Tecartus Real-World Adverse Event Reporting System in a Middle-Aged and Elderly Population: A FAERS-Based Pharmacovigilance Study

Abstract

Background

Tecartus (brexucabtagene autoleucel) is an autologous CAR-T product targeting CD19, but there are many undetected and unreported adverse events (AEs).

Methods

We counted data from the US Food and Drug Administration Adverse Event Reporting System (FAERS) from Q4 2020 through Q4 2024 for proportionate analyses to assess the association between Tecartus and adverse events and important medical events (IMEs).

Result

A total of 792 datasets related to Tecartus were collected. The following SOCs are significant signals: Nervous system disorders, Infections and infestations, Neoplasms benign, malignant and unspecified (incl cysts and polyps). The most common AEs are cytokine release syndrome, immune effector cell-associated neurotoxicity syndrome and neurotoxicity. With Tecartus, most AEs occur within one month, but AEs can still occur after one year of use.

Conclusion

By analysing data from the real-world FAERS database, we identified additional disproportionately reported AEs, providing clinicians with more information about disproportionately reported AEs before and during treatment.

Keywords

disproportionality analysis drug safety FAERS pharmacovigilance Tecartus

Introduction

Chimeric antigen receptor T-cell (CAR-T) immunotherapy activates T cells to recognise tumour-specific target antigens and exert anti-tumour effects. This type of immunotherapy has already made significant breakthroughs in haematological cancers, bringing new hope to many cancer patients. Among these, CAR-T cell therapy targeting CD19, a transmembrane glycoprotein of the immunoglobulin superfamily consisting of two extracellular immunoglobulin-like structural domains, a transmembrane region and a long intracellular tail.¹ It has been the most rapidly developed and has not only been successfully commercialised, but is also undergoing a number of investigational clinical trials.² This transmembrane protein is expressed only on normal and malignant B cells, making CD19 an ideal antigen for cell type-specific immunotherapy. CD19 CAR-T has shown promising efficacy in haematological tumours, partly because their tumour microenvironment is less immunosuppressive and more accessible compared to that of many solid tumours.³ Recent studies have shown that CD19 in combination with PD-1 inhibitors, epigenetic drugs such as demethylating agents, has the effect of reversing T-cell depletion or increasing tumour antigen expression.^4,5

The FDA has approved a total of six CAR-T products, four of which are CD19-targeted CAR-T therapies, and Tecartus is one of them and one of the world’s first CAR-T therapies for the treatment of mantle cell lymphoma (MCL). It is a type of B-cell non-Hodgkin’s lymphoma (NHL) with distinctive biological behaviour and clinical features. MCL is a B-cell non-Hodgkin’s lymphoma. MCL accounts for approximately 5-7% of all lymphomas.^6,7 It predominantly affects middle-aged and older men, is aggressive and has a poor prognosis.⁸ Despite improved patient survival with novel targeted and cellular therapies, its high relapse rate and proportion of late diagnoses remain clinical challenges.

While chimeric antigen receptor (CAR) T-cell therapies demonstrate remarkable clinical efficacy in relapsed/refractory mantle cell lymphoma (MCL), their safety profile remains a critical concern. Notably, cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) are frequently reported dose-limiting toxicities, with severity grades correlating with adverse outcomes in elderly cohorts. Current clinical trials, primarily designed to establish efficacy endpoints, often enroll limited patient numbers (e.g., ZUMA-2 trial: N=74) and prioritize younger, fitter populations with fewer comorbidities.⁹ This inherent selection bias may underestimate the incidence and severity of adverse events (AEs) in real-world MCL patients, who are predominantly male (male-to-female ratio 2-3:1) and aged ≥65 years (median age 68). Importantly, age-related immunosenescence and multimorbidity (e.g., cardiovascular disease, renal dysfunction) synergistically amplify CAR-T toxicity risks.¹⁰

To date, there is a lack of comprehensive and systematic studies of Tecartus-related drug toxicity. The limited number of subjects during the clinical trial period and the often highly restricted nature of the trial process have resulted in trial results that may not be fully representative of the variety of AEs induced by CAR-T therapy.¹¹ As Tecartus has been marketed, real-world data has progressively increased, with the occurrence of some unreported AE events. The middle-aged and elderly population is a core group of Tecartus users, but their physiological vulnerabilities, comorbidities and immune profiles pose unique safety risks, making a comprehensive, systematic summary of AE events critical.¹²

Method

Data Sources

The FDA Adverse Event Reporting System (FAERS) serves as a pharmacovigilance platform that underpins the FDA’s post-marketing safety monitoring initiatives for approved medications and therapeutic biologics (https://fis._fda._gov/_exten_sions/_FPD-_QDE-_FA_ER_S/_F_PD_-_QDE_-_FA_ER_S.html). This database contains all adverse event and medication error information collected by the FDA. Typically, each quarterly FAERS package consists of seven data files: DEMO (patient demographic and administrative information), DRUG (drug information), REAC (AE code), OUTC (patient outcome), RPSR (reporting source), THER (start and end dates of treatment for reported drugs), INDI (indication for use), and deleted case information. This information allows a comprehensive analysis of AES events. Our study also adheres to the REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS-PV) statement guidelines for analyses based on the FAERS database.¹³

Pre-Processing of Data

In our study, all adverse events were identified and categorized based on the Medical Dictionary for Regulatory Activities (MedDRA). The definition of adverse drug events analyzed was derived from MedDRA ([https://www.meddra.org](https://www.meddra.org)). Adverse events were coded using MedDRA® Preferred Terms (PTs), which are standardized terms used to consistently and accurately describe specific medical conditions or events.^14-18 And we collected all uploaded AE data from Q4 2020 to Q4 2024. We defined our target population as all individuals aged 45 years or older who used Tacartus medication between Q4 2020 and Q4 2024. All of thesse individuals were included in the demographic analysis, with no sampling or subgroup selection methods employed.

Quantitative signal detection for assessing the association between the drug and reported AEs was conducted using disproportionality analysis. A significant safety signal for the target drug was conservatively defined as the concurrent satisfaction of all four of the following validated criteria: (1) For the Reporting Odds Ratio (ROR) method, ≥3 reports for the specific AE-drug pair and the lower limit of the 95% confidence interval (95% CI) > 1; (2) For the Proportional Reporting Ratio (PRR) method, ≥3 reports, PRR > 2, and χ² > 4; (3) Under the Bayesian Confidence Propagation Neural Network (BCPNN) framework, the Information Component lower 95% credibility interval (IC₀₂₅) > 0; and (4) For the Multi-item Gamma Poisson Shrinker (MGPS) approach, the Empirical Bayes Geometric Mean 05th percentile (EBGM₀₅) > 2. As duplicate reporting of data is inevitable, we removed duplicate reports and reports with missing data according to specific criteria set by the FDA. If the CASEID was the same, we chose the most recent FDA_DT; if both CASEID and FDA_DT were the same, we chose the PRIMARYID with the larger value.

In actual clinical practice, a patient is often taking multiple medications, and once a patient experiences a combination of medications, the need to investigate the association between a particular drug and a particular AE greatly increases the difficulty of the study. This results in a large number of false-positive reports of adverse drug reactions. For this reason, the FAERS database has created a role_cod (drug’s reported role code in the event) in the DRUG table to identify true drug-adverse event signals. role_cod is classified into four categories: PS (primary suspect), SS (secondary suspect), C (concomitant) and I (interdependent). interactions). In this study, we identified cases in the DRUG file and selected role_cod as PS to improve accuracy. It is worth noting that the trial data used in this study were obtained from databases, so ethical approval was not required.¹⁹

Time-to-onset (TTO) was calculated as the interval (in days) between the therapy start date (START_DT) and the adverse event onset date (EVENT_DT) recorded in the FAERS database. Only reports with complete and logically consistent date information were included in the analysis. Reports with missing therapy start dates or event onset dates, implausible values (e.g., negative intervals), or obvious data entry errors were excluded to ensure data reliability. The calculated time intervals were subsequently grouped into predefined categories (0–30, 31–60, 61–90, 91–120, 121–150, 151–180, 181–360, and >360 days) to describe the distribution of reported onset times. These time bins were defined to reflect early, intermediate, and delayed reporting patterns after treatment initiation. It should be emphasized that the time-to-onset analysis was restricted to cases with available date information and reflects the distribution of reported intervals in FAERS rather than the true temporal risk or incidence of adverse events in clinical practice.

Statistical Analysis

Disproportionality analysis algorithms are widely used in pharmacovigilance for signal detection in spontaneous reporting systems.²⁰ Their primary objective is to identify statistically disproportionate reporting of drug–AE pairs compared with a reference reporting background, rather than to estimate incidence, risk, or causality. In spontaneous reporting system–based studies, the entire database is commonly used as the reference comparator for signal detection. Accordingly, in this study, the entire FAERS database served as the comparator framework. To enhance the robustness of signal detection, we applied four complementary disproportionality methods, as recommended in previous pharmacovigilance methodological studies. These included two frequentist approaches—the ROR and PRR—and two Bayesian methods—the BCPNN and the MGPS.^17,21-25 All four methods are based on 2×2 contingency table calculations comparing the reporting frequency of a specific drug–AE pair with that of all other drugs and events in the database. A signal is considered present when the disproportionality estimate meets predefined statistical thresholds, indicating higher-than-expected reporting relative to the overall FAERS background. The formulas and corresponding signal detection criteria for each method are presented in Table 1.

Table 1.

Formulas and Signal Detection Criterias for Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN) and Multi-Item Gamma Poisson Shrinker (MGPS)

Algorithms	Equation	Criteria
ROR	ROR=ad/bc	lower limitof 95%CI>1,a≥3
ROR	95%CI=eIn(ROR)±1.96(1/a+1/b+1/c+1/d)^0.5	lower limitof 95%CI>1,a≥3
PRR	PRR=a(c+d)/c/(a+b)	PRR>2,χ²>4,a>3
PRR	χ²=[(ad-bc)^2](a+b+c+d)/[(a+b)(c+d)(a+c)(b+d)]	PRR>2,χ²>4,a>3
BCPNN	IC=log2a(a+b+c+d)(a+c)(a+b)	IC025>0
BCPNN	95%CI=E(IC)±2V(IC)^0.5	IC025>0
MGPS	EBGM=a(a+b+c+d)/(a+c)/(a+b)	EBGM05>2
MGPS	95%CI=[eIn(EBGM±1.96(1/a+1/b+1/c+1/d)^0.5	EBGM05>2

Result

Descriptive Analysis

This study collected all relevant AE reports from the launch of Tecartus in Q4 2020 to Q4 2024, totaling 792 events. Figure 1A shows the annual number and trends of reported AEs, with an increasing number of reports over time, peaking at 274 in 2024. Table 2 summarizes the characteristics of patients with reported adverse events (AEs). As shown in Figure 1B, most AE reports originated from the United States (73.7%), followed by France and Germany. The majority of reported cases involved male patients (75.9%), predominantly aged 65–85 years (58.7%), with most having a body weight between 50 and 100 kg. Regarding treatment indications (Figure 1C), mantle cell lymphoma was the most frequently reported (54.8%), followed by unknown indications (11.7%), acute lymphoblastic leukemia (8.5%), and non-Hodgkin’s lymphoma (8.5% and 5.8%). Regarding time-to-onset analysis, 51.8% of the eligible reports lacked complete onset time data. Therefore, the following results are based only on the subset of reports with available information and reflect reporting distributions rather than true temporal incidence. Among these reports, a higher proportion of events were reported within one month after treatment initiation, while some events were also reported after one year. These findings are presented for descriptive purposes only and should not be interpreted as indicating temporal risk patterns or causal relationships.

Figure 1.

Distribution trends of Tecartus-related adverse event reports in the FAERS database.

Table 2.

Demographic Information Reported by AE in FAERS

Brexu-cel(n=792)	n(%)
Sex
Female	187 (23.6%)
Male	601 (75.9%)
Unknow	4 (0.5%)
Age
45∼65	325 (41.0%)
65∼85	465 (58.7%)
>85	2 (0.3%)
Weight
<50 kg	7 (0.9%)
>100 kg	83 (10.5%)
50∼100 kg	313 (39.5%)
Unknow	389 (49.1%)
Years
2020	17 (2.15%)
2021	142 (17.93%)
2022	158 (19.95%)
2023	201 (25.38%)
2024	274 (34.60%)
Geographical distribution
United states	584 (73.7%)
France	45 (5.7%)
Germany	39 (4.9%)
Italy	33 (4.2%)
Spain	20 (2.5%)
United kingdom	13 (1.6%)
Switzerland	9 (1.1%)
Austria	8 (1.0%)
Greece	8 (1.0%)
Netherlands	8 (1.0%)
Canada	6 (0.8%)
Poland	4 (0.5%)
Czechia	3 (0.4%)
Israel	3 (0.4%)
Australia	2 (0.3%)
Portugal	2 (0.3%)
Saudi arabia	1 (0.1%)
Sweden	1 (0.1%)
Belgium	1 (0.1%)
China	1 (0.1%)
Luxembourg	1 (0.1%)
Indication
Mantle cell lymphoma	434 (54.8%)
Product used for unknown indication	93 (11.7%)
Acute lymphocytic leukaemia	67 (8.5%)
Non-hodgkin’s lymphoma	46 (5.8%)
Mantle cell lymphoma refractory	16 (2.0%)
B-cell type acute leukaemia	10 (1.3%)
Mantle cell lymphoma recurrent	13 (1.6%)
Lymphoma	6 (0.8%)
Acute lymphocytic leukaemia recurrent	4 (0.5%)
B-cell lymphoma	3 (0.4%)
Burkitt’s lymphoma	3 (0.4%)
Mantle cell lymphoma stage iv	5 (0.6%)
Diffuse large b-cell lymphoma	5 (0.6%)
B-cell lymphoma refractory	2 (0.3%)
B-cell small lymphocytic lymphoma	1 (0.1%)
B precursor type acute leukaemia	1 (0.1%)
Diffuse large b-cell lymphoma refractory	1 (0.1%)
Haematological malignancy	2 (0.3%)
Ligament disorder	1 (0.1%)
Mantle cell lymphoma stage i	1 (0.1%)
Mcleod neuroacanthocytosis syndrome	1 (0.1%)
Neurotoxicity	1 (0.1%)
Non-hodgkin’s lymphoma stage iv	1 (0.1%)
Philadelphia positive acute lymphocytic leukaemia	1 (0.1%)
Richter’s syndrome	1 (0.1%)
Missing	73 (9.2%)
Occupation Code
CN	44 (5.6%)
HP	335 (42.3%)
MD	289 (36.5%)
PH	28 (3.5%)
Missing	96 (12.1%)
Outcome Code
DE	179 (22.6%)
DS	3 (0.4%)
HO	273 (34.5%)
LT	61 (7.7%)
OT	205 (25.9%)
missing	71 (9.0%)

Tecartus AEs Signal Distribution and Signal Strength at PT Level and SOC Level

At the SOC level, the distribution presented in Table 4 reflects the number of statistically significant disproportionate reporting signals identified within each system organ class, rather than the total number of reported cases. Based on the number of positive signals, the SOCs with the highest signal counts were Nervous system disorders, Infections and infestations, and Neoplasms benign, malignant and unspecified (including cysts and polyps) (Figure 2A). At the PT level, the volcano plot illustrates the distribution of disproportionate reporting signals based on log₂(ROR) and adjusted P values. Among the PTs meeting predefined signal detection criteria, events such as encephalopathy, hypoxia, hypotension, pancytopenia, confusional state, pyrexia, and tremor showed relatively strong disproportionality signals (Figure 2B). To further characterize these findings, we visualized the signal strength of positive PT-level signals using a heatmap and presented corresponding ROR estimates with 95% confidence intervals in a forest plot (Figures 3A and B). Detailed statistical results, including case counts and signal metrics (PRR, ROR, EBGM, and IC), are provided in Table 3, while the SOC-level signal distribution is summarized in Table 4. It should be noted that these results represent disproportionate reporting patterns in the FAERS database and do not indicate incidence, comparative risk, or causal relationships.

Figure 2.

SOC distribution and positive disproportionality analysis signals of Tecartus-related adverse events.

Figure 3.

Heatmap and forest plot of Tecartus disproportionality analysis results.

Table 3.

The Positive Signals at the PT Level and the Positive Signals at the SOC Level in FAERS Database

Tecartus
No	PT	Case reports	PRR(χ2)	ROR(95%CI)	EBGM(EBGM05)	IC(IC025)
1	Cytokine release syndrome	414	201.52 (77491.91)	235 (211.15 - 261.54)	188.97 (172.79)	7.56 (7.41)
2	Immune effector cell-associated neurotoxicity syndrome	274	450.08 (106864.14)	497.04 (435.32 - 567.51)	391.79 (350.65)	8.61 (8.42)
3	Neurotoxicity	111	102.93 (10839.14)	107 (88.24 - 129.75)	99.57 (84.74)	6.64 (6.36)
4	Hypotension	70	6.43 (321.79)	6.57 (5.18 - 8.33)	6.42 (5.27)	2.68 (2.34)
5	Pyrexia	70	4.26 (175.66)	4.35 (3.43 - 5.51)	4.26 (3.49)	2.09 (1.74)
6	Confusional state	43	5.74 (168.33)	5.81 (4.3 - 7.85)	5.73 (4.45)	2.52 (2.08)
7	Encephalopathy	42	30.17 (1173.28)	30.6 (22.53 - 41.56)	29.88 (23.13)	4.9 (4.46)
8	Tremor	35	5.46 (127.41)	5.51 (3.95 - 7.69)	5.45 (4.12)	2.45 (1.96)
9	Hypoxia	33	15.07 (431.72)	15.23 (10.8 - 21.49)	15 (11.25)	3.91 (3.41)
10	Pancytopenia	29	8.66 (196.2)	8.74 (6.06 - 12.61)	8.64 (6.36)	3.11 (2.58)
11	Squamous cell carcinoma of skin	29	66.04 (1818.19)	66.7 (46.08 - 96.54)	64.65 (47.45)	6.01 (5.48)
12	Tachycardia	28	7.85 (167.02)	7.91 (5.45 - 11.49)	7.83 (5.73)	2.97 (2.43)
13	Disease progression	26	3.7 (51.41)	3.73 (2.53 - 5.49)	3.7 (2.68)	1.89 (1.33)
14	Aphasia	25	17.85 (395.42)	17.99 (12.12 - 26.71)	17.75 (12.75)	4.15 (3.58)
15	Myelodysplastic syndrome	21	30.05 (584)	30.26 (19.66 - 46.58)	29.76 (20.74)	4.9 (4.27)
16	Infusion related reaction	20	5.24 (68.6)	5.27 (3.39 - 8.18)	5.23 (3.62)	2.39 (1.76)
17	Basal cell carcinoma	20	20.4 (366.54)	20.53 (13.21 - 31.92)	20.27 (14.01)	4.34 (3.71)
18	Febrile neutropenia	19	4.69 (55.19)	4.72 (3 - 7.41)	4.69 (3.21)	2.23 (1.58)
19	Haemophagocytic lymphohistiocytosis	18	30.5 (508.5)	30.68 (19.26 - 48.88)	30.2 (20.45)	4.92 (4.25)
20	Agitation	16	7.74 (93.79)	7.78 (4.76 - 12.73)	7.73 (5.12)	2.95 (2.25)
21	Depressed level of consciousness	16	8.08 (99.07)	8.12 (4.96 - 13.28)	8.06 (5.34)	3.01 (2.31)
22	Respiratory failure	14	3.86 (29.7)	3.88 (2.29 - 6.56)	3.86 (2.49)	1.95 (1.2)
23	Delirium	14	7.11 (73.36)	7.14 (4.22 - 12.08)	7.09 (4.57)	2.83 (2.08)
24	Mental status changes	14	12.74 (150.88)	12.8 (7.56 - 21.66)	12.69 (8.17)	3.67 (2.92)
25	Lymphocyte adoptive therapy	14	276.11 (3515.95)	277.45 (160.32 - 480.16)	253.05 (159.91)	7.98 (7.2)
26	Pleural effusion	14	4.36 (36.24)	4.38 (2.59 - 7.4)	4.36 (2.81)	2.12 (1.38)
27	Aspartate aminotransferase increased	13	6.01 (54.24)	6.03 (3.5 - 10.41)	6 (3.8)	2.59 (1.81)
28	Multiple organ dysfunction syndrome	13	6.11 (55.51)	6.14 (3.56 - 10.58)	6.1 (3.87)	2.61 (1.84)
29	Mantle cell lymphoma	11	142.46 (1475.49)	143 (78.02 - 262.09)	136.08 (81.97)	7.09 (6.23)
30	Cytopenia	11	12.04 (110.99)	12.09 (6.68 - 21.87)	12 (7.3)	3.58 (2.75)

Table 4.

Distribution of ae Signals in Each System Organ Class

SOC	Tecartus
SOC	N	Percentage
Nervous system disorders	15	17.86%
Infections and infestations	11	13.10%
Neoplasms benign, malignant and unspecified (incl cysts and polyps)	10	11.90%
Metabolism and nutrition disorders	7	8.33%
Respiratory, thoracic and mediastinal disorders	7	8.33%
Blood and lymphatic system disorders	6	7.14%
Investigations	6	7.14%
Psychiatric disorders	5	5.95%
General disorders and administration site conditions	4	4.76%
Immune system disorders	4	4.76%
Cardiac disorders	2	2.38%
Renal and urinary disorders	2	2.38%
Vascular disorders	2	2.38%
Hepatobiliary disorders	1	1.19%
Injury, poisoning and procedural complications	1	1.19%
Surgical and medical procedures	1	1.19%

Time of the AE Event

A total of 382 AE reports contained available time-to-onset information in the FAERS database after removal of duplicate records and reports with missing data. The distribution of reported onset times is summarized in Table 5. Among reports with available onset data, nervous system disorder–related AEs were reported across a broad time range after treatment initiation (Figure 4A). The reported time-to-onset for disproportionately reported AEs showed variability. A larger proportion of reports indicated onset within one month (81.68%), followed by two months (5.76%) and three months (7.07%) after treatment initiation. In addition, a small proportion of reports described AE onset more than one year after treatment initiation (2.36%) (Figure 4B–D). It should be emphasized that these findings reflect reporting patterns in FAERS among cases with available data and do not represent incidence, risk, or the true temporal distribution of adverse events.

Table 5.

The Evoked Time of Adverse Reactions was Grouped

Days	Number	Percentage(%)
0-30	312	81.68%
31-60	22	5.76%
91-120	27	7.07%
121-150	2	0.52%
151-180	2	0.52%
181-360	8	2.09%
>360	9	2.36%

Figure 4.

Time-to-onset distribution of Tecartus-related adverse events.

Discussion

Chimeric antigen receptor T-cell (CAR-T) therapy represents an important advancement in immunotherapy for hematologic malignancies.²⁶ Tecartus has demonstrated significant efficacy in patients with relapsed/refractory mantle cell lymphoma (R/R MCL); however, its safety profile in large real-world populations remains incompletely characterized. In this pharmacovigilance study based on the FAERS database, we identified signals of disproportionate reporting within its indicated population. These observations may complement existing clinical trial data by providing additional post-marketing safety context; however, they reflect reporting patterns rather than causal relationships and should be interpreted with caution given the inherent limitations of spontaneous reporting systems. Recent pharmacovigilance studies in oncology and CAR-T therapy have highlighted the importance of analytical framework selection, particularly the choice of comparator and the interpretation of disproportionality signals in populations with high baseline risks and complex treatment pathways.^24,25 Consistent with these studies, our analysis used the entire FAERS database as the reference, which enables broad signal detection but may also amplify reporting imbalances for therapies used in severely ill populations. Furthermore, prior work has emphasized that adverse event signals in CAR-T settings are often influenced by underlying disease characteristics, prior therapies, and treatment context. In line with these considerations, our findings should be interpreted as indicative of reporting patterns within a specific clinical context rather than as evidence of drug-specific toxicity.

Previous clinical trials have consistently described cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) as frequently reported adverse events in patients receiving Tecartus and other CD19-directed CAR-T therapies.^27,28 CRS typically occurs early after infusion and may manifest as fever, hypotension, hypoxia, or organ dysfunction in severe cases.²⁹ Neurotoxicity (ICANS) has also been frequently described, with clinical manifestations ranging from confusion and aphasia to seizures or cerebral edema.^29-34 These events are well recognized in clinical studies and form part of established CAR-T–related toxicity profiles. Our findings of disproportionate reporting of related events in FAERS are generally consistent with these known safety concerns. However, given the nature of spontaneous reporting data, these signals should be interpreted as reporting associations rather than evidence of increased incidence or causal confirmation. In addition, patients receiving Tecartus are typically heavily pretreated and often older, which may further complicate attribution of specific events to the therapy itself. Therefore, the present analysis should be viewed as hypothesis-generating and supportive of ongoing post-marketing surveillance rather than definitive risk quantification.

We consider this population to be the main incidence of Tecartus. Therefore, this study focused on the middle-aged and elderly population when exploring pharmacovigilance in Tecartus. We examined the clinical characteristics of Tecartus medication records (e.g. age, gender, reported time trends, reporting country, outcomes). A comprehensive understanding of the characteristics of AE events in Tecartus will facilitate the development of individualised risk mitigation strategies for this vulnerable population. The results of the study showed that 75.9% of AE events in Tecartus occurred in men, with a predominant age of onset between 65 and 85 years. This is generally in line with previously reported characteristics. The incidence of AEs in these patients was dominated by cytokine release syndrome (n=414), immune effector cell-associated neurotoxicity syndrome (n=274) and neurotoxicity (n=111). Overall, the symptoms reported in previous clinical trials remained the most common AEs. Among neurological disorders, confusion (n=43), encephalopathy (n=42) and tremor (n=35) were the most commonly reported. These findings are broadly consistent with current evidence on common neurological symptoms in CAR-T treated patients.^35,36

The incidence of AEs in these patients was dominated by cytokine release syndrome (n=414), immune effector cell-associated neurotoxicity syndrome (n=274) and neurotoxicity (n=111). Overall, the symptoms reported in previous clinical trials remained the most common AEs. Among neurological disorders, confusion (n=43), encephalopathy (n=42) and tremor (n=35) were the most commonly reported. These findings are broadly consistent with current evidence regarding common neurological symptoms in CAR-T patients. However, in terms of frequency, previous studies have identified tremor, headache and dysgraphia as common neurological symptoms,³⁷ whereas our findings do not have a high signal for dysgraphia. Cerebral oedema has always been an important AE event that should not be ignored, and severe cerebral oedema is easily associated with a risk of death.³⁸ However, in this study, cerebral oedema was not a high signal outcome. Although cerebral oedema was not a high signal outcome in this study, this does not mean that treatment with Tecartus does not cause cerebral oedema events. Previous studies have confirmed that CAR-T treatment with brain oedema, when it occurs, is associated with a poor prognosis.³⁹

These newly identified adverse events suggest that Tecartus may present potential safety risks in real-world clinical settings that extend beyond those documented in the official product label. Signals detected in the FAERS database that are not explicitly listed in the FDA-approved labeling include basal cell carcinoma, squamous cell carcinoma, gastrointestinal cancer, genitourinary tumors, mucormycosis, bronchopulmonary aspergillosis, cytomegalovirus (CMV) reactivation, staphylococcal bacteremia, perforated colonic diverticulitis, hypernatremia, facial paralysis, respiratory arrest, pulmonary atelectasis, and hypervolemia, among others. The occurrence of malignant solid tumors may be related to long-term immunosuppression or T-cell dysregulation, while the emergence of invasive fungal and viral infections highlights the need for enhanced prophylactic and monitoring strategies. Neurological and gastrointestinal complications such as facial paralysis, altered consciousness, or intestinal perforation, although infrequent, may significantly affect treatment tolerance and prognosis. Therefore, these real-world pharmacovigilance signals provide crucial insights that supplement clinical trial data, emphasizing the need for proactive monitoring and individualized risk management in clinical practice. They also underscore the importance of including these events in future safety assessments and post-marketing surveillance studies.

We then examined the distribution of each AE across the SOCs. In addition to the common nervous system disorders (n=15), infections and infestations (n=11), Tecartus treatment also presents cardiac disorders (n=2), renal and urinary disorders (n=2), vascular disorders (n=2), hepatobiliary disorders (n=1). Despite the low distribution of AEs in these SOCs, cardiac disorders, for example, cannot be ignored with 28 AEs reporting tachycardia.

Finally, we grouped the time of onset of AEs to explore the temporal pattern of AEs following Tecartus treatment. We found that the vast majority of AEs occurred within one month (n=312). Clinical reports of ICANS tended to be rapid, occurring mostly 4-5 days after infusion. This may be the main reason for the short time to AE with Tecartus. We also found that the onset of AE after Tecartus was after one year. The disproportionality analysis indicates a statistically supported safety signal linking Tecartus to specific chronic conditions, though causality requires further validation. However, it should be noted that only 382 records reported the specific onset time of AEs, and the small number of cases may lead to an inability to accurately reflect the actual onset time, so it is worthwhile to further verify the onset time of Tecartus-related AEs. In conclusion, healthcare professionals should pay close attention to the time frame of Tecartus treatment, proactively identify and prevent adverse events, and then take timely and effective measures to intervene.

There is currently a lack of large real-world safety studies of Tecartus. Therefore, our study provides additional post-marketing safety information based on the FAERS database. However, several important limitations should be acknowledged. First, FAERS is a spontaneous reporting system that collects reports from different countries and healthcare professionals, which may result in underreporting, reporting bias, missing or incomplete data, and variable report quality. These factors may affect the stability and interpretation of detected signals. Second, the disproportionality analyses in this study use the entire FAERS database as the comparator. Therefore, the reported ROR and IC values reflect reporting imbalances relative to all other drugs in the database rather than relative to alternative CAR-T products or other hematologic therapies. This approach may lead to inflated signals for Tecartus, as it is typically administered to critically ill patients with relapsed or refractory mantle cell lymphoma in highly specialized treatment settings. Consequently, signals observed for events such as neurologic toxicities, infections, and hematologic abnormalities may largely reflect indication-related factors, disease severity, and treatment context rather than Tecartus-specific adverse effects. Third, patients receiving Tecartus are typically heavily pretreated individuals who often undergo lymphodepleting chemotherapy and intensive supportive care. Many reported events—including infections, cytopenias, ICU-related complications, and disease progression—may therefore be strongly influenced by underlying disease status, prior therapies, comorbidities, and clinical management. The FAERS database does not allow adequate adjustment for these important confounders, and such events should not be interpreted as directly attributable to Tecartus. Fourth, some potential confounding factors, including concomitant medications, drug-drug interactions, and comorbid conditions, could not be fully assessed due to the inherent limitations of the database. Finally, although disproportionality analyses are useful for signal detection, they are based on reporting frequencies relative to the background database and do not provide incidence rates, absolute risk estimates, or evidence of causality. Therefore, the associations identified in this study should be interpreted as signals of disproportionate reporting rather than confirmed drug-related adverse effects.

Conclusion

Our analysis of the real-world FAERS database identified signals of disproportionate reporting for Tecartus that may complement its established safety profile. These findings are hypothesis-generating and reflect reporting patterns rather than confirmed causal associations. While they may help enhance clinical awareness of potential adverse events, they should be interpreted with caution and in the context of underlying disease characteristics and treatment setting. Further studies are warranted to validate these observations and clarify their clinical significance.

Supplemental Material

Supplemental Material - Tecartus Real-World Adverse Event Reporting System in a Middle-Aged and Elderly Population: A FAERS-Based Pharmacovigilance Study

Supplemental Material for Tecartus Real-World Adverse Event Reporting System in a Middle-Aged and Elderly Population: A FAERS-Based Pharmacovigilance Study by Haipeng Li, Jiaqi Yan, Yanjun Zhang, Yanqiu Li, Xinyu Liu, Luyuan Chang, Chunyu Ma, Zhenghong Li, Jingting Min in Technology in Cancer Research & Treatment

Footnotes

ORCID iDs

Haipeng Li

Jingting Min

Author Contributions

Haipeng Li, Zhenghong Li and Jingting Min conceived of or designed study. Haipeng Li, Yanjun Zhang and Yanqiu Li performed research. Jiaqi Yan and Xinyu Liu analyzed data. Chunyu Ma and Luyuan Chang contributed new methods or models. Haipeng Li wrote the paper.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Scientific Research Project of Higher Education Institutions in Anhui Province (2023AH051991). National Student Innovation and Entrepreneurship Training Programme (202410367005). Innovation and Entrepreneurship Training Programme for College Students in Anhui Province (S202410367025).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The dataset supporting the conclusions of this article is available via the public FDA Adverse Event Reporting System database found at . The ‘FAERS Public Dashboard’ option was then selected prompting to its home page. The ‘Search’ tab was then selected, and the rest of the information can be found within the Methods section of the manuscript.

Supplemental Material

Supplemental material for this article is available online.

Appendix

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