Next Article in Journal
Emerging Concepts and Novel Strategies in Radiation Therapy for Laryngeal Cancer Management
Previous Article in Journal
Exploiting Cancer’s Tactics to Make Cancer a Manageable Chronic Disease

Safety of BRAF+MEK Inhibitor Combinations: Severe Adverse Event Evaluation

Ella Lemelbaum Institute for Immuno-oncology, Sheba Medical Center, Ramat-Gan 526260, Israel
Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Tel-Aviv 6997801, Israel
Author to whom correspondence should be addressed.
Cancers 2020, 12(6), 1650;
Received: 31 May 2020 / Revised: 12 June 2020 / Accepted: 15 June 2020 / Published: 22 June 2020


Aim: The selective BRAF and MEK inhibitors (BRAFi+MEKi) have substantially improved the survival of melanoma patients with BRAF V600 mutations. However, BRAFi+MEKi can also cause severe or fatal outcomes. We aimed to identify and compare serious adverse events (sAEs) that are significantly associated with BRAFi+MEKi. Methods: In this pharmacovigilance study, we reviewed FDA Adverse Event Reporting System (FAERS) data in order to detect sAE reporting in patients treated with the combination therapies vemurafenib+cobimetinib (V+C), dabrafenib+trametinib (D+T) and encorafenib+binimetinib (E+B). We evaluated the disproportionate reporting of BRAFi+MEKi-associated sAEs. Significant associations were further analyzed to identify combination-specific safety signals among BRAFi+MEKi. Results: From January 2018 through June 2019, we identified 11,721 sAE reports in patients receiving BRAFi+MEKi. Comparison of BRAFi+MEKi combinations demonstrates that skin toxicities, including Stevens–Johnson syndrome, were disproportionally reported using V+C, with an age-adjusted reporting odds ratio (adj. ROR) of 3.4 (95%CI, 2.9–4.0), whereas fever was most significantly associated with D+T treatment with an adj. ROR of 1.9 (95%CI, 1.5–2.4). Significant associations using E+B treatment include peripheral neuropathies (adj. ROR 2.7; 95%CI, 1.2–6.1) and renal disorders (adj. ROR 4.1; 95%CI, 1.3–12.5). Notably, we found an increase in the proportion of Guillain–Barré syndrome reports (adj. ROR 8.5; 95%CI, 2.1–35.0) in patients administered E+B. Conclusion: BRAFi+MEKi combinations share a similar safety profile attributed to class effects, yet concomitantly, these combinations display distinctive effects that can dramatically impact patients’ health. Owing to the limitations of pharmacovigilance studies, some findings warrant further validation. However, the possibility of an increased risk for these events should be considered in patient care.
Keywords: BRAF and MEK inhibitors; melanoma; pharmacovigilance; disproportionality analysis BRAF and MEK inhibitors; melanoma; pharmacovigilance; disproportionality analysis

1. Introduction

Combination therapy with BRAF plus MEK inhibitors (BRAFi+MEKi) has transformed the treatment landscape and improved the clinical outcomes of patients with melanoma harboring BRAFv600 mutations [1,2,3,4,5,6,7]. Treatment with BRAFi+MEKi produces response rates of 68% for vemurafenib+cobimetinib (V+C) [1], 67% for dabrafenib+trametinib (D+T) [2] and 63% for encorafenib+binimetinib (E+B) [8] in BRAF-mutated metastatic melanoma. Landmark data showed 3 year overall survival rates of 38.5% for V+C [3,4], 45% for D+T [6] and 47% for E+B [8]. However, despite similar inclusion criteria in general, comparison of the clinical outcomes cannot be made directly since some criteria differed between trials—for example, variable definitions of brain metastasis control [9]. Moreover, the studies show significant differences in patient populations with poor prognostic markers, such as elevated serum LDH [9].
The safety profiles of BRAFi+MEKi monotherapy and combination therapies were evaluated during the confirmatory trials [1,2,3,4,5,6,10,11,12,13,14,15,16,17,18]. Some adverse reactions can be ascribed both to BRAFi and MEKi class effects, including gastrointestinal side effects, increases in transaminases and cutaneous toxicities [19]; others are attributed to class-specific reactions such as squamous cell carcinomas (SCCs) and arthralgia in BRAFi [19,20,21] compared to ocular, edema and cardiovascular toxicities in MEKi therapy [22,23]. The addition of MEKi to BRAFi was associated with higher risk for developing various adverse events (AE); however, the co-administration of MEKi may mitigate some of the toxicities of BRAFi induced by ‘paradoxical activation’ of the mitogen-activated protein kinase (MAPK) pathway in BRAF wild-type cells [1,2,6,24]. In patients treated with the combination therapy, almost all developed AEs of any grade; severe adverse events (sAEs) occurred in 34% for V+C, 43% for D+T and 34% for E+B [19]. Combination-specific side effects include photosensitivity for V+C and fever for D+T, which are attributed to vemurafenib and dabrafenib, as they were already identified in BRAFi monotherapy trials [15,16,25]. However, combination-specific side effects for E+B are less clear. Nevertheless, encorafenib was associated with increased incidences of transient Bell’s palsy which also occurred with the respective combination [5,18].
Considering the similar clinical efficacy demonstrated by the three BRAFi+MEKi combinations, the therapeutic decision often relies on the safety profile that characterizes each of the combinations. However, despite numerous studies comparing different agents, there are no head-to-head clinical trials comparing the different combinations, and most safety data originate from the confirmatory phase III trials [19]. Moreover, there are no studies directly comparing the safety profile of all the combinations, and instead, are either comparing only between two products (e.g., vemurafenib versus dabrafenib) [26,27,28,29,30,31], focusing on specific adverse events [26,28,29,30,31,32,33,34], or based on confirmatory studies alone [19]. Defining and characterizing BRAFi+MEKi combination-specific events is a crucial issue for patient safety, especially given that a large proportion of patients are expected to develop severe toxicities. Here, we aimed to characterize and compare sAEs of BRAFi+MEKi captured in the real-world setting by performing a large-scale post-marketing surveillance using the FAERS database.

2. Methods

2.1. Study Design and Data Sources

The observational, retrospective, pharmacovigilance study is based on reports linked to suspect drugs in the FDA Adverse Events Reporting System (FAERS) [35,36]. The FAERS serves as the FDA’s repository of safety reports submitted by patients, health care professionals and pharmaceutical companies, and currently contains over 18 million reports [37]. Since the FAERS is a publicly available and anonymized database, institutional review board approval and informed consent was waived.

2.2. Procedures

This study included serious events leading to death, life-threatening conditions, disability, or hospitalization from January 2018 to December 2019. Despite available reports before 2018, this period was used to compare the reports of three BRAFi+MEKi combinations during the same timeframe. Of note, reports of AEs associated with E+B started in 2018. Adverse events were classified according to the Medical Dictionary for Regular Activities (MedDRA), which organizes terms into a hierarchy [38]. Duplicate cases were removed and general AEs such as ‘off-label use’ were excluded. Adverse events specific to combination therapy in the analysis were those notified as suspected to be caused by both BRAFi and MEKi as primary or secondary suspects. Additional administrative information was extracted for each report, including patient characteristics (sex and age); country of origin; drugs (dosage regimen, start and end dates of administration); indication for the drug; reactions (onset date, outcome, and response to rechallenge and dechallenge).

2.3. Statistical Analysis

Disproportionality analysis using the ‘case-control’ approach allows assessing whether the reporting rate of AEs is differentially elevated relative to expected rates of reporting in a pharmacovigilance database [39,40]. Disproportionate reporting rates of AEs could be indicative of potential risks associated with a particular agent or drug combination. To compare treatment-induced toxicities among different BRAFi+MEKi regimens, we also performed a disproportionate analysis between the combinations as a case–noncase study (e.g., V+C versus D+T and E+B). Standard measures of disproportionality include the information component (IC) when comparing to all the records in the dataset and the reporting odds ratio (ROR) when comparing between different treatment subgroups [41,42]. Thus, the latter analysis (ROR) involves only patients with BRAFi+MEKi and the rest of the database is excluded. The IC, given by a Bayesian confidence propagation neural network [41], and ROR, a point estimate calculated as the observed odds ratio [43], are widely used and are currently employed by various reporting agencies, as well as by the World Health Organization (WHO) [41,44,45].
The decision rule for a potential signal generation was based on a positive IC value (>0), a minimum number of reactions of 3, and a threshold of 0.05 for the Bayesian version of the false discovery rate (FDR) [46]. This analysis was performed on high-level group terms of drug-specific adverse events versus the full database (Supplementary Table S1). To compare reactions between BRAFi+MEKi combinations that are also disproportionally reported in the full database, we used only AEs linked to the identified signals above. In the ROR analysis, the signal-generation method was based on mid-p-values, as proposed by Ahmed et al. [47], and a signal was considered significant if ROR > 1 and the corrected p-value for multiple testing using the Benjamini–Hochberg (BH) correction was <0.05 [48]. Only combinations with a minimum amount of 0.5% reports were selected, corresponding to five reports in the smallest combination regimen (E+B). Given the considerable overlap between symptoms for various causes as well as redundancies in reporting AEs, we considered events for further evaluation if they were also significant in associated MedDRA terms. Since relevant medical information such as cancer staging was absent and therefore could not be accounted for, death and neoplasm occurrences as AEs were excluded from subsequent analysis. The disproportionality analysis and the age-adjusted ROR analysis were performed using the PhViD [49] and epiR [50] packages, respectively, in the R statistical computing environment (v3.5.2) [51].

3. Results

3.1. Patient Characteristics

The FAERS database included 11,721 reports of sAEs among patients receiving V+C (2345), D+T (8411) and E+B (965), corresponding to the time period (January 2018 to December 2019) (Table 1). The total number of sAEs reported in the FAERS was 3,285,265. Patient characteristics, drug indications, and country of origin are shown in Table 1.

3.2. Toxicity Profile of BRAFi and MEKi Compared to the Full Database

In order to compare reactions between different BRAFi+MEKi combinations that are also differentially elevated compared to other drugs, we first performed a disproportionality analysis compared to the background (excluding fatal outcomes and neoplasm-related events). The most common sAE reports in patients exposed to V+C were epidermal and dermal conditions with 311 (13.3%) cases (IC = 2.1; FDR < 10−130), followed by general systems disorders not elsewhere classified (NEC) with 249 (10.6%) cases (IC = 0.42; FDR < 10−6) (Supplementary Table S1). For patients treated with D+T, the most frequent sAE reports were general systems disorders NEC with 890 (10.6%) cases (IC = 0.45; FDR < 10−20) and body temperature conditions with 496 (5.9%) cases (IC = 2.28; FDR < 10−258). For patients treated with E+B, the most common sAE reports were general systems disorders with 116 (12.0%) cases (IC = 0.74; FDR < 10−8), followed by gastrointestinal signs and symptoms with 68 (7.0%) cases (IC = 1.22; FDR < 10−12).

3.3. Toxicity Profile of BRAFi and MEKi Combinations

The reported frequencies of sAEs for the three BRAFi+MEKi combinations can be compared directly using ROR analysis. The detailed ROR estimates of V+C, D+T, and E+B for sAEs detected as signals are listed in Supplementary Table S2. To facilitate the analysis and interpretation of the data, the ROR for each identified sAE was compared: the respective shifts in BRAFi+MEKi ROR, expressed as an increase or decrease in a logarithmic scale, are displayed in Figure 1. ROR of dermatological side effects including rash, generalized rash, maculo-papular rash, erythema multiforme and rash with eosinophilia and systemic symptoms were considerably higher in V+C compared to D+T and E+B, whereas pyrexia and elevated C-reactive protein (CRP) were disproportionally higher in D+T compared to V+C and E+B. Furthermore, gastrointestinal events including colitis, diarrhea, nausea and upper abdominal pain and renal side effects including renal disorder/impairment/failure and increased creatinine were disproportionally higher in E+B compared to other BRAFi+MEKi combinations. Acute kidney injury (AKI) was higher in V+C, compared to E+B; however, both were considerably higher than D+T. Notably, Guillain–Barré syndrome (GBS) and seizures were significantly higher in E+B compared to V+C and D+T.
We conducted further analysis and calculated the age-adjusted ROR of the identified disproportional sAEs. The adjusted ROR of the sAEs (Table 2) showed similar patterns, where the most disproportional reactions were skin toxicities (3.4; 95%CI, 2.9–4.0) including Stevens–Johnson syndrome (10.4; 95%CI, 4.0–26.9) with V+C, pyrexia (1.9; 95%CI, 1.5–2.4) and elevated CRP (2.3; 95%CI, 1.2–4.8) with D+T, and renal disorders NEC (4.1; 95%CI, 1.3–12.5) and neurological disorders (peripheral neuropathies—2.7; 95%CI, 1.2–6.1 and seizures 3.8; 95%CI, 1.8–8.0) with E+B. Albeit rare, the adjusted ROR for GBS (8.5; 95%CI, 2.1–35) was substantially higher compared to V+C (0.9; 95%CI, 0.2–4.2) and D+T (0.2; 95%CI, 0.1–0.8). Colitis was also significantly higher (3.3; 95%CI, 1.5–7.1) with E+B; however, the adjusted ROR for GI motility conditions (diarrhea) for E+B (1.8; 95%CI, 1.2–2.6) was slightly lower compared to D+T (2.0; 95%CI, 1.6–2.6) after adjusting for age.

3.4. Clinical Characteristics Using BRAFi and MEKi Combinations

We described the clinical characteristics of cases with sAEs occurring in patients treated with BRAFi+MEKi (Table 3). Among patients with skin disorders treated with V+C, 9% (19/212) have died versus 24% (57/238) and 14% (4/29) in D+T and E+B, respectively. The onset of dermal toxicities occurred soon after the first V+C administration, with a median time to onset of 9 days (IQR 7–14), compared to 30 (IQR 13–50) and 12 (IQR 2–32) using D+T and E+B, respectively. Most reported serious skin disorders (65%, 154/238) occurred in patients treated with doses below 150/2 mg of D+T compared to 15% (32/212) and 3% (1/29) of patients using low V+C (<960/60 mg) and E+B (<450/45 mg) doses, respectively. Most reports of pyrexia were associated with lower doses of D+T (58%, 281/485) doses compared to V+C (7%, 5/69) and E+B (6%, 2/33), with death occurring in 25% (119/485) with D+T versus 14% (10/69) and 6% (2/33) in V+C and E+B, respectively. Pyrexia occurred earlier using V+C (9; IQR 8–22) compared to D+T (28; IQR 16–52) and E+B (35; IQR 12–122). Peripheral neuropathies induced by BRAFi+MEKi combinations were associated with similar death rates of 11% and 12% using V+C (1/9) and E+B (1/8), respectively, and 6% using D+T (2/32). The median time to onset of peripheral neuropathies occurred earlier in E+B (54; IQR 41–57) versus V+C (237; IQR 154–320) and D+T (107; IQR 44–206); however, there were very few cases with details regarding the timing. There were no clear patterns in drug dosing using V+C and D+T; however, most reported reactions with E+B (75%, 6/8) occurred using the recommended dose. Renal disorders were associated with similar death rates with 20% and 21% of the reports in patients using D+T (22/109) and E+B (6/28), respectively, and 13% using V+C (7/53). Most reports of renal toxicities occurred using the recommend dose of E+B with 54% of patients (15/28) and the treatment showed a similar median time to onset of 17.5 days (IQR 5–54) as compared to V+C with 14 days (IQR 7–80), but shorter than D+T, which occurred at 71 days (IQR 54–138). In most cases with sufficient details, the effects of BRAFi+MEKi exposure were resolved after treatment hold, regardless of the type of sAE or the combination.

4. Discussion

Novel therapeutic strategies relying on the selective inhibition of the MAPK pathway have proven to significantly prolong the survival of patients with advanced BRAF-mutated melanoma [1,2,3,4,5,6,7]. However, almost all patients treated with BRAFi+MEKi combination therapy develop AEs, with grade 3–4 AEs occurring in most patients [19]. Since the combination therapy with D+T has been approved for adjuvant use in melanoma patients [7] and the indications for BRAFi+MEKi have been extended to other cancer types [52,53,54], the pool of patients is expanding, and thus more patients are expected to develop life-threatening events. As no specific BRAFi+MEKi combination has shown superiority over the rest, there are no evidence-based standard-of-care recommendations of which therapy to choose in the metastatic setting. Therefore, knowledge of the safety profile can be used to tailor the therapy to patients and diagnose adverse reactions early. To our knowledge, we report the most extensive characterization of sAEs associated with BRAFi+MEKi combinations so far, through analysis of the FAERS pharmacovigilance database.
The results show a significant incidence of skin disorders, fever, and GI conditions associated with BRAFi+MEKi combinations, corresponding to the recognized class effects of the treatment [1,13,19]. A comparison between the combinations shows that skin toxicities and fever are overrepresented in V+C and D+T, respectively, while GI conditions were reported significantly less using D+T. These findings are consistent with safety data from phase III clinical studies [1,2,3,4,5,6,19], and corroborate our disproportionality analysis.
Confirmatory studies are necessary to determine therapeutic activity but are limited in providing an accurate depiction of the safety profile, as the conditions under which medicines are used post-marketing may not be reflected in clinical trials, and they also lack sufficient power to detect infrequent or rare events [55,56]. Pharmacovigilance surveillance thus remains the cornerstone for identifying drug-related complications in spite of recognized drawbacks [57]. Herein, we report renal toxicities, neurological disorders and hypotension as additional possible safety considerations for clinicians involved in the care of patients treated with BRAFi+MEKi therapy, and specifically with E+B combination. Data on renal toxicities from V+C and D+T are relatively sparse [2,4,26,58,59] and the association with E+B is even less clear [19,60,61]. We show that nephrotoxicity associated with E+B is significantly more disproportionate among BRAFi+MEKi and portends poor outcomes. Serious renal disorders can occur as early as 17.5 days (IQR 5–54) after initial exposure to E+B. Although the incidence of these renal toxicities cannot be established with the FAERS, some data are available. In the COLUMBUS study, 3% of grade 3–4 AEs encompass renal toxicities [5], which is similar to the proportion reflected in our results of 3.5% (n = 34). Of note, despite having longer follow-up durations, only 1% (grade 3–4 AE) and 0% (any grade AE) of the events were reported in phase III studies for V+C [4] and D+T [62], respectively. This pattern agrees with our findings and provides further support for our results. Furthermore, our report includes the identification of numerous cases with serious peripheral neuropathies and seizures associated with BRAFi+MEKi, which is, to our knowledge, the largest collection of such reports to date. Compared to the full database and BRAFi+MEKi combinations, E+B was associated with a notable reporting odds ratio of peripheral neuropathies and specifically GBS, representing 0.5% of the reports. Nonetheless, previous treatment with immune checkpoint inhibitors (ICIs) cannot be ruled out as a possible contributor or the source for GBS. Several lines of evidence support a major role of MAPK inhibitors as well as encorafenib in the pathophysiology of neurological disorders and GBS. First, the MAPK pathway mediates the dedifferentiation of Schwann cells in the presence of normal axonal signaling and required during the repair process following nerve injury [63]. In addition, the MAPK signaling pathway was found to be significantly enriched in overexpressed genes among patients with GBS [64]. Second, increasing number of case reports and case series were published in the literature, implicating BRAFi or MEKi in neuropathy [65,66,67,68,69,70,71] as well as GBS [69,72,73]. Third, a relatively frequent drug-related AE occurring with encorafenib monotherapy was transient Bell’s palsy [5,18], a mononeuritic variant of GBS, in most cases [74]. This reaction appeared in 8% of the patients and 11% in the dose expansion phase using encorafenib, but has rarely been reported in association with other BRAFi [18]. Facial paresis has also been reported using the combination E+B, but was considerably decreased [5], which can be explained by the suppression of the paradoxical activation of the MAPK pathway [24].
Few limitations of disproportionality analysis need to be recognized. Pharmacovigilance studies have complementary strengths in signal detection that require further validation, optimally in prospective trials. Replication of safety issues in other continental reporting agencies such as the WHO pharmacovigilance database and European Medicine Agency is biased by overlapping safety reports between databases [57]. Further, not all sAEs are submitted to the FAERS, and thus, some reports could be underreported or misrepresented. However, the FAERS provides a key advantage in signal detection, especially of less frequent events, such as GBS, due to the extensive collection of reports from 190 countries, according to the dataset utilized in this study.
One of the major limitations of the analysis involves the inability to quantify the incidence of identified risks, as the total number of patients exposed to a given product is unknown [75]. Further, the background reporting rate could be inflated by an event associated with other drugs, thereby masking the detection of a suspected drug [76]. This phenomenon tends to reduce the sensitivity of some events [77]. Inherent weakness involving the FAERS as well as other spontaneous reporting systems is the inability to verify the clinical findings to justify the reported diagnosis and access relevant data, such as co-morbid illness. Further, a lack of accurate information regarding the line of treatment limits the interpretation of the results. Previous treatment with ICIs could have contributed to the observed toxicities, such as hyperthyroidism which is a common immune-related AE [78]. Furthermore, inconsistent reporting results in missing information regarding demographics, time to onset of AE, drug dosing, dechallenge, rechallenge and comedications. Yet, many successful efforts exist, showing the value of disproportionality to detect safety signals after approval, including examples as early as in the 1980s, where a significant association was found between valproic acid use during pregnancy and spina bifida aperta [79]. Nonetheless, a risk still exists that disproportionality analysis of post-marking data could be misleading. An example of another validated association involves the identification of cefaclor to induce serum sickness-like syndrome [80] and the association between rofecoxib and thrombotic reactions [81]. This relationship, among others, which could have been predicted soon after marketing and long before withdrawal [81], demonstrates the ability to find signals quickly after drug approval [82,83]. Although causality cannot be established conclusively based on post-marketing data alone, it is often served as a basis for adjudicating relationships with sufficient certainty in pharmacovigilance settings [84].
In conclusion, our study shows that beyond skin disorders and fever associated with V+C and D+T, respectively, BRAFi+MEKi therapy can cause other combination-specific toxicities that include gastrointestinal, renal and neurological disorders associated with E+B. Most of the serious adverse reactions commence soon after administration and resolve after dechallenge. These complications should be considered in therapeutic decision making, and careful monitoring is recommended.

Supplementary Materials

The following are available online at, Table S1: Information component of high-level adverse events, Table S2: Reporting odds ratio of serious adverse events (detected as signals) reported for BRAK-MEK inhibitors.

Author Contributions

N.A. and T.M. conceived the idea. All authors contributed to the design of the study. T.M. acquired the data for the study, performed the analyses, and drafted the report. G.M. supervised the study. All authors interpreted the results and contributed to the writing of the final report. All authors have read and agreed to the published version of the manuscript.


This work was supported by the Ella Lemelbaum Institute Funds, by the Samueli Foundation Grant for Integrative Immuno-Oncology and by the Israel Science Foundation Personalized Medicine Grant 3495/19. Tomer Meirson was supported by the Foulkes Foundation fellowship for MD/PhD students. The funding sources had no role in the study design, collection, analysis and interpretation of data.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Larkin, J.; Ascierto, P.A.; Dréno, B.; Atkinson, V.; Liszkay, G.; Maio, M.; Mandalà, M.; Demidov, L.; Stroyakovskiy, D.; Thomas, L.; et al. Combined Vemurafenib and Cobimetinib in BRAF-Mutated Melanoma. N. Engl. J. Med. 2014, 371, 1867–1876. [Google Scholar] [CrossRef] [PubMed]
  2. Long, G.V.; Stroyakovskiy, D.; Gogas, H.; Levchenko, E.V.; De Braud, F.; Larkin, J.; Garbe, C.; Jouary, T.; Hauschild, A.; Grob, J.-J.; et al. Combined BRAF and MEK Inhibition versus BRAF Inhibition Alone in Melanoma. N. Engl. J. Med. 2014, 371, 1877–1888. [Google Scholar] [CrossRef] [PubMed]
  3. Dreno, B.; Ascierto, P.A.; McArthur, G.A.; Atkinson, V.; Liszkay, G.; Di Giacomo, A.M.; Mandalà, M.; Demidov, L.V.; Stroyakovskiy, D.; Thomas, L.; et al. Efficacy and safety of cobimetinib (C) combined with vemurafenib (V) in patients (pts) with BRAF V600 mutation–positive metastatic melanoma: Analysis from the 4-year extended follow-up of the phase 3 coBRIM study. J. Clin. Oncol. 2018, 36, 9522. [Google Scholar] [CrossRef]
  4. Ascierto, P.A.; McArthur, G.; Dréno, B.; Atkinson, V.; Liszkay, G.; Di Giacomo, A.M.; Mandalà, M.; Demidov, L.; Stroyakovskiy, D.; Thomas, L.; et al. Cobimetinib combined with vemurafenib in advanced BRAFV600-mutant melanoma (coBRIM): Updated efficacy results from a randomised, double-blind, phase 3 trial. Lancet Oncol. 2016, 17, 1248–1260. [Google Scholar] [CrossRef]
  5. Dummer, R.; Ascierto, P.A.; Gogas, H.; Arance, A.; Mandalà, M.; Liszkay, G.; Garbe, C.; Schadendorf, D.; Krajsová, I.; Gutzmer, R.; et al. Overall survival in COLUMBUS: A phase 3 trial of encorafenib (ENCO) plus binimetinib (BINI) vs. vemurafenib (VEM) or enco in BRAF-mutant melanoma. J. Clin. Oncol. 2018, 36, 9504. [Google Scholar] [CrossRef]
  6. Robert, C.; Karaszewska, B.; Schachter, J.; Rutkowski, P.; Mackiewicz, A.; Stroyakovskiy, D.; Dummer, R.; Grange, F.; Mortier, L.; Chiarion-Sileni, V.; et al. Three-year estimate of overall survival in COMBI-v, a randomized phase 3 study evaluating first-line dabrafenib (D)+ trametinib (T) in patients (pts) with unresectable or metastatic BRAF V600E/K–mutant cutaneous melanoma. Ann. Oncol. 2016, 27, vi575. [Google Scholar] [CrossRef]
  7. Long, G.V.; Hauschild, A.; Santinami, M.; Atkinson, V.; Mandalà, M.; Sileni, V.C.; Larkin, J.; Nyakas, M.; Dutriaux, C.; Haydon, A.; et al. Adjuvant Dabrafenib plus Trametinib in Stage IIIBRAF-Mutated Melanoma. N. Engl. J. Med. 2017, 377, 1813–1823. [Google Scholar] [CrossRef]
  8. Ascierto, P.A.; Dummer, R.; Gogas, H.J.; Flaherty, K.T.; Arance, A.; Mandala, M.; Liszkay, G.; Garbe, C.; Schadendorf, D.; Krajsova, I.; et al. Update on tolerability and overall survival in COLUMBUS: Landmark analysis of a randomised phase 3 trial of encorafenib plus binimetinib vs. vemurafenib or encorafenib in patients with BRAF V600-mutant melanoma. Eur. J. Cancer 2020, 126, 33–44. [Google Scholar] [CrossRef]
  9. Ugurel, S.; Röhmel, J.; Ascierto, P.A.; Flaherty, K.T.; Grob, J.-J.; Hauschild, A.; Larkin, J.; Long, G.V.; Lorigan, P.; McArthur, G.; et al. Survival of patients with advanced metastatic melanoma: The impact of novel therapies–update 2017. Eur. J. Cancer 2017, 83, 247–257. [Google Scholar] [CrossRef]
  10. Ascierto, P.A.; Schadendorf, D.; Berking, C.; Agarwala, S.S.; Van Herpen, C.M.; Queirolo, P.; Blank, C.U.; Hauschild, A.; Beck, J.T.; St-Pierre, A.; et al. MEK162 for patients with advanced melanoma harbouring NRAS or Val600 BRAF mutations: A non-randomised, open-label phase 2 study. Lancet Oncol. 2013, 14, 249–256. [Google Scholar] [CrossRef]
  11. Rosen, L.S.; LoRusso, P.; Ma, W.W. A first-in-human phase I study to evaluate the MEK1/2 inhibitor, cobimetinib, administered daily in patients with advanced solid tumors. Investig. New Drugs 2016, 34, 604–613. [Google Scholar] [CrossRef] [PubMed]
  12. Flaherty, K.T.; Robert, C.; Hersey, P.; Nathan, P.; Garbe, C.; Milhem, M.; Demidov, L.V.; Hassel, J.C.; Rutkowski, P.; Mohr, P.; et al. Improved Survival with MEK Inhibition in BRAF-Mutated Melanoma. N. Engl. J. Med. 2012, 367, 107–114. [Google Scholar] [CrossRef] [PubMed]
  13. Kim, K.B.; Kefford, R.; Pavlick, A.C. Phase II study of the MEK1/MEK2 inhibitor Trametinib in patients with metastatic BRAF-mutant cutaneous melanoma previously treated with or without a BRAF inhibitor. J. Clin. Oncol. 2013, 31, 482. [Google Scholar] [CrossRef] [PubMed]
  14. Chapman, P.B.; Hauschild, A.; Robert, C.; Haanen, J.B.; Ascierto, P.; Larkin, J.; Dummer, R.; Garbe, C.; Testori, A.; Maio, M.; et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N. Engl. J. Med. 2011, 364, 2507–2516. [Google Scholar] [CrossRef]
  15. Sosman, J.A.; Kim, K.B.; Schuchter, L.; Gonzalez, R.; Pavlick, A.C.; Weber, J.S.; McArthur, G.; Hutson, T.E.; Moschos, S.J.; Flaherty, K.T.; et al. Survival in BRAF V600-mutant advanced melanoma treated with vemurafenib. N. Engl. J. Med. 2012, 366, 707–714. [Google Scholar] [CrossRef]
  16. Hauschild, A.; Grob, J.-J.; Demidov, L.V.; Jouary, T.; Gutzmer, R.; Millward, M.; Rutkowski, P.; Blank, C.U.; Miller, W.H.; Kaempgen, E.; et al. Dabrafenib in BRAF-mutated metastatic melanoma: A multicentre, open-label, phase 3 randomised controlled trial. Lancet 2012, 380, 358–365. [Google Scholar] [CrossRef]
  17. Ascierto, P.A.; Minor, D.; Ribas, A.; Lebbé, C.; O’Hagan, A.; Arya, N.; Guckert, M.; Schadendorf, D.; Kefford, R.; Grob, J.-J.; et al. Phase II Trial (BREAK-2) of the BRAF Inhibitor Dabrafenib (GSK2118436) in Patients with Metastatic Melanoma. J. Clin. Oncol. 2013, 31, 3205–3211. [Google Scholar] [CrossRef]
  18. Delord, J.-P.; Robert, C.; Nyakas, M.; McArthur, G.; Kudchakar, R.; Mahipal, A.; Yamada, Y.; Sullivan, R.; Arance, A.; Kefford, R.; et al. Phase I Dose-Escalation and -Expansion Study of the BRAF Inhibitor Encorafenib (LGX818) in Metastatic BRAF -Mutant Melanoma. Clin. Cancer Res. 2017, 23, 5339–5348. [Google Scholar] [CrossRef]
  19. Heinzerling, L.; Eigentler, T.K.; Fluck, M.; Hassel, J.C.; Heller-Schenck, D.; Leipe, J.; Pauschinger, M.; Vogel, A.; Zimmer, L.; Gutzmer, R. Tolerability of BRAF/MEK inhibitor combinations: Adverse event evaluation and management. ESMO Open 2019, 4, e000491. [Google Scholar] [CrossRef]
  20. Zhang, W. BRAF inhibitors: The current and the future. Curr. Opin. Pharmacol. 2015, 23, 68–73. [Google Scholar] [CrossRef]
  21. Boussemart, L.; Routier, E.; Mateus, C.; Opletalova, K.; Sebille, G.; Kamsu-Kom, N.; Thomas, M.; Vagner, S.; Favre, M.; Tomasic, G.; et al. Prospective study of cutaneous side-effects associated with the BRAF inhibitor vemurafenib: A study of 42 patients. Ann. Oncol. 2013, 24, 1691–1697. [Google Scholar] [CrossRef] [PubMed]
  22. Indini, A.; Tondini, C.A.; Mandalà, M. Cobimetinib in malignant melanoma: How to MEK an impact on long-term survival. Futur. Oncol. 2019, 15, 967–977. [Google Scholar] [CrossRef] [PubMed]
  23. Van Dijk, E.H.C.; Duits, D.E.M.; Versluis, M.; Luyten, G.P.M.; Bergen, A.A.B.; Kapiteijn, E.; De Lange, M.J.; Boon, C.J.F.; Van Der Velden, P. Loss of MAPK Pathway Activation in Post-Mitotic Retinal Cells as Mechanism in MEK Inhibition-Related Retinopathy in Cancer Patients. Medicine 2016, 95, e3457. [Google Scholar] [CrossRef] [PubMed]
  24. Su, F.; Viros, A.; Milagre, C.; Trunzer, K.; Bollag, G.; Spleiss, O.; Reis-Filho, J.S.; Kong, X.; Koya, R.C.; Flaherty, K.T.; et al. RAS mutations in cutaneous squamous-cell carcinomas in patients treated with BRAF inhibitors. N. Engl. J. Med. 2012, 366, 207–215. [Google Scholar] [CrossRef]
  25. Daud, A.N.A.; Tsai, K. Management of Treatment-Related Adverse Events with Agents Targeting the MAPK Pathway in Patients with Metastatic Melanoma. Oncologist 2017, 22, 823–833. [Google Scholar] [CrossRef]
  26. Jhaveri, K.D.; Sakhiya, V.; Fishbane, S.; Sakhiya, M.V. Nephrotoxicity of the BRAF Inhibitors Vemurafenib and Dabrafenib. JAMA Oncol. 2015, 1, 1133. [Google Scholar] [CrossRef] [PubMed]
  27. Aguiar, J.P.; Borges, F.C.; Murteira, R.; Ramos, C.; Gouveia, E.; Passos, M.J.; Miranda, A.; Da Costa, F.A. Using a cancer registry to capture signals of adverse events following immune and targeted therapy for melanoma. Int. J. Clin. Pharm. 2018, 40, 852–861. [Google Scholar] [CrossRef]
  28. Dumas, M.; Laly, P.; Gottlieb, J.; Vercellino, L.; Paycha, F.; Bagot, M.; Baroudjian, B.; Madelaine, I.; Basset-Séguin, N.; Eftekhari, P.; et al. Osteopenia and fractures associated with long-term therapy with MEK inhibitors. Melanoma Res. 2018, 28, 641–644. [Google Scholar] [CrossRef]
  29. Cornet, L.; Khouri, C.; Roustit, M.; Guignabert, C.; Chaumais, M.-C.; Humbert, M.; Revol, B.; Despas, F.; Montani, D.; Cracowski, J.-L. Pulmonary arterial hypertension associated with protein kinase inhibitors: A pharmacovigilance–pharmacodynamic study. Eur. Respir. J. 2019, 53, 1802472. [Google Scholar] [CrossRef]
  30. Sanlorenzo, M.; Choudhry, A.; Vujic, I.; Posch, C.; Chong, K.; Johnston, K.; Meier, M.; Osella-Abate, S.; Quaglino, P.; Daud, A.N.A.; et al. Comparative profile of cutaneous adverse events: BRAF/MEK inhibitor combination therapy versus BRAF monotherapy in melanoma. J. Am. Acad. Dermatol. 2014, 71, 1102–1109.e1. [Google Scholar] [CrossRef]
  31. Alves, C.; Ribeiro, I.; Penedones, A.; Mendes, D.; Batel-Marques, F. Risk of Ophthalmic Adverse Effects in Patients Treated with MEK Inhibitors: A Systematic Review and Meta-Analysis. Ophthalmic Res. 2016, 57, 60–69. [Google Scholar] [CrossRef]
  32. Mackin, A.G.; Pecen, P.E.; Dinsmore, A.L.; Patnaik, J.L.; Gonzalez, R.; Robinson, W.A.; Palestine, A.G. Inflammatory side effects of BRAF and MEK inhibitors. Melanoma Res. 2019, 29, 522–526. [Google Scholar] [CrossRef]
  33. Bronte, E.; Bronte, G.; Novo, G., Jr.; Rinaldi, G.; Bronte, F.; Passiglia, F.; Russo, A. Cardiotoxicity mechanisms of the combination of BRAF-inhibitors and MEK-inhibitors. Pharmacol. Ther. 2018, 192, 65–73. [Google Scholar] [CrossRef]
  34. Mourad, N.; Lourenço, N.; Delyon, J.; Eftekhari, P.; Bertheau, P.; Allayous, C.; Ballon, A.; Pagès, C.; Allez, M.; Lebbé, C.; et al. Severe gastrointestinal toxicity of MEK inhibitors. Melanoma Res. 2019, 29, 556–559. [Google Scholar] [CrossRef]
  35. Goldman, S.A. Limitations and strengths of spontaneous reports data. Clin. Ther. 1998, 20, C40–C44. [Google Scholar] [CrossRef]
  36. Kessler, D.A. Introducing MEDWatch. A new approach to reporting medication and device adverse effects and product problems. JAMA 1993, 269, 2765–2768. [Google Scholar] [CrossRef]
  37. US Food and Drug Administration. FDA Adverse Event Reporting System (FAERS) Public Dashboard; US Food Drug Administration, Silver Spring: Maryland, MD, USA, 2018.
  38. Mozzicato, P. MedDRA. Pharm. Med. 2009, 23, 65–75. [Google Scholar] [CrossRef]
  39. Moore, N.; Kreft-Jais, C.; Haramburu, F.; Noblet, C.; Andrejak, M.; Ollagnier, M.; Bégaud, B. Reports of hypoglycaemia associated with the use of ACE inhibitors and other drugs: A case/non-case study in the French pharmacovigilance system database. Br. J. Clin. Pharmacol. 1997, 44, 513–518. [Google Scholar] [CrossRef]
  40. Wilson, A.; Thabane, L.; Holbrook, A. Application of data mining techniques in pharmacovigilance. Br. J. Clin. Pharmacol. 2004, 57, 127–134. [Google Scholar] [CrossRef]
  41. Bate, A.; Lindquist, M.; Edwards, I.R.; Olsson, S.; Orre, R.; Lansner, A.; De Freitas, R.M. A Bayesian neural network method for adverse drug reaction signal generation. Eur. J. Clin. Pharmacol. 1998, 54, 315–321. [Google Scholar] [CrossRef]
  42. Rothman, K.J.; Lanes, S.; Sacks, S.T. The reporting odds ratio and its advantages over the proportional reporting ratio. Pharmacoepidemiol. Drug Saf. 2004, 13, 519–523. [Google Scholar] [CrossRef] [PubMed]
  43. Van Der Heijden, P.G.M.; Van Buuren, S.; Van Der Hofstede, J.W.; Van Puijenbroek, E.P. On the assessment of adverse drug reactions from spontaneous reporting systems: The influence of under-reporting on odds ratios. Stat. Med. 2002, 21, 2027–2044. [Google Scholar] [CrossRef] [PubMed]
  44. 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, 796–803. [Google Scholar] [CrossRef] [PubMed]
  45. Star, K.; Sandberg, L.; Bergvall, T.; Choonara, I.; Caduff-Janosa, P.; Edwards, I.R. Paediatric safety signals identified in VigiBase: Methods and results from Uppsala Monitoring Centre. Pharmacoepidemiol. Drug Saf. 2019, 28, 680–689. [Google Scholar] [CrossRef]
  46. Ahmed, I.; Haramburu, F.; Fourrier-Réglat, A.; Thiessard, F.; Kreft-Jais, C.; Miremont-Salamé, G.; Begaud, B.; Tubert-Bitter, P. Bayesian pharmacovigilance signal detection methods revisited in a multiple comparison setting. Stat. Med. 2009, 28, 1774–1792. [Google Scholar] [CrossRef]
  47. Ahmed, I.; Dalmasso, C.; Haramburu, F.; Thiessard, F.; Broët, P.; Tubert-Bitter, P. False Discovery Rate Estimation for Frequentist Pharmacovigilance Signal Detection Methods. Biometrics 2009, 66, 301–309. [Google Scholar] [CrossRef]
  48. Benjamini, Y.; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B (Methodol.) 1995, 57, 289–300. [Google Scholar] [CrossRef]
  49. Ahmed, I.; Poncet, A. PhViD: An R Package for Pharmacovigilance Signal Detection, R Package Version; 2013. Available online: (accessed on 28 July 2019).
  50. Stevenson, M.; Nunes, T.; Sanchez, J. EpiR: An R Package for the Analysis of Epidemiological Data; R Package Version 0.9-48; R Foundation for Statistical Computing: Vienna, Austria, 2013. [Google Scholar]
  51. ArrayExpress—A Database of Functional Genomics Experiments. Available online: (accessed on 12 November 2012).
  52. Oneal, P.A.; Kwitkowski, V.; Luo, L.; Shen, Y.L.; Subramaniam, S.; Shord, S.; Goldberg, K.B.; McKee, A.E.; Kaminskas, E.; Farrell, A.; et al. FDA Approval Summary: Vemurafenib for the Treatment of Patients with Erdheim-Chester Disease with the BRAF V600 Mutation. Oncologist 2018, 23, 1520–1524. [Google Scholar] [CrossRef]
  53. Falchook, G.S.; Millward, M.; Hong, D.S.; Naing, A.; Piha-Paul, S.A.; Waguespack, S.; Cabanillas, M.E.; I Sherman, S.; Ma, B.; Curtis, M.; et al. BRAF Inhibitor Dabrafenib in Patients with Metastatic BRAF-Mutant Thyroid Cancer. Thyroid 2015, 25, 71–77. [Google Scholar] [CrossRef]
  54. Planchard, D.; Besse, B.; Groen, H.J.M.; Souquet, P.-J.; Quoix, E.; Baik, C.S.; Barlesi, F.; Kim, T.M.; Mazieres, J.; Novello, S.; et al. Dabrafenib plus trametinib in patients with previously treated BRAF(V600E)-mutant metastatic non-small cell lung cancer: An open-label, multicentre phase 2 trial. Lancet Oncol. 2016, 17, 984–993. [Google Scholar] [CrossRef]
  55. Pirmohamed, M.; Darbyshire, J. Collecting and sharing information about harms. BMJ 2004, 329, 6–7. [Google Scholar] [CrossRef]
  56. Talbot, J.C.; Nilsson, B.S. Pharmacovigilance in the pharmaceutical industry. Br. J. Clin. Pharmacol. 1998, 45, 427–431. [Google Scholar] [CrossRef]
  57. Salem, J.-E.; Manouchehri, A.; Moey, M.; Lebrun-Vignes, B.; Bastarache, L.; Pariente, A.; Gobert, A.; Spano, J.-P.; Balko, J.M.; Bonaca, M.P.; et al. Cardiovascular toxicities associated with immune checkpoint inhibitors: An observational, retrospective, pharmacovigilance study. Lancet Oncol. 2018, 19, 1579–1589. [Google Scholar] [CrossRef]
  58. Launay-Vacher, V.; Zimner-Rapuch, S.; Poulalhon, N.; Fraisse, T.; Garrigue, V.; Gosselin, M.; Amet, S.; Janus, N.; Deray, G. Acute renal failure associated with the new BRAF inhibitor vemurafenib: A case series of 8 patients. Cancer 2014, 120, 2158–2163. [Google Scholar] [CrossRef]
  59. Perico, L.; Mandalà, M.; Schieppati, A.; Carrara, C.; Rizzo, P.; Conti, S.; Longaretti, L.; Benigni, A.; Remuzzi, G. BRAF Signaling Pathway Inhibition, Podocyte Injury, and Nephrotic Syndrome. Am. J. Kidney Dis. 2017, 70, 145–150. [Google Scholar] [CrossRef]
  60. Maanaoui, M.; Saint-Jacques, C.; Gnemmi, V.; Frimat, M.; Lionet, A.; Hazzan, M.; Noël, C.; Provot, F. Glomerulonephritis and granulomatous vasculitis in kidney as a complication of the use of BRAF and MEK inhibitors in the treatment of metastatic melanoma. Medicine 2017, 96, e7196. [Google Scholar] [CrossRef]
  61. Nussbaum, E.Z.; Perazella, M.A. Update on the nephrotoxicity of novel anticancer agents. Clin. Nephrol. 2018, 89, 149–165. [Google Scholar] [CrossRef]
  62. Long, G.V.; Flaherty, K.T.; Stroyakovskiy, D.; Gogas, H.; Levchenko, E.V.; De Braud, F.; Larkin, J.; Garbe, C.; Jouary, T.; Hauschild, A.; et al. Dabrafenib plus trametinib versus dabrafenib monotherapy in patients with metastatic BRAF V600E/K-mutant melanoma: Long-term survival and safety analysis of a phase 3 study. Ann. Oncol. 2017, 28, 1631–1639. [Google Scholar] [CrossRef]
  63. Harrisingh, M.C.; Pérez-Nadales, E.; Parkinson, D.B.; Malcolm, D.S.; Mudge, A.W.; Lloyd, A.C. The Ras/Raf/ERK signalling pathway drives Schwann cell dedifferentiation. EMBO J. 2004, 23, 3061–3071. [Google Scholar] [CrossRef]
  64. Chang, K.-H.; Chuang, T.-J.; Lyu, R.-K.; Ro, L.-S.; Wu, Y.-R.; Chang, H.-S.; Huang, C.-C.; Kuo, H.-C.; Hsu, W.-C.; Chu, C.-C.; et al. Identification of Gene Networks and Pathways Associated with Guillain-Barré Syndrome. PLoS ONE 2012, 7, e29506. [Google Scholar] [CrossRef]
  65. Urner-Bloch, U.; Urner, M.; Stieger, P.; Galliker, N.; Winterton, N.; Zubel, A.; Parseval, L.M.-D.; Dummer, R.; Goldinger, S.M. Transient MEK inhibitor-associated retinopathy in metastatic melanoma. Ann. Oncol. 2014, 25, 1437–1441. [Google Scholar] [CrossRef] [PubMed]
  66. Stjepanovic, N.; Velazquez-Martin, J.P.; Bedard, P.L. Ocular toxicities of MEK inhibitors and other targeted therapies. Ann. Oncol. 2016, 27, 998–1005. [Google Scholar] [CrossRef] [PubMed]
  67. Niro, A.; Strippoli, S.; Alessio, G.; Sborgia, L.; Recchimurzo, N.; Guida, M. Ocular Toxicity in Metastatic Melanoma Patients Treated with Mitogen-Activated Protein Kinase Kinase Inhibitors: A Case Series. Am. J. Ophthalmol. 2015, 160, 959–967.e1. [Google Scholar] [CrossRef]
  68. Libenciuc, C.; Mateus, C.; Routier, E.; Reigneau, M.; Fahmy, J.; Ghoufi, L.; Boutros, C.; Cauquil, C.; Robert, C. Neuropathies sensitives sous la combinaison inhibiteurs de BRAF et de MEK: Dabrafénib et tramétinib. Ann. Dermatol. Vénéréol. 2016, 143, S207. [Google Scholar] [CrossRef]
  69. Compter, A.; Boogerd, W.; Van Thienen, J.V.; Brandsma, D. Acute polyneuropathy in a metastatic melanoma patient treated with vemurafenib and cobimetinib. Neurol. Clin. Pr. 2017, 7, 418–420. [Google Scholar] [CrossRef] [PubMed]
  70. Chen, X.; Schwartz, G.K.; DeAngelis, L.M.; Kaley, T.J.; Carvajal, R.D. Dropped head syndrome: Report of three cases during treatment with a MEK inhibitor. Neurology 2012, 79, 1929–1931. [Google Scholar] [CrossRef]
  71. Boasberg, P.; Redfern, C.H.; Daniels, G.A.; Bodkin, D.; Garrett, C.R.; Ricart, A.D. Pilot study of PD-0325901 in previously treated patients with advanced melanoma, breast cancer, and colon cancer. Cancer Chemother. Pharmacol. 2011, 68, 547–552. [Google Scholar] [CrossRef]
  72. Taha, T.; Tzuk-Shina, T.; Forschner, I.; Bar-Sela, G. Acute motor and sensory axonal neuropathy related to treatment with MEK inhibitors in a patient with advanced melanoma. Melanoma Res. 2017, 27, 632–634. [Google Scholar] [CrossRef]
  73. Maurice, C.; Marcus, B.; Mason, W. Guillain-Barre Syndrome after Treatment with Dabrafenib for Metastatic Recurrent Melaloma. (P4. 232); Neurology: Alphen aan den Rijn, The Netherlands, 2015. [Google Scholar]
  74. Greco, A.; Gallo, A.; Fusconi, M.; Marinelli, C.; Macri, G.; De Vincentiis, M. Bell’s palsy and autoimmunity. Autoimmun. Rev. 2012, 12, 323–328. [Google Scholar] [CrossRef]
  75. Grampp, G.; Felix, T. Pharmacovigilance Considerations for Biosimilars in the USA. BioDrugs 2015, 29, 309–321. [Google Scholar] [CrossRef]
  76. Wang, H.-W.; Hochberg, A.M.; Pearson, R.K.; Hauben, M.; Hauben, M. An Experimental Investigation of Masking in the US FDA Adverse Event Reporting System Database. Drug Saf. 2010, 33, 1117–1133. [Google Scholar] [CrossRef] [PubMed]
  77. Pariente, A.; Avillach, P.; Salvo, F. Effect of competition bias in safety signal generation. Drug Saf. 2012, 35, 855–864. [Google Scholar] [CrossRef] [PubMed]
  78. Morganstein, D.L.; Lai, Z.; Spain, L.; Diem, S.; Levine, D.; Mace, C.; Gore, M.; Larkin, J. Thyroid abnormalities following the use of cytotoxic T-lymphocyte antigen-4 and programmed death receptor protein-1 inhibitors in the treatment of melanoma. Clin. Endocrinol. (Oxf.) 2017, 86, 614–620. [Google Scholar] [CrossRef] [PubMed]
  79. Robert, E.; Rosa, F. Valproate and Birth Defects. Lancet 1983, 322, 1142. [Google Scholar] [CrossRef]
  80. Stricker, B.H.; Tijssen, J.G. Serum sickness-like reactions to cefaclor. J. Clin. Epidemiol. 1992, 45, 1177–1184. [Google Scholar] [CrossRef]
  81. Sommet, A.; Grolleau, S.; Bagheri, H.; Lapeyre-Mestre, M.; Montastruc, J.L.; French Network of Regional Pharmacovigilance Centres. Was the thrombotic risk of rofecoxib predictible from the French Pharmacovigilance Database before 30 September 2004? Eur. J. Clin. Pharmacol. 2008, 64, 829–834. [Google Scholar] [CrossRef]
  82. Souyri, C.; Olivier, P.; Grolleau, S.; Lapeyre-Mestre, M.; Centres, T.F.N.O.P. Severe necrotizing soft-tissue infections and nonsteroidal anti-inflammatory drugs. Clin. Exp. Dermatol. 2008, 33, 249–255. [Google Scholar] [CrossRef]
  83. Montastruc, J.L.; 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]
  84. Hauben, M.; Madigan, D.; Gerrits, C.M.; Walsh, L.; Van Puijenbroek, E.P. The role of data mining in pharmacovigilance. Expert Opin. Drug Saf. 2005, 4, 929–948. [Google Scholar] [CrossRef]
Figure 1. Comparison of serious adverse events (detected as signals) reported for BRAK-MEK inhibitors. The disproportionality analysis included serious adverse events leading to death, life-threatening conditions, disability, or hospitalization from January 2018 to June 2019. Adverse events associated with BRAFi+MEKi as primary or secondary suspects, which were also disproportionally reported compared to the full database, are displayed. The proportion of the events is shown on top of the bars. A constant of 0.1 was added to all ROR values to avoid bias of near zero values. AKI—acute kidney injury. CRP—C-reactive protein.
Figure 1. Comparison of serious adverse events (detected as signals) reported for BRAK-MEK inhibitors. The disproportionality analysis included serious adverse events leading to death, life-threatening conditions, disability, or hospitalization from January 2018 to June 2019. Adverse events associated with BRAFi+MEKi as primary or secondary suspects, which were also disproportionally reported compared to the full database, are displayed. The proportion of the events is shown on top of the bars. A constant of 0.1 was added to all ROR values to avoid bias of near zero values. AKI—acute kidney injury. CRP—C-reactive protein.
Cancers 12 01650 g001
Table 1. Characteristics of patients with BRAF+MEK-associated adverse events.
Table 1. Characteristics of patients with BRAF+MEK-associated adverse events.
Patients 5841893350
Reports, No. (%) 2345 (20)8411 (71.8)965 (8.2)
Age, Mean (SD), y 58.8 (14.05)59.4 (16)62.93 (12.96)
Sex, No. (%)
Female276 (47.26)789 (41.68)24 (6.86)
Male275 (47.09)943 (49.82)26 (7.43)
Not reported33 (5.65)161 (8.51)300 (85.71)
Indications, No. (%)
Melanoma480 (80.9)1219 (62.6)204 (57.8)
Lung cancer3 (0.5)111 (5.7)4 (1.1)
Gastrointestinal cancer4 (0.7)29 (1.5)48 (13.6)
Thyroid cancer20 (3.4)34 (1.7)2 (0.6)
Hematological malignancy18 (3)17 (0.9)1 (0.3)
Other/Unspecified71 (12)542 (27.8)96 (27.2)
Country, No. (%)
Americas389 (16.59)1695 (20.15)600 (62.18)
Europe1570 (66.95)3389 (40.29)233 (24.15)
Australia33 (1.41)178 (2.12)19 (1.97)
Asia70 (2.99)1097 (13.04)41 (4.25)
Africa19 (0.81)10 (0.12)0 (0)
Other264 (11.26)2032 (24.16)72 (7.46)
Table 2. Age-adjusted reporting odds ratio for selected serious adverse events.
Table 2. Age-adjusted reporting odds ratio for selected serious adverse events.
V/C Reports
(n = 2345)
D/T Reports (n = 8411)E/B Reports (n = 965)Reports in Full Database; (n = 3,285,265)Adj. ROR 1 (95% CI) V+C vs. D+T and E+BAdj. ROR 1 (95% CI) D+T vs. V+C and E+BAdj. ROR 1 (95% CI) E+B vs. V+C and D+T
Epidermal and dermal conditions311 (13.3%)355 (4.2%)47 (4.9%)303188 (9.2%)3.4 (2.9–4.0) 0.4 (0.3–0.4) 0.8 (0.6–1.1)
Stevens–Johnson syndrome17 (0.7%)6 (0.1%)0 (0%)7099 (0.2%)10.4 (4–26.9) 0.1 (0.1–0.3)
Pyrexia67 (2.9%)472 (5.6%)32 (3.3%)75,155 (2.3%)0.5 (0.4–0.6) 1.9 (1.5–2.4) 0.7 (0.5–1.1)
Increased CRP7 (0.3%)58 (0.7%)2 (0.2%)13,829 (0.4%)0.4 (0.2–0.9)2.3 (1.2–4.8) 0.6 (0.1–2.6)
Renal disorders NEC5 (0.2%)8 (0.1%)6 (0.6%)20,466 (0.6%)1.7 (0.6–4.7)0.3 (0.1–0.8) 4.1 (1.3–12.5)
Renal disorders (excl. nephropathies)61 (2.6%)115 (1.4%)28 (2.9%)335,796 (10.2%)1.8 (1.3–2.4) 0.5 (0.4–0.7) 1.8 (1.2–2.9)
GI motility and defecation conditions91 (3.9%)155 (1.8%)38 (3.9%)151,750 (4.6%)2 (1.6–2.6) 0.5 (0.4–0.6) 1.8 (1.2–2.6)
Colitis13 (0.6%)19 (0.2%)12 (1.2%)8681 (0.3%)1.9 (1–3.7)0.3 (0.2–0.6) 3.3 (1.5–7.1)
Nausea40 (1.7%)127 (1.5%)29 (3%)86,674 (2.6%)1 (0.7–1.5)0.7 (0.5–1.0)2.3 (1.4–3.7)
Seizure2 (0.1%)31 (0.4%)14 (1.5%)25,004 (0.8%)0.2 (0–0.8) 0.9 (0.5–1.6)3.8 (1.8–8.0)
Peripheral neuropathies16 (0.7%)35 (0.4%)9 (0.9%)35,676 (1.1%)1.3 (0.8–2.4)0.6 (0.3–0.9)2.7 (1.2–6.1)
Guillain–Barre syndrome2 (0.1%)4 (0%)5 (0.5%)1374 (0%)0.9 (0.2–4.2)0.2 (0.1–0.8)8.5 (2.1–35.0)
Hypotension11 (0.5%)49 (0.6%)12 (1.2%)57,946 (1.8%)0.7 (0.4–1.3)0.9 (0.5–1.4)2.5 (1.2–5.1)
CRP—C-reactive protein. NEC—not elsewhere classified. GI—gastrointestinal. 1 Age-adjusted; Adjusted p-value < 0.05.
Table 3. Clinical characteristics of patients with selected serious adverse events.
Table 3. Clinical characteristics of patients with selected serious adverse events.
Dermal ConditionsBody Temp. ConditionsRenal Disorders *GI Motility and Defac. ConditionsSeizures (Incl. Subtypes)Periph. Neurop.Vascular Hypoten. Disorders
Drug dosing
<960/<60 mg32/212 (15%)5/69 (7%)7/53 (13%)12/71 (17%)3/14 (21%)1/9 (11%)2/16 (12%)
960/60 mg67/212 (32%)25/69 (36%)18/53 (34%)19/71 (27%)3/14 (21%)2/9 (22%)6/16 (38%)
>960/>60 mg26/212 (12%)9/69 (13%)6/53 (11%)9/71 (13%)1/14 (7%)3/9 (33%)2/16 (12%)
Unreported87/212 (41%)30/69 (43%)22/53 (42%)31/71 (44%)7/14 (50%)3/9 (33%)6/16 (38%)
<150/<2 mg154/238 (65%)281/485 (58%)58/109 (53%)80/141 (57%)27/58 (47%)11/32 (34%)29/55 (53%)
150/2 mg10/238 (4%)56/485 (12%)9/109 (8%)8/141 (6%)5/58 (9%)2/32 (6%)9/55 (16%)
>150/>2 mg7/238 (3%)31/485 (6%)12/109 (11%)8/141 (6%)2/58 (3%)1/32 (3%)3/55 (5%)
Unreported67/238 (28%)117/485 (24%)30/109 (28%)45/141 (32%)24/58 (41%)18/32 (56%)14/55 (25%)
<450/<45 mg1/29 (3%)2/33 (6%)1/28 (4%)1/37 (3%)0/15 (0%)0/8 (0%)0/12 (0%)
450/45 mg9/29 (31%)14/33 (42%)15/28 (54%)16/37 (43%)6/15 (40%)6/8 (75%)7/12 (58%)
>450/>45 mg3/29 (10%)7/33 (21%)2/28 (7%)7/37 (19%)1/15 (7%)0/8 (0%)1/12 (8%)
Unreported16/29 (55%)10/33 (30%)10/28 (36%)13/37 (35%)8/15 (53%)2/8 (25%)4/12 (33%)
Time to AE onset, days (IQR)
V/C9 (7–14); n = 1389 (8–22); n = 4214 (7–80); n = 359 (5–27); n = 3937 (11–185); n = 9237 (154–320); n = 29 (7–13); n = 8
D/T30 (13–50); n = 2028 (16–52); n = 8870.5 (54–138); n = 2423 (10–50); n = 19141.5 (22–303); n = 8107 (44–206); n = 711 (6–76); n = 12
E/B12 (2–32); n = 534.5 (12–122); n = 1417.5 (5–54); n = 1221 (12–27); n = 13124 (64–128); n = 354 (41–57); n = 354 (14–131); n = 5
V/C123/134 (92%)38/44 (86%)34/36 (94%)25/27 (93%)2/2 (100%)1/1 (100%)11/11 (100%)
D/T116/120 (97%)239/251 (95%)60/64 (94%)57/60 (95%)15/16 (94%)10/12 (83%)32/33 (97%)
E/B7/8 (88%)17/17 (100%)10/11 (91%)14/16 (88%)0/1 (0%)1/2 (50%)4/5 (80%)
V/C4/9 (44%)0/2 (0%)2/2 (100%)1/2 (50%)
D/T1/2 (50%)8/19 (42%)3/3 (100%)0/1 (0%) 1/1 (100%)
E/B2/2 (100%)2/3 (67%)1/3 (33%)2/3 (67%) 0/1 (0%)
Death19/212 (9%)10/69 (14%)7/53 (13%)16/71 (23%)6/14 (43%)1/9 (11%)0/16 (0%)
Life-threatening18/212 (8%)7/69 (10%)5/53 (9%)3/71 (4%)3/14 (21%)2/9 (22%)2/16 (12%)
Hospitalization202/212 (95%)68/69 (99%)49/53 (92%)65/71 (92%)13/14 (93%)7/9 (78%)16/16 (100%)
Other58/212 (27%)28/69 (41%)18/53 (34%)37/71 (52%)10/14 (71%)8/9 (89%)6/16 (38%)
Death57/238 (24%)119/485 (25%)22/109 (20%)43/141 (30%)31/58 (53%)2/32 (6%)9/55 (16%)
Life-threatening22/238 (9%)48/485 (10%)18/109 (17%)6/141 (4%)2/58 (3%)5/32 (16%)7/55 (13%)
Hospitalization204/238 (86%)441/485 (91%)103/109 (94%)132/141 (94%)52/58 (90%)32/32 (100%)53/55 (96%)
Other140/238 (59%)269/485 (55%)65/109 (60%)89/141 (63%)43/58 (74%)20/32 (62%)31/55 (56%)
Death4/29 (14%)2/33 (6%)6/28 (21%)2/37 (5%)2/15 (13%)1/8 (12%)4/12 (33%)
Life-threatening5/29 (17%)3/33 (9%)5/28 (18%)3/37 (8%)4/15 (27%)0/8 (0%)1/12 (8%)
Hospitalization26/29 (90%)30/33 (91%)22/28 (79%)33/37 (89%)14/15 (93%)6/8 (75%)11/12 (92%)
Other12/29 (41%)12/33 (36%)12/28 (43%)12/37 (32%)9/15 (60%)3/8 (38%)5/12 (42%)
* Renal disorders (excluding nephropathies).
Back to TopTop