Exploring the Link Between Mtor Inhibitors and Interstitial Lung Diseases: A Disproportionality Analysis Using Fda Adverse Event Reporting System Database

doi:10.21203/rs.3.rs-2527867/v1

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Exploring the Link Between Mtor Inhibitors and Interstitial Lung Diseases: A Disproportionality Analysis Using Fda Adverse Event Reporting System Database

https://doi.org/10.21203/rs.3.rs-2527867/v1

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Background: The mammalian target of rapamycin (mTOR) inhibitors are immunosuppressive agents used to prevent organ transplant rejection. This class of drugs has shown significant high incidence of interstitial lung disease (ILD).

Objective: This study aims to find out the potential safety signal associated between mTOR inhibitors and ILD by Food and Drug Administration Adverse Event Reporting System (FAERS) databases.

Methods: Publicly available FAERS database was used to perform a case/non-case retrospective disproportionality analysis (2009Q1 to 2022Q3). OpenVigil 2.1MedDRA v24 was used to extract and filter data from FAERS database. The preferred term used for the study was “interstitial lung disease” and the drugs included ere everolimus, sirolimus and temsirolimus. Reporting odds ratio (ROR), proportional reporting ratio (PRR) and information component (IC) were used to detect signal.

Results: Overall, 62,117 AE reports related to mTOR inhibitors were recorded during the period. Of these 1,027 AE reports on ILD were submitted to the FAERS database. Everolimus accounted for 723, sirolimus for 191 and temsirolimus for 113 cases of ILD reports associated with mTOR inhibitors. On performing age and gender stratification, it was found that the age group of 19-64yrs and female sex had the highest signal strength.

Conclusion: This study has shown positive signals for ILD with everolimus, sirolimus, and temsirolimus which were not yet established in clinical trials. Further study with well-designed epidemiological data is required to validate these results.

Mammalian target of rapamycin inhibitor

Interstitial lung disease

FAERS

Pharmacovigilance

Disproportionality analysis

Data mining algorithms

safety signals.

Pharmacovigilance is the science related to the collection, monitoring and prevention of adverse events or any other drug related problems. Consequently, its purpose is to advocate the effective and secure use of drugs and evaluation of the risk-benefit profile of marketed medicines [1]. FDA Adverse Event Reporting system (FAERS) is a pharmacovigilance database that includes US and International data on spontaneous report of suspected adverse drug reactions and relevant administrative information, patient demographics, information about drug regimen and patient outcomes. Hence, it is mandatory for physicians, pharmacists and pharmaceutical companies to report the adverse events [2]. The United States Food and Drug Administration (US FDA) has maintained the Adverse Event Reporting System (AERS) database since 1968. The purpose of this database is the continuous monitoring of drugs during post- marketing surveillance [3].

According to the pharmacovigilance programmes of World Health Organization (WHO), a signal is described as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously. Usually, more than a single report is required to generate a signal, depending upon the seriousness of the event and the quality of the information” (WHO, 2002). Hence, signal detection is the modern method of detecting new reaction of a drug in pharmacovigilance and it is important for the public health safety. Presently, signal detection is done by Data Mining Algorithms (DMAs). ‘Data mining’ is defined information sources for extracting an unknown information and data mining techniques are employed to identify potential associations between drugs and adverse events. Currently, three major DMAs are the proportional reporting ratio (PRR), the reporting odds ratio (ROR) and the information component (IC) of the WHO are mainly used. These are calculated by 2x2 contingency table [4, 5]. Finally, the whole practice can be summarised as disproportionality analysis, which is considered primarily a tool for generating hypotheses on possible causal relationships between drugs and adverse reactions [6, 7]. Open Vigil FDA is a data mining tool used to extract and filter pharmacovigilance data from FAERS. Its function is to compare the safety profile of drugs, identify drug induced new adverse event, check unknown drug – drug interaction of combinational drugs and improve the significance of the result by identifying confounding factors and eliminate them using background correction [8, 9, 10].

The mammalian target of rapamycin (mTOR) inhibitors are US FDA approved immunosuppressors that are currently used in antineoplastic therapies for various cancers such as progressive pancreatic neuroendocrine tumors, advanced renal cell carcinoma and advanced breast cancer [11]. Three commercially available mTOR inhibitors are sirolimus, temsirolimus and everolimus which were approved by FDA in 1999, 2007 and 2009 respectively. It is noteworthy that mTOR pathway plays crucial roles in mammalian metabolism and physiology and the de-regulated activity of mTOR can cause many pathophysiological conditions, such as diabetes, ageing, obesity, alzheimer’s disease and cancer. Also, the upregulated activity of mTOR pathway is involved in tumor growth and progression through various mechanisms including the promotion of growth factor receptor signalling, angiogenesis, glycolytic metabolism, lipid metabolism, cancer cell migration, and suppression of autophagy. Thus, mTOR is a target for cancer therapy [12]. Besides, it has appeared as a treatment option for tuberous sclerosis complex, a genetic disorder that occurs as a result of overactivation of mTOR pathway [13]. Rapamycin, which was clinically named sirolimus (SRL), is a macrolide antibiotic discovered in the 1970 and it is mainly used in the prevention of organ rejection in solid organ transplantation and also for the treatment of autoimmune diseases [14]. Temsirolimus and everolimus are rapamycin analogues approved for the treatment of renal cell carcinoma. Everolimus is approved by US FDA for kidney and liver transplant rejection prophylaxis [15].

Interstitial lung disease (ILD), also known as diffused parenchymal diseases, are a heterogeneous group of disorders characterized by fibrosis (scarring) of the lungs and drug reactions are one of the main causes of ILD [16]. Over 1300 drugs, substances or procedures were reported to cause respiratory problems like ILD. It is noteworthy that mTOR inhibitors has shown a significantly higher incidence of drug-induced interstitial lung disease (DILLD) [17]. It can lead to respiratory insufficiency and even mortality. The mechanism of action of mTOR inhibitors induced ILD rests unknown even though earlier study proposed cell-mediated autoimmune response, i.e., T cell-mediated, delayed-type hypersensitivity, proinflammatory cytokines, may be concerned in its pathogenesis [18, 19]. Numerous fatal outcomes have been reported for sirolimus, everolimus, and temsirolimus due to ILD. Usually, diagnosis of mTOR inhibitor induced ILD is difficult and non-specific [20, 21].

Moreover, drug indued ILD is a life-threatening disease. It is often reversible and resolved with discontinuation of drug therapy and resume after improvement. Consequently, physician must be careful in monitoring and providing proper treatment. Hence, this study aims to find out potential safety signal between mTOR inhibitors and ILD by using FAERS databases and do a demographic and outcome stratification of the same. It is expected that the results of this study pave way for better clinical and formulary decisions.

Disproportionality analysis has been done using FAERS data from first quarter of 2009 to third quarter of 2022 by using OpenVigil2.1-MedDRA software. It was quantified using following data mining techniques such as Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR) and Information Component (IC).

2.1. DATASOURCE

The case/non-case retrospective disproportionality analysis was executed to quantify the association between mTOR inhibitors and interstitial lung disease. Adverse Event reports from the FAERS database were utilized in the study. This study included all the reports of mTOR inhibitors received by the FDA from 2009Q1–2022Q3.

2.2. STUDY PROCEDURE

Data mining, filtration and extraction were carried out by using the OpenVigil 2.1-MedDRA-v24, which is a software used for pharmacovigilance data analysis. It runs with clean data, by removing the duplicate case reports and reports with incomplete information. It incorporates with MedDRA terminology and we coded with preferred terms of MedDRA. The preferred adverse event (AE) for this study was Interstitial lung disease (ILD). We retrieved reports of ILD associated with mTOR inhibitors, namely everolimus, sirolimus and temsirolimus by excluding confounding factors obtained from OpenVigil FDA. Boolean algebraic functions (AND, OR, AND NOT) are applied to govern the signal strength of mTOR inhibitors by eliminating concomitantly administered drug i.e., confounding factors. ‘AND NOT’ was the function used to exclude the confounding factors. Various filters such as gender, age, outcome, reporter country and indication were used to confine the data. The disproportionality of each drug was measured using reporting odds ratio (ROR), proportional reporting ratio (PRR) and information component (IC) with 95% confidence interval (CI). ROR, PRR, and RRR were estimated by 2x2 contingency table acquired from OpenVigil data tool. IC value was calculated separately by using the formula; log2 base of RRR in an excel sheet.

2.3. STATISTICAL ANALYSIS

Descriptive analysis was used to review the reports of patients with ILD associated with mTOR inhibitors. We calculated the ROR, PRR and IC as standard disproportionality approach to find out signal strength associated between ILD and mTOR inhibitors.

The threshold used for data mining algorithm was predefined as PRR ≥ 2.0 with a chi-squared test of ≥ 4.0, at least three reports (n ≥ 3) of that preferred term (PT), IC with IC-2SD > 0 and ROR with ROR- 1.96SE > 1 with 95% CI, which were considered to be statistically significant. The higher the threshold values indicate greater disproportionality, that means our drug is probable to cause a specific AE than all other drugs.

Overall, 62,117 AE reports related to mTOR inhibitors were recorded during the period of 2009Q1 to 2022Q3. Of these, 1,027 AE reports on ILD were submitted to the FAERS database. Detailed patient characteristics of ILD reports submitted for mTOR inhibitors can be found in Table 1–6.

Table 1

Clinical characteristics of patients with everolimus -induced interstitial lung diseases
Variables	Everolimus
Variables	DE	ROR (CI- 95%)	PRR (CI- 95%)	IC (CI-95%)
Gender
Male	126	5.239 (4.389–6.254)	5.188 (4.355–6.18)	2.356
Female	178	8.815 (7.589–10.239)	8.727 (7.525–10.119)	3.091
Age
0–18yrs	4	5.08 (1.888–13.668)	5.062 (1.89–13.559)	2.322
19–64yrs	119	10.482 (8.725–12.594)	10.334 (8.625–12.38)	3.334
65–100yrs	126	5.577 (4.667–6.665)	5.466 (4.594–6.504)	2.432
Outcome
Congenital Anomaly	0	0	0	0
Death	81	2.591 (2.077–3.233)	2.572 (2.067–3.201)	1.346
Disability	7	10.645 (4.995–22.684)	10.412 (4.975–21.792)	3.357
Hospitalization Initial/prolong	173	5.585 (4.795–6.506)	5.476 (4.718–6.356)	2.426
Life -Threatening	24	4.814 (3.194–7.255)	4.688 (3.153–6.972)	2.210
Other	246	7.449 (6.555–8.465)	7.299 (6.442–8.27)	2.841
Required intervention to prevent permanent damage	0	0	0	0
Abbreviations: - DE: Case reports, ROR: Reporting odds ratio, PRR: Proportional reporting ratio, IC: Information component, CI: Confidence interval

3.1. Everolimus associated ILD

A total of 723 reports were identified from the FAERS Database for Everolimus and the signal strength of ROR, PRR and IC were 6.475, 6.416 and 2.65 respectively. The clinical characteristics of the patient have been taken. On gender subset analysis showed that, female gender (n = 178) had a greater signal strength of ROR 8.815, PRR 8.727 and IC 3.091 than male gender(n = 126) in which ROR, PRR and IC were 5.239, 5.188 and 2.356 respectively. The age stratification was done by dividing the study population into three groups: 0-18yrs, 19-64yrs and 65-100yrs. It was found that the age group of 19-64yrs (n = 119) had the greatest signal strength of ROR 10.482, PRR 10.334 and IC 3.334 than other age groups. The outcome-based analysis showed that disability (n = 7) had the greatest signal strength of ROR 10.645, PRR 10.412 and IC 3.357 than other outcomes. From the reporter country analysis, South America leads the signal strength list with ROR value 9.162, PRR 9.122 and IC 3.138. In the case of indication analysis, the signal strength was higher for liver transplant recipients (ROR 12.235, PRR 12.136 and IC 2.573). Age, gender and outcome of adverse event are listed in Table 1. The reporter country and indication are given in Table 2. Graphical representation of everolimus illustrate IC value related to the patient demographics depicted in Fig. 1.

Table 2

Reporter country and indication-based analysis of everolimus- induced interstitial lung diseases
Variables	Everolimus
Variables	DE	ROR (CI- 95%)	PRR (CI- 95%)	IC (CI-95%)
Reporter Country
Africa	1	7.906 (1.068–58.525)	7.856 (1.076–57.333)	2.930
Asia	185	4.577 (3.933 − 5.326)	4.298 (3.737–4.943)	2.081
Europe	114	8.068 (6.681 − 9.743)	7.907 (6.575–9.509)	2.943
North America	28	3.918 (2.7–5.684)	3.912 (2.698–5.672)	1.960
Oceania	0	0	0	0
South America	3	9.162 (2.881–29.142)	9.122 (2.884–28.854)	3.138
Indication
Renal cell carcinoma	49	2.531 (1.835–3.49)	2.49 (1.819–3.408)	1.110
Renal transplant	28	10.618 (6.373 − 17.69)	10.41 (6.289–17.233)	2.589
Liver transplant	5	12.235 (3.276–45.695)	12.136 (3.268–45.067)	2.573

Abbreviations: - DE: Case reports, ROR: Reporting odds ratio, PRR: Proportional reporting ratio, IC: Information component, CI: Confidence interval

3.2. Sirolimus associated ILD

A total of 191 reports were identified from the FAERS Database for Sirolimus and the signal strength of ROR, PRR and IC were 3.016, 3.006 and 1.586 respectively. The clinical characteristics of the patient were taken. On gender subset analysis showed that, female gender (n = 14) had a greater signal strength of ROR 4.949, PRR 4.923 and IC 2.297 than male gender(n = 24) in which ROR, PRR, and IC were 3.917, 3.89, and 1.957 respectively. The age stratification was done by dividing the study population into three groups: 0-18yrs, 19-64yrs and 65-100yrs. It was found that the age group of 0-18yrs (n = 4) had the greatest signal strength of ROR 7.257, PRR 7.217 and IC 2.832 than other age groups. The outcome-based analysis showed that Hospitalization (n = 28) had the greatest signal strength of ROR 4.128, PRR 4.071 and IC 2.022 than other outcomes. From the reporter country analysis, North America leads the signal strength list with ROR value 7.223, PRR 7.2 and IC 2.843. In the case of indication analysis, the signal strength was higher for liver transplant recipients (ROR 4.622, PRR 4.601, and IC 1.926). Age, gender and outcome of adverse event are listed in Table 3. The reporter country and indication are given in Table 4. Graphical representation of sirolimus illustrate IC value related to the patient demographics depicted in Fig. 2.

Table 3

Clinical characteristics of patients with sirolimus -induced interstitial lung diseases
Variables	Sirolimus
Variables	DE	ROR (CI- 95%)	PRR (CI- 95%)	IC (CI- 95%)
Gender
Male	24	3.917 (2.619–5.858)	3.89 (2.611–5.797)	1.957
Female	14	4.949 (2.925–8.376)	4.923 (2.919–8.303	2.297
Age
0–18yrs	4	7.257 (2.695–19.543)	7.217 (2.697–19.313)	2.832
19–64yrs	17	4.696 (2.912–7.575)	4.67 (2.905–7.505)	2.219
65–100yrs	14	3.337 (1.968–5.66)	3.303 (1.963–5.557	1.722
Outcome
Congenital Anomaly	0	0	0	0
Death	6	1.454 (0.651–3.247)	1.451 (0.653– 3.222)	0.536
Disability	0	0	0	0
Hospitalization Initial/prolong	28	4.128 (2.838–6.003)	4.071 (2.819–5.881)	2.022
Life -Threatening	1	0.8 (0.112–5.713)	0.801 (0.113–5.657)	-0.320
Other	25	2.885 (1.945–4.28)	2.868 (1.94–4.238)	1.518
Required intervention to prevent permanent damage	0	0	0	0
Abbreviations: - DE: Case reports, ROR: Reporting odds ratio, PRR: Proportional reporting ratio, IC: Information component, CI: Confidence interval

Table 4

Reporter country and indication-based analysis of sirolimus- induced interstitial lung diseases
Variables	Sirolimus
Variables	DE	ROR (CI- 95%)	PRR (CI- 95%)	IC (CI-95%)
Reporter Country
Africa	0	0	0	0
Asia	7	1.145 (0.541 − 2.422)	1.142 (0.549–2.377)	0.191
Europe	12	4.93 (2.786–8.725)	4.873 (2.776–8.554)	2.281
North America	15	7.223 (4.346–12.004)	7.2 (4.34–11.944)	2.843
Oceania	0	0	0	0
South America	0	0	0	0
Indication
Renal cell carcinoma	0	0	0	0
Renal transplant	2	1.133 (0.276–4.651)	1.132 (0.277–4.623)	0.174
Liver transplant	2	4.622 (0.957–22.33)	4.601 (0.959–22.069)	1.926
Abbreviations: - DE: Case reports, ROR: Reporting odds ratio, PRR: Proportional reporting ratio, IC: Information component, CI: Confidence interval

3.3. Temsirolimus associated ILD

A total of 113 reports were identified from the FAERS Database for Temsirolimus and the signal strength of ROR, PRR and IC were 11.131, 10.94 and 3.449 respectively.. The clinical characteristics of the patient were taken. On gender subset analysis showed that, male gender (n = 44) had a greater signal strength of ROR 14.018, PRR 13.604 and IC 3.758 than female gender(n = 8) in which ROR, PRR, and IC were9.427, 9.323, and 3.219 respectively. The age stratification was done by dividing the study population into three groups: 0-18yrs, 19-64yrs and 65-100yrs. It was found that the age group of 19-64yrs (n = 14) had the greatest signal strength of ROR 13.495, PRR 13.238 and IC 3.722 than other age groups. The outcome-based analysis showed that disability (n = 3) had the greatest signal strength of ROR 44.128, PRR 40.085 and IC 5.314 than other outcomes. From the reporter country analysis, Oceania leads the signal strength list with ROR value 32.41, PRR 31.268, and IC 4.936. In the case of indication analysis, the signal strength was higher for renal cell carcinoma subjects (ROR 5.134, PRR 4.908, and IC 2.135). Age, gender and outcome of adverse event are listed in Table 5. The reporter country and indication are given in Table 6. Graphical representation of temsirolimus illustrate IC value related to the patient demographics depicted in Fig. 3.

Table 5

Clinical characteristics of patients with temsirolimus-induced interstitial lung diseases
Variables	Temsirolimus
Variables	DE	ROR (CI- 95%)	PRR (CI- 95%)	IC (CI- 95%)
Gender
Male	44	14.018 (10.373–18.944)	13.604 (10.163–18.21)	3.758
Female	8	9.427 (4.692–18.941)	9.323 (4.681–18.57)	3.219
Age
0–18yrs	0	0	0	0
19–64yrs	14	13.495 (7.942–22.929)	13.238 (7.876–22.251)	3.722
65–100yrs	24	7.478 (4.976–11.239)	7.267 (4.9-10.777)	2.857
Outcome
Congenital Anomaly	0	0	0	0
Death	18	6.998 (4.372–11.203)	6.808 (4.317–10.737)	2.762
Disability	3	44.128 (13.386-145.469)	40.085 (13.588-118.254)	5.314
Hospitalization Initial/prolong	25	6.002 (4.032–8.934)	5.872 (3.985–8.651)	2.550
Life -Threatening	6	7.688 (3.374–17.518)	7.339 (3.362–16.022)	2.871
Other	34	11.818 (8.387–16.655)	11.418 (8.206–15.887)	3.509
Required intervention to prevent permanent damage	0	0	0	0
Abbreviations: - DE: Case reports, ROR: Reporting odds ratio, PRR: Proportional reporting ratio, IC: Information component, CI: Confidence interval

Table 6

Reporter country and indication-based analysis of temsirolimus- induced interstitial lung diseases
Variables	Temsirolimus
Variables	DE	ROR (CI- 95%)	PRR (CI- 95%)	IC (CI- 95%)
Reporter Country
Africa	0	0	0	0
Asia	24	5.559 (3.649-8.468)	5.128 (3.503- 7.508)	2.355
Europe	12	5.751 (3.247-10.185)	5.671 (3.233-9.948)	2.500
North America	15	22.949 (13.78-38.219)	22.694 (13.708-37.573)	4.499
Oceania	2	32.41 (7.781-134.989)	31.268 (7.899-123.779)	4.936
South America	0	0	0	0
Indication
Renal cell carcinoma	29	5.134 (3.439-7.663)	4.908 (3.353-7.184)	2.135
Renal transplant	0	0	0	0
Liver transplant	0	0	0	0

Abbreviations: - DE: Case reports, ROR: Reporting odds ratio, PRR: Proportional reporting ratio, IC: Information component, CI: Confidence interval

Our study intended to determine mTOR inhibitor-induced ILD. mTOR inhibitors are primarily used as immunosuppressive agents. [22]. A systematic review of drug-induced interstitial lung diseases was conducted by Skeoch S et al. and its result indicates that mTOR inhibitors have an association with pulmonary toxicity. A meta-analysis of 2233 cancer patients treated with everolimus reported a 10.4% of DIILD [23]. The diagnosis of DIILD remains challenging. Patients with ILD can show asymptomatic or symptomatic symptoms. Drug-induced ILD toxicity depends on the dose, age, and gender of the patient. Early diagnosis and rapid intervention by reducing the dose or discontinuation of the drug therapy can reduce the incidence. According to the study piloted by Lopez P et al., among 1473 transplant recipients who received everolimus, the incidence rate of everolimus-induced ILD was 0.4%. On discontinuation of everolimus therapy, ILD was resolved in the case of heart transplant recipients. However, the outcome was fatal for both kidney and liver transplant recipients even though the subjects discontinued the everolimus therapy [24].

This study performed disproportionality analysis using DMAs, which are ROR, PRR, and IC. It helps to detect AE associated with drugs. OpenVigil 2.1 MEDRA was used to extract, filter, and determine DMAs from the 2x2 contingency table [25]. Our research mainly focused on the identification of signal strength of mTOR inhibitors (everolimus, sirolimus, and temsirolimus) induced ILD using a pharmacovigilance approach. Regulatory agencies and pharmaceutical companies use this technique to identify AEs that can be caused by newly marketed medications. A total of 62,117 AE reports related to mTOR inhibitors were identified from the FAERS database and of these 1,027 AE reports associated with ILD were retrieved. We have observed everolimus has shown the highest signal strength for ILD with the greatest number of reported cases. The age stratification of the study showed that there is a disproportionate association of ILD among mTOR inhibitor users, particularly in the 19-64yrs age group for everolimus and temsirolimus. At the same time, sirolimus shows ILD cases prominently at 0-18yrs of age. On gender stratification, females showed more signal strength than males for sirolimus and everolimus, whereas temsirolimus shows more signal strength for males. From the data, disability has more signal strength for everolimus and temsirolimus while hospitalization accounts for higher signal strength for sirolimus. The indication-based results show that the liver transplant subjects have higher signal strength for everolimus and sirolimus. In addition to the previous result, temsirolimus has greater signal strength for renal cell carcinoma. From the reporter country analysis, South America, North America, and Oceania have more signal strength for everolimus, sirolimus, and temsirolimus respectively [26, 27]. In a study conducted by Willemsen AE et al., the ILD risk factor was greater for higher age and male sex. Our study contradicts this statement. The former study was performed in 2015 whereas this study evaluates the data up to 2022 which is more significant [20].

Although recent findings showed that there is a significant correlation between the risk estimated from disproportionality analysis and clinical studies, in general, disproportionality analysis cannot be used as a self-reliant method to assess drug-related problems in the real world or replace formal clinical trials. But, even in the absence of data from clinical trials, disproportionality analysis provides minimum information about AEs and helps in the design of future studies [28]. It may help in establishing a drug-AE association link but does not establish any causal relationship. More intensive research with well-designed epidemiological protocols is required to validate these findings.

Our study identified an association between mTOR inhibitors and ILD risk using FAERS database. A disproportionality analysis was done to detect and re-evaluate risk-benefit profile of drugs. We performed ROR, PRR, and IC algorithms using 2x2 contingency table to find out reliable signals. The clinical characteristics of AE reports of patient using mTOR inhibitors were summarized.

6.1 Ethical Approval:

Not applicable

6.2 Consent to participate and for publication:

Not applicable

6.3 Competing Interest:

The authors declare that there are no potential conflicts of interest with respect to the study.

6.4 Author’s Contributions:

The authors contributed equally to this work and take responsibility for the overall content. The Author Anakha, conceived and designed the study, and wrote the manuscript; The Author Aliena, performed data analysis and provided critical revisions; The Author Shefin, provided technical support and manuscript revisions; The corresponding author guided and reviewed the literature. All authors have read and approved the final version of the manuscript.

6.5 Funding:

No additional funding was received for this research

6.6 ACKNOWLEDGEMENTS: Nil

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No competing interests reported.

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Exploring the Link Between Mtor Inhibitors and Interstitial Lung Diseases: A Disproportionality Analysis Using Fda Adverse Event Reporting System Database

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Abstract

Figures

1. Introduction

2. Methodology

2.1. DATASOURCE

2.2. STUDY PROCEDURE

2.3. STATISTICAL ANALYSIS

3. Result

3.1. Everolimus associated ILD

3.2. Sirolimus associated ILD

3.3. Temsirolimus associated ILD

4. Disscussion

5. Conculsion

Declarations

References

Additional Declarations

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