The LC for Pharma Resource Hub is a comprehensive platform designed to support professionals in the pharmaceutical industry by providing valuable resources related to liquid chromatography (LC). This hub offers a variety of materials, including webinars, technical articles, application notes, and case studies, all aimed at enhancing knowledge and proficiency in LC methodologies within pharmaceutical applications.​<h3>Key features of the LC for Pharma Resource Hub include:</h3><h4>Educational Webinars</h4>Access on-demand webinars presented by industry experts, covering topics such as method development, validation, and troubleshooting in LC.​<ul><li>Episode 1: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/3437">USP 621 Harmonization - Method Scaling</a><ul><li>Guest Speakers: Amanda Guiraldelli, Ph.D. &amp; Catharine Layton</li></ul></li><li>Episode 2: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/3438">USP 621 Revisions - S/N Calculation, Sensitivity and Peak Symmetry</a><ul><li>Guest Speakers: Edmond Biba, Ph.D. &amp; Neil Lander</li></ul></li><li>Episode 3: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/3439">Enhanced Method Development</a><ul><li>Guest Speakers: Amanda Guiraldelli, Ph.D. &amp; Richard Verseput</li></ul></li><li>Episode 4: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/3440">aQbD for OTC Products</a><ul><li>Guest Speakers: Joshua Bhattacharaya &amp; Fadi Alkhateeb, Ph.D.</li></ul></li><li>Episode 5: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/3441">Impurity Profile Characterization</a><ul><li>Guest Speakers: Christian Zeine, Henry Foddy &amp; Margaret Maziarz</li></ul></li><li>Episode 6: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/4123">USP Chapter 129 Updates and SEC-MALS</a></li><li>Episode 7: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/4167">Dietary Supplement Quality</a></li><li>Episode 8: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/3444">Lipid Nanoparticles</a></li><li>Episode 9: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/3445">Nitrosamines</a></li><li>Episode 10: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/4169">Dissolution</a></li><li>Episode 11: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/4170">LG Polymers</a></li><li>Episode 12: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/4669">Method Validation</a></li><li>Episode 13: <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/4670">Multi-Attribute Method</a></li></ul><img src="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/Waters_Solving_Problems_That_Matter_a_Waters_webinar_series_featuring_USP_speakers_ecf3502756/Waters-Solving-Problems-That-Matter-a-Waters-webinar-series-featuring-USP-speakers.jpg" alt="Waters Corporation: Solving Problems That Matter - a Waters webinar series featuring USP speakers" srcset="https://storage.googleapis.com/labrulez-bucket-strapi-h3hsga3/thumbnail_Waters_Solving_Problems_That_Matter_a_Waters_webinar_series_featuring_USP_speakers_ecf3502756/thumbnail_Waters-Solving-Problems-That-Matter-a-Waters-webinar-series-featuring-USP-speakers.jpg 245w," sizes="100vw" width="800" height="418"><h4>Waters 2024 LC Symposium - Pushing the boundaries of LC to address today's challenges</h4><ul><li>Waters 2024 LC Symposium - Day 1 - <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/3935">Advanced LC Technologies for Increasingly Complex Molecules</a><ul><li>Guest Speakers: Davy Guillarme, Ph.D., Guilherme (Gui) Guimaraes, Ph.D., &amp; Michelle Chen, Ph.D.</li></ul></li><li>Waters 2024 LC Symposium - Day 2 - <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/webinar/3936">Next Generation HPLC: the Quest for Performance and Simplicity</a><ul><li>Guest Speakers: Amanda Guiraldelli, Ph.D., Jennifer Simeone, &amp; Andrea Gheduzzi</li></ul></li></ul><h4>Brochures and specifications</h4><ul><li>eBook: Improving Chromatographic Separations of Biopharmaceuticals With MaxPeak High Performance Surfaces (HPS) Technology<ul><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/720008011en_dd0b025cd5/720008011en.pdf">Full PDF for download</a></li></ul></li><li>White Paper: MaxPeak Premier Solutions: Improving Consumer Safety Through Innovative Science<ul><li><a target="_blank" rel="noopener noreferrer" href="https://www.waters.com/waters/library.htm?locale=en_US&amp;lid=135123293&amp;cid=511436&amp;srsltid=AfmBOoripwfSGOjTFXNWqjDHknCTbtr0G9sKgb9BsUD-L9avphhNaGuH">Full PDF for download</a></li></ul></li><li>Application Notebook: Alliance iS<ul><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/720008280en_e9d283fb41.pdf">Full PDF for download</a></li></ul></li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.cz/products/210">Alliance iS </a>Product Information</li><li>ICH Q14 Enhanced Approach: Tools in Empower Software to Support Your Analytical Procedure Development<ul><li><a target="_blank" rel="noopener noreferrer" href="https://cdn.technologynetworks.com/TN/resources/PDF/Tools-in-Empower-Software-to-Support-Your-Analytical-Procedure-Development.pdf">Full PDF for download</a></li></ul></li><li><a target="_blank" rel="noopener noreferrer" href="https://www.wyatt.com/products/instruments/multi-angle-light-scattering-detectors-sec-mals.html">MALS Detectors Product Information</a></li></ul><h4>Guides</h4><ul><li>LC Adjustments Made Simple: A Practical Guide<ul><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/720008065en_88843a7860/720008065en.pdf">Full PDF for download</a></li></ul></li><li>Guidebook: Characterization of New Modalities</li><li>Multi-Attribute Methods for Biopharmaceutical Analysis<ul><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/waters_mam_ebook_2023_84c6ae3f04/waters_mam_ebook_2023.pdf">Full PDF for download</a></li></ul></li><li><a target="_blank" rel="noopener noreferrer" href="https://www.waters.com/waters/library.htm?lid=135083997&amp;icid=hm-inno_00326&amp;srsltid=AfmBOopUZ0Cbncgz3zb34VIy1ysiVknnwF8Bca5aLAmt2KFOLtB3ZGhc">AQbD Method Development Infographic - Achieve Consistent Reportable Results</a></li></ul>By offering these resources, the LC for Pharma Resource Hub serves as a valuable tool for professionals seeking to enhance their understanding and application of liquid chromatography in the pharmaceutical industry.<blockquote>For complete information, please visit the <a target="_blank" rel="noopener noreferrer" href="https://gateway.on24.com/wcc/experience/elitewatersregional/2426651/4494230/method-management-campaign?partnerref=CZLabrulez">LC Regulated Workflows: Access insights from Pharma industry experts website</a></blockquote>

LC Regulated Workflows: Access insights from Pharma industry experts

Evaluating the potential of hydrophilic interaction liquid chromatography for collagen peptide mapping analysis

Our Library never stops expanding. What are the most recent contributions to LabRulezLCMS Library in the week of 14th July 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 posters by Agilent Technologies / ASMS, Shimadzu / ASMS, Thermo Fisher Scientific / ASMS and Waters Corporation / ASMS!<h3>1. Agilent Technologies / ASMS: Faster Than the Speed of Life: A Rapid Screening Method for Select PFAS Compounds in Serum by RapidFire-MS</h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/ps_rapidfire_ms_pfas_serum_asms_2025_wp_243_en_agilent_c8b56f4dd5.pdf">Full PDF for download</a></li></ul>Per- and polyfluoroalkyl substances (PFAS) are a broad class of compounds with a high degree of fluorination used in a variety of industries and consumer products over the past seventy years. Because of their widespread use, as well as their persistence and poorly understood effects on the human body, laboratories have started to monitor their presence in biological fluids, including serum. Biological samples are commonly prepared with protein precipitation. Additional cleanup steps such as solid phase extraction (SPE) with either C18 or weak anion exchange (WAX) sorbent can be used for increased sensitivity and cleanup. Alternatively, a pass-through cleanup using Captiva EMR-Lipid removes proteins and phospholipids through filtration, which significantly reduces sample complexity.<h4>Experimental</h4>Purchased serum was aliquoted into two sets of samples, one for protein precipitation (ppt) only and one for a protein precipitation plus Captiva EMR-Lipid based cleanup. For each preparation, 100 µL serum was aliquoted into a tube and spiked with 25 native and extracted isotopically labeled (EIS) compounds (EPA 533 list). The samples were vortexed for 1 minute and then crashed with 1% formic acid in cold ACN. They were vortexed for 3 minutes and then spun for 10 minutes to complete the crash. Samples undergoing the Captiva-EMR Lipid cleanup were then loaded onto a pre-rinsed cartridge and allowed to filter through (Figure 1). All samples were then spiked with the additional non-extracted isotopically labelled (NIS) compounds, vortexed, and transferred to a plate for analysis.The RF/TQ system consisted of a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?manufacturerItems=1&amp;search=RapidFire+400">RapidFire 400</a> front end with a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?manufacturerItems=1&amp;search=6495D+triple+quadrupole">6495D triple quadrupole mass spectrometer</a> for detection. Samples were introduced into the mass spectrometer using a RapidFire cartridge for additional cleanup, and the total cycle time was about 12.5 seconds, with analytes detected in negative polarity. Specific method conditions are detailed in Tables 1 (RF) and 2 (MS).<h4>Results and Discussion</h4><h5>Early Development Work&nbsp;</h5>Feasibility studies for detection of PFAS compounds in serum using the RapidFire started by looking for PFAS background in the instrument and in all steps of the workflow. Early testing demonstrated no detectable background from any stage of the process.RapidFire method development tested a variety of cartridges for optimal binding of the PFAS compounds, as well as determining the best state times and solvent conditions. For the preliminary work, the C18 cartridge appeared to have the best results, while the optimized elution solvent was 5 mM ammonium acetate in methanol.Early experiments were performed to understand the effects of dilution and sample prep on the overall sensitivity of the method. When only a protein precipitation preparation was employed, dilution had a significant positive effect on analyte sensitivity, as shown in Figure 2. However, when the sample prep routine utilized protein precipitation in combination with a Captiva EMR-Lipid cleanup, additional dilution was not necessary and in fact decreased overall sensitivity, as shown in Figure 3. This is attributed to the removal of extraneous matrix using the Captiva EMR-Lipid device, which has a unique selectivity for unbranched alkane chains, capturing phospholipids while allowing PFAS compounds to pass through the sorbent bed unretained.<h5>Custom Cartridge Work&nbsp;</h5>This preliminary work was done using the C18 cartridge, but the cartridge had poor retention of the short-chain PFAS compounds (C5 and below). To address this issue, a cartridge was custom-packed with mixed mode material that included C18 and anion exchange resins. Further testing and method optimization for this specific packing material yielded better results across the board, but especially for the short-chain PFAS compounds. Sensitivity for most analytes was at or below 0.1 ppb, as summarized in Table 3, with a calibration range of 0.01- 10 ppb tested for all analytes.<h4>Conclusions</h4><ul><li>LOQs for most PFAS analytes of interest in serum were 0.1 ppb or below</li><li>Samples were injected every 12 seconds, allowing for high throughput analysis</li><li>Custom mixed mode cartridge had best overall performance&nbsp;</li><li>There was no background PFAS contribution from the RapidFire system</li></ul><h3>2. Shimadzu / ASMS: Benchtop Matrix Assisted Laser Desorption Ionization Mass Spectrometry of PFAS for preliminary screening of environmental samples</h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/ASMS_2025_MP_588_4c415fced0.pdf">Full PDF for download</a></li></ul>Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants which negatively impact human health and the environment.&nbsp; High concentrations of PFAS in test samples can cause contamination of sensitive LC-MS/MS instruments requiring extensive decontamination procedures, resulting in significant downtime and costly delays in sample processing.&nbsp;We present the development of a simple, cost-effective method for the rapid screening of PFAS samples using an entry-level linear benchtop MALDI-TOF mass spectrometer to identify samples containing high levels before they enter the analytical workflow.<h4>Methods</h4>PFOA and PFOS standards were purchased from Merck, UK. Stock solutions (1 ppm prepared in acetonitrile) were diluted in water to the required concentrations.&nbsp;Samples were tested with a variety of MALDI matrices (CHCA, DHB, DHAP, DAN, NRM and TMGN), with norharmane (NRM) and 1,8-bis(tetramethylguanidino)-naphthalene (TMGN) identified for further investigation. Diluted samples were mixed with NRM (1 mg/mL) or TMGN (5 mg/mL) in 70:30 Acetonitrile:Water. Blank samples of UHP water were prepared to ensure no contamination was present. 1 µL of the mixture was spotted onto a FlexiMassSR48 slide and the samples were analysed using a <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/library?manufacturerItems=3&amp;search=MALDI-8030">MALDI-8030 MALDI-TOF mass spectrometer</a> (Shimadzu). Analysis conditions are shown in Fig 1. Peaks at m/z 314.2 and m/z 499.2 were monitored for PFOA and PFOS, respectively. 13C8PFOS (m/z 507) was used as an internal standard for PFOS samples with a final sample concentration of 6.25 ng/mL (see Fig. 2). MALDI-MSI was used to assess spot homogeneity and limits of detection were established.Subsequently, PFHxA and PFBS standards were purchased from Merck, UK and analysed using the same method<h4>Conclusion</h4><ul><li>We have shown, in principle, that it is possible to use MALDITOF mass spectrometry to implement a simple pre-screening method for aqueous samples to prevent widespread contamination in a PFAS monitoring laboratory. Using just a 200 µL sample and minimal preparation, a full slide of samples can be acquired in less than 15 minutes&nbsp;</li><li>Spot imaging during method development highlighted the differences in homogeneity of PFOS and PFOA spots suggesting that multiple internal standards would be necessary to pursue semi-quantitative analysis.&nbsp;</li><li>Whole slide imaging can provide quick results for multiple samples across a range of PFAS concentrations in a single run with easy visual identification of potentially problematic samples.&nbsp;</li><li>As a linear MALDI-TOF method, these results do not confirm the presence of PFAS but may be used as an indicative result to modify further testing protocols. Confirmatory LC-MS/MS analysis is required to determine the presence and concentration of any PFAS.</li></ul><h3>3. Thermo Fisher Scientific / ASMS: Enhanced resolution with multi-pass mode on the Orbitrap Astral Zoom mass spectrometer&nbsp;</h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/PO_319_ms_ASMS_25_Astral_Zoom_TMT_po319_na_en_a16d57e473.pdf">Full PDF for download</a></li></ul>The introduction of Thermo Scientific TMT 32-plex, with reporter ions clustered into quadruplets spaced by 3 mDa, demands a resolving power of approximately 90 k at low m/z, which challenges state-of-the-art time-of-flight mass analyzers. The Thermo Scientific Orbitrap Astral mass spectrometer [1] typically achieves around 60 k resolution at m/z = 138, falling to 50 k under high space charge influence.&nbsp;Here we present an enhanced resolution multi-pass method implemented in the new Thermo Scientific <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1959">Orbitrap Astral Zoom mass spectrometer</a> [2], significantly improving the resolving power at low m/z enabling TMT 32-plex on the Thermo Scientific Astral analyzer. The ions are repeatedly guided through a switchable prism deflector to extend the ion path while the principal aberrations are compensated, thereby more than doubling the resolution.<h4><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1959">The Orbitrap Astral Zoom Mass Spectrometer</a></h4>The Orbitrap Astral Zoom mass spectrometer incorporates a range of novel technologies for higher speed, sensitivity, dynamic range and resolving power. A faster ion filter and quadrupole switching in combination with improved ion transfer times in the ion-routing multipole (IRM) and ion processor allow DIA methods (Thermo Scientific Pierce FlexMix calibration solution, 2 Th isolation window, 350-980 m/z range, Thermo Scientific Orbitrap MS and Astral MS/MS) with scan rates faster than 270 Hz (DDA methods &gt; 180 Hz). A duty cycle of up to 80 % at 270 Hz is made possible by a new accumulation stage in the bent trap (linear to 106 ions) which increases the effective ion injection into the IRM by up to 40 % (additional ~0.75 ms on top of 2 ms IT in a DIA method). Furthermore, an additional mode of the high-dynamic range detector of the Astral analyzer for low-input sample applications like single-cell proteomics boosts the single ion detection efficiency by about 10 % and thereby the sensitivity. In combination with a new peak deconvolution algorithm for enhanced spectral processing, this increases the depth of analysis. In addition, modified IRM hardware allows for faster stepped collision energy experiments for deeper spectral coverage (2 CE: 80 Hz (OT Astral), 180 Hz (Orbitrap Astral Zoom MS), 3 CE: 60 Hz, 150 Hz). A modified AGC filling in the Orbitrap by a new enhanced dynamic range mode (EDR) provides additional spectral complexity and sensitivity. Here, multiplexed ion injections with different accumulation times are used for different mass ranges taking the variation in signal intensity into account. In addition, the Astral analyzer incorporates a new multi-pass mode which enhances the resolving power for small masses by more than a factor of two, enabling TMT 32-plex measurement in the Astral analyzer.<h4>Conclusions&nbsp;</h4>The TMT HR Mode advances the capabilities of the Astral analyzer. Fast electronic switching of the relay prism opens an ion trap and guides the ions after three times their normal flight distance to the detector. This greatly boosts the resolving power, especially for small m/z ratios, thereby enabling baseline-resolved quadruplets of reporter ions in a TMT 32-plex sample. First results show a peak resolution well above 100 k while maintaining high mass accuracy and shot-to-shot stability. The TMT HR Mode is utilized within a novel acquisition scheme delivering greater throughput for protein identification and quantitation for multiplexing up to 32 samples. Achieving a throughput of over 440 samples per day, the proteome coverage included 4,299 quantified protein groups and 22,146 peptides.<h3>4. Waters Corporation / ASMS: Assessing the Impact of Part-per-Billion Mass Accuracy Upon Forensic Toxicology Data-Independent Analysis Screening&nbsp;</h3><ul><li>Poster</li><li><a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/labrulez-bucket-strapi-h3hsga3/720008882_68ab66ac31.pdf">Full PDF for download</a></li></ul>This poster presents a forensic toxicology screening approach using ultra-high mass accuracy to improve specificity and efficiency in the analysis of complex biological matrices, such as urine. The need for rapid, accurate, and cost-effective screening is critical in forensic laboratories. The study evaluates the impact of parts-per-billion (ppb) mass accuracy using the <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/1148">Waters Xevo MRT Mass Spectrometer</a> compared to previous DIA methods.The method involved analyzing a commercial standard and ten anonymized urine samples, diluted and analyzed via the <a target="_blank" rel="noopener noreferrer" href="https://lcms.labrulez.com/products/199">ACQUITY UPLC I-Class FTN PLUS System</a> coupled with the Xevo MRT MS. Data acquisition was performed in MSE mode (data-independent acquisition) under positive electrospray ionization (ESI+) using a 15-minute gradient. Data were processed in waters_connect software, leveraging a comprehensive spectral library with over 2000 compounds.Results demonstrated a root mean square (RMS) mass error of just 571 ppb for precursor ions, with 94% of compounds detected within 1 ppm, and 96% of fragment ions within the same tolerance. A key example illustrated how reducing fragment ion tolerance from 2 mDa to 0.2 mDa eliminated a false positive (Anabasine), correctly identifying Nicotine as the true analyte, showing how ppb-level accuracy increases identification confidence and minimizes misidentifications.The study concludes that ppb mass accuracy significantly reduces false positives, lowers review workloads, and facilitates independent analysis by less experienced personnel. Additionally, it supports cost savings by minimizing the need for confirmatory testing, potentially reducing both financial and environmental burdens in forensic laboratories.

News from LabRulezLCMS Library - Week 29, 2025

News from LabRulezLCMS Library - Week 31, 2025

Low-Cost Heating Modalities Allow the Detection of Biomarkers for Plant Infection Using Rapid Evaporative Ionization Mass Spectrometry (REIMS) That Are Pathogen Specific

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

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