Bob Pirok wins HTC Innovation Award

The HTC Innovation Award was launched by LCGC International and the HTC Scientific Committee and Industry Board in honour of separation scientists who make pioneering contributions to the field of analytical separation science, with a strong focus on applications that benefit society. At the award ceremony Prof. Deirdre Cabooter (KU Leuven), Chair of the HTC-18 Scientific Committee, acknowledged Pirok’s “impressive research output”, working on polymer characterization (including pyrolysis-gas chromatography and hydrodynamic chromatography), and multidimensional liquid chromatography separations, using the interface between separation dimensions as a point of chemical transformation employing light degradation or digestion with immobilized enzymes.

Teamwork achievements

According to Cabooter, the award particularly recognizes Pirok’s research on using cutting-edge machine learning and chemometric approaches to automate method development for both one-dimensional as well as two-dimensional liquid chromatography separations. “Since method development for complex samples, such as synthetic polymers and oligonucleotides in medicinal drugs, is currently a true bottleneck, the impact of automating this process on many research fields and industries cannot be underestimated. The same approaches can also be extended to other techniques, such as comprehensive two-dimensional gas chromatography (GC×GC), further broadening the application field to, for example, hydrocarbons.”

Upon receiving the award, Pirok said to be very honoured and he accepted the prize “as a representative of a great team of people who made this work possible, in particular Tijmen Bos, Stef Molenaar, and Jim Boelrijk. These achievements are due to our teamwork. I feel blessed with the many academic and industrial collaborations and opportunities that have come on my path with great scientists around the globe. They have contributed massively to the fully automatic AutoLC method-development system that helped me earn the award”. Pirok’s research team runs several industrial-academic projects, mainly with the pharmaceutical and polymer industry. His group is known for bringing its research into education and vice versa.

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Publications

Parallel Gradients 2DLC-HRMS of complex protein digest

2D-LC System
2D-LC System

Investigating the proteins in biological samples can help us understand and identify diseases and improve the effectiveness of medication. To study proteins in these samples, they are typically digested into peptides and subsequently analyzed by liquid chromatography (LC) hyphenated with high-resolution mass spectrometry (HRMS).

Comprehensive two-dimensional LC (LC×LC) offers increased separation power over traditional LC methods. However, most common gradient designs require re-equilibration of every second-dimension run, resulting in high flow rate operations to limit the empty separation space. This also limits MS sensitivity as flow splitting is required to handle such flow rates.

In this work, we developed an LC×LC method using a so-called parallel-gradient design, which omits the need for column re-equilibration and enables the use of the entire separation space. Moreover, this allows for lower flow rates and maintains the sensitivity for low-abundant analytes. The parallel-gradient design achieved higher surface coverages and sensitivity at lower effective peak capacities. Most importantly, both methods were applied to analyze a Human IMR90 lung fibroblast cell line digest to assess its applicability to real complex samples. The parallel-gradient method was able to identify significantly more proteins than the current state-of-the-art methods while using the same analysis time and at a lower solvent consumption. The applicability of the parallel-gradient design could be improved even further by shortening the modulation times, as it was not limited by column re-equilibration.

The study is a collaborative work done thanks for the contribution of many colleagues and students. The link to the publication is reported below.

https://doi.org/10.1021/acs.analchem.4c02172

 

 

 

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Publications

Optimisation of 2D-LC separations by AutoLC

Optimisation of 2D-LC separation technology
Optimisation of 2D-LC separation technology

In their latest work, the AutoLC team of the CAST group of Pirok at the University of Amsterdam extended their method-development workflow to facilitate optimisation of 2D-LC separations [1].

The demonstrated workflow was capable of unsupervised gradient optimization for comprehensive 2D-LC-MS methods without needing to specify sample information. The required algorithms were designed by CAST researchers Stef Molenaar, Tijmen Bos and Jim Boelrijk, under the supervision of Bob Pirok.

The workflow was inspired by the original theoretical paper by Pirok in 2016 [2], in which retention models are constructed to computationally simulate methods and select the optimal parameters. In a nutshell, the system first utilizes very generic methods to measure the sample. This data is then used to construct retention models that describe the retention behavior of the analytes as a function of the mobile-phase composition. A large number of methods are then simulated computationally, and a computed optimum is submitted to the LC system. The LC system then carries out the proposed method and submits its data back to the system afterwards. Consequently, unsupervised optimisation of 2D-LC separations may be attained.

Unsupervised optimization of comprehensive 2D-LC separations
Figure 1. The AutoLC algorithm was demonstrated on a peptide digest separation using RPLC in both dimensions. Panels A, B, D and E show scanning conditions, with Panels C and F showing a computed method that was proposed unsupervised by the algorithm. Reproduced with permission from [1].

The Pirok group earlier had demonstrated their AutoLC platform on 1D separations [3], but now extended this to the more complicated 2D separation methods [1]. To accomplish this, algorithms were designed to compute and optimize complex second-dimension shifted-gradient assemblies.

The researchers also investigated the robustness of the retention models and the influence of the errors in peak-width predictions.

The study was conducted in a collaboration with the group of Dwight Stoll at Gustavus Adolphus College where the measurements were conducted. The developed tool for optimisation of 2D-LC separations was demonstrated on a complex peptide digest sample with RPLC in both of the dimensions.

The work was a product of Pirok’s UPSTAIRS (Unleashing the Potential of Separation Technology to Achieve Innovation in Research and Society) project, which is financed by the Dutch Research Council (NWO), as well as the synergetic UNMATCHED project, which is supported by BASF, DSM and Nouryon, and also receives funding from NWO.

The work was published open-access in Journal of Chromatography A and can be accessed free of charge here.

References

  1. Computer-driven optimization of complex gradients in comprehensive two-dimensional liquid chromatography, S.R.A. Molenaar, T.S. Bos, J. Boelrijk, T.A. Dahlseid, D.R. Stoll, B.W.J. Pirok, Chromatogr. A, 2023, 1707, 464396, DOI: 10.1016/j.chroma.2023.464306.
  2. Program for the interpretive optimization of two-dimensional resolution, B.W.J. Pirok, S. Pous-Torres, C. Ortiz-Bolsico, G. Vivó-Truyols and P.J. Schoenmakers, J. Chromatogr. A, 2016, 1450, 29–37, DOI: 10.1016/j.chroma.2016.04.061.
  3. Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography S. Bos, J. Boelrijk, S.R.A. Molenaar, B. van ‘t Veer, L.E. Niezen, D. van Herwerden, S. Samanipour, D.R. Stoll, P. Forré, B. Ensing, G.W. Somsen, B.W.J. Pirok, Anal. Chem. 2022, 94(46), 16060–16068, DOI: 10.1021/acs.analchem.2c03160.
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Publications

2D-LC in industry: technological innovations reviewed

2D-LC in industry: technological innovations reviewed
2D-LC in industry: technological innovations reviewed

CAST scientist Rick van den Hurk wrote a review on recent developments in 2D-LC and the use of 2D-LC in industry. He did this under the supervision of Bob Pirok and in collaboration with Matthias Pursh (Dow) and Dwight Stoll (Gustavus Adolphus College).

Two-dimensional liquid chromatography (2D-LC) greatly advances the separation powered of analytical separation sciences through a better peak capacity as well as offering more-tailored selectivity combinations.

However, the field of 2D-LC is, in particular in contrast to 2D-GC, still very immature and under significant development. In 2019, Bob Pirok, Dwight Stoll and Peter Schoenmakers published a review in which they examined the latest trends from 2015 until 2019 [1].

In this recent installment [2], Rick van den Hurk reviewed the recent innovations between 2019 and 2023. In addition the review also devotes significant focus to the implementation of the technique in industry. The review was co-written by Bob Pirok, Dwight Stoll (Gustavus Adolphus College) and Matthias Pursch (Dow).

The authors examined over 200 articles and also compared these with the articles published prior to 2019. In their review, the authors concluded that mobile-phase mismatch continues to be an important focus area for the field, and several modulation strategies and new variants were discussed.

Van den Hurk and co-workers also noticed that a third of the publications had at least one author affiliated with industry. Application fields that particularly demonstrated involvement were the polymer characterization, metabolomics, and pharmaceutical and biopharmaceutical analysis. Furthermore, industrial applications favored the use of heart-cut 2D-LC and largely employed on-line hyphenation. The authors did note that the database was likely to be missing out on a number of industrial works that are not published for confidentiality reasons.

Other important developments were the increased popularity of computer-aided strategies, alternative gradient-elution methods to facilitate modulation, as well as multi-stage, multi-dimensional separations, the latter of which were applied to the characterization of protein therapeutics.

2D-LC in industry: technological innovations reviewed
Figure. Number of applications per application area distributed by non-comprehensive (light blue) and comprehensive (dark blue) applications between 2019 and 2023. Reproduced with permission from [2].

Two-dimensional liquid chromatography is of paramount importance to the PARADISE project of CAST scientist Bob Pirok in which multi-dimensional separation technology is used to achieve separation of highly complex samples. In addition, the project aims to characterize the correlation of different sample properties within a single analytical solution. The outcomes of this recent review are thus of value to the ongoing progress in the PARADISE project. Rick van den Hurk is a PhD candidate in the PARADISE project, which stands for Propelling Analysts by Removing Analytical-, Data-, Instrument- and Sample-related Encumbrances and receives funding from the Dutch Research Council (NWO), as well as a number of public and private organisations. Read more about the PARADISE project here.

In addition, the review was part of Pirok’s UPSTAIRS project, which aims to improve the accessibility of advanced separation technology by developing computational methods to leverage chromatographic theory in an unsupervised workflow. This project also receives funding from NWO.

The work was published in TrAC Trends in Analytical Chemistry as open access, and can thus be accessed for free here.

References

  1. Recent Developments in Two-Dimensional Liquid Chromatography: Fundamental Improvements for Practical Applications, B.W.J. Pirok, D.R. Stoll and P.J. Schoenmakers, Anal. Chem., 2019, 91(1), 240-263, DOI: 10.1021/acs.analchem.8b04841
  2. Recent trends in two-dimensional liquid chromatography, R.S. van den Hurk, M. Pursch, D.R. Stoll, B.W.J. Pirok, TrAC Trends in Analytical Chemistry, 2023, 166, 117166, DOI: 1016/j.trac.2023.117166
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Publications

Latest developments in 2D gas chromatography reviewed

In this article, the latest developments in 2D gas chromatography are reviewed by scientists Nino Milani, Peter Schoenmakers and Bob Pirok.
In this article, the latest developments in 2D gas chromatography are reviewed by scientists Nino Milani, Peter Schoenmakers and Bob Pirok.

Comprehensive two-dimensional GC (GC×GC) has developed significantly in the three decades since the technique was first demonstrated experimentally. Consequently, the number of users, published methods, and scientific papers about 2D GC has increased dramatically. In their recent review in Journal of Separation Sciences, CAST member Nino Milani along with Eric van Gilst discuss the latest developments in 2D gas chromatography [1]. The authors reviewed the latest developments in modulation methods and also touched upon detection, retention modelling and data analysis.

Interestingly, Milani et al. found a surprising large number of technology-oriented publications that investigated new modulation technology. Thermal modulators – which are mostly used in application-papers still – yield excellent performance, even without the need for coolant consumables. Novel flow modulators can compete with thermal modulators, yet tend to be simpler and easier to operate.

The authors found a discrepancy between the use of flow modulation in technology-driven publications versus those in application studies. This was considered a testimony to the fact that the field is still highly dynamic, despite the widely-perceived maturity of GC×GC.

Milani also concluded that benchmark datasets were required to properly evaluate the latest developments in the field of signal processing and data analysis.

GC×GC is, along with GC×GC, of high interest to the PARADISE project, in which 2D chromatography is used to establish simultaneous determination and correlation of multiple sample dimensions. The analysis of datasets originating from 2D separations is also of high interest. Therefore, this review on the latest developments in 2D gas chromatography was written in the context of the PARADISE project. The PARADISE project is funded by public and private organisations and also receives funding from the Dutch Research Council (NWO).

The review was published open access in Journal of Separation Science and can be accessed free of charge here.

References

[1] Comprehensive two‐dimensional gas chromatography—A discussion on recent innovations, N.B.L. Milani, E. van Gilst, B.W.J. Pirok, P.J. Schoenmakers, J. Sep. Sci., 2023, DOI: 10.1002/jssc.202300304

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Publications

Unsupervised LC method development with AutoLC

Unsupervised LC method development with AutoLC
Unsupervised LC method development with AutoLC

In an international and interdisciplinary collaboration, CAST members Tijmen Bos, Stef Molenaar, Jim Boelrijk, Leon Niezen and Bob Pirok have demonstrated unsupervised LC method development with AutoLC. This is the first automated LC-MS method development workflow. It was applied it to a complex antibody digest sample. The work was recently published in Analytical Chemistry as cover article [1].

The majority of liquid chromatography (LC) methods are still developed in a conventional manner, that is, by analysts who rely on their knowledge and experience to make method development decisions. To tackle this problem, several tools utilizing design-of-experiment workflows, retention modeling based on experimental data and/or chemical structure information have been developed and even commercialized.

However, these approaches are generally difficult to scale with sample complexity and require significant user input to operate. Consequently, high-resolution separation technology and multi-dimensional systems have not been economically feasible for routine use. To improve the accessibility of state-of-the-art separation technology, the Pirok group at the University of Amsterdam is developing a workflow capable of unsupervised method development.

This has led to the present demonstration of a novel, open-source algorithm for automated and interpretive method development of LC(−mass spectrometry) separations (“AutoLC”). The scientists constructed a closed-loop workflow that interacted directly with the LC system and ran unsupervised in an automated fashion. 

The first demonstration of AutoLC was published as front cover article in Analytical Chemistry
The study was published as feature article in Analytical Chemistry.
Unsupervised LC method development with AutoLC
Schematic overview of the generic workflow employed by the AutoLC algorithm using retention modeling (top, blue) or BO (bottom, pink).

The team tested the algorithm using two newly designed method development strategies. The first utilized retention modeling, whereas the second used a Bayesian-optimization machine learning approach. In both cases, the algorithm could arrive within 4–10 iterations (i.e., sets of method parameters) at an optimum of the objective function, which included resolution and analysis time as measures of performance.

Retention modeling was found to be more efficient while depending on peak tracking, whereas Bayesian optimization was more flexible but limited in scalability. We have deliberately designed the algorithm to be modular to facilitate compatibility with previous and future work (e.g., previously published data handling algorithms).

AutoLC was tested on a peptide digest mixture.
The AutoLC framework was tested on an antibody digest sample. A) example of a generic scouting measurement, B) proposed optimum at the 4th iteration. Reproduced with permission of [1].

The degree of separation is often quantified as the resolution between chromatographic peaks, which can be written as a product of retention, selectivity and chromatographic efficiency. Currently, the AutoLC framework largely focuses on retention, but contemporary efforts have shifted focus to include selectivity. Support of validation is the logical next step thereafter.

AutoLC leverages earlier studies and interdisciplinary expertise

The AutoLC framework is the product of a several years of scientific studies that were conducted within public-private partnerships by the group of Pirok. These projects focused relevant aspects such as peak tracking [2,3], machine learning [4], and gradient deformation [5]. The AutoLC framework was designed to be modular so as to leverage global initiatives by the scientific community that were published in literature. Currently, the development of the framework is supported by funding from several grants from the Dutch Research Council (NWO). It is the prime topic of the UPSTAIRS project.

The present study was conducted in collaboration with Dr. Bernd Ensing (Computational Chemistry, University of Amsterdam), Dr. Saer Samanipour (Analytical Chemistry, University of Amsterdam), Dr. Patrick Forré (Institute for Informatics, University of Amsterdam), as well as scientists from Gustavus Adolphus College.

Special acknowledgement to Peter Schoenmakers

In the article, the authors acknowledged Prof. Peter Schoenmakers for his founding contributions. In one of his first papers in 1978 on gradient selection for RPLC method development Schoenmakers already envisaged the use of scouting data to facilitate automated method development [6].

Schoenmakers was the promotor of Bob Pirok, who first published about this topic in his 2016 paper in which the theoretical possibility of leveraging these concepts for 2D-LC were investigated [7]. This study was marked the start of this research line that, ultimately, led to the present publication of AutoLC.

References

  1. Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography S. Bos, J. Boelrijk, S.R.A. Molenaar, B. van ‘t Veer, L.E. Niezen, D. van Herwerden, S. Samanipour, D.R. Stoll, P. Forré, B. Ensing, G.W. Somsen, B.W.J. Pirok, Anal. Chem. 2022, 94(46), 16060–16068, DOI: 10.1021/acs.analchem.2c03160.
  2. Peak-Tracking Algorithm for Use in Automated Interpretive Method-Development Tools in Liquid Chromatography, B.W.J. Pirok, S.R.A. Molenaar, L.S. Roca and P.J. Schoenmakers, Anal. Chem., 2018, 90(23), 14011-14019, DOI: 10.1021/acs.analchem.8b03929.
  3. Peak-tracking algorithm for use in comprehensive two-dimensional liquid chromatography – application to monoclonal antibody peptides, R.A. Molenaar, T.A. Dahlseid, G. Leme, D.R. Stoll, P.J. Schoenmakers, B.W.J. Pirok, J. Chromatogr. A, 2021, 1639, 461922, DOI: 10.1016/j.chroma.2021.461922.
  4. Bayesian Optimization of Comprehensive Two-dimensional Liquid Chromatography Separations, J. Boelrijk, B.W.J. Pirok, B. Ensing, P. Forré, Chromatogr. A, 1659, 2021, 462628, DOI: 10.1016/j.chroma.2021.462628.
  5. Reducing the influence of geometry-induced gradient deformation in liquid chromatographic retention modellingS. Bos, L.E. Niezen, M.J. den Uijl, S.R.A. Molenaar, S. Lege, P.J. Schoenmakers, G.W. Somsen, B.W.J. Pirok, J. Chromatogr. A, 2021, 1635, 461714, DOI: 10.1016/j.chroma.2020.461714.
  6. Gradient selection in reversed-phase liquid chromatography, P.J. Schoenmakers, H.A.H. Billiet, R. Tussen, L. De Galan, J. Chromatogr. A, 1978, 149,  519-537, DOI: 10.1016/S0021-9673(00)81008-0.
  7. Program for the interpretive optimization of two-dimensional resolution, B.W.J. Pirok, S. Pous-Torres, C. Ortiz-Bolsico, G. Vivó-Truyols and P.J. Schoenmakers, J. Chromatogr. A, 2016, 1450, 29–37, DOI: 10.1016/j.chroma.2016.04.061.
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Recognition

Gargano and Pirok featured in ‘Top 40 Under 40’ of analytical scientists worldwide

The Analytical Scientist Power List

CAST researchers Andrea Gargano and Bob Pirok were featured for the second time in a row in the Top 40 Under 40 by The Analytical Scientists. The magazine has published profiles of all listed scientists, including their analyses of the current state of affairs, their predictions for the future, and their personal mission statements. 

The magazine presents the list as a celebration of ‘analytical science’s rising stars, who will, hopefully, provide the answers to the 21st century’s biggest questions’. It was compiled upon nomination by the readers of the magazine and shortlisting by an independent panel of expert judges.

"Modern separation technology, especially multi-dimensional, has the capabilities to crack many analytical problems in public and private labs. It is unacceptable that you rarely see this stuff applied in routine environments."

Bob Pirok

I think analytical science needs to grow even more into a more multidisciplinary and broader community rather than following a spiral of hyper-specialization.

Andrea Gargano

Further Reading