Local immune response to food antigens drives meal-induced abdominal pain.

Up to 20% of people worldwide develop gastrointestinal symptoms following a meal1, leading to decreased quality of life, substantial morbidity and high medical costs. Although the interest of both the scientific and lay communities in this issue has increased markedly in recent years, with the worldwide introduction of gluten-free and other diets, the underlying mechanisms of food-induced abdominal complaints remain largely unknown. Here we show that a bacterial infection and bacterial toxins can trigger an immune response that leads to the production of dietary-antigen-specific IgE antibodies in mice, which are limited to the intestine. Following subsequent oral ingestion of the respective dietary antigen, an IgE- and mast-cell-dependent mechanism induced increased visceral pain. This aberrant pain signalling resulted from histamine receptor H1-mediated sensitization of visceral afferents. Moreover, injection of food antigens (gluten, wheat, soy and milk) into the rectosigmoid mucosa of patients with irritable bowel syndrome induced local oedema and mast cell activation. Our results identify and characterize a peripheral mechanism that underlies food-induced abdominal pain, thereby creating new possibilities for the treatment of irritable bowel syndrome and related abdominal pain disorders.

Assisted Active Learning for Model-Based Method Development in Liquid Chromatography.

In recent decades, there has been a growing interest in fully automated methods for tackling complex optimization problems across various fields. Active learning (AL) and its variant, assisted active learning (AAL), incorporating guidance or assistance from external sources into the learning process, play key roles in this automation by enabling the autonomous selection of optimal experimental conditions to efficiently explore the problem space. These approaches are particularly valuable in situations wherein experimentation is costly or time-consuming. This study explores the application of AAL in model-based method development (MD) for liquid chromatography (LC) by using Bayesian statistics to incorporate historical data and analyte information for the generation of initial retention models. The process involves updating the model parameters based on new experiments, coupled with an active data selection method to choose the most informative experiment to run in a subsequent step. This iterative process balances model exploitation and experimental exploration until a satisfactory separation is achieved. The effectiveness of this approach is demonstrated via two practical examples, resulting in optimized separations in a limited number of experiments by optimizing the gradient slope. It is shown that the ability of AAL to leverage past knowledge and compound information to improve accuracy and reduce experimental runs offers a flexible alternative approach to fixed design methods.

Comprehensive two-dimensional liquid chromatography with high resolution mass spectrometry to investigate the photoelectrochemical degradation of environmentally relevant pharmaceuticals and their degradation products in water.

The widespread presence of organic micropollutants in the environment reflects the inability of traditional wastewater treatment plants to remove them. In this context, advanced oxidation processes (AOPs) have emerged as promising quaternary wastewater treatment technologies since they efficiently degrade recalcitrant components by generating highly reactive free radicals. Nonetheless, the chemical characterization of potentially harmful byproducts is essential to avoid the contamination of natural water bodies with hazardous substances. Given the complexity of wastewater matrices, the implementation of comprehensive analytical methodologies is required. In this work, the simultaneous photoelectrochemical degradation of seven environmentally relevant pharmaceuticals and one metabolite from the EU Watch List 2020/1161 was examined in ultrapure water and simulated wastewater, achieving excellent removal efficiencies (overall >95%) after 180 min treatment. The reactor unit was linked to an online LC sample manager, allowing for automated sampling every 15 min and near real-time process monitoring. Online comprehensive two-dimensional liquid chromatography (LC × LC) coupled with high resolution mass spectrometry (HRMS) was subsequently used to tentatively identify degradation products after photoelectrochemical degradation. Two reversed-phase liquid chromatography (RPLC) columns were used: an SB-C18 column operated with 5 mM ammonium formate at pH 5.8 (1A) and methanol (1B) as the mobile phases in the first dimension and an SB-Aq column using acidified water at pH 3.1 (2A) and acetonitrile (2B) as the mobile phases in the second dimension. This resulted in a five-fold increase in peak capacity compared to one-dimensional LC while maintaining the same total analysis time of 50 min. The LC x LC method allowed the tentative identification of 12 venlafaxine, 7 trimethoprim and 10 ciprofloxacin intermediates. Subsequent toxicity predictions suggested that some of these byproducts were potentially harmful. This study presents an effective hybrid technology for the simultaneous removal of pharmaceuticals from contaminated wastewater matrices and demonstrates how multidimensional liquid chromatography techniques can be applied to better understand the degradation mechanisms after the treatment of micropollutants with AOPs.

In-Depth Performance Analysis and Comparison of Monolithic and Particulate Zwitterionic Hydrophilic Interaction Liquid Chromatography Polymer Columns.

The kinetic performance of different zwitterionic hydrophilic interaction liquid chromatography polymer columns is evaluated and compared in-depth. For this purpose, two lab-made monolithic columns, synthesized with different crosslinkers, and a commercial particle packed column are considered. It is found that performance evaluation techniques, such as comparing plate height curves or fitted A-, B- and C-terms, obtained by fitting experimental plate height data to a plate height model, are complicated by the determination of a reliable characteristic length. This is due to the very different morphology of these column types, and the heterogeneity of the monolithic columns. The occurrence of a convective flow through the packed particle column further complicates the interpretation of the obtained fitting parameters, as part of the C-term is wrongfully attributed to the A-term. Therefore, the use of the kinetic plot method is suggested for the comparative evaluation of these columns, as kinetic plots do not require the determination of a characteristic length, nor rely on any fitting parameters. With the kinetic plot method, it is demonstrated that the lab-made monolithic columns outperform the packed particle column for plate counts between 10,000 and 800,000. This is attributed to the higher column efficiency of these columns, due to their small domain and skeleton sizes, and their high permeability, resulting from their high external porosity and the occasional occurrence of preferential flow paths.

Application of partial ozonation on tank truck cleaning concentrate and the influence on biodegradability and ecotoxicity: a pilot-scale study.

This study investigates the pilot-scale ozone treatment of reverse osmosis concentrate (ROC), originating from variable tank truck cleaning wastewater. The influence of ozonation on short- and long-term biodegradation potential was examined through respirometry and Zahn-Wellens, respectively. Ecotoxicity was also examined for several concentrate batches and ozonation steps. Chemical oxidation through ozone had a beneficial effect on chemical oxygen demand removal, with a removal efficiency up to 56%. Formation of short-term biochemical oxygen demand (BODst) was induced for several, but not all batches, showing the potential of subsequent biological treatment of ozonated ROC. An increase in the inherent biodegradability through Zahn-Wellens was observed for all tested samples after ozonation, rising to a maximum of 68% after 3 hours of ozonation, highlighting the importance of sludge adaptation. Ecotoxicity, tested with Artemia franciscana and the saltwater algae P. tricornutum, showed initial decreases in algae inhibition after short ozonation periods. An increase in algae inhibition was, however, seen after prolonged ozonation for all tested ROC samples, pointing to the formation of ecotoxic by-products. Artemia showed no significant toxicity effects. When applying biological treatment through Zahn-Wellens, a decrease in ecotoxicity was observed for several samples, likely through biological oxidation of the produced degradation products.

Graph Convolutional Networks for Improved Prediction and Interpretability of Chromatographic Retention Data.

Machine learning is a popular technique to predict the retention times of molecules based on descriptors. Descriptors and associated labels (e.g., retention times) of a set of molecules can be used to train a machine learning algorithm. However, descriptors are fixed molecular features which are not necessarily optimized for the given machine learning problem (e.g., to predict retention times). Recent advances in molecular machine learning make use of so-called graph convolutional networks (GCNs) to learn molecular representations from atoms and their bonds to adjacent atoms to optimize the molecular representation for the given problem. In this study, two GCNs were implemented to predict the retention times of molecules for three different chromatographic data sets and compared to seven benchmarks (including two state-of-the art machine learning models). Additionally, saliency maps were computed from trained GCNs to better interpret the importance of certain molecular sub-structures in the data sets. Based on the overall observations of this study, the GCNs performed better than all benchmarks, either significantly outperforming them (5-25% lower mean absolute error) or performing similar to them (<5% difference). Saliency maps revealed a significant difference in molecular sub-structures that are important for predictions of different chromatographic data sets (reversed-phase liquid chromatography vs hydrophilic interaction liquid chromatography).

Efficacy of Fenfluramine and Norfenfluramine Enantiomers and Various Antiepileptic Drugs in a Zebrafish Model of Dravet Syndrome.

Dravet syndrome (DS) is a rare genetic encephalopathy that is characterized by severe seizures and highly resistant to commonly used antiepileptic drugs (AEDs). In 2020, FDA has approved fenfluramine (FFA) for treatment of seizures associated with DS. However, the clinically used FFA is a racemic mixture (i.e. (±)-FFA), that is substantially metabolized to norfenfluramine (norFFA), and it is presently not known whether the efficacy of FFA is due to a single enantiomer of FFA, or to both, and whether the norFFA enantiomers also contribute significantly. In this study, the antiepileptic activity of enantiomers of FFA (i.e. (+)-FFA and (-)-FFA) and norFFA (i.e. (+)-norFFA and (-)-norFFA) was explored using the zebrafish scn1Lab-/- mutant model of DS. To validate the experimental conditions used, we assessed the activity of various AEDs typically used in the fight against DS, including combination therapy. Overall, our results are highly consistent with the treatment algorithm proposed by the updated current practice in the clinical management of DS. Our results show that (+)-FFA, (-)-FFA and (+)-norFFA displayed significant antiepileptic effects in the preclinical model, and thus can be considered as compounds actively contributing to the clinical efficacy of FFA. In case of (-)-norFFA, the results were less conclusive. We also investigated the uptake kinetics of the enantiomers of FFA and norFFA in larval zebrafish heads. The data show that the total uptake of each compound increased in a time-dependent fashion. A somewhat similar uptake was observed for the (+)-norFFA and (-)-norFFA, implying that the levo/dextrotation of the structure did not dramatically affect the uptake. Significantly, when comparing (+)-FFA with the less lipophilic (+)-norFFA, the data clearly show that the nor-metabolite of FFA is taken up less than the parent compound.

Pilot-scale evaluation of ozone as a polishing step for the removal of nonylphenol from tank truck cleaning wastewater.

The presence of nonylphenol (NP) in the wastewater of the tank truck cleaning industry is a major concern because of its endocrine disruptive properties. In this paper, the use of ozone for degrading NP from tank truck cleaning wastewater was investigated by operating a pilot-scale biological wastewater treatment in combination with an ozonation unit. The impact of the added ozonation step on the removal of NP, soluble chemical oxygen demand (sCOD) and total organic carbon (TOC) was monitored over one year. sCOD and TOC removal were not significantly enhanced, but the NP peak concentrations in the effluent were significantly lower than those obtained after biological treatment only: a relatively low NP concentration was observed, even when peak loads were present in the influent of the pilot-scale biological wastewater treatment plant (influentbio). Contrariwise, the effluent of the sole biological treatment follows the peak load trends of the influentbio. During the ozonation period, the average NP concentration in the combined biological-ozone unit was 0.29 µg/L, compared to 1.89 µg/L for the effluent obtained after a sole biological treatment, resulting in an improved average removal efficiency of 32%.

Elongator subunit 3 (ELP3) modifies ALS through tRNA modification.

Amyotrophic lateral sclerosis (ALS) is a fatal degenerative motor neuron disorder of which the progression is influenced by several disease-modifying factors. Here, we investigated ELP3, a subunit of the elongator complex that modifies tRNA wobble uridines, as one of such ALS disease modifiers. ELP3 attenuated the axonopathy of a mutant SOD1, as well as of a mutant C9orf72 ALS zebrafish model. Furthermore, the expression of ELP3 in the SOD1G93A mouse extended the survival and attenuated the denervation in this model. Depletion of ELP3 in vitro reduced the modified tRNA wobble uridine mcm5s2U and increased abundance of insoluble mutant SOD1, which was reverted by exogenous ELP3 expression. Interestingly, the expression of ELP3 in the motor cortex of ALS patients was reduced and correlated with mcm5s2U levels. Our results demonstrate that ELP3 is a modifier of ALS and suggest a link between tRNA modification and neurodegeneration.

Current developments in LC-MS for pharmaceutical analysis.

Liquid chromatography (LC) based techniques in combination with mass spectrometry (MS) detection have had a large impact on the development of new pharmaceuticals in the past decades. Continuous improvements in mass spectrometry and interface technologies, combined with advanced liquid chromatographic techniques for high-throughput qualitative and quantitative analysis, have resulted in a wider scope of applications in the pharmaceutical field. LC-MS tools are increasingly used to analyze pharmaceuticals across a variety of stages in their discovery and development. These stages include drug discovery, product characterization, metabolism studies (in vitro and in vivo) and the identification of impurities and degradation products. The increase in LC-MS applications has been enormous, with retention times and molecular weights (and related fragmentation patterns) emerging as crucial analytical features in the drug development process. The goal of this review is to give an overview of the main developments in LC-MS based techniques for the analysis of small pharmaceutical molecules in the last decade and give a perspective on future trends in LC-MS in the pharmaceutical field.

Fast liquid chromatography-tandem mass spectrometry methodology for the analysis of alkylphenols and their ethoxylates in wastewater samples from the tank truck cleaning industry.

A fast methodology to quantify 4-tert-octylphenol (4-t-OP) and 4-nonylphenol (4-NP) and their mono- and di-ethoxylates was developed, validated, and applied to real wastewater samples. Dispersive liquid-liquid microextraction was employed as a sample preparation step, leading to a pre-concentration factor of roughly 30. Analysis was carried out by liquid chromatography-tandem mass spectrometry with electrospray ionisation in multiple reaction monitoring mode. Average recoveries were generally between 80 and 120% for both the alkylphenols and their mono- and di-ethoxylates in influent and effluent wastewater. A minimum of 5 concentration levels per compound, ranging between 1 and 500 ng/mL, were prepared to construct calibration curves making use of isotopically labelled internal standards. The method presented good linearity and repeatability over the whole range of concentrations. Taking into account the concentration factor, and the recovery of the compounds, lower limits of quantification obtained in effluent wastewater were 0.04 ng/mL for 4-t-OP and 0.14 ng/mL for 4-NP, complying with European regulations, and between 0.03 ng/mL and 0.39 ng/mL for the ethoxylates. In influent wastewater, these limits were slightly higher. The total run time of 5 min for the alkylphenols and 8 min for the ethoxylates ensured high throughput. The developed method was applied to determine 4-t-OP and 4-NP and their mono- and di-ethoxylates in wastewater from several tank truck cleaning companies, which was subjected to ozonation and/or biological treatment. It was demonstrated that ozonation was best applied after the biological treatment, since in this case, the biological treatment could degrade most of the biodegradable organic matter, after which ozone could react directly with the recalcitrant organic pollutants. In this case, the concentrations of the target compounds in the wastewater of the investigated company decreased below the legally allowed concentration of the European water legislation.

Safety Assessment of Compounds after In Vitro Metabolic Conversion Using Zebrafish Eleuthero Embryos.

Zebrafish-based platforms have recently emerged as a useful tool for toxicity testing as they combine the advantages of in vitro and in vivo methodologies. Nevertheless, the capacity to metabolically convert xenobiotics by zebrafish eleuthero embryos is supposedly low. To circumvent this concern, a comprehensive methodology was developed wherein test compounds (i.e., parathion, malathion and chloramphenicol) were first exposed in vitro to rat liver microsomes (RLM) for 1 h at 37 °C. After adding methanol, the mixture was ultrasonicated, placed for 2 h at -20 °C, centrifuged and the supernatant evaporated. The pellet was resuspended in water for the quantification of the metabolic conversion and the detection of the presence of metabolites using ultra high performance liquid chromatography-Ultraviolet-Mass (UHPLC-UV-MS). Next, three days post fertilization (dpf) zebrafish eleuthero embryos were exposed to the metabolic mix diluted in Danieau's medium for 48 h at 28 °C, followed by a stereomicroscopic examination of the adverse effects induced, if any. The novelty of our method relies in the possibility to quantify the rate of the in vitro metabolism of the parent compound and to co-incubate three dpf larvae and the diluted metabolic mix for 48 h without inducing major toxic effects. The results for parathion show an improved predictivity of the toxic potential of the compound.

Development of a sensitive and quantitative capillary LC-UV method to study the uptake of pharmaceuticals in zebrafish brain.

The present study explores the potential of 10-day-old zebrafish (Danio rerio) as a predictive blood-brain-barrier model using a set of 7 pharmaceutical agents. For this purpose, zebrafish were incubated with each of these 7 drugs separately via the route of immersion and the concentration reaching the brain was determined by applying a brain extraction procedure allowing isolation of the intact brain from the head of the zebrafish larvae. Sample analysis was performed utilizing capillary ultra-high performance liquid chromatography (cap-UHPLC) on a Pepmap RSLC C18 capillary column (150 mm × 300 µm, dp = 2 µm) coupled to a variable wavelength UV detector. Gradient separation was performed in 28 min at a flow rate of 5 µL/min and the optimal injection volume was determined to be 1 µL. The brain extraction procedure was established for the zebrafish strain TG898 exhibiting red fluorescence of the brain, allowing control of the integrity of the extracted parts. Quantitative experiments carried out on pooled samples of six zebrafish (n = 6) demonstrated the selective semipermeable nature of the blood-brain barrier after incubating the zebrafish at the maximum tolerated concentration for the investigated pharmaceuticals. The obtained brain-to-trunk ratios ranged between 0.3 for the most excluded compound and 1.2 for the pharmaceutical agent being most accumulated in the brain of the fish. Graphical abstract Workflow of brain extraction to study the uptake of pharmaceuticals in the brain of zebrafish larvae.

Cell Imaging Counting as a Novel Ex Vivo Approach for Investigating Drug-Induced Hepatotoxicity in Zebrafish Larvae.

Drug-induced liver injury (DILI) is the most common reason for failures during the drug development process and for safety-related withdrawal of drugs from the pharmaceutical market. Therefore, having tools and techniques that can detect hepatotoxic properties in drug candidates at an early discovery stage is highly desirable. In this study, cell imaging counting was used to measure in a fast, straightforward, and unbiased way the effect of paracetamol and tetracycline, (compounds known to cause hepatotoxicity in humans) on the amount of DsRed-labeled hepatocytes recovered by protease digestion from Tg(fabp10a:DsRed) transgenic zebrafish. The outcome was in general comparable with the results obtained using two reference methods, i.e., visual analysis of liver morphology by fluorescence microscopy and size analysis of fluorescent 2D liver images. In addition, our study shows that administering compounds into the yolk is relevant in the framework of hepatotoxicity testing. Taken together, cell imaging counting provides a novel and rapid tool for screening hepatotoxicants in early stages of drug development. This method is also suitable for testing of other organ-related toxicities subject to the organs and tissues expressing fluorescent proteins in transgenic zebrafish lines.

Evaluation of the Kinetic Performance Differences between Hydrophilic-Interaction Liquid Chromatography and Reversed-Phase Liquid Chromatography under Conditions of Identical Packing Structure.

A protocol using trifluoroacetic acid at a temperature of 60 °C is developed for the adequate removal of the stationary phase of reversed-phase liquid chromatography (RPLC) columns. This procedure allows for studying the same column first under RPLC and subsequently under hydrophilic-interaction liquid chromatography (HILIC) conditions to isolate intrinsic differences between mass transfer properties in HILIC and RPLC from differences in packing quality. The established procedure allows for a complete removal of the stationary phase (confirmed by retention studies and thermogravimetry analyses) while leaving the structure of the packing unaffected (witnessed by an unchanged external porosity and pressure drop). Accurate plate height analysis comparing compounds at the same zone retention factor indicates a significant difference in reduced c-term (typically 40-80% larger under HILIC conditions), despite the columns otherwise being identical. Correcting for the known contributions of longitudinal diffusion (b-term) and mass transfer (cm- and cs-term) to focus on band broadening originating from eddy dispersion, similar strong differences are observed (differences of some h = 0.3 up to 1.2). These findings show that the interior structure and retention mechanism of the particles have a very strong effect on the observed eddy dispersion, a factor typically ascribed to phenomena occurring outside the particles. This also implies that comparing the quality of packings of different particle types is virtually impossible without the availability of a sound model to correct for the intraparticle effect on the observed eddy dispersion.

Graphical data representation methods to assess the quality of LC columns.

We discuss the most important plot types for the kinetic performance of liquid chromatography columns and elaborate on how these plots should best be constructed and can be made dimensionless. Distinction is made between plots that are most suited for practitioners (column users) versus those most suited for theoreticians and column manufacturers.

A perspective on the use of deep deterministic policy gradient reinforcement learning for retention time modeling in reversed-phase liquid chromatography.

Artificial intelligence and machine learning techniques are increasingly used for different tasks related to method development in liquid chromatography. In this study, the possibilities of a reinforcement learning algorithm, more specifically a deep deterministic policy gradient algorithm, are evaluated for the selection of scouting runs for retention time modeling. As a theoretical exercise, it is investigated whether such an algorithm can be trained to select scouting runs for any compound of interest allowing to retrieve its correct retention parameters for the three-parameter Neue-Kuss retention model. It is observed that three scouting runs are generally sufficient to retrieve the retention parameters with an accuracy (mean relative percentage error MRPE) of 1 % or less. When given the opportunity to select additional scouting runs, this does not lead to a significantly improved accuracy. It is also observed that the agent tends to give preference to isocratic scouting runs for retention time modeling, and is only motivated towards selecting gradient scouting runs when penalized (strongly) for large analysis/gradient times. This seems to reinforce the general power and usefulness of isocratic scouting runs for retention time modeling. Finally, the best results (lowest MRPE) are obtained when the agent manages to retrieve retention time data for % ACN at elution of the compound under consideration that spread the entire relevant range of ACN (5 % ACN to 95 % ACN) as well as possible, i.e., resulting in retention data at a low, intermediate and high % ACN. Based on the obtained results, we believe reinforcement learning holds great potential to automate and rationalize method development in liquid chromatography in the future.

Deep reinforcement learning for the direct optimization of gradient separations in liquid chromatography.

While Reinforcement Learning (RL) has already proven successful in performing complex tasks, such as controlling large-scale epidemics, mitigating influenza and playing computer games beyond expert level, it is currently largely unexplored in the field of separation sciences. This paper therefore aims to introduce RL, specifically proximal policy optimization (PPO), in liquid chromatography, and evaluate whether it can be trained to optimize separations directly, based solely on the outcome of a single generic separation as input, and a reward signal based on the resolution between peak pairs (taking a value between [-1,1]). More specifically, PPO algorithms or agents were trained to select linear (1-segment) or multi-segment (2-, 3-, or 16-segment) gradients in 1 experiment, based on the outcome of an initial, generic linear gradient (ϕstart=0.3, ϕend=1.0, and tg=20min), to improve separations. The size of the mixtures to be separated varied between 10 and 20 components. Furthermore, two agents, selecting 16-segment gradients, were trained to perform this optimization using either 2 or 3 experiments, in sequence, to investigate whether the agents could improve separations further, based on previous outcomes. Results showed that the PPO agent can improve separations given the outcome of one generic scouting run as input, by selecting ϕ-programs tailored to the mixture under consideration. Allowing agents more freedom in selecting multi-segment gradients increased the reward from 0.891 to 0.908 on average; and allowing the agents to perform an additional experiment increased the reward from 0.908 to 0.918 on average. Finally, the agent outperformed random experiments as well as standard experiments (ϕstart=0.0, ϕend=1.0, and tg=20min) significantly; as random experiments resulted in average rewards between 0.220 and 0.283, and standard experiments resulted in average rewards of 0.840. In conclusion, while there is room for improvement, the results demonstrate the potential of RL in chromatography and present an interesting future direction for the automated optimization of separations.

Automated method development in high-pressure liquid chromatography.

Method development in liquid chromatography is a crucial step in the optimization of analytical separations for various applications. However, it is often a challenging endeavour due to its time-consuming, resource intensive and costly nature, which is further hampered by its complexity requiring highly skilled and experienced scientists. This review presents an examination of the methods that are required for a completely automated method development procedure in liquid chromatography, aimed at taking the human out of the decision loop. Some of the presented approaches have recently witnessed an important increase in interest as they offer the promise to facilitate, streamline and speed up the method development process. The review first discusses the mathematical description of the separation problem by means of multi-criteria optimization functions. Two different strategies to resolve this optimization are then presented; an experimental and a model-based approach. Additionally, methods for automated peak detection and peak tracking are reviewed, which, upon integration in an instrument, allow for a completely closed-loop method development process. For each of these approaches, various currently applied methods are presented, recent trends and approaches discussed, short-comings pointed out, and future prospects highlighted.

Detailed comparison of in-house developed and commercially available heart-cutting and selective comprehensive two-dimensional liquid chromatography systems.

Recently, two-dimensional liquid chromatography (2D-LC) has become a popular approach to analyze complex samples. This is partly due to the introduction of commercial 2D-LC systems. In the past, 2D-LC was carried out on in-house developed setups, typically consisting of several switching valves and sample loops as the interface between the two dimensions. Commercial systems usually offer different 2D-LC modes in combination with specialized software to operate the instrument and analyze the data. This makes them highly user-friendly, however, at an increased cost compared to in-house developed setups. This study aims to make a comparison between an in-house developed 2D-LC setup and a commercially available 2D-LC instrument. The comparison is made based on experimental differences, in addition to more general differences, including cost price, flexibility, and ease of operation. Special attention is also paid to the different strategies to deal with the mobile phase incompatibility between the highly orthogonal separation mechanisms considered in this work: hydrophilic interaction liquid chromatography (HILIC) and reversed-phase LC (RPLC). For the commercial 2D-LC instrument, this is done using active solvent modulation (ASM), a valve-based approach allowing the on-line dilution of the effluent eluting from the first dimension column before transfer to the second dimension (2D) column. For the in-house developed setup, a combination of restriction capillaries and a trap column is used. Using a sample of 28 compounds with a large polarity range, peak shapes and recoveries of the 2D-chromatograms are compared for both setups. For early eluting compounds, the selective comprehensive approach, currently only possible on the commercial 2D-LC instrument, results in the best peak shapes and recoveries, however, at the cost of an increased analysis time. In general, depending on the analytical goal (single heart-cut versus full-comprehensive 2D-LC), an in-house developed system can be satisfactory for the analysis of specific target compounds/samples. For more complex problems, it can be interesting to use a more specialized commercial 2D-LC instrument. Overall, this comparison study provides advice for analytical scientists, who are considering to use 2D-LC, on the type of equipment to consider, depending on the needs of their particular applications.