Nephrotoxicity of Immune Checkpoint Inhibitors: A Disproportionality Analysis from 2013 to 2020

Abstract

Nephrotoxicity occasionally occurs during treatment with immune checkpoint inhibitors (ICIs). Few related studies compare the differences between these drugs. This study aimed to systematically characterize nephrotoxicity after ICI initiation. Data were extracted from the US FDA Adverse Event Reporting System (FAERS) database. Disproportionality analysis, including information components (ICs) and reporting odds ratios (RORs), was performed to determine the potential renal toxicity of ICIs. A total of 7,204 reports of renal adverse events (AEs) were identified in the FAERS database. Renal AEs were most commonly reported for nivolumab (46.84%). Strong signals were detected in male patients combined with ICIs. In the clinical application of ICIs, attention should be paid to patients, especially male patients, with acute kidney injury, nephritis, autoimmune nephritis and other nephrotoxic AEs. The use of ICIs is likely to aggravate their condition.

Introduction

Immune checkpoint inhibitors (ICIs) are a novel class of medications in the treatment of cancer. They have rapidly obtained a popularity for their success in improving clinical outcomes in a great many cancer types (Wrangle et al. 2018). The immune checkpoints programmed cell death protein 1 pathway (PD-1/PD-L1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) acted as a “brake” role in immune function, and suggested that immune checkpoint inhibition may reactivate T cells and eliminate cancer cells effectively (Ljunggren et al. 2018).

Accumulating evidence indicates that the inhibition of PD-1 promotes an effective immune response against cancer cells (Messenheimer et al. 2017). In addition, the development of PD-L1 checkpoint inhibitors has changed the landscape of non-small-cell lung cancer (NSCLC) therapy, with 2 approvals from the US Food and Drug Administration (FDA) of PD-1 inhibitors for second-line therapy (Sacher and Gandhi 2016). About the clinical manifestations of CTLA-4, first-line treatment with nivolumab plus ipilimumab resulted in a longer duration of overall survival than did chemotherapy in patients with NSCLC, independent of the PD-L1 expression level (Hellmann et al. 2019).

However, the risk of adverse events (AEs) of ICIs has attracted attention in clinical practice. Nephrotoxicity is one very common AE. It may induce serious and fatal events if doctors do not recognize and treat it promptly. Renal immune-related adverse events are rare, with an estimated incidence of 2% with anti-PD-1/PD-L1 and 5% with combination therapy in a review of published phase 2 and 3 trials, but more recent studies have suggested that the incidence of acute kidney injury is higher than that initially reported (Li et al. 2021).

In this study, a disproportionality analysis was conducted to characterize and evaluate nephrotoxicity associated with ICI regimens using AE data in the FDA Adverse Event Reporting System (FAERS) database. Although the data in FAERS database may lack detailed clinical information, this approach may help discover potential drug-toxicity associations.

Materials and Methods

Study design and data sources

This retrospective, pharmacovigilance study is a disproportionality analysis based on the FAERS database, a collection of reports of AEs by consumers, healthcare providers, drug manufacturers, and others. It allows for the signal detection and quantification of the association between drugs and the report s of AEs (Min et al. 2018). Input data for this study were taken from the public release of the FAERS database, covering the period from the first quarter of 2013 to the second quarter of 2020.

Procedure

The drugs studied included antibodies targeting PD-1 (nivolumab and pembrolizumab), PD-L1 (atezolizumab, avelumab and durvalumab), and CTLA-4 (ipilimumab and tremelimumab). Generic names and brand names were used to identify the records associated with the ICIs because there is no uniform coding system for medications.

This study included all renal disorders (excluding nephropathies) (Medical Dictionary for Regulatory Activities (MedDRA) code 10038430) and all nephropathies (MedDRA code 10029149) according to MedDRA version 23.0. In the FAERS database, each report is coded using the preferred terms (PTs) from MedDRA, the international medical terminology developed by the International Council for Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use.

Statistical analysis

Disproportionality appears when a drug is associated with a specific AE in pharmacovigilance studies. Two-by-two contingency tables were used to count the AE reports of the suspected drugs and other drugs (Table 1). Two data mining methods, reporting odds ratios (RORs) and Bayesian confidence propagation neural networks (BCPNNs) of information components (ICs), were used to calculate disproportionality.

The ROR can be expressed as (Stricker and Tijssen 1992)

The standard error of ln (ROR) and 95% confidence interval can be calculated by

The BCPNN and its variance can be calculated as (Weinstein et al. 2009)

where

and γ_ij = 1, α_i = 1, α = 2, β_j = 1, and β = 2; C is the total number of reports in the database, C_ij is the number of combinations between the ICI drugs (i) and the nephrotoxicity reaction (j), C_i is the total number of reports on the ICI drugs (i) in the database and C_j is the total number of reports on the nephrotoxicity reaction (j) in the database.

The signal was considered significant if the lower limit of the 95% confidence interval (ROR025) was higher than 1, or if the lower end of the 95% confidence interval of information component (IC025) was greater than 0. All analyses were performed with SAS version 9.4 and R version 3.6.3. Ethics approval and consent to participate are not applicable.

Table 1.

2*2 contingency tables for immune checkpoint inhibitor (ICI)-induced nephrotoxicity.

Results

Descriptive analysis

A total of 52,583,692 records from the FAERS database were involved in this study, and 7,204 reports were AEs of nephrotoxicity after treatment with ICIs. The clinical characteristics of patients with kidney toxicity using ICIs are shown in Tables 2 and 3. Among all reports of nephrotoxicity associated with ICIs, the proportion of males was larger than that of females (63.66% vs. 29.96%). With further analysis, the signal was also detected (ROR025 = 2.32, IC025 = 0.49). Among all reports of nephrotoxicity associated with each ICI, the signal of males was larger than that of females (Table 4). Significant difference existed between different age groups, too. The proportion of the elderly (> 65) was larger than that of the non-elderly (< 65) (51.94% vs. 33.94%), and the difference was significant (ROR025 = 1.12, IC025 = 0.04) , which might be attributed to degenerative change of the elderly. The most frequently reported severe outcomes were other serious medical events and hospitalization. Hospitalization (ROR025 = 2.18, IC025 = 0.79), death (ROR025 = 1.50, IC025 = 0.53) and life-threatening (ROR025 = 2.08, IC025 = 1.01) events associated with renal AEs after ICI treatment were reported, indicating the potentially life-threatening nature of ICI-related nephrotoxicity.

However, differences in various specific nephrotoxic AEs were observed in all ICI regimens. Among all the reports of AEs of nephrotoxicity, acute kidney injury (2,404, 33.37%), renal failure (1,128, 15.66%), renal impairment (778, 10.8%), tubulointerstitial nephritis (617, 8.56%), nephritis (350, 4.86%), and renal disorder (299, 4.15%) were the most frequently reported (Table 5). These reports account for 77.47% of the all the reports. The other reports of AEs of nephrotoxicity are considered rare.

Table 2.

Sex and age of patients with immune checkpoint inhibitor (ICI)-induced nephrotoxicity.

Table 2 shows the clinical characteristics of patients with kidney toxicity using ICIs and any other drugs. The number and the percentage are shown.

^*Missing value in FAERS database.

AEs, adverse events; IC025, the lower end of the 95% confidence interval of IC; ROR025, the lower limit of the 95% confidence interval of ROR; Empty cells; there are not enough data to compute.

Table 3.

Outcome and country of patients with immune checkpoint inhibitor (ICI)-induced nephrotoxicity.

Table 3 shows the outcome and countries of patients with kidney toxicity using ICIs and any other drugs. The number and the percentage are shown.

^&Required intervention to prevent permanent impairment/damage.

^*Missing value in FAERS database.

AEs, adverse events; IC025, the lower end of the 95% confidence interval of IC; ROR025, the lower limit of the 95% confidence interval of ROR; Empty cells, there are not enough data to compute.

Table 4.

The signals of patients’ sex with immune checkpoint inhibitor (ICI)-induced nephrotoxicity.

Table 4 shows the signals of patients’ sex with kidney toxicity using ICIs. Cemiplimab has little reports to find signals.

N, number of total records; ROR, reporting odds ratio; ROR025, the lower end of the 95% confidence interval of ROR; IC, information component; IC025, the lower end of the 95% confidence interval of IC.

Table 5.

Frequency of nephrotoxic adverse events (AEs) related to immune checkpoint inhibitor (ICI) drugs.

^*Other nephrotoxic.

AEs; N, the number of records with kidney AEs reported for ICIs.

The spectrum of nephrotoxicity AEs differs in immunotherapy regimens

In general, ICIs were hardly associated with renal AEs. When further analysis was done, there was significant difference between AEs related to nephrotoxicity and atezolizumab in total (IC025: 0.05; ROR025: 1.04), but not the other ICIs (Table 6).

Eleven preferred terms (PTs) were significantly associated with atezolizumab treatment, ranging from pyelonephritis (ROR025 = 1.20, IC025 = 0.07) to glomerulonephritis chronic (ROR025 = 51.95, IC025 = 1.17). Nivolumab was with a broadest spectrum of renal AEs with 26 PTs detected as signals, ranging from anuria ( ROR025 = 1.02, IC025 = −0.03) to autoimmune nephritis (ROR025 = 29.03, IC025 = 3.46). Twenty-one PTs were significantly associated with pembrolizumab treatment, ranging from renal tubular necrosis (ROR025 = 1.06, IC025 = 0.04) to immune-mediated renal disorder (ROR025 = 185.22, IC025 = 1.69). Nine PTs were significantly associated with ipilimumab treatment, ranging from nephrotic syndrome (ROR025 = 1.08, IC025 = −0.04) to autoimmune nephritis (ROR025 = 72.67, IC025 = 3.77). For durvalumab, 7 disproportionality signals were detected, ranging from thrombotic microangiopathy (ROR025= 1.97, IC025 = 0.77) to autoimmune nephritis (ROR025 = 18.27, IC025 = 0.72). Small number of the other signals were reported. But some very strong signals were found. Avelumab was much stronger associated with pyelonephritis (ROR025 = 5.27, IC025 = 1.17). Cemiplimab was much stronger associated with autoimmune nephritis (ROR025 = 375.35, IC025 = 0.83) and kidney transplant rejection (ROR025 = 13.22, IC025 = 0.20). Tremelimumab was much stronger associated with nephritis (ROR025 = 27.90, IC025 = 2.83), etc (Figs. 1 and 2).

Table 6.

Association of total kidney adverse events (AEs) in total or class-specific immune checkpoint inhibitor (ICI) use

Table 6 shows the information of total and specific ICI count in 2*2 contingency tables and calculation results by disproportionality analysis. The font will be bold if ROR025 is higher than 1 or IC025 is higher than 0.

(a) The number of records with kidney AEs reported for ICIs. (b) The number of records with any other AEs reported for ICIs. (c) The number of records with any kidney AEs for other drugs. (d) The number of records reported other AEs for other drugs. ROR, reporting odds ratio; ROR025, the lower end of the 95% confidence interval of ROR; IC, information component; IC025, the lower end of the 95% confidence interval of IC.

Fig. 1

All signals showing reporting odds ratios (RORs) of immune checkpoint inhibitor (ICI) drugs in detailed nephrotoxic adverse events (AEs).

PT, preferred term; ROR025, the lower end of the 95% confidence interval of ROR.

ROR025 greater than 1 was deemed a signal.

Fig. 2

All signals showing information components (ICs) of immune checkpoint inhibitor (ICI) drugs in detailed nephrotoxic adverse events (AEs).

PT, preferred term; IC025, the lower end of the 95% confidence interval of IC.

IC025 greater than 0 was deemed a signal.

Discussion

Statistical analyses have been shown to be useful tools in aiding signal detection in spontaneous reporting systems. In previous study, the ROR was selected as the disproportionality analysis algorithms to characterize the spectrum, frequency, and clinical features of ICI-related adverse events (Raschi et al. 2019). However, no individual approach to detect signals is adequate and the concurrent use of other methods is essential (van Puijenbroek et al. 2002). Therefore, two methods, ROR and BCPNN, are used in this study. The selection of time intervals needs to pay attention to. In previous study, disproportionality and Bayesian analysis were used in data mining to screen the suspected renal adverse effects after the administration of different ICIs, based on FAERS from January 2004 to September 2019 (Chen et al. 2020). But the ICI relatively larger market, ipilimumab, is listed in 2011. Redundant data increases the analysis results in probability errors. Our study is a pharmacovigilance study on ICI-associated nephrotoxicities based on more than 50 million records in the appropriate period of time. This makes our conclusion more stable compared with those of other studies.

Most clinical trials of ICIs have assessed clinical effects other than AEs and only given a short brief description of these severe or even fatal AEs. To the best of our knowledge, this study is the the most systematic and comprehensive collection until recently to compare the associations of renal adverse effects after different ICIs in the real-world practice based on the FAERS pharmacovigilance database. Previous researches show that ICIs can increase the risk of organ system toxicities such as endocrine, hematological, ear and labyrinth toxicity (Zhai et al. 2019; Hu et al. 2020; Ye et al. 2020). Nephrotoxicity is rare. Accounting for 2.47% among all ICI records with the latest data from January 1, 2013, to June 30, 2020.

Nephrotoxicity based on sex

Notably, compared with male patients, nephrotoxic AEs were reported more than those reported among female patients (63.66% vs. 29.96% ). The result reflects that males are high-risky population to occur nephrotoxic AEs after treatment with ICI drugs. Previous research has also confirmed this (Chen et al. 2020). The reason for the difference of the occurring frequency of nephrotoxicity between males and females may be related to the level of sex hormones. Male hormones exert a deleterious effect in terms of increasing oxidative stress, activating the renin-angiotensin system, and worsening fibrosis within the damaged kidney, but female hormones exert a renoprotective effect (Valdivielso et al. 2019). Therefore, the clinical application of ICI drugs should focus on the occurrence of nephrotoxicity in male patients.

Association of total nephrotoxicity in total ICI use

The kidney is a vital organ for urine production, regulation of electrolytes and water, and homeostasis of nutrients and metabolites in the body (Liu et al. 2019). Medications are a relatively common cause of kidney injury (Markowitz and Perazella 2005; Perazella 2005, 2009, 2012; Uchino et al. 2007; Izzedine et al. 2009; Moffett and Goldstein 2011; Hoste et al. 2015). Drug-induced nephrotoxicity is more common in hospitalized patients, particularly intensive care unit patients (Mehta et al. 2004; Uchino et al. 2007; Moffett and Goldstein 2011; Hoste et al. 2015), and is affected by the inherent nephrotoxic potential of the drug, underlying patient characteristics that enhance the risk for kidney injury, and the metabolism and excretion of the potential offending agent by the kidney (Perazella 2003, 2005, 2009; Markowitz and Perazella 2005). The control group may express a higher proportion of renal toxicity. Also, ICI records may express too many other organ system toxicities, which may result in the expression of no significant association between renal AEs and ICIs.

Nephrotoxicity and atezolizumab

The kidney is composed of glomeruli and tubules. No tendency of toxicity of each part is apparently in atezolizumab group (Table 7), which might be attributed to the mechanism of this drug. Atezolizumab is an engineered humanized monoclonal IgG1 antibody that binds selectively to PD-L1 and prevents its interaction with PD-1 and B7.1 while it is sparing the interaction between PD-L2 and PD-1. To some degree, atezolizumab may have higher appetency of the PD-L1 receptors that scattered over the kidney, which should be confirmed by more empirical studies.

Table 7.

Frequency of nephrotoxic adverse events (AEs) related to atezolizumab.

^*Other nephrotoxic AEs related to atezolizumab.

Proven nephrotoxicity and ICIs

The results show that acute kidney injury, IgA nephropathy and nephritis show a significant difference with partial ICIs, and some of these signals were stronger than others (Fig. 2). This may be related to the mechanism of the ICIs. CTLA-4 is a part of the B7:CD28 immunoglobulin family found on the surface of T-cells and transmits an inhibitory signal to the T-cell. PD-1 is expressed on T-cells and binds to its ligands PD-L1 and PD-L2 that are expressed on cancer cells and other immune cells. Antibodies against PD-1, such as nivolumab, pembrolizumab and pidilizumab, or PD-L1, such as MEDI4736 and MPDL3280A increase the anti-tumor T-cell response by blocking the interaction of PD1 and PD-L1 to prevent T-cell inactivation (Curran et al. 2010). Acute kidney injury, IgA nephropathy and nephritis are complex diseases with many subtypes, and there are many causes of the disease (Cortazar et al. 2016; Mamlouk et al. 2019). Previous research has not been able to elaborate on the pathological mechanism. But what is certain is that changes in the immune system will affect other tissues, which is in line with the characteristics of the human body.

Autoimmune nephritis and ICIs

The results show that autoimmune nephritis and a variety of drugs are related, including atezolizumab (ROR025 = 20.97, IC025 = 1.11), cemiplimab (ROR025 = 375.35, IC025 = 0.83), ipilimumab (ROR025 = 72.67, IC025 = 3.77), durvalumab (ROR025 = 18.27, IC025 = 0.72), nivolumab (ROR025 = 29.03, IC025 = 3.46) and pembrolizumab (ROR025 = 38.15, IC025 = 3.21). It has not been mentioned in previous studies. Autoimmune nephritis disease is a disease caused by impaired autoimmune system. The aforementioned medicinal mechanism of the ICIs is very relevant with autoimmune nephritis. Therefore, medical staff must pay attention to the physical condition of such patients in clinical use.

Conclusion

This study comprehensively evaluated the association of ICIs and potential nephrotoxicities from real-world practice. Overall, no significant association was detected between ICIs and kidney AEs, while there was significant difference between AEs related to nephrotoxicity and atezolizumab in total . And class-specific nephrotoxicities were detected in several ICIs Immunotherapy strategies. Partial results were consistent with previous literatures. The results show that acute kidney injury, IgA nephropathy and nephritis show a significant difference with partial ICIs. At the same time new discoveries have also emerged. Autoimmune nephritis and a variety of drugs are related, including atezolizumab, cemiplimab, ipilimumab, nivolumab and pembrolizumab. In the clinical application of ICIs, attention should be paid to patients, especially male patients and elderly patients, with acute kidney injury, nephritis, autoimmune nephritis and other nephrotoxic AEs. Related AEs of nephrotoxicity are warranted to be reminded by clinicians.

Acknowledgments

This study was funded by the National Natural Science Foundation of China (81303315) and the Natural Science Foundation of Liaoning Province (20180550342).

Conflict of Interest

The authors declare no conflict of interest.

References

•  Chen,  G.,  Qin,  Y.,  Fan,  Q.,  Zhao,  B.,  Mei,  D. &  Li,  X. (2020) Renal adverse effects following the use of different immune checkpoint inhibitor regimens: a real-world pharmacoepidemiology study of post-marketing surveillance data. Cancer Med., 9, 6576-6585.
•  Cortazar,  F.B.,  Marrone,  K.A.,  Troxell,  M.L.,  Ralto,  K.M.,  Hoenig,  M.P.,  Brahmer,  J.R.,  Le,  D.T.,  Lipson,  E.J.,  Glezerman,  I.G.,  Wolchok,  J.,  Cornell,  L.D.,  Feldman,  P.,  Stokes,  M.B.,  Zapata,  S.A.,  Hodi,  F.S., et al. (2016) Clinicopathological features of acute kidney injury associated with immune checkpoint inhibitors. Kidney Int., 90, 638-647.
•  Curran,  M.A.,  Montalvo,  W.,  Yagita,  H. &  Allison,  J.P. (2010) PD-1 and CTLA-4 combination blockade expands infiltrating T cells and reduces regulatory T and myeloid cells within B16 melanoma tumors. Proc. Natl. Acad. Sci. U. S. A., 107, 4275-4280.
•  Hellmann,  M.D.,  Paz-Ares,  L.,  Bernabe Caro,  R.,  Zurawski,  B.,  Kim,  S.W.,  Carcereny Costa,  E.,  Park,  K.,  Alexandru,  A.,  Lupinacci,  L.,  de la Mora Jimenez,  E.,  Sakai,  H.,  Albert,  I.,  Vergnenegre,  A.,  Peters,  S.,  Syrigos,  K., et al. (2019) Nivolumab plus ipilimumab in advanced non-small-cell lung cancer. N. Engl. J. Med., 381, 2020-2031.
•  Hoste,  E.A.J.,  Bagshaw,  S.M.,  Bellomo,  R.,  Cely,  C.M.,  Colman,  R.,  Cruz,  D.N.,  Edipidis,  K.,  Forni,  L.G.,  Gomersall,  C.D.,  Govil,  D.,  Honoré,  P.M.,  Joannes-Boyau,  O.,  Joannidis,  M.,  Korhonen,  A.-M.,  Lavrentieva,  A., et al. (2015) Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med., 41, 1411-1423.
•  Hu,  F.,  Ye,  X.,  Zhai,  Y.,  Xu,  J.,  Guo,  X.,  Guo,  Z.,  Zhou,  X.,  Ruan,  Y.,  Zhuang,  Y. &  He,  J. (2020) Ear and labyrinth toxicities induced by immune checkpoint inhibitors: a disproportionality analysis from 2014 to 2019. Immunotherapy, 12, 531-540.
•  Izzedine,  H.,  Harris,  M. &  Perazella,  M.A. (2009) The nephrotoxic effects of HAART. Nat. Rev. Nephrol., 5, 563-573.
•  Li,  H.,  Xu,  J.,  Bai,  Y.,  Zhang,  S.,  Cheng,  M. &  Jin,  J. (2021) Nephrotoxicity in patients with solid tumors treated with anti-PD-1/PD-L1 monoclonal antibodies: a systematic review and meta-analysis. Invest. New Drugs. 39, 860-870.
•  Liu,  C.P.,  Hu,  Y.,  Lin,  J.C.,  Fu,  H.L.,  Lim,  L.Y. &  Yuan,  Z.X. (2019) Targeting strategies for drug delivery to the kidney: from renal glomeruli to tubules. Med. Res. Rev., 39, 561-578.
•  Ljunggren,  H.G.,  Jonsson,  R. &  Höglund,  P. (2018) Seminal immunologic discoveries with direct clinical implications: The 2018 Nobel Prize in Physiology or Medicine honours discoveries in cancer immunotherapy. Scand. J. Immunol., 88, e12731.
•  Mamlouk,  O.,  Selamet,  U.,  Machado,  S.,  Abdelrahim,  M.,  Glass,  W.F.,  Tchakarov,  A.,  Gaber,  L.,  Lahoti,  A.,  Workeneh,  B.,  Chen,  S.,  Lin,  J.,  Abdel-Wahab,  N.,  Tayar,  J.,  Lu,  H.,  Suarez-Almazor,  M., et al. (2019) Nephrotoxicity of immune checkpoint inhibitors beyond tubulointerstitial nephritis: single-center experience. J. Immunother. Cancer, 7, 2.
•  Markowitz,  G.S. &  Perazella,  M.A. (2005) Drug-induced renal failure: a focus on tubulointerstitial disease. Clin. Chim. Acta., 351, 31-47.
•  Mehta,  R.L.,  Pascual,  M.T.,  Soroko,  S.,  Savage,  B.R.,  Himmelfarb,  J.,  Ikizler,  T.A.,  Paganini,  E.P. &  Chertow,  G.M. (2004) Spectrum of acute renal failure in the intensive care unit: the PICARD experience. Kidney Int., 66, 1613-1621.
•  Messenheimer,  D.J.,  Jensen,  S.M.,  Afentoulis,  M.E.,  Wegmann,  K.W.,  Feng,  Z.,  Friedman,  D.J.,  Gough,  M.J.,  Urba,  W.J. &  Fox,  B.A. (2017) Timing of PD-1 blockade is critical to effective combination immunotherapy with Anti-OX40. Clin. Cancer Res., 23, 6165-6177.
•  Min,  J.,  Osborne,  V.,  Kowalski,  A. &  Prosperi,  M. (2018) Reported adverse events with painkillers: data mining of the US Food and Drug Administration Adverse Events Reporting System. Drug Saf., 41, 313-320.
•  Moffett,  B.S. &  Goldstein,  S.L. (2011) Acute kidney injury and increasing nephrotoxic-medication exposure in noncritically-ill children. Clin. J. Am. Soc. Nephrol., 6, 856-863.
• Perazella, M.A. (2003) Drug-induced renal failure: update on new medications and unique mechanisms of nephrotoxicity. Am. J. Med. Sci., 325, 349-362.
• Perazella, M.A. (2005) Drug-induced nephropathy: an update. Expert Opin. Drug Saf., 4, 689-706.
• Perazella, M.A. (2009) Renal vulnerability to drug toxicity. Clin. J. Am. Soc. Nephrol. 4, 1275-1283.
• Perazella, M.A. (2012) Drug use and nephrotoxicity in the intensive care unit. Kidney Int., 81, 1172-1178.
•  Raschi,  E.,  Mazzarella,  A.,  Antonazzo,  I.C.,  Bendinelli,  N.,  Forcesi,  E.,  Tuccori,  M.,  Moretti,  U.,  Poluzzi,  E. &  De Ponti,  F. (2019) Toxicities with immune checkpoint inhibitors: emerging priorities from disproportionality analysis of the FDA Adverse Event Reporting System. Target. Oncol., 14, 205-221.
•  Sacher,  A.G. &  Gandhi,  L. (2016) Biomarkers for the clinical use of PD-1/PD-L1 inhibitors in non-small-cell lung cancer: a review. JAMA Oncol., 2, 1217-1222.
•  Stricker,  B.H. &  Tijssen,  J.G. (1992) Serum sickness-like reactions to cefaclor. J. Clin. Epidemiol., 45, 1177-1184.
•  Uchino,  S.,  Bellomo,  R.,  Morimatsu,  H.,  Morgera,  S.,  Schetz,  M.,  Tan,  I.,  Bouman,  C.,  Macedo,  E.,  Gibney,  N.,  Tolwani,  A.,  Oudemans-van Straaten,  H.,  Ronco,  C. &  Kellum,  J.A. (2007) Continuous renal replacement therapy: a worldwide practice survey. The beginning and ending supportive therapy for the kidney (B.E.S.T. kidney) investigators. Intensive Care Med., 33, 1563-1570.
•  Valdivielso,  J.M.,  Jacobs-Cacha,  C. &  Soler,  M.J. (2019) Sex hormones and their influence on chronic kidney disease. Curr. Opin. Nephrol. Hypertens., 28, 1-9.
•  van Puijenbroek,  E.P.,  Bate,  A.,  Leufkens,  H.G.,  Lindquist,  M.,  Orre,  R. &  Egberts,  A.C. (2002) A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions. Pharmacoepidemiol. Drug Saf., 11, 3-10.
•  Wrangle,  J.M.,  Velcheti,  V.,  Patel,  M.R.,  Garrett-Mayer,  E.,  Hill,  E.G.,  Ravenel,  J.G.,  Miller,  J.S.,  Farhad,  M.,  Anderton,  K.,  Lindsey,  K.,  Taffaro-Neskey,  M.,  Sherman,  C.,  Suriano,  S.,  Swiderska-Syn,  M.,  Sion,  A., et al. (2018) ALT-803, an IL-15 superagonist, in combination with nivolumab in patients with metastatic non-small cell lung cancer: a non-randomised, open-label, phase 1b trial. Lancet Oncol., 19, 694-704.
•  Weinstein,  L.,  Radano,  T.A.,  Jack,  T.,  Kalina,  P. &  Eberhardt,  J.S. 3rd. (2009) Perspect Health Inf. Manag., 6, 1b.
•  Ye,  X.,  Hu,  F.,  Zhai,  Y.,  Qin,  Y.,  Xu,  J.,  Guo,  X.,  Zhuang,  Y. &  He,  J. (2020) Hematological toxicities in immune checkpoint inhibitors: a pharmacovigilance study from 2014 to 2019. Hematol. Oncol., 38, 565-575.
•  Zhai,  Y.,  Ye,  X.,  Hu,  F.,  Xu,  J.,  Guo,  X.,  Zhuang,  Y. &  He,  J. (2019) Endocrine toxicity of immune checkpoint inhibitors: a real-world study leveraging US Food and Drug Administration adverse events reporting system. J. Immunother. Cancer, 7, 286.