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Assessment of the real-world safety profile of vedolizumab using the United States Food and Drug Administration adverse event reporting system

  • Raymond K. Cross ,

    Roles Conceptualization, Methodology, Writing – review & editing

    rcross@medicine.umaryland.edu

    Affiliation University of Maryland School of Medicine, Baltimore, Maryland, United States of America

  • Michael Chiorean,

    Roles Conceptualization, Methodology, Writing – review & editing

    Affiliation Virginia Mason Medical Center, Seattle, Washington, United States of America

  • Francis Vekeman,

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Validation, Writing – review & editing

    Affiliation Groupe d’analyse, Ltée, Montréal, Québec, Canada

  • Yongling Xiao,

    Roles Data curation, Formal analysis, Methodology, Validation, Writing – review & editing

    Affiliation Groupe d’analyse, Ltée, Montréal, Québec, Canada

  • Eric Wu,

    Roles Data curation, Methodology, Writing – review & editing

    Affiliation Analysis Group, Inc., Boston, Massachusetts, United States of America

  • Jingdong Chao,

    Roles Conceptualization, Methodology, Writing – review & editing

    Affiliation AbbVie Inc., North Chicago, Illinois, United States of America

  • Anthony W. Wang

    Roles Conceptualization, Methodology, Writing – review & editing

    Affiliation AbbVie Inc., North Chicago, Illinois, United States of America

Assessment of the real-world safety profile of vedolizumab using the United States Food and Drug Administration adverse event reporting system

  • Raymond K. Cross, 
  • Michael Chiorean, 
  • Francis Vekeman, 
  • Yongling Xiao, 
  • Eric Wu, 
  • Jingdong Chao, 
  • Anthony W. Wang
PLOS
x

Abstract

Vedolizumab is the first gut-selective integrin blocker indicated for patients with Crohn's disease (CD) and ulcerative colitis (UC). This study aimed to examine the adverse events (AEs) profile of vedolizumab compared to anti-tumor necrosis factors (anti-TNFs) indicated for CD and UC using the FDA Adverse Event Reporting System (FAERS) database. AE reports with vedolizumab (5/20/2014–6/30/2015) and CD/UC-indicated anti-TNF drugs (adalimumab, infliximab, certolizumab pegol, and golimumab, during 8/1/1998–6/30/2015) as primary suspects were extracted from the FAERS database. AEs associated with vedolizumab were compared for signals of disproportionate reporting against anti-TNF drugs and all other drugs (1969–6/30/2015), using the proportional reporting ratio (PRR) and the empirical Bayesian geometric mean (EBGM) algorithms. The search retrieved 499 reports for vedolizumab and 119,620 reports for anti-TNFs, with 35.9% and 32.1% of these, respectively, being serious AEs. With the PRR approach, vedolizumab-associated reports had signals for 22 groups of AEs (9 were associated with serious outcomes) relative to anti-TNFs and had 34 signals relative to all other drugs. Signals detected included those reported as warnings in prescribing information and new AEs related to cardiovascular disease. Due to the voluntary nature of FAERS, this finding should be considered hypothesis generating (rather than hypothesis testing). Longer-term observational studies are required to evaluate the safety of vedolizumab.

Introduction

Biologic drugs target specific components of the immune system and have revolutionized the treatment of inflammatory bowel disease (IBD).[13] Anti-tumor necrosis factor (TNF) agents, which include adalimumab (Humira® [AbbVie, Inc., North Chicago, IL]), certolizumab pegol (Cimzia® [UCB, Inc., Smryna, GA]), golimumab (Simponi® [Janssen Biotech Inc., Horsham, PA]), and infliximab (Remicade® [Janssen Biotech Inc., Horsham, PA]), have shown to be effective, have an acceptable safety profile, and have been the standard of care for nearly 20 years.[47] Recently biologic therapies with novel mechanisms of action such as vedolizumab (Entyvio® [Takeda Inc, Tokyo, Japan]), the first gut-targeted integrin blocker, have entered the market and provide alternative treatment options for IBD patients.[3] While vedolizumab has demonstrated a favorable safety profile in randomized control trials and in early real-world studies, its safety profile has yet to be compared with anti-TNF therapies. While the development of new therapies to treat IBD is crucial to patients, it is important to place the safety of new therapies–especially those with novel mechanisms of action–in the context of existing therapies.

Anti-TNF drugs work by systemically suppressing the activity of the pro-inflammatory and pro-apoptotic TNFα cytokine,[8] and thus suppressing the activation of downstream immune responses.[8] By contrast, integrin blockers disrupt leukocyte migration to sites of inflammation, providing a more selective inhibition of the chronic inflammatory response in IBD.[9] Vedolizumab mainly impacts the immune response in the gut through its interaction with the gut-associated alpha4-beta7 integrin on the surface of memory T cells.[10, 11] Vedolizumab was approved in May 2014 for the treatment of moderate-to-severe IBD and is still the only gut-selective leukocyte migration inhibitor approved in the US for the treatment of both UC and CD.[10]

The balance between clinical benefit and possible risks is essential in determining optimal treatment choice.[1214] While the safety profiles of anti-TNF drugs are well-established from both randomized clinical trials (RCTs)[1517] and real-world studies,[1821] to date, information on possible adverse events (AEs) after treatment with vedolizumab comes mainly from clinical trials such as the two Phase III trials for vedolizumab in patients with UC and CD (GEMINI 1 and 2),[22, 23] with limited information derived from a few real-world studies with small sample sizes or relatively short follow-up that assessed the effectiveness and safety of vedolizumab.[2427],[28] However, while RCTs are the gold standard for assessing the efficacy of drugs, they are not ideal for detecting rare safety events.[29] The main shortcoming of the RCT study design is its limited external validity namely due to its often short duration of follow-up, limited study population size, stringent entry criteria that often exclude patients with significant comorbidities, older age, real-world population heterogeneity, and an artificially high level of adherence to treatment.[3033] As a result, infrequent serious adverse events (SAEs) are often discovered through voluntary reporting systems or from nonrandomized post-marketing studies.[32] Thus, real-world observational studies and mining of pharmacovigilance data are used to augment safety information derived from RCTs and assist in detecting possible areas of caution when using the drug of interest.[34, 35] The FDA Adverse Event Reporting Systems (FAERS) is a voluntary reporting system developed by the FDA for the purpose of post-marketing surveillance for all approved drugs and therapeutic biologics. It gathers reports of AEs voluntarily submitted by health care professionals and consumers (directly or through the manufacturer of the drug), which may contain AEs that were already observed in RCTs and others that may not have been detected during RCTs.[36] Statistical methods have been developed to allow the effective interpretation of findings based on voluntary reporting systems.[3739]

The objectives of this study were to assess the rates of real-world AE reporting associated with vedolizumab using the FAERS database. Three specific objectives were addressed in this study. The first objective was to explore the real-world profile of reported AEs with vedolizumab, as the unique mechanism of action (MOA) of vedolizumab provides the opportunity to compare gut-selective with systemic immunosuppressive drugs. The second objective was to identify whether there are any reported AEs that are disproportionately associated with vedolizumab relative to anti-TNFs for the treatment of UC and CD. The third objective was to identify whether there are any reported AEs disproportionately associated with vedolizumab relative to all other drugs reported in the FAERS database.

Materials and methods

Data source

The FAERS is a spontaneous reporting database maintained by the FDA,[40] whose aim is to support the FDA’s post-marketing safety surveillance program for drug and therapeutic biologic products by collecting information on adverse drug reactions from two principal sources: (1) mandatory reports from pharmaceutical companies (who must report any AE within 14 days of becoming aware of the AE), and (2) voluntary AE reports from healthcare professionals, consumers, and manufacturers. The database has over 9 million reports, with the earliest entries dating from 1969. In recent years (since 2012), the FAERS database has captured over 1 million reports annually. The data structure of the database adheres to the safety reporting guidance issued by the International Conference on Harmonisation (ICH) E2B. The FAERS public database includes seven datasets containing demographic and administrative information of the AEs (date FDA received the report, AE date, patient age, gender, reporting country, etc.), pharmaceutical details (drug names, indications, role of drugs, etc.), AE information, source of reports, and patient outcome information. In the database, AE names and indications for drug use are coded using the Medical Dictionary for Regulatory Activities (MedDRA) preferred terms.[41] Drugs are recorded by valid trade names if available or by verbatim names. Drug roles are classified as primary suspect, secondary suspect, concomitant, or interacting. Serious outcomes attributed to AEs, including death, life-threatening event, hospitalization (initial and prolonged), disability or permanent damage, congenital anomaly/birth defect, required intervention to prevent permanent impairment/damage (devices), or other serious outcomes (important medical events), are also captured.[42] Other drug-related information (such as dosage, administration route, etc.) is recorded using verbatim text. The FAERS data used in this study were provided by DrugLogic, Inc. (Reston, VA), which cleaned, mapped, de-duplicated, and normalized the raw FAERS data. This data has been utilized in multiple post-marketing adverse event analyses.[4345]

Sample selection

Reports associated with vedolizumab or anti-TNF drugs (i.e., adalimumab, certolizumab pegol, golimumab, and infliximab) were extracted by searching the generic and branded drug names along with potential misspellings and abbreviations, which were generated in the database cleaning/normalization process. Specifically, reports for vedolizumab were extracted from the date of FDA approval (May 20, 2014) to June 30, 2015 (the most recent update of the FAERS database at the time the study was conducted). Reports for anti-TNFs (i.e., adalimumab, certolizumab pegol, golimumab, and infliximab) were extracted from August 24, 1998 (the date of approval of infliximab—the first anti-TNF drug for CD) to June 30, 2015. Only reports listing the agents of interest (i.e., vedolizumab or anti-TNFs) as the primary suspect were kept (88% of reports for vedolizumab and 97.6% of reports for anti-TNFs). All vedolizumab reports were included without respect to indication because vedolizumab is only indicated for IBD. As anti-TNFs are indicated for multiple non-IBD inflammatory conditions, only reports where these drugs were indicated for UC or CD were included to ensure a homogeneous comparison with vedolizumab. Reports for all other drugs that were recorded in the FAERS database were extracted from inception to date of study execution (i.e., from 1969 to June 30, 2015). Ustekinumab (approved for Crohn’s disease after the last date available in the dataset) and natalizumab (primarily a treatment for multiple sclerosis with a black box warning for progressive multifocal leukoencephalopathy) were not included in the analysis.

Reports that satisfied the above inclusion criteria and were associated with serious outcomes were extracted for the subgroup analyses.

All data accessible within the FAERS database are fully anonymized and comply with the patient confidentiality requirements of the Health Insurance Portability and Accountability Act (HIPAA). Ethics approval was not required as the research does not contain any new studies with human or animal subjects performed by any of the authors.

Study outcomes

Grouped AEs associated with vedolizumab were analyzed. Specifically, all individual AEs based on MedDRA preferred terms[41] recorded on vedolizumab reports were first identified, and then the preferred terms belonging to the same MedDRA High Level Term (HLT) class were grouped to form 254 grouped AEs (S1 Appendix).

Statistical analysis

AE characteristics and patient demographics were reported using descriptive statistics (i.e., frequency, proportion, mean, and standard deviation) for vedolizumab and anti-TNF reports, respectively. Data mining algorithms were then used to identify AEs associated with vedolizumab that were reported more frequently than expected using the information on all drugs of interest as a reference (i.e., the reports for vedolizumab and anti-TNF drugs for Objective 2, and the reports for vedolizumab and all other drugs in the FAERS database for Objective 3). Two FDA-recommended data-mining algorithms for the analysis of spontaneous reports were used in this study: the proportional reporting ratio (PRR) and the empirical Bayesian geometric mean (EBGM).[38, 39, 46]

The PRR is one of the most commonly used frequentist methods of measuring reporting disproportionality.[39] In this study, the PRR was calculated as the ratio of the reporting proportion of AEs associated with vedolizumab divided by the reporting proportion of AEs associated with the comparator (i.e., anti-TNF drugs or all the other drugs in the FAERS database). The reporting proportion of a given AE for vedolizumab and the comparator, respectively, was calculated by dividing the number of reports with the given AE by the total number of reports for the drug of interest. A signal of disproportionate reporting was defined as a PRR ≥2, the lower bound of the PRR confidence interval ≥1, an associated chi-square ≥4, and the number of events in each group (i.e., vedolizumab and the comparator) ≥3.[47]

The EBGM calculation is conceptually similar to that of the PRR, but incorporates Bayesian “shrinkage” to produce more robust and stable disproportionality results when there are limited data and small numbers of events.[38, 39] The EBGM was calculated using the Bayesian multi-item gamma Poisson shrinker method.[38] A signal of disproportionate reporting was defined as the EB05 (i.e., the lower bound of the 90% confidence limit of EBGM) for vedolizumab ≥2, the EB05 for vedolizumab greater than EB95 (i.e., the upper bound of the 90% confidence limit of EBGM) for the comparator, and the number of events in each group ≥3.

The same two disproportionality analysis measures (PRR and EBGM) were calculated to examine the presence of signals of disproportionate reporting for reports associated with serious outcomes only.

SAS version 9.3 (SAS Institute, Inc., Cary, NC) and R version 3.2.2 (R Foundation, Vienna, Austria)[48] were used to conduct the statistical analyses.

Results

A total of 499 reports were identified in which vedolizumab was listed as the primary suspect drug. Patient demographics are presented in Table 1. Vedolizumab was used in 44.3% of reports for CD, 23.4% for UC, and in 28.2% of reports the indication was not specified. Overall, 35.9% of vedolizumab reports were associated with serious outcomes. Among the 499 reports, 459 different MedDRA preferred terms were recorded and a total of 254 grouped AEs were created and compared for disproportionate reporting between vedolizumab, anti-TNF drugs and all other drugs in the database.

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Table 1. Characteristics of reports—all reports for vedolizumab and anti-TNF drugs from August 1998 to June 2015.

https://doi.org/10.1371/journal.pone.0225572.t001

A total of 119,620 reports were identified in which anti-TNF drugs were listed as primary suspects with indication for UC or CD (Table 1, left panel). Overall, 84.7% and 15.3% of all anti-TNF reports had an indication for CD and UC, respectively, and 32.1% were associated with serious outcomes. Vedolizumab users and anti-TNF users were similar in terms of age and gender (Table 1).

Table 2 presents the PRRs calculated for grouped AEs containing known AEs listed in the prescribing information for vedolizumab (i.e., nasopharyngitis, headache, arthralgia, nausea, pyrexia, upper respiratory tract infection, fatigue, cough, bronchitis, influenza, back pain, rash, pruritus, sinusitis, oropharyngeal pain, and pain in extremities). A total of 309 out of 499 of reported vedolizumab AEs occurred for the AEs listed in the prescribing information for vedolizumab. No PRR signals were detected among vedolizumab reports for any of these known AEs compared with anti-TNF drugs (Table 2, left panel). In comparison with all other drugs, 3 PRR signals were detected including joint related symptoms (e.g., arthralgia), upper respiratory tract infections (e.g., nasopharyngitis and sinusitis), and upper respiratory tract symptoms (e.g., oropharyngeal pain) (Table 2, right panel).

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Table 2. Proportional reporting ratios for known adverse events for vedolizumab versus anti-TNF drugs or all other drugsa.

https://doi.org/10.1371/journal.pone.0225572.t002

Among all 254 grouped AEs identified for vedolizumab in the FAERS database, PRR signals were detected for 22 when compared with anti-TNFs (Table 3), of which 7 also had an EBGM signal. These 22 grouped AEs include AEs related to cardiovascular disease (pulmonary thrombotic and embolic conditions, pulmonary edemas, as well as central nervous system hemorrhages and cerebrovascular accidents) and some AEs discussed in the Warnings and Precautions section of vedolizumab’s prescribing information (infusion site reactions, infections, liver abnormalities, and colorectal neoplasms) (Table 3, left panel). A total of 17 vedolizumab and 1074 anti-TNF-associated reports detailed an AE related to cardiovascular disease; most of these reports were associated with serious outcomes (88.2% and 85.7%, respectively). The safety signal for central nervous system hemorrhages and cerebrovascular accidents for vedolizumab compared with anti-TNFs were detected by both PRR and EBGM.

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Table 3. Proportional reporting ratios of adverse events with a signal for vedolizumab compared with anti-TNF drugs.

https://doi.org/10.1371/journal.pone.0225572.t003

A total of 179 and 38,346 reports of AEs with serious outcomes were identified for vedolizumab and anti-TNFs, respectively (Table 1, right panel). Among the vedolizumab-associated reports with serious outcomes, the drug was used for CD in 52.5% and UC in 27.4% compared with 86.1% and 13.9% for anti-TNFs-associated reports, respectively. Of the 22 grouped AEs with a PRR signal identified using all eligible reports (Table 3, left panel), PRR signals were detected for 9 AEs using reports associated with serious outcomes, of which 2 also had an EBGM signal (Table 3, right panel).

In comparison with all other drugs reported in the FAERS database, the PRR analysis identified 34 signals for grouped AEs associated with vedolizumab. Of these, 14 also had an EBGM signal (Table 4, left panel). When restricting the analysis to reports with serious outcomes only, 21 of the 34 grouped AEs associated with vedolizumab had a PRR signal and 8 also had an EBGM signal (Table 4, right panel).

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Table 4. Proportional reporting ratios for adverse events with a signal for vedolizumab compared with all other drugs.

https://doi.org/10.1371/journal.pone.0225572.t004

Discussion

Safety is a crucial element of determining optimal treatment choice and is always a key concern when new therapies are introduced into the clinical practice. The safety profile of vedolizumab that has emerged from the analysis of clinical trial data in over 3,000 patients suggests an increased susceptibility to upper respiratory infections (nasopharyngitis, sinusitis), and other serious and opportunistic infections as well as non-specific AEs (nausea, fatigue, headache, arthralgia, rash, and pruritus).[9, 4951] However, data from RCTs may underestimate the occurrence of rare but SAEs for which clinical trials have no adequate detection power due to sample size and relatively short-duration.[29] Post-marketing surveillance is currently required for all FDA-approved drugs so that rare AEs or AEs undetected in RCTs can be detected sooner after use in the real-world clinical setting.[36] Examples of examining potential novel side-effects associated with anti-TNF agents in post-marketing analyses of the FAERS database include the analysis of sporadic reports of non-Hodgkin's lymphoma[5254] and of possible congenital anomalies (if the drug was administered to gestating patients).[55] The assessment of long-term safety of a drug is of the upmost importance to better educate patients and healthcare providers regarding the risks and benefits of treatment. Therefore, in the current study we have investigated the real-world safety profile of vedolizumab compared to anti-TNF and other drugs using the FAERS database.

In this study, no signals were detected for known AEs listed in vedolizumab’s prescribing information relative to anti-TNF use. However, signals were detected for arthralgia, nasopharyngitis, sinusitis, and oropharyngeal pain when comparing vedolizumab with all other drugs reported in the FAERS database.

Among the 254 grouped AEs identified for vedolizumab in the FAERS database, 22 and 34 were identified with signals of disproportionate reporting compared with anti-TNF drugs and all other drugs in the database, respectively. Grouped AEs with a PRR signal include AEs related to cardiovascular disease and AEs that had been reported in vedolizumab clinical trials and discussed in the Warnings and Precautions section of vedolizumab’s prescribing information (including infusion site reactions, infections, liver abnormalities, and colorectal neoplasms). AE signals detected in both comparisons include pulmonary edemas, infusion site reactions, infections, liver abnormalities, and colorectal neoplasms.

Several hypotheses exist for the signal detected for central nervous system hemorrhages and cerebrovascular accidents associated with vedolizumab relative to anti-TNF drugs. First, the reported cardiovascular disease AEs may be related to the different mechanisms of action of vedolizumab and anti-TNFs. Second, cardiovascular disease signals are commonly missed in IBD clinical trials as they are uncommon.[34] In the case of IBD, cardiovascular AEs may be linked to the underlying IBD: chronic low-grade inflammation has been associated with both venous and arterial thromboembolic events[5660] and, overall, the development of cardiovascular disease.[56, 57, 61] An alternative explanation for the reported cardiovascular disease AEs could be that vedolizumab would be utilized in moderately to severely active CD and UC patients with who have had an inadequate response or intolerance to a TNF inhibitor or corticosteroids as indicated by the FDA. Such patients may be at greater risk for cardiovascular complications secondary to poor long-term control of disease. However, due to the nature of the voluntary AE reporting system and the types of data it collects, the study could not examine or control for various potential confounding factors which could influence the incidence of cardiovascular disease-related AEs. Such confounding factors include patient’s lifestyle habits (smoking, daily physical activity levels, diet), prior or concurrent treatments (e.g., NSAIDs), and other comorbidities (e.g., obesity, diabetes), among others.

Our study has several limitations. First, we relied on a database which is subject to incomplete and inconsistent reporting, as well as ascertainment bias. For example, missing information on indications for drug use or having multiple indications for a same drug might affect the identification of eligible reports included in the study. However, such ascertainment bias should be minimal as less than 2% of selected reports had either missing (for vedolizumab reports only) or multiple indications. Second, the Weber effect, or the phenomenon of increased reporting in the early years of a product’s launch, is often found in spontaneous reporting. Thus, we also included the early years after anti-TNF launches to account for this issue. It should be noted that we are analyzing reports from the first 13 months following vedolizumab approval compared with nearly 17 years of anti-TNF reports. However, the cardiovascular AEs we observed will not likely be impacted by the Weber effect since these AEs will likely be reported regardless of time from launch. Third, data quality greatly varies across reports and by the type of reporters (e.g., specialists, general practitioners, patients, pharmaceutical companies) and there is no internal or external validation. Fourth, since spontaneous reporting systems are numerator-based, they provide no denominator for the number of drug users. Due to this limitation, there is no way to factor in actual drug utilization and no way to calculate population based incidence rates from FAERS. Fifth, for some AEs with very small counts, the reporting rates estimated using the PRR approach might have large variations, which may result in false-positive signals (to limit false positives a minimum of three reported events were required to detect a signal in the current analysis). However, for all signals detected by the PRR approach, the EBGM approach, a method developed to produce more robust and stable disproportionality results when there are limited data and small numbers of events, was also used. It should be noted that there is no gold standard for data mining algorithms. An alternative approach such as reporting odds ratio (ROR) in contrast to PRR could be used to adjusted to different confounding factors in multivariate logistic regression [47]. However, as previously noted, there is limited information on other potential confounders, such as comorbidities, information on disease duration and severity, disease phenotype, surgical history, smoking history, and concurrent IBD treatment. Nonetheless, the FAERS database has successfully been used in previous analyses of real-world post-marketing pharmacovigilance studies.[53, 55, 6265] Given these limitations, it is important to consider our results as hypothesis-generating and deserving of further study. Taken together, findings from this study detect signals of vedolizumab with an elevated reporting of certain AEs, including cardiovascular and thromboembolic disease, compared to anti-TNFs in IBD patients. These findings should be confirmed in further studies.

Supporting information

S1 Appendix. Supplemental Appendix A: Unique Reaction HLTs.

https://doi.org/10.1371/journal.pone.0225572.s001

(DOCX)

Acknowledgments

Medical writing assistance and analytic support was provided by Ana Bozas, PhD, and Keith Betts, PhD, employees of Analysis Group, Inc. Financial support for these activities was provided by AbbVie. Anthony Wang accepts full responsibility for the conduct of the study. The study guarantor had full access to the data and has control of the decision to publish this work.

All authors participated in the design of the study and contributed to the manuscript development (planning and conducting the study, analyzing and interpreting data, and drafting the manuscript). Data were collected by Analysis Group and analyzed and interpreted in collaboration with all other authors. Manuscript drafts were prepared by the authors with editorial assistance from a professional medical writer ultimately paid by the sponsor. All the authors vouch for the accuracy and completeness of the data reported and the adherence of the study to the protocol. All authors approved the final draft submitted here and independently made the decision to submit the manuscript for publication.

References

  1. 1. Stidham RW, Lee TC, Higgins PD, Deshpande AR, Sussman DA, Singal AG, et al. Systematic review with network meta-analysis: the efficacy of anti-TNF agents for the treatment of Crohn's disease. Aliment Pharmacol Ther. 2014;39(12):1349–62. Epub 2014/04/23. pmid:24749763.
  2. 2. Stidham RW, Lee TC, Higgins PD, Deshpande AR, Sussman DA, Singal AG, et al. Systematic review with network meta-analysis: the efficacy of anti-tumour necrosis factor-alpha agents for the treatment of ulcerative colitis. Aliment Pharmacol Ther. 2014;39(7):660–71. Epub 2014/02/11. pmid:24506179.
  3. 3. Cohen LB, Nanau RM, Delzor F, Neuman MG. Biologic therapies in inflammatory bowel disease. Translational research: the journal of laboratory and clinical medicine. 2014;163(6):533–56. Epub 2014/01/29. pmid:24467968.
  4. 4. Gomollon F, Dignass A, Annese V, Tilg H, Van Assche G, Lindsay JO, et al. 3rd European evidence-based consensus on the diagnosis and management of Crohn's disease 2016: part 1: diagnosis and medical management. J Crohns Colitis. 2017;11(1):3–25. Epub 2016/09/24. pmid:27660341.
  5. 5. Lichtenstein GR, Hanauer SB, Sandborn WJ. Management of Crohn's disease in adults. Am J Gastroenterol. 2009;104(2):465–83; quiz 4, 84. Epub 2009/01/29. pmid:19174807.
  6. 6. Magro F, Portela F. Management of inflammatory bowel disease with infliximab and other anti-tumor necrosis factor alpha therapies. BioDrugs. 2010;24 Suppl 1:3–14. Epub 2010/12/24. pmid:21175228.
  7. 7. Janssen Biotech I. SIMPONI (golimumab) prescribing information 2013 [cited 2017 March 2]. 1–33]. Available from: http://www.simponi.com/shared/product/simponi/prescribing-information.pdf.
  8. 8. Sedger LM, McDermott MF. TNF and TNF-receptors: from mediators of cell death and inflammation to therapeutic giants—past, present and future. Cytokine Growth Factor Rev. 2014;25(4):453–72. pmid:25169849.
  9. 9. de Mattos BR, Garcia MP, Nogueira JB, Paiatto LN, Albuquerque CG, Souza CL, et al. Inflammatory bowel disease: an overview of immune mechanisms and biological treatments. Mediators Inflamm. 2015;2015:493012. pmid:26339135; PubMed Central PMCID: PMC4539174.
  10. 10. McLean LP, Shea-Donohue T, Cross RK. Vedolizumab for the treatment of ulcerative colitis and Crohn's disease. Immunotherapy. 2012;4(9):883–98. pmid:23046232; PubMed Central PMCID: PMC3557917.
  11. 11. Ala A, Dhillon AP, Hodgson HJ. Role of cell adhesion molecules in leukocyte recruitment in the liver and gut. Int J Exp Pathol. 2003;84(1):1–16. pmid:12694483; PubMed Central PMCID: PMC2517541.
  12. 12. Dignass A, Lindsay JO, Sturm A, Windsor A, Colombel JF, Allez M, et al. Second European evidence-based consensus on the diagnosis and management of ulcerative colitis part 2: current management. J Crohns Colitis. 2012;6(10):991–1030. pmid:23040451.
  13. 13. Dignass A, Van Assche G, Lindsay JO, Lemann M, Soderholm J, Colombel JF, et al. The second European evidence-based consensus on the diagnosis and management of Crohn's disease: current management. J Crohns Colitis. 2010;4(1):28–62. pmid:21122489.
  14. 14. Terdiman JP, Gruss CB, Heidelbaugh JJ, Sultan S, Falck-Ytter YT, Practice AGAIC, et al. American Gastroenterological Association Institute guideline on the use of thiopurines, methotrexate, and anti-TNF-alpha biologic drugs for the induction and maintenance of remission in inflammatory Crohn's disease. Gastroenterology. 2013;145(6):1459–63. Epub 2013/11/26. pmid:24267474.
  15. 15. Huang X, Lv B, Jin HF, Zhang S. A meta-analysis of the therapeutic effects of tumor necrosis factor-alpha blockers on ulcerative colitis. Eur J Clin Pharmacol. 2011;67(8):759–66. Epub 2011/06/22. pmid:21691804.
  16. 16. Zhang D, Xiong B, Li X, Xu T, Yu M. Meta-analysis: serious adverse events in Crohn's disease patients treated with TNF-alpha inhibitors. Hepatogastroenterology. 2013;60(126):1333–42. Epub 2013/01/24. pmid:23340167.
  17. 17. Singh JA, Wells GA. Adverse effects of biologics: a network meta-analysis and Cochrane overview. Cochrane Database of Syst Rev. 2012;3:1–82.
  18. 18. Freling E, Baumann C, Cuny JF, Bigard MA, Schmutz JL, Barbaud A, et al. Cumulative incidence of, risk factors for, and outcome of dermatological complications of anti-TNF therapy in inflammatory bowel disease: a 14-year experience. Am J Gastroenterol. 2015;110(8):1186–96. Epub 2015/07/22. pmid:26195181.
  19. 19. Lees CW, Ali AI, Thompson AI, Ho GT, Forsythe RO, Marquez L, et al. The safety profile of anti-tumour necrosis factor therapy in inflammatory bowel disease in clinical practice: analysis of 620 patient-years follow-up. Aliment Pharmacol Ther. 2009;29(3):286–97. Epub 2009/01/10. pmid:19132970.
  20. 20. Lichtenstein GR, Feagan BG, Cohen RD, Salzberg BA, Diamond RH, Price S, et al. Serious infection and mortality in patients with Crohn's disease: more than 5 years of follow-up in the TREAT registry. Am J Gastroenterol. 2012;107(9):1409–22. Epub 2012/08/15. pmid:22890223; PubMed Central PMCID: PMC3438468.
  21. 21. Marehbian J, Arrighi HM, Hass S, Tian H, Sandborn WJ. Adverse events associated with common therapy regimens for moderate-to-severe Crohn's disease. Am J Gastroenterol. 2009;104(10):2524–33. Epub 2009/06/18. pmid:19532125.
  22. 22. Feagan BG, Rutgeerts P, Sands BE, Hanauer S, Colombel JF, Sandborn WJ, et al. Vedolizumab as induction and maintenance therapy for ulcerative colitis. N Engl J Med. 2013;369(8):699–710. Epub 2013/08/24. pmid:23964932.
  23. 23. Sandborn WJ, Feagan BG, Rutgeerts P, Hanauer S, Colombel JF, Sands BE, et al. Vedolizumab as induction and maintenance therapy for Crohn's disease. N Engl J Med. 2013;369(8):711–21. Epub 2013/08/24. pmid:23964933.
  24. 24. Baumgart DC, Bokemeyer B, Drabik A, Stallmach A, Schreiber S, Vedolizumab Germany C. Vedolizumab induction therapy for inflammatory bowel disease in clinical practice—a nationwide consecutive German cohort study. Aliment Pharmacol Ther. 2016;43(10):1090–102. pmid:27038247.
  25. 25. Amiot A, Grimaud JC, Peyrin-Biroulet L, Filippi J, Pariente B, Roblin X, et al. Effectiveness and safety of vedolizumab induction therapy for patients with inflammatory bowel disease. Clin Gastroenterol Hepatol. 2016;14(11):1593–601 e2. pmid:26917043.
  26. 26. Vivio EE, Kanuri N, Gilbertsen JJ, Monroe K, Dey N, Chen CH, et al. Vedolizumab effectiveness and safety over the first year of use in an IBD clinical practice. J Crohns Colitis. 2016;10(4):402–9. pmid:26681763.
  27. 27. Shelton E, Allegretti JR, Stevens B, Lucci M, Khalili H, Nguyen DD, et al. Efficacy of vedolizumab as induction therapy in refractory IBD patients: a multicenter cohort. Inflamm Bowel Dis. 2015;21(12):2879–85. Epub 2015/08/20. pmid:26288002; PubMed Central PMCID: PMC4745906.
  28. 28. Dulai PS, Singh S, Jiang X, Peerani F, Narula N, Chaudrey K, et al. The real-world effectiveness and safety of vedolizumab for moderate-severe Crohn's disease: results from the US VICTORY Consortium. Am J Gastroenterol. 2016;111(8):1147–55. Epub 2016/06/15. pmid:27296941.
  29. 29. Booth CM, Tannock IF. Randomised controlled trials and population-based observational research: partners in the evolution of medical evidence. Br J Cancer. 2014;110(3):551–5. pmid:24495873; PubMed Central PMCID: PMC3915111.
  30. 30. Concato J, Peduzzi P, Huang GD, O'Leary TJ, Kupersmith J. Comparative effectiveness research: what kind of studies do we need? J Investig Med. 2010;58(6):764–9. pmid:20479661.
  31. 31. Armstrong K. Methods in comparative effectiveness research. J Clin Oncol. 2012;30(34):4208–14. pmid:23071240; PubMed Central PMCID: PMC3504326.
  32. 32. Rothman KJ, Greenland S, Lash TL. Modern Epidemiology. 3rd ed. Philadelphia, PA: Lippincott, Williams & Wilkins; 2008.
  33. 33. Meyer RM. Generalizing the results of cancer clinical trials. J Clin Oncol. 2010;28(2):187–9. pmid:19933900.
  34. 34. Xu R, Wang Q. Large-scale combining signals from both biomedical literature and the FDA Adverse Event Reporting System (FAERS) to improve post-marketing drug safety signal detection. BMC bioinformatics. 2014;15(1):17. pmid:24428898; PubMed Central PMCID: PMC3906761.
  35. 35. Bailey S, Singh A, Azadian R, Huber P, Blum M. Prospective data mining of six products in the US FDA Adverse Event Reporting System: disposition of events identified and impact on product safety profiles. Drug Saf. 2010;33(2):139–46. Epub 2010/01/20. pmid:20082540.
  36. 36. Weaver J, Willy M, Avigan M. Informatic tools and approaches in postmarketing pharmacovigilance used by FDA. The AAPS journal. 2008;10(1):35–41. Epub 2008/05/01. pmid:18446503; PubMed Central PMCID: PMC2751449.
  37. 37. Sakaeda T, Tamon A, Kadoyama K, Okuno Y. Data mining of the public version of the FDA Adverse Event Reporting System. Int J Med Sci. 2013;10(7):796–803. pmid:23794943; PubMed Central PMCID: PMC3689877.
  38. 38. Almenoff JS, LaCroix KK, Yuen NA, Fram D, DuMouchel W. Comparative performance of two quantitative safety signalling methods: implications for use in a pharmacovigilance department. Drug safety. 2006;29(10):875–87. pmid:16970511
  39. 39. Bate A, Edwards IR. Data mining in spontaneous reports. Basic Clin Pharmacol Toxicol. 2006;98(3):324–30. pmid:16611210.
  40. 40. Center for Drug Evaluation and Research, U. S. Food and Drug Administration. FDA Adverse Event Reporting System (FAERS) Silver Spring, MD: U.S. Food and Drug Administration; 2015 [cited 2016 February 19, 2016]. Available from: http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/.
  41. 41. Giese P. MedDRA–Beyond that basic AE report. How SMQs and MedDRA structure can enhance reporting. 2013.
  42. 42. Office of the Commissioner, U. S. Food and Drug Administration. Reporting serious problems to the FDA—What is a serious adverse event? Silver Spring, MD: U.S. Food and Drug Administration; 2016 [cited 2016 February 19, 2016]. Available from: http://www.fda.gov/Safety/MedWatch/HowToReport/ucm053087.htm.
  43. 43. Weiss-Smith S, Deshpande G, Chung S, Gogolak V. The FDA drug safety surveillance program: adverse event reporting trends. Arch Intern Med. 2011;171(6):591–3. Epub 2011/03/30. pmid:21444854.
  44. 44. Chhabra P, Chen X, Weiss SR. Adverse event reporting patterns of newly approved drugs in the USA in 2006: an analysis of FDA Adverse Event Reporting System data. Drug safety. 2013;36(11):1117–23. Epub 2013/10/01. pmid:24078293.
  45. 45. Duh MS, Cremieux P, Audenrode MV, Vekeman F, Karner P, Zhang H, et al. Can social media data lead to earlier detection of drug-related adverse events? Pharmacoepidemiol Drug Saf. 2016;25(12):1425–33. Epub 2016/09/08. pmid:27601271; PubMed Central PMCID: PMC5157765.
  46. 46. Duggirala HJ, Tonning JM, Smith E, Bright RA, Baker JD, Ball R, et al. Data Mining at FDA. Silver Spring, MD: U.S. Food and Drug Administration, 2015.
  47. 47. Deshpande G, Gogolak V, Smith SW. Data Mining in drug safety. Pharmaceutical Medicine. 2010;24(1):37–43.
  48. 48. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2014.
  49. 49. Colombel JF, Sands BE, Rutgeerts P, Sandborn W, Danese S, D'Haens G, et al. The safety of vedolizumab for ulcerative colitis and Crohn's disease. Gut [Internet]. 2016 Feb 18. Available from: https://www.ncbi.nlm.nih.gov/pubmed/26893500.
  50. 50. Wang MC, Zhang LY, Han W, Shao Y, Chen M, Ni R, et al. PRISMA—efficacy and safety of vedolizumab for inflammatory bowel diseases: a systematic review and meta-analysis of randomized controlled trials. Medicine. 2014;93(28):e326. Epub 2014/12/20. pmid:25526490; PubMed Central PMCID: PMC4603082.
  51. 51. Luthra P, Peyrin-Biroulet L, Ford AC. Systematic review and meta-analysis: opportunistic infections and malignancies during treatment with anti-integrin antibodies in inflammatory bowel disease. Aliment Pharmacol Ther. 2015;41(12):1227–36. Epub 2015/04/24. pmid:25903741.
  52. 52. Brown SL, Greene MH, Gershon SK, Edwards ET, Braun MM. Tumor necrosis factor antagonist therapy and lymphoma development: twenty-six cases reported to the Food and Drug Administration. Arthritis Rheum. 2002;46(12):3151–8. pmid:12483718.
  53. 53. Diak P, Siegel J, La Grenade L, Choi L, Lemery S, McMahon A. Tumor necrosis factor alpha blockers and malignancy in children: forty-eight cases reported to the Food and Drug Administration. Arthritis Rheum. 2010;62(8):2517–24. Epub 2010/05/28. pmid:20506368.
  54. 54. Deepak P, Sifuentes H, Sherid M, Stobaugh D, Sadozai Y, Ehrenpreis ED. T-cell non-Hodgkin's lymphomas reported to the FDA AERS with tumor necrosis factor-alpha (TNF-alpha) inhibitors: results of the REFURBISH study. Am J Gastroenterol. 2013;108(1):99–105. pmid:23032984.
  55. 55. Carter JD, Ladhani A, Ricca LR, Valeriano J, Vasey FB. A safety assessment of tumor necrosis factor antagonists during pregnancy: a review of the Food and Drug Administration database. J Rheumatol. 2009;36(3):635–41. Epub 2009/01/10. pmid:19132789.
  56. 56. Filimon AM, Negreanu L, Doca M, Ciobanu A, Preda CM, Vinereanu D. Cardiovascular involvement in inflammatory bowel disease: dangerous liaisons. World J Gastroenterol. 2015;21(33):9688–92. Epub 2015/09/12. pmid:26361415; PubMed Central PMCID: PMC4562952.
  57. 57. Fumery M, Xiaocang C, Dauchet L, Gower-Rousseau C, Peyrin-Biroulet L, Colombel JF. Thromboembolic events and cardiovascular mortality in inflammatory bowel diseases: a meta-analysis of observational studies. J Crohns Colitis. 2014;8(6):469–79. Epub 2013/11/05. pmid:24183231.
  58. 58. Singh S, Singh H, Loftus EV Jr., Pardi DS. Risk of cerebrovascular accidents and ischemic heart disease in patients with inflammatory bowel disease: a systematic review and meta-analysis. Clin Gastroenterol Hepatol. 2014;12(3):382–93 e1: quiz e22. Epub 2013/08/28. pmid:23978350.
  59. 59. Vizzardi E, Sciatti E, Bonadei I, Bordonali T, Ricci C, Lanzarotto F, et al. Subclinical cardiac involvement in Crohn's disease and ulcerative colitis: an echocardiographic case-control study. Panminerva medica. 2016;58(2):115–20. Epub 2016/03/11. pmid:26964076.
  60. 60. Steyers CM 3rd, Miller FJ Jr. Endothelial dysfunction in chronic inflammatory diseases. International journal of molecular sciences. 2014;15(7):11324–49. Epub 2014/06/27. pmid:24968272; PubMed Central PMCID: PMC4139785.
  61. 61. Papa A, Scaldaferri F, Danese S, Guglielmo S, Roberto I, Bonizzi M, et al. Vascular involvement in inflammatory bowel disease: pathogenesis and clinical aspects. Dig Dis. 2008;26(2):149–55. Epub 2008/04/24. pmid:18431065.
  62. 62. Deepak P, Stobaugh DJ, Ehrenpreis ED. Infectious complications of TNF-alpha inhibitor monotherapy versus combination therapy with immunomodulators in inflammatory bowel disease: analysis of the Food and Drug Administration Adverse Event Reporting System. J Gastrointestin Liver Dis. 2013;22(3):269–76. Epub 2013/10/01. pmid:24078983.
  63. 63. Kip KE, Swoger JM, Grandinetti LM, Barrie AM 3rd, Greer JB, Regueiro MD. Tumor necrosis factor alpha antagonist-associated psoriasis in inflammatory diseases: an analysis of the FDA adverse event reporting system. Inflamm Bowel Dis. 2013;19(6):1164–72. Epub 2013/03/23. pmid:23518804.
  64. 64. McKenna MR, Stobaugh DJ, Deepak P. Melanoma and non-melanoma skin cancer in inflammatory bowel disease patients following tumor necrosis factor-alpha inhibitor monotherapy and in combination with thiopurines: analysis of the Food and Drug Administration Adverse Event Reporting System. J Gastrointestin Liver Dis. 2014;23(3):267–71. Epub 2014/10/01. pmid:25267954.
  65. 65. Deepak P, Stobaugh DJ, Sherid M, Sifuentes H, Ehrenpreis ED. Neurological events with tumour necrosis factor alpha inhibitors reported to the Food and Drug Administration Adverse Event Reporting System. Aliment Pharmacol Ther. 2013;38(4):388–96. Epub 2013/06/28. pmid:23802849.