Electrolyte Disorders Induced by Multikinase Inhibitors Therapy for Renal Cell Carcinoma：A Large-Scale Pharmacovigilance Analysis

doi:10.21203/rs.3.rs-3402473/v1

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Electrolyte Disorders Induced by Multikinase Inhibitors Therapy for Renal Cell Carcinoma：A Large-Scale Pharmacovigilance Analysis

https://doi.org/10.21203/rs.3.rs-3402473/v1

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Journal Publication

published 07 Mar, 2024

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Objective

To provide evidence for optimization of multi-kinase inhibitors (MKIs) use in the clinic, we use the public database to describe and evaluate electrolyte disorders related to various MKIs treated for renal cell carcinoma.

Methods

We analyzed spontaneous reports submitted to the Food and Drug Administration Adverse Events Reporting System (FAERS) in an observational and retrospective manner. Selecting electrolyte disorders' adverse events to multikinase inhibitors (axitinib, cabozantinib, lenvatinib, pazopanib, sunitinib, and sorafenib). We used Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and multi-item gamma Poisson shrinker (MGPS) algorithms to analyze suspected adverse reactions of electrolyte disorders induced by MKIs (which were treated for renal cell carcinoma) between January 2004 and December 2022.

Results

As of December 2022, 2772 MKIs (which were treated for renal cell carcinoma) ICSRs were related to electrolyte disorders AEs. In general, there were more ADR cases in males, except lenvatinib and 71.8% of the cases were submitted from North America. ICSRs in this study, the age group most frequently affected by electrolyte disorders AEs was individuals aged 45–64 years for axitinib, cabozantinib, pazopanib, and sunitinib, whereas electrolyte disorders AEs were more common in older patients (65–74 years) for sorafenib and lenvatinib. For all EDs documented in ICSRs (excluding missing data), the most common adverse outcome was hospitalization(1429/2674, 53.4%), and the most serious outcome was death/life-threat(281/2674, 10.5%). The prevalence of mortality was highest for sunitinib-related EDs (145/616, 23.5%), excluding missing data (n = 68), followed by cabozantinib-related EDs (20/237, 8.4%), excluding missing data (n = 1). The distribution of time-to-onset of Each drug-related ICSRs was not all the same, and the difference was statistically significant (P = 0.001). With the criteria of ROR, the six MKIs were all significantly associated with electrolyte disorders AEs, the strongest association was the association between cabozantinib and hypermagnesaemia.

Conclusions

MKIs have been reported to have significant electrolyte disorders side effects. Patients and physicians need to recognize and monitor these potentially fatal adverse events.

Biological sciences/Cancer

Health sciences/Health care

Health sciences/Oncology

multi-kinase inhibitors

electrolyte disorders

FAERS

real-world data

pharmacovigilance

Renal cell carcinoma (RCC) accounts for about 3% of all cancers, with the highest incidence occurring in Western countries and an annual increase of about 2% over the past 20 years[1]. In 2023, in the United States, RCC is expected to be the sixth and ninth most frequently diagnosed malignancy in men and women, respectively[1]. In recent years, several tyrosine kinase inhibitors (TKIs) have been approved for use in cancer therapy[2], especially multi-kinase inhibitors (MKIs). According to National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology for Kidney Cance (Version 4.2023 - January 18, 2023), axitinib, cabozantinib, sunitinib, lenvatinib, pazopanib, and sorafenib can be used to treat for RCC. Their efficacy, however, varies. One of their major problems is their lack of specificity, resulting in a high rate of drug-related toxicity and therefore a reduction, withdrawal, or discontinuation of therapy.

Apart from efficacy issues, the main concern of MKIs is adverse events (AEs). In fact, previous studies indicate that the adverse events caused by MKIs need to be addressed since the incidence of adverse events ranges from 87.2–100%. In particular, MKIs are known to cause AEs related to proteinuria, diarrhea, hypertension, and palmar-plantar erythrodysesthesia[3], other significant side effects, including hyponatremia and other electrolyte abnormalities, have not been well characterized in patients treated with MKIs. Adverse events are essential for the use and success of MKI treatment. Stopping or reducing the MKI dose is detrimental to the therapy's long-term efficacy[4]. Furthermore, several studies[5–8] stress that the timely management of adverse events is key to the treatment prognosis[9].

The FDA Adverse Event Reporting System (FAERS) is an important tool for post-marketing safety surveillance in the United States, as it gathers information regarding all AEs for the FDA. The FAERS data shall be publicly accessible and, if a potential safety concern is identified, further assessment shall be carried out, this assessment may lead to changes in the label information of the product, implementation of restrictions on the duration of use of the medicine, or withdrawal of the medication from the market[10].

Based on the high incidence of AEs from MKIs, as previously mentioned, this study aimed to describe better AEs and to evaluate the reporting frequency of some toxicities through the analysis of electrolyte disorders related individual case safety reports (ICSRs) among multikinase inhibitors therapy in RCC (Table 1) collected into the publicly available FAERS database. The results of this study will be useful for the clinical optimization of tumor treatment drugs to provide a basis.

Table 1

Summary of multikinase inhibitors (treated for renal cell carcinoma)
	First time of approval	Targeted tyrosine kinases	FDA or EMA-approved indications
Sorafenib	12/01/2005	VEGFR-2 and − 3, FLT-3, PDGFR β, c-KIT, RET, and RAF	HCC, RCC, DTC
Sunitinib	01/26/2006	VEGFR- 1, -2, -3, PDGFR, c-KIT, FLT-3	GIST, MRCC, pNET
Pazopanib	10/19/2009	FGFR, ITK, c-KIT, PDGFR, VEGFR- 1, -2, -3, LCK, and FLT-3	RCC, STS
Axitinib	01/27/2012	VEGFR- 1, -2, -3	RCC
Cabozantinib	11/29/2012	VEGFR-2, RET, MET	RCC, HCC, MTC
Lenvatinib	02/13/2015	VEGFR-1, -2, -3, FGFR-1, -2, -3, -4, PDGFR α, RET, and c-KIT	HCC, DTC, RCC, EC
FDA: the US Food and Drug Administration; EMA: the European Union European Medical Agency; GIST: gastrointestinal stromal tumour; RCC: renal cell carcinoma; DTC: differentiated thyroid carcinoma; HCC: hepatocellular carcinoma; MRCC: metastatic renal cell carcinoma ; pNET: pancreatic neuroendocrine tumours; MTC: medullary thyroid cancer; EC: endometrial carcinoma; VEGFR: vascular endothelial growth factor receptor; PDGFR: platelet-derived growth factor receptor; c-KIT: the stem cell factor receptor; MET: the hepatocyte growth factor receptor; FLT-3: FMS-like tyrosine kinase 3; EGFR: the epidermal growth factor receptor; RET: rearranged during transfection; FGFR: fibroblast growth factor receptor1; RAF: rapidly accelerated fibrosarcoma; STS: advanced soft tissue sarcoma

Study design and data sources

This observational and retrospective pharmacovigilance study is a disproportionality analysis according to the individual case safety reports (ICSRs) taken from FAERS (range from 2004 quarter 1 to 2022 quarter 4). This database includes anonymous reports from healthcare professionals, consumers, producers, and others (FDA Adverse Event Reporting System (FAERS)-FDA Adverse Event Reporting System (FAERS): Latest Quarterly Data Files). The FAERS dataset is made up of seven data tables, which contain patient demographic information (e.g., sex, age, weight), source and type of the report, country of the reports, drugs with dates of start and end (when available), doses and routes of administration, AEs and their outcomes, and indications of use were contained to investigate the potential reporting association of electrolyte disorders with MKIs for RCC. Ethical approval was not required because this study was conducted using de-identified data. Since the target drug data used comes from private sources and there is no public reference to access it, the detailed data information is considered to be intellectual property. Readers who wish to have access to the data or consult the relevant information should contact the relevant author via e-mail.

Definition of Cases and Drugs of Interest

In the database, AEs were coded using the preferred terms (PTs) and System Organ Classes (SOCs) in the Medical Dictionary for Regulatory Activities (MedDRA). Drugs of interest included axitinib, cabozantinib, sunitinib, lenvatinib, pazopanib, and sorafenib, which were recommended by National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology for Kidney Cance (Version 4.2023 - January 18, 2023).

Descriptive Analysis

For this study, the following data were retrieved from FAERS—the patient’s sex and age at the time of adverse event, the type of reporter (health professional or not), reporting year and country, the event, as well as its occurrence date, seriousness criteria and the outcome of the event. The following variables concerning MKIs were also extracted-drug name, drug role in event occurrence (suspect drug, concomitant or interacting), and prescription dates when available. A major problem in spontaneous reporting data is the presence of duplicates (i.e., the same report submitted by different sources) and multiple reports (i.e., a follow-up of the same case with additional and updated information). In the present study, a two-step procedure of deduplication was applied. Firstly, only the last version of cases for which a follow-up was available was used. Secondly, cases with the same event, event date, age, gender, and country of origin were considered duplicated[11]. The reports in which the reported date of the start of a drug was after the date of the AEs were considered aberrant and excluded from the analysis.

Statistical Analyses and Signal Detection

A descriptive analysis of all the ICSRs was conducted to assess the demographic characteristics and variables related to the drugs under study. The analyses were performed considering the demographic data of ICSRs (gender and age), reporter country, indication, time to report, and outcome of AEs. The outcome of the ADRs was classified as” Death”, “Death/ Disability/ Other “, “Disability”, “Disability/ Congenital Anomaly/ Other”, et, al. In the case of two or more ADRs with different results reported in a single ICSR, the result with the lowest resolution level was chosen for classification. The calculation of the time-to-onset of EAD events was carried out according to the following formula—(Time-to-onset = Event onset date–Therapy start date). The Kruskal-Wallis H test was used to compare the distribution of time-to-onset of ICSRs.

Using a reporting odds ratio (ROR) method[12, 13] and the proportional reporting ratio (PRR), bayesian confidence propagation neural network (BCPNN) method[14], and the multi-item gamma Poisson shrinker (MGPS) method to evaluate the potential association between the target drug and AEs. ROR and its 95% confidence interval, PRR, BCPNN, and MGPS are calculated based on the four-grid table of proportional imbalance measurement (Table 2). The equations and criteria[15] for the above four algorithms are shown in Table 3. At least one of the four algorithms meets the standard and should be regarded as a risk signal. All the calculations were performed with Microsoft Excel 2021(Microsoft Corporation, Redmond, WA, USA).

The datasets generated and/or analysed during the current study are not publicly available due [REASON WHY DATA ARE NOT PUBLIC] but are available from the corresponding author on reasonable request.

Table 2

Two-by-two contingency table for analysis.
Drugs	Electrolyte disorders adverse reactions cases	All other adverse event cases	Total
Multikinase inhibitors (treated for renal cell carcinoma)	a	b	a + b
All other drugs	c	d	c + d
Total	a + c	b + d	a + b + c + d

Table 3

Summary of major algorithms used for signal detection.
Algorithms	Indicator	Equation	Criteria
ROR	ROR	ROR = ad/c/b	ROR05 > 1, N ≥ 2
ROR	ROR	95CI = e^{ln(ROR)±1.96(1/a+1/b+1/c+1/d)^0.5}	ROR05 > 1, N ≥ 2
PRR	PRR	PRR = a/(a + b)/c/(c + d)	PRR ≥ 2
PRR	χ²	χ²=[(ad-bc)²(a + b + c + d)]/[(a + b)(c + d)(a + c)(b + d)]	χ² ≥ 4, N ≥ 3
BCPNN	IC	IC = log₂[a(a + b + c + d)]/[(a + c)(a + b)]	IC025 > 0
BCPNN	IC	95CI = e^{ln(IC)±1.96(1/a+1/b+1/c+1/d)^0.5}	IC025 > 0
MGPS	EBGM	EBGM = a(a + b + c + d)/(a + c)/(a + b)	EBGM05 > 2, N ≥ 0
MGPS	EBGM	95CI = e^{ln(EBGM)±1.96(1/a+1/b+1/c+1/d)^0.5}	EBGM05 > 2, N ≥ 0

N, number of adverse event reports; CI, confidence interval; ROR, reporting odds ratio; ROR05, the lower limit of the 95 two-sided CI of the ROR; N, the number of co-occurrences; PRR, proportional reporting ratio; χ², chi-squared; BCPNN, Bayesian confidence propagation neural network; IC, information component; IC025, the lower limit of the 95 two-sided CI of the IC; MGPS, multi-item gamma Poisson shrinker; EBGM, empirical Bayesian geometric mean; EBGM05, the lower 95 two-sided CI of EBGM.

Demographic Characteristics

From January 2004 to December 2022, the FAERS database contained 19494698 reports, of which 16134686 reports were retained after duplication records were excluded, 90324 ICSRs (sunitinib 21840, cabozantinib 4324, lenvatinib 13802, axitinib 12697, pazopanib 23265, sorafenib 14396) suspected to be related to MKIs had been reported. 2772 were related to electrolyte disorders AEs. Specifically, 687 (24.78%) ICSRs were linked to lenvatinib, 684 (24.68%) to sunitinib, 512 (18.47%) to sorafenib, 420 (15.15%) to pazopanib, 238 (8.59%) to cabozantinib, and 231 (8.33%) to axitinib (Fig. 1). As shown in Fig. 2, there were a total of 2 SOCs ( Investigations, Metabolism and nutrition disorders), 26 PTs, of which 15 and 11 were associated with Investigations and Metabolism and nutrition disorders, respectively. Fifteen PTs, blood calcium decreased, blood calcium increased, blood calcium abnormal, blood potassium increased, blood magnesium decreased, blood potassium decreased, hyperkalaemia, hypercalcaemia, blood sodium decreased, hypernatraemia, hyponatraemia, hypocalcaemia, hypokalaemia, hypophosphataemia, hypomagnesaemia, were reported for all the six drugs. Four PTs, blood sodium abnormal, blood sodium increased, blood potassium abnormal, and blood magnesium increased, were reported for 5 drugs. Blood magnesium increased was reported for axitinib (n = 1), lenvatinib (n = 4), cabozantinib (n = 1), sunitinib (n = 2), sorafenib (n = 1); blood potassium abnormal was reported for pazopanib (n = 3), lenvatinib (n = 9), cabozantinib (n = 1), sunitinib (n = 7), sorafenib (n = 3); blood sodium abnormal and blood sodium increased was reported for pazopanib (n = 5), axitinib (n = 1), lenvatinib (n = 3), sunitinib (n = 1), sorafenib (n = 1). One PT, blood phosphorus decreased, was reported for 4 drugs, pazopanib (n = 2), lenvatinib (n = 3), sunitinib (n = 6), sorafenib (n = 12). Three PTs, blood magnesium abnormal, blood phosphorus increased, hypermagnesaemia, reported for 3 drugs, blood magnesium abnormal reported for lenvatinib (n = 4), cabozantinib (n = 1), sunitinib (n = 3); Blood phosphorus increased reported for pazopanib (n = 5), axitinib (n = 3), sunitinib (n = 4); Hypermagnesaemia reported for pazopanib (n = 2), lenvatinib (n = 2), cabozantinib (n = 3). One PT, blood phosphorus abnormal reported for lenvatinib (n = 1), sunitinib (n = 1). Hyperphosphataemia was reported for lenvatinib (n = 1). Hyponatraemic syndrome reported for sunitinib (n = 1).

In electrolyte disorders AEs ICSRs, sorafenib and sunitinib were collected since 2006, and the highest ICSRs were reported in 2010 and 2015, respectively; for pazopanib, electrolyte disorders AEs were collected since 2010, and the highest ICSRs were reported in 2019; for axitinib, electrolyte disorders AEs were collected since 2012, and the highest ICSRs were reported in 2022; for cabozantinib, electrolyte disorders AEs were collected since 2013, and the highest ICSRs were reported in 2020; and for lenvatinib, electrolyte disorders AEs were collected since 2015, and the highest ICSRs were reported in 2020 and 2022. The total number of electrolyte disorders AEs ICSRs was most reported in 2019. In general, more males suffered AEs (except lenvatinib use) and 71.8% of cases were from North America. For individuals taking axitinib, cabozantinib, pazopanib, and sunitinib, aged 45–64 years were affected most frequently by EDs. But for sorafenib and lenvatinib, aged 65–74 years were affected most frequently by EDs. For sunitinib, renal cancer was the most common indication (56.58%). For lenvatinib, thyroid cancer was the most common indication (56.58%). For cabozantinib, hepatic cancer and thyroid cancer was the most common indication (29.83% and 28.15%, respectively). For pazopanib, sorafenib, and axitinib, hepatic cancer was the most common indication (34.76%, 50.20%, and 64.50%, respectively). Overall, hepatic cancer (28.72%) was the most common indication for MKIs, followed by renal cancer (17.58%). For all EDs documented in ICSRs (excluding missing data), the most common adverse outcome was hospitalization(1429/2674, 53.4%), and the most serious outcome was death/life-threat (281/2674, 10.5%).The prevalence of mortality was highest for sunitinib-related EDs (145/616, 23.5%) ,excluding missing data ( n = 68), followed by cabozantinib-related EDs (20/237, 8.4%), excluding missing data ( n = 1). As shown in Table 4.

Table 4

Demographic and clinical data of electrolyte disorders with interested multikinase inhibitors.
	Axitinib ( N = 231 )		Cabozantinib ( N = 238 )		Lenvatinib ( N = 687 )		Pazopanib (N = 420)		Sunitinib ( N = 684 )		Sorafenib ( N = 512)		Total (N = 2772)
	N	%	N	%	N	%	N	%	N	%	N	%	N	%
Sex
Females	96	41.6	93	39.1	422	61.4	149	35.5	259	37.9	162	31.6	1181	42.6
Males	124	53.7	117	49.2	258	37.6	245	58.3	394	57.6	343	67.0	1481	53.4
Missing	11	4.76	28	11.8	7	1.0	26	6.2	31	4.5	7	1.4	110	4.0
Age distribution (years)
＜18	0	0.0	2	0.8	4	0.6	5	1.2	0	0.0	4	0.8	15	0.5
18–44	5	2.2	17	7.1	7	1.0	11	2.6	34	5.0	12	2.3	86	3.1
45–64	93	40.3	70	29.4	238	34.6	121	28.8	251	36.7	167	32.6	940	33.9
65–74	74	32.0	55	23.1	265	38.6	110	26.2	213	31.1	174	34.0	891	32.1
75–84	34	14.7	35	14.7	99	14.4	45	10.7	113	16.5	118	23.1	444	16.0
≥ 85	4	1.7	5	2.1	12	1.8	11	2.6	9	1.3	8	1.6	49	1.8
other	21	9.1	54	22.7	62	9.0	117	27.9	64	9.4	29	5.7	347	12.6
Reporter country
North America	143	61.9	148	62.2	433	63.0	170	40.5	368	53.8	205	40.0	1467	52.9
South America	6	2.6	2	0.8	3	0.4	10	2.4	46	6.7	22	4.3	89	3.2
Europe	20	8.7	80	33.6	81	11.8	125	29.8	123	18.0	74	14.5	503	18.1
Asia	62	26.8	6	2.5	158	23.0	83	19.8	135	19.7	207	40.4	651	23.5
Other	0	0.0	1	0.4	7	1.0	6	1.4	9	1.3	4	0.8	27	1.0
Missing	0	0.0	1	0.4	5	0.7	26	6.2	3	0.4	0	0.0	35	1.3
Outcome
Death/life-threat	17	7.4	20	8.4	39	5.7	25	6.0	145	21.2	35	6.8	281	10.1
Disability	0	0.0	3	1.3	0	0.0	1	0.2	4	0.6	2	0.4	10	0.4
Hospitalization	75	32.5	125	52.5	549	79.9	143	34.1	364	53.2	173	33.8	1429	51.6
Other serious	130	56.3	89	37.4	99	14.4	241	57.4	103	15.1	292	57.0	954	34.4
Missing	9	3.9	1	0.4	0	0.0	10	2.4	68	9.9	10	2.0	98	3.5
Indication
Renal cancer	149	64.5	71	29.8	90	13.1	146	34.8	83	12.1	257	50.2	796	28.7
Hepatic cancer	0	0.0	11	4.6	95	13.8	1	0.2	387	56.6	0	0.0	494	17.8
Thyroid cancer	1	0.4	67	28.2	160	23.3	7	1.7	19	2.8	1	0.2	255	9.2
EC/EOC	0	0.0	0	0.0	2	0.3	1	0.2	2	0.3	127	24.8	132	4.8
CRC	0	0.0	13	5.5	130	18.9	4	1.0	0	0.0	0	0.0	147	5.3
Other	81	35.1	76	31.9	210	30.6	261	62.1	193	28.2	127	24.8	948	34.2
Reported time-to-onset (days)
0–30	18	7.8	36	15.1	62	9.0	89	21.2	94	13.7	121	23.6	420	15.2
31–60	11	4.8	14	5.9	18	2.6	31	7.4	40	5.9	15	2.9	129	4.7
61–90	2	0.9	10	4.2	14	2.0	17	4.1	19	2.8	10	2.0	72	2.6
91–180	9	3.9	21	8.8	26	3.8	17	4.1	22	3.2	14	2.7	109	3.9
181–360	10	4.3	15	6.3	10	1.5	13	3.1	11	1.6	8	1.6	67	2.4
＞360	3	1.3	3	1.3	13	1.9	20	4.8	37	5.4	14	2.7	90	3.3
Other	30	13.0	51	21.4	103	15.0	67	16.0	47	6.9	145	28.3	443	16.0
Missing	148	64.1	88	37.0	441	64.2	166	39.5	414	60.5	185	36.1	1442	52.0
Time to report
2006	0	0.0	0	0.0	0	0.0	0	0.0	6	0.9	23	4.5	29	1.1
2007	0	0.0	0	0.0	0	0.0	0	0.0	1	0.2	30	5.9	31	1.1
2008	0	0.0	0	0.0	0	0.0	0	0.0	2	0.3	19	3.7	21	0.8
2009	0	0.0	0	0.0	0	0.0	0	0.0	41	6.0	49	9.6	90	3.3
2010	0	0.0	0	0.0	0	0.0	16	3.8	43	6.3	76	14.8	135	4.9
2011	0	0.0	0	0.0	0	0.0	25	6.0	29	4.2	56	10.9	110	4.0
2012	7	3.0	0	0.0	0	0.0	20	4.8	8	1.2	48	9.4	83	3.0
2013	20	8.7	11	4.6	0	0.0	36	8.6	7	1.0	21	4.1	95	3.4
2014	21	9.1	17	7.1	0	0.0	31	7.4	38	5.6	18	3.5	125	4.5
2015	24	10.4	29	12.2	12	1.8	43	10.2	108	15.8	36	7.0	252	9.1
2016	14	6.1	23	9.7	37	5.4	39	9.3	98	14.3	20	3.9	231	8.3
2017	15	6.5	18	7.6	46	6.7	35	8.3	96	14.0	29	5.7	239	8.6
2018	11	4.8	24	10.1	67	9.8	39	9.3	72	10.5	27	5.3	240	8.7
2019	17	7.4	16	6.7	119	17.3	55	13.1	60	8.8	24	4.7	291	10.5
2020	24	10.4	38	16.0	142	20.7	36	8.6	28	4.1	20	3.9	288	10.4
2021	24	10.4	26	10.9	122	17.8	31	7.4	17	2.5	15	3.0	235	8.5
2022	54	23.4	36	15.1	142	20.7	14	3.3	30	4.4	1	0.2	277	10.0

EC:Endometrial cancer; EOC: Epithelial ovarian cancer; CRC: Colorectal cancer.

A total of 887 cases were suitable for calculating the median value of time to onset of the ICSRs. We found that the median time to onset of axitinib-related ICSRs was 53 days, cabozantinib-related ICSRs was 56 days, lenvatinib-related ICSRs was 45 days, pazopanib-related ICSRs was 37 days, sunitinib-related ICSRs was 40 days, sorafenib-related ICSRs was 12 days, and the overall median value of onset time was 35 days, as shown in Fig. 3. The Kruskal-Wallis H test was used to compare the distribution of time-to-onset of ICSRs. We found that each drug-related ICSRs was not all the same, and the difference was statistically significant (P = 0.001). Pairwise comparisons with Bonferroni correction for significance levels found that time-to-onset distribution was significantly different in sorafenib versus pazopanib(adjusted P = 0.002), sorafenib versus lenvatinib(adjusted P = 0.001), sorafenib versus sunitinib, cabozantinib, and axitinib (adjusted P < 0.001), but not among the other groups.

As shown in Fig. 4, among the AEs of the electrolyte sodium associated with MKI, whether hyponatraemia or blood sodium decreased, lenvatinib-related ICSRs were the most (142 and 83, respectively), followed by sunitinib (135 and 75, respectively). Among the AEs of the electrolyte phosphorus associated with MKI, whether hypophosphataemia or blood phosphorus decreased, sorafenib-related ICSRs were the most (75 and 12, respectively). As shown in Fig. 5, among the AEs of the electrolyte potassium associated with MKI, it is interesting that lenvatinib-related blood potassium decrease ICSRs was the most (n = 89), but cabozantinib-related hypokalaemia ICSRs were the most (n = 31); sunitinib-related blood potassium increase ICSRs was the most (n = 49), but pazopanib-related hyperkalaemia ICSRs was the most (n = 72). As shown in Fig. 6, among the AEs of the electrolyte calcium associated with MKI, lenvatinib-related blood calcium decreased ICSRs was the most (n = 37), but sorafenib-related hypocalcaemia ICSRs were the most (n = 50); whether hypercalcaemia or blood calcium increased, sunitinib-related ICSRs was the most (39 and 26, respectively). As shown in Fig. 7, among the AEs of the electrolyte magnesium associated with MKI, lenvatinib-related blood magnesium decreased ICSRs were the most (n = 57), but sunitinib-related hypomagnesaemia ICSRs were the most (n = 24); lenvatinib-related blood magnesium increased ICSRs was the most (n = 4), only cabozantinib-related hypermagnesaemia ICSRs was reported (n = 3). As shown in Table 4.

Signal Values of electrolyte disorders AEs Associated with MKIs

As shown in Table 5. With the criteria of ROR, the six MKIs were all significantly associated with electrolyte disorders AEs, the strongest association was the association between cabozantinib and hypermagnesaemia (ROR 16.10, 95% CI 5.18–50.11; PRR 16.10, χ² 42.24; IC025 2.33; EBGM05 6.19), followed by the association of lenvatinib and blood magnesium abnormal (ROR 9.95, 95% CI 3.72–26.64; PRR 9.95, χ² 31.88; IC025 1.63; EBGM05 4.33). Based on ROR, PRR, BCPNN, and MGPS methods, axitinib was significantly associated with blood potassium increased and blood calcium increased; cabozantinib was significantly associated with hypocalcaemia, hypomagnesaemia, blood calcium decreased, hypophosphataemia, blood magnesium decreased, blood calcium abnormal, hypermagnesaemia; lenvatinib was significantly associated with blood potassium decreased, blood sodium decreased, blood magnesium decreased, blood calcium decreased, blood potassium abnormal, blood magnesium abnormal, blood magnesium increased; pazopanib was significantly associated with blood sodium abnormal; sunitinib was significantly associated with blood calcium abnormal; sorafenib was significantly associated with hypophosphataemia.

Table 5

Associations of different interested multikinase inhibitors regimens with electrolyte disorders
	Advers Event	N	ROR (95 CI)	PRR (χ2 )	IC(IC025)	EBGM (EBGM05)
Axitinib	Blood potassium increased	34	3.28 (2.34)	3.28 (53.65)	1.71(0.04)	3.27(2.47)
	Blood calcium increased	20	3.56 (2.30)	3.56 (36.72)	1.83(0.16)	3.55(2.46)
	Blood sodium decreased	20	1.68 (1.09 )	1.68 (5.54 )	0.75(-0.92)	1.68(1.17)
	Hypercalcaemia	17	2.21 (1.37)	2.21 (11.25)	1.14(-0.52)	2.21(1.48)
	Blood magnesium decreased	10	1.94 (1.04)	1.94 (4.52 )	0.95(-0.71)	1.93(1.15)
Cabozantinib	Hyponatraemia	51	3.02 (2.29)	3.01 (68.53)	1.59(-0.08)	3.01(2.39)
	Hypocalcaemia	33	6.07 (4.31)	6.06 (139.03)	2.60(0.93)	6.04(4.54)
	Hypokalaemia	31	2.32 (1.63)	2.32 (23.28 )	1.21(-0.45)	2.32(1.73)
	Hypomagnesaemia	20	5.31 (3.42)	5.30(69.68 )	2.40(0.74)	5.29(3.67)
	Blood calcium decreased	19	5.42 (3.45 )	5.41 (68.24)	2.43(0.77)	5.40(3.71)
	Hypophosphataemia	13	6.30 (3.66 )	6.30 (57.82)	2.65(0.99)	6.29(3.99)
	Blood magnesium decreased	12	4.92 (2.79 )	4.92 (37.41)	2.30(0.63)	4.91(3.06)
	Hypercalcaemia	10	2.75 (1.48 )	2.75 (11.13 )	1.46(-0.21)	2.75(1.64)
	Blood calcium abnormal	4	8.20 (3.07 )	8.20 (25.20)	3.03(1.36)	8.17(3.60)
	Hypermagnesaemia	3	16.10(5.18)	16.10(42.24)	4.00(2.33)	16.01(6.19)
Lenvatinib	Hyponatraemia	142	3.16 (2.68 )	3.15 (207.81)	1.65(-0.01)	3.14(2.74)
	Blood potassium decreased	89	3.72 (3.02)	3.71(175.88)	1.89(0.22)	3.70(3.11)
	Blood sodium decreased	83	5.57 (4.49)	5.56 (309.06)	2.47(0.80)	5.54(4.62)
	Blood magnesium decreased	57	8.83 (6.80)	8.82 (391.70)	3.13(1.46)	8.75(7.03)
	Hypocalcaemia	44	3.03 (2.25)	3.03 (59.61)	1.60(-0.07)	3.02(2.36)
	Blood potassium increased	40	3.06(2.24 )	3.06 (55.27)	1.61(-0.06)	3.05(2.35)
	Blood calcium decreased	37	3.96(2.87)	3.96 (81.49)	1.98(0.31)	3.95(3.01)
	Hypercalcaemia	29	3.00 (2.08)	2.99 (38.41 )	1.58(-0.09)	2.99(2.20)
	Hypomagnesaemia	24	2.39 (1.60 )	2.38 (19.26)	1.25(-0.41)	2.38(1.70)
	Blood calcium increased	19	2.68 (1.71)	2.68 (20.00)	1.42(-0.25)	2.68(1.84)
	Blood potassium abnormal	9	4.61 (2.39 )	4.61 (25.29 )	2.20(0.53)	4.59(2.65)
	Blood magnesium abnormal	4	9.95 (3.72)	9.95 (31.88 )	3.30(1.63)	9.86(4.33)
	Blood magnesium increased	4	5.50 (2.06)	5.49 (14.63)	2.45(0.78)	5.47(2.40)
	Blood calcium abnormal	4	3.07 (1.15 )	3.07 (5.57)	1.62(-0.05)	3.07(1.35)
	Blood sodium abnormal	3	4.09 (1.32)	4.09 (6.98 )	2.03(0.36)	4.08(1.58)
Pazopanib	Hyponatraemia	82	1.26 (1.02 )	1.26 (4.46)	0.34(-1.33)	1.26(1.05)
	Hyperkalaemia	72	1.83 (1.45)	1.83 (27.03 )	0.87(-0.80)	1.83(1.51)
	Blood sodium decreased	39	1.81 (1.32)	1.81 (14.15)	0.86(-0.81)	1.81(1.39)
	Blood potassium increased	38	2.02 (1.47)	2.02(19.50 )	1.01(-0.65)	2.02(1.54)
	Hypercalcaemia	29	2.08 (1.45)	2.08(16.24)	1.06(-0.61)	2.08(1.53)
	Blood sodium abnormal	5	4.75 (1.97)	4.75(14.73)	2.24(0.57)	4.73(2.27)
Sunitinib	Hyponatraemia	135	1.54 (1.30)	1.54 (25.33)	0.62(-1.05)	1.54(1.33)
	Blood sodium decreased	75	2.58 (2.06 )	2.58 (72.29)	1.36(-0.30)	2.57(2.13)
	Blood potassium decreased	67	1.43 (1.13)	1.43 (8.80)	0.52(-1.15)	1.43(1.17)
	Blood potassium increased	49	1.93 (1.46)	1.93 (21.74)	0.94(-0.72)	1.92(1.52)
	Hypocalcaemia	43	1.52 (1.13)	1.52 (7.63)	0.60(-1.06)	1.52(1.18)
Sorafenib	Hypercalcaemia	39	2.07 (1.51 )	2.07(21.51)	1.05(-0.62)	2.07(1.59)
	Blood magnesium decreased	32	2.53 (1.79)	2.53 (29.55)	1.34(-0.33)	2.53(1.89)
	Blood calcium decreased	28	1.54 (1.06)	1.54 (5.24)	0.62(-1.05)	1.54(1.13)
	Blood calcium increased	26	1.89 (1.28)	1.89(10.81)	0.91(-0.75)	1.88(1.37)
	Blood calcium abnormal	9	3.56 (1.85)	3.56 (16.48)	1.83(0.16)	3.55(2.05)
	Blood magnesium abnormal	3	3.82 (1.23 )	3.82(6.21)	1.93(0.25)	3.80(1.47)
	Hypophosphataemia	75	9.92 (7.90)	9.91 (593.11)	3.29(1.63)	9.79(8.09)
	Blood sodium decreased	51	2.46 (1.87)	2.46(43.88)	1.29(-0.37)	2.45(1.95)
	Hypocalcaemia	50	2.48 (1.88)	2.48 (43.96)	1.31(-0.36)	2.47(1.96)
	Blood potassium increased	44	2.42 (1.80)	2.42 (36.66)	1.27(-0.39)	2.42(1.89)
	Blood magnesium decreased	16	1.77 (1.08 )	1.77 (5.36)	0.82(-0.84)	1.77(1.17)
	Blood phosphorus decreased	12	3.14 (1.78 )	3.14 (17.38)	1.64(-0.02)	3.13(1.95)

To the authors’ knowledge, this is the first study investigating the relationship between MKIs and the risk of EDs using a pharmacovigilance approach. MKIs are used widely to treat RCC, and EDs are encountered commonly in patients suffering from cancer. EDs can lead to life-threatening complications[16, 17].

The reporting signals identified in this study for the investigated MKIs are an important finding. Spontaneous reporting systems are required for the early identification and characterization of individual AEs in real-time, which, in turn, supports awareness among oncologists regarding the need to manage those AEs promptly.

Over the years, the distribution of AEs has been influenced by the different timings of approval and clinical use of TKIs [18]. In the present study, the main indications for MKI use were liver cancer and RCC (28.72% and 17.82%, respectively). Males and those aged 45–74 years were the main groups affected by EDs in our study. A causal relationship between EDs and TKI use has not been shown. Other studies have demonstrated a marked association between hyponatremia and mortality in patients with non-Hodgkin’s lymphoma, RCC, gastric cancer, and small-cell lung cancer[17].

EDs are common in cancer patients and may be secondary to the cancer[19], specific pharmacodynamic mechanisms, or acute renal impairment, albuminuria, and hypertension (2017). For example, some antineoplastic agents may interfere with the tubular effects of electrolytes and urinary excretion by interfering with antidiuretic hormones. Hyponatremia is the most frequently observed ED in cancer patients. The major cause of hyponatremia is volume depletion, which is most commonly associated with bleeding, diarrhea, vomiting, ascites, pleural effusion, peritonitis, or ileus[20]. The syndrome of inappropriate antidiuretic hormone (SIADH) is considered to be another frequent cause of hyponatremia in cancer patients[21]. Dose-dependent use of axitinib has been associated with hyponatremia. The use of sorafenib, sunitinib[22], pazopanib [23] has also been associated with a high prevalence of hyponatremia. The major underlying mechanism is SIADH induction[24–26]. In the present study, ICSRs for hyponatremia were documented for all six medications.

The release of phosphorus from damaged cells and the presence of extracellular phosphorus causes hyperphosphatemia, as noted in tumor lysis syndrome, rhabdomyolysis, lactic acidosis, ketoacidosis, and respiratory alkalosis[21]. Use of axitinib, sunitinib, and sorafenib has been associated with hypocalcemia in patients with advanced cancer [27, 28]. Sunitinib, by inhibiting bone turnover, interferes with phosphate homeostasis. Sorafenib can influence bone turnover through the inhibition of platelet-derived growth factor receptors and the induction of acquired Fanconi syndrome [25]. Hypophosphatemia can be worsened by concomitant conditions, and induce diarrhea upon consequent malabsorption of vitamin D [29, 30]. In the present study, ICSRs for hyponatremia were documented for all six MKIs.

Most cancer patients undergo targeted therapy and chemotherapy (which can cause nausea and vomiting). Excessive vomiting (especially for long periods) results in hypovolemia and hypochloremic metabolic alkalosis due to the loss of chloride ions and hydrogen ions, which may be related to hypokalemia and hypomagnesemia [25]. Concerning the mechanism of hypokalemia, poor nutrition, anorexia, and volume depletion may lead to insufficient intake of potassium [21]. Axitinib induces hyperkalemia via the development of tumor lysis syndrome and distal tubular dysfunction (e.g., type-4 renal tubular acidosis during hyperkalemia [31]. In the present study, ICSRs for hyperkalemia were documented for lenvatinib, cabozatinib, sunitinib, and sorafenib.

Hypocalcemia can result from malnutrition, hypoalbuminemia, sepsis, or tumor lysis syndrome [21]. The use of sorafenib [32], axitinib [27], or sunitinib [33] can cause hypocalcemia. The mechanism of the hypocalcemia elicited by sorafenib might be due to endoplasmic reticulum stress coupled with calcium mobilization. However, how sunitinib and axitinib may cause hypocalcemia is not clear. In the present study, ICSRs for hypocalcemia were documented for lenvatinib, cabozantinib, sunitinib, and sorafenib.

Magnesium has a crucial role in various physiological processes but is frequently overlooked[34]. Such lack of attention to the magnesium level may be due to: an absence of magnesium testing in routine chemistry panels; the subtlety of magnesium-deficiency symptoms; the wide range of processes in which magnesium is involved, making it challenging to attribute specific symptoms to magnesium deficiency. Hypomagnesemia is observed frequently in cancer patients and has been ascribed to malnutrition, diarrhea, hypercalcemia, and therapy with antineoplastic drugs[23].

Interestingly, some studies have found that cabozantinib is not associated with EDs [28, 35]. In our study, hyperphosphatemia, hyperkalemia, hypocalcemia, hypomagnesemia, and hyponatremia were associated with the use of cabozantinib, as well as lenvatinib and pazopanib.

Studies have reported that hypertension[11], heart failure[36], renal/liver insufficiency [37], chemotherapy, hematologic malignancies, or cancer with distant metastasis can aggravate the risk of death in patients with EDs. Diabetes mellitus is an independent risk factor for hyponatremia. This indicates that EDs may be prevented by early correction of renal/hepatic impairment and other biochemical markers at the time of hospital admission. The synergistic action of antitumoral drugs and EDs can lead to fatal arrhythmias. Hence, periodic monitoring of electrolytes, an optimal mode of intravenous infusion, correction of variable factors for infusion, and appropriate management of EDs during anticancer therapy should not be overlooked, especially for those who are older, have a low body mass index, underlying disease, or receiving surgery/chemotherapy. Short-term results and quality of life could be improved by taking these measures[21]

In order to achieve a higher quality of evidence in this field, attention should be given to EDs due to MKIs in randomized controlled trials. Furthermore, more data are needed to assess ED development's incidence and risk factors. In addition, it is necessary to clarify the mechanism of these adverse events at both the molecular and cellular levels in order to develop more effective drugs. Evidence-based guidelines for the management of EDs in patients treated with MKIs are required.

There are three major limitations to our research. Firstly, data mining fails to compensate for the inherent deficiencies of FAERS, such as incomplete, inaccurate reporting and false, all of which can lead to bias. Secondly, because of the intrinsic properties of FAERS, only qualitative studies were possible in our study. Thirdly, although pharmacovigilance databases indicate a strong relationship between the target medicinal product and the target adverse reaction, there is no causal relationship. Although it has limitations, the results of this study prove the prevalence and concealment of MKI-related EDs. At the same time, this study has a warning effect and reveals the potential safety risks of MKIs, which provides the direction for further clinical research and provides the basis for more optimal clinical use of MKIs.

Overall, our findings support the assumption that EDs AEs are a safety concern for MKIs (which are treated for renal cell carcinoma). Periodic electrolytes monitoring, optimal mode of intravenous infusion, correction of variable factors for infusion, and proper management of EDs during anticancer therapy should not be overlooked. Furthermore, a large prospective population-based study is required to determine the true incidence of AEs and to fully clarify the risk factors, which would support appropriate risk management.

Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contributions XS, SW, and XW provided a substantial contribution to the conception/design of the work; XS, SW, and XW had an important role in the acquisition of data and the following analysis. SW and XS worked on the interpretation of data together with DY. XS and SW drafted the work, SW and XW revised it critically for important intellectual content.

Funding This study was supported by the Shanxi Patent Transformation Program (Grant number No.202304011).

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No competing interests reported.

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Electrolyte Disorders Induced by Multikinase Inhibitors Therapy for Renal Cell Carcinoma：A Large-Scale Pharmacovigilance Analysis

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