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Abstract

The combination of immune checkpoint inhibitors (ICIs) and angiogenesis inhibitors (AGIs) is widely used in cancer treatment; however, drug–drug reactions (DDIs) remain unknown. We aimed to identify interaction signals for the concomitant use of ICIs and AGIs. Data were obtained from the US FDA Adverse Event Reporting System (FAERS) from January 1, 2015, to December 31, 2023. Disproportionality analysis was used for data mining by calculating the reporting odds ratio (ROR) and 95% confidence interval (95% CI). Adjusted RORs were analysed using logistic regression analysis, considering age, sex and reporting year. Further confirmation was assessed via additive and multiplicative models. We identified 75,936 reports on ICIs combined with AGIs. Significant interaction signals were observed for hepatobiliary disorders (RORcrude: 5.25, 95% CI: 5.07–5.44, RORadj: 5.01, 95% CI: 4.82–5.22, additive models: 0.2323), investigations (RORcrude: 1.66, 95% CI: 1.62–1.70, RORadj: 1.63, 95% CI: 1.58–1.67, additive models: 0.2187, multiplicative models: 1.1265), renal and urinary disorders (RORcrude: 1.87, 95% CI: 1.80–1.95, RORadj: 1.72, 95% CI: 1.64–1.79, additive models: 0.3239, multiplicative models: 1.1799) and vascular disorders (RORcrude: 1.94, 95% CI: 1.87–2.02, RORadj: 1.87, 95% CI: 1.80–1.95, additive models: 0.5823, multiplicative models: 1.5676). Subset data analysis showed positive interaction signals for PDL-1/CTLA-4 inhibitors + AGI in hepatobiliary disorders, PD-1 inhibitors + AGI in investigations, or PD-1/PDL-1 inhibitors + AGI in renal and urinary/ vascular disorders. Based on FAERS data, four systemic disorders were identified as having DDIs related to the combined use of ICIs and AGIs. Pre-clinical trials are required to explore the mechanisms underlying these interactions.

Keywords

angiogenesis inhibitors drug–drug interaction FDA adverse event reporting system (FAERS)immune checkpoint inhibitors pharmacovigilance

What’s new

This is the first study to identify interaction signals for the concomitant use of ICIs and AGIs through a pharmacovigilance analysis. Four systemic disorders, including drug interactions, hepatobiliary disorders, renal and urinary disorders and vascular disorders, were found to be associated with the concomitant use of ICIs and AGIs.

Introduction

Over the past decade, immune checkpoint inhibitors (ICIs), including programmed death-1 (PD-1), programmed death ligand-1 (PDL-1) and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors, have revolutionised the strategic therapies for a wide variety of tumours.¹ The effectiveness of ICIs in treating various cancers has been demonstrated in several clinical trials, and immunotherapy has been positively correlated with the rate of a complete response to treatment.^2–4 However, this approach does not meet expectations and encounters issues with response duration, resistance, treatment eligibility and safety because of tumour heterogeneity and the complexity of the tumour microenvironment.^5,6 Consequently, immunotherapy and other therapies, such as chemotherapy and radiotherapy, are currently under investigation.⁷ The combination of ICIs and angiogenesis inhibitors (AGIs) could have potential antitumor effects, given the growing understanding of the importance of the tumor microenvironment.^8–10

Although the combination of ICIs and AGIs is highly effective, it has significantly more negative effects.¹¹ The risk of cardiovascular adverse events (AE) is higher with ICIs combined with AGIs than with ICIs alone.¹² Furthermore, combination therapy is an independent risk factor for adverse drug reactions (ADRs) associated with interstitial lung disease, hypertension and gastrointestinal bleeding.¹³ The occurrence of high-grade AEs was significantly different between patients with renal cell carcinoma (RCC) treated with cabozantinib combined with nivolumab and ipilimumab and those treated with nivolumab and ipilimumab alone.¹⁴ While combination therapies often seek to exploit complementary mechanisms of action to achieve superior antitumor efficacy, the concomitant use of multiple drugs may lead to drug–drug interactions (DDIs), resulting in drug toxicities, suboptimal therapy and treatment failure, all of which affect the full benefit of treatment.¹⁵

However, there is currently no research on the DDIs through the combined use of ICIs and AGIs. To better understand the overall aspects of the concomitant use of ICIs and AGIs, it is crucial to obtain sufficient information on their DDIs from real-world data. We conducted descriptive and disproportionality analyses of DDIs to determine the possible harmful effects of combinations of ICIs and AGIs by examining the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database. Our use of big data allowed us to summarise their interaction characteristics, confirm the intensity and occurrence patterns of their AEs, and provide a reference for clinical decision-making.

Methods

Data source

The FAERS database was used for this retrospective pharmacovigilance study.¹⁶ The FAERS database comprises AE reports from health professionals, patients and manufacturers worldwide. The public has access to these data. Data were extracted from the first quarter (Q1) of 2015 to the fourth quarter (Q4) of 2023.

Procedures

Data were obtained from the REAC files according to the Medical Dictionary for Regulatory Activities (MedDRA, version 23.0) at the preferred term (PT) level based on all 27 Standardised MedDRA Query System Organ Class (SOC).¹⁷ The 27 SOCs are listed in Table S1 in the supplemental material. The DRUG file contained generic name records of all drugs, with the role_code selected being either ‘PS’ (primary suspect) or ‘SS’ (secondary suspect). Combined immunotherapy drugs included anti-PD-1 drugs (pembrolizumab, nivolumab, cemiplimab, dostarlimab, prolongolimab, tislelizumab, toripalimab, sintilimab, camrelizumab, penpulimab, zimberelimab and serplulimab), anti-PDL-1 drugs (atezolizumab, durvalumab, avelumab, envafolimab and sugemalimab) and anti-CTLA4 drugs (ipilimumab and tremelimumab). AGIs included bevacizumab, ramucirumab, aflibercept, sorafenib, sunitinib, pazopanib, vandetanib, cabozantinib, regorafenib, axitinib, nintedanib/intedanib, lenvatinib, cediranib, tivozanib, erdafitinib, vatalanib, apatinib (China), anlotinib (China), fruquintinib (China) and endostatin (China). To remove duplicate reports, the last FDA_DT was selected when the CASEID was identical, and the PRIMARY_ID with the greater value was selected when the CASEID and FDA_DT were identical, as directed in the FAERS user instructions.¹⁶ The clinical features of patients taking ICIs combined with AGIs were synthesised using descriptive analyses of data collected from the FAERS database.

Data analysis

In spontaneous reporting databases, the detection of possible interactions is based on the demonstration that a suspected AE is reported more frequently with a combination of two drugs than when they are used alone.^18–20 Therefore, the reports were divided into three index groups: (i) reports of patients exposed to ICIs alone, but not AGIs; (ii) reports of patients exposed to AGIs alone, not to ICIs and (iii) reports of patients exposed to both ICIs and AGIs at the time of the event. The reference group comprised patients who were not exposed to ICIs or AGIs.

Disproportionality was calculated using the reporting odds ratio (ROR) with a relevant 95% confidence interval (95% CI), which was defined as statistically significant when the lower limit of the 95% CI exceeded 1, with at least three cases of interest reported.²¹ To identify potential confounders in the database, crude RORs were recalculated using unconditional logistic regression analysis based on age, sex and reporting year.²² The logistic model was as follows: Log (risk of the event) = β₀ + β₁ ICIs + β₂ AGI + β₃ ICIs × AGI + β₄ age + β₅ gender + β₆ reporting year. An interaction can be claimed when the combination is linked to an enhanced ROR compared with other index groups.²³

To assess the consistency and reliability of DDIs, we used multiplicative and additive models.²² The analysis provided a measurement of the threshold for detecting DDI signals. In the multiplicative model, the risk associated with a drug is multiplied by the background risk, whereas in the additive model, the risk associated with the drug is added to the background risk. The risk(drug 1 × drug 2)/((risk(drug 1) × risk(drug 2)) > 1 and risk(drug 1 × drug 2) – (risk(drug 1) + risk(drug 2)) > 0, respectively, indicate that the multiplicative and additive models generate a drug interaction signal. If the value (interaction term) goes beyond 0 or 1 in the additive or multiplicative models, it indicates a positive interaction.²⁴ Data management and analyses were performed using SPSS (version 22.0; IBM Corp., Armonk, NY, USA).

Results

Descriptive analysis

From January 2014 to December 2023, 51,336,49,755 reports were extracted from the FAERS database and 39,523,594 reports were included in the final analysis, of which 75,936 reports of ICIs combined with AGIs were identified. The clinical features of the patients are summarised in Table 1. The median patient age was 55 years (IQR: 64–72 years). The proportion of male patients (48.82%) was slightly higher than that of female patients (42.27%). The number of reports submitted by healthcare professionals was high (49.25%). Asia reported the highest rate of ADRs (31.74%), followed by North America (29.79%), Europe (20.01%), South America (1.01%), Oceania (1.00%) and Africa (0.04%; Figure 1a). The number of reports steadily increased over time, from 0.05% in 2014 to 31.20% in 2023 (Figure 1b). A Sankey diagram was used to display the distribution of the clinical characteristics (Figure 1c).

Table 1.

Characteristics of patients treated with immune checkpoint inhibitors combined with angiogenesis inhibitors in the FAERS database (January 1, 2014 to December 31, 2023).

Characteristics	Overall
Total number of cases	28,326
Patient’s age, years, median (Q1–Q3)
<18 years	1908 (6.74%)
18–65 years	8983 (31.71%)
⩾65 years	10,761 (37.99%)
Unknown	6674 (23.56%)
Patient’s gender (n (%))
Male	13,830 (48.82%)
Female	11,972 (42.27%)
Unknown	2524 (8.91%)
Type of reporter
Health professional	13,951 (49.25%)
Non-health professional	3206 (11.32%)
Unknown	11,169 (39.43%)
Outcome
Hospitalisation	11,437 (40.38%)
Life-threatening	924 (3.26%)
Death	4618 (16.30%)
Reported regions
North America	8438 (29.79%)
South America	288 (1.01%)
Europe	5668 (20.01%)
Asia	8990 (31.74%)
Oceania	282 (1.00%)
Africa	10 (0.04%)
Unknown	4650 (16.42%)
Reported year
2014	13 (0.05%)
2015	59 (0.21%)
2016	191 (0.67%)
2017	449 (1.59%)
2018	801 (2.83%)
2019	1686 (5.95%)
2020	3071 (10.84%)
2021	5115 (18.06%)
2022	8104 (28.61%)
2023	8837 (31.20%)

Figure 1.

(a) Geographical regions reporting immune checkpoint inhibitors (ICIs) combined with angiogenesis inhibitors (AGIs). (b) Annual reported adverse reactions of ICIs combined with AGI from 2014 to 2023. (c) The distribution of clinical characteristics of ICIs combined with AGIs using Sankey diagram.

Disproportionality analysis

The signal detection results of the DDIs with the concomitant use of ICIs and AGIs for all 27 SOCs are presented in Table 2. The crude/adjusted RORs and 95% CIs for all comparisons are presented using a disproportionality method. We estimated the DDIs according to additive/multiplicative models. Positive signals were detected in four SOCs: investigations, hepatobiliary, renal and urinary and vascular disorders (Table 3). For hepatobiliary disorders, the crude/adjusted ROR for the use of AGI alone was 1.91 (95% CI: 1.87–1.94)/1.73 (95% CI: 1.69–1.76), and the crude/adjusted ROR for ICIs alone was 4.11 (95% CI: 4.04–4.18)/3.95 (95% CI: 3.87–4.03). An increase in the signal for the concomitant use of ICIs and AGI emerged (RORcrude: 5.25, 95% CI: 5.07–5.44, RORadj: 5.01, 95% CI: 4.82–5.22). The positive interaction signal was supported by additive models (0.2323). For investigations, the crude/adjusted ROR for the use of AGI alone was 1.37 (95% CI: 1.36–1.38)/1.38 (95% CI: 1.36–1.39), and the crude/adjusted ROR for ICIs alone was 1.07 (95% CI: 1.06–1.09)/1.05 (95% CI: 1.04–1.07). An increase in the signal for the concomitant use of ICIs and AGI emerged (RORcrude: 1.66, 95% CI: 1.62–1.70, RORadj: 1.63, 95% CI: 1.58–1.67). The positive interaction signal was supported by additive (0.2187) and multiplicative (1.1265) models. For renal and urinary disorders, the crude/adjusted ROR for the use of AGI alone was 1.07 (95% CI: 1.05–1.08)/0.96 (95% CI: 0.94–0.98), and the crude/adjusted ROR for ICIs alone was 1.48 (95% CI: 1.45–1.51)/0.94 (95% CI: 1.35–1.41). An increase in the signal for the concomitant use of ICIs and AGI emerged (RORcrude: 1.87, 95% CI: 1.80–1.95, RORadj: 1.72, 95% CI: 1.64–1.79). The positive interaction signal was supported by additive (0.3239) and multiplicative (1.1799) models. For vascular disorders, the crude/adjusted ROR for the use of AGI alone was 1.57 (95% CI: 1.55–1.59)/1.36 (95% CI: 1.34–1.38), and the crude/adjusted ROR for ICIs alone was 0.79 (95% CI: 0.77–0.81)/0.74 (95% CI: 0.72–0.76). An increase in the signal for the concomitant use of ICIs and AGI emerged (RORcrude: 1.94, 95% CI: 1.87–2.02, RORadj: 1.87, 95% CI: 1.80–1.95). The positive interaction signal was supported by additive (0.5823) and multiplicative (1.5676) models.

Table 2.

Reporting odds ratios (RORs) and drug interaction approaches for immune checkpoint inhibitors (ICIs) and angiogenesis inhibitors (AGIs).

SOC	Exposure	Cases	Non-cases	RORcrude (95% CI)	RORadj (95% CI)	Additive model	Multiplicative model
Blood and lymphatic system disorders	No ICIs, no AGI	5,88,930	376,61,071	Reference	Reference	−1.2697	0.4447
	AGI, no ICIs	22,948	7,73,583	1.81 (1.78–1.83)	1.45 (1.43–1.47)
	ICIs, no AGI	15,782	3,85,344	2.47 (2.43–2.51)	2.15 (2.12–2.19)
	ICIs, AGI	2428	73,508	2.01 (1.93–2.09)	1.66 (1.59–1.74)
Cardiac disorders	No ICIs, no AGI	8,81,965	373,68,036	Reference	Reference	0.0841	1.1440
	AGI, no ICIs	13,729	7,82,802	0.75 (0.74–0.76)	0.60 (0.59–0.61)
	ICIs, no AGI	11,467	3,89,659	1.24 (1.22–1.27)	1.09 (1.06–1.11)
	ICIs, AGI	1880	74,056	1.08 (1.03–1.13)	1.03 (0.98–1.08)
Congenital, familial and genetic disorders	No ICIs, no AGI	1,08,403	381,41,598	Reference	Reference	0.6838	2.4483
	AGI, no ICIs	406	7,96,125	0.18 (0.17–0.20)	0.50 (0.44–0.56)
	ICIs, no AGI	306	4,00,820	0.28 (0.25–0.31)	0.70 (0.62–0.80)
	ICIs, AGI	30	75,906	0.15 (0.10–0.21)	0.34 (0.24–0.50)
Ear and labyrinth disorders	No ICIs, no AGI	1,64,820	380,85,181	Reference	Reference	0.2653	1.1315
	AGI, no ICIs	1902	7,94,629	0.56 (0.54–0.59)	0.55 (0.52–0.58)
	ICIs, no AGI	923	4,00,203	0.54 (0.51–0.58)	0.53 (0.49–0.57)
	ICIs, AGI	119	75,817	0.37 (0.31–0.44)	0.37 (0.30–0.45)
Endocrine disorders	No ICIs, no AGI	76,666	381,73,335	Reference	Reference	−0.4826	0.6166
	AGI, no ICIs	3190	7,93,341	1.67 (1.61–1.73)	2.23 (2.14–2.33)
	ICIs, no AGI	12,664	3,88,462	13.13 (12.89–13.39)	18.62 (18.20–19.05)
	ICIs, AGI	2436	73,500	13.32 (12.79–13.87)	17.11 (16.32–17.95)
Eye disorders	No ICIs, no AGI	7,44,213	375,05,788	Reference	Reference	−0.3021	0.4614
	AGI, no ICIs	14,751	7,81,780	0.96 (0.94–0.97)	0.83 (0.81–0.85)
	ICIs, no AGI	4988	3,96,138	0.64 (0.63–0.66)	0.67 (0.65–0.70)
	ICIs, AGI	438	75,498	0.30 (0.27–0.33)	0.30 (0.26–0.33)
Gastrointestinal disorders	No ICIs, no AGI	30,77,456	351,72,545	Reference	Reference	−0.5457	0.6984
	AGI, no ICIs	1,38,858	6,57,673	2.11 (2.10–2.12)	2.49 (2.47–2.50)
	ICIs, no AGI	39,024	3,62,102	1.18 (1.16–1.19)	1.35 (1.33–1.36)
	ICIs, AGI	10,926	65,010	1.74 (1.71–1.78)	2.08 (2.03–2.13)
General disorders and administration site conditions	No ICIs, no AGI	67,56,946	314,93,055	Reference	Reference	−0.0670	0.8999
	AGI, no ICIs	1,32,577	6,63,954	0.95 (0.94–0.95)	1.13 (1.12–1.13)
	ICIs, no AGI	59,853	3,41,273	0.85 (0.84–0.85)	0.89 (0.89–0.90)
	ICIs, AGI	9703	66,233	0.73 (0.71–0.74)	0.85 (0.82–0.87)
Hepatobiliary disorders	No ICIs, no AGI	2,91,509	379,58,492	Reference	Reference	0.2323	0.6703
	AGI, no ICIs	12,306	7,84,225	1.91 (1.87–1.94)	1.73 (1.69–1.76)
	ICIs, no AGI	13,368	3,87,758	4.11 (4.04–4.18)	3.95 (3.87–4.03)
	ICIs, AGI	3233	72,703	5.25 (5.07–5.44)	5.01 (4.82–5.22)
Immune system disorders	No ICIs, no AGI	4,47,177	378,02,824	Reference	Reference	0.2236	1.6293
	AGI, no ICIs	2694	7,93,837	0.29 (0.28–0.31)	0.32 (0.31–0.33)
	ICIs, no AGI	4393	3,96,733	0.95 (0.92–0.98)	0.91 (0.88–0.95)
	ICIs, AGI	410	75,526	0.47 (0.43–0.52)	0.45 (0.40–0.50)
Infections and infestations	No ICIs, no AGI	20,17,498	362,32,503	Reference	Reference	0.0505	1.0850
	AGI, no ICIs	32,404	7,64,127	0.77 (0.77–0.78)	0.74 (0.73–0.75)
	ICIs, no AGI	24,122	3,77,004	1.14 (1.13–1.16)	1.13 (1.12–1.15)
	ICIs, AGI	3865	72,071	0.97 (0.94–1.00)	0.90 (0.87–0.93)
Injury, poisoning and procedural complications	No ICIs, no AGI	38,18,693	344,31,308	Reference	Reference	0.2489	1.2569
	AGI, no ICIs	39,696	7,56,835	0.51 (0.50–0.51)	0.51 (0.50–0.52)
	ICIs, no AGI	27,163	3,73,963	0.69 (0.68–0.70)	0.60 (0.59–0.61)
	ICIs, AGI	3314	72,622	0.44 (0.43–0.46)	0.34 (0.33–0.36)
Investigations	No ICIs, no AGI	21,82,725	360,67,276	Reference	Reference	0.2187	1.1265
	AGI, no ICIs	62,889	7,33,642	1.37 (1.36–1.38)	1.38 (1.36–1.39)
	ICIs, no AGI	24,835	3,76,291	1.07 (1.06–1.09)	1.05 (1.04–1.07)
	ICIs, AGI	7278	68,658	1.66 (1.62–1.70)	1.63 (1.58–1.67)
Metabolism and nutrition disorders	No ICIs, no AGI	7,54,407	374,95,594	Reference	Reference	−0.7933	0.5487
	AGI, no ICIs	31,582	7,64,949	1.94 (1.92–1.97)	2.10 (2.02–2.19)
	ICIs, no AGI	16,493	3,84,633	2.02 (1.99–2.05)	1.97 (1.94–2.01)
	ICIs, AGI	3,357	72,579	2.17 (2.09–2.24)	2.10 (2.02–2.19)
Musculoskeletal and connective tissue disorders	No ICIs, no AGI	20,22,136	362,27,865	Reference	Reference	0.2240	1.2661
	AGI, no ICIs	28,197	7,68,334	0.68 (0.67–0.68)	0.68 (0.67–0.69)
	ICIs, no AGI	15,631	3,85,495	0.74 (0.73–0.76)	0.74 (0.73–0.75)
	ICIs, AGI	2563	73,373	0.64 (0.62–0.67)	0.62 (0.59–0.65)
Neoplasms benign, malignant and unspecified (incl cysts and polyps)	No ICIs, no AGI	10,90,340	371,59,661	Reference	Reference	−1.2449	0.4375
	AGI, no ICIs	37,910	7,58,621	1.62 (1.61–1.64)	1.55 (1.53–1.57)
	ICIs, no AGI	25,878	3,75,248	2.20 (2.17–2.23)	1.70 (1.67–1.72)
	ICIs, AGI	3520	72,416	1.58 (1.53–1.64)	1.03 (0.99–1.07)
Nervous system disorders	No ICIs, no AGI	30,58,964	351,91,037	Reference	Reference	0.1999	1.2462
	AGI, no ICIs	52,448	7,44,083	0.83 (0.82–0.84)	0.86 (0.85–0.87)
	ICIs, no AGI	22,296	3,78,830	0.70 (0.69–0.71)	0.74 (0.73–0.75)
	ICIs, AGI	4397	71,539	0.73 (0.71–0.75)	0.78 (0.75–0.80)
Pregnancy, puerperium and perinatal conditions	No ICIs, no AGI	1,60,124	380,89,877	Reference	Reference	0.8819	2.8826
	AGI, no ICIs	82	7,96,449	0.03 (0.02–0.03)	0.05 (0.03–0.07)
	ICIs, no AGI	174	4,00,952	0.11 (0.09–0.12)	0.28 (0.23–0.34)
	ICIs, AGI	4	75,932	0.01 (0.01–0.04)	-
Product issues	No ICIs, no AGI	6,61,680	375,88,321	Reference	Reference	0.8933	3.2577
	AGI, no ICIs	985	7,95,546	0.07 (0.07–0.08)	0.08 (0.07–0.09)
	ICIs, no AGI	324	4,00,802	0.05 (0.04–0.05)	0.01 (0.01–0.03)
	ICIs, AGI	19	75,917	0.02 (0.01–0.02)	0.04 (0.04–0.05)
Psychiatric disorders	No ICIs, no AGI	20,89,895	361,60,106	Reference	Reference	0.6479	2.6751
	AGI, no ICIs	13,514	7,83,017	0.32 (0.31–0.32)	0.39 (0.38–0.40)
	ICIs, no AGI	5590	3,95,536	0.26 (0.25–0.27)	0.31 (0.31–0.32)
	ICIs, AGI	919	75,017	0.23 (0.21–0.24)	0.28 (0.26–0.30)
Renal and urinary disorders	No ICIs, no AGI	7,22,887	375,27,114	Reference	Reference	0.3239	1.1799
	AGI, no ICIs	16,192	7,80,339	1.07 (1.05–1.08)	0.96 (0.94–0.98)
	ICIs, no AGI	11,332	3,89,794	1.48 (1.45–1.51)	0.94 (1.35–1.41)
	ICIs, AGI	2711	73,225	1.87 (1.80–1.95)	1.72 (1.64–1.79)
Reproductive system and breast disorders	No ICIs, no AGI	3,08,782	379,41,219	Reference	Reference	0.8139	5.0200
	AGI, no ICIs	2321	7,94,210	0.37 (0.35–0.38)	0.64 (0.61–0.67)
	ICIs, no AGI	663	4,00,463	0.21 (0.19–0.23)	0.35 (0.32–0.38)
	ICIs, AGI	235	75,701	0.39 (0.35–0.45)	0.61 (0.52–0.71)
Respiratory, thoracic and mediastinal disorders	No ICIs, no AGI	17,40,576	365,09,425	Reference	Reference	−0.6559	0.5934
	AGI, no ICIs	48,624	7,47,907	1.32 (1.31–1.34)	1.25 (1.23–1.26)
	ICIs, no AGI	29,657	3,71,469	1.60 (1.58–1.62)	1.58 (1.56–1.60)
	ICIs, AGI	4449	71,487	1.27 (1.23–1.31)	1.24 (1.20–1.28)
Skin and subcutaneous tissue disorders	No ICIs, no AGI	20,85,041	361,64,960	Reference	Reference	−0.0212	0.9766
	AGI, no ICIs	47,647	7,48,884	1.10 (1.09–1.11)	1.31 (1.29–1.32)
	ICIs, no AGI	20,846	3,80,280	0.95 (0.94–0.97)	1.09 (1.08–1.11)
	ICIs, AGI	4255	71,681	1.03 (1.00–1.06)	1.08 (1.04–1.12)
Social circumstances	No ICIs, no AGI	1,59,066	380,90,935	Reference	Reference	0.6597	2.6229
	AGI, no ICIs	1051	7,95,480	0.32 (0.31–0.35)	0.41 (0.39–0.44)
	ICIs, no AGI	349	4,00,777	0.21 (0.19–0.24)	0.21 (0.18–0.23)
	ICIs, AGI	61	75,875	0.20 (0.15–0.26)	0.17 (0.12–0.22)
Surgical and medical procedures	No ICIs, no AGI	5,27,918	377,22,083	Reference	Reference	0.1927	0.7836
	AGI, no ICIs	5614	7,90,917	0.52 (0.51–0.53)	0.51 (0.49–0.53)
	ICIs, no AGI	2775	3,98,351	0.51 (0.49–0.53)	0.39 (0.38–0.41)
	ICIs, AGI	228	75,708	0.22 (0.19–0.25)	0.18 (0.15–0.21)
Vascular disorders	No ICIs, no AGI	7,62,560	374,87,441	Reference	Reference	0.5823	1.5676
	AGI, no ICIs	25,263	7,71,268	1.57 (1.55–1.59)	1.36 (1.34–1.38)
	ICIs, no AGI	6386	3,94,740	0.79 (0.77–0.81)	0.74 (0.72–0.76)
	ICIs, AGI	2978	72,958	1.94 (1.87–2.02)	1.87 (1.80–1.95)

ICIs: immune checkpoint inhibitors; AGIs: angiogenesis inhibitors.

The boldfaced values represent meaningful data.

Table 3.

Summary of methods used to analyse drug-drug interactions for all SOCs.

SOC	RORadj	Additive model	Multiplicative model
Blood and lymphatic system disorders	–	–	–
Cardiac disorders	–	√	√
Congenital, familial and genetic disorders	–	√	√
Ear and labyrinth disorders	–	√	√
Endocrine disorders	–	–	–
Eye disorders	–	–	–
Gastrointestinal disorders	–	–	–
General disorders and administration site conditions	–	–	–
Hepatobiliary disorders	√	√	–
Immune system disorders	–	√	√
Infections and infestations	–	√	√
Injury, poisoning and procedural complications	–	√	√
Investigations	√	√	√
Metabolism and nutrition disorders	–	–	–
Musculoskeletal and connective tissue disorders	–	√	√
Neoplasms benign, malignant and unspecified (incl cysts and polyps)	–	–	–
Nervous system disorders	–	√	√
Pregnancy, puerperium and perinatal conditions	–	√	√
Product issues	–	√	√
Psychiatric disorders	–	√	√
Renal and urinary disorders	√	√	√
Reproductive system and breast disorders	–	√	√
Respiratory, thoracic and mediastinal disorders	–	–	–
Skin and subcutaneous tissue disorders	–	–	–
Social circumstances	–	√	√
Surgical and medical procedures	–	√	–
Vascular disorders	√	√	√

Subset data analyses

For the four positive SOCs, we conducted a subset analysis of AGIs combined with PD-1, PDL-1 and CTLA-4 (Tables 4 and 5). For hepatobiliary disorders, both PDL-1 and CTLA-4 combined with AGIs revealed positive DDI signals, which were verified using additive or multiplicative models. For investigations, PD-1 combined with AGIs revealed positive DDI signal, which were verified using additive or multiplicative models. For renal and urinary disorders, PD-1 and PDL-1 in combination with AGIs showed positive DDI signals, which were verified using additive or multiplicative models. In vascular disorders, the combination of PD-1 and PDL-1 with AGIs revealed positive DDI signals, which were verified using additive or multiplicative models.

Table 4.

Disproportionality analyses and drug interaction approaches for the various drug combinations.

SOC	Drug–drug interaction of interest	Exposure	Cases	Non-cases	RORcrude (95% CI)	RORadj (95% CI)	Additive model	Multiplicative model
Hepatobiliary disorders	PD-1+AGI	No PD-1, no AGI	2,93,838	380,38,194	Reference	Reference	−0.9663	0.4956
		PD-1, no AGI	11,039	3,08,056	4.27 (4.19–4.35)	4.06 (3.97–4.14)
		AGI, no PD-1	13,756	8,08,364	2.06 (2.03–2.10)	1.86 (1.82–1.90)
		PD-1, AGI	1783	48,564	4.36 (4.16–4.58)	4.23 (4.01–4.46)
	PDL-1+AGI	No PDL-1, no AGI	3,02,917	382,85,096	Reference	Reference	2.1489	0.8951
		PDL-1, no AGI	1960	61,154	3.83 (3.66–4.00)	3.16 (3.00–3.34)
		AGI, no PDL-1	14,085	8,32,915	2.05 (2.02–2.09)	1.77 (1.74–1.81)
		PDL-1, AGI	1454	24,013	7.03 (6.66–7.41)	5.95 (5.60–6.31)
	CTLA-4+AGI	No CTLA-4, no AGI	3,01,038	382,57,572	Reference	Reference	0.0963	0.5738
		CTLA-4, no AGI	3839	88,678	5.12 (4.95–5.28)	4.48 (4.32–4.65)
		AGI, no CTLA-4	15,319	8,52,952	2.18 (2.14–2.21)	1.91 (1.87–1.94)
		CTLA-4, AGI	220	3976	6.39 (5.58–7.32)	5.19 (4.46–6.03)
Investigations	PD-1+AGI	No PD-1, no AGI	21,88,395	361,43,637	Reference	Reference	0.3545	1.2368
		PD-1, no AGI	19,165	2,99,930	1.04 (1.03–1.06)	1.01 (0.99–1.03)
		AGI, no PD-1	65,033	7,57,087	1.37 (1.36–1.38)	1.37 (1.36–1.38)
		PD-1, AGI	5134	45,213	1.77 (1.72–1.82)	1.73 (1.68–1.79)
	PDL-1+AGI	No PDL-1, no AGI	22,02,955	363,85,058	Reference	Reference	−0.2172	0.8176
		PDL-1, no AGI	4605	58,509	1.27 (1.23–1.30)	1.25 (1.21–1.29)
		AGI, no PDL-1	68,049	7,78,951	1.39 (1.38–1.41)	1.38 (1.37–1.39)
		PDL-1, AGI	2118	23,349	1.44 (1.38–1.51)	1.38 (1.31–1.45)
	CTLA-4+AGI	No CTLA-4, no AGI	22,02,399	363,56,211	reference	reference	0.4440	1.3385
		CTLA-4, no AGI	5161	87,356	0.97 (0.94–1.00)	0.94 (0.91–0.97)
		AGI, no CTLA-4	69,730	7,98,541	1.39 (1.38–1.40)	1.38 (1.36–1.39)
		CTLA-4, AGI	437	3759	1.81 (1.64–1.99)	1.24 (1.19–1.30)
Renal and urinary disorders	PD-1+AGI	No PD-1, no AGI	7,24,928	376,07,104	Reference	Reference	0.0681	1.0083
		PD-1, no AGI	9291	3,09,804	1.53 (1.50–1.56)	1.39 (1.36–1.42)
		AGI, no PD-1	17,273	8,04,847	1.10 (1.09–1.12)	0.99 (0.97–1.01)
		PD-1, AGI	1630	48,717	1.70 (1.62–1.78)	1.61 (1.52–1.70)
	PDL-1+AGI	No PDL-1, no AGI	7,32,481	378,55,532	Reference	Reference	0.6623	1.3857
		PDL-1, no AGI	1738	61,376	1.44 (1.38–1.52)	1.31 (1.24–1.38)
		AGI, no PDL-1	17,829	8,29,171	1.10 (1.09–1.12)	0.98 (0.96–1.00)
		PDL-1, AGI	1074	24,393	2.21 (2.08–2.35)	1.82 (1.70–1.95)
	CTLA-4+AGI	No CTLA-4, no AGI	7,31,805	378,26,805	Reference	Reference	0.4179	1.2376
		CTLA-4, no AGI	2414	90,103	1.37 (1.31–1.43)	1.77 (1.59–1.97)
		AGI, no CTLA-4	18,749	8,49,522	1.13 (1.12–1.15)	1.01 (0.99–1.02)
		CTLA-4, AGI	154	4042	1.92 (1.63–2.26)	1.52 (1.27–1.82)
Vascular disorders	PD-1+AGI	No PD-1, no AGI	7,64,053	375,67,979	Reference	Reference	0.6534	1.6595
		PD-1, no AGI	4893	3,14,202	0.76 (0.74–0.78)	0.71 (0.71–0.73)
		AGI, no PD-1	26,215	7,95,905	1.58 (1.56–1.60)	1.37 (1.35–1.39)
		PD-1, AGI	2026	48,321	1.99 (1.91–2.09)	1.94 (1.84–2.04)
	PDL-1+AGI	No PDL-1, no AGI	7,67,733	378,20,280	Reference	Reference	0.2121	1.1577
		PDL-1, no AGI	1213	61,901	0.95 (0.90–1.01)	0.90 (0.84–0.96)
		AGI, no PDL-1	27,334	8,19,666	1.60 (1.58–1.62)	1.38 (1.36–1.40)
		PDL-1, AGI	907	24,560	1.76 (1.65–1.89)	1.64 (1.52–1.77)
	CTLA-4+AGI	No CTLA-4, no AGI	7,67,540	377,91,070	Reference	Reference	0.1506	1.2479
		CTLA-4, no AGI	1406	91,111	0.75 (0.71–0.79)	0.70 (0.66–0.74)
		AGI, no CTLA-4	28,113	8,40,158	1.61 (1.59–1.62)	1.39 (1.37–1.41)
		CTLA-4, AGI	128	4068	1.51 (1.27–1.80)	1.41 (1.16–1.70)

PD-1: programmed death-1 inhibitors; PDL-1: programmed death ligand-1 inhibitors; CTLA-4: cytotoxic T-lymphocyte antigen-4 inhibitors.

The boldfaced values represent meaningful data.

Table 5.

Summary of methods used to analyse drug-drug interactions in four SOCs.

SOC	Exposure	RORadj	Additive model	Multiplicative model
Hepatobiliary disorders	PD-1 + AGI	√	–	–
	PDL-1 + AGI	√	√	–
	CTLA-4 + AGI	√	√	–
Investigations	PD-1 + AGI	√	√	√
	PDL-1 + AGI	–	–	–
	CTLA-4 + AGI	–	√	√
Renal and urinary disorders	PD-1 + AGI	√	√	√
	PDL-1 + AGI	√	√	√
	CTLA-4 + AGI	–	√	√
Vascular disorders	PD-1 + AGI	√	√	√
	PDL-1 + AGI	√	√	√
	CTLA-4 + AGI	–	√	√

PD-1: programmed death-1 inhibitors; PDL-1: programmed death ligand-1 inhibitors; CTLA-4: cytotoxic T-lymphocyte antigen-4 inhibitors.

Discussion

To the best of our knowledge, this is the first pharmacovigilance study to assess DDIs between ICIs and AGIs. Our study provides the most comprehensive information on the clinical characteristics and DDIs of ICIs combined with AGIs in a real-world setting. Several main findings emerged: (1) DDIs of ICIs combined with AGIs occurred in four systems: hepatobiliary disorders, investigations, renal and urinary disorders and vascular disorders. (2) PDL-1/CTLA-4 combined with AGIs is associated with hepatobiliary disorders, whereas PD-1 combined with AGIs is not. (3) PD-1/CTLA-4 combined with AGIs was associated with AEs, whereas PDL-1 combined with AGIs was not. (4) PD-1, PDL-1, and CTLA-4 combined with AGIs are associated with renal and urinary disorders. (5) PD-1/PDL-1 combined with AGIs is associated with vascular disorders, whereas CTLA-4 combined with AGIs is not.

The host immune response against tumours is activated by ICIs, which block negative regulatory immune signals and trigger a T-cell-mediated response.^10,25 The formation of new blood vessels from the pre-existing vasculature, known as angiogenesis, is crucial for tumour growth. Angiogenesis is promoted by tumours secreting pro-angiogenic factors that aid in the provision of nutrients for their expansion and metastatic spread.²⁶ Vascular endothelial growth factor (VEGF) is crucial for angiogenesis.²⁷ Tumour cells hinder vessel maturation, leading to suboptimal blood flow, tumour hypoxia and an immunosuppressive tumour microenvironment.^28,29 The inhibition of VEGF signalling results in the normalisation of tumour vasculature, which has significant positive effects on the infiltration of immune cells into tumours.^30,31 The use of anti-angiogenic drugs, such as sorafenib, sunitinib, or bevacizumab, has been shown to increase PDL-1 expression in models of RCC and hepatocellular carcinoma.^32,33 Tumour immune escape and treatment resistance can be attributed to the interactions between immunity and angiogenesis.¹⁰ The synergistic antitumor effects of a combinatory VEGFI/ICI treatment approach have been verified in the clinical setting, displayed by promising results in various tumor types, including RCC and non-small cell lung cancer, and will become an important treatment strategy for various malignancies.^34–36

Drug interactions can be classified into pharmacodynamic and pharmacokinetic interactions. Pharmacodynamic interactions can occur when different drugs have mechanisms of action that affect the same physiological processes, whereas pharmacokinetic interactions occur when a drug affects its absorption, distribution, metabolism and excretion.³⁷ DDIs can result in either increased or decreased therapeutic or AEs, or a unique response that is not caused by either agent alone.¹⁵ This study examined drug interactions that lead to increased toxicity from the combination of ICIs and AGIs.

There are several methods for detecting interaction signals. The logistic regression model is a statistical method used to detect DDI signals from a spontaneous reporting system. The ROR, like the odds ratio, is a statistical model that can be adjusted for age and gender using the logistic regression model. The additive/multiplicative model is one of the methods to detect the signals of potential DDIs. According to the additive model, drug-related risks increase additively, while the multiplicative model assumes they increase synergistically.²² Because there is no gold standard statistical method established for capturing DDI signals from spontaneous reporting data, we use adjusted ROR and additive/multiplicative model to detect the signals.

Abnormal liver function is one of the most common AEs associated with ICIs combined with bevacizumab in the FAERS database.¹³ Liver toxicity is prevalent in ICI-treated patients and typically manifests as an asymptomatic increase in hepatic enzymes with or without hyperbilirubinemia, which can occur in 2–25% of patients.^38,39 The proposed explanation involves increased autoimmunity of hepatocytes caused by T-cell activation from ICIs. The liver is among the most commonly involved.⁴⁰ The immune response is the cause of immunomediated hepatotoxicity caused by ICIs, whereas conventional drug-induced liver injury occurs from either direct or idiosyncratic effects.⁴¹ The onset of immuno-related hepatitis typically occurs 8–12 weeks after the initiation of ICI therapy.⁴² The role of VEGF in maintaining the structural and functional integrity is well-documented.⁴³ VEGF and VEGF receptor agonists exhibit more consistent growth, regenerative and cytoprotective effects than other agents used in the treatment of chemically induced hepatitis or partial hepatectomy.^44,45 Under these conditions, transaminase elevations are due to a direct effect of the agent on hepatocytes, possibly partly due to the inhibition of the vascular endothelial growth factor receptor, or due to an effect on endothelial cells of small hepatic blood vessels, which is not fully clear.⁴⁶

Hypertension was the most frequent side effect experienced by patients with metastatic RCC who were treated with avelumab or pembrolizumab with axitinib as a first-line treatment.^35,47 The risk of developing all-grade hypertension in RCC is higher when ICIs are used in combination than when sunitinib monotherapy is used.⁴⁸ Patients with advanced solid tumours were found to have a 67% incidence of hypertension when they received a combination of bevacizumab and sorafenib.⁴⁹ In patients with advanced lung cancer, there was an 8.41% rate of vascular disorders in those with combined ICIs and AGIs and an 18.37% rate in those with ICIs combined with AGI grade 3 or higher.⁵⁰ VEGF plays a crucial role in regulating vascular homeostasis and cardiac development and function.⁵¹ Up to 36% of the patients reported hypertension while taking the humanised VEGF antibody bevacizumab; however, their blood pressure returned to normal after treatment discontinuation.^52,53 Sunitinib-induced hypertension rates ranged from 16% to 47%.^54–56 Both chemotherapy and angiogenesis inhibition increases the risk of venous and arterial thrombosis,^57,58 and sporadic cases of arterial thromboembolism, particularly myocardial infarction, have been documented.⁵⁷ Thromboembolic events were more common among patients treated with ICIs, with an incidence rate of 2.6–18%.^59,60 According to a pharmacovigilance study, ICIs are associated with an increased risk of venous and arterial thromboembolism.⁶¹

ICIs plus anti-VEGF therapy was associated with a significant increase in the risk of all-grade proteinuria in RCC compared to sunitinib monotherapy.⁴⁸ The rate of renal and urinary disorders in patients with advanced lung cancer is 4.74% for all grades of ICIs combined with AGIs, and 4.08% for grades ⩾3.⁵⁰ ICI therapy leads to adverse renal events in 2.2% of patients.⁶² Acute interstitial nephritis is the most frequently reported pathology.⁶³ The symptoms included oliguria, haematuria, peripheral oedema, worsening hypertension, electrolyte imbalance, altered urinary output and increased creatinine.^63,64 Renal toxicity is a common side effect of angiogenesis inhibition and proteinuria is the most prominent manifestation.⁶⁵ VEGF plays a significant role in the normal functioning of the glomerulus.⁶⁶ The reason for VEGF inhibitor-induced proteinuria may be acute hypertension and the direct effects of VEGF antagonism on the glomeruli.⁶⁷ Proteinuria related to treatment with bevacizumab is a dose-dependent side effect and has been reported in up to 41–63% of patients, and the risk of proteinuria and hypertension related to bevacizumab treatment was not altered after the exclusion of patients with RCC.⁶⁸ Patients treated with sunitinib or sorafenib experienced significant proteinuria, which decreased or disappeared after treatment was reduced or stopped.⁶⁹

Our study has some limitations. First, FAERS is a spontaneous reporting system (SRS). The quantification of adverse reaction signals based on the total number of AEs was not possible using the collected data. The purpose was to provide a qualitative indicator using the signal intensity between the drugs and the reactions alone. Second, compared with clinical trials and cohort studies, SRS data are generally less reliable. The identification and reporting of AEs within a SRS are subject to less stringent controls. Third, it is difficult to identify significant risk factors between disorders and drugs because of deficiencies in preexisting disorders and comorbidities that may affect the disease. Furthermore, this study was not restricted to a particular disease, which is significantly different from clinical trials. Fourth, this study used SOC as the target reaction to encompass all AEs; however, its scope was too broad, and subgroup analyses were missing. Fifth, calculation, justification and power analyses for the selected sample size in this study were not conducted because the intention was to include all eligible ADRs. Sixth, FAERS database has reporting bias, including weber effect (reports increase in the first 2 years after launching, but then decline), ripple effect (the reporting of other drugs in the same class can be expedited due to the notoriety of a drug) and masking or cloaking effect (reports with the same adverse event connected to other drugs can suppress signal scores). For DDIs, the relative reporting rate of drug-induced AEs when either drug is used alone will be overestimated if there is a lack of information on one of the two drugs.^70,71 Finally, data mining revealed imperfect reporting, with inaccuracies and incomplete entries, potentially causing analytical bias. Our findings, although limited by FAERS, shed light on DDIs related to ICIs combined with AGIs and provide a framework for rigorous research to validate the findings.

Conclusion

Our study identified four types of DDIs associated with the combined use of ICIs and AGIs using real-world FAERS data. Therefore, close attention should be paid when these two drugs are administered together. Preclinical trials are required to explore the mechanisms underlying these interactions, and further robust clinical studies are necessary to elucidate these relationships, understand the risk of DDIs associated with this combination therapy, and provide more granular details.

Supplemental Material

sj-docx-1-iji-10.1177_03946320241305390 – Supplemental material for Adverse reactions of immune checkpoint inhibitors combined with angiogenesis inhibitors: A pharmacovigilance analysis of drug–drug interactions

Supplemental material, sj-docx-1-iji-10.1177_03946320241305390 for Adverse reactions of immune checkpoint inhibitors combined with angiogenesis inhibitors: A pharmacovigilance analysis of drug–drug interactions by Xiayang Ren, Lei Deng, Xin Dong, Ying Bai, Guohui Li and Yanfeng Wang in International Journal of Immunopathology and Pharmacology

Footnotes

Acknowledgements

We thank Xin Zhou for his contributions regarding the data processing and image editing.

List of abbreviations

AE, adverse event; AGI, angiogenesis inhibitor; CI, confidence interval; CTLA-4, cytotoxic T-lymphocyte antigen-4; DDI, drug-drug reaction; FAERS, FDA Adverse Event Reporting System; FDA, US Food and Drug Administration; IC, information component; ICI, immune checkpoint inhibitor; IQR, interquartile range; MedDRA, Medical Dictionary for Regulatory Activities; PD-1, programmed death-1; PD-L1, programmed death ligand-1; PT, preferred term; RCC, renal cell carcinoma; ROR, reporting odds ratio; ROR025, lower 95% confidence limit of the ROR; SOC, System Organ Class; VEGF, vascular endothelial growth factor

Author contributions

XR drafted the manuscript. All authors participated in data analysis and interpretation, and manuscript revision. All authors have read and approved the manuscript.

Data availability statement

The data that support the findings of this study are available upon from the corresponding author, Yanfeng Wang, on reasonable request. The raw data can be obtained from the FAERS database at the following link: FAERS Quarterly Data Extract Files (fda.gov).

Declaration of conflicting interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This study was funded by Beijing Xisike Clinical Oncology Research FoundationY-HR2020MS-0671.

Ethical considerations

Not applicable. Ethical approval was not required for this study because we used the FAERS database, which is a free open-access database.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

ORCID iD

Xiayang Ren

Supplemental material

Supplemental material for this article is available online.

References

Michielin

Lalani

Robert

, et al (2022) Defining unique clinical hallmarks for immune checkpoint inhibitor-based therapies. Journal for ImmunoTherapy of Cancer 10(1): e003024.

Borghaei

Paz-Ares

Horn

, et al (2015) Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. New England Journal of Medicine 373(17): 1627–1639.

Hellmann

Paz-Ares

Bernabe Caro

, et al (2019) Nivolumab plus ipilimumab in advanced non-small-cell lung cancer. New England Journal of Medicine 381(21): 2020–2031.

Santoni

Rizzo

Kucharz

, et al (2023) Complete remissions following immunotherapy or immuno-oncology combinations in cancer patients: The MOUSEION-03 meta-analysis. Cancer Immunology, Immunotherapy 72(6): 1365–1379.

Bagchi

Yuan

Engleman

(2021) Immune checkpoint inhibitors for the treatment of cancer: Clinical impact and mechanisms of response and resistance. Annual Review of Pathology: Mechanisms of Disease 16: 223–249.

Zhou

Qiao

Zhou

(2021) The cutting-edge progress of immune-checkpoint blockade in lung cancer. Cellular & Molecular Immunology 18(2): 279–293.

Meng

, et al (2021) The combination of radiotherapy with immunotherapy and potential predictive biomarkers for treatment of non-small cell lung cancer patients. Frontiers in Immunology 12: 723609.

Padda

Reckamp

(2021) Combination of immunotherapy and antiangiogenic therapy in cancer-a rational approach. Journal of Thoracic Oncology 16(2): 178–182.

Garber

(2014) Promising early results for immunotherapy-antiangiogenesis combination. Journal of the National Cancer Institute 106(11): dju392.

10.

Jiao

Qin

, et al (2019) Synergistic effect of immune checkpoint blockade and anti-angiogenesis in cancer treatment. Molecular Cancer 18(1): 60.

11.

Chambers

Kundranda

Rao

, et al (2021) Anti-angiogenesis revisited: Combination with immunotherapy in solid tumors. Current Oncology Reports 23(9): 100.

12.

Wang

Cui

Deng

, et al (2023) Cardiovascular toxicity profiles of immune checkpoint inhibitors with or without angiogenesis inhibitors: A real-world pharmacovigilance analysis based on the FAERS database from 2014 to 2022. Frontiers in Immunology 14: 1127128.

13.

Jiang

Zhou

, et al (2023) Adverse reactions associated with immune checkpoint inhibitors and bevacizumab: A pharmacovigilance analysis. International Journal of Cancer 152(3): 480–495.

14.

Choueiri

Powles

Albiges

, et al (2023) Cabozantinib plus nivolumab and ipilimumab in renal-cell carcinoma. New England Journal of Medicine 388(19): 1767–1778.

15.

Blower

de Wit

Goodin

, et al (2005) Drug-drug interactions in oncology: Why are they important and can they be minimized? Critical Reviews in Oncology/Hematology 55(2): 117–142.

16.

U.S. Food and Drug Administration (2021) FDA adverse event reporting system (FAERS) quarterly data extract files. Available at: https://fis.fda.gov/extensions/fpd-qde-faers/fpd-qde-faers.html (accessed 20 October 2021).

17.

Maintenance and Support Service Organization (2021) Support documentation: MedDRA version 23.0 English March/April 2020. Available at: https://www.meddra.Org/how-to-use/support-documentation/English (accessed 6 October 2021).

18.

van Puijenbroek

Egberts

Heerdink

, et al (2000) Detecting drug-drug interactions using a database for spontaneous adverse drug reactions: An example with diuretics and non-steroidal anti-inflammatory drugs. European Journal of Clinical Pharmacology 56(9–10): 733–738.

19.

Yue

Shi

Jiang

, et al (2014) Acute kidney injury during concomitant use of valacyclovir and loxoprofen: Detecting drug-drug interactions in a spontaneous reporting system. Pharmacoepidemiology and Drug Safety 23(11): 1154–1159.

20.

Capogrosso Sansone

Convertino

Galiulo

, et al (2017) Muscular adverse drug reactions associated with proton pump inhibitors: A disproportionality analysis using the Italian national network of pharmacovigilance database. Drug Safety 40(10): 895–909.

21.

van Puijenbroek

Bate

Leufkens

, et al (2002) A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiology and Drug Safety 11(1): 3–10.

22.

Noguchi

Tachi

Teramachi

(2019) Review of statistical methodologies for detecting drug-drug interactions using spontaneous reporting systems. Frontiers in Pharmacology 10: 1319.

23.

Thakrar

Grundschober

Doessegger

(2007) Detecting signals of drug-drug interactions in a spontaneous reports database. British Journal of Clinical Pharmacology 64(4): 489–495.

24.

Noguchi

Tachi

Teramachi

(2020) Comparison of signal detection algorithms based on frequency statistical model for drug-drug interaction using spontaneous reporting systems. Pharmaceutical Research 37(5): 86.

25.

Lee

Gupta

Sahasranaman

(2016) Immune checkpoint inhibitors: An introduction to the next-generation cancer immunotherapy. The Journal of Clinical Pharmacology 56(2): 157–169.

26.

van Dorst

DCH

van Doorn

Mirabito Colafella

, et al (2021) Cardiovascular toxicity of angiogenesis inhibitors and immune checkpoint inhibitors: Synergistic anti-tumour effects at the cost of increased cardiovascular risk? Clinical Science 135(14): 1649–1668.

27.

Falcon

O’Clair

McClure

, et al (2013) Development and characterization of a high-throughput in vitro cord formation model insensitive to VEGF inhibition. Journal of Hematology & Oncology 6: 31.

28.

Jain

(2003) Molecular regulation of vessel maturation. Nature Medicine 9(6): 685–693.

29.

Jain

(2014) Antiangiogenesis strategies revisited: From starving tumors to alleviating hypoxia. Cancer Cell 26(5): 605–622.

30.

Huinen

Huijbers

EJM

van Beijnum

, et al (2021) Anti-angiogenic agents - overcoming tumour endothelial cell anergy and improving immunotherapy outcomes. Nature Reviews Clinical Oncology 18(8): 527–540.

31.

Fukumura

Kloepper

Amoozgar

, et al (2018) Enhancing cancer immunotherapy using antiangiogenics: Opportunities and challenges. Nature Reviews Clinical Oncology 15(5): 325–340.

32.

Chen

Ramjiawan

Reiberger

, et al (2015) CXCR4 inhibition in tumor microenvironment facilitates anti-programmed death receptor-1 immunotherapy in sorafenib-treated hepatocellular carcinoma in mice. Hepatology 61(5): 1591–602.

33.

Liu

Hoang

Zhou

, et al (2015) Resistance to antiangiogenic therapy is associated with an immunosuppressive tumor microenvironment in metastatic renal cell carcinoma. Cancer Immunology Research 3(9): 1017–1029.

34.

Reck

Mok

TSK

Nishio

, et al (2019) Atezolizumab plus bevacizumab and chemotherapy in non-small-cell lung cancer (IMpower150): Key subgroup analyses of patients with EGFR mutations or baseline liver metastases in a randomised, open-label phase 3 trial. The Lancet Respiratory Medicine 7(5): 387–401.

35.

Motzer

Penkov

Haanen

, et al (2019) Avelumab plus axitinib versus sunitinib for advanced renal-cell carcinoma. New England Journal of Medicine 380(12): 1103–1115.

36.

Meric-Bernstam

Larkin

Tabernero

, et al (2021) Enhancing anti-tumour efficacy with immunotherapy combinations. Lancet 397(10278): 1010–1022.

37.

Pajiep

Lapeyre-Mestre

Despas

(2023) Drug-drug interactions of protein kinase inhibitors in chronic myeloid leukaemia patients: A study using the French health insurance database. Fundamental & Clinical Pharmacology 37(5): 994–1005.

38.

Haanen

JBAG

Carbonnel

Robert

, et al (2017) Management of toxicities from immunotherapy: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Annals of Oncology 28(suppl_4): iv119–iv142.

39.

Puzanov

Diab

Abdallah

, et al (2017) Managing toxicities associated with immune checkpoint inhibitors: Consensus recommendations from the society for immunotherapy of cancer (SITC) toxicity management working group. Journal for ImmunoTherapy of Cancer 5(1): 95.

40.

Peeraphatdit

Wang

Odenwald

, et al (2020) Hepatotoxicity from immune checkpoint inhibitors: A systematic review and management recommendation. Hepatology 72(1): 315–329.

41.

Hoofnagle

Björnsson

(2019) Drug-induced liver injury - types and phenotypes. New England Journal of Medicine 381(3): 264–273.

42.

Cramer

Bresalier

(2017) Gastrointestinal and hepatic complications of immune checkpoint inhibitors. Current Gastroenterology Reports 19(1): 3.

43.

LeCouter

Moritz

, et al (2003) Angiogenesis-independent endothelial protection of liver: Role of VEGFR-1. Science 299(5608): 890–893.

44.

Tsuchihashi

Kaldas

, et al (2006) Vascular endothelial growth factor antagonist modulates leukocyte trafficking and protects mouse livers against ischemia/reperfusion injury. American Journal of Pathology 168(2): 695–705.

45.

Donahower

McCullough

Kurten

, et al (2006) Vascular endothelial growth factor and hepatocyte regeneration in acetaminophen toxicity. American Journal of Physiology-Gastrointestinal and Liver Physiology 291(1): G102–G109.

46.

Eskens

Verweij

(2006) The clinical toxicity profile of vascular endothelial growth factor (VEGF) and vascular endothelial growth factor receptor (VEGFR) targeting angiogenesis inhibitors; a review. European Journal of Cancer 42(18): 3127–139.

47.

Rini

Plimack

Stus

, et al (2019) Pembrolizumab plus axitinib versus sunitinib for advanced renal-cell carcinoma. New England Journal of Medicine 380(12): 1116–1127.

48.

Tao

Zhang

, et al (2021) Balancing the risk-benefit ratio of immune checkpoint inhibitor and anti-VEGF combination therapy in renal cell carcinoma: A systematic review and meta-analysis. Frontiers in Oncology 11: 739263.

49.

Azad

Posadas

Kwitkowski

, et al (2008) Combination targeted therapy with sorafenib and bevacizumab results in enhanced toxicity and antitumor activity. Journal of Clinical Oncology 26(22): 3709–3714.

50.

Zheng

Dong

, et al (2023) Treatment-related adverse events of immune checkpoint inhibitors combined with angiogenesis inhibitors in advanced lung cancer: A systematic review and meta-analysis. International Immunopharmacology 123: 110785.

51.

Touyz

Herrmann

(2018) Cardiotoxicity with vascular endothelial growth factor inhibitor therapy. NPJ Precision Oncology 2: 13.

52.

Yang

Haworth

Sherry

, et al (2003) A randomized trial of bevacizumab, an anti-vascular endothelial growth factor antibody, for metastatic renal cancer. New England Journal of Medicine 349(5): 427–434.

53.

Miller

Chap

Holmes

, et al (2005) Randomized phase III trial of capecitabine compared with bevacizumab plus capecitabine in patients with previously treated metastatic breast cancer. Journal of Clinical Oncology 23(4): 792–799.

54.

Chu

Rupnick

Kerkela

, et al (2007) Cardiotoxicity associated with tyrosine kinase inhibitor sunitinib. Lancet 370(9604): 2011–2019.

55.

Demetri

van Oosterom

Garrett

, et al (2006) Efficacy and safety of sunitinib in patients with advanced gastrointestinal stromal tumour after failure of imatinib: A randomised controlled trial. Lancet 368(9544): 1329–1338.

56.

Rixe

Billemont

Izzedine

(2007) Hypertension as a predictive factor of sunitinib activity. Annals of Oncology 18(6): 1117.

57.

Elice

Rodeghiero

Falanga

, et al (2009) Thrombosis associated with angiogenesis inhibitors. Best Practice & Research Clinical Haematology 22(1): 115–128.

58.

Nalluri

Chu

Keresztes

, et al (2008) Risk of venous thromboembolism with the angiogenesis inhibitor bevacizumab in cancer patients: A meta-analysis. JAMA 300(19): 2277–2285.

59.

Sussman

Hobbs

, et al (2021) Incidence of thromboembolism in patients with melanoma on immune checkpoint inhibitor therapy and its adverse association with survival. Journal for ImmunoTherapy of Cancer 9(1): e001719.

60.

Solinas

Saba

Sganzerla

, et al (2020) Venous and arterial thromboembolic events with immune checkpoint inhibitors: A systematic review. Thrombosis Research 196: 444–453.

61.

Sun

, et al (2021) Thromboembolic events associated with immune checkpoint inhibitors: A real-world study of data from the food and drug administration adverse event reporting system (FAERS) database. International Immunopharmacology 98: 107818.

62.

Rassy

Kourie

Rizkallah

, et al (2016) Immune checkpoint inhibitors renal side effects and management. Immunotherapy 8(12): 1417–1425.

63.

Wanchoo

Karam

Uppal

, et al (2017) Adverse renal effects of immune checkpoint inhibitors: A narrative review. American Journal of Nephrology 45(2): 160–169.

64.

Belliere

Meyer

Mazieres

, et al (2016) Acute interstitial nephritis related to immune checkpoint inhibitors. British Journal of Cancer 115(12): 1457–1461.

65.

Kappers

van Esch

Sleijfer

, et al (2009) Cardiovascular and renal toxicity during angiogenesis inhibition: Clinical and mechanistic aspects. Journal of Hypertension 27(12): 2297–2309.

66.

Stylianou

Lioudaki

Papadimitraki

, et al (2011) Crescentic glomerulonephritis associated with vascular endothelial growth factor (VEGF) inhibitor and bisphosphonate administration. Nephrology Dialysis Transplantation 26(5): 1742–1745.

67.

Gurevich

Perazella

(2009) Renal effects of anti-angiogenesis therapy: Update for the internist. American Journal of Medicine 122(4): 322–328.

68.

Zhu

Dahut

, et al (2007) Risks of proteinuria and hypertension with bevacizumab, an antibody against vascular endothelial growth factor: Systematic review and meta-analysis. American Journal of Kidney Diseases 49(2): 186–193.

69.

Patel

Morgan

Demetri

, et al (2008) A preeclampsia-like syndrome characterized by reversible hypertension and proteinuria induced by the multitargeted kinase inhibitors sunitinib and sorafenib. Journal of the National Cancer Institute 100(4): 282–284.

70.

Noguchi

Tachi

Teramachi

(2021) Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source. Briefings in Bioinformatics 22(6): bbab347.

71.

Sakaeda

Tamon

Kadoyama

, et al (2013) Data mining of the public version of the FDA adverse event reporting system. International Journal of Medical Sciences 10(7): 796–803.

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