Mass Spectrometry for the Analysis of Payloads and Related Impurities in Antibody–Drug Conjugates

Mass Spectrometry for the Analysis of Payloads and Related Impurities in Antibody–Drug Conjugates | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mass Spectrometry for the Analysis of Payloads and Related Impurities in Antibody–Drug Conjugates gang WU, gangling XU, yue zhao, Tie Gao, yongbo NI, Xiaolei LV, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9212596/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract In antibody–drug conjugate research, the small molecule impurities requiring evaluation include not only free payloads, but also potential payload-related small molecule impurities. In this study, high-resolution mass spectrometry and triple quadrupole mass spectrometry were employed for systematically and comprehensively investigate small molecule related impurities in antibody–drug conjugate product. First, the quantitation of free payload was analyzed. High-resolution mass spectrometry enabled quantification at both precursor ion and product ion levels, the free payload exhibited good linearity over the concentration range of 0.05–100 ng/mL (R² ≥ 0.991), with a lower limit of quantification of 0.05 ng/mL. The accuracy ranged from 80% to 120%, and the precision was below 5%. The high-resolution mass spectrometry results were confirmed using triple quadrupole mass spectrometry. Second, other potential payload related small molecule impurities in the antibody–drug conjugate was investigated in depth. Multiple possible hydrolysis pathways of the payload were predicted based on molecular structure analysis, and the corresponding payload related impurities were subsequently identified using triple quadrupole mass spectrometry in accordance with the predicted precursor–product ion information. Third, stability studies of the antibody–drug conjugate were conducted under forced degradation conditions. Qualitative analyses were performed to characterize unknown payload related degradation products generated under stress conditions, including light exposure, heat, acidic, and alkaline environments. The major degradation products and their relative abundances were identified, with alkaline conditions exerting the greatest impact on sample stability. Clinical trial number: not applicable. antibody–drug conjugate small molecular related impurities high-resolution mass spectrometry Quantitation Stability study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Rapid progress in the biopharmaceutical market has led to the emergence of antibody–drug conjugate (ADC) drugs as a new approach to cancer treatment. ADCs link cytotoxic small-molecule drugs to monoclonal antibodies via chemical bonds. The high affinity of antibodies for specific tumor cell surface antigens enables the precision delivery of cytotoxic drugs to cancer cells, enhancing efficacy and reducing damage to normal tissues. Several generations of development have occurred since the concept of ADCs was first proposed. The first-generation ADC is represented by Pfizer’s Mylotarg (gemtuzumab ozogamicin). Mylotarg, the first ADC worldwide, was approved in 2000, representing the first clinical use of this drug type. Mylotarg is formed by conjugating an anti-CD33 monoclonal antibody to the antitumor antibiotic calicheamicin via a cleavable hydrazone linker and is used to treat CD33-positive acute myeloid leukemia (AML) [ 1 ]. However, in clinical practice, linker stability was found to be poor in the blood circulation; furthermore, calicheamicin was prone to early release, leading to severe off-target toxicity, a narrow therapeutic window, and limited patient benefit. Thus, Mylotarg was withdrawn from the market in 2010. The issues encountered with this first-generation ADC provided direction for subsequent research and development. Second-generation ADCs are optimized with respect to linkers, antibodies, and the cytotoxic payload. The linker is designed to be more stable, for example, using non-cleavable linkers or modifications to the structure of cleavable linkers that improve their stability in the blood circulation, reducing the risk of early release. Regarding antibodies, humanized or fully human antibodies are often used to reduce immunogenicity [ 2 – 3 ]. For example, Adcetris (brentuximab vedotin) and Kadcyla (ado-trastuzumab emtansine), which are representative second-generation ADCs for treating solid tumors, have cleavable linkers that couple different toxin molecules [ 4 – 5 ]. Continuous technical progress has enabled the development of more innovative, third-generation ADCs that also retain the advantages of the previous two generations. For example, regarding the coupling technique, the site-specific conjugation method results in a more even drug-to-antibody ratio (DAR), enhancing product quality and stability and improving the ADC’s pharmacokinetic properties. For example, Daiichi Sankyo’s Enhertu (trastuzumab deruxtecan) is formed by coupling trastuzumab and a topoisomerase I inhibitor (exatecan derivative) via a cleavable linker. This ADC has demonstrated excellent efficacy in treating several cancers, including HER2-positive breast cancer and gastric cancer, expanding the applicable target population of this drug type [ 6 – 7 ]. To date, a total of 19 antibody–drug conjugates (ADCs) have been approved globally, covering a broad range of indications for hematological malignancies and solid tumors. In addition, approximately 40 ADC candidates worldwide are currently undergoing phase III clinical trials. Given their therapeutic potential in oncology, ADCs are expected to experience rapid and transformative growth in the near future. [ 8 – 10 ]. On April 23, 2021, loncastuximab tesirine was approved by the United States Food and Drug Administration (FDA) for the treatment of relapsed or refractory diffuse large B-cell lymphoma (DLBCL) after two or more systemic therapies. On December 10, 2024, loncastuximab tesirine was approved by the National Medical Products Administration (NMPA) of China, representing the first CD19 ADC drug approved and marketed in China. Loncastuximab tesirine comprises a humanized IgG1 kappa monoclonal antibody attached to SG3249 (tesirine) by cysteine, and its DAR value is approximately 2.3 [ 11 – 12 ]. SG3249 comprises the pyrrolo–benzodiazepine (PBD) dimer SG3199, para-aminobenzoic acid (PABA), a dipeptide (Val-Ala), a PEG8 spacer to increase solubility, and a maleimide linker. Its structure is shown in Fig. 1 . This study focuses on this product. ADC products require multidimensional quality analysis during research and manufacturing. In particular, comprehensive quality characterization of the naked antibody is essential, including extensive structural and physicochemical analyses such as amino acid sequence determination, glycosylation profiling, and higher-order structure characterization, to ensure the correctness and stability of the antibody structure. Meanwhile, free payload and payload-related impurities are also critical quality attributes (CQAs), as they directly determine the safety and efficacy of the drug product. Quality control of small-molecule toxins and linkers involves structural confirmation and limit testing, among other requirements [ 13 – 15 ], all of which rely on appropriate analytical methodologies. In studies of drug metabolism and pharmacokinetics (DMPK), the quantification of free payload requires substantially higher analytical sensitivity due to its low in vivo concentrations and pronounced matrix effects. To date, the most widely reported approach for quantifying free payloads in blood samples is triple quadrupole mass spectrometry, operated in the multiple reaction monitoring (MRM) mode. From the perspective of in vivo drug metabolism studies, this method has been extensively applied to characterize the time-dependent release profiles of ADC payloads in circulation [ 16 – 18 ]. In certain investigations of ADC in vivo metabolism, the lower limit of quantification for unconjugated cytotoxic drugs in blood has been reported to reach 0.015 ng/mL using triple quadrupole mass spectrometry [ 19 ]. Owing to the excellent selectivity of the MRM quantification mode, triple quadrupole mass spectrometry is also applicable to quality control of ADC products, where it can be employed to monitor free payload levels and thereby assess drug safety and efficacy [ 20 – 21 ]. As the sensitivity requirements for quantifying free payloads in in vitro ADC products are generally lower than those for in vivo bioanalysis, high-resolution mass spectrometry (HRMS) is also suitable for meeting analytical needs in ADC product testing. With its superior mass resolution, HRMS enables quantification based on both full-scan (TOFMS) data and fragment ions in MS/MS-based quantitative workflows, such as high-resolution multiple reaction monitoring (MRM hr ). Reported studies have demonstrated that the sensitivity of HRMS-based methods for quantifying free payloads in ADCs can reach the ppb level or even lower [ 22 ]. Researchers have also used specialized pretreatment methods to quantify the free ADC payload using an optimized liquid chromatography (LC) method; for example, samples labeled with fluorescent dye during pretreatment, purified using solid-phase extraction (SPE), and then quantified by LC [ 22 ]. However, the pretreatment required for this method is complicated, recovery is unstable, and sensitivity is limited, usually in the range of nanograms per milliliter to micrograms per milliliter. TQMS is the most reported method for quantifying the free ADC payload in drug metabolism and pharmacokinetics studies. In vivo drug metabolism research has been employed to demonstrate the difference in the released ADC payload content over time [23–27]. This technique, using the targeted quantification mode, is also used to quantitatively analyze the free payload in ADC quality control. Similarly, HRMS has been used to quantify the free payload in ADC drugs; the free payload is measured while the ADC product is characterized [28–30]. For example, Leanne combined HRMS and TQMS to quantitatively analyze the free payload in ADC products. However, deeper-level structural characterizations and drug stability analyses remain lacking. Forced degradation experiments are critical core components in the study of ADC drug stability. In the context of drug stability, artificial applications of extreme conditions, such as high temperatures, acidic or basic environments, and oxidative conditions, can systematically reveal potential degradation pathways and key degradation products, providing the critical evidence needed for drug formulation optimization, packaging material selection, and storage condition determination. In this study, we analyzed the newly approved ADC loncastuximab tesirine using multiple HRMS quantification methods. In addition to the secondary quantification of free payload using MRM hr , the super-high-resolution capability of HRMS was exploited to achieve primary free payload quantification. Experimental validation revealed a good quantification range. TQMS was applied in the same context. In the quantitative free-payload analysis of the ADC product, the HRMS primary and secondary quantification results were consistent with those obtained via TQMS, with coefficients of variation below 10%. In the study of payload stability, specific ions were assessed via TQMS in product-ion scan mode to identify hydrolysis products predicted from hydrolysis-prone sites in the chemical structure, providing reliable data to support the quantification of the payload and its potential hydrolysis products. In the complete stability study, the ADC was subjected to various forced degradation conditions, including strong acidic, strong basic, heated, and light-illuminated environments. For the first time, we employed HRMS coupled with nanoflow liquid chromatography to analyze the degradation products of the ADC drug payload. The degradation pathways of the ADC products under various extreme conditions were elucidated according to the number of degradation products and their common products, providing data to support drug stability studies. 2 Materials and samples 2.1 Materials Liquid chromatography–mass spectrometry (LC–MS) grade acetonitrile and water were purchased from Thermo Fisher; 10 kDa ultrafiltration membrane filters were purchased from Sartorius. 2.2 Samples The ADC product loncastuximab tesirine and N-acetylcysteine-modified tesirine (NAC-SG3249) were sourced as retained from the National Institutes for Food and Drug Control (China). In the ADC manufacturing process, N-acetylcysteine (NAC) was used as the antioxidant, which binds to free tesirine; thus, the free payload exists as NAC-SG3249. 2.2.1 Preparation of the NAC-SG3249 standard The NAC-SG3249 standard was sequentially diluted with LC–MS-grade water, and standard curve gradient concentrations were prepared as follows: 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, and 100 ng/mL. 2.2.2 ADC sample preparation The sample was ultrafiltered using a 10 kDa ultrafiltration membrane. Subsequently, the filtrate was evaporated to dryness and reconstituted in a 1/10 volume of LC–MS water to prepare the test sample. The free NAC-SG3249 content in the test sample was measured. 2.2.3 Stress test sample preparation Four forced degradation modes were designed to process the ADC sample: acid treatment (pH adjusted to 2–3 using formic acid), base treatment (pH adjusted to 11–12 using aqueous ammonia), strong light illumination, and heating at 60℃. The four conditions were maintained in an enclosed environment for one day. Subsequently, the samples were ultrafiltered using a 10 kDa membrane. The filtrate was then evaporated to dryness and reconstituted in a 1/10 volume of LC–MS-grade water to prepare the test samples. The potential degradation products in the test samples were measured. 3 Methods Liquid chromatography–high-resolution mass spectrometry (LC–HRMS) was used to quantitatively analyze NAC-SG3249, and the results were compared with those from the LC-TQMS method. The potential hydrolysis products were identified and structurally characterized by TQMS. The degradation impurities in the payload under forced degradation conditions were analyzed by HRMS coupled with nanoflow liquid chromatography. 3.1 LC method A SCIEX ExionLC™ AE system was employed using a Waters ACQUITY UPLC Peptide BEH C18 chromatography column (130 Å, 1.7 µm, 2.1 × 100 mm) under the following conditions: mobile phase A, water, 0.1% formic acid; mobile phase B, acetonitrile, 0.1% formic acid; column temperature, 40°C; and injection volume, 20 µL. The gradient elution program is shown in Table 1 . Table 1 Liquid chromatography gradient elution program Time (min) Flow (mL/min) B. Conc (%) 0.0 0.3 10.0 1.0 0.3 10.0 7.0 0.3 95.0 10.0 0.3 95.0 10.1 0.3 10.0 12.0 0.35 10.0 A Waters ACQUITY MCLASS LC system was employed using a Waters ACQUITY UPLC Peptide BEH C18 chromatography column (1.7 µm, 75 µm × 25 cm) under the following conditions: mobile phase A, water, 0.1% formic acid; mobile phase B, acetonitrile, 0.1% formic acid; column temperature, 60°C; and injection volume, 20 µL. The gradient elution program is shown in Table 2 . Table 2 Nanoflow liquid chromatography gradient elution program Time (min) Flow (µL/min) B. Conc (%) 0.0 0.3000 3.0 1.0 0.3000 8.0 41.0 0.3000 45.0 43.0 0.3000 95.0 50.0 0.3000 95.0 51.0 0.3000 3.0 60.0 0.3000 3.0 3.2 QTOF method A SCIEX ZenoTOF 7600 system was employed for MRM hr data collection in positive-ion mode. The conditions were as follows: nebulizer gas (GS1), 70 psi; auxiliary gas (GS2), 20 psi; curtain gas (CUR), 35 psi; ion source temperature (TEM), 500°C; ionization source voltage (IS), 5500 V; and collision gas (CAD), 7 psi. MRM hr ion pair data are shown in Table 3 . Table 3 MRM hr ion pairs and MS parameters Compound Precursor ion product ion Accumulation time (ms) Declustering potential Collision energy NAC-SG3249-1 830.3729 358.1073 25 80 46 NAC-SG3249-2 830.3729 585.2713 25 80 30 NAC-SG3249-3 830.3729 908.4179 25 80 26 The SCIEX ZenoTOF 7600 system was also employed for information-dependent acquisition (IDA) of data in positive-ion mode. The conditions were as follows: GS1, 8 psi; CUR, 25 psi; TEM, 175°C; IS, 4000 V; and CAD, 7 psi. For the TOF MS scan, the range was 350–1500, the accumulation time was 0.25 s, and the maximum number of candidate ions was 20. Exclude Former candidate ions were excluded for 3 s. For the TOF MSMS scan, the range was 100–1500, and the accumulation time was 0.05 s. Zeno pulsing was enabled, and the Zeno threshold was set to 100,000. The dynamic collision energy (CE) spread was 10, and the time bins were set to sum 8. 2.3.3 Triple quad method The Triple Quad™ 6500 + system, a SCIEX localized product in China, was used; the data collection mode was MRM, and the positive ion mode was employed. The conditions were as follows: GS1, 70 psi; GS2, 20 psi; CUR, 35 psi; TEM, 500°C; IS, 5500 V; and CAD, 7 psi. The MRM ion pair data are shown in Table 4 . Table 4 MRM ion pairs and MS parameters Compound Precursor ion Product ion Dwell time (ms) Declustering potential Entrance potential Collision energy Collision cell exit potential NAC-SG3249-1 830.7 358.1 20 100 10 50 10 NAC-SG3249-2 830.7 585.2 20 100 10 30 10 NAC-SG3249-2 830.7 908.4 20 110 10 30 10 3 Results and discussion 3.1 Quantitative analysis of payload (NAC-SG3249) in the ADC product The molecular formula of payload (NAC-SG3249) is C 80 H 110 O 26 N 10 S. The information-dependent acquisition (IDA) mode in HRMS was used to identify the quantitative ion pairs (Fig. 2 ). The mass-to-charge ratio (m/z) of the doubly charged ion detected by the TOFMS was 830.3741, and this precursor ion displayed the highest response, showing only 1.44 ppm in the mass error of the theoretical m/z. Thus, it could be used for primary HRMS quantification. In the analysis of the fragments, the characteristic fragment ions of 358.1072 (m/z), 585.2708 (m/z), and 908.4172 (m/z), whose corresponding structural information could be found in the structural elucidation, were detected. The corresponding ion pairs could act as the theoretical ion pairs set in the MRM hr method of HRMS and the MRM method of TQMS. TOFMS and TOFMS/MS analyses were performed to quantitatively detect the NAC-SG3249 in the ADC sample according to the identified ion pairs. NAC-SG3249 gradient standard samples were used to investigate linearity to verify the method’s reliability (Fig. 3 ). In the TOFMS and TOFMS/MS analyses, the concentration gradient for NAC-SG3249 was set to 0.05–100 ng/mL, and the linear correlation coefficients (R 2 ) were 0.991 and 0996, respectively. We performed the TQMS quantitative analysis in parallel to verify the accuracy of the HRMS results. The linear correlation coefficient (R 2 ) reached 0.999. The limit of quantitation values for HRMS and TQMS in the NAC-SG3249 analysis were 0.05 ng/mL and 0.005 ng/mL, respectively. Furthermore, the accuracy values at all linear concentration points stabilized within 80%–120%, indicating that both methods are suitable for quantitative analyses. In addition to assessing the linear relationship, detection limit, and accuracy, we conducted a systematic analysis of precision and recovery across different instruments and analytical methods. Precision was investigated using low, medium, and high concentration gradients; three injections were conducted per concentration point (Table 5 ). The precision values of all concentration points were below 5%, indicating good repeatability. Furthermore, recovery was stable across all concentrations, at 80%–120%, further validating the method's reliability and accuracy. Table 5 Precision analysis results obtained with different techniques and different instruments Concentration point Precision investigation TOFMS TOFMSMS Triple Quad MS 0.5 ng/mL 1.33% 3.03% 2.82% 5 ng/mL 1.38% 2.73% 1.07% 50 ng/mL 0.84% 0.41% 0.48% We employed three methods to analyze payload (NAC-SG3249) in ADC samples: TOFMS quantification, TOFMS/MS MRM hr , and TQMS MRM; measured concentrations of NAC-SG3249 residue in the ADC samples were 812.5 ng/mL, 846.6 ng/mL, and 781.4 ng/mL, respectively. The three quantification methods showed good consistency, with an RSD below 10%. These findings indicate that HRMS can reach the same level of detection as TQMS for the free payload in ADC samples. 3.2 Assessment of NAC-SG3249 stability In the in vitro synthesis of NAC-SG3249, NAC attacks the double-bonded carbon atom on the maleimide ring of SG3249 via a nucleophilic reaction mechanism, thus combining with SG3249 to form NAC-SG3249. We assessed NAC-SG3249 stability via molecular structure analyses, revealing relatively low amide and ester bond energies, indicating bonds prone to hydrolysis or cleavage (Fig. 4 ). Notably, the specific cleavage sites of the three characteristic ions used in the quantification analysis could be determined by analyzing the NAC-SG3249 hydrolysis products. Furthermore, we investigated the potential hydrolysis products of NAC-SG3249 during the stability analysis. Six possible hydrolysis products were predicted according to the sites prone to hydrolysis (Fig. 5 ). MRM mode was employed in the TQMS measurements. The predicted hydrolysis product was selected as the precursor ion, and the fragment ion 358.1 m/z was selected as the product ion. The molecular weight of predicted hydrolysis product A was too small for analysis; thus, this product was not investigated. The precursor ion signal of 756.3 m/z was detected at 3.8 min (as shown in Fig. 6 ), indicating the hydrolysis product of NAC-SG3249 (Fig. 4 B). The retention behavior of this hydrolysis product on the C18 chromatography column revealed that its hydrophilicity was even stronger than that of NAC-SG3249, consistent with the theoretical structure characteristics. Following MRM mode application, an information-dependent acquisition (IDA)-enhanced product ion (EPI) was set to conduct fragmentation treatment on this hydrolysis product, and the characteristic ions of 696.55 m/z, 710.76 m/z, and 738.48 m/z were detected in the product ion signal (Fig. 7 ). Additionally, the cleavage positions corresponding to these fragments could be found in the structure of this hydrolysis product, and they were related to three different cleavage positions (Fig. 7 D). Thus, it can be determined that this substance is an effectively detectable hydrolysis product of NAC-SG3249. 3.3 Stability Study of Small Molecules with Forced Degradation in ADC Samples To assess the stability of the ADC product, we designed four forced degradation experiments with different conditions: acid treatment, base treatment, light illumination, and heating treatment. The metabolites of the payload in the ADC drug were analyzed under each condition. HRMS coupled with nanoflow liquid chromatography in IDA scan mode was employed to measure metabolites in the different forced degradation samples. Under acid, base, light illumination, and heating conditions, 23, 39, 26, and 28 types of small molecular related impurities were detected. The greatest number of small molecular related impurities was observed with base treatment. Twelve common small molecular related impurities were identified across the four forced degradation conditions (Fig. 8 ). The common small molecular related impurities are shown in Table 6 . Table 6 Identification of the common degradation products using four forced degradation method No. Name Formula Neutral Mass Average Mass m/z ppm 1 Loss of C44H47N5O10 and C12H14N2O6S+Demethylation to Carboxylic Acid [M + H]+ C24H47N3O12 569.3148 569.4921 570.3221 -2.1 2 Loss of C5H7NO3 and C49H56N6O11 + Thioalcohol to Alcohol [M + H]+ C26H47N3O13 609.3091 609.5364 610.3164 -3 3 Loss of C12H15N3O6S and C49H56N6O11 + Amine to Carboxylic Acid [M + H]+ C20H38O11 454.2398 454.4053 455.2471 -3.5 4 Loss of C52H61N7O12 and C12H14N2O6S+Oxidation [M + H]+ C16H35NO9 385.2298 385.3778 386.2371 -3.5 5 Loss of C12H15N3O6S and C49H56N6O11 + Oxidation [M + H]+ C19H39NO10 441.256 441.4348 442.2633 -3.1 6 Loss of C12H15N3O6S and C52H61N7O12 + Oxidation [M + H]+ C16H34O9 370.2195 370.3921 371.2267 -2.2 7 Loss of C52H61N7O12 and C12H14N2O6S [M + H]+ C16H35NO8 369.2348 369.6364 370.2421 -4 8 Loss of C49H56N6O11 and C14H19N3O7S+Amine to Carboxylic Acid [M + H]+ C18H34O10 410.2135 410.3629 411.2208 -4 9 Loss of C12H15N3O6S and C56H69N7O14 + Glucose Conjugation [M + H]+ C18H36O11 428.2247 428.383 429.232 -2.4 10 Loss of C49H56N6O11 and C16H23N3O8S+Amine to Carboxylic Acid [M + H]+ C16H30O9 366.1877 366.3189 367.195 -3.4 11 Loss of C49H57N7O11 and C16H23N3O7S+Oxidation [M + H]+ C15H30O9 354.1885 354.3265 355.1958 -1.2 12 Loss of C34H36N4O8 and C26H43N3O12S+Glucose Conjugation [M + H]+ C26H41N3O11 571.2721 571.473 572.2793 -3.6 4 Conclusions This study employed high-resolution mass spectrometry (HRMS) and triple quadrupole mass spectrometry (TQMS) as the core analytical technologies to comprehensively investigate the payload and payload-related small-molecule impurities in the commercially available antibody–drug conjugate (ADC) product loncastuximab tesirine. The study focused on the quantitative determination of the free payload (NAC-SG3249), the characterization of potential small-molecule impurities associated with the payload, and the investigation of payload-related impurities generated under forced degradation conditions. The objective of this work was to establish a clear and reliable analytical strategy for payload analysis in ADC products. For the quantitative analysis of the free payload, characteristic precursor–product ion transitions of NAC-SG3249 were selected. Both TOFMS and TOFMS/MS quantification approaches were evaluated by taking advantage of the superior mass resolution of high-resolution mass spectrometry, and the analytical methodology was systematically assessed. The results demonstrated excellent linearity across different concentration levels, with coefficients of determination (R²) greater than 0.99. The lower limit of quantification (LLOQ) was determined to be 0.05 ng/mL. Method accuracy and precision were evaluated at multiple concentration levels; accuracy ranged from 80% to 120%, and precision showed relative standard deviations (RSDs) of less than 5%. Recovery values also fell within the acceptable range of 80%–120%. The quantitative results obtained by HRMS were consistent with those generated using triple quadrupole mass spectrometry. For the determination of free payload (NAC-SG3249) in ADC samples, the deviation among the three analytical approaches was less than 10%. In the study of potential impurities associated with the payload (NAC-SG3249), labile bonds within the molecular structure that are susceptible to hydrolysis were systematically analyzed. Using the MRM-IDA-EPI acquisition mode of triple quadrupole mass spectrometry, hydrolysis products of NAC-SG3249 were accurately detected and structurally characterized. These findings provide a theoretical basis for the subsequent quantitative analysis of payload-related impurities. For the stability assessment of the ADC samples, forced degradation studies were conducted under acidic, alkaline, photolytic, and thermal conditions. Leveraging the high sensitivity and mass accuracy of high-resolution mass spectrometry, trace-level degradation products were detected and confidently identified through matching experimental data with theoretical mass calculations using dedicated data analysis software. Among the four forced degradation conditions, alkaline treatment had the most pronounced impact on sample stability, generating a total of 39 degradation products. Twelve degradation products were found to be common across all four stress conditions. These results enable the elucidation of degradation pathways of the payload and provide a solid foundation for subsequent stability evaluation and impurity control strategies. Abbreviations ADC Antibody–drug conjugate NAC N-acetylcysteine CQAs Constitute critical quality attributes MS Mass spectrometry LC Liquid chromatography HRMS High-resolution mass spectrometry TQMS Triple quadrupole mass spectrometry TOFMS Time of flight mass spectrometry TOFMS/MS Tandem time-of-flight mass spectrometry MRM hr High-resolution multiple reaction monitoring MRM Multiple reaction monitoring RSD Relative standard deviation CD33 Cluster of differentiation 33‌ DAR Drug-to-antibody ratio IDA Information-dependent acquisition EPI Enhanced product ion FDA United states food and drug administration NMPA National medical products administration Declarations Clinical trial number: not applicable. Funding This research was supported by Beijing Municipal Science and Technology Commission (Grant number Z251100004625003), National Key Research and Development Program of China (Grant number 2023YFC3404004), Project of State Key Laboratory of Drug Regulatory science (Grant number: 2025SKLDRS0336), and Standardization Improvement Project of Chinese Pharmacopeia (Grant number: BZ2025137). Competing Interests Consent for Publication Manuscript is approved by all authors for publication. Competing Interests The authors declare no competing interests. Author’s Contribution GW and GX conceived and designed the research; YZ, TG and XL collected and analyze the data; YN, HC and ML wrote the manuscript; JD and CY revised the manuscript and gave meaningful discussion and suggestions. All authors have read and agreed to the published version of the manuscript. Data Availability Data are available from the corresponding authors on reasonable request. Authors and Affiliations Gang Wu 1* , Gangling Xu 1,2* , Yue Zhao 3* , Tie Gao 4 , Yongbo Ni 1 , Xiaolei Lv 4 , Hongxu Chen 4 , Meng Li 1 , Jialiang Du 1 , Chuanfei Yu 1# * These authors contributed equally to the work. #Corresponding author. Email addresses: [email protected] 1 National Institutes for Food and Drug Control, State Key Laboratory of Drug Regulatory Science, NHC Key Laboratory of Research on Quality and Standardization of Biotech Products, NMPA Key Laboratory for Quality Research and Evaluation of Biological Products, No. 31 Huatuo Road, Daxing District, Beijing 102629, China. 2 School of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, No. 103 Wenhua Road, Shenyang 110016, China. 3 Beijing Institute for Drug Control (Beijing Center for Vaccine Control), NMPA Center for Innovation and Research in Regulatory Science., No. 25 Keyuan Road, Changping District, Beijing102206, China. 4 SCIEX China, No.18 Science Park Road, Changping District, Beijing 102206, P. R. China, China. References Akram, F., Ali, A. M., Akhtar, M. T. (2025). Bioorganic & Medicinal Chemistry 117, 118010. Tumey, L. N. (2020). Methods Mol. Biol. Lambert, J. M., & Morris, C. Q. (2017). Adv Ther 34, 1015–1035. Mahmood, I. 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Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 08 Apr, 2026 Editor invited by journal 04 Apr, 2026 Editor assigned by journal 03 Apr, 2026 First submitted to journal 01 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9212596","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619963606,"identity":"880a8fea-bb6f-43cd-b613-6724fb93ecf6","order_by":0,"name":"gang WU","email":"","orcid":"","institution":"NIFDC: National Institutes for Food and Drug Control","correspondingAuthor":false,"prefix":"","firstName":"gang","middleName":"","lastName":"WU","suffix":""},{"id":619963607,"identity":"b2c00724-640a-40be-9c05-f3df0588bc83","order_by":1,"name":"gangling XU","email":"","orcid":"","institution":"NIFDC: National Institutes for Food and Drug Control","correspondingAuthor":false,"prefix":"","firstName":"gangling","middleName":"","lastName":"XU","suffix":""},{"id":619963608,"identity":"79306240-4afd-46b6-a9a1-b94a51b2fd18","order_by":2,"name":"yue zhao","email":"","orcid":"","institution":"Shenyang Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"yue","middleName":"","lastName":"zhao","suffix":""},{"id":619963609,"identity":"0c1a66e2-e934-4a2c-9a6f-6e9b71015a7d","order_by":3,"name":"Tie Gao","email":"","orcid":"","institution":"SCIEX","correspondingAuthor":false,"prefix":"","firstName":"Tie","middleName":"","lastName":"Gao","suffix":""},{"id":619963610,"identity":"0a1d8992-2bdb-419a-aceb-fa84e30fcdac","order_by":4,"name":"yongbo NI","email":"","orcid":"","institution":"NIFDC: National Institutes for Food and Drug Control","correspondingAuthor":false,"prefix":"","firstName":"yongbo","middleName":"","lastName":"NI","suffix":""},{"id":619963611,"identity":"fc4e2058-f894-437e-bb04-fae70c3ee0fb","order_by":5,"name":"Xiaolei LV","email":"","orcid":"","institution":"SCIEX","correspondingAuthor":false,"prefix":"","firstName":"Xiaolei","middleName":"","lastName":"LV","suffix":""},{"id":619963612,"identity":"b2193b60-167c-4fe8-a921-42b71d06defc","order_by":6,"name":"hongxu chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYDACCQbGBx8qbHj42RuI18JsOONMmoxkzwHitbBJ87YdtjG44UCkDv7ZPQYSvG3neRhuMDB++JhDjCV3jiUYSJy7zcM4u4FZcuY2IrQYSCQfSDAou83DLHOAjZmXOC2JDQcS2M7xsEkkEK0l+WDDgbYDPDxEa5G4kZbM2HAmmUeC52AzcX7hn5Fj/vtPhZ29/fHmgx8+EqMFCTA2kKZ+FIyCUTAKRgFuAACcjTU7f7FgegAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4537-0382","institution":"SCIEX","correspondingAuthor":true,"prefix":"","firstName":"hongxu","middleName":"","lastName":"chen","suffix":""},{"id":619963613,"identity":"d33916d0-ce2b-4365-838d-f110ad2c62d6","order_by":7,"name":"meng Li","email":"","orcid":"","institution":"NIFDC: National Institutes for Food and Drug Control","correspondingAuthor":false,"prefix":"","firstName":"meng","middleName":"","lastName":"Li","suffix":""},{"id":619963614,"identity":"c53f79af-5f7c-4da7-a9c2-7a3e947ee38c","order_by":8,"name":"Jialiang Du","email":"","orcid":"","institution":"NIFDC: National Institutes for Food and Drug Control","correspondingAuthor":false,"prefix":"","firstName":"Jialiang","middleName":"","lastName":"Du","suffix":""},{"id":619963615,"identity":"dfba2420-d7e7-421e-b03f-15b4d7e920ab","order_by":9,"name":"chuanfei Yu","email":"","orcid":"","institution":"NIFDC: National Institutes for Food and Drug Control","correspondingAuthor":false,"prefix":"","firstName":"chuanfei","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2026-03-24 13:32:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9212596/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9212596/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107087888,"identity":"21ff59cb-5121-4704-982b-7bb293f699db","added_by":"auto","created_at":"2026-04-16 15:23:32","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61161,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the SG3249 (tesirine) structure\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9212596/v1/0b1c85be3288676e31bd6580.jpeg"},{"id":107087877,"identity":"41a13d27-e963-4b58-b1bd-3a64c2def60f","added_by":"auto","created_at":"2026-04-16 15:23:30","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":155795,"visible":true,"origin":"","legend":"\u003cp\u003eHRMS IDA analysis results for NAC‑SG3249. A: TIC, B: TOFMS, C: TOFMSMS\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9212596/v1/80c0b63a45661e411b82289e.jpeg"},{"id":107087878,"identity":"a8f2e9a4-5f90-45e4-9a65-2907838d5544","added_by":"auto","created_at":"2026-04-16 15:23:30","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82948,"visible":true,"origin":"","legend":"\u003cp\u003eLinearity investigation of NAC‑SG3249 using different instruments and methods. A: TOFMS, B: TOFMS/MS, C: TQMS\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9212596/v1/f7cef3a454613817194a0407.jpeg"},{"id":107087873,"identity":"ad0bf013-dfc9-4c34-a270-56e475bb12fe","added_by":"auto","created_at":"2026-04-16 15:23:29","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53626,"visible":true,"origin":"","legend":"\u003cp\u003eStructural formula of NAC‑SG3249 and positions prone to cleavage or hydrolysis (marked in red).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9212596/v1/85ee0946d0ff842cccdbaa60.jpeg"},{"id":107087982,"identity":"dea4c485-7b09-4562-91fc-312bde5daa60","added_by":"auto","created_at":"2026-04-16 15:24:00","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":81702,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted hydrolysis products of NAC‑SG3249. The precursor ion m/z values for A–F are: 333.0, 756.3, 855.4, 926.4, 1031.5, and 1015.5, respectively.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9212596/v1/2377debe47802ab61bb75880.jpeg"},{"id":107088143,"identity":"37dd2870-0469-47fb-aacf-08d9c5dcd6e7","added_by":"auto","created_at":"2026-04-16 15:24:45","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":203639,"visible":true,"origin":"","legend":"\u003cp\u003eMS results of hydrolysis products analyzed in MRM mode using TQMS. A: TIC, B: XICs of different channels, C: XIC of m/z 756.3.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9212596/v1/a832802383cd484ee87c34ca.jpeg"},{"id":107088029,"identity":"bdc5854c-cab3-4136-af41-dd3a0eb95e83","added_by":"auto","created_at":"2026-04-16 15:24:13","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":142189,"visible":true,"origin":"","legend":"\u003cp\u003eSecondary analysis of hydrolysis products using MRM and IDA‑EPI modes. A: TIC, B: XIC of 756.3, C: Product ions of 756.3, D: Cleavage positions of the hydrolysis product (marked in red).\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9212596/v1/9787852e5628cbbdbb1f0287.jpeg"},{"id":107087883,"identity":"55c1aa30-ab2e-4b36-ac22-6ca24bda98a2","added_by":"auto","created_at":"2026-04-16 15:23:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":50557,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the degradation product numbers under four forced degradation conditions\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9212596/v1/fcba96238b073cd77b039e85.png"},{"id":107088477,"identity":"99362b09-a54f-48db-84d6-c282e3a8881b","added_by":"auto","created_at":"2026-04-16 15:26:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1743741,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9212596/v1/9887bcd9-322f-4860-98dd-735e6d3dc2dc.pdf"}],"financialInterests":"","formattedTitle":"Mass Spectrometry for the Analysis of Payloads and Related Impurities in Antibody–Drug Conjugates","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eRapid progress in the biopharmaceutical market has led to the emergence of antibody\u0026ndash;drug conjugate (ADC) drugs as a new approach to cancer treatment. ADCs link cytotoxic small-molecule drugs to monoclonal antibodies via chemical bonds. The high affinity of antibodies for specific tumor cell surface antigens enables the precision delivery of cytotoxic drugs to cancer cells, enhancing efficacy and reducing damage to normal tissues. Several generations of development have occurred since the concept of ADCs was first proposed. The first-generation ADC is represented by Pfizer\u0026rsquo;s Mylotarg (gemtuzumab ozogamicin). Mylotarg, the first ADC worldwide, was approved in 2000, representing the first clinical use of this drug type. Mylotarg is formed by conjugating an anti-CD33 monoclonal antibody to the antitumor antibiotic calicheamicin via a cleavable hydrazone linker and is used to treat CD33-positive acute myeloid leukemia (AML) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, in clinical practice, linker stability was found to be poor in the blood circulation; furthermore, calicheamicin was prone to early release, leading to severe off-target toxicity, a narrow therapeutic window, and limited patient benefit. Thus, Mylotarg was withdrawn from the market in 2010.\u003c/p\u003e \u003cp\u003eThe issues encountered with this first-generation ADC provided direction for subsequent research and development. Second-generation ADCs are optimized with respect to linkers, antibodies, and the cytotoxic payload. The linker is designed to be more stable, for example, using non-cleavable linkers or modifications to the structure of cleavable linkers that improve their stability in the blood circulation, reducing the risk of early release. Regarding antibodies, humanized or fully human antibodies are often used to reduce immunogenicity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For example, Adcetris (brentuximab vedotin) and Kadcyla (ado-trastuzumab emtansine), which are representative second-generation ADCs for treating solid tumors, have cleavable linkers that couple different toxin molecules [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eContinuous technical progress has enabled the development of more innovative, third-generation ADCs that also retain the advantages of the previous two generations. For example, regarding the coupling technique, the site-specific conjugation method results in a more even drug-to-antibody ratio (DAR), enhancing product quality and stability and improving the ADC\u0026rsquo;s pharmacokinetic properties. For example, Daiichi Sankyo\u0026rsquo;s Enhertu (trastuzumab deruxtecan) is formed by coupling trastuzumab and a topoisomerase I inhibitor (exatecan derivative) via a cleavable linker. This ADC has demonstrated excellent efficacy in treating several cancers, including HER2-positive breast cancer and gastric cancer, expanding the applicable target population of this drug type [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. To date, a total of 19 antibody\u0026ndash;drug conjugates (ADCs) have been approved globally, covering a broad range of indications for hematological malignancies and solid tumors. In addition, approximately 40 ADC candidates worldwide are currently undergoing phase III clinical trials. Given their therapeutic potential in oncology, ADCs are expected to experience rapid and transformative growth in the near future. [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn April 23, 2021, loncastuximab tesirine was approved by the United States Food and Drug Administration (FDA) for the treatment of relapsed or refractory diffuse large B-cell lymphoma (DLBCL) after two or more systemic therapies. On December 10, 2024, loncastuximab tesirine was approved by the National Medical Products Administration (NMPA) of China, representing the first CD19 ADC drug approved and marketed in China. Loncastuximab tesirine comprises a humanized IgG1 kappa monoclonal antibody attached to SG3249 (tesirine) by cysteine, and its DAR value is approximately 2.3 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. SG3249 comprises the pyrrolo\u0026ndash;benzodiazepine (PBD) dimer SG3199, para-aminobenzoic acid (PABA), a dipeptide (Val-Ala), a PEG8 spacer to increase solubility, and a maleimide linker. Its structure is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This study focuses on this product.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eADC products require multidimensional quality analysis during research and manufacturing. In particular, comprehensive quality characterization of the naked antibody is essential, including extensive structural and physicochemical analyses such as amino acid sequence determination, glycosylation profiling, and higher-order structure characterization, to ensure the correctness and stability of the antibody structure. Meanwhile, free payload and payload-related impurities are also critical quality attributes (CQAs), as they directly determine the safety and efficacy of the drug product. Quality control of small-molecule toxins and linkers involves structural confirmation and limit testing, among other requirements [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], all of which rely on appropriate analytical methodologies.\u003c/p\u003e \u003cp\u003eIn studies of drug metabolism and pharmacokinetics (DMPK), the quantification of free payload requires substantially higher analytical sensitivity due to its low in vivo concentrations and pronounced matrix effects. To date, the most widely reported approach for quantifying free payloads in blood samples is triple quadrupole mass spectrometry, operated in the multiple reaction monitoring (MRM) mode. From the perspective of in vivo drug metabolism studies, this method has been extensively applied to characterize the time-dependent release profiles of ADC payloads in circulation [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In certain investigations of ADC in vivo metabolism, the lower limit of quantification for unconjugated cytotoxic drugs in blood has been reported to reach 0.015 ng/mL using triple quadrupole mass spectrometry [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Owing to the excellent selectivity of the MRM quantification mode, triple quadrupole mass spectrometry is also applicable to quality control of ADC products, where it can be employed to monitor free payload levels and thereby assess drug safety and efficacy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As the sensitivity requirements for quantifying free payloads in in vitro ADC products are generally lower than those for in vivo bioanalysis, high-resolution mass spectrometry (HRMS) is also suitable for meeting analytical needs in ADC product testing. With its superior mass resolution, HRMS enables quantification based on both full-scan (TOFMS) data and fragment ions in MS/MS-based quantitative workflows, such as high-resolution multiple reaction monitoring (MRM\u003csup\u003ehr\u003c/sup\u003e). Reported studies have demonstrated that the sensitivity of HRMS-based methods for quantifying free payloads in ADCs can reach the ppb level or even lower [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearchers have also used specialized pretreatment methods to quantify the free ADC payload using an optimized liquid chromatography (LC) method; for example, samples labeled with fluorescent dye during pretreatment, purified using solid-phase extraction (SPE), and then quantified by LC [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, the pretreatment required for this method is complicated, recovery is unstable, and sensitivity is limited, usually in the range of nanograms per milliliter to micrograms per milliliter. TQMS is the most reported method for quantifying the free ADC payload in drug metabolism and pharmacokinetics studies. \u003cem\u003eIn vivo\u003c/em\u003e drug metabolism research has been employed to demonstrate the difference in the released ADC payload content over time [23\u0026ndash;27]. This technique, using the targeted quantification mode, is also used to quantitatively analyze the free payload in ADC quality control. Similarly, HRMS has been used to quantify the free payload in ADC drugs; the free payload is measured while the ADC product is characterized [28\u0026ndash;30]. For example, Leanne combined HRMS and TQMS to quantitatively analyze the free payload in ADC products. However, deeper-level structural characterizations and drug stability analyses remain lacking. Forced degradation experiments are critical core components in the study of ADC drug stability. In the context of drug stability, artificial applications of extreme conditions, such as high temperatures, acidic or basic environments, and oxidative conditions, can systematically reveal potential degradation pathways and key degradation products, providing the critical evidence needed for drug formulation optimization, packaging material selection, and storage condition determination.\u003c/p\u003e \u003cp\u003eIn this study, we analyzed the newly approved ADC loncastuximab tesirine using multiple HRMS quantification methods. In addition to the secondary quantification of free payload using MRM\u003csup\u003ehr\u003c/sup\u003e, the super-high-resolution capability of HRMS was exploited to achieve primary free payload quantification. Experimental validation revealed a good quantification range. TQMS was applied in the same context. In the quantitative free-payload analysis of the ADC product, the HRMS primary and secondary quantification results were consistent with those obtained via TQMS, with coefficients of variation below 10%. In the study of payload stability, specific ions were assessed via TQMS in product-ion scan mode to identify hydrolysis products predicted from hydrolysis-prone sites in the chemical structure, providing reliable data to support the quantification of the payload and its potential hydrolysis products. In the complete stability study, the ADC was subjected to various forced degradation conditions, including strong acidic, strong basic, heated, and light-illuminated environments. For the first time, we employed HRMS coupled with nanoflow liquid chromatography to analyze the degradation products of the ADC drug payload. The degradation pathways of the ADC products under various extreme conditions were elucidated according to the number of degradation products and their common products, providing data to support drug stability studies.\u003c/p\u003e"},{"header":"2 Materials and samples","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Materials\u003c/h2\u003e \u003cp\u003eLiquid chromatography\u0026ndash;mass spectrometry (LC\u0026ndash;MS) grade acetonitrile and water were purchased from Thermo Fisher; 10 kDa ultrafiltration membrane filters were purchased from Sartorius.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Samples\u003c/h2\u003e \u003cp\u003eThe ADC product loncastuximab tesirine and N-acetylcysteine-modified tesirine (NAC-SG3249) were sourced as retained from the National Institutes for Food and Drug Control (China). In the ADC manufacturing process, N-acetylcysteine (NAC) was used as the antioxidant, which binds to free tesirine; thus, the free payload exists as NAC-SG3249.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Preparation of the NAC-SG3249 standard\u003c/h2\u003e \u003cp\u003eThe NAC-SG3249 standard was sequentially diluted with LC\u0026ndash;MS-grade water, and standard curve gradient concentrations were prepared as follows: 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10, 50, and 100 ng/mL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 ADC sample preparation\u003c/h2\u003e \u003cp\u003eThe sample was ultrafiltered using a 10 kDa ultrafiltration membrane. Subsequently, the filtrate was evaporated to dryness and reconstituted in a 1/10 volume of LC\u0026ndash;MS water to prepare the test sample. The free NAC-SG3249 content in the test sample was measured.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Stress test sample preparation\u003c/h2\u003e \u003cp\u003eFour forced degradation modes were designed to process the ADC sample: acid treatment (pH adjusted to 2\u0026ndash;3 using formic acid), base treatment (pH adjusted to 11\u0026ndash;12 using aqueous ammonia), strong light illumination, and heating at 60℃. The four conditions were maintained in an enclosed environment for one day. Subsequently, the samples were ultrafiltered using a 10 kDa membrane. The filtrate was then evaporated to dryness and reconstituted in a 1/10 volume of LC\u0026ndash;MS-grade water to prepare the test samples. The potential degradation products in the test samples were measured.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Methods","content":"\u003cp\u003eLiquid chromatography\u0026ndash;high-resolution mass spectrometry (LC\u0026ndash;HRMS) was used to quantitatively analyze NAC-SG3249, and the results were compared with those from the LC-TQMS method. The potential hydrolysis products were identified and structurally characterized by TQMS. The degradation impurities in the payload under forced degradation conditions were analyzed by HRMS coupled with nanoflow liquid chromatography.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 LC method\u003c/h2\u003e \u003cp\u003eA SCIEX ExionLC\u0026trade; AE system was employed using a Waters ACQUITY UPLC Peptide BEH C18 chromatography column (130 \u0026Aring;, 1.7 \u0026micro;m, 2.1 \u0026times; 100 mm) under the following conditions: mobile phase A, water, 0.1% formic acid; mobile phase B, acetonitrile, 0.1% formic acid; column temperature, 40\u0026deg;C; and injection volume, 20 \u0026micro;L. The gradient elution program is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLiquid chromatography gradient elution program\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlow (mL/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB. Conc (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA Waters ACQUITY MCLASS LC system was employed using a Waters ACQUITY UPLC Peptide BEH C18 chromatography column (1.7 \u0026micro;m, 75 \u0026micro;m \u0026times; 25 cm) under the following conditions: mobile phase A, water, 0.1% formic acid; mobile phase B, acetonitrile, 0.1% formic acid; column temperature, 60\u0026deg;C; and injection volume, 20 \u0026micro;L. The gradient elution program is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNanoflow liquid chromatography gradient elution program\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlow (\u0026micro;L/min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB. Conc (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 QTOF method\u003c/h2\u003e \u003cp\u003eA SCIEX ZenoTOF 7600 system was employed for MRM\u003csup\u003ehr\u003c/sup\u003e data collection in positive-ion mode. The conditions were as follows: nebulizer gas (GS1), 70 psi; auxiliary gas (GS2), 20 psi; curtain gas (CUR), 35 psi; ion source temperature (TEM), 500\u0026deg;C; ionization source voltage (IS), 5500 V; and collision gas (CAD), 7 psi. MRM\u003csup\u003ehr\u003c/sup\u003e ion pair data are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMRM\u003csup\u003ehr\u003c/sup\u003e ion pairs and MS parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecursor ion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eproduct ion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAccumulation time (ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDeclustering potential\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCollision energy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAC-SG3249-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e830.3729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e358.1073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAC-SG3249-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e830.3729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e585.2713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAC-SG3249-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e830.3729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e908.4179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe SCIEX ZenoTOF 7600 system was also employed for information-dependent acquisition (IDA) of data in positive-ion mode. The conditions were as follows: GS1, 8 psi; CUR, 25 psi; TEM, 175\u0026deg;C; IS, 4000 V; and CAD, 7 psi. For the TOF MS scan, the range was 350\u0026ndash;1500, the accumulation time was 0.25 s, and the maximum number of candidate ions was 20. Exclude Former candidate ions were excluded for 3 s. For the TOF MSMS scan, the range was 100\u0026ndash;1500, and the accumulation time was 0.05 s. Zeno pulsing was enabled, and the Zeno threshold was set to 100,000. The dynamic collision energy (CE) spread was 10, and the time bins were set to sum 8.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Triple quad method\u003c/h2\u003e \u003cp\u003eThe Triple Quad\u0026trade; 6500\u0026thinsp;+\u0026thinsp;system, a SCIEX localized product in China, was used; the data collection mode was MRM, and the positive ion mode was employed. The conditions were as follows: GS1, 70 psi; GS2, 20 psi; CUR, 35 psi; TEM, 500\u0026deg;C; IS, 5500 V; and CAD, 7 psi. The MRM ion pair data are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMRM ion pairs and MS parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecursor ion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProduct ion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDwell time (ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDeclustering potential\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEntrance potential\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCollision energy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCollision cell exit potential\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAC-SG3249-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e830.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e358.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAC-SG3249-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e830.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e585.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAC-SG3249-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e830.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e908.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results and discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Quantitative analysis of payload (NAC-SG3249) in the ADC product\u003c/h2\u003e \u003cp\u003eThe molecular formula of payload (NAC-SG3249) is C\u003csub\u003e80\u003c/sub\u003eH\u003csub\u003e110\u003c/sub\u003eO\u003csub\u003e26\u003c/sub\u003eN\u003csub\u003e10\u003c/sub\u003eS. The information-dependent acquisition (IDA) mode in HRMS was used to identify the quantitative ion pairs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The mass-to-charge ratio (m/z) of the doubly charged ion detected by the TOFMS was 830.3741, and this precursor ion displayed the highest response, showing only 1.44 ppm in the mass error of the theoretical m/z. Thus, it could be used for primary HRMS quantification. In the analysis of the fragments, the characteristic fragment ions of 358.1072 (m/z), 585.2708 (m/z), and 908.4172 (m/z), whose corresponding structural information could be found in the structural elucidation, were detected. The corresponding ion pairs could act as the theoretical ion pairs set in the MRM\u003csup\u003ehr\u003c/sup\u003e method of HRMS and the MRM method of TQMS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTOFMS and TOFMS/MS analyses were performed to quantitatively detect the NAC-SG3249 in the ADC sample according to the identified ion pairs. NAC-SG3249 gradient standard samples were used to investigate linearity to verify the method\u0026rsquo;s reliability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the TOFMS and TOFMS/MS analyses, the concentration gradient for NAC-SG3249 was set to 0.05\u0026ndash;100 ng/mL, and the linear correlation coefficients (R\u003csup\u003e2\u003c/sup\u003e) were 0.991 and 0996, respectively. We performed the TQMS quantitative analysis in parallel to verify the accuracy of the HRMS results. The linear correlation coefficient (R\u003csup\u003e2\u003c/sup\u003e) reached 0.999. The limit of quantitation values for HRMS and TQMS in the NAC-SG3249 analysis were 0.05 ng/mL and 0.005 ng/mL, respectively. Furthermore, the accuracy values at all linear concentration points stabilized within 80%\u0026ndash;120%, indicating that both methods are suitable for quantitative analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition to assessing the linear relationship, detection limit, and accuracy, we conducted a systematic analysis of precision and recovery across different instruments and analytical methods. Precision was investigated using low, medium, and high concentration gradients; three injections were conducted per concentration point (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The precision values of all concentration points were below 5%, indicating good repeatability. Furthermore, recovery was stable across all concentrations, at 80%\u0026ndash;120%, further validating the method's reliability and accuracy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrecision analysis results obtained with different techniques and different instruments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConcentration point\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePrecision investigation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOFMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTOFMSMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTriple Quad MS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.5 ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.07%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50 ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe employed three methods to analyze payload (NAC-SG3249) in ADC samples: TOFMS quantification, TOFMS/MS MRM\u003csup\u003ehr\u003c/sup\u003e, and TQMS MRM; measured concentrations of NAC-SG3249 residue in the ADC samples were 812.5 ng/mL, 846.6 ng/mL, and 781.4 ng/mL, respectively. The three quantification methods showed good consistency, with an RSD below 10%. These findings indicate that HRMS can reach the same level of detection as TQMS for the free payload in ADC samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Assessment of NAC-SG3249 stability\u003c/h2\u003e \u003cp\u003eIn the \u003cem\u003ein vitro\u003c/em\u003e synthesis of NAC-SG3249, NAC attacks the double-bonded carbon atom on the maleimide ring of SG3249 via a nucleophilic reaction mechanism, thus combining with SG3249 to form NAC-SG3249. We assessed NAC-SG3249 stability via molecular structure analyses, revealing relatively low amide and ester bond energies, indicating bonds prone to hydrolysis or cleavage (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, the specific cleavage sites of the three characteristic ions used in the quantification analysis could be determined by analyzing the NAC-SG3249 hydrolysis products.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, we investigated the potential hydrolysis products of NAC-SG3249 during the stability analysis. Six possible hydrolysis products were predicted according to the sites prone to hydrolysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). MRM mode was employed in the TQMS measurements. The predicted hydrolysis product was selected as the precursor ion, and the fragment ion 358.1 m/z was selected as the product ion. The molecular weight of predicted hydrolysis product A was too small for analysis; thus, this product was not investigated. The precursor ion signal of 756.3 m/z was detected at 3.8 min (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), indicating the hydrolysis product of NAC-SG3249 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The retention behavior of this hydrolysis product on the C18 chromatography column revealed that its hydrophilicity was even stronger than that of NAC-SG3249, consistent with the theoretical structure characteristics. Following MRM mode application, an information-dependent acquisition (IDA)-enhanced product ion (EPI) was set to conduct fragmentation treatment on this hydrolysis product, and the characteristic ions of 696.55 m/z, 710.76 m/z, and 738.48 m/z were detected in the product ion signal (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Additionally, the cleavage positions corresponding to these fragments could be found in the structure of this hydrolysis product, and they were related to three different cleavage positions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Thus, it can be determined that this substance is an effectively detectable hydrolysis product of NAC-SG3249.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Stability Study of Small Molecules with Forced Degradation in ADC Samples\u003c/h2\u003e \u003cp\u003eTo assess the stability of the ADC product, we designed four forced degradation experiments with different conditions: acid treatment, base treatment, light illumination, and heating treatment. The metabolites of the payload in the ADC drug were analyzed under each condition. HRMS coupled with nanoflow liquid chromatography in IDA scan mode was employed to measure metabolites in the different forced degradation samples. Under acid, base, light illumination, and heating conditions, 23, 39, 26, and 28 types of small molecular related impurities were detected. The greatest number of small molecular related impurities was observed with base treatment. Twelve common small molecular related impurities were identified across the four forced degradation conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The common small molecular related impurities are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIdentification of the common degradation products using four forced degradation method\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeutral Mass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage Mass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003em/z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eppm\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss of C44H47N5O10 and C12H14N2O6S+Demethylation to Carboxylic Acid [M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC24H47N3O12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e569.3148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e569.4921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e570.3221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss of C5H7NO3 and C49H56N6O11\u0026thinsp;+\u0026thinsp;Thioalcohol to Alcohol [M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC26H47N3O13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e609.3091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e609.5364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e610.3164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss of C12H15N3O6S and C49H56N6O11\u0026thinsp;+\u0026thinsp;Amine to Carboxylic Acid [M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC20H38O11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e454.2398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e454.4053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e455.2471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss of C52H61N7O12 and C12H14N2O6S+Oxidation [M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC16H35NO9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e385.2298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e385.3778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e386.2371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss of C12H15N3O6S and C49H56N6O11\u0026thinsp;+\u0026thinsp;Oxidation [M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC19H39NO10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e441.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e441.4348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e442.2633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss of C12H15N3O6S and C52H61N7O12\u0026thinsp;+\u0026thinsp;Oxidation [M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC16H34O9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e370.2195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e370.3921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e371.2267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss of C52H61N7O12 and C12H14N2O6S [M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC16H35NO8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e369.2348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e369.6364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e370.2421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss of C49H56N6O11 and C14H19N3O7S+Amine to Carboxylic Acid [M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC18H34O10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e410.2135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e410.3629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e411.2208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss of C12H15N3O6S and C56H69N7O14\u0026thinsp;+\u0026thinsp;Glucose Conjugation [M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC18H36O11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e428.2247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e428.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e429.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss of C49H56N6O11 and C16H23N3O8S+Amine to Carboxylic Acid [M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC16H30O9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e366.1877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e366.3189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e367.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss of C49H57N7O11 and C16H23N3O7S+Oxidation [M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC15H30O9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e354.1885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e354.3265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e355.1958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss of C34H36N4O8 and C26H43N3O12S+Glucose Conjugation [M\u0026thinsp;+\u0026thinsp;H]+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC26H41N3O11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e571.2721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e571.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e572.2793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThis study employed high-resolution mass spectrometry (HRMS) and triple quadrupole mass spectrometry (TQMS) as the core analytical technologies to comprehensively investigate the payload and payload-related small-molecule impurities in the commercially available antibody\u0026ndash;drug conjugate (ADC) product loncastuximab tesirine. The study focused on the quantitative determination of the free payload (NAC-SG3249), the characterization of potential small-molecule impurities associated with the payload, and the investigation of payload-related impurities generated under forced degradation conditions. The objective of this work was to establish a clear and reliable analytical strategy for payload analysis in ADC products.\u003c/p\u003e \u003cp\u003eFor the quantitative analysis of the free payload, characteristic precursor\u0026ndash;product ion transitions of NAC-SG3249 were selected. Both TOFMS and TOFMS/MS quantification approaches were evaluated by taking advantage of the superior mass resolution of high-resolution mass spectrometry, and the analytical methodology was systematically assessed. The results demonstrated excellent linearity across different concentration levels, with coefficients of determination (R\u0026sup2;) greater than 0.99. The lower limit of quantification (LLOQ) was determined to be 0.05 ng/mL. Method accuracy and precision were evaluated at multiple concentration levels; accuracy ranged from 80% to 120%, and precision showed relative standard deviations (RSDs) of less than 5%. Recovery values also fell within the acceptable range of 80%\u0026ndash;120%. The quantitative results obtained by HRMS were consistent with those generated using triple quadrupole mass spectrometry. For the determination of free payload (NAC-SG3249) in ADC samples, the deviation among the three analytical approaches was less than 10%.\u003c/p\u003e \u003cp\u003eIn the study of potential impurities associated with the payload (NAC-SG3249), labile bonds within the molecular structure that are susceptible to hydrolysis were systematically analyzed. Using the MRM-IDA-EPI acquisition mode of triple quadrupole mass spectrometry, hydrolysis products of NAC-SG3249 were accurately detected and structurally characterized. These findings provide a theoretical basis for the subsequent quantitative analysis of payload-related impurities.\u003c/p\u003e \u003cp\u003eFor the stability assessment of the ADC samples, forced degradation studies were conducted under acidic, alkaline, photolytic, and thermal conditions. Leveraging the high sensitivity and mass accuracy of high-resolution mass spectrometry, trace-level degradation products were detected and confidently identified through matching experimental data with theoretical mass calculations using dedicated data analysis software. Among the four forced degradation conditions, alkaline treatment had the most pronounced impact on sample stability, generating a total of 39 degradation products. Twelve degradation products were found to be common across all four stress conditions. These results enable the elucidation of degradation pathways of the payload and provide a solid foundation for subsequent stability evaluation and impurity control strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntibody\u0026ndash;drug conjugate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eN-acetylcysteine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCQAs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConstitute critical quality attributes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMass spectrometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLiquid chromatography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHRMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-resolution mass spectrometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTQMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriple quadrupole mass spectrometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTOFMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTime of flight mass spectrometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTOFMS/MS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTandem time-of-flight mass spectrometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRM\u003csup\u003ehr\u003c/sup\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-resolution multiple reaction monitoring\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultiple reaction monitoring\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRelative standard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCD33\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCluster of differentiation 33\u0026zwnj;\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDrug-to-antibody ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInformation-dependent acquisition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEnhanced product ion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited states food and drug administration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNMPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational medical products administration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eClinical trial number:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was supported by Beijing Municipal Science and Technology Commission (Grant number Z251100004625003), National Key Research and Development Program of China (Grant number 2023YFC3404004), Project of State Key Laboratory of Drug Regulatory science (Grant number: 2025SKLDRS0336), and Standardization Improvement Project of Chinese Pharmacopeia (Grant number: BZ2025137).\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eConsent for Publication Manuscript is approved by all authors for publication.\u003c/p\u003e\n\u003cp\u003eCompeting Interests The authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eAuthor\u0026rsquo;s Contribution\u003c/h2\u003e\n\u003cp\u003eGW and GX conceived and designed the research; YZ, TG and XL collected and analyze the data; YN, HC and ML wrote the manuscript; JD and CY revised the manuscript and gave meaningful discussion and suggestions. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eData are available from the corresponding authors on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGang Wu\u003csup\u003e1*\u003c/sup\u003e, Gangling Xu\u003csup\u003e1,2*\u003c/sup\u003e, Yue Zhao\u003csup\u003e3*\u003c/sup\u003e, Tie Gao\u003csup\u003e4\u003c/sup\u003e, Yongbo Ni\u003csup\u003e1\u003c/sup\u003e, Xiaolei Lv\u003csup\u003e4\u003c/sup\u003e, Hongxu Chen\u003csup\u003e4\u003c/sup\u003e, Meng Li\u003csup\u003e1\u003c/sup\u003e, Jialiang Du\u003csup\u003e1\u003c/sup\u003e, Chuanfei Yu\u003csup\u003e1#\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e*\u0026nbsp;These authors contributed equally to the work.\u003c/p\u003e\n\u003cp\u003e#Corresponding author. Email addresses: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e National Institutes for Food and Drug Control, State Key Laboratory of Drug Regulatory Science, NHC Key Laboratory of Research on Quality and Standardization of Biotech Products, NMPA Key Laboratory for Quality Research and Evaluation of Biological Products, No. 31 Huatuo Road, Daxing District, Beijing 102629, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eSchool of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, No. 103 Wenhua Road, Shenyang 110016, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u0026nbsp;\u003c/sup\u003eBeijing Institute for Drug Control (Beijing Center for Vaccine Control), NMPA Center for Innovation and Research in Regulatory Science., No. 25 Keyuan Road, Changping District, Beijing102206, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u0026nbsp;\u003c/sup\u003eSCIEX China, No.18 Science Park Road, Changping District, Beijing 102206, P. R. China, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eAkram, F., Ali, A. M., Akhtar, M. T. (2025). \u003cem\u003eBioorganic \u0026amp; Medicinal Chemistry\u003c/em\u003e 117, 118010.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTumey, L. N. (2020). \u003cem\u003eMethods Mol. Biol.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLambert, J. M., \u0026amp; Morris, C. Q. (2017). \u003cem\u003eAdv Ther\u003c/em\u003e 34, 1015\u0026ndash;1035.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMahmood, I. (2021). \u003cem\u003eAntibodies\u003c/em\u003e 10, 40.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSau, S., Alsaab, H. O., Kashaw, S. K. (2017). \u003cem\u003eDrug Discov Today\u003c/em\u003e 22, 1547\u0026ndash;1556.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLi, Y., Wang, Y., Shenoy, V. M. 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(2016). \u003cem\u003emAbs\u003c/em\u003e 8, 306\u0026ndash;317.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDi Ianni, A., Cowan, K. J., Riccardi Sirtori, F., \u0026amp; Barbero, L. (2025). \u003cem\u003eInternational Journal Of Molecular Sciences\u003c/em\u003e 26, 3080.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGrafmueller, L., Wei, C., Ramanathan, R. (2016). \u003cem\u003eBioanalysis\u003c/em\u003e 8, 1663\u0026ndash;1678.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"applied-biochemistry-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"abab","sideBox":"Learn more about [Applied Biochemistry and Biotechnology](https://www.springer.com/journal/12010)","snPcode":"12010","submissionUrl":"https://submission.nature.com/new-submission/12010/3","title":"Applied Biochemistry and Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"antibody–drug conjugate, small molecular related impurities, high-resolution mass spectrometry, Quantitation, Stability study","lastPublishedDoi":"10.21203/rs.3.rs-9212596/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9212596/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn antibody\u0026ndash;drug conjugate research, the small molecule impurities requiring evaluation include not only free payloads, but also potential payload-related small molecule impurities. In this study, high-resolution mass spectrometry and triple quadrupole mass spectrometry were employed for systematically and comprehensively investigate small molecule related impurities in antibody\u0026ndash;drug conjugate product. First, the quantitation of free payload was analyzed. High-resolution mass spectrometry enabled quantification at both precursor ion and product ion levels, the free payload exhibited good linearity over the concentration range of 0.05\u0026ndash;100 ng/mL (R\u0026sup2; \u0026ge; 0.991), with a lower limit of quantification of 0.05 ng/mL. The accuracy ranged from 80% to 120%, and the precision was below 5%. The high-resolution mass spectrometry results were confirmed using triple quadrupole mass spectrometry. Second, other potential payload related small molecule impurities in the antibody\u0026ndash;drug conjugate was investigated in depth. Multiple possible hydrolysis pathways of the payload were predicted based on molecular structure analysis, and the corresponding payload related impurities were subsequently identified using triple quadrupole mass spectrometry in accordance with the predicted precursor\u0026ndash;product ion information. Third, stability studies of the antibody\u0026ndash;drug conjugate were conducted under forced degradation conditions. Qualitative analyses were performed to characterize unknown payload related degradation products generated under stress conditions, including light exposure, heat, acidic, and alkaline environments. The major degradation products and their relative abundances were identified, with alkaline conditions exerting the greatest impact on sample stability. Clinical trial number: not applicable.\u003c/p\u003e","manuscriptTitle":"Mass Spectrometry for the Analysis of Payloads and Related Impurities in Antibody–Drug Conjugates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 15:23:04","doi":"10.21203/rs.3.rs-9212596/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-09T06:50:01+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T01:56:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Applied Biochemistry and Biotechnology","date":"2026-04-05T03:41:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-03T15:06:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Applied Biochemistry and Biotechnology","date":"2026-04-01T21:01:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"applied-biochemistry-and-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"abab","sideBox":"Learn more about [Applied Biochemistry and Biotechnology](https://www.springer.com/journal/12010)","snPcode":"12010","submissionUrl":"https://submission.nature.com/new-submission/12010/3","title":"Applied Biochemistry and Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"abe546ae-f2e4-49ae-8664-38c8aa916dd3","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-16T15:23:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 15:23:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9212596","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9212596","identity":"rs-9212596","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}