Next Article in Journal
Transgenic Drosophila Expressing Active Human LH Receptor in the Gonads Exhibit a Decreased Fecundity: Towards a Platform to Identify New Orally Active Modulators of Gonadotropin Receptor Activity
Previous Article in Journal
NMDA Receptors: Distribution, Role, and Insights into Neuropsychiatric Disorders
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Pharmaceuticals
Volume 17
Issue 10
10.3390/ph17101266
pharmaceuticals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Neuropsychiatric Adverse Events with Monoclonal Antibodies Approved for Multiple Myeloma: An Analysis from the FDA Adverse Event Reporting System

by
Giuseppe Cicala
1,
Giulia Russo
1,
Vincenza Santoro
2,
Tindara Franchina
3,
Nicola Silvestris
3,
Mariacarmela Santarpia
3,
Edoardo Spina
1 and
Maria Antonietta Barbieri
1,*
1
Department of Clinical and Experimental Medicine, University of Messina, 98125 Messina, Italy
2
Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98125 Messina, Italy
3
Department of Human Pathology in Adulthood and Childhood Gaetano Barresi, University of Messina, 98125 Messina, Italy
*
Author to whom correspondence should be addressed.
Pharmaceuticals 2024, 17(10), 1266; https://doi.org/10.3390/ph17101266
Submission received: 2 September 2024 / Revised: 20 September 2024 / Accepted: 23 September 2024 / Published: 25 September 2024
(This article belongs to the Section Pharmacology)
Browse Figures
Versions Notes

Abstract

:
Background/Objectives: Monoclonal antibodies (mAbs) have revolutionized multiple myeloma (MM) treatment. However, post-marketing data on their neuropsychiatric safety are limited. This study aimed to evaluate neuropsychiatric adverse events (AEs) related to mAbs used for MM through a retrospective pharmacovigilance analysis using the Food and Drug Administration (FDA) Adverse Events Reporting System (FAERS) database. Methods: Individual case safety reports (ICSRs) from 2015 to 2023 with at least one neuropsychiatric AE and one of the MM-approved mAbs as the suspected drug (i.e., daratumumab, elotuzumab, isatuximab, belantamab mafodotin, teclistamab, elranatamab, and talquentamab) were analyzed using descriptive and disproportionality approaches. Results: Unknown signals of disproportionate reporting (SDR) included the following: cerebral infarction for daratumumab (n = 45; reporting odds ratio (ROR) = 2.39, 95% confidence interval (CI) = 1.79–3.21; information component (IC) = 1.54, IC025–IC075 = 1.05–1.9), elotuzumab (25; 7.61, 5.13–11.28; 3.03, 2.37–3.51), and isatuximab (10; 2.56, 1.38–4.76; 1.67, 0.59–2.4); mental status changes for daratumumab (40; 2.66, 1.95–3.63; 1.67, 1.14–2.04) and belantamab mafodotin (10; 4.23, 2.28–7.88; 2.3, 1.22–3.03); an altered state of consciousness for daratumumab (32; 1.97, 1.39–2.78; 1.32, 0.73–1.74) and belantamab mafodotin (6; 2.35, 1.05–5.23; 1.6, 0.19–2.52); Guillain-Barre syndrome (GBS) for daratumumab (23; 6.42, 4.26–9.69; 2.81, 2.11–3.3), isatuximab (8; 10.72, 5.35–21.48; 3.57, 2.35–4.37), and elotuzumab (3; 4.74, 1.53–14.7; 2.59, 0.52–3.8); and orthostatic intolerance for daratumumab (10; 12.54, 6.71–23.43; 3.75, 2.67–4.48) and elotuzumab (4; 28.31, 10.58–75.73; 5, 3.24–6.08). Conclusions: Our analysis highlighted several previously unacknowledged SDRs for MM-approved mAbs. Given the complex and not entirely understood etiology of some neuropsychiatric AEs, including GBS, further investigations are necessary.

1. Introduction

Multiple myeloma (MM) is characterized by the abnormal growth of plasma cells, which produce monoclonal immunoglobulins. This proliferation of cells within the bone marrow frequently leads to bone lesions, kidney damage, anemia, and elevated calcium levels [1]. Monoclonal antibodies (mAbs) have transformed MM treatment, offering significant effectiveness in both newly diagnosed MM (NDMM) and relapsed/refractory MM (RRMM) cases, improving survival rates and treatment compliance while reducing toxicity [2,3]. The five-year overall survival (OS) rates for MM have now surpassed 50% [4]. Daratumumab combined with lenalidomide and dexamethasone extend median OS to 67.6 months compared to 51.8 months with lenalidomide and dexamethasone alone [5]. Elotuzumab improves median progression-free survival (PFS) to 19.4 months [6], while teclistamab shows a median PFS of 11.3 months [7]. By targeting plasma cell antigens, mAbs induce apoptosis through mechanisms such as antibody-dependent cellular cytotoxicity (ADCC), complement-dependent cytotoxicity, inhibition of mitochondrial transfer, and antibody-dependent cellular phagocytosis [8]. MAbs approved by the Food and Drug Administration (FDA) for MM include daratumumab, isatuximab, elotuzumab, belantamab mafodotin (withdrawn from the market), teclistamab, elranatamab, and talquetamab [9,10,11,12,13,14,15].
Although generally well-tolerated, mAbs can cause several adverse events (AEs) [9,10,11,12,13,14,15], including neuropsychiatric ones. While known neuropsychiatric AEs such as neuropathy for daratumumab, elotuzumab, teclistamab, elranatamab, and talquetamab and immune effector cell-associated neurotoxicity syndrome (ICANS) for teclistamab, elranatamab, and talquetamab are documented in the FDA Prescribing Information for these drugs, the literature suggests other undetected potential neuropsychiatric AEs for mAbs. For example, there have been case series reporting leukoencephalopathy and encephalitis with daratumumab [16,17,18] as well as other neurotoxicities, including movement and/or neurocognitive disorders not reported in FDA labels [15,19,20]. However, a comprehensive post-marketing study investigating the neuropsychiatric profile of the new MM therapies is lacking. The present study aims to evaluate and characterize neuropsychiatric AEs related to all mAbs used for MM by analyzing the US FDA Adverse Event Reporting System (FAERS) database to detect new potential neuropsychiatric safety signals.

2. Results

2.1. Selection Process and Descriptive Analysis

After applying the preliminary exclusion criteria and performing the final cleaning of the database, a total of 13,496,241 individual case safety reports (ICSRs) were identified. Among those, 4061 ICSRs met the previously specified inclusion criteria and were classified as cases because they were related to neuropsychiatric AEs and had one of the mAbs approved for MM listed as the suspected drug. Most of these cases (n = 2862; 70.5%) were related to daratumumab, followed by isatuximab (n = 345; 8.5%) and elotuzumab (n = 321; 7.9%) (Figure 1).
Nearly half of the ICSRs were reported for elderly patients (n = 1947; 47.9%). This percentage was significantly higher than that observed in the non-cases (n = 2,895,017; 21.5%). A higher frequency of male patients was also observed in cases compared to non-cases (n = 1849; 45.5% vs. n = 4,670,150; 34.6%) (Table 1). A variation in age frequency was noted when stratifying neuropsychiatric ICSRs by each mAb. Specifically, lower frequencies of elderly patients were shown for teclistamab (n = 86; 39.8%), belantamab mafodotin (n = 69; 28.6%), and talquetamab (n = 13; 27.7%) (Table S1). Neuropsychiatric ICSRs were mainly issued by physicians (n = 2114; 52.1%), while the main geographical area of reporting was Europe (n = 1668; 41.1%). In terms of codified outcomes, neuropsychiatric ICSRs were mainly deemed to be linked to AEs of medical importance (n = 1801; 44.4%), followed by AEs leading to or prolonging hospitalization (n = 1397; 34.4%). Additionally, 351 ICSRs (8.6%) reported death as an outcome (Table 1). Considering neuropsychiatric AEs by each mAb, belantamab mafodotin and teclistamab-related ICSRs presented higher frequencies of death (n = 52; 21.6% and n = 42; 19.4%, respectively) (Table S1).
The shortest median (Q1–Q3) time to onset (TTO) for neuropsychiatric AEs was observed with teclistamab at 8 (3–11) days, while the highest median (Q1–Q3) TTO was observed with elranatamab at 72 (18–98) days (Figure 2).

2.2. Disproportionality Analysis

New and previously undetected signals of disproportionate reporting (SDRs) using neuropsychiatric AEs were detected by calculating the Reporting Odds Ratios (ROR) and their 95% confidence intervals (CI). The Bayesian information component (IC) was also computed to gauge the association strength between mAbs and AEs. Unexpected AEs were considered as such if not listed in the FDA Prescribing Information. Further details are provided in the Section 4.
Several already acknowledged AEs related to mAbs approved for the treatment of MM emerged as SDRs from our analysis. These included syncope for daratumumab, ICANS for both talquetamab and teclistamab, and peripheral neuropathy for both elotuzumab and elranatamab. The entire disproportionality analysis is available in Table S2. Moreover, some SDRs were linked to other similar known neuropsychiatric AEs. Daratumumab was associated with both polyneuropathies, which could include peripheral sensory neuropathy, and encephalopathies possibly linked to the known posterior reversible encephalopathy syndrome. Elranatamab-related ICSRs reported syncope, with a depressed level of consciousness being a known AE. Furthermore, postherpetic neuralgia, possibly tied to herpes zoster infections, was reported for elotuzumab. However, some unknown AEs also emerged as SDRs (Table 2).
Daratumumab had several SDRs, which included some unknown nervous system-related AEs as follows: cerebral infarction (n = 45; ROR = 2.39, 95% CI = 1.79–3.21), a depressed level of consciousness (42; 1.65, 1.22–2.24), ischaemic stroke (33; 2.24, 1.59–3.15; 1.47, 0.89–1.88), an altered state of consciousness (32; 1.97, 1.39–2.78), partial seizures (27; 6.77, 4.63–9.89), spinal cord compression (23; 6.48, 4.29–9.77), Guillain-Barre syndrome (GBS) (23; 6.42, 4.26–9.69), ICANS (18; 5.36, 3.37–8.53), neurotoxicity (17; 1.69, 1.05–2.72), and incoherent (12; 2.61, 1.48–4.61). Considering psychiatric disorders, the AEs not reported in the FDA Prescribing Information for daratumumab were delirium (n = 54; ROR = 2.29, 95% CI = 1.75–2.99), mental status changes (40; 2.66, 1.95–3.63), and body dysmorphic disorder (15; 58.08, 34.3–98.33).
Focusing on belantamab mafodotin, unknown SDRs related to nervous system disorders included neuropathy peripheral (n = 38; ROR = 2.62, 95% CI = 1.90–3.61), an altered state of consciousness (6; 2.35, 1.05–5.23), muscle tone disorder (4; 59.56, 22.19–159.81), Bell’s palsy (3; 12.77, 4.11–39.68), and neurological decompensation (3; 12.49, 4.02–38.8). Moreover, regarding psychiatric disorders, the only unknown SDR was mental status changes (10; 4.23, 2.28–7.88).
Undocumented nervous system disorders for isatuximab that emerged as SDRs in our analysis included polyneuropathy (n = 21; ROR = 9.26, 95% CI = 6.03–14.22), transient ischaemic attack (17; 3.37, 2.09–5.42), ischaemic stroke (14; 4.59, 2.71–7.75), peripheral sensory neuropathy (11; 12.23, 6.76–22.13), cerebral infarction (10; 2.56, 1.38–4.76), cerebral ischaemia (9; 12.64, 6.56–24.34), GBS (8; 10.72, 5.35–21.48), haemorrhage intracranial (7; 2.76, 1.31–5.79), basal ganglia infarction (6; 132.39, 58.54–299.42), peripheral motor neuropathy (6; 29.61, 13.25–66.18), and subarachnoid haemorrhage (5; 2.95, 1.23–7.09). The only unknown SDR for psychiatric disorders was acute psychosis (3; 8.8, 2.83–27.34).
Spinal cord compression was the only unknown neuropsychiatric AE for teclistamab (n = 4; ROR = 15.87, 95% CI = 5.94–42.38).
Focusing on elotuzumab, unknown nervous system disorders with SDR included syncope (n = 27; ROR = 1.75; 95% CI = 1.2–2.56), cerebral infarction (25; 7.61, 5.13–11.28), cerebral hemorrhage (12; 2.27, 1.29–4; 1.52, 0.54–2.18), cerebrovascular disorder (4; 16.68, 6.24–44.56), orthostatic intolerance (4; 28.31, 10.58–75.73), VIth nerve paralysis (4; 36.99, 13.81–99.05), GBS (3; 4.74, 1.53–14.7), intention tremor (3; 49.87, 15.97–155.8), monoplegia (3; 4.55, 1.46–14.11), and spinal cord compression (3; 4.78, 1.54–14.83). Considering psychiatric disorders, the only unknown SDR was listlessness (3; 5.28, 1.7–16.38). An association between the drug and all unknown AEs was confirmed by the 95% credibility interval limit being greater than 0 for the IC. Further details are available in Table 2.

3. Discussion

To the best of our knowledge, this is the first study based on mAb-related neuropsychiatric AEs for the treatment of MM using a large-scale spontaneous reporting system database. Focusing on demographic characteristics, we observed a higher frequency of neuropsychiatric ICSRs involving male patients. The different incidence of MM between male and females might be a key factor in interpreting this result. Male sex is a well-recognized risk factor for the onset of MM. Indeed, a population-based study in the US revealed that, from 2000 to 2019, the age-standardized incidence rates of MM per 100,000 people were 8.49 (95% CI 8.43–8.54) for men and 5.58 (95% CI 5.55–5.62) for women [21]. Literature sources have hypothesized that this increased risk might be related to genetic factors [22]. Additionally, possible lifestyle-dependent risk factors more frequent in male patients (such as smoking or obesity) have also been hypothesized to contribute to the onset of monoclonal gammopathy of undetermined significance, a premalignant precursor to MM [23,24,25]. However, no conclusive evidence exists regarding this in MM at present. Elderly patients were the age category with the highest frequency of neuropsychiatric ICSRs. Over 60% of MM diagnoses in the US are made in patients aged 65 years and older [26]. This might be due to early nonspecific symptoms of MM, such as back pain, fatigue, and anemia, which can often be mistaken for age-related issues, leading to delays in MM diagnosis and treatment [27]. Furthermore, elderly patients are known to be more susceptible to the onset of AEs in general [28,29], and age is also considered a risk factor for the development of neuropsychiatric AEs, such as peripheral neuropathy and polyneuropathy, in MM patients [30].
Serious outcomes, including hospitalization and important medical events, were mainly observed in neuropsychiatric ICSRs compared to the non-case group. The line therapy of mAbs in MM treatment should be considered in this context. Indeed, daratumumab is the only mAb currently approved for NDMM. Thus, a relevant portion of the ICSRs could pertain to patients with RRMM. These patients are typically older, have undergone several lines of previous therapies, and may have disease-related comorbidities [31].
Considering the TTO, elranatamab-related ICSRs exhibited the highest median TTO among all mAbs for neuropsychiatric AEs. Elranatamab-related neuropsychiatric AEs with a longer TTO were mainly associated with alterations in consciousness, such as syncope, a depressed level of consciousness, and an altered state of consciousness. These manifestations have previously been observed as part of cytokine release syndromes [32]. However, these AEs are mostly reported during the step-up phases of treatment, with randomized controlled trial data highlighting a median (Q1–Q3) TTO of 2 (1–9) days [33]. Thus, the observed prolonged TTO might be due to other factors, such as dose delays or interruptions, which could be implemented as mitigation strategies following the onset of previous AEs such as infections or hematologic AEs [33].
The disproportionality analysis highlighted SDRs in vascular disorders involving the central nervous system (CNS). Both cerebral infarction and ischaemic stroke were previously unknown for daratumumab, isatuximab, and elotuzumab. Literature data regarding specific CNS vascular complications in MM patients treated with mAbs are currently lacking. However, pre-marketing safety data for both daratumumab and isatuximab highlighted non-relevant effects on the frequency of vascular thromboembolic events (VTE) in general [34,35]. Other factors might play a key role in the onset of these AEs. Indeed, MM patients are frequently characterized by hypercoagulability states, which could facilitate the onset of VTEs [36,37]. Furthermore, the co-administration of mAbs with immunomodulatory drugs, such as lenalidomide and pomalidomide, represents a well-recognized risk factor for VTEs [38]. Additionally, several disproportional haemorrhage-related AEs were observed, such as cerebellar haemorrhage for daratumumab and intracranial haemorrhage for isatuximab. In these cases, disease progression in MM might play a key role in the onset of these AEs. Indeed, MM patients exhibit the highest incidence of thrombocytopenia among those with haematological cancers, which is a significant risk factor for bleeding [39]. Moreover, dysfibrinogenemia, often observed in MM patients due to interactions between MM paraproteins and coagulation proteins, can also lead to bleeding complications [40,41,42].
AEs associated with alterations in state of consciousness were also identified as unknown SDRs. Specifically, a depressed or altered level of consciousness, an incoherent state, stupor, and a worsening of senile dementia had higher RORs for daratumumab. Alterations in consciousness and mental status were also SDRs for belantamab mafodotin, along with neurological decompensation. Delirium was identified as an SDR for elotuzumab, together with listless. Finally, acute psychotic episodes were unknown SDRs for isatuximab. Altered mental status (AMS) in MM patients is often due to metabolic disturbances such as uremia, hypercalcemia, and hyperviscosity. Elevated levels of serum ammonia have also been reported as a rare but clinically impactful cause of AMS in these patients [43]. Furthermore, a population-based study showed a strong correlation between peripheral neuropathies (PNs) and the degradation of cognitive performance, which could lead to AMS in elderly patients [44]. AMS conditions might also result from the co-administration of immunomodulators, which could themselves be related to neurotoxicity [45]. Moreover, AMS might be a part of more complex clinical issues, such as encephalopathies [46], which were disproportionally reported for daratumumab and elotuzumab. The posterior reversible encephalopathy syndrome is an already-documented AE for daratumumab. This condition is characterized by reversible vasogenic cerebral edema that manifests acutely with neurological symptoms such as seizures, headaches, and visual disturbances, in addition to AMS [47].
Neuropathies were also identified as SDRs in several mAbs. The onset of PNs was not mentioned in the FDA Prescribing Information for isatuximab and belantamab mafodotin. The neuronal damage that could lead to PNs might theoretically be caused by isatuximab and belantamab mafodotin through mechanisms such as ADCC [48,49] and CDC [50]. However, PNs can also emerge as consequences of worsening MM [30,51] due to deposits of the M-protein produced by myeloma cells on neurons [52]. Furthermore, isatuximab is currently approved only as a third-line treatment, while belantamab mafodotin was approved as a fifth-line therapy before it was withdrawn. Thus, compromised patient conditions should be considered as a possible influencing factor [53,54]. Moreover, the concomitant use of pomalidomide and carfilzomib with daratumumab or isatuximab could also be associated with the onset of PNs in MM patients [55,56]. Neuropathies can be associated with both sensory symptoms (e.g., numbness, tingling, pain) and motor symptoms (e.g., muscle weakness). In some cases, PNs can also be associated with paralysis [30]. Our data were in line with this, as unknown VIth nerve paralyses were SDRs for daratumumab, belantamab mafodotin, and elotuzumab. Moreover, a rare severe form of PN, characterized by a rapidly-advancing, symmetrical limb weakness [51,52] and known as GBS, was also disproportionally reported for daratumumab, isatuximab, and elotuzumab [49,50]. The mechanisms underlying the onset of GBS remain unclear; however, the presence of a previous infection is considered an important factor [57]. Although immunodeficiency is a common feature of MM [58,59], both daratumumab and isatuximab-based therapies have been linked to an increased risk of infections [60]. Indeed, the results of a recent meta-analysis showed that among anti-CD38-treated patients, the relative risk for any grade infections compared with the control group was 1.27 (95% CI, 1.17–1.37) [61]. This increased susceptibility to infections could potentially trigger the onset of GBS in predisposed individuals.

Strengths and Limitations

Spontaneous reporting system database-based analyses are among the most widely used methodologies for generating hypotheses about drug safety in pharmacovigilance [62,63]. The large-scale nature of the FAERS database enables the detection of AEs not previously identified in controlled environment studies [64]. However, some limitations inherent to the chosen methodology are present. The absence of a proper denominator prevents us from determining the incidence of the observed AEs [65]. Additionally, pharmacovigilance databases are mainly based on spontaneous reporting, which can lead to underreporting or overreporting of events due to various external factors [66,67]. Another limitation is the potential presence of duplicate ICSRs. To mitigate this issue, we implemented a multi-step control process based on key information fields, as detailed in the Section 4. Several additional measures were also implemented to improve data quality, such as eliminating undescriptive AEs and using validated data extraction and processing tools as well as a standardized drug naming dictionary [68]. Most of the mAbs considered are prescribed as a second or subsequent line of treatment for patients with RRMM. Therefore, the influence of disease progression on the reporting of neuropsychiatric AEs cannot be excluded. The observed disproportionalities may also have been influenced by the presence of other co-administered drugs, which complicates establishing a causal relationship between the observed AEs and mAbs. Furthermore, the lack of complete patient clinical histories, which are not available in the open FAERS data, limits our ability to conduct a more comprehensive evaluation. Despite these limitations, we believe our study provides valuable insights for oncologists, aiding in the understanding of the neuropsychiatric safety profile of mAbs and assisting in the management of MM patients.

4. Materials and Methods

4.1. Study Design

A retrospective pharmacovigilance study was conducted to identify neuropsychiatric AEs associated with mAbs approved for MM using the FAERS database. The FAERS database, a widely utilized public resource, has consistently demonstrated its reliability as a platform for drug safety evaluation studies [69,70,71,72]. This database aggregates over 20 million ICSRs from patients, healthcare providers, and pharmaceutical companies across the US, Europe, and Asia. Each ICSR includes a primary ID, data related to the individual (e.g., gender, age, and weight), reporting details such as the reporting country and the qualification of primary sources, information on suspected and concomitant drugs—including their indications and administration dates—and suspected AEs classified by the Medical Dictionary for Regulatory Activities (MedDRA®) Preferred Term (PT) [73], along with details on the date of onset and the outcome.

4.2. Selection of Cases

Using the zipped ASCII FAERS quarterly data extract files accessible at https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html (accessed on 29 January 2024), we downloaded data from the first quarter (Q1) of 2015 to the fourth quarter (Q4) of 2023, covering the period since the approval of the first mAbs for MM.
In detail, we retrieved data from each DEMO, DRUG, INDI, OUTC, REAC, and THER file. These files were merged based on the primary ID and the case ID. Information from INDI and THER files was combined with DRUG data to create a comprehensive file named DRUG_ALL. Similarly, OUTC data were merged with DEMO data to generate a file renamed DEMO_ALL. Additionally, the REAC_ALL file contained data exclusively from the REAC file.
Each of these three files—DRUG_ALL, DEMO_ALL, and REAC_ALL—was cleaned by removing all duplicated ICSRs based on primary ID and case ID as well as key fields including type of AEs, date of onset, gender, age, reporting country, and suspected drug. This process followed FDA recommendations, wherein in cases with multiple ICSRs sharing the same primary ID, only the most recent case ID version are retained [74].
From the DEMO_ALL file, premarketing ICSRs with supporting literature were excluded. Additionally, for the DRUG_ALL file, we utilized the DiAna dictionary—a dynamic, open-source tool known for its dynamic nature, transparency, and adaptability. This dictionary was used to map all drug names in active substances within each ICSR according to the Anatomical Therapeutic Chemical (ATC) classifications [68]. We also excluded ICSRs that contained at least one investigational product, investigational biosimilar product, or blinded product. Similarly, from the REAC_ALL file, all cases with the PT “no adverse event” were excluded.
For defining our cases, we selected all ICSRs for which one of the following drugs was listed as the primary or secondary suspect: daratumumab, elotuzumab, isatuximab, belantamab mafodotin, teclistamab, elranatamab, and talquentamab. To avoid therapeutic biases, ICSRs with indications other than MM were excluded. Moreover, to analyze neuropsychiatric AEs, we considered all ICSRs containing at least one AE classified under the SOC “nervous system disorders” or “psychiatric disorders”.

4.3. Data Analyses

The demographic and clinical characteristics of FAERS ICSRs were analyzed using a descriptive statistical approach with a case–non-case comparison. Continuous variables are presented as medians with quartiles (Q1–Q3), while categorical variables are shown as absolute values with corresponding percentages. Key variables analyzed include gender, age, the primary source of information, year of reporting, reporting country, and detailed descriptions of AEs, including their outcome and the TTO. The TTO was calculated as the interval between the drug administration (start date) and the AE manifestation (event date) and was presented as a median (Q1–Q3) for clarity.
A disproportionality analysis was conducted to detect new and previously undetected SDRs for neuropsychiatric PTs by calculating the ROR and its 95% CI. Statistical significance was determined if the lower limit of the 95% CI for the ROR was greater than one, with a minimum of three ICSRs for each drug-event combination [70].
To reduce the risk of identifying spurious associations and to assess the strength of the association between mAbs and AEs, the Bayesian IC was computed. An association between the drug and the AE was indicated by a 95% credibility interval limit greater than 0 (IC025 > 0). AEs not listed in the FDA Full Prescribing Information for each mAb at the time of this study were considered unexpected [9,10,11,12,13,14,15].
The significance level for statistical analyses was set at a p value < 0.05. All data processing and statistical analyses were conducted using R (version 4.3.1) with the RStudio (version 2024.04.2+764) [75,76].

5. Conclusions

This study underscores the crucial role of large-scale spontaneous reporting system databases in evaluating AEs. Our findings are consistent with the limited existing literature on neuropsychiatric AEs associated with mAbs used in the treatment of MM. We identified several previously unrecognized neuropsychiatric AEs related to mAbs, including VTE, AMS, and GBS. Further research is needed to better understand and contextualize these tolerability issues. Additionally, our study highlights the importance of the ongoing monitoring of MM patients for neuropsychiatric AEs. Timely management of these AEs can enhance patient quality of life and, in some cases, such as alterations in consciousness, may help reduce the impact of associated complications.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/ph17101266/s1: Table S1. Characteristics of neuropsychiatric mAb-related reports involving mAbs used for the treatment of myeloma multiple collected into FAERS. Table S2. Disproportionality analyses with ROR and IC for neuropsychiatric AEs related to mAbs approved for MM (including not signal).

Author Contributions

Conceptualization, G.R. and M.A.B.; methodology, G.R. and M.A.B.; software, M.A.B.; validation, M.A.B. and E.S.; formal analysis, G.C., G.R. and M.A.B.; investigation, G.C. and M.A.B.; data curation, G.R.; writing—original draft preparation, G.C. and G.R.; writing—review and editing, M.A.B., T.F., M.S. and E.S.; visualization, G.C., G.R., V.S., T.F., N.S., M.S., E.S. and M.A.B.; supervision, E.S. and M.A.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study used publicly available safety ICSR data that were provided in an anonymous form and were already compliant with ethical standards. Therefore, no further ethical evaluation was necessary.

Informed Consent Statement

Not applicable, as the study involved pre-anonymized data provided directly by the U.S. Food and Drug Administration.

Data Availability Statement

This study was entirely based on publicly anonymized data made available by the Food and Drug Administration. The raw data can be downloaded at the following link: https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html (accessed on 29 January 2024).

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

ADCCantibody-dependent cellular cytotoxicity
AEadverse event
AMSaltered mental status
ATCAnatomical Therapeutic Chemical
CIconfidence interval
FAERSFood and Drug Administration Adverse Events Reporting System
FDAFood and Drug Administration
GBSGuillain-Barre syndrome
ICinformation component
ICANSimmune effector cell-associated neurotoxicity syndrome
ICSRIndividual Case Safety Report
mAbsmonoclonal antibodies
MedDRAMedical Dictionary for Regulatory Activities
MMmultiple myeloma
NDMMnewly diagnosed multiple myeloma
PNperipheral neuropathy
PTPreferred Term
RORReporting Odds Ratio
RRMMrelapsed/refractory multiple myeloma
SDRsignal of disproportionate reporting
TTOtime to onset
VTEvascular thromboembolic events

References

  1. Rajkumar, S.V.; Dimopoulos, M.A.; Palumbo, A.; Blade, J.; Merlini, G.; Mateos, M.-V.; Kumar, S.; Hillengass, J.; Kastritis, E.; Richardson, P.; et al. International Myeloma Working Group Updated Criteria for the Diagnosis of Multiple Myeloma. Lancet Oncol. 2014, 15, e538–e548. [Google Scholar] [CrossRef] [PubMed]
  2. Lapietra, G.; Fazio, F.; Petrucci, M.T. Race for the Cure: From the Oldest to the Newest Monoclonal Antibodies for Multiple Myeloma Treatment. Biomolecules 2022, 12, 1146. [Google Scholar] [CrossRef]
  3. Wudhikarn, K.; Wills, B.; Lesokhin, A.M. Monoclonal Antibodies in Multiple Myeloma: Current and Emerging Targets and Mechanisms of Action. Best Pract. Res. Clin. Haematol. 2020, 33, 101143. [Google Scholar] [CrossRef] [PubMed]
  4. Van de Donk, N.W.C.J.; Pawlyn, C.; Yong, K.L. Multiple Myeloma. Lancet 2021, 397, 410–427. [Google Scholar] [CrossRef] [PubMed]
  5. Dimopoulos, M.A.; Oriol, A.; Nahi, H.; San-Miguel, J.; Bahlis, N.J.; Usmani, S.Z.; Rabin, N.; Orlowski, R.Z.; Suzuki, K.; Plesner, T.; et al. Overall Survival with Daratumumab, Lenalidomide, and Dexamethasone in Previously Treated Multiple Myeloma (POLLUX): A Randomized, Open-Label, Phase III Trial. J. Clin. Oncol. 2023, 41, 1590–1599. [Google Scholar] [CrossRef]
  6. Lonial, S.; Dimopoulos, M.; Palumbo, A.; White, D.; Grosicki, S.; Spicka, I.; Walter-Croneck, A.; Moreau, P.; Mateos, M.-V.; Magen, H.; et al. Elotuzumab Therapy for Relapsed or Refractory Multiple Myeloma. N. Engl. J. Med. 2015, 373, 621–631. [Google Scholar] [CrossRef]
  7. Moreau, P.; Garfall, A.L.; van de Donk, N.W.C.J.; Nahi, H.; San-Miguel, J.F.; Oriol, A.; Nooka, A.K.; Martin, T.; Rosinol, L.; Chari, A.; et al. Teclistamab in Relapsed or Refractory Multiple Myeloma. N. Engl. J. Med. 2022, 387, 495–505. [Google Scholar] [CrossRef]
  8. Romano, A.; Storti, P.; Marchica, V.; Scandura, G.; Notarfranchi, L.; Craviotto, L.; Di Raimondo, F.; Giuliani, N. Mechanisms of Action of the New Antibodies in Use in Multiple Myeloma. Front. Oncol. 2021, 11, 684561. [Google Scholar] [CrossRef]
  9. US Food and Drug Administration. Full Prescribing Information Talvey® (Talquetamab-Tgvs). Available online: https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/761342s000lbl.pdf (accessed on 29 April 2024).
  10. US Food and Drug Administration. Full Prescribing Information Elrexfio TM® (Elranatamab Bcmm). Available online: https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/761345Orig1s000lbl.pdf (accessed on 29 April 2024).
  11. US Food and Drug Administration. Full Prescribing Information Texvayli® (Teclistamab-Cqyv). Available online: https://www.accessdata.fda.gov/drugsatfda_docs/label/2022/761291s000lbl.pdf (accessed on 30 October 2023).
  12. US Food and Drug Administration. Full Prescribing Information Blenrep® (Belantamab Mafodotin-Blmf). Available online: https://www.accessdata.fda.gov/drugsatfda_docs/label/2020/761158s000lbl.pdf (accessed on 30 October 2023).
  13. US Food and Drug Administration. Full Prescribing Information Empliciti® (Elotuzumab). Available online: https://www.accessdata.fda.gov/drugsatfda_docs/label/2022/761035s015lbl.pdf (accessed on 30 October 2023).
  14. US Food and Drug Administration. Full Prescribing Information Sarclisa® (Isatuximab-Irfc). Available online: https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/761113s009lbl.pdf (accessed on 30 October 2023).
  15. US Food and Drug Administration. Full Prescribing Information Darzalex® (Daratumumab). Available online: https://www.accessdata.fda.gov/drugsatfda_docs/label/2023/761036s044lbl.pdf (accessed on 30 October 2023).
  16. Kareem, S.S.; Viswanathan, N.; Sahebjam, S.; Tran, N.D.; Gatewood, T.; Tobon, K.; Baz, R.; Piña, Y.; Shain, K.H.; Mokhtari, S. Leukoencephalopathy during Daratumumab-Based Therapy: A Case Series of Two Patients with Multiple Myeloma. OncoTargets Ther. 2022, 15, 953–962. [Google Scholar] [CrossRef]
  17. Smets, I.; Titulaer, M.J. Antibody Therapies in Autoimmune Encephalitis. Neurotherapeutics 2022, 19, 823–831. [Google Scholar] [CrossRef]
  18. Ratuszny, D.; Skripuletz, T.; Wegner, F.; Groß, M.; Falk, C.; Jacobs, R.; Ruschulte, H.; Stangel, M.; Sühs, K.-W. Daratumumab in a Patient with Severe Refractory Anti-NMDA Receptor Encephalitis. Front. Neurol. 2020, 11, 602102. [Google Scholar] [CrossRef] [PubMed]
  19. Mohyuddin, G.R.; Banerjee, R.; Alam, Z.; Berger, K.E.; Chakraborty, R. Rethinking Mechanisms of Neurotoxicity with BCMA Directed Therapy. Crit. Rev. Oncol. Hematol. 2021, 166, 103453. [Google Scholar] [CrossRef] [PubMed]
  20. Borrelli, E.P.; McGladrigan, C.G. Differences in Safety Profiles of Newly Approved Medications for Multiple Myeloma in Real-World Settings versus Randomized Controlled Trials. J. Oncol. Pharm. Pract. 2021, 27, 887–896. [Google Scholar] [CrossRef] [PubMed]
  21. Mousavi, S.E.; Ilaghi, M.; Aslani, A.; Yekta, Z.; Nejadghaderi, S.A. A Population-Based Study on Incidence Trends of Myeloma in the United States over 2000–2020. Sci. Rep. 2023, 13, 20705. [Google Scholar] [CrossRef] [PubMed]
  22. Bird, S.; Cairns, D.; Menzies, T.; Boyd, K.; Davies, F.; Cook, G.; Drayson, M.; Gregory, W.; Jenner, M.; Jones, J.; et al. Sex Differences in Multiple Myeloma Biology but Not Clinical Outcomes: Results from 3894 Patients in the Myeloma XI Trial. Clin. Lymphoma Myeloma Leuk. 2021, 21, 667–675. [Google Scholar] [CrossRef]
  23. Lee, D.J.; El-Khoury, H.; Tramontano, A.C.; Alberge, J.-B.; Perry, J.; Davis, M.I.; Horowitz, E.; Redd, R.; Sakrikar, D.; Barnidge, D.; et al. Mass Spectrometry-Detected MGUS Is Associated with Obesity and Other Novel Modifiable Risk Factors in a High-Risk Population. Blood Adv. 2024, 8, 1737–1746. [Google Scholar] [CrossRef]
  24. Muscogiuri, G.; Verde, L.; Vetrani, C.; Barrea, L.; Savastano, S.; Colao, A. Obesity: A Gender-View. J. Endocrinol. Investig. 2023, 47, 299–306. [Google Scholar] [CrossRef]
  25. U.S. Centers for Disease Control and Prevention. Current Cigarette Smoking Among Adults in the United States. Available online: https://www.cdc.gov/tobacco/php/data-statistics/adult-data-cigarettes/?CDC_AAref (accessed on 1 August 2024).
  26. Padala, S.A.; Barsouk, A.; Barsouk, A.; Rawla, P.; Vakiti, A.; Kolhe, R.; Kota, V.; Ajebo, G.H. Epidemiology, Staging, and Management of Multiple Myeloma. Med. Sci. 2021, 9, 3. [Google Scholar] [CrossRef]
  27. Boyle, E.M.; Legrand, C.; Demarquette, H.; Guidez, S.; Herbaux, C.; Leleu, X.; Facon, T. Treatment of Elderly Patients with Myeloma. In Handbook of Multiple Myeloma; Springer International Publishing: Cham, Switzerland, 2015; pp. 41–63. [Google Scholar]
  28. Palumbo, A.; Oliva, S. Latest Advances in the Management of Elderly Patients with Multiple Myeloma. Int. J. Hematol. Oncol. 2013, 2, 431–434. [Google Scholar] [CrossRef]
  29. Palumbo, A.; Bringhen, S.; Ludwig, H.; Dimopoulos, M.A.; Bladé, J.; Mateos, M.V.; Rosiñol, L.; Boccadoro, M.; Cavo, M.; Lokhorst, H.; et al. Personalized Therapy in Multiple Myeloma According to Patient Age and Vulnerability: A Report of the European Myeloma Network (EMN). Blood 2011, 118, 4519–4529. [Google Scholar] [CrossRef]
  30. Richardson, P.G.; Laubach, J.P.; Schlossman, R.L.; Mitsiades, C.; Anderson, K. Complications of Multiple Myeloma Therapy, Part 1: Risk Reduction and Management of Peripheral Neuropathy and Asthenia. J. Natl. Compr. Cancer Netw. 2010, 8, S-4–S-12. [Google Scholar] [CrossRef] [PubMed]
  31. Pozzi, S.; Bari, A.; Pecherstorfer, M.; Vallet, S. Management of Adverse Events and Supportive Therapy in Relapsed/Refractory Multiple Myeloma. Cancers 2021, 13, 4978. [Google Scholar] [CrossRef] [PubMed]
  32. Shimabukuro-Vornhagen, A.; Gödel, P.; Subklewe, M.; Stemmler, H.J.; Schlößer, H.A.; Schlaak, M.; Kochanek, M.; Böll, B.; von Bergwelt-Baildon, M.S. Cytokine Release Syndrome. J. Immunother. Cancer 2018, 6, 56. [Google Scholar] [CrossRef] [PubMed]
  33. Lesokhin, A.M.; Tomasson, M.H.; Arnulf, B.; Bahlis, N.J.; Miles Prince, H.; Niesvizky, R.; Rodrίguez-Otero, P.; Martinez-Lopez, J.; Koehne, G.; Touzeau, C.; et al. Elranatamab in Relapsed or Refractory Multiple Myeloma: Phase 2 MagnetisMM-3 Trial Results. Nat. Med. 2023, 29, 2259–2267. [Google Scholar] [CrossRef]
  34. Sborov, D.W.; Baljevic, M.; Reeves, B.; Laubach, J.; Efebera, Y.A.; Rodriguez, C.; Costa, L.J.; Chari, A.; Silbermann, R.; Holstein, S.A.; et al. Daratumumab plus Lenalidomide, Bortezomib and Dexamethasone in Newly Diagnosed Multiple Myeloma: Analysis of Vascular Thrombotic Events in the GRIFFIN Study. Br. J. Haematol. 2022, 199, 355–365. [Google Scholar] [CrossRef]
  35. Moreau, P.; Dimopoulos, M.-A.; Mikhael, J.; Yong, K.; Capra, M.; Facon, T.; Hajek, R.; Špička, I.; Baker, R.; Kim, K.; et al. Isatuximab, Carfilzomib, and Dexamethasone in Relapsed Multiple Myeloma (IKEMA): A Multicentre, Open-Label, Randomised Phase 3 Trial. Lancet 2021, 397, 2361–2371. [Google Scholar] [CrossRef]
  36. Papageorgiou, L.; Alhaj Hussen, K.; Thouroude, S.; Mbemba, E.; Cost, H.; Garderet, L.; Elalamy, I.; Larsen, A.; Van Dreden, P.; Dimopoulos, M.A.; et al. Modelization of Blood-Borne Hypercoagulability in Myeloma: A Tissue-Factor-Bearing Microparticle-Driven Process. TH Open 2019, 3, e340–e347. [Google Scholar] [CrossRef]
  37. Guo, L.; Tong, D.; Yu, M.; Zhang, Y.; Li, T.; Wang, C.; Zhou, P.; Jin, J.; Li, B.; Liu, Y.; et al. Phosphatidylserine-Exposing Cells Contribute to the Hypercoagulable State in Patients with Multiple Myeloma. Int. J. Oncol. 2018, 52, 1981–1990. [Google Scholar] [CrossRef]
  38. Covut, F.; Sanfilippo, K.M. Mitigating the Risk of Venous Thromboembolism in Patients with Multiple Myeloma Receiving Immunomodulatory-Based Therapy. Hematology 2022, 2022, 363–367. [Google Scholar] [CrossRef]
  39. Shaw, J.L.; Nielson, C.M.; Park, J.K.; Marongiu, A.; Soff, G.A. The Incidence of Thrombocytopenia in Adult Patients Receiving Chemotherapy for Solid Tumors or Hematologic Malignancies. Eur. J. Haematol. 2021, 106, 662–672. [Google Scholar] [CrossRef]
  40. Kulkarni, A.; Bazou, D.; Santos-Martinez, M.J. Bleeding and Thrombosis in Multiple Myeloma: Platelets as Key Players during Cell Interactions and Potential Use as Drug Delivery Systems. Int. J. Mol. Sci. 2023, 24, 15855. [Google Scholar] [CrossRef] [PubMed]
  41. Siddiq, N.; Bergstrom, C.; Anderson, L.D.; Nagalla, S. Bleeding Due to Acquired Dysfibrinogenemia as the Initial Presentation of Multiple Myeloma. BMJ Case Rep. 2019, 12, e229312. [Google Scholar] [CrossRef] [PubMed]
  42. Rahman, S.; Veeraballi, S.; Chan, K.H.; Shaaban, H.S. Bleeding Diathesis in Multiple Myeloma: A Rare Presentation of a Dreadful Emergency with Management Nightmare. Cureus 2021, 13, e13990. [Google Scholar] [CrossRef] [PubMed]
  43. Sandhu, G.; Farias, A.A.; Ranade, A.; Meisels, I. Altered Mental Status in a Case of Multiple Myeloma Not Related to a Metabolic Cause. Clin. Kidney J. 2009, 2, 434–435. [Google Scholar] [CrossRef] [PubMed]
  44. Lin, Y.-J.; Kao, T.-W.; Chen, W.-L. Relationship between Peripheral Neuropathy and Cognitive Performance in the Elderly Population. Medicine 2021, 100, e26071. [Google Scholar] [CrossRef]
  45. Patel, U.H.; Mir, M.A.; Sivik, J.K.; Raheja, D.; Pandey, M.K.; Talamo, G. Central Neurotoxicity of Immunomodulatory Drugs in Multiple Myeloma. Hematol. Rep. 2015, 7, 5704. [Google Scholar] [CrossRef]
  46. Fugate, J.E.; Rabinstein, A.A. Posterior Reversible Encephalopathy Syndrome: Clinical and Radiological Manifestations, Pathophysiology, and Outstanding Questions. Lancet Neurol. 2015, 14, 914–925. [Google Scholar] [CrossRef]
  47. Ghanem, R.; Glaisner, S.; Bobin, A.; Ronchetti, A.-M.; Cereja, S.; Joly, B.; Salanoubat, C.; Fouquet, G. Posterior Reversible Encephalopathy Syndrome (PRES) and Myeloma. Leuk. Res. Rep. 2024, 21, 100407. [Google Scholar] [CrossRef]
  48. Lonial, S.; Lee, H.C.; Badros, A.; Trudel, S.; Nooka, A.K.; Chari, A.; Abdallah, A.-O.; Callander, N.; Lendvai, N.; Sborov, D.; et al. Belantamab Mafodotin for Relapsed or Refractory Multiple Myeloma (DREAMM-2): A Two-Arm, Randomised, Open-Label, Phase 2 Study. Lancet Oncol. 2020, 21, 207–221. [Google Scholar] [CrossRef]
  49. Tai, Y.-T.; de Weers, M.; Li, X.-F.; Song, W.; Nahar, S.; Bakker, J.M.; Vink, T.; Jacobs, D.; Oomen, L.; Bleeker, W.K.; et al. Daratumumab, a Novel Potent Human Anti-CD38 Monoclonal Antibody, Induces Significant Killing of Human Multiple Myeloma Cells: Therapeutic Implication. Blood 2009, 114, 608. [Google Scholar] [CrossRef]
  50. Markham, A. Belantamab Mafodotin: First Approval. Drugs 2020, 80, 1607–1613. [Google Scholar] [CrossRef] [PubMed]
  51. Mohty, B.; El-Cheikh, J.; Yakoub-Agha, I.; Moreau, P.; Harousseau, J.-L.; Mohty, M. Peripheral Neuropathy and New Treatments for Multiple Myeloma: Background and Practical Recommendations. Haematologica 2010, 95, 311–319. [Google Scholar] [CrossRef] [PubMed]
  52. Živković, S.A.; Lacomis, D.; Lentzsch, S. Paraproteinemic Neuropathy. Leuk. Lymphoma 2009, 50, 1422–1433. [Google Scholar] [CrossRef] [PubMed]
  53. Nahi, H.; Walinder, G.; Patel, V.; Qu, Y.; Levine, A.; Majer, I.; Kutikova, L.; Hellqvist Franck, E.; Svensson, M.K.; Hansson, M. Burden of Treatment-Induced Peripheral Neuropathy in Patients with Multiple Myeloma in Sweden. Acta Haematol. 2021, 144, 519–527. [Google Scholar] [CrossRef] [PubMed]
  54. Richardson, P.G.; Delforge, M.; Beksac, M.; Wen, P.; Jongen, J.L.; Sezer, O.; Terpos, E.; Munshi, N.; Palumbo, A.; Rajkumar, S.V.; et al. Management of Treatment-Emergent Peripheral Neuropathy in Multiple Myeloma. Leukemia 2012, 26, 595–608. [Google Scholar] [CrossRef]
  55. Richardson, P.G.; Siegel, D.S.; Vij, R.; Hofmeister, C.C.; Baz, R.; Jagannath, S.; Chen, C.; Lonial, S.; Jakubowiak, A.; Bahlis, N.; et al. Pomalidomide Alone or in Combination with Low-Dose Dexamethasone in Relapsed and Refractory Multiple Myeloma: A Randomized Phase 2 Study. Blood 2014, 123, 1826–1832. [Google Scholar] [CrossRef]
  56. Siegel, D.S.; Martin, T.; Wang, M.; Vij, R.; Jakubowiak, A.J.; Lonial, S.; Trudel, S.; Kukreti, V.; Bahlis, N.; Alsina, M.; et al. A Phase 2 Study of Single-Agent Carfilzomib (PX-171-003-A1) in Patients with Relapsed and Refractory Multiple Myeloma. Blood 2012, 120, 2817–2825. [Google Scholar] [CrossRef]
  57. Van den Berg, B.; Walgaard, C.; Drenthen, J.; Fokke, C.; Jacobs, B.C.; van Doorn, P.A. Guillain–Barré Syndrome: Pathogenesis, Diagnosis, Treatment and Prognosis. Nat. Rev. Neurol. 2014, 10, 469–482. [Google Scholar] [CrossRef]
  58. Díaz-Tejedor, A.; Lorenzo-Mohamed, M.; Puig, N.; García-Sanz, R.; Mateos, M.-V.; Garayoa, M.; Paíno, T. Immune System Alterations in Multiple Myeloma: Molecular Mechanisms and Therapeutic Strategies to Reverse Immunosuppression. Cancers 2021, 13, 1353. [Google Scholar] [CrossRef]
  59. Nightingale, B.; Decker, M.; Ryan, R.; Kaczmarczyk, K.; Jandir, P.; Waykole, T.; Ashkar, R.; Harmon, G.; Mathur, A.; Levitt, M. Multiple Myeloma: A Review of the Literature and a Case Report Highlighting the Immunocompromised State of Myeloma Patients. World J. Oncol. 2024, 15, 348–354. [Google Scholar] [CrossRef]
  60. Hong, J.; Zhou, B.; Pak, A.; Yang, N.; Barmettler, S. Hypogammaglobulinemia and Risk of Infection Following Daratumumab in Patients with Multiple Myeloma. J. Allergy Clin. Immunol. 2024, 153, AB231. [Google Scholar] [CrossRef]
  61. Vassilopoulos, S.; Vassilopoulos, A.; Kalligeros, M.; Shehadeh, F.; Mylonakis, E. Cumulative Incidence and Relative Risk of Infection in Patients with Multiple Myeloma Treated with Anti-CD38 Monoclonal Antibody-Based Regimens: A Systematic Review and Meta-Analysis. Open Forum Infect. Dis. 2022, 9, ofac574. [Google Scholar] [CrossRef] [PubMed]
  62. Barbieri, M.A.; Sorbara, E.E.; Cicala, G.; Santoro, V.; Cutroneo, P.M.; Franchina, T.; Spina, E. Adverse Drug Reactions with HER2-Positive Breast Cancer Treatment: An Analysis from the Italian Pharmacovigilance Database. Drugs Real World Outcomes 2022, 9, 91–107. [Google Scholar] [CrossRef] [PubMed]
  63. Barbieri, M.A.; Sorbara, E.E.; Cicala, G.; Santoro, V.; Cutroneo, P.M.; Franchina, T.; Santarpia, M.; Silvestris, N.; Spina, E. Safety Profile of Tyrosine Kinase Inhibitors Used in Non-Small-Cell Lung Cancer: An Analysis from the Italian Pharmacovigilance Database. Front. Oncol. 2022, 12, 1005626. [Google Scholar] [CrossRef]
  64. Celi, L.A.; Moseley, E.; Moses, C.; Ryan, P.; Somai, M.; Stone, D.; Tang, K. From Pharmacovigilance to Clinical Care Optimization. Big Data 2014, 2, 134–141. [Google Scholar] [CrossRef] [PubMed]
  65. Montastruc, J.; Sommet, A.; Bagheri, H.; Lapeyre-Mestre, M. Benefits and Strengths of the Disproportionality Analysis for Identification of Adverse Drug Reactions in a Pharmacovigilance Database. Br. J. Clin. Pharmacol. 2011, 72, 905–908. [Google Scholar] [CrossRef]
  66. Cicala, G.; de Filippis, R.; Barbieri, M.A.; Cutroneo, P.M.; De Fazio, P.; Schoretsanitis, G.; Spina, E. Tolerability Profile of Paliperidone Palmitate Formulations: A Pharmacovigilance Analysis of the EUDRAVigilance Database. Front. Psychiatry 2023, 14, 1130636. [Google Scholar] [CrossRef]
  67. Pozsgai, K.; Szűcs, G.; Kőnig-Péter, A.; Balázs, O.; Vajda, P.; Botz, L.; Vida, R.G. Analysis of Pharmacovigilance Databases for Spontaneous Reports of Adverse Drug Reactions Related to Substandard and Falsified Medical Products: A Descriptive Study. Front. Pharmacol. 2022, 13, 964399. [Google Scholar] [CrossRef]
  68. Fusaroli, M.; Giunchi, V.; Battini, V.; Puligheddu, S.; Khouri, C.; Carnovale, C.; Raschi, E.; Poluzzi, E. Enhancing Transparency in Defining Studied Drugs: The Open-Source Living DiAna Dictionary for Standardizing Drug Names in the FAERS. Drug Saf. 2024, 47, 271–284. [Google Scholar] [CrossRef]
  69. Barbieri, M.A.; Russo, G.; Sorbara, E.E.; Cicala, G.; Franchina, T.; Santarpia, M.; Speranza, D.; Spina, E.; Silvestris, N. Neuropsychiatric Adverse Drug Reactions with Oral Tyrosine Kinase Inhibitors in Metastatic Colorectal Cancer: An Analysis from the FDA Adverse Event Reporting System. Front. Oncol. 2023, 13, 1268672. [Google Scholar] [CrossRef]
  70. Russo, G.; Barbieri, M.A.; Sorbara, E.E.; Cicala, G.; Franchina, T.; Santarpia, M.; Silvestris, N.; Spina, E. Renal Disorders with Oral Tyrosine Kinase Inhibitors in Metastatic Colorectal Cancer: An Analysis from the FDA Adverse Event Reporting System Database. Biomedicines 2023, 11, 2311. [Google Scholar] [CrossRef] [PubMed]
  71. Cicala, G.; Barbieri, M.A.; Russo, G.; Salvo, F.; Spina, E. Safety of Dual Orexin Receptor Antagonist Daridorexant: A Disproportionality Analysis of Publicly Available FAERS Data. Pharmaceuticals 2024, 17, 342. [Google Scholar] [CrossRef] [PubMed]
  72. Raschi, E.; Fusaroli, M.; Giunchi, V.; Repaci, A.; Pelusi, C.; Mollica, V.; Massari, F.; Ardizzoni, A.; Poluzzi, E.; Pagotto, U.; et al. Adrenal Insufficiency with Anticancer Tyrosine Kinase Inhibitors Targeting Vascular Endothelial Growth Factor Receptor: Analysis of the FDA Adverse Event Reporting System. Cancers 2022, 14, 4610. [Google Scholar] [CrossRef] [PubMed]
  73. Brown, E.G.; Wood, L.; Wood, S. The Medical Dictionary for Regulatory Activities (MedDRA). Drug Saf. 1999, 20, 109–117. [Google Scholar] [CrossRef]
  74. Khaleel, M.A.; Khan, A.H.; Ghadzi, S.M.S.; Adnan, A.S.; Abdallah, Q.M. A Standardized Dataset of a Spontaneous Adverse Event Reporting System. Healthcare 2022, 10, 420. [Google Scholar] [CrossRef]
  75. RStudio Team. RStudio: Integrated Development for R; Posit Software, PBC: Boston, MA, USA, 2023. [Google Scholar]
  76. The R Foundation for Statistical Computing. R: A Language and Environment for Statistical Computing. Available online: https://www.R-project.org/ (accessed on 10 January 2024).
Pharmaceuticals 17 01266 g001
Figure 1. Database cleaning and cases selection flowchart. AE = adverse event; ICSR = individual case safety report; MM = multiple myeloma; and PT = Preferred Term.
Figure 1. Database cleaning and cases selection flowchart. AE = adverse event; ICSR = individual case safety report; MM = multiple myeloma; and PT = Preferred Term.
Pharmaceuticals 17 01266 g001
Pharmaceuticals 17 01266 g002
Figure 2. Time to onset of neuropsychiatric AEs. The data are sorted in descending order of frequency and presented as a box plot, with the box extending from the first quartile (Q1) to the third quartile (Q3) and a horizontal line in the middle representing the median time to onset (TTO).
Figure 2. Time to onset of neuropsychiatric AEs. The data are sorted in descending order of frequency and presented as a box plot, with the box extending from the first quartile (Q1) to the third quartile (Q3) and a horizontal line in the middle representing the median time to onset (TTO).
Pharmaceuticals 17 01266 g002
Table 1. Characteristics of cases related to neuropsychiatric adverse events of monoclonal antibodies approved for multiple myeloma compared to non-cases.
Table 1. Characteristics of cases related to neuropsychiatric adverse events of monoclonal antibodies approved for multiple myeloma compared to non-cases.
CharacteristicNeuropsychiatric Cases
(n = 4061)		Non-Cases
(n = 13,492,180)		Total
(n = 13,496,241)
Age group, n (%)
  Neonate3 (<0.1%)39,379 (0.3%)39,382 (0.3%)
  Infants 15,400 (0.1%)15,400 (0.1%)
  Child7 (0.2%)150,436 (1.1%)150,443 (1.1%)
  Adolescent10 (0.3%)196,410 (1.5%)196,420 (1.5%)
  Adult1006 (24.8%)4,157,969 (30.8%)4,158,975 (30.8%)
  Elderly1947 (47.9%)2,895,017 (21.5%)2,896,964 (21.5%)
  Not available1088 (26.8%)6,037,569 (44.8%)6,038,657 (44.7%)
Sex, n (%)
  Female1588 (39.1%)7,145,404 (53.0%)7,146,992 (53.0%)
  Male1849 (45.5%)4,670,150 (34.6%)4,671,999 (34.6%)
  Not available 624 (15.4%)1,676,626 (12.4%)1,677,250 (12.4%)
Primary source qualification, n (%)
  Consumers580 (14.3%)6,659,308 (49.4%)6,659,888 (49.4%)
  Health professional685 (16.9%)1,185,741 (8.8%)1,186,426 (8.8%)
  Physician2114 (52.1%)2,801,856 (20.8%)2,803,970 (20.8%)
  Other health-professional352 (8.7%)1,140,098 (8.5%)1,140,450 (8.5%)
  Pharmacist312 (7.7%)884,427 (6.6%)884,739 (6.6%)
  Lawyer 502,463 (3.7%)502,463 (3.7%)
  Not available18 (0.4%)318,287 (2.4%)318,305 (2.4%)
Outcome codification, n (%)
  Death351 (8.6%)780,158 (5.8%)780,509 (5.8%)
  Disability69 (1.7%)147,322 (1.1%)147,391 (1.1%)
  Hospitalization—Initial or prolonged1397 (34.4%)2,090,657 (15.5%)2,092,054 (15.5%)
  Life-threatening112 (2.8%)143,135 (1.1%)143,247 (1.1%)
  Other serious (Important Medical Event)1801 (44.4%)4,233,022 (31.4%)4,234,823 (31.4%)
  Required intervention to prevent permanent impairment/damage5 (0.1%)12,674 (0.1%)12,679 (0.1%)
  Congenital anomaly 21,535 (0.2%)21,535 (0.2%)
  Not available326 (8.0%)6,063,677 (44.9%)6,064,003 (44.9%)
Reporter Country, n (%)
  Africa19 (0.5%)37,622 (0.3%)37,641 (0.3%)
  Asia635 (15.6%)667,858 (5.0%)668,493 (5.0%)
  Central America18 (0.4%)28,633 (0.2%)28,651 (0.2%)
  Europe1668 (41.1%)1,749,620 (13.0%)1,751,288 (13.0%)
  North America1414 (34.8%)10,100,868 (74.9%)10,102,282 (74.9%)
  Oceania77 (1.9%)95,927 (0.7%)96,004 (0.7%)
  South America172 (4.2%)227,114 (1.7%)227,286 (1.7%)
  Not available 58 (1.4%)584,538 (4.3%)584,596 (4.3%)
Year of reporting, n (%)
  201517 (0.4%)1,239,483 (9.2%)1,239,500 (9.2%)
  2016251 (6.2%)1,300,142 (9.6%)1,300,393 (9.6%)
  2017250 (6.2%)1,356,259 (10.1%)1,356,509 (10.1%)
  2018414 (10.2%)1,616,069 (12.0%)1,616,483 (12.0%)
  2019471 (11.6%)1,628,852 (12.1%)1,629,323 (12.1%)
  2020469 (11.6%)1,681,724 (12.5%)1,682,193 (12.5%)
  2021535 (13.2%)1,706,194 (12.7%)1,706,729 (12.7%)
  2022748 (18.4%)1,628,953 (12.1%)1,629,701 (12.1%)
  2023906 (22.3%)1,334,504 (9.9%)1,335,410 (9.9%)
Median age (Q1–Q3), years69 (61–75)60 (44–71)60 (44–71)
Median weights (Q1–Q3), Kgs70 (60–85)73 (60–88)73 (60–88)
Table 2. Disproportionality analyses and notoriety evaluations based on the Food and Drug Administration Prescribing Information for neuropsychiatric adverse events related to monoclonal antibodies approved for multiple myeloma.
Table 2. Disproportionality analyses and notoriety evaluations based on the Food and Drug Administration Prescribing Information for neuropsychiatric adverse events related to monoclonal antibodies approved for multiple myeloma.
Daratumumab
SOCPTNROR (95% CI)IC (IC025–IC075)Expected in FDA Prescribing Information
Nervous system disordersNeuropathy peripheral5335.89 (5.4–6.42)2.64 (2.49–2.74)Uk (peripheral sensory neuropathy)
Polyneuropathy18917.74 (15.34–20.5)4.15 (3.91–4.32)Uk (peripheral sensory neuropathy)
Syncope1451.66 (1.41–1.95)1.11 (0.83–1.31)Yes
Encephalopathy715.07 (4.01–6.41)2.48 (2.08–2.76)Uk (posterior reversible encephalopathy syndrome)
Peripheral sensory neuropathy4510.49 (7.81–14.08)3.45 (2.96–3.81)Yes
Cerebral infarction452.39 (1.79–3.21)1.54 (1.05–1.9)No
Depressed level of consciousness421.65 (1.22–2.24)1.12 (0.6–1.48)No
Ischaemic stroke332.24 (1.59–3.15)1.47 (0.89–1.88)No
Altered state of consciousness321.97 (1.39–2.78)1.32 (0.73–1.74)No
Presyncope321.42 (1–2.01)0.96 (0.37–1.37)Yes
Posterior reversible encephalopathy syndrome296.13 (4.25–8.84)2.74 (2.12–3.18)Yes
Nervous system disorder281.69 (1.16–2.45)1.15 (0.52–1.59)Yes
Partial seizures276.77 (4.63–9.89)2.87 (2.23–3.33)No
Leukoencephalopathy2614.8 (10.04–21.84)3.93 (3.28–4.4)Uk (posterior reversible encephalopathy syndrome)
Spinal cord compression236.48 (4.29–9.77)2.82 (2.12–3.31)No
Guillain-Barre syndrome236.42 (4.26–9.69)2.81 (2.11–3.3)No
Brain oedema202.51 (1.62–3.9)1.62 (0.87–2.14)Uk (peripheral oedema)
Facial paralysis191.6 (1.02–2.52)1.1 (0.33–1.64)Uk (peripheral sensory neuropathy)
Peripheral sensorimotor neuropathy1822.42 (14.02–35.85)4.51 (3.72–5.07)Uk (peripheral sensory neuropathy)
ICANS185.36 (3.37–8.53)2.58 (1.79–3.13)No
Neurotoxicity171.69 (1.05–2.72)1.16 (0.35–1.73)No
Peripheral motor neuropathy1414.48 (8.53–24.58)3.93 (3.02–4.55)Uk (peripheral sensory neuropathy)
Incoherent122.61 (1.48–4.61)1.68 (0.71–2.35)No
Orthostatic intolerance1012.54 (6.71–23.43)3.75 (2.67–4.48)No
Stupor84.48 (2.23–8.98)2.39 (1.18–3.19)No
Senile dementia610.24 (4.57–22.93)3.52 (2.1–4.43)No
Intracranial mass64.61 (2.07–10.3)2.45 (1.04–3.36)No
Cytotoxic oedema541.17 (16.71–101.45)5.44 (3.88–6.42)Uk (peripheral oedema)
Allodynia58.64 (3.57–20.86)3.31 (1.74–4.29)Uk (nerve damage causing tingling, numbness or pain)
Hyperammonaemic encephalopathy57.57 (3.13–18.26)3.13 (1.57–4.11)Uk (posterior reversible encephalopathy syndrome)
Paraparesis53.69 (1.53–8.89)2.18 (0.62–3.17)Uk (peripheral sensory neuropathy)
Pleocytosis411.42 (4.25–30.68)3.72 (1.95–4.8)No
VIth nerve paralysis46.48 (2.42–17.35)2.95 (1.19–4.03)Uk (nerve damage causing tingling, numbness or pain)
Cerebellar haemorrhage43.04 (1.14–8.11)1.97 (0.2–3.04)No
Loss of proprioception312.5 (3.99–39.14)3.89 (1.82–5.1)No
Cerebellar haematoma311.42 (3.65–35.74)3.77 (1.7–4.98)No
Toxic neuropathy310.67 (3.41–33.38)3.68 (1.61–4.88)Uk (peripheral sensory neuropathy)
Autonomic neuropathy33.93 (1.26–12.21)2.34 (0.27–3.55)Uk (peripheral sensory neuropathy)
Psychiatric disordersDelirium542.29 (1.75–2.99)1.49 (1.04–1.81)No
Mental status changes402.66 (1.95–3.63)1.67 (1.14–2.04)No
Body dysmorphic disorder1558.08 (34.3–98.33)5.81 (4.94–6.41)No
Anxiety disorder123.6 (2.04–6.35)2.08 (1.1–2.75)Yes
Belantamab Mafodotin
SOCPTNROR (95% CI)IC (IC025–IC075)Expected in FDA Prescribing Information
Nervous system disordersNeuropathy peripheral382.62 (1.9–3.61)1.65 (1.11–2.03)No
Altered state of consciousness62.35 (1.05–5.23)1.6 (0.19–2.52)No
Muscle tone disorder459.56 (22.19–159.81)6.06 (4.29–7.14)No
Bell’s palsy312.77 (4.11–39.68)3.94 (1.87–5.15)No
Neurological decompensation312.49 (4.02–38.8)3.91 (1.84–5.12)No
Psychiatric disordersMental status changes104.23 (2.28–7.88)2.3 (1.22–3.03)No
Elranatamab
SOCPTNROR (95% CI)IC (IC025–IC075)Expected in FDA Prescribing Information
Nervous system disordersAltered state of consciousness320.7 (6.61–64.83)4.6 (2.53–5.81)Yes
Syncope33.82 (1.22–11.98)2.29 (0.22–3.5)Uk (depressed level of consciousness)
Neuropathy peripheral33.6 (1.15–11.28)2.22 (0.15–3.42)Yes
Isatuximab
SOCPTNROR (95% CI)IC (IC025–IC075)Expected in FDA Prescribing Information
Nervous system disordersPolyneuropathy219.26 (6.03–14.22)3.31 (2.58–3.82)No
Transient ischaemic attack173.37 (2.09–5.42)1.98 (1.17–2.55)No
Ischaemic stroke144.59 (2.71–7.75)2.39 (1.48–3.01)No
Peripheral sensory neuropathy1112.23 (6.76–22.13)3.72 (2.7–4.42)No
Cerebral infarction102.56 (1.38–4.76)1.67 (0.59–2.4)No
Cerebral ischaemia912.64 (6.56–24.34)3.78 (2.64–4.54)No
Guillain-Barre syndrome810.72 (5.35–21.48)3.57 (2.35–4.37)No
Haemorrhage intracranial72.76 (1.31–5.79)1.79 (0.48–2.64)No
Basal ganglia infarction6132.39 (58.54–299.42)7.11 (5.7–8.02)No
Peripheral motor neuropathy629.61 (13.25–66.18)5.01 (3.6–5.92)No
Subarachnoid haemorrhage52.95 (1.23–7.09)1.9 (0.34–2.89)No
Acute motor-sensory axonal neuropathy493.02 (34.43–251.26)6.68 (4.91–7.76)No
Meningoradiculitis3179.87 (56.31–574.58)7.64 (5.57–8.85)No
Chronic inflammatory demyelinating polyradiculoneuropathy311.95 (3.84–37.14)3.85 (1.78–5.05)No
Psychiatric disordersAcute psychosis38.8 (2.83–27.34)3.42 (1.36–4.63)No
Talquetamab
SOCPTNROR (95% CI)IC (IC025–IC075)Expected in FDA Prescribing Information
Nervous system disordersDysgeusia1317.71 (10.11–31.02)4.16 (3.22–4.8)Yes
ICANS7185.55 (87.3–394.35)7.59 (6.29–8.44)Yes
Taste disorder726.81 (12.63–56.93)4.82 (3.52–5.68)Yes
Ageusia518.81 (7.75–45.66)4.37 (2.81–5.36)Yes
Neurotoxicity326.11 (8.35–81.62)4.93 (2.86–6.14)Yes
Teclistamab
SOCPTNROR (95% CI)IC (IC025–IC075)Expected in FDA Prescribing Information
Nervous system disordersICANS96450.7 (364.76–556.89)8.66 (8.32–8.9)Yes
Neurotoxicity2231.49 (20.65–48.02)5 (4.29–5.51)Yes
Polyneuropathy56.45 (2.68–15.53)2.92 (1.36–3.91)Yes
Nervous system disorder54.27 (1.78–10.28)2.37 (0.81–3.36)Yes
Depressed level of consciousness52.79 (1.16–6.71)1.83 (0.27–2.82)Yes
Spinal cord compression415.87 (5.94–42.38)4.19 (2.43–5.27)No
Encephalopathy44.02 (1.51–10.72)2.32 (0.56–3.4)Yes
Unresponsive to stimuli43.44 (1.29–9.19)2.13 (0.36–3.21)Uk (depressed level of consciousness)
Psychiatric disordersMental status changes54.7 (1.95–11.31)2.5 (0.94–3.48)Yes
Elotuzumab
SOCPTNROR (95% CI)IC (IC025–IC075)Expected in FDA Prescribing Information
Nervous system disordersNeuropathy peripheral412.52 (1.85–3.43)1.6 (1.08–1.97)Yes
Syncope271.75 (1.2–2.56)1.19 (0.55–1.64)No
Cerebral infarction257.61 (5.13–11.28)3.03 (2.37–3.51)No
Cerebral haemorrhage122.27 (1.29–4)1.52 (0.54–2.18)No
Cerebrovascular disorder416.68 (6.24–44.56)4.26 (2.5–5.34)No
Clumsiness47.82 (2.93–20.86)3.21 (1.45–4.29)Uk (peripheral motor neuropathy)
Orthostatic intolerance428.31 (10.58–75.73)5 (3.24–6.08)No
VIth nerve paralysis436.99 (13.81–99.05)5.38 (3.62–6.46)No
Guillain-Barre syndrome34.74 (1.53–14.7)2.59 (0.52–3.8)No
Intention tremor349.87 (15.97–155.8)5.86 (3.79–7.06)No
Monoplegia34.55 (1.46–14.11)2.54 (0.47–3.74)No
Post herpetic neuralgia311.39 (3.67–35.39)3.78 (1.71–4.99)Uk (herpes zoster)
Spinal cord compression34.78 (1.54–14.83)2.6 (0.53–3.81)No
Toxic encephalopathy36.51 (2.1–20.22)3.01 (0.95–4.22)No
Psychiatric disordersDelirium153.63 (2.18–6.02)2.08 (1.21–2.68)Uk (mood altered)
Listless35.28 (1.7–16.38)2.73 (0.66–3.94)No
CI = Confidence Interval; FDA = Food and Drug Administration; IC = Information Component; ICANS = Immune effector Cell-Associated Neurotoxicity Syndrome; PT = Preferred Term; ROR = Reporting Odds Ratio; SOC = System Organ Class; and Uk = Unknown.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Cicala, G.; Russo, G.; Santoro, V.; Franchina, T.; Silvestris, N.; Santarpia, M.; Spina, E.; Barbieri, M.A. Neuropsychiatric Adverse Events with Monoclonal Antibodies Approved for Multiple Myeloma: An Analysis from the FDA Adverse Event Reporting System. Pharmaceuticals 2024, 17, 1266. https://doi.org/10.3390/ph17101266

AMA Style

Cicala G, Russo G, Santoro V, Franchina T, Silvestris N, Santarpia M, Spina E, Barbieri MA. Neuropsychiatric Adverse Events with Monoclonal Antibodies Approved for Multiple Myeloma: An Analysis from the FDA Adverse Event Reporting System. Pharmaceuticals. 2024; 17(10):1266. https://doi.org/10.3390/ph17101266

Chicago/Turabian Style

Cicala, Giuseppe, Giulia Russo, Vincenza Santoro, Tindara Franchina, Nicola Silvestris, Mariacarmela Santarpia, Edoardo Spina, and Maria Antonietta Barbieri. 2024. "Neuropsychiatric Adverse Events with Monoclonal Antibodies Approved for Multiple Myeloma: An Analysis from the FDA Adverse Event Reporting System" Pharmaceuticals 17, no. 10: 1266. https://doi.org/10.3390/ph17101266

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop