Abstract
BACKGROUND:
Monoclonal antibodies (mAbs) are frequently used in the treatment of various types of cancer and autoimmune diseases, but their causal association with Gastrointestinal (GI) disorders is still unclear. The purpose of this study is to evaluate the frequency and nature of GI adverse events (AEs) associated with mAbs reported in the FDA Adverse Event Reporting System (FAERS) database.
OBJECTIVES:
The study’s primary objective was to investigate the GI-AEs linked with mAbs, determine the demographic trends, and evaluate the clinical characteristics (age groups, seriousness, fatality, etc.) from the reported cases. Secondary objectives included using disproportionality signal analysis to find mAb-AEs combinations with significant associations.
MATERIALS AND METHODS:
Cases involving mAbs from the FAERS database were examined for the period from first authorization until June 30, 2023. The AEs were taken from the “GI Disorders” System Organ Class as standardized Preferred Terms. The characteristics of the cases, such as gender, age, seriousness, and co-suspect drug usage, were examined in detail. Proportional relative risk, IC025, χ2, and Ncomb metrics were used in disproportionality analysis to find the meaningful associations between a particular mAb and GI-AE.
RESULTS:
A total of 115,346 cases were evaluated, and the most common GI-AEs were caused by rituximab, bevacizumab, and denosumab. Constipation, vomiting, nausea, abdominal pain, and diarrhea were the most commonly reported AEs. 89.17% of cases had serious AEs, and 14.33% had fatal outcomes. Bevacizumab, atezolizumab, and rituximab had the highest death rates. 29 signals were detected by performing the disproportionality analysis for mAbs-GI-AEs combinations having significant values, especially for enterocolitis, ascites, GI perforation, and some other AEs as well, from GI disorders. The highest number of signals was identified by bevacizumab.
CONCLUSIONS:
This study highlights the strong correlation between mAbs and GI disorders, especially for mAbs such as atezolizumab, rituximab, and bevacizumab. The results emphasise the necessity of increased pharmacovigilance to keep an eye on these AEs and enhance patient safety. Improving patient safety during mAb therapy requires constant safety surveillance and additional research. Further investigation into the mechanisms underlying these relationships could lead to more effective management options for patients undergoing mAb therapy.
Keywords: Disproportionality signal analysis, FDA Adverse Event Reporting System Database, gastrointestinal disorders, monoclonal antibodies, pharmacovigilance
Introduction
Behring and Shibasaburo first and isolated the antibodies as a neutralizing constituent found in blood during their investigations of animal models of diphtheria in 1890.[1] Since Kohler and Milstein developed the hybridoma technology in 1975, the development of therapeutic antibodies has revolutionized targeted cancer treatment over the last 30 years. Therefore, Monoclonal antibodies (mAb) based immunotherapy emerged as a key therapeutic modality for cancer in recent years, supplementing traditional approaches including radiation, chemotherapy, and surgery.[2]
There are many obstacles in the way of using mAbs as therapeutic agents for conditions like leukemia, breast cancer, Crohn’s disease, arthritis, transplantation, and macular degeneration. In addition to cancer and autoimmune diseases, mAbs are used to treat conditions such as osteoporosis, multiple sclerosis, asthma, and age-related macular degeneration. Their potential for treating Alzheimer’s disease, controlling diabetes, and preventing migraines is currently being studied.[3]
Anti-angiogenic mAbs, such as bevacizumab and ramucirumab, are often linked to Gastrointestinal (GI) disorders, notably GI perforation (GIP), which has a mortality rate of 8.8%. In addition, anti-HER2 agents like trastuzumab, particularly when combined with chemotherapies, show an increased incidence of diarrhea among patients.[4] Due to EGFR inhibitors such as cetuximab and panitumumab, grade 3–4 diarrhea is 66% more common during treatment. In addition to extra-intestinal symptoms such as ulcerations, patchy distribution, extensive colitis, ileal inflammation, persistent duodenitis, and various forms of inflammatory bowel disease, studies have revealed that mAbs like ipilimumab and tremelimumab were frequently associated with diarrhea.[5,6]
Through improved techniques, stakeholders can query and categorize data pertaining to adverse events (AEs) reported to the FDA across various pharmacological and biologic products.[7] FAERS, itself an extensive database containing real-world safety information, helps in discovering AEs that might not have been observed during clinical trials.[8] It is also well known that data mining is a highly effective tool for identifying drug safety signals quickly, which could be used to predict and understand the safety profile of pharmaceutical products.[9] Given this background, the goal of this study is to evaluate FAERS data to identify relevant safety signals and outline the clinical characteristics of Individual Case Safety Reports (ICSRs) related to AEs of GI disorders associated with mAbs.
Materials and Methods
Sources of data
The FDA recently released the FAERS data via an online dashboard, marking a significant milestone in the data collection process. Anyone using a computer or smartphone and Internet connection may go through the AEs documented for different pharmaceutical drugs from 1968 to the present.[10] As of September 2024, the FAERS database contained over 29 million ICSRs, serving as a cornerstone for the FDA’s postmarketing safety (PMS) surveillance initiatives for medicinal and therapeutic biological products.[11] The current study used the FAERS database to analyze ICSRs concerning suspected adverse drug reactions (ADRs) in the category of GI disorders, focusing specifically on mAbs as the implicated drugs.
We chose the top 10 commercially available mAbs that have received FDA and EMA (European Medicines Agency) approval to simplify our analysis, for diverse indications and held by various market authorization holders, as mentioned in Tables 1 and 2.[12,13]
Table 1.
List of approved monoclonal antibodies taken in this study
| mAbs | Approval year by regulatory authority | |
|---|---|---|
|
| ||
| By FDA | By EMA | |
| Rituximab | 1997 | 1998 |
| Trastuzumab | 1998 | 2000 |
| Bevacizumab | 2004 | 2006 |
| Panitumumab | 2006 | 2007 |
| Denosumab | 2010 | 2010 |
| Ipilimumab | 2011 | 2011 |
| Pembrolizumab | 2014 | 2015 |
| Nivolumab | 2014 | 2015 |
| Daratumumab | 2015 | 2016 |
| Atezolizumab | 2016 | 2017 |
Table 2.
2×2 table for the calculation of disproportionality[13]
| Target AE | All other AE | Total | |
|---|---|---|---|
| Target drug | a | b | a+b |
| All other drugs | c | d | c+d |
| Total | a+c | b+d | a+b+c+d |
Following formulas (required threshold values) for Ncomb, PRR, IC025 and χ2
Ncomb (a ≥3)
Proportional Relative Risk (PRR ≥2):
Information Component (IC025 ≥0):
Chi-square statistic (with 1 degree of freedom) (χ2 ≥4):
Study design
A cross-sectional observational study was conducted using the Line listing (LL) generated from the FAERS database containing reports of both serious as well as nonserious AEs for the relevant drugs from the initial authorization date through 30 June 2023. Every ICSR that was part of the study had to include at least one suspected medication (mAb), along with at least one PT (which is AE’s standardized name) that comes under SOC “GI disorders” using the latest version of the standard Medical Dictionary for Regulatory Activities (MedDRA) coding [Figure 1]. Cases were chosen from the safety database’s LL, paying particular attention to cases in which required mAbs [Table 1] were identified as Potential Active Ingredients. The steps for generating LL from the FAERS safety database are outlined in the subsequent sections of this research paper. The line-listing generation procedure did not consider the brand names of products.
Figure 1.
The Medical Dictionary for Regulatory Activities hierarchy with terms corresponding to the PT “Example: - Diarrhoea”
Line-listing generation from the FDA Adverse Event Reporting System database
LL from the FAERS database was generated by the following steps given below:
First, we have gone to the FAERS Public Dashboard via the mentioned link https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers/fda-adverse-event-reporting-system-faers-public-dashboard. Where we need to read and understand the “Disclaimer” and click on the “Accept” for reaching to reach the home page of the database. In the home page of the database, click on “Search” and a new window will be opened, where we need to type the name of the desired product (a maximum of 5 products at once), for example, Rituximab, in the “Search by Product” search bar and click on “Go” and a new window will be opened. While searching the product in the “Search by Product” tab, we can search for individual molecule or group of 1–5 molecules. As we need to search all the cases for the SOC “GI Disorders” with selected products, click on the “Reaction group” and a tab of all the SOC will be opened from where we check the SOC “GI Disorders” and click on the “Listing of cases” where all the relevant fields will be opened such as received year (for the selecting the time period of the cases received), seriousness, age groups, sex, outcome, etc. from which we can selected the desired fields required for our study purpose. After filling in the required tabs, download the LL (Excel/PDF) from the database by clicking on the 3 circle dots 
, parallel to the name of the product and a snapshot [Figure 2] of the FAERS database with the selected fields have been mentioned below. During our study, we have generated the LL in Excel format.[11]
Figure 2.
Snapshot of the FDA Adverse Event Reporting System database with the selected fields to generate the line listing
Extracted variables
The LL included patient information, including age, gender, and weight at the time of the AE, among other parameters required for analysis. Furthermore, product details were reviewed, including trade names, indications for usage, concurrent prescriptions, and active ingredients (suspected drugs). The analysis’s main emphasis was on AEs that fell under the SOC for GI disorders. The name of the AE (PT name), the date the event occurred, the severity of the reaction, and information about the reporter and case, including reporter type, sender (the company submitting the case), manufacturer received date, manufacturer control number, case ID, priority of the case (whether expedited or not), the nation where the event occurred, and the first and latest FDA receive dates for the case, were all extracted information regarding the particular case for the detailed investigation.
Due to anonymization and multiple submission sources, identifying duplicate case reports is a major data quality challenge in this safety database. Through a manual assessment process that considered variables such as case ID, patient demographics, product names, seriousness, and outcomes, the study concentrated on extracting and eliminating duplicates. Enhancing data authenticity for statistical disproportionality signal analysis is the goal of this endeavor.
Demonstrative assessment
In this study, we have selected a total of 30 GI events with the highest number of cases reported with respective selected mAbs during the defined period for case-by-case safety evaluation, remaining events, apart from these selected 30 GI AEs, reported with the same selected mAbs only were considered “other GI events” category. The clinical relevance of each case was assessed using several analytical factors, including patient age (in years), gender, and the seriousness criteria, some other factors were case Priority (Expedited/Non-Expedited), Co-suspect drugs, Concomitant drugs, Type of reporter, event’s geography (In US or Non-US country), and fatality rate with respect to particular mAbs.
Statistical tools
Quantitative signal detection provides a wide range of methods for data assessment. Quantitative signal detection is used by several statistical data mining methods to calculate the information from safety databases. In this investigation, disproportionality analysis was carried out using three distinct methods for quantitative signal evaluation: the proportional relative risk (PRR) and the Information Component (IC025), and the Chi-square statistic (χ2).
Characterization of cases
Once the duplicate cases for the chosen mAbs from the FAERS database have been rectified, a total of 115,346 cases remained for analysis. In this study, highest number of cases reported for the rituximab 28952 (25.10%) followed by bevacizumab 21506 cases (18.64%), denosumab 15820 cases (13.72%), nivolumab 13472 cases (11.68%), trastuzumab 9222 cases (8.00%), pembrolizumab 9022 cases (7.82%), ipilimumab 8051 cases (6.98%), atezolizumab 4477 cases (3.88%), panitumumab 2982 cases (2.59%), and daratumumab 1842 cases (1.60%) [Figure 3].
Figure 3.
Percentage of cases reported for each monoclonal antibody associated with gastrointestinal disorder events
The study found that the PTs – diarrhea, nausea, vomiting, abdominal pain, constipation, abdominal discomfort, colitis, stomatitis, abdominal pain upper, glossodynia, tooth disorder, dysphagia, ascites, and dyspepsia – had the highest number of cases linked to mAbs. mAbs were indicated for various conditions, including breast cancer, malignant melanoma, osteoporosis postmenopausal, hypertension, rheumatoid arthritis, metastatic breast cancer, pain, colon cancer, nausea, non-Hodgkin’s lymphoma, ovarian cancer, lung adenocarcinoma, etc.
The summary features of related mAb case reports show that 38,749 (33.59%) cases were reported for male patients, 61,834 (53.61%) cases for female patients, and 14,763 (12.80%) cases did not specify gender. In terms of age structure, patients between the aged of 51 to 75 years reported majority number of case 51,781 (44.89%), followed by 26 to 50 years 13,727 cases (11.90%), above 76 years 11,764 cases (10.20%), and up to 25 years 1438 cases (1.25%), while in in 36,636 cases (31.76%) age group of patients was not specified. In a study of patient drug usage, 33.68% involved no co-suspect drugs, 23.42% cases involved at-least one co-suspect drug, 13.59% involved two, 8.46% involved three, 5.19% involved four, and 15.66% involved five or more co-suspect drugs. Healthcare professionals reported 79.07% of cases, non-healthcare professionals reported 20.20%, and unidentified reporters accounted for 0.73%. Of the ICSRs, 29.60% indicated the United States as the country of incidence, 63.13% reported other countries, and 7.27% provided no information on the country of incidence [Table 3].
Table 3.
Clinical evaluation of cases of gastrointestinal adverse events due to monoclonal antibodies
| Characteristics | Name of drugs | |||||
|---|---|---|---|---|---|---|
|
| ||||||
| Pembrolizumab ICSRs (n=5331) | Nivolumab ICSRs (n=8311) | Atezolizumab ICSRs (n=2677) | Bevacizumab ICSRs (n=13,441) | Trastuzumab ICSRs (n=7138) | Rituximab ICSRs (n=23,394 | |
| Age (years) (%) | ||||||
| ≤25 | 27 (0.30) | 120 (0.89) | 13 (0.29) | 219 (1.02) | 20 (0.22) | 917 (3.17) |
| 26–50 | 774 (8.58) | 1467 (10.89) | 361 (8.06) | 2607 (12.12) | 2018 (21.88) | 4534 (15.66) |
| 51–75 | 4678 (51.85) | 7306 (54.23) | 2266 (50.61) | 10,592 (49.25) | 3673 (39.83) | 9728 (33.60) |
| ≥76 | 1050 (11.64) | 1500 (11.13) | 468 (10.45) | 1552 (7.21) | 415 (4.50) | 1574 (5.44) |
| Unknown | 2493 (27.63) | 3079 (22.85) | 1369 (30.58) | 6536 (30.39) | 3096 (33.57) | 12,199 (42.14) |
| Gender (%) | ||||||
| Male | 4262 (47.24) | 7831 (58.13) | 2261 (50.50) | 8462 (39.35) | 477 (5.17) | 6795 (23.47) |
| Female | 4319 (47.87) | 4588 (34.06) | 1685 (37.63) | 10,104 (46.98) | 7600 (82.41) | 15,945 (55.07) |
| Unknown/not specified | 441 (4.89) | 1053 (7.82) | 531 (11.86) | 2940 (13.67) | 1145 (12.42) | 6212 (21.46) |
| Case seriousness (%) | ||||||
| Serious | 7833 (86.82) | 12,409 (92.11) | 4336 (96.85) | 20,755 (96.51) | 8453 (91.66) | 27,997 (96.70) |
| Nonserious | 1189 (13.18) | 1063 (7.89) | 141 (3.15) | 751 (3.49) | 769 (8.34) | 955 (3.30) |
| Case priority (%) | ||||||
| Expedited | 6528 (72.36) | 11,800 (87.59) | 4284 (95.69) | 19,968 (92.85) | 8318 (90.20) | 27,548 (95.15) |
| Nonexpedited | 2299 (25.48) | 1490 (11.06) | 173 (3.86) | 1482 (6.89) | 877 (9.51) | 1261 (4.36) |
| Direct (sender “Fda-Ctu”) | 195 (2.16) | 182 (1.35) | 20 (0.45) | 56 (0.26) | 27 (0.29) | 143 (0.49) |
| Co-suspect drugs (%) | ||||||
| 0 | 3571 (39.58) | 5210 (38.67) | 903 (20.17) | 5300 (24.64) | 1268 (13.75) | 5442 (18.80) |
| 1 | 2929 (32.47) | 5691 (42.24) | 1779 (39.74) | 5405 (25.13) | 2000 (21.69) | 3279 (11.33) |
| 2 | 1555 (17.24) | 1399 (10.38) | 817 (18.25) | 4618 (21.47) | 2386 (25.87) | 2609 (9.01) |
| 3 | 445 (4.93) | 675 (5.01) | 548 (12.24) | 3315 (15.41) | 1433 (15.54) | 1713 (5.92) |
| 4 | 208 (2.31) | 243 (1.80) | 187 (4.18) | 1450 (6.74) | 813 (8.82) | 2434 (8.41) |
| ≥5 | 314 (3.48) | 254 (1.89) | 243 (5.43) | 1418 (6.59) | 1322 (14.34) | 13475 (46.54) |
| Concomitant drugs | ||||||
| Taken | 3822 (42.36) | 5870 (43.57) | 2562 (57.23) | 11,735 (54.57) | 5287 (57.33) | 13,835 (47.79) |
| Not taken | 5200 (57.64) | 7602 (56.43) | 1915 (42.77) | 9771 (45.43) | 3935 (42.67) | 15,117 (52.21) |
| Type of reporter (%) | ||||||
| Health professional | 7167 (79.44) | 10,674 (79.23) | 4213 (94.10) | 18,535 (86.19) | 7635 (82.79) | 24,079 (83.17) |
| Nonhealth professional (consumer) | 1840 (20.39) | 2787 (20.69) | 263 (5.87) | 2574 (11.96) | 1474 (15.98) | 4607 (15.91) |
| Unknown | 15 (0.17) | 11 (0.08) | 1 (0.02) | 397 (1.85) | 113 (1.23) | 266 (0.92) |
| Country where event occurred (%) | ||||||
| US | 3083 (34.17) | 4646 (34.49) | 557 (12.44) | 5478 (25.47) | 1926 (20.88) | 3660 (12.64) |
| Non-US | 5263 (58.34) | 8755 (64.99) | 2892 (64.60) | 13,353 (62.09) | 6368 (69.05) | 22,483 (77.66) |
| Unknown | 676 (7.49) | 71 (0.53) | 1028 (22.96) | 2675 (12.44) | 928 (10.06) | 2809 (9.70) |
| Fatal outcome (%) | ||||||
| Fatal cases | 1467 (16.26) | 2292 (17.01) | 898 (20.06) | 5138 (23.89) | 1082 (11.73) | 3018 (10.42) |
| Other cases | 7555 (83.74) | 11,180 (82.99) | 3579 (79.94) | 16,368 (76.11) | 8140 (88.27) | 25,934 (89.58) |
|
| ||||||
| Characteristics | Name of drugs | |||||
|
| ||||||
| Ipilimumab ICSRs (n=3803) | Daratumumab ICSRs (n=2239) | Denosumab ICSRs (n=11,095) | Panitumumab ICSRs (n=1593) | Cumulative count | ||
|
| ||||||
| Age (years) (%) | ||||||
| ≤25 | 60 (0.75) | 5 (0.27) | 39 (0.25) | 18 (0.60) | 1438 (1.25) | |
| 26–50 | 1076 (13.36) | 112 (6.08) | 424 (2.68) | 354 (11.87) | 13,727 (11.90) | |
| 51–75 | 4164 (51.72) | 904 (49.08) | 6782 (42.87) | 1688 (56.61) | 51,781 (44.89) | |
| ≥76 | 861 (10.69) | 291 (15.80) | 3786 (23.93) | 267 (8.95) | 11,764 (10.20) | |
| Unknown | 1890 (23.48) | 530 (28.77) | 4789 (30.27) | 655 (21.97) | 36,636 (31.76) | |
| Gender (%) | ||||||
| Male | 4526 (56.22) | 813 (44.14) | 1744 (11.02) | 1578 (52.92) | 38,749 (33.59) | |
| Female | 2723 (33.82) | 741 (40.23) | 13,101 (82.81) | 1028 (34.47) | 61,834 (53.61) | |
| Unknown/not specified | 802 (9.96) | 288 (15.64) | 975 (6.16) | 376 (12.61) | 14,763 (12.80) | |
| Case seriousness (%) | ||||||
| Serious | 7581 (94.16) | 1657 (89.96) | 9048 (57.19) | 2782 (93.29) | 102,851 (89.17) | |
| Nonserious | 470 (5.84) | 185 (10.04) | 6772 (42.81) | 200 (6.71) | 12,495 (10.83) | |
| Case priority (%) | ||||||
| Expedited | 7147 (88.77) | 1516 (82.30) | 7007 (44.29) | 2271 (76.16) | 96,387 (83.56) | |
| Nonexpedited | 824 (10.23) | 301 (16.34) | 8392 (53.05) | 709 (23.78) | 17,808 (15.44) | |
| Direct (sender “Fda-Ctu”) | 80 (0.99) | 25 (1.36) | 421 (2.66) | 2 (0.07) | 1151 (1.00) | |
| Co-suspect drugs (%) | ||||||
| 0 | 2302 (28.59) | 417 (22.64) | 13,130 (83.00) | 1304 (43.73) | 38,847 (33.68) | |
| 1 | 4137 (51.38) | 294 (15.96) | 1179 (7.45) | 318 (10.66) | 27,011 (23.42) | |
| 2 | 871 (10.82) | 495 (26.87) | 542 (3.43) | 387 (12.98) | 15,679 (13.59) | |
| 3 | 478 (5.94) | 327 (17.75) | 240 (1.52) | 585 (19.62) | 9759 (8.46) | |
| 4 | 132 (1.64) | 109 (5.92) | 214 (1.35) | 192 (6.44) | 5982 (5.19) | |
| ≥5 | 131 (1.63) | 200 (10.86) | 515 (3.26) | 196 (6.57) | 18,068 (15.66) | |
| Concomitant drugs | ||||||
| Taken | 3329 (41.35) | 865 (46.96) | 6353 (40.16) | 1622 (54.39) | 55,280 (47.93) | |
| Not taken | 4722 (58.65) | 977 (53.04) | 9467 (59.84) | 1360 (45.61) | 60,066 (52.07) | |
| Type of reporter (%) | ||||||
| Health professional | 6695 (83.16) | 1636 (88.82) | 7857 (49.66) | 2716 (91.08) | 91,207 (79.07) | |
| Nonhealth professional (consumer) | 1348 (16.74) | 203 (11.02) | 7939 (50.18) | 261 (8.75) | 23,296 (20.20) | |
| Unknown | 8 (0.10) | 3 (0.16) | 24 (0.15) | 5 (0.17) | 843 (0.73) | |
| Country where event occurred (%) | ||||||
| US | 3525 (43.78) | 588 (31.92) | 10,186 (64.39) | 491 (16.47) | 34,140 (29.60) | |
| Non-US | 4482 (55.67) | 1249 (67.81) | 5516 (34.87) | 2462 (82.56) | 72,823 (63.13) | |
| Unknown | 44 (0.55) | 5 (0.27) | 118 (0.75) | 29 (0.97) | 8383 (7.27) | |
| Fatal outcome (%) | ||||||
| Fatal cases | 1278 (15.87) | 257 (13.95) | 589 (3.72) | 514 (17.24) | 16,533 (14.33) | |
| Other cases | 6773 (84.13) | 1585 (86.05) | 15231 (96.28) | 2468 (82.76) | 98,813 (85.67) | |
ICSR=Individual case safety reports
In total, 102,851 (89.17%) cases were reported as serious, while 12,495 (10.83%) cases were reported as nonserious, where highest percentage of serious cases were reported for the drug atezolizumab (96.85%), rituximab (96.70%), bevacizumab (96.51%), ipilimumab (94.16%), panitumumab (93.29%), nivolumab (92.11%), trastuzumab (91.66%), daratumumab (89.96%), and pembrolizumab (86.82%), respectively, and the lowest percentage of serious cases was for denosumab (57.19%) [Figure 4].
Figure 4.
Evaluation of reported percentages of serious and nonserious cases for monoclonal antibodies associated with gastrointestinal disorder events
Among the total cases, the cumulative average fatal outcome was 14.33%. For the individual products, the highest fatal outcome was reported for bevacizumab (23.89%), atezolizumab (20.06%), panitumumab (17.24%), nivolumab (17.01%), pembrolizumab (16.26%), ipilimumab (15.87%), daratumumab (13.95%), trastuzumab (11.73%), and rituximab (10.73%), respectively, and for the product denosumab (3.72%), the lowest cases with fatal outcomes were reported, even as compared to other mAbs [Figure 5].
Figure 5.
Percentage of fatality rate in the reported individual case safety reports for monoclonal antibodies linked to gastrointestinal disorder events
Disproportionality signal detection
Based on our study utilizing disproportionality signal analysis, 29 pairs of drug-events have exhibited favorable outcomes across all selected parameters, encompassing PRR, IC025, and χ2, alongside the Ncomb metric [Table 4]. Bevacizumab, denosumab, nivolumab, pembrolizumab, ipilimumab, panitumumab, and rituximab were the mAbs that demonstrated the maximum number of desirable values across all statistical parameters concerning AEs associated with GI disorders. According to all three algorithms, a total of 22 PTs showed a significant value in disproportionality analysis, where enterocolitis/enterocolitis infectious/enterocolitis bacterial, ascites, intestinal obstruction, intestinal perforation, rectal hemorrhage, GIP, ileus, tooth disorder, gastrooesophageal reflux disease, toothache, colitis, immune-mediated enterocolitis, pancreatitis, stomatitis, abdominal discomfort, glossodynia, GI disorder, duodenal ulcer perforation, irritable bowel syndrome, and diarrhea have demonstrated significant statistical significance with all 04 parameters, indicating potential value for signal assessment of these PTs [Table 4].
Table 4.
Disproportionality signal analysis derived from the food and drug administration adverse event reporting system safety database, satisfactory values were found for all four statistical techniques (Ncomb, proportional relative risk, information component025, and χ2) for the safety reports of monoclonal antibodies related with the preferred terms of gastrointestinal disorders (system organ class)
| Products (mAbs) | PTs | Ncomb | PRR | IC025 | χ 2 |
|---|---|---|---|---|---|
| Atezolizumab | Enterocolitis/Enterocolitis Infectious/Enterocolitis Bacterial | 116 | 2.63 | 1.01 | 105.91 |
| Ascites | 329 | 3.27 | 1.41 | 463.55 | |
| Bevacizumab | Ascites | 1101 | 2.86 | 0.97 | 789.95 |
| Intestinal Obstruction | 1364 | 3.97 | 1.23 | 1548.58 | |
| Intestinal Perforation | 1895 | 6.74 | 1.56 | 3518.28 | |
| Rectal Haemorrhage | 458 | 2.39 | 0.74 | 234.02 | |
| GI Perforation | 974 | 14.88 | 1.82 | 2671.96 | |
| Ileus | 666 | 2.40 | 0.77 | 344.92 | |
| Denosumab | Tooth Disorder | 2717 | 25.85 | 2.28 | 12134.99 |
| Gastro-esophageal Reflux Disease | 548 | 2.99 | 0.90 | 483.81 | |
| Toothache | 1001 | 16.64 | 1.90 | 3811.26 | |
| Ipilimumab | Colitis | 3219 | 6.15 | 1.98 | 10151.20 |
| Enterocolitis/Enterocolitis Infectious/Enterocolitis Bacterial | 283 | 4.13 | 0.97 | 506.04 | |
| Immune-Mediated Enterocolitis | 554 | 8.99 | 1.63 | 2292.81 | |
| Nivolumab | Colitis | 3355 | 3.70 | 1.33 | 4726.87 |
| Enterocolitis/Enterocolitis Infectious/Enterocolitis Bacterial | 370 | 3.40 | 0.79 | 423.85 | |
| Immune-Mediated Enterocolitis | 758 | 9.53 | 1.51 | 2458.08 | |
| Pancreatitis | 556 | 2.07 | 0.55 | 241.01 | |
| Panitumumab | Stomatitis | 478 | 3.25 | 0.94 | 719.77 |
| Ileus | 119 | 2.51 | 0.23 | 102.95 | |
| Pembrolizumab | Colitis | 1678 | 2.28 | 0.84 | 1097.26 |
| Immune-Mediated Enterocolitis | 395 | 4.67 | 0.85 | 805.61 | |
| Pancreatitis | 432 | 2.36 | 0.55 | 282.96 | |
| Rituximab | Abdominal Discomfort | 5471 | 10.24 | 1.41 | 10090.91 |
| Glossodynia | 3724 | 43.24 | 1.57 | 9284.59 | |
| GI Disorder | 1712 | 2.63 | 0.68 | 902.90 | |
| Duodenal Ulcer Perforation | 1447 | 53.67 | 1.33 | 3596.93 | |
| Irritable Bowel Syndrome | 1094 | 16.79 | 1.13 | 2256.49 | |
| Trastuzumab | Diarrhoea | 4220 | 2.05 | 0.80 | 2503.81 |
In our study, bevacizumab exhibited the highest number of potential signals (06) based on disproportionality signal analysis, followed by rituximab (05), nivolumab (04), denosumab (03), ipilimumab (03), pembrolizumab (03), atezolizumab (02), panitumumab (02), and trastuzumab (01), which had the fewest signals for GI disorders PTs.
When compared with all mAb, bevacizumab exhibited associations with highest number of PTs: Ascites (PRR - 2.86, IC025-0.97 and χ2 statistic - 789.95), intestinal obstruction (PRR - 3.97, IC025-1.23 and χ2-1548.58) intestinal perforation (PRR - 6.74, IC025-1.56 and χ2 statistic - 3518.28), rectal hemorrhage (PRR - 2.39, IC025-0.74 and χ2-234.02), GI Perforation (PRR - 14.88, IC025 – 1.82 and χ2-2671.96), and ileus (PRR - 2.40, IC025-0.77 and χ2-344.92). Atezolizumab was related to enterocolitis/enterocolitis infectious/enterocolitis bacterial with (PRR - 2.63, IC025-1.01 and χ2-105.91) and ascites (PRR - 3.27, IC025-1.41 and χ2-463.55). Denosumab displayed strongly associations with tooth disorder (PRR - 25.85, IC025-2.28 and χ2-12134.99), gastroesophageal reflux disease (PRR - 2.99, IC025-0.90 and χ2-483.81) and toothache (PRR - 16.64, IC025-1.90 and χ2-3811.26) followed by ipilimumab with colitis (PRR - 6.15, IC025-1.98 and χ2-10151.20), enterocolitis/enterocolitis infectious/enterocolitis bacterial (PRR - 4.13, IC025-0.97 and χ2-506.04), immune-mediated enterocolitis (PRR - 8.99, IC025-1.63 and χ2-2292.81), nivolumab associated with enterocolitis/enterocolitis infectious/enterocolitis bacterial (PRR - 3.40, IC025-0.79 and χ2-423.85), immune-mediated enterocolitis (PRR - 9.53, IC025-1.51 and χ2-2458.08), and pancreatitis (PRR - 2.07, IC025-0.55 and χ2-241.01), panitumumab was associated with stomatitis (PRR - 3.25, IC025-0.94 and χ2 – 719.77) and ileus (PRR - 2.51, IC025-0.23 and χ2-102.95). Pembrolizumab was associated with colitis (PRR - 2.28, IC025-0.84 and χ2 – 1097.26), immune-mediated enterocolitis (PRR - 4.67, IC025-0.85 and χ2 – 805.61) and pancreatitis (PRR - 2.36, IC025-0.55 and χ2 – 282.96). Trastuzumab was only associated with single event diarrhea (PRR - 2.05, IC025-0.80 and χ2 – 2503.81). Rituximab showed associations with abdominal discomfort (PRR 10.24, IC025-1.41 and χ2-10090.91), glossodynia (PRR - 43.24, IC025-1.57 and χ2 – 9284.59), GI disorder (PRR - 2.63, IC025-0.68 and χ2-902.90) duodenal ulcer perforation (PRR - 53.67, IC025-1.33 and χ2-3596.93) and irritable bowel syndrome (PRR - 16.79, IC025-1.13 and χ2-2556.49), indicating the greatest number of drug-AE pairs with significantly greater values across all disproportionality techniques employed during this research, suggesting a potential signal [Table 4].
In this study, it is found that nine different drug-AE combinations (mAbs-GI disorders PTs) were showing at least three satisfactory disproportionality algorithmic values (Ncomb, IC025 and χ2), as shown in Table 5.
Table 5.
Disproportionality signal analysis derived from the food and drug administration adverse event reporting system safety database, satisfactory values were found for 3 or more than 3 statistical techniques (Ncomb, proportional relative risk, information component025, and χ2) for the safety reports of monoclonal antibodies related with the preferred terms of gastrointestinal disorders (system organ class)
| Products (mAbs) | PTs | Ncomb | PRR | IC0.25 | χ 2 |
|---|---|---|---|---|---|
| Atezolizumab | Constipation | 428 | 1.51 | 0.42 | 73.69 |
| Colitis | 709 | 1.81 | 0.70 | 263.08 | |
| GI Haemorrhage | 197 | 1.85 | 0.63 | 73.07 | |
| Intestinal Obstruction | 160 | 1.42 | 0.25 | 19.33 | |
| Immune-Mediated Enterocolitis | 100 | 1.89 | 0.56 | 38.87 | |
| Pancreatitis | 176 | 1.79 | 0.57 | 58.45 | |
| Bevacizumab | Nausea | 4701 | 1.08 | 0.04 | 25.21 |
| Vomiting | 3640 | 1.35 | 0.29 | 286.90 | |
| Abdominal Pain/Abdominal Pain Upper/Abdominal Pain Lower | 2793 | 1.28 | 0.22 | 143.84 | |
| Constipation | 1732 | 1.33 | 0.25 | 111.59 | |
| GI Haemorrhage | 800 | 1.74 | 0.49 | 177.49 | |
| Daratumumab | Diarrhoea | 596 | 1.33 | 0.28 | 64.69 |
| Vomiting | 287 | 1.17 | 0.04 | 7.61 | |
| Constipation | 180 | 1.52 | 0.31 | 33.75 | |
| Pancreatitis | 66 | 1.60 | 0.10 | 14.66 | |
| Ileus | 55 | 1.83 | 0.11 | 20.43 | |
| Denosumab | Dyspepsia | 394 | 1.20 | 0.05 | 10.86 |
| Abdominal Distension | 319 | 1.28 | 0.10 | 16.69 | |
| Ipilimumab | Diarrhoea | 2519 | 1.31 | 0.29 | 225.48 |
| Pancreatitis | 297 | 1.72 | 0.35 | 79.54 | |
| Nivolumab | Diarrhoea | 3573 | 1.10 | 0.07 | 38.39 |
| Dysphagia | 452 | 1.24 | 0.09 | 18.44 | |
| Ascites | 391 | 1.21 | 0.05 | 12.25 | |
| Dry Mouth | 327 | 1.33 | 0.11 | 22.66 | |
| Panitumumab | Diarrhoea | 1220 | 1.71 | 0.57 | 454.84 |
| Vomiting | 475 | 1.19 | 0.07 | 16.76 | |
| Intestinal Obstruction | 137 | 1.84 | 0.17 | 51.01 | |
| Pembrolizumab | Diarrhoea | 2345 | 1.07 | 0.03 | 13.90 |
| Dysphagia | 297 | 1.20 | 0.01 | 9.31 | |
| Dry Mouth | 225 | 1.35 | 0.06 | 18.40 | |
| Rituximab | Nausea | 7250 | 1.32 | 0.24 | 474.84 |
| Vomiting | 4051 | 1.06 | 0.01 | 11.60 | |
| Abdominal Pain/Abdominal Pain Upper/Abdominal Pain Lower | 3605 | 1.24 | 0.16 | 123.63 | |
| Stomatitis | 1977 | 1.47 | 0.28 | 202.74 | |
| Dysphagia | 931 | 1.22 | 0.08 | 27.10 | |
| Dyspepsia | 961 | 1.95 | 0.41 | 263.88 | |
| Dry Mouth | 730 | 1.51 | 0.21 | 81.36 | |
| GI Haemorrhage | 786 | 1.15 | 0.02 | 10.55 | |
| Gastrooesophageal Reflux Disease | 520 | 1.29 | 0.07 | 23.30 | |
| Mouth Ulceration | 464 | 1.81 | 0.25 | 101.19 | |
| Trastuzumab | Nausea | 2931 | 1.62 | 0.52 | 768.46 |
| Vomiting | 1982 | 1.70 | 0.53 | 568.00 | |
| Abdominal Pain/Abdominal Pain Upper/Abdominal Pain Lower | 1083 | 1.10 | 0.03 | 10.90 | |
| Constipation | 857 | 1.50 | 0.31 | 132.89 | |
| Stomatitis | 671 | 1.44 | 0.24 | 84.48 | |
| Dyspepsia | 306 | 1.64 | 0.20 | 66.84 | |
| Rectal Haemorrhage | 170 | 1.72 | 0.10 | 44.26 | |
| Mouth Ulceration | 157 | 1.67 | 0.07 | 36.45 |
As shown in Table 5, a total of 48 different drug-event combinations (mAbs linked to GI disorders PTs) showed satisfactory disproportionality values in any three algorithms (out of Ncomb, PRR, IC025, and χ²), in addition to the drug-event pairs that showed significant disproportionality across four statistical algorithms [Table 4]. Although these drug and AE combinations might not satisfy the required thresholds for every parameter, they are nonetheless of considerable significance for further signal evaluation, as they represent potential sources of scientific evidence for assessing safety signals.
Discussion
mAbs are a type of targeted therapy widely used in the treatment of numerous illnesses, including tumors, infections, and various autoimmune disorders. However, they are very effective in various serious conditions, but simultaneously, these mAbs are known to cause various GI disorders. It is essential to comprehend these AEs and the role of a gastroenterologist in managing them for patient safety as well as treatment outcomes.
Our analysis of data extracted from the FAERS has elucidated a notable prevalence of GI toxicity among patients undergoing treatment with mAbs. Specifically, the investigation revealed that GI adverse effects, including but not limited to nausea, vomiting, diarrhea, abdominal pain, and more severe conditions such as GI hemorrhage and perforation, occur with a frequency that warrants attention. This finding underscores a significant correlation between the use of mAbs and the manifestation of GI symptoms. The literature reports that a range of GI toxicities, including nausea, severe diarrhea (grades 3–4), abdominal pain, enterocolitis, vomiting, GIP, and colitis, have been documented with various mAbs such as panitumumab, cetuximab, bevacizumab, and rituximab, which are utilized in cancer therapy.[6,14]
In a dataset comprising 102,851 selected cases, 89.17% of ICSRs were classified as serious, while 10.83% were categorized as nonserious. Among the serious cases, atezolizumab was associated with the highest frequency, followed in descending order by Rituximab, bevacizumab, ipilimumab, panitumumab, nivolumab, trastuzumab, daratumumab, and pembrolizumab. Gender distribution among the reported cases was as follows: 53.61% were female, 33.59% were male, and 12.80% had unspecified gender. Though this can vary by age group, GI events like Crohn’s disease, ulcerative colitis, etc., are more common in women than in males when it comes to mAbs. Although the distribution of genders is more balanced, some research indicates that males may experience a higher incidence during adolescence and early adulthood. Few studies also indicate that there is no proof of a correlation (P = 0.244) between the severity of ADRs and gender.[14,15]
Disproportionality analysis reveals significant correlations between various mAbs and GI AEs. For instance, bevacizumab demonstrates a strong association with serious GI complications, including ascites, intestinal obstruction, intestinal perforation, rectal hemorrhage, ileus and GIP. In each of these drug-event combinations, the calculated statistical parameters meet the predefined criteria necessary for identifying potential signals, underscoring the importance of ongoing monitoring and further investigation into these associations to enhance patient safety. Use of bevacizumab has been reported to increase the risk of bleeding. Cases of severe (with grade ≤3) bleeding had also been reported, even though bleeding events usually featured mild epistaxis and other autonomous mucosal bleeding.[16] Potential threat of GIP and ileus has been associated with bevacizumab in a study with significant statistical values, which are very serious GI conditions. A small number of further investigations have shown that bevacizumab usage has been linked to reports of ileus and intestinal perforation.[17]
Rituximab exhibited the highest association with glossodynia and duodenal ulcer perforation based on disproportionality statistical values, indicating significant signals related to these AEs. This was followed by other notable associations with abdominal discomfort, irritable bowel syndrome, and GI disorders, as shown in Table 4. Rituximab has been linked to severe colonic perforation, abdominal pain that necessitates ICU admission and potentially surgery, inflammatory bowel disease, and other GI diseases, according to the literature.[18,19] Literature reports describe that patients with oral lichenoid reactions have been reported, while the treatment of follicular non-Hodgkin lymphoma with rituximab. Given that rituximab is commonly used for lymphoma and is increasingly employed in the treatment of autoimmune disorders, it is important to recognize this rare adverse effect, enabling timely management to reduce associated morbidity.[20] A case report also highlighted grade 2 mucositis, weight loss, and Stevens–Johnson syndrome, along with an additional diagnosis of pemphigus or toxic epidermal necrolysis following rituximab treatment.[21]
This study reported the potential signals for GI AEs, including enterocolitis/infectious enterocolitis/bacterial enterocolitis, associated with various mAbs such as atezolizumab, ipilimumab, and nivolumab. In addition, colitis was observed with multiple mAbs, including ipilimumab, nivolumab, and pembrolizumab. Immune-mediated enterocolitis was noted with ipilimumab, nivolumab, and pembrolizumab, while pancreatitis was linked to nivolumab and pembrolizumab, with all signals meeting the required threshold in disproportionality signal analysis tools. There have been reports of new immune-mediated inflammatory bowel disorders, including Crohn’s disease, enterocolitis, ulcerative colitis, and severe colitis with intestinal perforation. These conditions can sometimes result in fatalities and manifest as more severe side effects. About 40% of instances are mostly immune-mediated side effects, and they have been linked to medications including ipilimumab, rituximab, bevacizumab, infliximab, adalimumab, and etanercept.[22] Cases of pancreatitis induced by pembrolizumab have been reported, suggesting that the frequency of such cases may rise with the growing use of these types of drugs. Nivolumab and ipilimumab recognized can result in immune-related AEs (irAEs), such as nephritis, dermatitis, pancreatitis, and enterocolitis. Much is still unclear about the pathophysiology and clinical significance.[23]
In our study, denosumab was found to be associated with gastroesophageal reflux disease, tooth disorders, and toothache, while panitumumab was linked to stomatitis and ileus. Additionally, trastuzumab showed an association with diarrhea, and the disproportionality signal values for these associations suggest they should be considered as potential safety signals. In the literature study, validated denosumab’s effectiveness by showing that its AEs, such as death, jaw osteonecrosis, back pain, dental issues, bone pain, and hypocalcemia, were consistent with known denosumab-related AEs, indicating that the results could accurately represent actual clinical procedures.[24] Cases of gastroesophageal reflux disease (2.1%) have been reported, along with several serious side effects, including jaw pain, unusual groin pain, muscle pain, joint pain, bone pain, osteoporosis, shortness of breath, and severe jawbone problems, associated with the use of denosumab.[25] Stomatitis is a common AE due to panitumumab, which could significantly affect the eating and quality of life, impacting approximately 4% of patients.[26] According to a case study, a patient developed ileus as a result of grade IV diarrhea, which led to deadly complications. In extreme situations, surgery may be necessary in addition to supportive care, hydration, and bowel rest.[27] According to a study, diarrhea affected 71% of individuals on pertuzumab and trastuzumab, whereas the placebo group suffered diarrhea in 45% of cases.[28] 10% of patients had diarrhea of grade 3 or worse, with symptoms ranging from moderate to severe. Targeting the HER2 receptor, trastuzumab may harm the intestinal mucosa and hinder healing, which might result in GI toxicity.[29]
Strengths and limitations
FAERS database supports the FDA’s PMS surveillance program for pharmaceuticals and biological products, has over 29 million (as of September 2024) ICSRs. The FAERS database is a continuously expanding source of safety reports, and all safety reports are classified by using exact terminology with the latest version of MedDRA.[11] The FAERS database available case reports allow researchers to examine large volumes of data and produce a variety of safety recommendations for medications and incidents. For additional evaluation of drug-event combinations, especially during mAbs treatment, the results from our study on disproportionality signal analysis are essential.[8]
Patients, physicians, and pharmaceutical companies voluntarily report adverse reactions to the FAERS database, but this reliance may introduce reporting bias due to varying willingness and accuracy in reporting, leading to potential overestimation or underestimation of negative reactions. Duplicate reports are common because both customers and manufacturers may report the same incident, complicating data evaluation. Reports may be attributed to other variables and do not prove a causal association between the reported drugs and the event. The trustworthiness of the FAERS reports is questioned since they are based on subjective judgments and have not been medically validated.[7]
Conclusions
This study examines the FAERS database to explore GI disorders associated with mAbs. It shows that mAbs were causing various AEs such as diarrhea, nausea, vomiting, and abdominal pain. Demographic trends show that AEs happen more often in women and people between the ages of 51 and 75. Severe adverse events were significantly associated with atezolizumab, bevacizumab, and rituximab, with fatal outcomes related to advanced preexisting medical conditions. Disproportionality signal analysis identified 29 drug-AE pairs significantly correlated with GI disorders, with bevacizumab, rituximab, and nivolumab exhibiting the most pronounced signals. Specific PTs like enterocolitis, ascites, and GI perforation showed statistical significance, highlighting their importance for further signal detection. These findings emphasize the need for continuous GI-AE monitoring while mAbs treatment, bolstering pharmacovigilance advancements and risk-mitigation tactics to improve patient safety and outcomes.
Conflicts of interest
There are no conflicts of interest.
Funding Statement
Nil.
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