Age-Specific Differences in Omalizumab-Related Adverse Drug Reaction Signals between Children and Adults: An Analysis Based on the FAERS Database

Age-Specific Differences in Omalizumab-Related Adverse Drug Reaction Signals between Children and Adults: An Analysis Based on the FAERS Database | 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 Age-Specific Differences in Omalizumab-Related Adverse Drug Reaction Signals between Children and Adults: An Analysis Based on the FAERS Database Hairui Zheng, Lu Liang, Ning Li, Yi Su This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9305106/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective： To analyze age-specific differences in post-marketing adverse drug reaction (ADR) signals of omalizumab between children (<18 years) and adults (≥18 years) using FAERS data, to inform optimized medication safety monitoring strategies. Methods： Omalizumab-related ADR reports (2003Q1–2025Q3) were extracted from FAERS and stratified by age. ADRs were coded with MedDRA. Signals were mined using four disproportionality methods (ROR, PRR, BCPNN, MGPS), with validity requiring all thresholds met. Cumulative incidence and time-to-onset (TTO) were analyzed. Results： Among 62,925 reports (4% children, 45% adults), both groups showed strong signals for respiratory/immune disorders. Children had an additional musculoskeletal signal; adults had more general disorders. At PT level, adults had broader coverage (allergic/infectious signals), while children had narrower coverage with a unique strong signal for asthmatic crisis (ROR=52.83). Children had shorter median TTO (34.4 vs 63.1 days) and faster cumulative incidence; adults had longer late-onset risk. Conclusion： Omalizumab shows significant age-specific ADR differences. Individualized monitoring strategies are needed to enhance clinical safety. Omalizumab Adverse Drug Reactions FAERS Real-world study Children Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Omalizumab is a humanized monoclonal antibody that effectively alleviates allergic reactions(Chow et al., 2022 ; Humbert et al., 2019 ; Pelaia et al., 2018 ) by specifically binding to free immunoglobulin E (IgE), blocking its interaction with surface receptors on mast cells and eosinophils, and inhibiting the release of inflammatory mediators. As the first biological agent endorsed by the Global Initiative for Asthma (GINA) for the treatment of IgE-mediated severe allergic asthma(Agache et al., 2020 ), it was approved by the U.S. FDA in 2003 for the treatment of moderate-to-severe allergic asthma in children aged 6 years and older and adults(Gon et al., 2022 ). Subsequently, its indications have gradually expanded to include chronic urticaria in patients aged 12 years and older(Maurer et al., 2018 ) and adult nasal polyps(Kariyawasam & James, 2020 ), with further approval for the treatment of IgE-mediated food allergy in 2024(Zhang et al., 2025 ). It has also demonstrated favorable efficacy in various other allergic conditions, such as allergic rhinitis(Ma et al., 2021 ) and atopic dermatitis(Chan et al., 2020 ). With the continuous expansion of its clinical applications(Pongdee & Li, 2025 ), particularly its extended use in children, the medication safety of omalizumab has garnered increasing attention. From the perspective of developmental pharmacology, there are significant differences in the drug exposure-response relationship between children and adults, leading to population-specific characteristics in the type and incidence of ADRs(Elzagallaai et al., 2017 ). Current studies on omalizumab-related ADRs are mostly based on clinical trials and meta-analyses. Although common ADRs such as headache, injection site reactions, and arthralgia have been identified(Deschildre et al., 2015 ; Hanania et al., 2011 ; Maurer et al., 2018 ), these studies are limited by small sample sizes and strict inclusion criteria, making it difficult to fully reflect the real-world medication safety profile. More importantly, there is a lack of systematic analysis on the differences between children and adults. Traditional ADR signal detection algorithms are mostly based on the entire population, which tend to overlook the unique medication risks in children. Given that children’s physiological functions are not yet fully developed, their tolerance to drugs is inherently different from that of adults(Elzagallaai et al., 2017 ). Clarifying the differences in ADR signals between the two populations is therefore crucial for clinical medication safety. The FDA Adverse Event Reporting System serves as a core tool for post-marketing drug safety monitoring(Feng et al., 2022 ), continuously collecting real-time ADR data submitted by healthcare professionals, consumers, and other stakeholders. It offers advantages such as a large sample size, frequent updates, and high data accessibility, providing reliable data support for real-world drug safety research(Shu et al., 2023 ). In view of this, the present study systematically analyzed the differences in omalizumab-related ADR signals between children and adults based on reports from the FAERS database spanning from the first quarter of 2003 to the third quarter of 2025, aiming to explore the occurrence characteristics and specific risks of ADRs in different populations. This study intends to provide a scientific basis for clinicians to accurately monitor medication risks in different age groups and formulate individualized medication plans, optimize the clinical risk management strategy of omalizumab, and promote the rational use of drugs and safety assurance for both children and adult patients. Materials and methods 1. Data accessing Publicly available data were retrieved from the Faers( https://www.fda.gov/drugs/development-approval-process-drugs/drug-approvals-and-databases ), a spontaneous pharmacovigilance reporting database. No ethical approval was required, and all analyses adhered to the Declaration of Helsinki. Data spanning Q1 2003 to Q3 2025 were extracted, covering omalizumab’s full post-marketing period. 2. Data cleaning and processing ADR reports with omalizumab as the primary suspected drug were identified using its generic name (Omalizumab), original brand (Xolair), and biosimilar (Omlyclo). All ADRs were coded into System Organ Classes (SOCs) and Preferred Terms (PTs) per the Medical Dictionary for Regulatory Activities (MedDRA, Version 26.0). Duplicate reports and those missing key variables (age, ADR description) were excluded to ensure data quality. The cleaned dataset was stratified into two age subgroups: children (< 18 years) and adults (≥ 18 years). Python and MySQL were used for data processing, merging and management to guarantee integrity and consistency. 3. ADR Signal Detection Analysis ADR signal detection was performed via disproportionality analysis, a widely used approach for alertness analysis in large spontaneous reporting databases that preliminarily evaluates potential drug-ADR causal associations (requiring subsequent comprehensive clinical case validation) 17 . This method quantifies discrepancies by comparing observed and expected report counts for specific drug-adverse event combinations 18 . Four internationally recognized disproportionality methods were integrated for signal detection: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS) 19 . A positive signal was defined only by simultaneous fulfillment of all thresholds: ROR: 95% confidence interval (CI) lower bound > 1 PRR: > 2 with χ² ≥ 4 BCPNN: Information Component (IC) 95% CI lower bound > 0 MGPS: Empirical Bayes Geometric Mean (EBGM) 95% CI lower bound > 1 This integrated multi-method approach reduced false positive signals, enhanced the reliability of drug-ADR association detection, and ensured robust drug safety assessment. 4. TTO Analysis and Weibull Distribution Modeling Time-to-onset (TTO) was defined as the interval from initial omalizumab administration to the first reported ADR. For reports with missing exact onset dates, the midpoint of the reported time window was imputed for TTO calculation. Cumulative ADR incidence over time was estimated by the Kaplan-Meier method, with intergroup TTO distribution differences compared via the log-rank test. Parametric survival analysis using the Weibull distribution model characterized ADR onset temporal dynamics—this model was selected for its flexibility in capturing variable hazard rates, making it well-suited for describing post-marketing ADR onset patterns. Key Weibull distribution parameters were defined as follows: Shape parameter (β): Reflects the ADR hazard rate trend (β 1 = increasing hazard rate) Scale parameter (α, characteristic life): Time point at which 63.2% of ADRs occurred in the study population All statistical analyses were performed in Python (Version 3.9) with the lifelines library for survival analysis and Weibull regression modeling. A two-sided p -value < 0.05 was considered statistically significant for all analyses. Results 1. ADR reports This study conducted systematic processing and analysis of omalizumab-related ADR reports based on three core data files of the FAERS database: the demographic file (DEMO), drug exposure file (DRUG), and adverse event file (REAC). The specific workflow was as follows: Starting with the DEMO file, which initially contained 23,488,769 records, 3,902,199 duplicate records were excluded, resulting in 19,586,570 unique demographic records. Subsequently, the de-duplicated DEMO data was linked and integrated with the DRUG file (71,482,606 records) and REAC file (58,226,317 records) to match patients’ demographic information, medication history, and adverse event data. The detailed data mining process and results are presented in Fig. 1 . From the integrated dataset, two types of ADR reports with omalizumab as the primary suspected drug were screened: omalizumab-related ADR reports (n = 62,925) and omalizumab-induced ADR reports (n = 251,695). Based on the age stratification criteria (children subgroup: <18 years; adult subgroup: ≥18 years), the above two types of reports were further categorized into the corresponding subgroups. Among the omalizumab-related ADR reports, there were 2,792 cases (4%) in the children subgroup, 28,075 cases (45%) in the adult subgroup, and 32,057 cases (51%) in the subgroup with missing age information. The distribution results are shown in Fig. 2 . Safety signal mining was performed on the stratified ADR reports using four internationally recognized disproportionality analysis methods: ROR, PRR, BCPNN, and MGPS. On this basis, an in-depth analysis of drug safety characteristics was conducted from four dimensions: clinical indications, outcome events and incidence rates, TTO of adverse events, and clinical characteristics of affected populations. A total of 62,925 ADR reports with omalizumab as the primary suspected drug were collected from the FAERS database during the period from the first quarter of 2003 to the third quarter of 2025. The number of reports showed a steady year-on-year growth trend (Fig. 3 ), which is consistent with the expansion of omalizumab’s indications and the increase in its clinical utilization rate(Pongdee & Li, 2025 ). We also analyzed the characteristics of omalizumab-related ADR reports in children and adult populations (Table 1 ). Regarding gender distribution, the proportion of female patients in ADR reports was higher than that of males in the adult subgroup (females: 20,537 cases, 73.15%; males: 7,296 cases, 25.99%), while the gender distribution was relatively balanced in the children subgroup (females: 1,380 cases, 49.43%; males: 1,308 cases, 46.85%). Additionally, in terms of geographic distribution, the United States contributed the largest number of ADR reports, accounting for 65.63% in the children subgroup and 64.92% in the adult subgroup. Regarding reporter types, physician-provided reports predominated in the children subgroup (1,216 cases, 43.55%), whereas consumer-initiated reports accounted for a higher proportion in the adult subgroup (12,190 cases, 43.42%). Table 1 Characteristics of ADRs reports on omalizumab in children and Adults. Characteristic Children Adults Total 2792 28075 Gender Female 1380(49.43%) 20537(73.15%) Male 1308(46.85%) 7296(25.99%) Missing 104(3.72%) 242(0.86%) The top 10 Reporting Country United States 1625(65.63%) 16443(64.92%) Canada 344(13.89%) 5701(22.51%) Switzerland 93(3.76%) 758(2.99%) Japan 112(4.52%) 559(2.21%) France 88(3.55%) 487(1.92%) Brazil 37(1.49%) 378(1.49%) United Kingdom 47(1.90%) 293(1.16%) Colombia 75(3.03%) 259(1.02%) Argentina 24(0.97%) 229(0.90%) Germany 31(1.25%) 220(0.87%) Outcome Other Serious 951(34.06%) 10673(38.02%) Hospitalization 586(20.99%) 5770(20.55%) Life-Threatening 98(3.51%) 846(3.01%) Disability 43(1.54%) 383(1.36%) Death 24(0.86%) 1007(3.59%) Congenital Anomaly 23(0.82%) 12(0.04%) Required Intervention to Prevent Permanent Impairment/Damage 14(0.50%) 128(0.46%) Missing 175(6.27%) 1472(5.24%) Reporter Type Consumer 910(32.59%) 12190(43.42%) Physician 1216(43.55%) 10060(35.83%) Other health-professional 290(10.39%) 2384(8.49%) Pharmacist 69(2.47%) 846(3.01%) Lawyer 1(0.04%) 2(0.01%) Others 279(9.99%) 2312(8.24%) Missing 27(0.97%) 281(1.00%) 2. Signal mining at the SOC level Regarding the signal characteristics of the adult subgroup, its ADR reports exhibited the feature of "high report volume concentrated in core systems" (Fig. 4 a). "General disorders and administration site conditions" had the highest number of reports (28,099 cases) but only a mild positive signal (ROR = 1.11, 95% CI 1.09–1.12), indicating that while this is the most common type of ADR in adults, it has a weak association with the drug. Instead, it is mostly cumulative events resulting from the large size of the treated population and prolonged exposure. In contrast, "Respiratory, thoracic and mediastinal disorders" (27,558 cases, ROR = 3.78, 95% CI 3.73–3.83) and "Immune system disorders" (5,517 cases, ROR = 3.33, 95% CI 3.24–3.42) were strong positive signal SOCs in the adult subgroup. Both had ROR values significantly greater than 1 with extremely narrow 95% CIs, and combined with extremely high PRR χ² values (48,711.11 and 8,544.34, respectively), indicating that these two systems are the core target systems for drug-related ADRs in adults, with strong association strength and high statistical reliability. Additionally, "Infections and infestations", "Skin and subcutaneous tissue disorders", and "Ear and labyrinth disorders" also showed moderate positive signals (ROR 1.41–1.60), suggesting that the risk of ADRs in these systems is significantly higher than the background level of the database. Regarding the children subgroup, its ADR signal characteristics showed age-specificity of "high association strength" (Fig. 4 b). "General disorders and administration site conditions" not only had the highest number of reports but also a moderate positive signal (ROR = 1.56, 95% CI 1.46–1.62), which is in stark contrast to the "high report volume but weak association" feature of the adult subgroup. This is related to the greater sensitivity of children’s organisms to local or systemic stimulation by biological agents. Meanwhile, "Respiratory, thoracic and mediastinal disorders" (ROR = 3.77, 95% CI 3.59–3.97) and "Immune system disorders" (ROR = 3.36, 95% CI 1.13–7.70) were also strong positive signals, consistent with the core target systems in the adult subgroup. This commonality stems from the pharmacological mechanism of omalizumab—specifically binding to free IgE and downregulating high-affinity IgE receptors. Abnormal reactions in the respiratory and immune systems are "class-effect ADRs" of this type of drug, independent of age. Furthermore, the children subgroup additionally exhibited positive signals for "Musculoskeletal and connective tissue disorders" (ROR = 1.38 and 1.20, respectively), while no valid signals for these SOCs were detected in the adult subgroup. This may be related to the physiological characteristics of the musculoskeletal system during childhood development, reflecting age-specific differences in the ADR profile. The signal characteristics of both populations not only confirm the inherent safety profile of the drug but also clarify age-specific monitoring priorities: both adults and children require priority monitoring of ADRs in the respiratory and immune systems. Adults need to pay attention to high-incidence general disorders, while children require additional vigilance against musculoskeletal discomfort. The absence of valid signals for certain SOCs (e.g., "Nervous system disorders", "Cardiac disorders") in both populations suggests that the safety of these systems is relatively controllable, allowing for a reduction in unnecessary monitoring burden. 3. Signal mining at the PT level Among omalizumab-related ADRs, 4,674 Preferred Terms (PTs) were reported in the adult subgroup, compared with 1,504 PTs in the children subgroup. To verify the reliability of omalizumab-related PT signals, four commonly used pharmacovigilance signal detection algorithms were employed in this study: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Multi-item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagation Neural Network (BCPNN). Venn diagrams were used to visualize the overlap characteristics of positive signals identified by each algorithm. In the adult subgroup, the consistency of multi-algorithm signal detection was significantly higher: the MGPS algorithm identified the most unique positive signals (159), the number of intersecting signals between MGPS and BCPNN reached 373, and the core consensus signals jointly recognized by all four algorithms were 96. Additionally, the intersecting signals between ROR and PRR were 63 (Fig. 5 a). This result suggests that ADR signals in adults are highly stable, and multi-algorithms show high consensus in identifying core risks, which is directly related to the large size of the adult treated population and sufficient ADR report volume. In contrast, the consistency of multi-algorithms in the children subgroup was significantly reduced: the number of unique signals identified by MGPS decreased to 119, the intersecting signals between MGPS and BCPNN were only 141, and the core consensus signals jointly recognized by all four algorithms were merely 12. Furthermore, ROR and PRR identified only 2 and 3 unique signals, respectively (Fig. 5 b). This characteristic is closely associated with the small size of the children treated population and limited ADR report volume. Insufficient sample size leads to greater fluctuations in signal intensity, making the identification results of different algorithms more prone to discrepancies. Systematic analysis and comparison of the distribution characteristics and differences of ADRs at the PT level between adult and children subgroups, based on the SOC-PT hierarchical mapping diagram of omalizumab-related ADRs, revealed significant age-specific differences in PT coverage, composition profile, and hierarchical association patterns with SOCs. The PTs in the adult subgroup mapping diagram exhibited a broad-coverage and multi-type distribution: not only was the total number of outer-layer PTs significantly higher, but the PT connections corresponding to each core SOC were also denser and more diverse. For example, "Respiratory, thoracic and mediastinal disorders" included multiple PTs such as asthma, dyspnea, cough, and wheezing, while other core SOCs (e.g., "General disorders and administration site conditions", "Skin and subcutaneous tissue disorders") also corresponded to diverse PT manifestations. In contrast, the PTs in the children subgroup mapping diagram showed a narrow-focus and low-type distribution: PT coverage was overall narrow with a small number, and the PT connections corresponding to each SOC were relatively sparse. Only core SOCs such as "Respiratory, thoracic and mediastinal disorders" and "General disorders and administration site conditions" focused on a few key PT types (Fig. 6 a). Regarding the composition of specific PT profiles, the age-related differences between adult and children subgroups were particularly prominent. The PT composition of the adult subgroup was centered on allergic skin reaction-related manifestations, while also including multiple infection-related PTs (e.g., urticaria, pruritus, idiopathic cutaneous erythematous papules, atopic dermatitis, pneumonia, sinusitis). Notably, PTs such as increased blood immunoglobulin E, chronic airway obstruction, and chronic sinusitis were unique to the adult subgroup. In contrast, the PT composition of the children subgroup exhibited distinct age-specificity, with a focus on local administration site reactions and acute severe respiratory-related PTs (e.g., asthmatic crisis, injection site pain, injection site erythema, injection site induration, growing pains), which were unique to the children subgroup (Fig. 6 b). As shown in the PT-level signal forest plot of the adult subgroup (Fig. 7 a), the PTs with the highest number of reports were asthma (4,185 cases) and urticaria (3,745 cases), with corresponding ROR values of 17.08 (95% CI: 16.54–17.63) and 9.17 (95% CI: 8.87–9.48), respectively. Their IC values also fell within the high range of 3.11–3.96. These PTs not only have an extremely high occurrence frequency but also a strong association with the drug, representing the core manifestations of drug-related ADRs in adults. Meanwhile, respiratory-related PTs such as dyspnea and cough, as well as allergy-related PTs such as pruritus, were all significant signals. Only a few PTs, including dizziness and pain, showed non-significant signals, indicating no clear association with the drug. In contrast to adults, the children subgroup exhibited more prominent signal intensities for core PTs (Fig. 7 b). The ROR for asthma was as high as 21.33 (95% CI: 19.26–23.63), with an IC value elevated to 4.22, suggesting a closer association between this reaction and the drug in children. Notably, the children subgroup had an additional unique strong positive signal PT: asthmatic exacerbation, with an ROR of 52.80 (95% CI: 40.77–68.45) and an IC of 5.36, representing a severe risk that requires high vigilance after omalizumab administration in children. Furthermore, PTs related to administration site reactions, such as injection site pain, were also significant signals, consistent with the physiological characteristic of children being more sensitive to local stimuli. In contrast, PTs such as vomiting and erythema showed non-significant signals, which differed from the signal distribution in adults. The PT signal characteristics of the two populations shared commonalities while exhibiting clear age-specific differences. The commonality lies in that respiratory and allergy-related PTs (e.g., asthma and urticaria) were all strongly significant signals, reflecting the inherent ADRs associated with the drug’s IgE-targeted pharmacological mechanism. The differences were manifested in two aspects: children had higher signal intensities and unique high-risk PTs (e.g., asthmatic crisis), while adults were characterized by high-report-volume allergic reactions. This result suggests that in clinical practice, adults require focused monitoring for high-frequency allergic or respiratory reactions, whereas children need enhanced vigilance against asthmatic crises and administration site reactions. Essentially, the series of PT-level differences between adults and children arise from the interplay of drug effects with the physiological characteristics and disease spectrum of different age groups, providing a clear direction for formulating age-specific targeted medication safety management strategies in clinical practice. 4. Cumulative Incidence of ADRs and Weibull Distribution of Onset Time The cumulative incidence of ADRs was calculated, and Weibull distribution plots were generated. As shown in the subgroup-specific Weibull distribution curves, both the adult and children subgroups exhibited a typical Weibull distribution pattern for cumulative ADR incidence: a rapid initial rise followed by a gradual plateau (Fig. 8 a, b). However, the children subgroup showed a steeper ascending rate: the cumulative incidence exceeded 90% within the first 1,000 days of drug administration, compared with approximately 85% in the adult subgroup at the same time point. This indicates that children have a more rapid exposure response to drug-related adverse events and a higher early-stage risk. Meanwhile, the curve of the children subgroup reached a plateau earlier (approximately 3,000 days), whereas the adult subgroup required a longer time (approximately 6,000 days). This reflects that the time window of ADR occurrence is more concentrated in children, while adults face a more persistent late-onset risk. Further highlighting these temporal differences was the combined population comparison curve (Fig. 8 c): the red curve of the children subgroup consistently lay above the yellow curve of the adult subgroup, with the most significant gap observed within the first 2,000 days of administration. By 2,000 days, the cumulative incidence in children was nearly 100%, compared with only approximately 95% in adults. This difference is closely related to the immature immune system development of children, which leads to more prominent acute responses after drug exposure. In contrast, the presence of late-onset risk in adults is highly consistent with their clinical characteristics of having more chronic comorbidities and longer drug exposure durations. The Weibull distribution fitting results demonstrated that the onset time of ADRs in children tends to follow an "early-onset and concentrated" pattern, while adults exhibit a "delayed-onset and persistent" pattern. These differences in risk temporal characteristics provide clear temporal targets for clinical phased monitoring strategies: children require intensive monitoring primarily within the first 1,000 days of administration, whereas adults need to balance early monitoring with the prevention and control of long-term late-onset risks. We also analyzed the time-to-onset (TTO) of ADRs in both subgroups. Based on the Weibull distribution parameters, the shape parameter β was 0.44 (95% CI: 0.44, 0.45) in the adult subgroup and 0.43 (95% CI: 0.42, 0.45) in the children subgroup, both of which were less than 1. This indicates that the risk of ADRs in both populations decreases rapidly over time, with the early stage being a high-risk period. Further comparison revealed that the median time to onset in the children subgroup was only 34.4 days (IQR: 1.0–216.0 days), which was significantly shorter than that in the adult subgroup (63.1 days, IQR: 2.0–343.0 days). Additionally, the characteristic life (scale parameter α) of the children subgroup (α = 80.03 days) was much shorter than that of adults (α = 143.85 days), reflecting a more concentrated time window for ADR occurrence and more prominent early-stage risk in children. Differences in the peak density of the density curves further confirmed this characteristic: the peak density of the children subgroup was approximately 0.0040, significantly higher than 0.0025 in the adult subgroup. This suggests that children have a higher ADR occurrence density and more concentrated risk in the very early stage (within 0–100 days) after drug administration (Fig. 9 a, b). These differences provide clear temporal targets for age-specific medication safety management in clinical practice: children require more intensive ADR monitoring within the first 34 days of administration to address the concentrated early-stage risk, while adults need to balance early monitoring within the first 63 days with long-term prevention and control of late-onset risks, thereby improving the precision of medication safety for different populations. Discussion Although the efficacy and safety of omalizumab have been verified in rigorous pre-marketing clinical trials, with the gradual expansion of its indications and continuous enlargement of its target population, coupled with the diversity and complexity of patient characteristics in real-world settings, the characteristics of some adverse drug reactions (ADRs) have not yet been fully elucidated. Based on real-world data from the U.S. FAERS spanning Q1 2003 to Q3 2025, this study systematically explored and compared the ADR signal characteristics of omalizumab in children (< 18 years) and adults (≥ 18 years) using four internationally recognized disproportionality analysis methods: ROR, PRR, BCPNN, and MGPS. For the first time, we revealed age-specific differences in ADRs across dimensions including SOC, PT, TTO, and cumulative incidence, providing an evidence-based basis for individualized medication safety monitoring strategies in patients of different age groups. Meanwhile, this study addressed the limitation of previous research that mostly focused on the overall population and lacked in-depth age-stratified analysis. A total of 62,925 omalizumab-related ADR reports were identified in this study, including 2,792 cases (4%) in the children subgroup, 28,075 cases (45%) in the adult subgroup, and the proportion of missing age information reached 51%. This missing data characteristic is associated with the inherent limitations of data collection in spontaneous reporting systems, which necessitates cautious interpretation of the results. Analysis of population characteristics revealed that the proportion of females (73.15%) was significantly higher than that of males (25.99%) in ADR reports of the adult subgroup, while the gender distribution was relatively balanced in the children subgroup (females: 49.43%; males: 46.85%). This discrepancy is highly consistent with the epidemiological features of diseases targeted by omalizumab’s core indications (asthma, chronic urticaria), where the prevalence is substantially higher in females than in males(Ciprandi & Gallo, 2018 ). Additionally, female sex hormones can induce immune responses and exacerbate inflammatory reactions(Sirufo et al., 2022 ), thereby increasing the risk of ADRs. In contrast, gender-related physiological differences are not fully manifested in childhood, resulting in no significant gender bias in children ADR reports. Regarding the characteristics of report sources, physician-submitted reports predominated in the children subgroup (43.55%), whereas consumer-initiated reports accounted for a higher proportion in the adult subgroup (43.42%). This difference may stem from the fact that children medication use strictly adheres to physician guidance, and their ADRs are mostly monitored and reported by healthcare professionals. In contrast, adults exhibit greater self-awareness of medication responses and face lower barriers to self-reporting, suggesting that adult subgroup reports may contain more subjective biases. Thus, the authenticity of signals requires further differentiation in conjunction with clinical information. In terms of annual distribution trends, the number of omalizumab-related ADR reports showed a continuous upward trend from 2004 to 2025, with a significantly accelerated growth rate after 2018. This change is directly associated with the gradual expansion of omalizumab’s indications, which now include the treatment of food allergy(Zhang et al., 2025 ), allergic rhinitis(Ma et al., 2021 ), atopic dermatitis(Chan et al., 2020 ), and other allergic diseases. At the SOC level, the core positive signals of the adult and children subgroups shared commonalities while exhibiting specificity. Both groups showed strong positive signals for "Respiratory, thoracic and mediastinal disorders" (adults: ROR = 3.78; children: ROR = 3.77) and "Immune system disorders" (adults: ROR = 3.33; children: ROR = 3.36). This common feature is linked to omalizumab’s core pharmacological mechanism: it exerts therapeutic effects by specifically binding to free IgE and blocking its interaction with surface receptors on mast cells and eosinophils(Chow et al., 2022 ; Humbert et al., 2019 ; Pelaia et al., 2018 ). The respiratory and immune systems are the primary target organs of IgE-mediated diseases, so the aforementioned ADRs are classified as "class-effect ADRs" of the drug, independent of age. In the adult subgroup, "General disorders and administration site conditions" had the highest number of reports (28,099 cases) but only a mild positive signal (ROR = 1.11), indicating that such reactions are mostly cumulative events resulting from the large adult treatment population and prolonged drug exposure, with a weak direct association with the drug. In contrast, this SOC not only ranked first in report volume in the children subgroup but also showed a moderate positive signal (ROR = 1.56), which is closely related to the immature development of children’s organisms and their increased sensitivity to local stimulation (e.g., injection site reactions) and systemic stress responses induced by biological agents(Elzagallaai et al., 2017 ). Furthermore, the children subgroup additionally exhibited positive signals for "Musculoskeletal and connective tissue disorders", while no valid signals for these SOCs were detected in the adult subgroup. This difference may be associated with the unique characteristics of the musculoskeletal system during childhood development, and the immunomodulatory effects of biological agents may indirectly affect the physiological state of this system, further reflecting age-specific differences in the ADR profile. Signal characteristics at the PT level further underscored the age specificity of omalizumab-related ADRs. The adult subgroup exhibited broad PT coverage (4,674 terms in total) with diverse manifestations, and its core signals were concentrated in allergic skin reactions and infectious manifestations (e.g., asthma: 4,185 cases, ROR = 17.08; urticaria: 3,745 cases, ROR = 9.17). This feature is associated with the large adult treatment population, prolonged drug exposure, and a higher prevalence of chronic comorbidities that elevate infection risk in adults. In contrast, the children subgroup had narrow PT coverage (1,504 terms in total) with highly focused manifestations; its core signals were dominated by local administration site reactions (e.g., injection site pain, erythema, induration) and acute severe respiratory reactions. Notably, asthmatic crisis was a unique strong positive signal in the children subgroup (ROR = 52.83, IC = 5.36), indicating that children face a significantly higher risk of acute asthma exacerbation following omalizumab administration— a finding consistent with the physiological characteristics of immature immune system development and more prominent airway hyperreactivity in children(Carr et al., 2016 ). Weibull distribution analysis revealed that the cumulative incidence of ADRs in both the children and adult subgroups exhibited a characteristic pattern of a rapid initial rise followed by a gradual plateau, yet the ascending rate was steeper in the children subgroup: the cumulative incidence exceeded 90% within the first 1,000 days of administration in children, compared with approximately 85% in adults. Concurrently, the median time to onset of ADRs in the children subgroup (34.4 days) was significantly shorter than that in the adult subgroup (63.1 days), and the characteristic life (α = 80.03 days) was also much lower than that of adults (α = 143.85 days). Further, the peak density of the Weibull distribution for ADR onset time demonstrated that the peak incidence density in children was 0.0040 within the first 0–100 days of administration, significantly higher than the 0.0025 observed in adults. These findings suggest a more concentrated time window for ADR occurrence and more prominent early-stage risk in children. The cumulative incidence curve of the adult subgroup reached a plateau at approximately 6,000 days, later than the 3,000 days in the children subgroup, indicating a more persistent late-onset risk in adults— a feature consistent with the clinical reality that adults often have more chronic comorbidities, a higher rate of polypharmacy, and longer drug exposure durations(Song et al., 2025 ). These age-related differences in temporal risk characteristics provide clear targets for phase-specific clinical monitoring: for children patients, intensive monitoring should be prioritized within the first 34 days (the median time to onset) of administration, with enhanced prevention and control of acute reactions in the first 100 days in particular; for adult patients, clinical practice must balance early monitoring within the first 63 days with long-term follow-up for late-onset risks to avoid missing chronic adverse reactions. The age-specific differences in omalizumab-related ADRs are essentially the result of the interplay between drug effects and the physiological characteristics and disease spectrum of different age groups. First, children have immature hepatic and renal function with impaired drug metabolism and excretion capacity, which easily leads to drug accumulation(Carr et al., 2016 ). Their immune systems are in a developmental stage with a higher proportion of naive immune cells, rendering them more sensitive to the immunomodulatory effects of biological agents and thus more prone to acute inflammatory or local stimulatory reactions. In contrast, adults have a relatively stable composition of immune cells and stronger adaptive capacity; however, thymic involution in old age can induce immune senescence, which, combined with the impact of chronic comorbidities, results in a significantly elevated risk of late-onset ADRs(Palmer, 2013 ). Second, omalizumab has broader indications in adults, who also frequently suffer from chronic comorbidities such as hypertension and diabetes; polypharmacy further increases the complexity of ADR occurrence. In children, by contrast, omalizumab use is mostly limited to core indications such as asthma, with relatively simple medication regimens. This study has several inherent limitations. First, there are limitations in the data source: over 64% of reports in the FAERS database are from the United States, with insufficient data from non-European and American populations, which may compromise the generalizability of the study results. Second, spontaneous reporting systems have inherent biases, including missing information in some reports and variability in report quality between consumer-submitted and healthcare professional-submitted data, which may affect the accuracy of signal detection. Third, disproportionality analysis can only indicate an association between omalizumab and ADRs, but cannot establish a causal relationship; further verification is required through prospective clinical trials. Conclusion Based on real-world data from the U.S. FAERS database, this study employed four disproportionality analysis algorithms to investigate the post-marketing ADRs of omalizumab in children and adult populations. We clearly identified significant age-specific differences in the ADR profiles, signal intensities, and onset times of omalizumab between the two age groups. Clinically, precise and tailored monitoring protocols should be developed for different populations to maximize the clinical benefits of medication and minimize associated safety risks. This study provides robust real-world data support for the safe clinical use of omalizumab, and also offers a methodological reference for age-stratified pharmacovigilance research on biological agents. Declarations Conflict of Interest The authors declare no competing interests in relation to this research, authorship and publication of this manuscript. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution S and HZ conceived and designed the study. HZ and LL collected and processed the data from the FAERS database. HZ and NL performed the statistical analysis, including signal detection with ROR, PRR, BCPNN, and MGPS methods. S supervised the study and critically revised the manuscript. All authors reviewed and approved the final version of the manuscript. Acknowledgement The authors acknowledge the U.S. FAERS database used in this study. Data Availability The data that support the findings of this study are openly available in the FDA Adverse Event Reporting System (FAERS) Public Dashboard at https://www.fda.gov/drugs/development-approval-process-drugs/drug-approvals-and-databases, maintained by the U.S. Food and Drug Administration (FDA). All raw data used in this analysis are accessible to the public via the official FAERS database portal. References Agache, I., Rocha, C., Beltran, J., Song, Y., Posso, M., Solà, I., Alonso-Coello, P., Akdis, C., Akdis, M., Canonica, G. W., Casale, T., Chivato, T., Corren, J., Del Giacco, S., Eiwegger, T., Firinu, D., Gern, J. E., Hamelmann, E., Hanania, N., … Jutel, M. (2020). Efficacy and safety of treatment with biologicals (benralizumab, dupilumab and omalizumab) for severe allergic asthma: A systematic review for the EAACI Guidelines - recommendations on the use of biologicals in severe asthma. Allergy , 75 (5), 1043～1057. https://doi.org/10.1111/all.14235 Carr, E. J., Dooley, J., Garcia-Perez, J. E., Lagou, V., Lee, J. C., Wouters, C., Meyts, I., Goris, A., Boeckxstaens, G., Linterman, M. A., & Liston, A. (2016). The cellular composition of the human immune system is shaped by age and cohabitation. Nature Immunology , 17 (4), 461～468. https://doi.org/10.1038/ni.3371 Caster, O., Aoki, Y., Gattepaille, L. M., & Grundmark, B. (2020). Disproportionality Analysis for Pharmacovigilance Signal Detection in Small Databases or Subsets: Recommendations for Limiting False-Positive Associations. Drug Safety , 43 (5), 479～487. https://doi.org/10.1007/s40264-020-00911-w Chan, S., Cornelius, V., Cro, S., Harper, J. I., & Lack, G. (2020). Treatment Effect of Omalizumab on Severe Pediatric Atopic Dermatitis: The ADAPT Randomized Clinical Trial. JAMA Pediatrics , 174 (1), 29～37. https://doi.org/10.1001/jamapediatrics.2019.4476 Chow, T. G., Franzblau, L. E., & Khan, D. A. (2022). Adverse Reactions to Biologic Medications Used in Allergy and Immunology Diseases. Current Allergy and Asthma Reports , 22 (12), 195～207. https://doi.org/10.1007/s11882-022-01048-9 Ciprandi, G., & Gallo, F. (2018). The impact of gender on asthma in the daily clinical practice. Postgraduate Medicine , 130 (2), 271～273. https://doi.org/10.1080/00325481.2018.1430447 Deschildre, A., Marguet, C., Langlois, C., Pin, I., Rittié, J.-L., Derelle, J., Abou Taam, R., Fayon, M., Brouard, J., Dubus, J. C., Siret, D., Weiss, L., Pouessel, G., Beghin, L., & Just, J. (2015). Real-life long-term omalizumab therapy in children with severe allergic asthma. The European Respiratory Journal , 46 (3), 856～859. https://doi.org/10.1183/09031936.00008115 Elzagallaai, A. A., Greff, M., & Rieder, M. J. (2017). Adverse Drug Reactions in Children: The Double-Edged Sword of Therapeutics. Clinical Pharmacology and Therapeutics , 101 (6), 725～735. https://doi.org/10.1002/cpt.677 Feng, Z., Li, X., Tong, W. K., He, Q., Zhu, X., Xiang, X., & Tang, Z. (2022). Real-world safety of PCSK9 inhibitors: A pharmacovigilance study based on spontaneous reports in FAERS. Frontiers in Pharmacology , 13 , 894685. https://doi.org/10.3389/fphar.2022.894685 Gon, Y., Maruoka, S., & Mizumura, K. (2022). Omalizumab and IgE in the Control of Severe Allergic Asthma. Frontiers in Pharmacology , 13 , 839011. https://doi.org/10.3389/fphar.2022.839011 Hanania, N. A., Alpan, O., Hamilos, D. L., Condemi, J. J., Reyes-Rivera, I., Zhu, J., Rosen, K. E., Eisner, M. D., Wong, D. A., & Busse, W. (2011). Omalizumab in severe allergic asthma inadequately controlled with standard therapy: A randomized trial. Annals of Internal Medicine , 154 (9), 573～582. https://doi.org/10.7326/0003-4819-154-9-201105030-00002 Humbert, M., Bousquet, J., Bachert, C., Palomares, O., Pfister, P., Kottakis, I., Jaumont, X., Thomsen, S. F., & Papadopoulos, N. G. (2019). IgE-Mediated Multimorbidities in Allergic Asthma and the Potential for Omalizumab Therapy. The Journal of Allergy and Clinical Immunology. In Practice , 7 (5), 1418～1429. https://doi.org/10.1016/j.jaip.2019.02.030 Kariyawasam, H. H., & James, L. K. (2020). Chronic Rhinosinusitis with Nasal Polyps: Targeting IgE with Anti-IgE Omalizumab Therapy. Drug Design, Development and Therapy , 14 , 5483～5494. https://doi.org/10.2147/DDDT.S226575 Luo, L., Wang, Y., Fu, Y., Chen, X., Liu, S., & Zhao, B. (2026). Comprehensive safety assessment of ribociclib: A real-world analysis using the FDA Adverse Event Reporting System (FAERS) database. British Journal of Clinical Pharmacology , 92 (1), 291～299. https://doi.org/10.1002/bcp.70265 Ma, T., Wang, H., & Wang, X. (2021). Effectiveness and Response Predictors of Omalizumab in Treating Patients with Seasonal Allergic Rhinitis: A Real-World Study. Journal of Asthma and Allergy , 14 , 59～66. https://doi.org/10.2147/JAA.S288952 Maurer, M., Kaplan, A., Rosén, K., Holden, M., Iqbal, A., Trzaskoma, B. L., Yang, M., & Casale, T. B. (2018). The XTEND-CIU study: Long-term use of omalizumab in chronic idiopathic urticaria. The Journal of Allergy and Clinical Immunology , 141 (3), 1138-1139.e7. https://doi.org/10.1016/j.jaci.2017.10.018 Montastruc, J.-L., Sommet, A., Bagheri, H., & Lapeyre-Mestre, M. (2011). Benefits and strengths of the disproportionality analysis for identification of adverse drug reactions in a pharmacovigilance database. British Journal of Clinical Pharmacology , 72 (6), 905～908. https://doi.org/10.1111/j.1365-2125.2011.04037.x Palmer, D. B. (2013). The effect of age on thymic function. Frontiers in Immunology , 4 , 316. https://doi.org/10.3389/fimmu.2013.00316 Pelaia, C., Calabrese, C., Terracciano, R., de Blasio, F., Vatrella, A., & Pelaia, G. (2018). Omalizumab, the first available antibody for biological treatment of severe asthma: More than a decade of real-life effectiveness. Therapeutic Advances in Respiratory Disease , 12 , 1753466618810192. https://doi.org/10.1177/1753466618810192 Pongdee, T., & Li, J. T. (2025). Omalizumab safety concerns. The Journal of Allergy and Clinical Immunology , 155 (1), 31～35. https://doi.org/10.1016/j.jaci.2024.11.005 Shu, Y., Wang, L., Ding, Y., & Zhang, Q. (2023). Disproportionality Analysis of Abemaciclib in the FDA Adverse Event Reporting System: A Real-World Post-Marketing Pharmacovigilance Assessment. Drug Safety , 46 (9), 881～895. https://doi.org/10.1007/s40264-023-01334-z Sirufo, M. M., De Pietro, F., Ginaldi, L., & De Martinis, M. (2022). Sex, Allergic Diseases and Omalizumab. Biomedicines , 10 (2), 328. https://doi.org/10.3390/biomedicines10020328 Song, Y., Wang, Z., Wang, N., Xie, X., Zhu, T., & Wang, Y. (2025). A real-world pharmacovigilance study of omalizumab using disproportionality analysis in the FDA adverse drug events reporting system database. Scientific Reports , 15 , 8045. https://doi.org/10.1038/s41598-025-91463-5 Zhang, Y., Liang, B., Tian, L., Chen, B., & Wu, S. (2025). Systematic review and meta-analysis of omalizumab for IgE-mediated food allergy in children and young adults. Frontiers in Immunology , 16 , 1690650. https://doi.org/10.3389/fimmu.2025.1690650 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 05 Apr, 2026 Submission checks completed at journal 05 Apr, 2026 First submitted to journal 02 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-9305106","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619602682,"identity":"52f6b326-e631-4bbd-b0af-3d201d7e8c75","order_by":0,"name":"Hairui Zheng","email":"","orcid":"","institution":"Guangzhou First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hairui","middleName":"","lastName":"Zheng","suffix":""},{"id":619602687,"identity":"06a9edf7-fe48-4874-af85-7a18f84ccadc","order_by":1,"name":"Lu Liang","email":"","orcid":"","institution":"Guangzhou First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Liang","suffix":""},{"id":619602689,"identity":"119b10d0-8809-4cc7-a3c6-c4837de81d43","order_by":2,"name":"Ning Li","email":"","orcid":"","institution":"Guangzhou First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Li","suffix":""},{"id":619602690,"identity":"7d2d87fb-0cd4-406a-85cd-37b6b13972bb","order_by":3,"name":"Yi Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACNmb+7x8kKmx4+NkbiNTCx95gxmBxJk1GsucAkVrkeA6YMVS2HbYxuJFArMMkEtIe3GxL4zG4+XjjDYYam2hitBw3nHHOhkfydlqxBcOxtNwGwloSG6QlytJ4+G7nmEkwNhwmRksyg/QftsM8DDfPEKuF5xibhETbYR6BGzzEamHvYTaQOJPGI9kD9EsCMX6Rb+ZhfACMSnt+9sMbb3yosSGsBRkYSCSQohyihVQdo2AUjIJRMDIAAG24PDCpIwpRAAAAAElFTkSuQmCC","orcid":"","institution":"Guangzhou First People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yi","middleName":"","lastName":"Su","suffix":""}],"badges":[],"createdAt":"2026-04-02 15:39:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9305106/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9305106/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106901704,"identity":"9e619111-76b8-4800-8f0a-0192321182ca","added_by":"auto","created_at":"2026-04-14 15:04:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":138979,"visible":true,"origin":"","legend":"\u003cp\u003eThe process of selecting omalizumab-associated ADRs from FAERS database.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9305106/v1/bb38092a42342602200d3eb5.png"},{"id":106901705,"identity":"12f9a98f-8dbd-4959-bcb2-8cc78ee66018","added_by":"auto","created_at":"2026-04-14 15:04:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49765,"visible":true,"origin":"","legend":"\u003cp\u003eAge Group Distribution of Omalizumab-Related ADRs Reports\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9305106/v1/51d1ed7e7d4735fd982086fa.png"},{"id":106901706,"identity":"21df1c52-6580-46e6-8b5d-c7fbc3fc4e05","added_by":"auto","created_at":"2026-04-14 15:04:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":98102,"visible":true,"origin":"","legend":"\u003cp\u003eThe annual distribution of omalizumab-related ADRs reports from 2004 to 2025\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9305106/v1/0c1d82e452c0732f2012aa40.png"},{"id":106961541,"identity":"337e43fe-565c-4ccf-8221-5cc07ccc3a1d","added_by":"auto","created_at":"2026-04-15 09:25:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":158155,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of SOC level signal detection in Adults(a) and Children (b)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9305106/v1/428b4a8dcca06f14c94deadb.png"},{"id":106961730,"identity":"7d35f7da-7610-4ae5-98c0-b99e2916401a","added_by":"auto","created_at":"2026-04-15 09:26:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":74751,"visible":true,"origin":"","legend":"\u003cp\u003eVenn Diagram of PT Signals Meeting the Criteria of Four Algorithms in Adults (a) and Children(b)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9305106/v1/64a98e30af49573497d23481.png"},{"id":106901709,"identity":"d4425469-4e32-4a27-a232-eda877f6d074","added_by":"auto","created_at":"2026-04-14 15:04:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":323819,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical Mapping of ADRs in Adults(a) and Children(b)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9305106/v1/2f029113ac6b995cb0b17ba9.png"},{"id":106961739,"identity":"377549fa-b845-4096-98fe-f7884ed53e1a","added_by":"auto","created_at":"2026-04-15 09:26:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":193071,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of PT level signal detection in Adults(a) and Children(b)\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9305106/v1/f8b0a3858a8dcb2c2f8d3220.png"},{"id":106901710,"identity":"bbe60a2c-c3bf-4ac3-9ce2-7f141a587a5a","added_by":"auto","created_at":"2026-04-14 15:04:01","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":77437,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative Incidence of ADRs Adults(a) and Children(b); Comparison of Cumulative Incidence of ADRs in Children and Adults(c)\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9305106/v1/a99e0a65c531acdd38349e52.png"},{"id":106960487,"identity":"15a331d9-572f-433e-b68f-930aa13d4ea9","added_by":"auto","created_at":"2026-04-15 09:21:24","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":66841,"visible":true,"origin":"","legend":"\u003cp\u003eWeibull Distribution Analysis of ADRs Onset Time in Adults(a) and Children(b)\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-9305106/v1/5ef13e65da9fad2452f31eee.png"},{"id":106963404,"identity":"5864577f-7678-4adc-b72d-d2ef8e85747d","added_by":"auto","created_at":"2026-04-15 09:44:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1663563,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9305106/v1/d6cfd9cd-cf7c-4931-9995-a55ea9a2864c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Age-Specific Differences in Omalizumab-Related Adverse Drug Reaction Signals between Children and Adults: An Analysis Based on the FAERS Database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOmalizumab is a humanized monoclonal antibody that effectively alleviates allergic reactions(Chow et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Humbert et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pelaia et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) by specifically binding to free immunoglobulin E (IgE), blocking its interaction with surface receptors on mast cells and eosinophils, and inhibiting the release of inflammatory mediators. As the first biological agent endorsed by the Global Initiative for Asthma (GINA) for the treatment of IgE-mediated severe allergic asthma(Agache et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), it was approved by the U.S. FDA in 2003 for the treatment of moderate-to-severe allergic asthma in children aged 6 years and older and adults(Gon et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Subsequently, its indications have gradually expanded to include chronic urticaria in patients aged 12 years and older(Maurer et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and adult nasal polyps(Kariyawasam \u0026amp; James, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with further approval for the treatment of IgE-mediated food allergy in 2024(Zhang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It has also demonstrated favorable efficacy in various other allergic conditions, such as allergic rhinitis(Ma et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and atopic dermatitis(Chan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). With the continuous expansion of its clinical applications(Pongdee \u0026amp; Li, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), particularly its extended use in children, the medication safety of omalizumab has garnered increasing attention.\u003c/p\u003e \u003cp\u003eFrom the perspective of developmental pharmacology, there are significant differences in the drug exposure-response relationship between children and adults, leading to population-specific characteristics in the type and incidence of ADRs(Elzagallaai et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Current studies on omalizumab-related ADRs are mostly based on clinical trials and meta-analyses. Although common ADRs such as headache, injection site reactions, and arthralgia have been identified(Deschildre et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hanania et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Maurer et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), these studies are limited by small sample sizes and strict inclusion criteria, making it difficult to fully reflect the real-world medication safety profile. More importantly, there is a lack of systematic analysis on the differences between children and adults. Traditional ADR signal detection algorithms are mostly based on the entire population, which tend to overlook the unique medication risks in children. Given that children\u0026rsquo;s physiological functions are not yet fully developed, their tolerance to drugs is inherently different from that of adults(Elzagallaai et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Clarifying the differences in ADR signals between the two populations is therefore crucial for clinical medication safety.\u003c/p\u003e \u003cp\u003eThe FDA Adverse Event Reporting System serves as a core tool for post-marketing drug safety monitoring(Feng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), continuously collecting real-time ADR data submitted by healthcare professionals, consumers, and other stakeholders. It offers advantages such as a large sample size, frequent updates, and high data accessibility, providing reliable data support for real-world drug safety research(Shu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In view of this, the present study systematically analyzed the differences in omalizumab-related ADR signals between children and adults based on reports from the FAERS database spanning from the first quarter of 2003 to the third quarter of 2025, aiming to explore the occurrence characteristics and specific risks of ADRs in different populations. This study intends to provide a scientific basis for clinicians to accurately monitor medication risks in different age groups and formulate individualized medication plans, optimize the clinical risk management strategy of omalizumab, and promote the rational use of drugs and safety assurance for both children and adult patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\n\u003ch3\u003e1. Data accessing\u003c/h3\u003e\n\u003cp\u003ePublicly available data were retrieved from the Faers(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fda.gov/drugs/development-approval-process-drugs/drug-approvals-and-databases\u003c/span\u003e\u003cspan address=\"https://www.fda.gov/drugs/development-approval-process-drugs/drug-approvals-and-databases\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a spontaneous pharmacovigilance reporting database. No ethical approval was required, and all analyses adhered to the Declaration of Helsinki. Data spanning Q1 2003 to Q3 2025 were extracted, covering omalizumab\u0026rsquo;s full post-marketing period.\u003c/p\u003e\n\u003ch3\u003e2. Data cleaning and processing\u003c/h3\u003e\n\u003cp\u003eADR reports with omalizumab as the primary suspected drug were identified using its generic name (Omalizumab), original brand (Xolair), and biosimilar (Omlyclo). All ADRs were coded into System Organ Classes (SOCs) and Preferred Terms (PTs) per the \u003cem\u003eMedical Dictionary for Regulatory Activities\u003c/em\u003e (MedDRA, Version 26.0). Duplicate reports and those missing key variables (age, ADR description) were excluded to ensure data quality. The cleaned dataset was stratified into two age subgroups: children (\u0026lt;\u0026thinsp;18 years) and adults (\u0026ge;\u0026thinsp;18 years). Python and MySQL were used for data processing, merging and management to guarantee integrity and consistency.\u003c/p\u003e\n\u003ch3\u003e3. ADR Signal Detection Analysis\u003c/h3\u003e\n\u003cp\u003eADR signal detection was performed via disproportionality analysis, a widely used approach for alertness analysis in large spontaneous reporting databases that preliminarily evaluates potential drug-ADR causal associations (requiring subsequent comprehensive clinical case validation)\u003csup\u003e17\u003c/sup\u003e. This method quantifies discrepancies by comparing observed and expected report counts for specific drug-adverse event combinations\u003csup\u003e18\u003c/sup\u003e. Four internationally recognized disproportionality methods were integrated for signal detection: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS)\u003csup\u003e19\u003c/sup\u003e. A positive signal was defined only by simultaneous fulfillment of all thresholds:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eROR: 95% confidence interval (CI) lower bound\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePRR: \u0026gt; 2 with χ\u0026sup2; \u0026ge; 4\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBCPNN: Information Component (IC) 95% CI lower bound\u0026thinsp;\u0026gt;\u0026thinsp;0\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMGPS: Empirical Bayes Geometric Mean (EBGM) 95% CI lower bound\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis integrated multi-method approach reduced false positive signals, enhanced the reliability of drug-ADR association detection, and ensured robust drug safety assessment.\u003c/p\u003e\n\u003ch3\u003e4. TTO Analysis and Weibull Distribution Modeling\u003c/h3\u003e\n\u003cp\u003eTime-to-onset (TTO) was defined as the interval from initial omalizumab administration to the first reported ADR. For reports with missing exact onset dates, the midpoint of the reported time window was imputed for TTO calculation. Cumulative ADR incidence over time was estimated by the Kaplan-Meier method, with intergroup TTO distribution differences compared via the log-rank test. Parametric survival analysis using the Weibull distribution model characterized ADR onset temporal dynamics\u0026mdash;this model was selected for its flexibility in capturing variable hazard rates, making it well-suited for describing post-marketing ADR onset patterns.\u003c/p\u003e \u003cp\u003eKey Weibull distribution parameters were defined as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eShape parameter (β): Reflects the ADR hazard rate trend (β\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026thinsp;=\u0026thinsp;decreasing hazard rate; β\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026thinsp;=\u0026thinsp;increasing hazard rate)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eScale parameter (α, characteristic life): Time point at which 63.2% of ADRs occurred in the study population\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAll statistical analyses were performed in Python (Version 3.9) with the \u003cem\u003elifelines\u003c/em\u003e library for survival analysis and Weibull regression modeling. A two-sided \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003e1. ADR reports\u003c/h3\u003e\n\u003cp\u003eThis study conducted systematic processing and analysis of omalizumab-related ADR reports based on three core data files of the FAERS database: the demographic file (DEMO), drug exposure file (DRUG), and adverse event file (REAC). The specific workflow was as follows: Starting with the DEMO file, which initially contained 23,488,769 records, 3,902,199 duplicate records were excluded, resulting in 19,586,570 unique demographic records. Subsequently, the de-duplicated DEMO data was linked and integrated with the DRUG file (71,482,606 records) and REAC file (58,226,317 records) to match patients\u0026rsquo; demographic information, medication history, and adverse event data. The detailed data mining process and results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. From the integrated dataset, two types of ADR reports with omalizumab as the primary suspected drug were screened: omalizumab-related ADR reports (n\u0026thinsp;=\u0026thinsp;62,925) and omalizumab-induced ADR reports (n\u0026thinsp;=\u0026thinsp;251,695).\u003c/p\u003e\n\u003cp\u003eBased on the age stratification criteria (children subgroup: \u0026lt;18 years; adult subgroup: \u0026ge;18 years), the above two types of reports were further categorized into the corresponding subgroups. Among the omalizumab-related ADR reports, there were 2,792 cases (4%) in the children subgroup, 28,075 cases (45%) in the adult subgroup, and 32,057 cases (51%) in the subgroup with missing age information. The distribution results are shown in Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eSafety signal mining was performed on the stratified ADR reports using four internationally recognized disproportionality analysis methods: ROR, PRR, BCPNN, and MGPS. On this basis, an in-depth analysis of drug safety characteristics was conducted from four dimensions: clinical indications, outcome events and incidence rates, TTO of adverse events, and clinical characteristics of affected populations.\u003c/p\u003e\n\u003cp\u003eA total of 62,925 ADR reports with omalizumab as the primary suspected drug were collected from the FAERS database during the period from the first quarter of 2003 to the third quarter of 2025. The number of reports showed a steady year-on-year growth trend (Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which is consistent with the expansion of omalizumab\u0026rsquo;s indications and the increase in its clinical utilization rate(Pongdee \u0026amp; Li, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). We also analyzed the characteristics of omalizumab-related ADR reports in children and adult populations (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Regarding gender distribution, the proportion of female patients in ADR reports was higher than that of males in the adult subgroup (females: 20,537 cases, 73.15%; males: 7,296 cases, 25.99%), while the gender distribution was relatively balanced in the children subgroup (females: 1,380 cases, 49.43%; males: 1,308 cases, 46.85%). Additionally, in terms of geographic distribution, the United States contributed the largest number of ADR reports, accounting for 65.63% in the children subgroup and 64.92% in the adult subgroup. Regarding reporter types, physician-provided reports predominated in the children subgroup (1,216 cases, 43.55%), whereas consumer-initiated reports accounted for a higher proportion in the adult subgroup (12,190 cases, 43.42%).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of ADRs reports on omalizumab in children and Adults.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eChildren\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAdults\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e28075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1380(49.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e20537(73.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1308(46.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7296(25.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e104(3.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e242(0.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eThe top 10 Reporting Country\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1625(65.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e16443(64.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e344(13.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5701(22.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSwitzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e93(3.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e758(2.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e112(4.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e559(2.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e88(3.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e487(1.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e37(1.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e378(1.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e47(1.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e293(1.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eColombia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e75(3.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e259(1.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eArgentina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e24(0.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e229(0.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e31(1.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e220(0.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOther Serious\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e951(34.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10673(38.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHospitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e586(20.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5770(20.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLife-Threatening\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e98(3.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e846(3.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDisability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e43(1.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e383(1.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e24(0.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1007(3.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCongenital Anomaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e23(0.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12(0.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRequired Intervention to Prevent Permanent Impairment/Damage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e14(0.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e128(0.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e175(6.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1472(5.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eReporter Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eConsumer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e910(32.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12190(43.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePhysician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1216(43.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e10060(35.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOther health-professional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e290(10.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2384(8.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePharmacist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e69(2.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e846(3.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLawyer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1(0.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2(0.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e279(9.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2312(8.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e27(0.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e281(1.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003e2. Signal mining at the SOC level\u003c/h3\u003e\n\u003cp\u003eRegarding the signal characteristics of the adult subgroup, its ADR reports exhibited the feature of \u0026quot;high report volume concentrated in core systems\u0026quot; (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). \u0026quot;General disorders and administration site conditions\u0026quot; had the highest number of reports (28,099 cases) but only a mild positive signal (ROR\u0026thinsp;=\u0026thinsp;1.11, 95% CI 1.09\u0026ndash;1.12), indicating that while this is the most common type of ADR in adults, it has a weak association with the drug. Instead, it is mostly cumulative events resulting from the large size of the treated population and prolonged exposure. In contrast, \u0026quot;Respiratory, thoracic and mediastinal disorders\u0026quot; (27,558 cases, ROR\u0026thinsp;=\u0026thinsp;3.78, 95% CI 3.73\u0026ndash;3.83) and \u0026quot;Immune system disorders\u0026quot; (5,517 cases, ROR\u0026thinsp;=\u0026thinsp;3.33, 95% CI 3.24\u0026ndash;3.42) were strong positive signal SOCs in the adult subgroup. Both had ROR values significantly greater than 1 with extremely narrow 95% CIs, and combined with extremely high PRR \u0026chi;\u0026sup2; values (48,711.11 and 8,544.34, respectively), indicating that these two systems are the core target systems for drug-related ADRs in adults, with strong association strength and high statistical reliability. Additionally, \u0026quot;Infections and infestations\u0026quot;, \u0026quot;Skin and subcutaneous tissue disorders\u0026quot;, and \u0026quot;Ear and labyrinth disorders\u0026quot; also showed moderate positive signals (ROR 1.41\u0026ndash;1.60), suggesting that the risk of ADRs in these systems is significantly higher than the background level of the database.\u003c/p\u003e\n\u003cp\u003eRegarding the children subgroup, its ADR signal characteristics showed age-specificity of \u0026quot;high association strength\u0026quot; (Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). \u0026quot;General disorders and administration site conditions\u0026quot; not only had the highest number of reports but also a moderate positive signal (ROR\u0026thinsp;=\u0026thinsp;1.56, 95% CI 1.46\u0026ndash;1.62), which is in stark contrast to the \u0026quot;high report volume but weak association\u0026quot; feature of the adult subgroup. This is related to the greater sensitivity of children\u0026rsquo;s organisms to local or systemic stimulation by biological agents. Meanwhile, \u0026quot;Respiratory, thoracic and mediastinal disorders\u0026quot; (ROR\u0026thinsp;=\u0026thinsp;3.77, 95% CI 3.59\u0026ndash;3.97) and \u0026quot;Immune system disorders\u0026quot; (ROR\u0026thinsp;=\u0026thinsp;3.36, 95% CI 1.13\u0026ndash;7.70) were also strong positive signals, consistent with the core target systems in the adult subgroup. This commonality stems from the pharmacological mechanism of omalizumab\u0026mdash;specifically binding to free IgE and downregulating high-affinity IgE receptors. Abnormal reactions in the respiratory and immune systems are \u0026quot;class-effect ADRs\u0026quot; of this type of drug, independent of age. Furthermore, the children subgroup additionally exhibited positive signals for \u0026quot;Musculoskeletal and connective tissue disorders\u0026quot; (ROR\u0026thinsp;=\u0026thinsp;1.38 and 1.20, respectively), while no valid signals for these SOCs were detected in the adult subgroup. This may be related to the physiological characteristics of the musculoskeletal system during childhood development, reflecting age-specific differences in the ADR profile.\u003c/p\u003e\n\u003cp\u003eThe signal characteristics of both populations not only confirm the inherent safety profile of the drug but also clarify age-specific monitoring priorities: both adults and children require priority monitoring of ADRs in the respiratory and immune systems. Adults need to pay attention to high-incidence general disorders, while children require additional vigilance against musculoskeletal discomfort. The absence of valid signals for certain SOCs (e.g., \u0026quot;Nervous system disorders\u0026quot;, \u0026quot;Cardiac disorders\u0026quot;) in both populations suggests that the safety of these systems is relatively controllable, allowing for a reduction in unnecessary monitoring burden.\u003c/p\u003e\n\u003ch3\u003e3. Signal mining at the PT level\u003c/h3\u003e\n\u003cp\u003eAmong omalizumab-related ADRs, 4,674 Preferred Terms (PTs) were reported in the adult subgroup, compared with 1,504 PTs in the children subgroup. To verify the reliability of omalizumab-related PT signals, four commonly used pharmacovigilance signal detection algorithms were employed in this study: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Multi-item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagation Neural Network (BCPNN). Venn diagrams were used to visualize the overlap characteristics of positive signals identified by each algorithm.\u003c/p\u003e\n\u003cp\u003eIn the adult subgroup, the consistency of multi-algorithm signal detection was significantly higher: the MGPS algorithm identified the most unique positive signals (159), the number of intersecting signals between MGPS and BCPNN reached 373, and the core consensus signals jointly recognized by all four algorithms were 96. Additionally, the intersecting signals between ROR and PRR were 63 (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). This result suggests that ADR signals in adults are highly stable, and multi-algorithms show high consensus in identifying core risks, which is directly related to the large size of the adult treated population and sufficient ADR report volume.\u003c/p\u003e\n\u003cp\u003eIn contrast, the consistency of multi-algorithms in the children subgroup was significantly reduced: the number of unique signals identified by MGPS decreased to 119, the intersecting signals between MGPS and BCPNN were only 141, and the core consensus signals jointly recognized by all four algorithms were merely 12. Furthermore, ROR and PRR identified only 2 and 3 unique signals, respectively (Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). This characteristic is closely associated with the small size of the children treated population and limited ADR report volume. Insufficient sample size leads to greater fluctuations in signal intensity, making the identification results of different algorithms more prone to discrepancies.\u003c/p\u003e\n\u003cp\u003eSystematic analysis and comparison of the distribution characteristics and differences of ADRs at the PT level between adult and children subgroups, based on the SOC-PT hierarchical mapping diagram of omalizumab-related ADRs, revealed significant age-specific differences in PT coverage, composition profile, and hierarchical association patterns with SOCs.\u003c/p\u003e\n\u003cp\u003eThe PTs in the adult subgroup mapping diagram exhibited a broad-coverage and multi-type distribution: not only was the total number of outer-layer PTs significantly higher, but the PT connections corresponding to each core SOC were also denser and more diverse. For example, \u0026quot;Respiratory, thoracic and mediastinal disorders\u0026quot; included multiple PTs such as asthma, dyspnea, cough, and wheezing, while other core SOCs (e.g., \u0026quot;General disorders and administration site conditions\u0026quot;, \u0026quot;Skin and subcutaneous tissue disorders\u0026quot;) also corresponded to diverse PT manifestations. In contrast, the PTs in the children subgroup mapping diagram showed a narrow-focus and low-type distribution: PT coverage was overall narrow with a small number, and the PT connections corresponding to each SOC were relatively sparse. Only core SOCs such as \u0026quot;Respiratory, thoracic and mediastinal disorders\u0026quot; and \u0026quot;General disorders and administration site conditions\u0026quot; focused on a few key PT types (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea).\u003c/p\u003e\n\u003cp\u003eRegarding the composition of specific PT profiles, the age-related differences between adult and children subgroups were particularly prominent. The PT composition of the adult subgroup was centered on allergic skin reaction-related manifestations, while also including multiple infection-related PTs (e.g., urticaria, pruritus, idiopathic cutaneous erythematous papules, atopic dermatitis, pneumonia, sinusitis). Notably, PTs such as increased blood immunoglobulin E, chronic airway obstruction, and chronic sinusitis were unique to the adult subgroup. In contrast, the PT composition of the children subgroup exhibited distinct age-specificity, with a focus on local administration site reactions and acute severe respiratory-related PTs (e.g., asthmatic crisis, injection site pain, injection site erythema, injection site induration, growing pains), which were unique to the children subgroup (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e\n\u003cp\u003eAs shown in the PT-level signal forest plot of the adult subgroup (Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea), the PTs with the highest number of reports were asthma (4,185 cases) and urticaria (3,745 cases), with corresponding ROR values of 17.08 (95% CI: 16.54\u0026ndash;17.63) and 9.17 (95% CI: 8.87\u0026ndash;9.48), respectively. Their IC values also fell within the high range of 3.11\u0026ndash;3.96. These PTs not only have an extremely high occurrence frequency but also a strong association with the drug, representing the core manifestations of drug-related ADRs in adults. Meanwhile, respiratory-related PTs such as dyspnea and cough, as well as allergy-related PTs such as pruritus, were all significant signals. Only a few PTs, including dizziness and pain, showed non-significant signals, indicating no clear association with the drug.\u003c/p\u003e\n\u003cp\u003eIn contrast to adults, the children subgroup exhibited more prominent signal intensities for core PTs (Fig. \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). The ROR for asthma was as high as 21.33 (95% CI: 19.26\u0026ndash;23.63), with an IC value elevated to 4.22, suggesting a closer association between this reaction and the drug in children. Notably, the children subgroup had an additional unique strong positive signal PT: asthmatic exacerbation, with an ROR of 52.80 (95% CI: 40.77\u0026ndash;68.45) and an IC of 5.36, representing a severe risk that requires high vigilance after omalizumab administration in children. Furthermore, PTs related to administration site reactions, such as injection site pain, were also significant signals, consistent with the physiological characteristic of children being more sensitive to local stimuli. In contrast, PTs such as vomiting and erythema showed non-significant signals, which differed from the signal distribution in adults.\u003c/p\u003e\n\u003cp\u003eThe PT signal characteristics of the two populations shared commonalities while exhibiting clear age-specific differences. The commonality lies in that respiratory and allergy-related PTs (e.g., asthma and urticaria) were all strongly significant signals, reflecting the inherent ADRs associated with the drug\u0026rsquo;s IgE-targeted pharmacological mechanism. The differences were manifested in two aspects: children had higher signal intensities and unique high-risk PTs (e.g., asthmatic crisis), while adults were characterized by high-report-volume allergic reactions. This result suggests that in clinical practice, adults require focused monitoring for high-frequency allergic or respiratory reactions, whereas children need enhanced vigilance against asthmatic crises and administration site reactions. Essentially, the series of PT-level differences between adults and children arise from the interplay of drug effects with the physiological characteristics and disease spectrum of different age groups, providing a clear direction for formulating age-specific targeted medication safety management strategies in clinical practice.\u003c/p\u003e\n\u003ch3\u003e4. Cumulative Incidence of ADRs and Weibull Distribution of Onset Time\u003c/h3\u003e\n\u003cp\u003eThe cumulative incidence of ADRs was calculated, and Weibull distribution plots were generated. As shown in the subgroup-specific Weibull distribution curves, both the adult and children subgroups exhibited a typical Weibull distribution pattern for cumulative ADR incidence: a rapid initial rise followed by a gradual plateau (Fig. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea, b). However, the children subgroup showed a steeper ascending rate: the cumulative incidence exceeded 90% within the first 1,000 days of drug administration, compared with approximately 85% in the adult subgroup at the same time point. This indicates that children have a more rapid exposure response to drug-related adverse events and a higher early-stage risk. Meanwhile, the curve of the children subgroup reached a plateau earlier (approximately 3,000 days), whereas the adult subgroup required a longer time (approximately 6,000 days). This reflects that the time window of ADR occurrence is more concentrated in children, while adults face a more persistent late-onset risk.\u003c/p\u003e\n\u003cp\u003eFurther highlighting these temporal differences was the combined population comparison curve (Fig. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec): the red curve of the children subgroup consistently lay above the yellow curve of the adult subgroup, with the most significant gap observed within the first 2,000 days of administration. By 2,000 days, the cumulative incidence in children was nearly 100%, compared with only approximately 95% in adults. This difference is closely related to the immature immune system development of children, which leads to more prominent acute responses after drug exposure. In contrast, the presence of late-onset risk in adults is highly consistent with their clinical characteristics of having more chronic comorbidities and longer drug exposure durations. The Weibull distribution fitting results demonstrated that the onset time of ADRs in children tends to follow an \u0026quot;early-onset and concentrated\u0026quot; pattern, while adults exhibit a \u0026quot;delayed-onset and persistent\u0026quot; pattern. These differences in risk temporal characteristics provide clear temporal targets for clinical phased monitoring strategies: children require intensive monitoring primarily within the first 1,000 days of administration, whereas adults need to balance early monitoring with the prevention and control of long-term late-onset risks.\u003c/p\u003e\n\u003cp\u003eWe also analyzed the time-to-onset (TTO) of ADRs in both subgroups. Based on the Weibull distribution parameters, the shape parameter \u0026beta; was 0.44 (95% CI: 0.44, 0.45) in the adult subgroup and 0.43 (95% CI: 0.42, 0.45) in the children subgroup, both of which were less than 1. This indicates that the risk of ADRs in both populations decreases rapidly over time, with the early stage being a high-risk period. Further comparison revealed that the median time to onset in the children subgroup was only 34.4 days (IQR: 1.0\u0026ndash;216.0 days), which was significantly shorter than that in the adult subgroup (63.1 days, IQR: 2.0\u0026ndash;343.0 days). Additionally, the characteristic life (scale parameter \u0026alpha;) of the children subgroup (\u0026alpha;\u0026thinsp;=\u0026thinsp;80.03 days) was much shorter than that of adults (\u0026alpha;\u0026thinsp;=\u0026thinsp;143.85 days), reflecting a more concentrated time window for ADR occurrence and more prominent early-stage risk in children. Differences in the peak density of the density curves further confirmed this characteristic: the peak density of the children subgroup was approximately 0.0040, significantly higher than 0.0025 in the adult subgroup. This suggests that children have a higher ADR occurrence density and more concentrated risk in the very early stage (within 0\u0026ndash;100 days) after drug administration (Fig. \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea, b).\u003c/p\u003e\n\u003cp\u003eThese differences provide clear temporal targets for age-specific medication safety management in clinical practice: children require more intensive ADR monitoring within the first 34 days of administration to address the concentrated early-stage risk, while adults need to balance early monitoring within the first 63 days with long-term prevention and control of late-onset risks, thereby improving the precision of medication safety for different populations.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAlthough the efficacy and safety of omalizumab have been verified in rigorous pre-marketing clinical trials, with the gradual expansion of its indications and continuous enlargement of its target population, coupled with the diversity and complexity of patient characteristics in real-world settings, the characteristics of some adverse drug reactions (ADRs) have not yet been fully elucidated.\u003c/p\u003e \u003cp\u003eBased on real-world data from the U.S. FAERS spanning Q1 2003 to Q3 2025, this study systematically explored and compared the ADR signal characteristics of omalizumab in children (\u0026lt;\u0026thinsp;18 years) and adults (\u0026ge;\u0026thinsp;18 years) using four internationally recognized disproportionality analysis methods: ROR, PRR, BCPNN, and MGPS. For the first time, we revealed age-specific differences in ADRs across dimensions including SOC, PT, TTO, and cumulative incidence, providing an evidence-based basis for individualized medication safety monitoring strategies in patients of different age groups. Meanwhile, this study addressed the limitation of previous research that mostly focused on the overall population and lacked in-depth age-stratified analysis.\u003c/p\u003e \u003cp\u003eA total of 62,925 omalizumab-related ADR reports were identified in this study, including 2,792 cases (4%) in the children subgroup, 28,075 cases (45%) in the adult subgroup, and the proportion of missing age information reached 51%. This missing data characteristic is associated with the inherent limitations of data collection in spontaneous reporting systems, which necessitates cautious interpretation of the results. Analysis of population characteristics revealed that the proportion of females (73.15%) was significantly higher than that of males (25.99%) in ADR reports of the adult subgroup, while the gender distribution was relatively balanced in the children subgroup (females: 49.43%; males: 46.85%). This discrepancy is highly consistent with the epidemiological features of diseases targeted by omalizumab\u0026rsquo;s core indications (asthma, chronic urticaria), where the prevalence is substantially higher in females than in males(Ciprandi \u0026amp; Gallo, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, female sex hormones can induce immune responses and exacerbate inflammatory reactions(Sirufo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), thereby increasing the risk of ADRs. In contrast, gender-related physiological differences are not fully manifested in childhood, resulting in no significant gender bias in children ADR reports.\u003c/p\u003e \u003cp\u003eRegarding the characteristics of report sources, physician-submitted reports predominated in the children subgroup (43.55%), whereas consumer-initiated reports accounted for a higher proportion in the adult subgroup (43.42%). This difference may stem from the fact that children medication use strictly adheres to physician guidance, and their ADRs are mostly monitored and reported by healthcare professionals. In contrast, adults exhibit greater self-awareness of medication responses and face lower barriers to self-reporting, suggesting that adult subgroup reports may contain more subjective biases. Thus, the authenticity of signals requires further differentiation in conjunction with clinical information. In terms of annual distribution trends, the number of omalizumab-related ADR reports showed a continuous upward trend from 2004 to 2025, with a significantly accelerated growth rate after 2018. This change is directly associated with the gradual expansion of omalizumab\u0026rsquo;s indications, which now include the treatment of food allergy(Zhang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), allergic rhinitis(Ma et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), atopic dermatitis(Chan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and other allergic diseases.\u003c/p\u003e \u003cp\u003eAt the SOC level, the core positive signals of the adult and children subgroups shared commonalities while exhibiting specificity. Both groups showed strong positive signals for \"Respiratory, thoracic and mediastinal disorders\" (adults: ROR\u0026thinsp;=\u0026thinsp;3.78; children: ROR\u0026thinsp;=\u0026thinsp;3.77) and \"Immune system disorders\" (adults: ROR\u0026thinsp;=\u0026thinsp;3.33; children: ROR\u0026thinsp;=\u0026thinsp;3.36). This common feature is linked to omalizumab\u0026rsquo;s core pharmacological mechanism: it exerts therapeutic effects by specifically binding to free IgE and blocking its interaction with surface receptors on mast cells and eosinophils(Chow et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Humbert et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pelaia et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The respiratory and immune systems are the primary target organs of IgE-mediated diseases, so the aforementioned ADRs are classified as \"class-effect ADRs\" of the drug, independent of age. In the adult subgroup, \"General disorders and administration site conditions\" had the highest number of reports (28,099 cases) but only a mild positive signal (ROR\u0026thinsp;=\u0026thinsp;1.11), indicating that such reactions are mostly cumulative events resulting from the large adult treatment population and prolonged drug exposure, with a weak direct association with the drug. In contrast, this SOC not only ranked first in report volume in the children subgroup but also showed a moderate positive signal (ROR\u0026thinsp;=\u0026thinsp;1.56), which is closely related to the immature development of children\u0026rsquo;s organisms and their increased sensitivity to local stimulation (e.g., injection site reactions) and systemic stress responses induced by biological agents(Elzagallaai et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, the children subgroup additionally exhibited positive signals for \"Musculoskeletal and connective tissue disorders\", while no valid signals for these SOCs were detected in the adult subgroup. This difference may be associated with the unique characteristics of the musculoskeletal system during childhood development, and the immunomodulatory effects of biological agents may indirectly affect the physiological state of this system, further reflecting age-specific differences in the ADR profile.\u003c/p\u003e \u003cp\u003eSignal characteristics at the PT level further underscored the age specificity of omalizumab-related ADRs. The adult subgroup exhibited broad PT coverage (4,674 terms in total) with diverse manifestations, and its core signals were concentrated in allergic skin reactions and infectious manifestations (e.g., asthma: 4,185 cases, ROR\u0026thinsp;=\u0026thinsp;17.08; urticaria: 3,745 cases, ROR\u0026thinsp;=\u0026thinsp;9.17). This feature is associated with the large adult treatment population, prolonged drug exposure, and a higher prevalence of chronic comorbidities that elevate infection risk in adults. In contrast, the children subgroup had narrow PT coverage (1,504 terms in total) with highly focused manifestations; its core signals were dominated by local administration site reactions (e.g., injection site pain, erythema, induration) and acute severe respiratory reactions. Notably, \u003cem\u003easthmatic crisis\u003c/em\u003e was a unique strong positive signal in the children subgroup (ROR\u0026thinsp;=\u0026thinsp;52.83, IC\u0026thinsp;=\u0026thinsp;5.36), indicating that children face a significantly higher risk of acute asthma exacerbation following omalizumab administration\u0026mdash; a finding consistent with the physiological characteristics of immature immune system development and more prominent airway hyperreactivity in children(Carr et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWeibull distribution analysis revealed that the cumulative incidence of ADRs in both the children and adult subgroups exhibited a characteristic pattern of a rapid initial rise followed by a gradual plateau, yet the ascending rate was steeper in the children subgroup: the cumulative incidence exceeded 90% within the first 1,000 days of administration in children, compared with approximately 85% in adults. Concurrently, the median time to onset of ADRs in the children subgroup (34.4 days) was significantly shorter than that in the adult subgroup (63.1 days), and the characteristic life (α\u0026thinsp;=\u0026thinsp;80.03 days) was also much lower than that of adults (α\u0026thinsp;=\u0026thinsp;143.85 days). Further, the peak density of the Weibull distribution for ADR onset time demonstrated that the peak incidence density in children was 0.0040 within the first 0\u0026ndash;100 days of administration, significantly higher than the 0.0025 observed in adults. These findings suggest a more concentrated time window for ADR occurrence and more prominent early-stage risk in children. The cumulative incidence curve of the adult subgroup reached a plateau at approximately 6,000 days, later than the 3,000 days in the children subgroup, indicating a more persistent late-onset risk in adults\u0026mdash; a feature consistent with the clinical reality that adults often have more chronic comorbidities, a higher rate of polypharmacy, and longer drug exposure durations(Song et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These age-related differences in temporal risk characteristics provide clear targets for phase-specific clinical monitoring: for children patients, intensive monitoring should be prioritized within the first 34 days (the median time to onset) of administration, with enhanced prevention and control of acute reactions in the first 100 days in particular; for adult patients, clinical practice must balance early monitoring within the first 63 days with long-term follow-up for late-onset risks to avoid missing chronic adverse reactions.\u003c/p\u003e \u003cp\u003eThe age-specific differences in omalizumab-related ADRs are essentially the result of the interplay between drug effects and the physiological characteristics and disease spectrum of different age groups. First, children have immature hepatic and renal function with impaired drug metabolism and excretion capacity, which easily leads to drug accumulation(Carr et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Their immune systems are in a developmental stage with a higher proportion of naive immune cells, rendering them more sensitive to the immunomodulatory effects of biological agents and thus more prone to acute inflammatory or local stimulatory reactions. In contrast, adults have a relatively stable composition of immune cells and stronger adaptive capacity; however, thymic involution in old age can induce immune senescence, which, combined with the impact of chronic comorbidities, results in a significantly elevated risk of late-onset ADRs(Palmer, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Second, omalizumab has broader indications in adults, who also frequently suffer from chronic comorbidities such as hypertension and diabetes; polypharmacy further increases the complexity of ADR occurrence. In children, by contrast, omalizumab use is mostly limited to core indications such as asthma, with relatively simple medication regimens.\u003c/p\u003e \u003cp\u003eThis study has several inherent limitations. First, there are limitations in the data source: over 64% of reports in the FAERS database are from the United States, with insufficient data from non-European and American populations, which may compromise the generalizability of the study results. Second, spontaneous reporting systems have inherent biases, including missing information in some reports and variability in report quality between consumer-submitted and healthcare professional-submitted data, which may affect the accuracy of signal detection. Third, disproportionality analysis can only indicate an association between omalizumab and ADRs, but cannot establish a causal relationship; further verification is required through prospective clinical trials.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBased on real-world data from the U.S. FAERS database, this study employed four disproportionality analysis algorithms to investigate the post-marketing ADRs of omalizumab in children and adult populations. We clearly identified significant age-specific differences in the ADR profiles, signal intensities, and onset times of omalizumab between the two age groups. Clinically, precise and tailored monitoring protocols should be developed for different populations to maximize the clinical benefits of medication and minimize associated safety risks. This study provides robust real-world data support for the safe clinical use of omalizumab, and also offers a methodological reference for age-stratified pharmacovigilance research on biological agents.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests in relation to this research, authorship and publication of this manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS and HZ conceived and designed the study. HZ and LL collected and processed the data from the FAERS database. HZ and NL performed the statistical analysis, including signal detection with ROR, PRR, BCPNN, and MGPS methods. S supervised the study and critically revised the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors acknowledge the U.S. FAERS database used in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are openly available in the FDA Adverse Event Reporting System (FAERS) Public Dashboard at https://www.fda.gov/drugs/development-approval-process-drugs/drug-approvals-and-databases, maintained by the U.S. Food and Drug Administration (FDA). All raw data used in this analysis are accessible to the public via the official FAERS database portal.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAgache, I., Rocha, C., Beltran, J., Song, Y., Posso, M., Sol\u0026agrave;, I., Alonso-Coello, P., Akdis, C., Akdis, M., Canonica, G. 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M., De Pietro, F., Ginaldi, L., \u0026amp; De Martinis, M. (2022). Sex, Allergic Diseases and Omalizumab. \u003cem\u003eBiomedicines\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(2), 328. https://doi.org/10.3390/biomedicines10020328\u003c/li\u003e\n \u003cli\u003eSong, Y., Wang, Z., Wang, N., Xie, X., Zhu, T., \u0026amp; Wang, Y. (2025). A real-world pharmacovigilance study of omalizumab using disproportionality analysis in the FDA adverse drug events reporting system database. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e, 8045. https://doi.org/10.1038/s41598-025-91463-5\u003c/li\u003e\n \u003cli\u003eZhang, Y., Liang, B., Tian, L., Chen, B., \u0026amp; Wu, S. (2025). Systematic review and meta-analysis of omalizumab for IgE-mediated food allergy in children and young adults. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e, 1690650. https://doi.org/10.3389/fimmu.2025.1690650\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":"naunyn-schmiedebergs-archives-of-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nsap","sideBox":"Learn more about [Naunyn-Schmiedeberg's Archives of Pharmacology](https://www.springer.com/journal/210)","snPcode":"210","submissionUrl":"https://submission.nature.com/new-submission/210/3","title":"Naunyn-Schmiedeberg's Archives of Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Omalizumab, Adverse Drug Reactions, FAERS, Real-world study, Children","lastPublishedDoi":"10.21203/rs.3.rs-9305106/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9305106/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective：\u003c/strong\u003eTo analyze age-specific differences in post-marketing adverse drug reaction (ADR) signals of omalizumab between children (\u0026lt;18 years) and adults (≥18 years) using FAERS data, to inform optimized medication safety monitoring strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods：\u003c/strong\u003eOmalizumab-related ADR reports (2003Q1–2025Q3) were extracted from FAERS and stratified by age. ADRs were coded with MedDRA. Signals were mined using four disproportionality methods (ROR, PRR, BCPNN, MGPS), with validity requiring all thresholds met. Cumulative incidence and time-to-onset (TTO) were analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults：\u003c/strong\u003eAmong 62,925 reports (4% children, 45% adults), both groups showed strong signals for respiratory/immune disorders. Children had an additional musculoskeletal signal; adults had more general disorders. At PT level, adults had broader coverage (allergic/infectious signals), while children had narrower coverage with a unique strong signal for asthmatic crisis (ROR=52.83). Children had shorter median TTO (34.4 vs 63.1 days) and faster cumulative incidence; adults had longer late-onset risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion：\u003c/strong\u003eOmalizumab shows significant age-specific ADR differences. Individualized monitoring strategies are needed to enhance clinical safety.\u003c/p\u003e","manuscriptTitle":"Age-Specific Differences in Omalizumab-Related Adverse Drug Reaction Signals between Children and Adults: An Analysis Based on the FAERS Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 15:03:50","doi":"10.21203/rs.3.rs-9305106/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-14T13:48:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202933025997325767222398213651492720106","date":"2026-04-08T11:14:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16347182742873594092601261395223837982","date":"2026-04-07T21:12:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T14:23:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T00:08:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-06T00:07:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Naunyn-Schmiedeberg's Archives of Pharmacology","date":"2026-04-02T15:24:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"naunyn-schmiedebergs-archives-of-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nsap","sideBox":"Learn more about [Naunyn-Schmiedeberg's Archives of Pharmacology](https://www.springer.com/journal/210)","snPcode":"210","submissionUrl":"https://submission.nature.com/new-submission/210/3","title":"Naunyn-Schmiedeberg's Archives of Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c6d9859b-0a35-4b36-82c1-0105fc99b319","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T15:03:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 15:03:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9305106","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9305106","identity":"rs-9305106","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}