Integrating NMR and multi-LC-MS-based untargeted metabolomics for comprehensive analysis of blood serum samples

The goal of this study was to develop and validate a novel LC-MS/MS assay capable of accurately measuring low concentrations of creatinine in sweat and saliva. These non-invasively obtainable biofluids are explored as alternatives to blood for continuous monitoring of kidney function, particularly during haemodialysis.By analyzing samples from forty dialysis patients, the study demonstrated strong correlations between creatinine levels in sweat, saliva, and plasma. The validated assay showed high accuracy and precision, with a low limit of quantitation. These findings support the potential use of sweat and saliva in wearable biosensor systems, offering a foundation for patient-friendly, non-invasive kidney monitoring technologies.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537">A novel LC-MS/MS assay for low concentrations of creatinine in sweat and saliva to validate biosensors for continuous monitoring of renal function</a></h4>Sophie Adelaars, Chyara S.M. Lapré, Patricia Raaijmakers, Constantijn J.A.M. Konings, Massimo Mischi, R. Arthur Bouwman, Daan van de KerkhofJournal of Chromatography B, Volume 1252, 2025, 124444<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1016/j.jchromb.2024.124444">https://doi.org/10.1016/j.jchromb.2024.124444</a><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.Accurately and timely monitoring of renal function is critical for managing patients at risk of acute kidney injury (AKI), a condition that affects approximately 12% of hospitalized patients <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#b0005">[1]</a> and up to 57% of ICU patients <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#b0010">[2]</a>. In about 20% of these cases, progression occurs towards higher stages of AKI, with significantly longer periods of hospitalization, increased mortality rates and more frequent need for invasive renal replacement therapy <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#b0005">[1]</a>. This progression could potentially be delayed when patients are proactively treated based on frequent or continuous monitoring of renal function related biomarkers, such as creatinine.As renal failure can progress rapidly, frequent venepuncture for creatinine analysis is required. Continuous monitoring of creatinine concentration can possibly improve effective patient management by further minimizing diagnostic delay. Recent developments in microfluidic devices with biosensors have sparked interest in alternative biofluids, such as sweat and saliva, as non-invasive matrices for continuous creatinine monitoring <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#b0015">[3]</a>. The existing literature demonstrates moderate to high correlation coefficients between saliva and plasma creatinine concentrations, implying a potential utility of saliva in the diagnosis and monitoring of patients with AKI <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#b0020">[4]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#b0025">[5]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#b0030">[6]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#b0035">[7]</a>. However, the correlation between sweat and plasma creatinine concentrations remains poorly investigated. Contrarily to the relatively high urea concentrations in sweat, which supposedly contributes significantly to urea clearance in renal failure <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#b0040">[8]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#b0045">[9]</a>, sweat creatinine concentration generally remains &lt;15&nbsp;µmol/L. Besides the fact that this complicates correlation studies, it also suggests that creatinine clearance via sweat is physiologically irrelevant in healthy conditions as well as in renal failure, as hypothesized by Al-Tamer et al. [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#b0080">16</a>].To enable accurate quantification of creatinine in these alternative biofluids, especially in sweat where concentrations are low, the development of a highly sensitive and specific analytical method is required. Conventional enzymatic assays, with limits of quantification (LoQ) around 10&nbsp;µmol/L, lack the sensitivity needed for this purpose. Our developed LC-MS/MS assay, with its lower limit of quantification, aims to serve as a reference method to validate emerging biosensors for real-time, continuous renal function monitoring. These biosensors hold the potential to minimize diagnostic delays, offering more immediate insight into renal function status [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#b0050">10</a>].In this study, we aimed to develop and validate a novel LC-MS/MS assay that meets these sensitivity requirements with a significantly lower LoQ than conventional enzymatic methods. This work builds upon our previous research, in which we explored the concentrations of urea and creatinine in sweat and saliva from the same cohort of patients <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#b0055">[11]</a>. However, the enzymatic assay used in that study faced limitations; it was not sensitive enough to quantify creatinine in many samples due to the assay’s LoQ, and it required larger sweat volumes than practically collected. The current study seeks to address these limitations by presenting an LC-MS/MS method that requires smaller sample volumes while offering a lower LoQ. Additionally, the assay can be utilized as a reference method to validate the performance of emerging biosensors aimed at continuous monitoring of renal function. Sweat and saliva samples were collected from forty haemodialysis patients to explore the feasibility of using sweat and saliva for continuous, non-invasive monitoring of renal function.<h4>2. Methods</h4><h5>2.1.3. LC-MS/MS</h5>The analysis was performed using an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/191">Acquity Ultraperformance Liquid Chromatograph (UP-LC) </a>from Waters (Milford, MA, USA). 5&nbsp;µL of sample was injected onto a 4&nbsp;mm&nbsp;×&nbsp;3&nbsp;mm strong cation exchange SecurityGuard column (Phenomenex, Macclesfield, UK). Every measurement started with 100% mobile phase A (water&nbsp;+&nbsp;2&nbsp;mmol/L ammoniumacetate&nbsp;+&nbsp;0.1% FA), stepping up at 0.40&nbsp;min to 100% mobile phase B (water&nbsp;+&nbsp;50&nbsp;mmol/L ammoniumacetate&nbsp;+&nbsp;10% MeOH + 0.1% FA), holding for 0.20&nbsp;min before returning to initial conditions until a total runtime of 1.10&nbsp;min, with a flow rate of 0.6&nbsp;mL/min and a column temperature of 30&nbsp;°C. A typical chromatograms obtained for sweat samples is shown in Supplemental Fig. S1.An <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products?category=20&amp;manufacturerItems=5&amp;search=xevo+tq-s">Acquity Xevo TQ-S triple MS/MS</a> from Waters (Milford, MA, USA) was used with <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/635">MassLynx software</a> from Waters (Milford, MA, USA) and results were analysed with <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?manufacturerItems=5&amp;search=TargetLynx+software">TargetLynx software</a> from Waters (Milford, MA, USA). Multiple Reaction Monitoring (MRM) in the positive electrospray ionization (ESI+) mode was used as MS acquisition mode. MS settings were set as follows: capillary voltage: 2.40&nbsp;kV, cone voltage: 40&nbsp;V, desolvation temperature: 200&nbsp;°C, source temperature: 150&nbsp;°C, desolvation gas flow: 550&nbsp;L/h, nebulizer gas: 7.0&nbsp;bar, collision gas: 0.15&nbsp;mL/min, dwell time: 0.063&nbsp;s. The specific transitions and collision voltage were monitored and optimized in Multiple Reaction Monitoring (MRM) (Supplemental Table S1).<h4>3. Results</h4><h5>3.4. Creatinine concentrations and correlation</h5>The results of the creatinine concentrations in plasma, sweat and saliva are summarized in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#t0010">Table 2</a> and visualized in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#f0010">Fig. 2</a>. Median creatinine concentrations in plasma decreased significantly during haemodialysis, from 677&nbsp;µmol/L to 232&nbsp;µmol/L (p&nbsp;&lt;&nbsp;0.001). Similarly, the concentrations decreased significantly in saliva, from 59.2&nbsp;µmol/L to 14.0&nbsp;µmol/L (p&nbsp;&lt;&nbsp;0.001), and in sweat, from 41.2&nbsp;µmol/L to 18.4&nbsp;µmol/L (p&nbsp;&lt;&nbsp;0.001).<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Journal_of_Chromatography_B_Volume_1252_2025_124444_Fig_2_Creatinine_concentrations_at_the_start_and_at_the_end_of_the_haemodialysis_cycle_14f23fe87d.jpg" alt="Journal of Chromatography B, Volume 1252, 2025, 124444: Fig. 2. Creatinine concentrations at the start and at the end of the haemodialysis cycle, measured in plasma, sweat and saliva. All creatinine concentrations in sweat and saliva were significantly (***) lower than in plasma. *** = p < 0.001." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Journal_of_Chromatography_B_Volume_1252_2025_124444_Fig_2_Creatinine_concentrations_at_the_start_and_at_the_end_of_the_haemodialysis_cycle_14f23fe87d.jpg 245w," sizes="100vw" width="1600" height="895">The mean sweat-to-plasma ratio was 0.071 (95% Confidence Interval (CI): 0.005–0.138). This ratio appeared to follow a decreasing pattern with increasing plasma creatinine concentrations (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#f0015">Fig. 3</a>A). The mean saliva-to-plasma ratio was 0.109 (95% CI: −0.066 to 0.283) and appeared constant across the entire range of measured plasma creatinine concentrations (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#f0015">Fig. 3</a>B).<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Journal_of_Chromatography_B_Volume_1252_2025_124444_Fig_3_A_The_mean_sweat_to_plasma_was_0_071_and_appeared_to_follow_a_decreasing_pattern_with_increasing_plasma_creatinine_concentrations_75af5c371a.jpg" alt="Journal of Chromatography B, Volume 1252, 2025, 124444: Fig. 3. (A) The mean sweat-to-plasma was 0.071 and appeared to follow a decreasing pattern with increasing plasma creatinine concentrations. (B) The mean saliva-to-plasma ratio was 0.109 and appears constant across the range of the measured plasma creatinine concentration." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Journal_of_Chromatography_B_Volume_1252_2025_124444_Fig_3_A_The_mean_sweat_to_plasma_was_0_071_and_appeared_to_follow_a_decreasing_pattern_with_increasing_plasma_creatinine_concentrations_75af5c371a.jpg 245w," sizes="100vw" width="1600" height="787">The Spearman’s correlation coefficient between creatinine concentrations in plasma with sweat was 0.68 (95% confidence interval (CI): 0.49–0.81, p&nbsp;&lt;&nbsp;0.001), while the correlation between creatinine concentrations in plasma with saliva was 0.80 (95% CI: 0.71–0.85, p&nbsp;&lt;&nbsp;0.001) (Supplemental Fig. S6). The results of the Passing–Bablok regression analysis, assessing the agreement between plasma and sweat and saliva creatinine concentrations, are also visualized in Supplemental Fig. S6.<h5>3.5. Sweat rate dependency</h5>Multivariable linear regression analysis to predict the plasma creatinine concentration based on sweat creatinine concentration revealed a significant positive effect of collected sweat volume (p&nbsp;&lt;&nbsp;0.001) (<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S1570023224004537#f0020">Fig. 4</a>). To enable visualization, sweat volumes were fixed at specific values (2, 5, 6, and 10&nbsp;µmol/L), and corresponding regression lines were plotted as examples. Specifically, higher sweat volumes were associated with lower concentrations in sweat and higher predicted concentrations in plasma. However, there was no direct correlation between the concentrations in sweat and sweat volume (r: −0.26, p&nbsp;&lt;&nbsp;0.05, R2adj: 0.06); therefore, the concentrations in sweat were not corrected for the collected volume (Supplemental Fig. S7).<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Journal_of_Chromatography_B_Volume_1252_2025_124444_Fig_4_Multivariable_linear_regression_analysis_b37b0335e5.jpg" alt="Journal of Chromatography B, Volume 1252, 2025, 124444: Fig. 4. Multivariable linear regression analysis revealed a significant effect of the collected sweat volume on the prediction of plasma creatinine concentration with sweat creatinine concentration. Regression lines at different sweat volumes are visualized in blue. Sweat volumes of 10, 40 and 70 µL were filled in as constants in the multivariable linear regression formula (y = ax1 + bx2 + c, where y = plasma creatinine concentrations, x1 = sweat creatinine concentration, x2 = sweat volume and a, b and c are constants determined by the model). To demonstrate, a sweat creatinine concentration of 40 µmol/L without taking volume into account would predict a plasma creatinine concentration of ±633 µmol/L (black square). With the volumes 10 µL, 40 µL and 70 µL, the plasma concentration would be predicted to be 528, 700 and 872 µmol/L (red squares), respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Journal_of_Chromatography_B_Volume_1252_2025_124444_Fig_4_Multivariable_linear_regression_analysis_b37b0335e5.jpg 132w," sizes="100vw" width="1378" height="1627"><h4>4. Discussion</h4>In summary, this study builds upon our previous research by employing a novel LC-MS/MS assay to quantify creatinine concentrations in sweat and saliva, enabling the measurement of lower analyte concentrations than conventional enzymatic methods. While our earlier work was the first to explore urea and creatinine in these biofluids within this patient cohort, this follow-up study provides a more comprehensive dataset that enhances our understanding of the correlations between these non-invasive biomarkers and plasma creatinine. The strong correlations observed support the potential of sweat and salivary creatinine as valid indicators of renal function. Moreover, these findings might pave the way for future applications in validating wearable biosensors for continuous monitoring of patients with AKI in low-resource settings, ultimately improving patient care through real-time assessment and timely intervention.

A novel LC-MS/MS assay for low concentrations of creatinine in sweat and saliva to validate biosensors for continuous monitoring of renal function

Systematic method optimisation approach for small molecule imaging on the SELECT SERIES MALDI MRT - Drug mapping in fingerprints, a case study

This study presents a novel and eco-friendly LC-QqQ-MS/MS method for the trace-level quantification of N-Nitroso Moxifloxacin (N-MOX) and di-Nitroso pyrrolopiperidine (DNPP) in moxifloxacin drug substances and products. As both NDSRIs fall into potency category 4 with an acceptable intake limit of 1500 ng/day, their control is vital for patient safety. The method achieves efficient chromatographic separation using a Cortecs T3 C18 column and gradient elution, and employs MRM transitions m/z 431.0 → 386.0 for N-MOX and 185.1 → 138.1 for DNPP.Validated per ICH guidelines, the method proved accurate, sensitive, and reproducible. Green assessment tools confirmed its low environmental impact in terms of time, solvent use, waste, and sample volume. Successfully applied to commercial moxifloxacin tablets and eye drops, this method offers a robust solution for routine NDSRI monitoring in pharmaceuticals.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971">A facile and eco-friendly simultaneous quantification LC-TQ-MS/MS approach for N-Nitroso Moxifloxacin and di-nitroso pyrrolopiperidine in Moxifloxacin tablets and eye drops</a></h4>Srinivas Nakka, Siva Krishna Muchakayala, Surendra Babu Manabolu Surya&nbsp;Green Analytical Chemistry, Volume 12, 2025, 100188<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1016/j.greeac.2024.100188">https://doi.org/10.1016/j.greeac.2024.100188</a><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.The emergence of nitrosamine impurities in active pharmaceutical ingredients (APIs) is a complex issue influenced by various factors related to chemical synthesis, raw material quality, and manufacturing processes. The concern of nitrosamine contamination in pharmaceuticals is quickly becoming more widespread, extending not only beyond smaller nitrosamines but also nitrosamine drug substance-related impurities (NDSRIs). These NDSRIs are particularly challenging because they are structurally related to the API, making their presence almost unavoidable [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0001">[1]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0002">[2]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0003">[3]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0004">[4]</a>]. Moreover, the lack of carcinogenicity or mutagenicity data for most NDSRIs complicates the establishment of acceptable intake levels (AIs). Based on the GSRS (Global Substance Registration System) database, 41.4 % of APIs and 30.2 % of API impurities are potential nitrosamine precursors. However, when tertiary amines are excluded from this classification, the percentages adjust to 14.7 %, and 12.8 % are for APIs and API impurities, respectively, that could potentially lead to nitrosamine formation [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0005">5</a>].In July 2023, the European Medicines Agency (EMA) introduced a novel assessment method known as the Carcinogenic Potency Categorization Approach (CPCA) for N-nitrosamines. This approach was designed to facilitate the determination of AI for nitrosamines, thereby simplifying the complexities associated with setting AI [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0006">6</a>,<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0007">7</a>]. Developing an analytical method with high sensitivity for regular analysis presents a considerable challenge, given the outstandingly low acceptable intake thresholds for these impurities and is fraught with sensitivity issues, while reactivity and stability concerns further complicate the analysis.Moxifloxacin (MOX) is a broad-spectrum antibiotic that acts against both Gram-positive and Gram-negative bacteria's from the fourth-generation fluoroquinolone class of medication and is prescribed for numerous bacterial ailments such as pneumonia, conjunctivitis, endocarditis, tuberculosis, and sinusitis. It can be administered orally, intravenously, or as eye drops. It obstructs the bacteria's DNA replication process, vital for their growth and proliferation [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0008">[8]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0009">[9]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0010">[10]</a>]. It is acknowledged worldwide for its efficacy in treating numerous bacterial infections and is listed among the World Health Organization (WHO)’s essential medicines.Green analytical chemistry (GAC) is dedicated to developing and applying methods that minimize environmental impact and improve sustainability. The core goal of this approach is to reduce the use of hazardous substances, minimize waste production, and encourage energy-efficient analytical processes. In 1998, Paul Anastas and his team introduced twelve principles to assess the environmental impact of chemical processes, which were later adapted for analytical methods [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0011">11</a>]. Gałuszka et al. developed twelve principles specifically for green analytical chemistry, incorporating the core concepts of green chemistry [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0012">12</a>]. It involves analyzing smaller sample quantities and reducing the usage or volume of toxic reagents, solvents, and energy consumption. Experts in green chemistry focus on the use of safe, environmentally friendly chemicals and low-waste assessment methods [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0013">[13]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0014">[14]</a>].<div>A comprehensive literature review reveals that, to date, there are no published research articles on the simultaneous trace-level quantification of N-MOX (1-cyclopropyl-6-fluoro-8-methoxy-7-((4aS,7aS)-1-nitrosooctahydro-6H-pyrrolo[3,4-b]pyridin-6-yl)-4-oxo-1,4-dihydroquinoline-3-carboxylic acid), and DNPP {(4aS,7aS)-1,6-dinitrosooctahydro-1H-pyrrolo[3,4-b]pyridine}. The current study aimed to assess genotoxicity by utilizing Quantitative structure-activity relationship ((Q)-SAR) models that apply carcinogenic potency category classification, while also developing a straightforward, rapid, and novel UPLC-triple quadrupole (QqQ)-MS/MS method for the simultaneous trace-level quantification of N-MOX and DNPP impurities. The MOX, N-MOX, and DNPP chemical structures are illustrated in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#fig0001">Fig. 1</a>.<h4>2. Materials and methods</h4><h5>2.1. Instruments and software</h5>The chromatographic study was performed on the <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/198">Waters H class UPLC</a> (Ultra Performance Liquid Chromatography) system, which includes a PDA-photo diode array detector model (L20UPD127A) modules, an Auto Sampler Manager with a Flow-Through Needle (FTN- L20FTP352G), Quaternary Solvent Manager (QSM- L20QSP358A) (Waters Corporation, USA). A <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/195">Waters Xevo TQ-XS LC-MS system</a> equipped with a triple quadrupole (QqQ) mass analyzer and Electrospray Ionization (ESI) ion source with multiple reaction monitoring (MRM) techniques was utilized (Waters Corporation, Milford, Massachusetts, USA). A Mettler-Toledo (Model: XPE205, Columbus, Ohio, USA) analytical balance was employed to weigh the samples and the impurity standard. The analysis utilized high-purity water consists of resistivity &lt;18.2 MΩ˙cm at 25 °C, TOC (Total Oxidizable Carbon) ≤5 ppb, Bacteria &lt;10 CFU (Colony-forming unit) / 100 mL obtained from the Millipore Milli-Q purification system (Bedford, MA, USA). Centrifuge (Model: 5810R, Hemburg, Germany) from Eppendorf. Millex-GV hydrophilic 0.22 µm PVDF filters (Burlington, Massachusetts, USA) have been obtained from Millipore. Using ChemDraw Professional 15.0, sketched the chemical structures of MOX, N-MOX, and DNPP. The two primary (Q)-SAR methodologies employed were the Derek Nexus (version 6.2.0, featuring the Derek KB 2022 1.0 knowledge base) and the Sarah Nexus (version 3.2.0, which includes the Nexus version 1.9 and the Sarah Model 2022.1). <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/635">MassLynx</a> and TargetLynx software (v 4.2) were used for all LC-QqQ-MS/MS acquisition parameters and to process the data, respectively.<h4>3. Results and discussion</h4><h5>3.4. Development and optimization of MS/MS conditions</h5>Multiple Reaction Monitoring (MRM) is an analytical method of high sensitivity and specificity, gaining prominence in different fields, including drug development, clinical research, and metabolomics [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0017">17</a>]. It enables precise detection and accurate quantification of impurities at very low concentrations. During the current study, N-MOX and DNPP impurities were detected and quantified using the MRM approach in an electrospray ionization positive mode (ESI). The capillary voltage (kV), desolvation temperature ( °C), and desolvation gas flow (L/Hr) are the three major ESI source parameters. Concerning the low pump flow rate of 0.3 mL/min, the recommended desolvation gas temperature and gas flow are 450 °C and 900 L/Hr. Both N-MOX and DNPP showed maximum intensity at a capillary voltage of 3.0 kV. The MS parameters for MRM channels, such as collision energy (CE) and cone voltage (CV), were individually optimized to ensure a stable and robust signal for the compounds. Intellistart software, built into Waters mass lynx software, is employed to fine-tune MS/MS parameters, including cone voltage and collision energy values with different mass transitions. MS/MS fragmentation pattern for both impurities shown in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#fig0003">Fig. 3</a>. After careful optimization of cone voltage and collision energies finalized, the quantifier ion transition as m/z 185.1→ 138.1 and the qualifier ion transition as m/z 185.1 → 96.10 for DNPP; the quantifier ion transition as m/z 431→ 386 and the qualifier ion transition as m/z 431 → 319 for N-MOX (Fig. S2). A mass spectrometer, a 50 µL loop, was installed, which improves the peak shape when injecting samples containing a high percentage of organic solvents. Later, studies of the impact of collision energies varying from 5 to 30 V and optimized collision energy for N-MOX and DNPP were 20 and 15 V, respectively (Fig. S3). The MRM quantifier qualifier and ESI source parameters for both impurities were tabulated in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#tbl0002">Table 2</a>. In the MS/MS method; the events program was configured to direct the flow from 0 to 2.8 min. for mass detection of DNPP. To prevent contamination from high concentrations of MOX from tablets and eye drops (5 mg/mL concentration), the flow was diverted to waste from 2.9 to 4.5 min. From 4.6 to 8 min, the flow was directed again to detect N-MOX.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Green_Analytical_Chemistry_Volume_12_2025_100188_Fig_3_MS_MS_fragmentation_pattern_for_A_DNPP_and_B_N_MOX_fce810c628.jpg" alt="Green Analytical Chemistry, Volume 12, 2025, 100188: Fig. 3. MS/MS fragmentation pattern for (A) DNPP, and (B) N-MOX." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Green_Analytical_Chemistry_Volume_12_2025_100188_Fig_3_MS_MS_fragmentation_pattern_for_A_DNPP_and_B_N_MOX_fce810c628.jpg 232w," sizes="100vw" width="1338" height="900"><h5>3.9. Method greenness assessment of the proposed method</h5>The environmental impact or “greenness” of the recently developed LC-QqQ-MS/MS method was evaluated using insights from published journals [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0024">[24]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0025">[25]</a>, <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0026">[26]</a>] and contemporary tools, including the Analytical Eco-scale, GAPI, AGREE, AGREEprep, and the Blue Applicability Grade Index (BAGI).<h5>3.9.2. Green analytical procedure index (GAPI) assessment tool</h5>In 2018, Płotka-Wasylka introduced the GAPI [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0028">28</a>], which uses a pictogram with red, yellow, and green colors to represent high, medium, and low environmental impacts, respectively. For the developed LC-QqQ-MS/MS method, pictograms 2, 3, 6, 8, 9, and 12 are green, indicating low environmental impact. Pictograms 4, 5, 10, 11, and 14 are yellow, reflecting medium impact, while pictograms 1, 7, 13, and 15 are red, indicating high impact. This visual representation highlights both qualitative and quantitative environmental impacts [<a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#bib0029">29</a>]. An illustration of the GAPI pictogram is provided in <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#fig0007">Fig. 7</a>A. <a target="_blank" rel="noopener noreferrer" href="https://www.sciencedirect.com/science/article/pii/S2772577424000971#tbl0006">Table 6</a> details the principles of GAPI, the selected method condition values, and their corresponding scores.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Green_Analytical_Chemistry_Volume_12_2025_100188_Fig_7_Pictograms_of_A_GAPI_B_AGREE_C_AGRE_Eprep_and_D_BAGI_11db2203ed.jpg" alt="Green Analytical Chemistry, Volume 12, 2025, 100188: Fig. 7. Pictograms of (A) GAPI (B) AGREE, (C) AGREEprep, and (D) BAGI." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Green_Analytical_Chemistry_Volume_12_2025_100188_Fig_7_Pictograms_of_A_GAPI_B_AGREE_C_AGRE_Eprep_and_D_BAGI_11db2203ed.jpg 106w," sizes="100vw" width="1090" height="1600"><h4>4. Conclusion</h4>A novel, highly sensitive, and eco-friendly UPLC-QqQ-MS/MS method has been developed for the simultaneous trace-level quantification of MOX NDSRI and DNPP in MOX drug substances, tablets, and eye drops. The method was rigorously validated according to ICH Q2(R2) guidelines, including parameters such as linearity, LOD, LOQ, accuracy, precision, and robustness, showing excellent performance with high recovery rates and lower % RSD values and detecting N-MOX from 0.1 ng/mL to 37.5 ng/mL and DNPP from 0.5 ng/mL to 37.5 ng/mL. The method's versatility and sensitivity were demonstrated through a successful application to the analysis of commercial MOX tablets and eye drops, thereby ensuring the safety of MOX formulations. Furthermore, the proposed LC-QqQ-MS/MS method was evaluated using advanced green tools such as Analytical Eco-scale, GAPI, AGREE, AGREEprep, and BAGI, which confirmed its sustainability. This method can be employed for the accurate trace-level quantification of N-MOX and DNPP in any of the drug substances and formulations adopting suitable sample preparation. This methodology offers several advantages, such as reduced sample and organic solvent consumption, as well as a shorter run time, allowing for the processing of more batch samples. Overall, this validated UPLC-QqQ-MS/MS method presents a precise, reliable, and environmentally friendly approach for the simultaneous quantification of N-MOX and DNPP, offering a valuable contribution to pharmaceutical research and development.</div>

A facile and eco-friendly simultaneous quantification LC-TQ-MS/MS approach for N-Nitroso Moxifloxacin and di-nitroso pyrrolopiperidine in Moxifloxacin tablets and eye drops

The rapid spread of new psychoactive substances (NPS) presents global challenges due to their unpredictable effects and ability to evade routine drug tests. This study applied UPLC-HRMS combined with multivariate analysis to classify and identify unknown NPS.A dataset of 159 standards was processed for PCA and SIMCA modeling, showing clear clustering for several NPS classes. The validated SIMCA models successfully classified test analytes and seized drug samples, particularly at lower collision energies. Despite some misclassifications caused by limited standards, the models proved highly effective, highlighting their value for forensic screening of NPS.<blockquote><h4>The original article</h4><h4><a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c02450">Soft Independent Modeling of Class Analogies for the Screening of New Psychoactive Substances through UPLC-HRMS/MS</a></h4>Ilenia Bracaglia, Sara Gamberoni, Camilla Montesano*, Francesco Bartolini, Sabino Napoletano, Claudio D’Alfonso, Chiara Nieri, Federico Marini, Manuel SergiAnal. Chem. 2025, 97, 28, 15420–15429<a target="_blank" rel="noopener noreferrer" href="https://doi.org/10.1021/acs.analchem.5c02450">https://doi.org/10.1021/acs.analchem.5c02450</a><h6>licensed under <a target="_blank" rel="noopener noreferrer" href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a></h6></blockquote>Selected sections from the article follow. Formats and hyperlinks were adapted from the original.The detection and analytical recognition of New Psychoactive Substances (NPS) represents a current challenge in forensic drug analysis. The spread of these substances has become a global phenomenon, involving more than 800 different compounds, as reported by the United Nations Office on Drugs and Crime (UNODC) through its Early Warning Advisory (EWA). <a href="javascript:void(0);">(1)</a> NPS are defined “substances of abuse, either in a pure form or a preparation, that are not controlled by the 1961 Single Convention on Narcotic Drugs or the 1971 Convention on Psychotropic Substances, but which may pose a public health threat”. <a href="javascript:void(0);">(2)</a> The development and production of NPS began decades ago with the aim of creating pharmacologically active compounds that could replicate the effects of the main categories of internationally controlled psychotropic drugs. <a href="javascript:void(0);">(3)</a> Many of these drugs share common chemical structural features with controlled substances, often differing by only a slight modification of the original molecule. <a href="javascript:void(0);">(4)</a> As a result, the detection and analytical identification of unknown NPS is difficult, since their rapid spread often exceeds existing analytical protocols, and is further complicated by the lack of reference standards. <a href="javascript:void(0);">(5)</a>When dealing with drug seizures, typically an initial qualitative analysis using low cost, portable instruments is carried out; these may identify the characteristic chemical groups of the substances but have low selectivity and specificity. <a href="javascript:void(0);">(6)</a> More sophisticated techniques including nuclear magnetic resonance (NMR), gas chromatography–mass spectrometry (GC-MS), liquid chromatography tandem mass spectrometry (LC-MS/MS) <a href="javascript:void(0);">(7,8)</a> and especially high-resolution mass spectrometry (HRMS) are then crucial for the identification of unexpected drugs.At present HRMS is the gold standard for NPS detection, given its ability to record accurate mass measurements that provide information about molecular structures; <a href="javascript:void(0);">(3)</a> but the complexity of data obtained limits its routinary application. For this reason, it is necessary to combine top-down and bottom-up approaches <a href="javascript:void(0);">(5,9)</a> to interpret and simplify data with additional support from databases (such as HighResNPS, <a href="javascript:void(0);">(10)</a> SWGDRUG, <a href="javascript:void(0);">(11)</a> mzCloud) and data mining tools for retrospective analysis. <a href="javascript:void(0);">(7,8,12,13)</a> An analytical approach for identifying both known and unpredicted NPS could involve the analysis of diagnostic fragment ions characteristic of each NPS core. This methodology can be particularly useful for preliminary MS screening, especially when comparative confirmation with analytical reference standards is unavailable. <a href="javascript:void(0);">(14−16)</a> Vincenti et al. <a href="javascript:void(0);">(17)</a> employed a molecular networking strategy to group similar compounds or those sharing the same m/z into clusters based on the similarity of their MS/MS fragments. This approach facilitated the identification of similarities among MS/MS spectra within a data set and enabled the correlation of unknown but related molecules. Ventura et al. <a href="javascript:void(0);">(18)</a> exploited DART-HRMS technology to observe neutral losses typical of the tryptamine class and employed Hierarchical Cluster Analysis (HCA) <a href="javascript:void(0);">(19)</a> for data analysis; the observed clusters were then used to create a supervised classification model (partial least-squares discriminant analysis, or PLS-DA) to support the identification of unknown tryptamine structures. Other classification models have been used to identify the structural characteristics of illicit drugs from MS, <a href="javascript:void(0);">(20,21)</a> chromatographic <a href="javascript:void(0);">(22)</a> or IR data, <a href="javascript:void(0);">(23−25)</a> however, they have never been applied to putatively identify new drugs.The present study combined analytical and chemometric tools to detect unknown NPS in seizures through UPLC-QTOF-MS analysis. The preliminary step consisted in analyzing 159 NPS standards to obtain chromatographic and MS/MS information. A data matrix, including m/z of precursor and fragments, retention time, and neutral losses, was then built and was split into training and test sets. Principal component analysis (PCA) <a href="javascript:void(0);">(16,26)</a> was then applied to explore the data, while a class modeling strategy based on Soft Independent Modeling of Class Analogies (SIMCA) <a href="javascript:void(0);">(20,27,28)</a> was employed to create robust models aimed at predicting the class of unknown NPS. The models were validated on the test set and with real seized samples obtained from the Scientific Police Service.<h4>Experimental Section</h4><h5>UPLC-MS/MS Analysis</h5>Data acquisition was performed using an UPLC-QTOF system consisting of an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/199">ACQUITY I-Class UPLC System </a>coupled to a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=5&amp;search=SYNAPT+G2-Si+HDMS">SYNAPT G2-Si HDMS</a> (Waters Corporation, Milford, MA). Instrument control, data acquisition, and initial data processing were performed with <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/635">MassLynxTM version 4.2</a>. Chromatographic separation was achieved using a C18 column (<a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1952">ACQUITY UPLC HSS C18 Column</a>, 100 Å, 1.8 μm, 2.1 150 mm) maintained at 50 °C with a flow rate of 0.4 mL min–1; injection volume was 10 μL. The mobile phases were 5 mM ammonium formate buffer, pH 3.0 (A), and acetonitrile 0.1% HCOOH (B). The chromatographic separation was performed with the following gradient: phase B was held at 13% in the first 0.5 min, then increased linearly to 50% in 10 min. Then, phase B was rapidly changed to 95% in 0.75 min and held for 1.5 min. After 0.25 min, the column was returned to the original ratio within 4 min. The total run time was 16.5 min.The TOF-MS was operated in positive electrospray ionization mode (Z-SprayTM, Waters) with the following settings: 800 L/h of nitrogen as the nebulization gas at 500 °C, 40 L/h cone gas flow at 120 °C, a capillary voltage of 3 kV and argon as the collision gas. Data were recorded in profile mode using Full Scan and then MS/MS mode. These scans were carried out in Resolution mode (fwhm 20,000) with a mass range from 50 to 700 Da. MS/MS spectra were acquired with CE ramped from 10 to 30 V, with a scan time of 1 s for each time window centered on the retention time of each analyte ± 30 s (see <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/suppl/10.1021/acs.analchem.5c02450/suppl_file/ac5c02450_si_001.pdf">Table S1</a>). Mass calibration was performed daily with the Major Mix.<h4>Result and Discussion</h4><h5>Data Matrix Building</h5>The analytical standards were selected based on the substances most frequently reported by the UNODC Early Warning Advisory (EWA), the first global monitoring system for NPS. <a href="javascript:void(0);">(32)</a> In this context, the classes with the largest number of substances chosen are synthetic cathinones, synthetic cannabinoids, and synthetic opioids, which mainly include fentanyl analogues. <a href="javascript:void(0);">(33,34)</a>To achieve optimal chromatographic separation and maximize MS spectral response, the chromatographic column, mobile phases, and gradient were carefully selected and tailored, considering the nature of the analytes under examination. The choice of the column and mobile phases was supported by extensive literature studies <a href="javascript:void(0);">(35−37)</a> and experimental testing.Full Scan and MS/MS acquisition modes were chosen to obtain fragmentation spectra for the analytes under study. The Full Scan analysis of the 159 NPS enabled the determination of characteristic retention times for these substances, allowing for simultaneous gradient adjustments to separate isomeric compounds, such as Ethcathinone, Dimethylcathinone, and 3-Methylmethcathinone, eluting at 1.86, 1.93, and 3.05 min, respectively, as well as α-methyl fentanyl and cis-3-methyl fentanyl eluting at 5.41 and 6.64 min, respectively.Subsequently, MS/MS analysis was performed to obtain specific fragmentation patterns for each analyte, testing two collision energy ramps: 10–30 and 30–50 V. To this aim, the chromatographic run was divided into distinct acquisition time windows of approximately ± 0.7 min. This approach allowed efficient and targeted fragmentation data acquisition across all analytes within the optimized timeframes.By analyzing the obtained spectra (some examples are shown in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c02450#fig2">Figure 2</a>), it was possible to identify and consider the most intense fragments characteristic of each analyte and compare them with the Waters Forensic Toxicology Library and HighResNPS. <a href="javascript:void(0);">(11)</a> Moreover, the neutral losses were calculated by examining the differences between the signal corresponding to the fragments, ranked in descending order of intensity and above a relative intensity threshold of 10%, and the base peak. This approach provided valuable information about the structure of the compounds, facilitating a deeper understanding of their chemical properties.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2025_97_28_15420_15429_Figure_2_MSMS_spectra_58ead7f820.jpg" alt="Anal. Chem. 2025, 97, 28, 15420–15429: Figure 2. MS/MS spectra (CE ramp of 10–30 V) for: (a) ethylenoxynitazene; (b) ADB-5Br-INACA; (c) 2-Methyl-α-PHP; (d) ADB-4en-PINACA(+H+)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2025_97_28_15420_15429_Figure_2_MSMS_spectra_58ead7f820.jpg 207w," sizes="100vw" width="2088" height="1581"><h5>Data Treatment and Multivariate Data Analysis Strategies</h5>All MS/MS spectral data acquired from the 159 NPS standards, including precursor m/z, the m/z values of the three most intense fragments, and the three corresponding neutral losses, were organized within two matrices, the first containing MS/MS spectra acquired in the CE range of 10–30 V (159 × 8), and the second containing MS/MS spectra acquired in the CE range of 30–50 V (142 × 8). These matrices also include chromatographic information such as retention time, which is a characteristic parameter influenced by the physicochemical properties, although it also depends on the chromatographic conditions used.The preliminary PCA conducted on the training set (122 substances) of the data acquired in the CE range of 10–30 V (<a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c02450#fig3">Figure 3</a>a,b) already highlighted some well-defined clusters corresponding to specific classes of substances. Only the results of PCA on this matrix are presented, as the exploratory analysis performed on the matrix derived from data acquired in the CE range of 30–50 V did not show any clear groupings.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2025_97_28_15420_15429_Figure_3_a_b_Scores_and_loadings_plot_of_the_PCA_calculated_on_the_training_set_including_all_compound_classes_a9aebb010b.jpg" alt="Anal. Chem. 2025, 97, 28, 15420–15429: Figure 3. (a, b) Scores and loadings plot of the PCA calculated on the training set including all compound classes; (c, d) Scores and loadings plot of the PCA calculated on the Training set including only the nine most represented classes." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2025_97_28_15420_15429_Figure_3_a_b_Scores_and_loadings_plot_of_the_PCA_calculated_on_the_training_set_including_all_compound_classes_a9aebb010b.jpg 245w," sizes="100vw" width="2088" height="1178">Following the graphical exploration of the data, we further subdivided the synthetic cannabinoid and synthetic opioid classes into the following respective subclasses: JWH, PINACA, WIN, SC1, SC2, and SC3 (synthetic cannabinoids), fentanyls and nitazenes (synthetic opioids). A graphical representation of the defined subclasses is shown in <a target="_blank" rel="noopener noreferrer" href="https://pubs.acs.org/doi/10.1021/acs.analchem.5c02450#fig4">Figure 4</a>, highlighting the common chemical groups.<img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Anal_Chem_2025_97_28_15420_15429_Figure_4_Defined_subclasses_1702675fce.jpg" alt="Anal. Chem. 2025, 97, 28, 15420–15429: Figure 4. Defined subclasses within (a) synthetic cannabinoids (JWH-green, PINACA-violet, WIN-gray, SC1-purple, SC2-red, SC3-orange) and (b) synthetic opioids (brorphine analogues-blue, nitazenes-yellow, and fentanyl analogues-dark orange and blue)." srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Anal_Chem_2025_97_28_15420_15429_Figure_4_Defined_subclasses_1702675fce.jpg 245w," sizes="100vw" width="2088" height="1308"><h4>Conclusions</h4>The proposed approach integrates advanced analytical strategies based on UPLC-HRMS technologies with multivariate analysis to monitor the rapid emergence of NPS. The supervised SIMCA approach proved effective in correctly identifying the class of various compounds, even in real contexts, as highlighted by the analysis of real samples. External validation conducted on three real samples confirmed the models’ ability to generalize and adapt to data not included in the training set, reducing the risk of overfitting and demonstrating strong predictive performance, as evidenced by the positive validation parameters obtained for each model.Despite the overall positive results, some cases of misclassification emerged. An example concerns the tryptamine model, which showed relatively low specificity, leading to false positives attributable to the scarcity of available standards in this class, thereby limiting the model’s robustness.To further improve performance and minimize classification errors, continuous model updates are essential, including a greater number of analytes for each class. In parallel, increasing the number of descriptive variables in the data set could enhance the model’s ability to more precisely differentiate analytes based on their physicochemical similarities.A key advantage of the SIMCA approach lies in its flexibility. Unlike other techniques, such as PLS-DA, which attempt to model all classes within a single model and require known samples, SIMCA constructs separate models for each class. This feature eliminates the need for samples from nontarget classes, allowing the possibility of not classifying samples into any of the modeled categories. This multivariate approach proves particularly useful for classifying unknown NPS structurally related to those already known identifying potential new classes and recognizing metabolites, paving the way for future applications in analyzing biological matrices. This capability represents an added value in the context of NPS monitoring, increasing the knowledge of this constantly evolving phenomenon and providing strong support for forensic investigations. In addition, the approach described can be valuable in other fields, such as doping analysis, metabolomics, and environmental analysis.

Soft Independent Modeling of Class Analogies for the Screening of New Psychoactive Substances through UPLC-HRMS/MS

Our Library never stops expanding. What are the most recent contributions to LabRulezLCMS Library in the week of 8th September 2025? Check out new documents from the field of liquid phase, especially HPLC and LC/MS techniques!<blockquote>👉 <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library">SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT LCMS AND RELATED TECHNIQUES</a></blockquote><blockquote>👉 Need info about different analytical techniques? Peek into <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library">LabRulezGCMS</a> or <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library">LabRulezICPMS</a> libraries.</blockquote>This week we bring you application notes by Agilent Technologies and Shimadzu and posters by Thermo Fisher Scientific / HPLC Symposium and Waters Corporation!<h3>1. Agilent Technologies: Automated Solid Phase Extraction of PFAS from Aqueous Samples</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_1633_automated_spe_wax_carbon_s_5994_8289en_agilent_1fc425207e.pdf">Full PDF for download</a></li></ul>In this study, three types of dual-phase SPE cartridges were evaluated based on the quality control requirements for aqueous matrices as specified in US EPA Method 1633 using automated SPE. The cartridges contained PFAS WAX and Carbon S for PFAS. In the first configuration, a 200 mg bed of PFAS WAX was layered on top of a 50&nbsp;mg bed of Carbon S with the sorbents separated by a frit. In the second configuration, 200 mg of PFAS WAX was mixed with 50 mg of Carbon S before cartridge packing. In the third configuration, 200 mg of PFAS WAX and 10&nbsp;mg of Carbon S were mixed before cartridge packing. All three configurations were evaluated under identical protocols using automated SPE and were compared to results obtained using the manual addition of carbon and separate WAX extraction. The blended sorbent containing 200 mg of PFAS WAX and 10 mg of Carbon S was selected for validation and further evaluation of several environmental matrices including groundwater from residential private wells, effluent from a wastewater storage lagoon, and a landfill monitoring&nbsp;well.<h4>Experimental</h4><h5>Equipment and materials</h5>The three Agilent dual-phase cartridges used in the study are listed in Table 1. As a performance benchmark, a single‑phase <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?search=Bond+Elut+PFAS+WAX">Bond Elut PFAS WAX sorbent</a> cartridge (Agilent, part number 5610‑2151) was used with either Carbon S bulk (Agilent, part number 5610-2095) or Superclean ENVI-Carb (Millipore Sigma, part number 57210-U).Samples were extracted using the PromoChrom eight‑channel automated SPE extractors with upside‑down shaker racks (part number SPE-03 with MOD004), anticlogging frits (part&nbsp;number CF-06), high-capacity inline filters (part&nbsp;number&nbsp;F-HC-30), and anticlogging tips (part&nbsp;number F-T-4). Sample analysis was performed using an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?search=1290+Infinity+II+LC">Agilent 1290 Infinity II LC</a> system consisting of an Agilent 1290 Infinity&nbsp;II high-speed pump (G7120A), an Agilent 1290 Infinity II multisampler (G7167B), and an Agilent 1290 Infinity II multicolumn thermostat (G7116B). The LC system was modified for PFAS analysis using the Agilent <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?search=InfinityLab+PFC%E2%80%91free+HPLC+conversion+kit">InfinityLab PFC‑free HPLC conversion kit </a>(part number 5004-0006). The LC system was coupled to an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?search=6495C">Agilent 6495C triple quadrupole LC/MS</a> equipped with an Agilent Jet Stream technology ion source. Agilent <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?search=MassHunter+Workstation">MassHunter Workstation software</a> was used for data acquisition (version 10.1) and <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?search=Quantitative+Analysis+">Quantitative Analysis </a>for QQQ (version 12.1) was used for data analysis. Instrumental conditions were determined based on detailed multiparametric optimization experiments developed in‑house.&nbsp;<h4>Conclusion&nbsp;</h4>The adoption of automated solid phase extraction (SPE) for PFAS analysis in environmental matrices significantly enhances laboratory efficiency and reduces the potential for human error. Our evaluation of five extraction protocols revealed that the blended sorbent containing 200 mg of PFAS WAX and 10 mg of Carbon S provided the best performance in terms of recovery, matrix interference reduction, and fewer outliers. This configuration was successfully applied to various environmental samples, demonstrating its versatility and reliability. The findings support the use of automated SPE with optimized sorbent cartridges as a robust method for PFAS analysis, aligning with the quality control metrics of US EPA Method 1633. Future work should focus on further refining automated SPE techniques and exploring their application to other environmental matrices.<h3>2. Shimadzu: Automated Preparation of Calibration Curves Using the Autosampler's Dilution Function</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_00914_en_089a8c34fa.pdf">Full PDF for download</a></li></ul><h5>User Benefits</h5><ul><li>The auto-dilution function equipped in the autosampler eliminates the need for manual dilution during calibration curve creation and sample preparation.</li><li>Samples can be automatically diluted to create a calibration curve by specifying the desired dilution factor in the batch table.</li><li>Because the same method file can be used across different dilution factors, only minor adjustments are needed when changing dilution conditions.&nbsp;</li></ul>In LC quantification, the dilution ofstandard and sample solutions is typically performed manually using pipettes, which is a laborintensive and time-consuming process. Recent advancements have called for automation to reduce labor and improve efficiency. By automating the dilution process, workflows can be streamlined, leading to increased productivity. The Nexera series autosamplers are equipped with an automatic pretreatment function that enables automated dilution. Using the auto-dilution function, samples can be automatically prepared at any desired dilution factor and directly introduced into the analytical column. This article presents a method to automate the creation of calibration curves and subsequent quantification workflows using the autosampler’s auto-dilution function.<h4>Automated Dilution Analysis of Organic Acid Standard Solution</h4>Automated dilution analysis was conducted using a 1000 mg/L mixed standard solution of ten organic acids. The analytical conditions are shown in Table 2 (pretreatment program : Table 1). 1.0 mL polypropylene vials were used as mixing vials, and septum-sealed vials were used for the undiluted standard solution. Rinse solution (ultrapure water) was used as the diluent. Fig. 3 shows the chromatogram of the 100-fold diluted organic acid mixed standard solution (concentration after auto-dilution : 10 mg/L for each organic acid). The <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=1&amp;search=Nexera+Organic+Acid+Analysis+System">Nexera Organic Acid Analysis System</a> (pH-buffered electric conductivity detection) was employed for this analysis.<h4>Conclusion</h4>By utilizing the auto-dilution function in the autosampler, the standard undiluted solution for calibration curve creation can be automatically diluted at any desired dilution ratio. This eliminates the need for manual sample dilution and streamlines the calibration curve creation workflow. Additionally, since the autodilution function can also be applied to sample solutions, only the preparation of undiluted standard and sample solutions is required, allowing for a fully automated process from calibration curve creation to quantitation. As a result, the reduction in pretreatment work contributes to improved work efficiency and productivity.<h3>3. Thermo Fisher Scientific / HPLC Symposium: Wide-ranging polynucleotide separation capabilities using Reversed Phase particles with variable pore geometry</h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/po_wide_ranging_polynucleotide_separation_capabilities_po_en_1fdaba9e08.pdf">Full PDF for download</a></li></ul>Polynucleotides such as DNA and RNA serve many uses in the areas of basic research, industrial manufacturing, therapeutics to up/down regulate cellular functions, and as therapeutic and vaccine intermediates (i.e., as an instructional template to produce proteins that 1) supplement/replace ineffective proteins or 2) function as antigens). CRISPR technology using guide RNA (gRNA, ~100mer) to directly modify cellular chromosomes was recently leveraged to create a customized treatment within 6-months for an infant with CPS1 deficiency1. This approach has provided long-term, life-saving enzyme production negating the need for daily treatments. Associated DNA and RNA such as gRNA and mRNA (1000+mer) require detailed characterization to ensure safety and efficacy (e.g., of sufficient quantity and fidelity in delivery vehicles such as lipid nanoparticles). Here, we present a RP particle with complex pore geometry and the effects of mobile phase and temperature for separating DNA/RNA with therapeutic benefits (siRNA-gRNA-mRNA:~ 20-100- 1000+mer). Semi-prep column separations, common for sample recovery for subsequent analyses or sample enrichment for in vitro or in vivo use, is also shown.<h4>Materials and methods</h4><h5>Test method(s)&nbsp;</h5><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=3&amp;search=DNAPac+RP+column">DNAPac RP columns</a> packed with 4 µm DNAPac RP media include 2.1 × 50 mm (P/N 088924), 2.1 × 50 mm (P/N 088924), and 21.2 × 150 mm (P/N 080922- 1521232). For instruments, mobile phases, gradient conditions, and other chromatographic operating parameters (flow rate, temperature, injection volume, etc.), please reference the figures and associated text. For all analyses, samples were detected by UV absorbance at 260nm. For 21.2x150mm semi-prep separations, a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products?category=40&amp;manufacturerItems=3">conventional (U)HPLC chromatography system</a> with a binary pump, autosampler and detector without column compartment (ambient temperature separations) was used for all analyses. Ion Pairing Agents (Cation Anion): TEAA – Triethylamine Acetate, HAA – Hexylamine Acetate, HFIP – Hexafluoroisopropanol.&nbsp;<h5>Data analysis&nbsp;</h5>The <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/76">Thermo Scientific Chromeleon </a>7.2.10 or 7.3 Chromatography Data Systems (CDS) software was used for data acquisition and analysis.<h4>Results&nbsp;</h4><h5>DNAPac RP particle characterization – chemistry and pore structure&nbsp;</h5>Figure 1 summarizes the chemical and physical properties of the resin, which is based on polymeric divinylbenzene (top left). Unlike conventional silica particles, the polymeric resin is not susceptible to hydrolytic degradation at neither extreme pH (1-14) nor high temperature (90+oC). The aromatic groups also provide π- π bonding with aromatic nucleotides giving different selectivity from conventional alkyl phases incapable of such interactions. The SEM image visually shows the highly variated porous structure of the particles. Barrett-Joyner-Halenda (BJH) N2 adsorption analysis details the surface area contributions of the different pore sizes. From this analysis it is clear that a continuum of pores from 50Å to 2500Å (suitable for small molecules up to macromolecule adsorption, respectively) contributes to the overall surface area of the particle. Based on the complex geometry and resulting pore structure, we commonly refer to the media as a supermacroporous (SMP) particle.<h4>Conclusions&nbsp;</h4><ul><li>High resolution separation of nucleic acids varying in length from 10 to 10,000 nucleotides is achieved using an SMP particle with a variable pore structure.</li><li>Polymeric SMP particles are compatible with different mobile phase IP compositions, high pH and high separation temperature, which are critical parameters for optimizing the separation of polynucleotides&nbsp;</li><li>SMP particles provide high resolution separations in both analytical and semi-prep formats</li></ul><h3>4. Waters Corporation / ASMS: Detection and Characterization of PFAS Molecules Using Inclusion and Exclusion Features of Data Directed Analysis (DDA)</h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/720008869_fb6d27e0a0.pdf">Full PDF for download</a></li></ul>Polyfluorinated alkyl substances (PFAS) are bioaccumulative and suspected to contribute to increased cancer risk in humans. Monitoring PFAS in biofluids is possible, but their very low concentrations present analytical challenges.Data-Directed Acquisition (DDA) isolates ions that meet defined criteria, fragments them at elevated collision energies, and generates product ion spectra. Because PFAS ions show a characteristic mass defect due to fluorine atoms, inclusion lists can enhance their detection. Conversely, exclusion lists derived from background matrix analyses help remove interferences, simplifying ion selection for fragmentation.Preliminary tests with mixed small-molecule samples demonstrated the utility of these lists before applying them to PFAS, where inclusion lists contained hundreds of entries and exclusion lists thousands.<h4>Methods</h4><ul><li>Instrument: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/202">SELECT SERIES™ Cyclic™ Ion Mobility Mass Spectrometer</a></li><li>Acquisition: ToF-ESI negative DDA mode, 100 ms survey/MSMS scan times, collision energy ramps optimized by PFAS class</li><li>LC System: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/191">ACQUITY Premier </a>with PFAS Kit and Atlantis Premier BEH C18 AX Isolator Column (2.1 × 50 mm, 5 µm)</li><li>Analytical Column: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1952">ACQUITY UPLC HSS T3 C18</a> (100 × 2.1 mm, 1.8 µm) at 35 °C</li><li>Flow Rate: 0.3 mL/min; Injection Volume: 5 µL; Autosampler Temp: 6 °C</li><li>Mobile Phases:<ul><li>A: H₂O:MeOH (95:5) with 2 mM ammonium acetate</li><li>B: MeOH with 2 mM ammonium acetate</li></ul></li><li>Samples: LCMS QC mixture, native PFAS mixture (PFAC30PAR, Wellington Labs), and human serum</li><li>Software: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/635">MassLynx™ 4.2 for acquisition</a>, <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1544">waters_connect™ with PFAS library</a> (130 entries) for processing, Tibco Spotfire® for visualization</li></ul><h4>Conclusion</h4><ul><li>ToF-DDA mode on a cyclic ion mobility mass spectrometer is effective for PFAS analysis.</li><li>Inclusion lists based on PFAS mass defect improve real-time detection and intelligent switching.</li><li>Background-derived exclusion lists (up to 9000 entries) further reduce interferences and enhance specificity.</li></ul>

News from LabRulezLCMS Library - Week 37, 2025

Our Library never stops expanding. What are the most recent contributions to LabRulezLCMS Library in the week of 22nd September 2025? Check out new documents from the field of liquid phase, especially HPLC and LC/MS techniques!<blockquote>👉 <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library">SEARCH THE LARGEST REPOSITORY OF DOCUMENTS ABOUT LCMS AND RELATED TECHNIQUES</a></blockquote><blockquote>👉 Need info about different analytical techniques? Peek into <a target="_blank" rel="noopener noreferrer" href="https://gcms.labrulez.com/library">LabRulezGCMS</a> or <a target="_blank" rel="noopener noreferrer" href="https://icpms.labrulez.com/library">LabRulezICPMS</a> libraries.</blockquote>This week we bring you application notes by Agilent Technologies, Shimadzu and Waters Corporation and poster by Thermo Fisher Scientific / HPLC Symposium!<h3>1. Agilent Technologies: Quantitation of N-Nitroso Nebivolol in Nebivolol Tablet Formulation</h3>Using an <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=10&amp;search=6495D+triple+quadrupole">Agilent 6495D triple quadrupole LC/MS system</a><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_nebivolol_tablet_formulation_6495_qqq_lc_ms_system_5994_8053en_agilent_e7f21eb12f.pdf">Full PDF for download</a></li></ul>N-Nitroso Nebivolol is a NDSRI of Nebivolol, a beta-1 adrenergic receptor antagonist commonly used in the treatment of hypertension and heart failure. In the carcinogenic potency categorization published by the FDA, N-Nitroso Nebivolol has been categorized as Class 4, indicating the maximum allowable intake limit for the impurity to be 1,500 ng/day. Nebivolol has maximum daily dosage limit of 40&nbsp;mg for adults.Considering a maximum daily dose of 40&nbsp;mg and allowable intake of 1,500&nbsp;ng/day for the nitrosamine impurity in the formulation under study, the specification limit of N-Nitroso Nebivolol has been calculated as 22.5 ng/mL for the 0.6 mg/mL test concentration used during this study.&nbsp;This application note demonstrates a highly specific, sensitive, and reproducible method for the quantitation of N-Nitroso Nebivolol in a 20 mg Nebivolol tablet formulation. The method uses a 1290 Infinity II LC coupled to a 6495D triple quadrupole LC/MS/MS. The multiple reaction monitoring (MRM) conditions and ion source parameters were automatically optimized using the optimizer tool available with MassHunter Acquisition software to maximize the sensitivity of the target analyte. The API peaks were diverted to waste with the help of an integrated, software-controlled diverter valve during quantitation of the&nbsp;impurity.<h4>Experimental</h4><h5>Equipment&nbsp;</h5>The analysis was performed on a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=10&amp;search=1290+Infinity+II+LC">1290 Infinity II LC system</a> coupled to a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=10&amp;search=6495D+triple+quadrupole">6495D triple quadrupole LC/MS/ MS</a>. The LC system was equipped with the following&nbsp;modules: – Agilent 1290 Infinity II high-speed pump (G7120A) – Agilent 1290 Infinity II multisampler (G7167B) – Agilent 1290 Infinity II multicolumn thermostat (G7116B) – Agilent 1290 Infinity II diode array detector (G7117A)<h4>Conclusion&nbsp;</h4>A highly sensitive and robust MRM method was developed for the quantitation of N-Nitroso Nebivolol in 20&nbsp;mg Nebivolol tablet formulation using an Agilent 6495D triple quadrupole LC/MS system. The chromatographic method developed provided good separation between the analyte and the API. The method showed LOD and LOQ of 2 and 5 pg/mL, respectively. The minimum signal-to-noise ratio at the LOQ level was found to be more than 50:1 (Peak to Peak). The calibration curve from 5 pg/mL to 50&nbsp;ng/mL was found to be linear, with 1/x weighting. R and R2 values were 0.999 and 0.997, respectively. Spike recovery analysis showed the efficiency of sample extraction with a recovery percentage of 116.8% in API, 108.3% in placebo, and 111.8% in tablet formulation at the spiking level of 200 pg/mL. The method was found to be highly reproducible at the LOQ level, while the %RSD value for the area response of seven replicate injections was approximately 3.1%.<h3>2. Shimadzu: Efficient Method Development for Synthetic Peptide and Related Impurities</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/an_01_00780_en_4d0aa014ea.pdf">Full PDF for download</a></li></ul><h5>User Benefits</h5><ul><li>LabSolutions MD improves the efficiency of the entire workflow for method development of synthetic peptide and impurities.&nbsp;</li><li>Screening for multiple mobile phases and columns can be automated using mobile phase and column switching valves.&nbsp;</li><li>Molecular weights of peptides and related impurities can be estimated and accurately tracked with LCMS-2050, a single quadrupole mass spectrometer.</li></ul>Peptide therapeutics, characterized by specific amino acid sequences crucial to their function, can be synthesized chemically, similar to small molecule drugs. The production of synthetic peptides involves multiple steps, including deprotection, activation, coupling, and the cleavage of the final sequence from the solid support. Impurities, such as those resulting from premature chain termination or missing amino acids, can affect the safety and efficacy of the final product. Therefore, these impurities must be separated by liquid chromatography (LC). In LC analysis, selecting the appropriate mobile phase and column across a wide range of combinations is critical for achieving optimal separation, as it significantly impacts the separation. However, since the separation pattern varies depending on the peptide chain lengths, amino acid compositions, and presence of modifications, optimizing separation for each peptide sequence is time-intensive. This study describes how to efficiently find the best separation conditions for peptides and related impurities utilizing LabSolutions MD, a dedicated software for supporting method development, through both screening and optimization phases.<h4>Experimental</h4><ul><li>LC System: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=1&amp;search=Nexera+X3">Nexera X3</a> (Method Scouting System)</li><li>MS System: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?mainauthorItems=1&amp;search=LCMS-2050">LCMS-2050</a></li></ul><h4>Automated Peak Tracking by LCMS-2050&nbsp;</h4>LC chromatograms obtained with a gradient time of 5 min, flow rate of 0.6 mL/min, and initial concentration of 5 % and 15 %, along with molecular weights for FLP and impurities, are shown in Fig. 18. The UV spectra for each impurity are displayed in Fig. 19. The similarity between UV spectra of Met(O2), p.A1del, p.A1_E2del, p.A1_K3del, and p.A1_D5del is greater than 0.99, suggesting that peak tracking based on UV spectra would be challenging. In contrast, LabSolutions MD enables peak tracking based on molecular weights acquired by LCMS-2050, facilitating accurate identification of compounds with similar UV spectra (Fig. 18). The estimated molecular weights for each compound show small mass errors compared to the theoretical values (Table 3), which can be used to confirm the molecular weights of known compounds as well as to approximate the molecular weights of unknown impurities.<h4>Conclusion</h4>The separation patterns of synthetic peptides vary depending on mobile phase composition, column type, and various LC parameters, such as gradient conditions, column oven temperature, and flow rate. Separation behavior can also differ based on peptide structure, including length, amino acid composition, and the presence of modifications. Therefore, it is necessary to optimize the separation for each peptide sequence individually. However, optimizing analytical conditions through numerous analyses and data processing can be time-consuming. LabSolutions MD automates the entire workflow, including generating analysis schedules, preparing mobile phases, and processing the data, thanks to functionalities such as automated peak tracking, ranking of chromatograms, and design space visualization. This article introduced a case where the optimal separation conditions for synthetic peptides were efficiently identified through screening and optimization phases. LabSolutions MD also offers fully automated gradient optimization by AI algorithm to meet user-defined criteria. For more details, please refer to the application news, “Automatic Optimization of Gradient Conditions by AI Algorithm on Synthetic Peptide and Impurities: 01-00814”.<h3>3. Thermo Fisher Scientific / HPLC Symposium: Sensitive cationic lipids impurities analysis with quantitation by charged aerosol detection and simultaneous mass confirmation by MS</h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/po_sensitive_cationic_lipids_impurities_analysis_quantitation_po_en_3dacf61554.pdf">Full PDF for download</a></li></ul>Cationic lipid is a critical component in lipid nanoparticle formulations for delivering nucleic acids such as mRNA, siRNA, antisense oligonucleotides etc. [2] Cationic lipids are amphiphilic molecules that possess a hydrophilic region, a hydrophobic region, and a linker structure connecting the two. [1] Charged aerosol detector (CAD) and evaporative light scattering detectors (ELSD) are preferred for characterizing lipid nanoparticle components, with CAD being the better choice for impurity quantification due to its higher sensitivity [3]. In a previous study, an inverse gradient method was developed to quantify impurities in cationic lipids raw materials.[1] To demonstrate the inverse gradient method on CAD HP, the same cationic lipids -- R-DOTAP, DLin-KC2- DMA and ALC-0315 -- were measured to this experiment as in Application note 003384 [1]. New diverter valve and software features were showcased, along with method’s sensitivity and repeatability.<h4>Materials and methods</h4>Using the inverse gradient method from Application note 003384 [1], to demonstrate impurity analysis in cationic lipids on the <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/2176">Thermo Scientific Vanquish Charged Aerosol Detector HP (CAD HP)</a> integrated in the <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/743">Thermo Scientific Vanquish Flex Inverse Gradient UHPLC System</a>, with parallel mass confirmation by the <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/413">Thermo Scientific ISQ EM Single Quadrupole Mass Spectrometer</a>.&nbsp;Methods: The workflow uses a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/743">Vanquish Flex Inverse Gradient LC system</a> with Vanquish CAD HP and <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1970">Thermo Scientific Hypersil GOLD C8 column</a>.<h5>Data Analysis&nbsp;</h5><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/76">Thermo Scientific Chromeleon Chromatography Data System (CDS)</a> 7.2.10 was used for data acquisition and analysis.<h4>Conclusions&nbsp;</h4>The CAD HP integrates perfectly into the Vanquish Inverse Gradient LC system and offers high sensitivity and excellent repeatability for quantitative cationic lipid impurity analysis, while the ISQ EM MS provides straightforward mass confirmation with high repeatability, all under compliance-ready conditions.<h3>4. Waters Corporation: Quantitative Analysis of Four Long-Chain Lysophosphatidylcholines (LPCs) in Dried Blood Spot using Liquid Chromatography Tandem Mass Spectrometry for Clinical Research</h3><ul><li>Application note</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/720008826_en_dc154872b6.pdf">Full PDF for download</a></li></ul><h5>Benefits</h5><ul><li>Good chromatographic resolution of all forms</li><li>Short extraction time compared to traditional methods for lipid extraction</li><li>Short analysis injection-to-injection 7 minutes</li></ul>Historically, very long chain fatty acids have been quantified using direct transesterification and gas chromatography methods which involve time-consuming and labor-intensive sample preparation and analysis. Consequently, simpler LC-MS/MS methods have been reported for the measurement of long-chain LPCs (LPCs generic chemical structure, Figure 1) in DBS. Interferences have been reported using positive ESI, thus researchers have developed methodologies utilizing negative ESI to circumvent isobaric interferences when there is lack of chromatographic resolution. Here a UHPLC-MS/MS method is described using acetate adducts for the reliable analysis of four long-chain LPCs in DBS for clinical research.&nbsp;A UHPLC-MS/MS method for quantifying a panel of LPCs (C20:0, C22:0, C24:0, and C26:0-LPCs) was developed. The mobile phase composition consisted of a mixture of water, acetonitrile, and isopropanol with 5 mM ammonium acetate as a modifier. The DBS sample extraction used a working solution prepared with the deuterated LPC internal standards in methanol. The method was developed with an ACQUITY UPLC I-Class PLUS System (Fixed Loop) using an ACQUITY Premier CSH C18 Column, which provided baseline resolution of endogenous isobaric interferences for the saturated forms of the targeted LPCs. The detector was a Xevo TQ-S micro Mass Spectrometer operated in negative ESI mode. This methodology resulted in a total analysis time (injection-to-injection) of 7 minutes.<h5>LC Conditions&nbsp;</h5><ul><li>LC system: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/199">ACQUITY UPLC I-Class PLUS FL&nbsp;</a></li><li>System Detection: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/209">Xevo TQ-S micro Mass Spectrometer&nbsp;</a></li><li>Plate: Waters 96-well Sample Collection Plate, 350 µL&nbsp;</li><li>Column(s): <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1952">ACQUITY Premier CSH C18 Column</a>, 2.1 mm x 50 mm, 1.7 µm (p/n: 186009460)</li></ul><h5>MS Conditions&nbsp;</h5><ul><li>MS system: Xevo TQ-S micro Mass Spectrometer</li></ul><h5>Data Management&nbsp;</h5><ul><li>MS software: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/635">MassLynx™ v4.2 Software SCN 1045&nbsp;</a></li><li>Informatics: TargetLynx™ XS v4.2 Software SCN1045 and QUAN Review</li></ul><h4>Results and Discussion&nbsp;</h4>During method development, different types of column chemistry, mobile phase composition, and ESI polarity modes were assessed to optimize chromatographic peak shape, analytical sensitivity and manage interferences. In positive mode, ammonium formate favored the formation of m/z 184 and m/z 104 as daughter ions for all the LPCs. The m/z 184 ion lacks specificity because most phospholipids share this moiety, so the ion m/z 104 was selected as quantifier. Having evaluated conditions in positive mode for the analysis of the four saturated LPCs, it was observed that some existing interferences were not only affecting the parent masses of the analytes but their corresponding internal standard parent ion masses too. Hence, negative ionization using ammonium acetate as modifier was assessed for the detection of LPCs. Acetate adducts [M+CH3COO]- were the most abundant parent ion masses, generated under these conditions for each LPC. The acyl-chain product ions generated in negative ionization reduce the detection of interferences coming from other phosphocholine headgroups. The latter detection conditions were used for the validation of the method presented herein.<h4>Conclusion&nbsp;</h4>An LC-MS/MS clinical research method for measuring C20:0, C22:0, C24:0, and C26:0 LPCs with the ACQUITY UPLC I-Class PLUS FL System, and the Xevo TQ-S micro Mass Spectrometer that:<ul><li>Demonstrates good linearity, with no significant carryover, or matrix effects</li><li>Offers good precision with a total reproducibility and repeatability ≤10% CV</li><li>Offers reliability in the identification of LPCs from interfering isobaric compounds Provides good selectivity and analytical sensitivity in negative mode and is relatively fast as approximately 220 samples can be analyzed in 24 hours</li></ul>

News from LabRulezLCMS Library - Week 39, 2025

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News from LabRulezLCMS Library - Week 39, 2025

News from LabRulezLCMS Library - Week 37, 2025

Soft Independent Modeling of Class Analogies for the Screening of New Psychoactive Substances through UPLC-HRMS/MS

A facile and eco-friendly simultaneous quantification LC-TQ-MS/MS approach for N-Nitroso Moxifloxacin and di-nitroso pyrrolopiperidine in Moxifloxacin tablets and eye drops

Systematic method optimisation approach for small molecule imaging on the SELECT SERIES MALDI MRT - Drug mapping in fingerprints, a case study

A novel LC-MS/MS assay for low concentrations of creatinine in sweat and saliva to validate biosensors for continuous monitoring of renal function

Integrating NMR and multi-LC-MS-based untargeted metabolomics for comprehensive analysis of blood serum samples

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