ABSTRACT
Background
Mogamulizumab is a monoclonal antibody targeting the C‐C chemokine receptor 4, used to treat T‐cell malignancies such as cutaneous T‐cell lymphoma, adult T‐cell leukemia/lymphoma, and peripheral T‐cell lymphoma. However, real‐world studies on mogamulizumab‐associated adverse events (AEs) are limited.
Methods
Disproportionality analyses were performed to assess the safety profile of mogamulizumab based on data from the US Food and Drug Administration Adverse Event Reporting System (FAERS) database for the period spanning from October 2018 to December 2023. The research investigated demographic characteristics, the onset timing of AEs, and the safety implications associated with mogamulizumab use.
Results
A total of 1182 significant preferred terms were identified among the 3661 mogamulizumab‐associated AE reports collected from the FAERS database. The frequently reported AEs including rash, infusion‐related reaction, and pyrexia were in line with drug instruction. Notably, several unexpectedly significant AEs were also found, including pemphigoid (ROR = 5.69 [95% CI 1.83–17.66]), unstable angina (ROR = 20.56 [95% CI 8.54–49.5]), bulbar palsy (ROR = 238.36 [95% CI 75.22–755.31]), myositis (ROR = 12.65 [95% CI 5.67–28.19]), and various autoimmune diseases such as autoimmune hepatitis (ROR = 21.33 [95% CI 11.08–41.07]), myocarditis (ROR = 15.29 [95% CI 8.67–26.97]), glomerulonephritis (ROR = 22.49 [95% CI 7.24–69.9]), nephrotic syndrome (ROR = 7.63 [95% CI 2.46–23.67]), myasthenia gravis (ROR = 8.54 [95% CI 3.2–22.77]), and autoimmune thyroiditis (ROR = 11.81 [95% CI 3.8–36.68]).
Conclusion
This study replicated previously identified AEs associated with mogamulizumab and uncovered additional signals of AEs, particularly emphasizing the risks associated with autoimmune diseases. It is essential to exercise vigilance in monitoring the occurrence of these AEs during the use of mogamulizumab in clinical practice.
Keywords: adverse events, disproportionality analyses, Food and Drug Administration Adverse Event Reporting System, mogamulizumab
Abbreviations
- ADCC
antibody‐dependent cell‐mediated cytotoxicity
- AER
adverse event reports
- AEs
adverse events
- ATLL
adult T‐cell leukemia/lymphoma
- BCPNN
Bayesian confidence propagation neural network
- CCR4
chemokine receptor 4
- CTCL
cutaneous T‐cell lymphoma
- FAERS
Food and Drug Administration's adverse event reporting system
- FDA
Food and Drug Administration
- MedDRA
medical dictionary for regulatory activities
- MGPS
multi‐item gamma Poisson shrinker
- PRR
proportional reporting ratio
- PTCL
peripheral T‐cell lymphoma
- ROR
reporting odds ratio
- SOC
system organ class
- Treg
regulatory T
1. Introduction
The transmembrane chemokine receptor 4 (CCR4) is predominantly expressed on the surface of Th2 helper cells and regulatory T (Treg) cells [1, 2]. However, in T‐cell malignancies such as cutaneous T‐cell lymphoma (CTCL), adult T‐cell leukemia/lymphoma (ATLL), and peripheral T‐cell lymphoma (PTCL), the malignant T‐cell tumor cells often exhibit an overexpression of this receptor [2, 3]. Mogamulizumab is a monoclonal antibody that targets CCR4, promoting antibody‐dependent cell‐mediated cytotoxicity (ADCC) and leading to the elimination of target cells [4, 5, 6]. In a phase III clinical trial, mogamulizumab demonstrated a significant extension in patients' survival compared to vorinostat, unveiling a novel therapeutic avenue for CTCL [5]. Consequently, the U.S. Food and Drug Administration (FDA) authorized the utilization of mogamulizumab in adult patients grappling with relapsed/refractory mycosis fungoides, Sezary syndrome, and those who have previously undergone at least one systemic therapy [7]. Additionally, mogamulizumab is also used for treating other T‐cell lymphomas, including ATLL and PTCL [8, 9].
Although mogamulizumab exhibits promising clinical efficacy, it is also linked to various adverse reactions. Previous studies have shown that the most frequent adverse events (AEs) associated with mogamulizumab include upper respiratory tract infections, musculoskeletal pain, diarrhea, fatigue, infusion‐related reactions, and rash (reported incidence ≥ 20%) [7]. Approximately 36% of patients report experiencing serious AEs, with infections accounting for 16% of all patients [7]. In addition, refractory graft‐versus‐host disease, autoimmune complications, and skin toxicity have been observed as AEs [7, 10]. However, the safety data mainly come from clinical trials and postmarketing observational studies. Real‐world studies can compensate for the small sample size in clinical trials, yet research on the safety of mogamulizumab in the real world is limited.
The U.S. Food and Drug Administration's Adverse Event Reporting System (FAERS) provides abundant real‐world data that can be used to evaluate the safety and effectiveness of drugs [11]. This study aims to assess the safety of mogamulizumab after its approval through data mining of the FAERS database.
2. Materials and Methods
2.1. Data Sources
This study utilized ASCII report files from the FAERS spanning from the last quarter of 2018 to the last quarter of 2023 as the primary data source. Data management and visualization processing were conducted using MySQL 15.0 [12].
2.2. Data Extraction
Duplicate and conflicting reports were eliminated from the files, retaining only the most recent case IDs to ensure data accuracy. The reports were identified using primary ID, and the drug names were standardized. Mogamulizumab was designated as the primary drug, and reports about AEs were collected for variable analysis, including patient age, gender, reporter types, and regions. The flowchart of data processing can be found in Figure 1.
FIGURE 1.
The process of selecting mogamulizumab‐associated AEs from FAERS database.
2.3. Definition of AEs
During the preprocessing phase of this study, reports with preferred terms (PTs) < 3 were excluded. Medical Dictionary for Regulatory Activities (MedDRA) was employed to reposition, categorize, and encode AEs based on PT and system organ class (SOC) to analyze their impact on organ systems.
2.4. Statistical Analysis
Disproportionality analysis was used to determine the potential association between mogamulizumab and AEs, aiming to evaluate the correlation between the drug and AEs through comparing the ratio of observed frequencies in exposed and unexposed populations (Table S1). In this study, four disproportionality methods were employed to identify signals of AEs: reporting odds ratio (ROR) [13], proportional reporting ratio (PRR) [14], multiple‐item shrinker of Bayesian confidence propagation neural network (BCPNN) [14], and multi‐item gamma Poisson shrinker (MGPS) [15]. ROR offers the advantage of correcting biases induced by low numbers of event reports, while PRR demonstrates higher specificity in comparison to ROR. BCPNN excels in integrating diverse data sources and conducting cross‐validation. MGPS is particularly adept at detecting signals arising from rare events. The specific formulas and thresholds for these four methods can be found in Table S2. Higher values in this analysis indicate stronger signal strength, reflecting a more robust relationship between the target drug and AEs.
3. Results
3.1. Basic Characteristics of Mogamulizumab‐Related AEs
In this study, we retrieved a total of 9,033,057 adverse event reports (AER) from the FAERS database, covering the period from the last quarter of 2018 to the last quarter of 2023. Among these reports, 1182 confirmed mogamulizumab as the primary suspected drug responsible for AEs. Notably, the majority of reports (95.01%) were submitted by healthcare professionals rather than consumers. The United States accounted for the highest proportion of reports (58.71%), followed by Japan (17.85%) and France (8.29%). Regarding clinical outcomes, AEs leading to hospitalization or prolonged hospital stay were the most frequently reported (22.23%), in addition to undisclosed severe AEs. Moreover, a total of 197 reports (19.47%) involved death. Notwithstanding, it is crucial to highlight that a substantial amount of data lacked age and gender information, thus limiting our comprehensive understanding of the relationship between age, gender, and AEs. Refer to Table 1 for further details.
TABLE 1.
Basic information on adverse events related to mogamulizumab from the FAERS database.
| Variable | Total |
|---|---|
| Year | |
| 2018 | 19 (1.61%) |
| 2019 | 209 (17.68%) |
| 2020 | 193 (16.33%) |
| 2021 | 254 (21.49%) |
| 2022 | 326 (27.58%) |
| 2023 | 181 (15.31%) |
| Reporter | |
| Pharmacist | 722 (61.08%) |
| Physician | 240 (20.30%) |
| Other health‐professional | 161 (13.62%) |
| Consumer | 58 (4.91%) |
| Unknown | 1 (0.08%) |
| Reported countries | |
| United States | 694 (58.71%) |
| Japan | 211 (17.85%) |
| France | 98 (8.29%) |
| Other | 179 (15.14%) |
| Outcomes | |
| Hospitalization | 225 (22.23%) |
| Death | 197 (19.47%) |
| Life threatening | 35 (3.46%) |
| Disability | 9 (0.89%) |
| Other serious | 546 (53.95%) |
3.2. Signal Detects at the SOC Level
Table 2 presents the signal strengths of mogamulizumab‐related AEs classified by SOC. Based on our statistical analysis, AEs related to mogamulizumab occurred in 22 organ systems. Within these organ systems, several important SOC categories were identified through the screening process, all of which met the criteria of having at least one indicator satisfying the standard among the four analyzed indicators. Significant SOCs included skin and subcutaneous tissue disease (cases = 793, ROR 4.37 [95% CI 4.04–4.73]); infections and invasions (cases = 276, ROR 1.35 [95% CI 1.2–1. 53]); blood and lymphatic system disorders (case = 155, ROR 2.49 [95% CI 2.12–2.93]); metabolism and nutrition disorders (cases = 105, ROR 1.45 [95% CI 1.2–1.77]); immune system disorders (cases = 88, ROR 1.92 [95% CI 1.56–2.38]); and hepatobiliary disorders (cases = 67, ROR 2.17 [95% CI 1.71–2.77]).
TABLE 2.
The signal strength of adverse events of mogamulizumab at the SOC level in FAERS database.
| SOC | Number of cases (n) | ROR (95% CI) | PRR (95% CI) | Chi‐squared | IC (IC025) | EBGM (EBGM05) |
|---|---|---|---|---|---|---|
| Skin and subcutaneous tissue disorders | 793 | 4.37 (4.04, 4.73) | 3.63 (3.42, 3.85) | 1606.29 | 1.86 (1.75) | 3.63 (3.4) |
| General disorders and administration site conditions | 701 | 1.07 (0.99, 1.17) | 1.06 (1, 1.12) | 2.93 | 0.08 (−0.03) | 1.06 (0.99) |
| Infections and infestations | 276 | 1.35 (1.2, 1.53) | 1.33 (1.18, 1.5) | 23.41 | 0.41 (0.23) | 1.33 (1.2) |
| Injury, poisoning, and procedural complications | 250 | 0.53 (0.47, 0.61) | 0.57 (0.51, 0.64) | 94.13 | −0.82 (−1) | 0.57 (0.51) |
| Gastrointestinal disorders | 226 | 0.74 (0.65, 0.85) | 0.76 (0.68, 0.85) | 18.62 | −0.4 (−0.59) | 0.76 (0.68) |
| Investigations | 218 | 1 (0.87, 1.14) | 1 (0.87, 1.15) | 0 | 0 (−0.2) | 1 (0.89) |
| Blood and lymphatic system disorders | 155 | 2.49 (2.12, 2.93) | 2.43 (2.08, 2.84) | 132.75 | 1.28 (1.05) | 2.43 (2.12) |
| Nervous system disorders | 139 | 0.48 (0.4, 0.57) | 0.5 (0.43, 0.58) | 76.34 | −1.01 (−1.25) | 0.5 (0.43) |
| Musculoskeletal and connective tissue disorders | 109 | 0.56 (0.46, 0.67) | 0.57 (0.48, 0.68) | 37.66 | −0.81 (−1.09) | 0.57 (0.48) |
| Metabolism and nutrition disorders | 105 | 1.45 (1.2, 1.77) | 1.44 (1.18, 1.75) | 14.47 | 0.53 (0.25) | 1.44 (1.23) |
| Respiratory, thoracic, and mediastinal disorders | 103 | 0.59 (0.49, 0.72) | 0.6 (0.49, 0.73) | 28.03 | −0.73 (−1.01) | 0.6 (0.51) |
| Immune system disorders | 88 | 1.92 (1.56, 2.38) | 1.9 (1.53, 2.36) | 38.1 | 0.93 (0.62) | 1.9 (1.59) |
| Cardiac disorders | 85 | 1.13 (0.91, 1.4) | 1.13 (0.91, 1.4) | 1.28 | 0.17 (−0.13) | 1.13 (0.94) |
| Psychiatric disorders | 73 | 0.34 (0.27, 0.43) | 0.36 (0.28, 0.46) | 89.73 | −1.49 (−1.82) | 0.36 (0.29) |
| Hepatobiliary disorders | 67 | 2.17 (1.71, 2.77) | 2.15 (1.7, 2.72) | 41.72 | 1.11 (0.76) | 2.15 (1.76) |
| Neoplasms benign, malignant, and unspecified (incl cysts and polyps) | 53 | 0.37 (0.28, 0.48) | 0.38 (0.29, 0.5) | 57.33 | −1.41 (−1.8) | 0.38 (0.3) |
| Vascular disorders | 52 | 0.73 (0.56, 0.96) | 0.73 (0.55, 0.96) | 5.07 | −0.44 (−0.84) | 0.73 (0.58) |
| Renal and urinary disorders | 52 | 0.66 (0.5, 0.87) | 0.67 (0.51, 0.88) | 8.87 | −0.59 (−0.98) | 0.67 (0.53) |
| Eye disorders | 31 | 0.43 (0.3, 0.61) | 0.43 (0.3, 0.61) | 23.65 | −1.21 (−1.71) | 0.43 (0.32) |
| Ear and labyrinth disorders | 17 | 1.09 (0.68, 1.76) | 1.09 (0.68, 1.74) | 0.13 | 0.13 (−0.54) | 1.09 (0.73) |
| Endocrine disorders | 14 | 1.42 (0.84, 2.41) | 1.42 (0.84, 2.41) | 1.76 | 0.51 (−0.22) | 1.42 (0.92) |
| Reproductive system and breast disorders | 7 | 0.29 (0.14, 0.62) | 0.3 (0.14, 0.63) | 11.86 | −1.76 (−2.76) | 0.3 (0.16) |
3.3. Signal of PTs
A total of 92 AEs related to mogamulizumab have been identified, covering 16 SOCs, all meeting the criteria of four algorithms (Table S3). After excluding AEs potentially caused by the disease itself, which could lead to inaccurate reporting (e.g., disease progression and pruritus), the top five PTs in terms of number of cases were rash (n = 218), infusion‐related reaction (n = 95), pyrexia (n = 73), erythema (n = 52), and drug eruption (n = 48). Table 3 displays the top 40 PTs sorted by ROR. Upon ranking by descending ROR values, the top five PTs are tumor flare (ROR 256.55 [95% CI 126.42–520.63]), cytomegalovirus enteritis (ROR = 244.37 [95% CI 114.76–520.32]), bulbar palsy (ROR = 238.36 [95% CI 75.22–755.31]), cytomegalovirus enterocolitis (ROR = 124.39 [95% CI 55.41–279.27]), and vitiligo (ROR = 66.41[95% CI 35.58–123.93]). Notably, several unexpectedly significant AEs were also identified, including pemphigoid (ROR = 5.69[95% CI 1.83–17.66]), unstable angina (ROR = 20.56 [95% CI 8.54–49.5]), bulbar palsy (ROR = 238.36 [95% CI 75.22–755.31]), myositis (ROR = 12.65 [95% CI 5.67–28.19]), and several autoimmune diseases, including autoimmune hepatitis (ROR = 21.33 [95% CI 11.08–41.07]), myocarditis (ROR = 15.29 [95% CI 8.67–26.97]), glomerulonephritis (ROR = 22.49 [95% CI 7.24–69.9]), nephrotic syndrome (ROR = 7.63 [95% CI 2.46–23.67]), myasthenia gravis (ROR = 8.54 [95% CI 3.2–22.77]), and autoimmune thyroiditis (ROR = 11.81 [95% CI 3.8–36.68]), as shown in Figure 2. Additionally, mogamulizumab was found to be associated with leukopenia (ROR = 3.35 [95% CI 2.24–5.01]), neutropenia (ROR = 8.4 [95% CI 6.02–11.72]), and thrombocytopenia (ROR = 6.79 [95% CI 2.19–21.08]).
TABLE 3.
The top 40 signal strength of adverse events of mogamulizumab ranked by ROR at the PT level in FAERS database.
| PT | Number of cases (n) | ROR (95% CI) | PRR (95% CI) | Chi‐squared | IC (IC025) | EBGM (EBGM05) |
|---|---|---|---|---|---|---|
| Tumor flare | 8 | 256.55 (126.42, 520.63) | 255.98 (126.41, 518.38) | 1952.23 | 7.94 (6.98) | 245.98 (136.06) |
| Cytomegalovirus enteritis | 7 | 244.37 (114.76, 520.32) | 243.89 (115.81, 513.64) | 1629.92 | 7.88 (6.85) | 234.8 (124.76) |
| Bulbar palsy | 3 | 238.36 (75.22, 755.31) | 238.16 (74.93, 756.98) | 682.58 | 7.84 (6.39) | 229.48 (87.43) |
| Cytomegalovirus enterocolitis | 6 | 124.39 (55.41, 279.27) | 124.19 (55.6, 277.39) | 718.96 | 6.93 (5.85) | 121.8 (61.91) |
| Vitiligo | 10 | 66.41 (35.58, 123.93) | 66.23 (35.37, 124.01) | 635.72 | 6.03 (5.18) | 65.54 (38.89) |
| Graft‐versus‐host disease | 21 | 55.42 (36.02, 85.27) | 55.11 (35.81, 84.82) | 1106.01 | 5.77 (5.16) | 54.63 (38.1) |
| Graft‐versus‐host disease in gastrointestinal tract | 10 | 54.73 (29.34, 102.08) | 54.58 (29.15, 102.19) | 521.5 | 5.76 (4.9) | 54.12 (32.12) |
| Drug eruption | 48 | 48.45 (36.4, 64.48) | 47.82 (36.34, 62.92) | 2184.39 | 5.57 (5.16) | 47.47 (37.37) |
| Graft‐versus‐host disease in skin | 8 | 45.1 (22.48, 90.47) | 45 (22.66, 89.36) | 341.73 | 5.48 (4.53) | 44.68 (24.96) |
| Cytomegalovirus test positive | 4 | 40.31 (15.07, 107.8) | 40.27 (15.11, 107.3) | 152.19 | 5.32 (4.05) | 40.02 (17.57) |
| Alopecia areata | 6 | 40.1 (17.96, 89.54) | 40.03 (17.92, 89.41) | 226.9 | 5.31 (4.24) | 39.78 (20.31) |
| Granuloma | 6 | 32.89 (14.73, 73.41) | 32.84 (14.7, 73.35) | 184.24 | 5.03 (3.96) | 32.67 (16.69) |
| Psoriasiform dermatitis | 4 | 30.48 (11.41, 81.44) | 30.44 (11.42, 81.11) | 113.36 | 4.92 (3.65) | 30.3 (13.31) |
| Infusion site reaction | 4 | 28.77 (10.77, 76.86) | 28.74 (10.79, 76.58) | 106.6 | 4.84 (3.57) | 28.61 (12.57) |
| Cytomegalovirus chorioretinitis | 4 | 28.7 (10.74, 76.68) | 28.67 (10.76, 76.39) | 106.34 | 4.84 (3.57) | 28.54 (12.54) |
| Generalized exfoliative dermatitis | 9 | 27.56 (14.31, 53.09) | 27.49 (14.4, 52.49) | 228.79 | 4.77 (3.88) | 27.38 (15.82) |
| Acute graft‐versus‐host disease in skin | 4 | 25.06 (9.38, 66.95) | 25.04 (9.4, 66.72) | 91.94 | 4.64 (3.37) | 24.94 (10.96) |
| Angina unstable | 5 | 24.7 (10.26, 59.49) | 24.67 (10.21, 59.6) | 113.12 | 4.62 (3.46) | 24.58 (11.78) |
| Glomerulonephritis | 3 | 22.63 (7.28, 70.35) | 22.61 (7.25, 70.47) | 61.75 | 4.49 (3.08) | 22.54 (8.72) |
| Autoimmune hepatitis | 9 | 21.72 (11.28, 41.82) | 21.67 (11.35, 41.38) | 176.83 | 4.43 (3.54) | 21.6 (12.48) |
| Cytomegalovirus viremia | 7 | 21.26 (10.11, 44.68) | 21.22 (10.08, 44.69) | 134.42 | 4.4 (3.4) | 21.15 (11.36) |
| Infusion‐related reaction | 95 | 20.96 (17.09, 25.71) | 20.43 (16.79, 24.85) | 1752.52 | 4.35 (4.06) | 20.37 (17.17) |
| Acute graft‐versus‐host disease | 5 | 20.02 (8.32, 48.21) | 20 (8.28, 48.31) | 89.96 | 4.32 (3.16) | 19.94 (9.56) |
| Hypoalbuminemia | 8 | 19.49 (9.73, 39.04) | 19.45 (9.79, 38.62) | 139.57 | 4.28 (3.33) | 19.39 (10.84) |
| Skin erosion | 4 | 18.49 (6.93, 49.37) | 18.47 (6.93, 49.21) | 65.92 | 4.2 (2.94) | 18.42 (8.1) |
| Lymphopenia | 18 | 18.24 (11.47, 29) | 18.15 (11.34, 29.05) | 290.97 | 4.18 (3.53) | 18.1 (12.28) |
| Hyperuricemia | 4 | 17.57 (6.58, 46.91) | 17.56 (6.59, 46.79) | 62.28 | 4.13 (2.86) | 17.51 (7.7) |
| Cytomegalovirus infection reactivation | 6 | 16.63 (7.46, 37.09) | 16.61 (7.44, 37.1) | 87.77 | 4.05 (2.98) | 16.56 (8.47) |
| Skin disorder | 35 | 16.53 (11.84, 23.07) | 16.38 (11.74, 22.86) | 504.41 | 4.03 (3.56) | 16.34 (12.36) |
| Skin lesion | 28 | 16.32 (11.24, 23.68) | 16.2 (11.16, 23.51) | 398.44 | 4.01 (3.49) | 16.16 (11.83) |
| Myocarditis | 12 | 15.29 (8.67, 26.97) | 15.24 (8.63, 26.91) | 159.36 | 3.93 (3.14) | 15.21 (9.46) |
| Cytomegalovirus infection | 16 | 14.59 (8.92, 23.86) | 14.53 (8.9, 23.72) | 201.21 | 3.86 (3.17) | 14.5 (9.61) |
| Skin plaque | 10 | 14.29 (7.68, 26.6) | 14.25 (7.61, 26.68) | 122.95 | 3.83 (2.97) | 14.22 (8.45) |
| Erythema multiforme | 6 | 13.9 (6.23, 30.98) | 13.88 (6.21, 31) | 71.53 | 3.79 (2.72) | 13.85 (7.08) |
| Skin weeping | 3 | 13.23 (4.26, 41.1) | 13.22 (4.24, 41.2) | 33.82 | 3.72 (2.31) | 13.2 (5.11) |
| Myositis | 6 | 12.65 (5.67, 28.19) | 12.63 (5.65, 28.21) | 64.12 | 3.66 (2.58) | 12.6 (6.44) |
| Hypercalcemia | 9 | 12.46 (6.48, 23.99) | 12.44 (6.52, 23.75) | 94.47 | 3.63 (2.74) | 12.41 (7.18) |
| Autoimmune thyroiditis | 3 | 12.05 (3.88, 37.41) | 12.04 (3.86, 37.53) | 30.31 | 3.59 (2.17) | 12.02 (4.66) |
| Autoimmune hemolytic anemia | 3 | 11.66 (3.75, 36.2) | 11.65 (3.74, 36.31) | 29.16 | 3.54 (2.12) | 11.63 (4.51) |
| Stevens–Johnson syndrome | 10 | 11.22 (6.03, 20.89) | 11.19 (5.98, 20.95) | 92.68 | 3.48 (2.63) | 11.18 (6.65) |
FIGURE 2.
The signal strength of adverse events for mogamulizumab‐associated autoimmune diseases. PT, preferred term; ROR, reporting odds ratio; TTO, time to onset.
3.4. Onset Time of Mogamulizumab‐Related AEs
We obtained data on the occurrence time of AEs related to mogamulizumab from the FAERS database. After excluding erroneous and missing reports, a total of 255 reports provided accurate information on the onset time. According to the data, the median onset time for AEs was 21 days, with an interquartile range (IQR) of 2–107 days. As shown in Figure S1, 33.73% of patients experienced adverse reactions within the first week of using mogamulizumab, and 20.78% of patients experienced adverse reactions within the first month. The probability of experiencing AEs was lowest in the second month of treatment, at only 9.80%. However, after 2 months, the probability of AEs significantly increased to 35.69%. These findings suggest the necessity of monitoring potential adverse reactions in patients even after several months of mogamulizumab treatment.
4. Discussion
It is worth pointing out that previous safety studies of mogamulizumab have been limited to clinical trials or have focused solely on specific AEs such as rash. This narrow focus may not provide a comprehensive understanding of potential safety concerns. In this large‐scale study of real‐world drug surveillance, we have identified previously unknown AEs associated with mogamulizumab. These newly identified AEs encompass pemphigoid, myocarditis, bulbar palsy, myositis, myasthenia gravis, among others, in addition to the AEs already documented on the drug label. To our knowledge, this is the first pharmacovigilance analysis conducted after the market approval of mogamulizumab, providing valuable insights for further optimizing its clinical usage.
At the organ level, our study found that the most common AEs associated with mogamulizumab occurred in the “skin and subcutaneous tissue disorders” category. Previous clinical trials mainly reported skin‐related AEs such as rash [16], with few case reports of other conditions including vitiligo [17], alopecia areata [18], psoriasiform dermatitis [19], toxic epidermal necrolysis [20], and photosensitivity reaction [21]. These AEs were also confirmed in our study. Additionally, our research suggested some previously unreported skin manifestations, such as pemphigus and bullae formation. Mechanistically, while the mechanism of action of mogamulizumab targeting CCR4 can effectively reduce the malignant T‐cell burden and promote antitumor immunity by depleting Treg cells in the tumor microenvironment, it also leads to hyperactivation of the Th1 immune response. Some researchers have found a reduction in circulating and skin‐infiltrating Tregs during mogamulizumab treatment, leading to severe inflammatory and immune‐mediated skin disorders [22]. Although some studies suggest that certain types of skin lesions might be markers for better tumor prognosis [23], a larger‐scale observational study is needed to assess the impact of mogamulizumab‐induced skin AEs, such as alopecia areata, on tumor prognosis [19]. Moreover, previous studies indicated that 7% of patients receiving mogamulizumab therapy discontinued treatment due to drug eruption [7], and severe skin conditions causing physical appearance changes could result in significant psychological trauma for patients. This emphasizes the need for further research to identify reliable biomarkers for predicting and managing mogamulizumab‐induced skin manifestations.
Similar to many monoclonal antibody drugs, mogamulizumab was found to be associated with infectious events, including cytomegalovirus infection, staphylococcal infection, and fungal infection (candidiasis). However, it is crucial to note that in T‐cell lymphoma patients who have not received mogamulizumab treatment, the incidence of staphylococcal infections and sepsis is also present. Furthermore, due to limitations in the FAERS database, there may be reporting errors in AEs related to bacterial infections as identified in this study. CCR4 is expressed in Th17 cells, Tregs, and T‐helper type 2 cells, which play important roles in maintaining immune homeostasis [1]. As a monoclonal antibody targeting CCR4, mogamulizumab partially impairs the functions of these immune cells, thereby affecting normal anti‐infection responses in the body.
Furthermore, we also observed the impact of mogamulizumab on the hematological system. Mogamulizumab was found to be associated with leukopenia, neutropenia, and thrombocytopenia and could potentially cause severe coagulation abnormalities such as disseminated intravascular coagulation. It may also result in hypercalcemia, hyperuricemia, and other metabolic disturbances associated with tumor destruction, tumor metabolic disruption, and enhanced immune response. Close monitoring of changes in blood biochemical indicators is vital for timely management during clinical practice.
We also observed new signals related to self‐immune enhancement, including autoimmune hepatitis, myocarditis, glomerulonephritis, nephrotic syndrome, myasthenia gravis, and autoimmune thyroiditis. The specific mechanisms underlying these side effects remain to be elucidated, and predicting the occurrence of these AEs is a challenging goal.
Although this study provides reliable scientific evidence for the safety evaluation of mogamulizumab, all pharmacovigilance databases have inherent flaws [24, 25]. Firstly, disproportionality analysis can only assess the strength of signals and explore potential associations, but cannot determine causal relationships. Secondly, due to the phenomena of underreporting and overreporting, accurately quantifying the real risk in clinical practice is challenging, and the incidence rates calculated based on spontaneous reports are not precise. Additionally, the data analysis did not consider various unmeasured confounding factors that may affect AEs, such as potential drug interactions, adjustments in treatment regimens, and laboratory and instrument testing. Therefore, further experimental and prospective clinical studies are necessary to validate these findings.
5. Conclusion
Using real‐world data from the FAERS database, we performed pharmacovigilance analysis to identify safety signals and potential risks related to the utilization of mogamulizumab. The AEs observed in this study exhibited a general consistency with those indicated in the prescribing information; however, several unexpected and significant AEs were also detected, including autoimmune diseases like autoimmune hepatitis, myocarditis, and glomerulonephritis, as well as disseminated intravascular coagulation. These results, characterized by robust signal‐like AEs, serve to partially offset the limitations imposed by the small sample size in the clinical studies of this drug. However, given the presence of population heterogeneity, incomplete data, reporting bias, and other potential factors that may impact the analysis results, additional basic research and prospective clinical studies are still required to validate and interpret the relationship between reporting bias and these AEs. The findings of this study contribute to the investigation of rare AEs, augment the FAERS database system, and offer an innovative perspective for the identification of analogous events in the future.
Author Contributions
Genshan Zhang: data curation (equal), formal analysis (equal), methodology (equal), project administration (equal), validation (equal), visualization (equal), writing – original draft (lead), writing – review and editing (equal). Haokun Zhang: formal analysis (equal), writing – review and editing (equal). Jie Fu: data curation (equal), supervision (equal), validation (equal), visualization (equal), writing – review and editing (equal). Zhixin Cao: funding acquisition (lead), project administration (equal), writing – review and editing (equal).
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Data S1.
Acknowledgments
Thanks to the US Food and Drug Administration (FDA) for providing access to the FAERS database for this study. We are deeply grateful to Ting Wu (Department of Dermatology, The Second Affiliated Hospital of Nanchang University), Yanjie Xu (Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology), and Xiangshang Xu (Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology) for their guidance during the writing of this paper.
Funding: This study was supported by the Chen Xiao‐Ping Foundation for the Development of Science and Technology of Hubei Province (CXPJJH121003‐2104) for Z.C.
Contributor Information
Jie Fu, Email: fujie@tjh.tjmu.edu.cn.
Zhixin Cao, Email: zxcao@tjh.tjmu.edu.cn.
Data Availability Statement
The original data involved in this analysis can be downloaded from http://www.fda.gov/Safety/MedWatch/, while other data can be obtained by contacting the corresponding author.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data S1.
Data Availability Statement
The original data involved in this analysis can be downloaded from http://www.fda.gov/Safety/MedWatch/, while other data can be obtained by contacting the corresponding author.