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Predictors of the Onset of Type 1 Diabetes Obtained from Real-World Data Analysis in Cancer Patients Treated with Immune Checkpoint Inhibitors

Medications that target programmed cell death protein-1 (PD-1) have proven effective. However, blockade of PD-1/Programmed death-ligand 1(PD-L1) causes immune-related adverse events (irAEs). Characteristics of this irAE include many symptom, low in frequency, and difficulty in prevention. The key to...

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Autores principales: Takada, Shinya, Hirokazu, Hashishita, Yamagishi, Kayo, Hideki, Sato, Masayuki, Endo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: West Asia Organization for Cancer Prevention 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568867/
https://www.ncbi.nlm.nih.gov/pubmed/32592366
http://dx.doi.org/10.31557/APJCP.2020.21.6.1697
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author Takada, Shinya
Hirokazu, Hashishita
Yamagishi, Kayo
Hideki, Sato
Masayuki, Endo
author_facet Takada, Shinya
Hirokazu, Hashishita
Yamagishi, Kayo
Hideki, Sato
Masayuki, Endo
author_sort Takada, Shinya
collection PubMed
description Medications that target programmed cell death protein-1 (PD-1) have proven effective. However, blockade of PD-1/Programmed death-ligand 1(PD-L1) causes immune-related adverse events (irAEs). Characteristics of this irAE include many symptom, low in frequency, and difficulty in prevention. The key to a successful ICI-related treatment lies in the management of irAEs resulting from immune checkpoint inhibitor (ICI) treatment. Although it is difficult to predict irAE, we tried to extract features of irAE expression from analysis of real-world database. This study used data extracted from the Japan Adverse Drug Event Report (JADER) database to assess risk factors associated with serious side effects of irAE, type 1 diabetes (T1DM). The analysis targets were nivolumab, atezolizumab, durvalumab, and pembrolizumab, and the study period was from July 2014 to June 2019. Analysis of Japanese population data confirmed that being women and having melanoma were risk factors for developing ICI-related T1DM. Analysis using this database in combination with information on ICI-related T1DM provides information and guidelines that will help in the safer treatment of ICI in the future.
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spelling pubmed-75688672020-10-30 Predictors of the Onset of Type 1 Diabetes Obtained from Real-World Data Analysis in Cancer Patients Treated with Immune Checkpoint Inhibitors Takada, Shinya Hirokazu, Hashishita Yamagishi, Kayo Hideki, Sato Masayuki, Endo Asian Pac J Cancer Prev Research Article Medications that target programmed cell death protein-1 (PD-1) have proven effective. However, blockade of PD-1/Programmed death-ligand 1(PD-L1) causes immune-related adverse events (irAEs). Characteristics of this irAE include many symptom, low in frequency, and difficulty in prevention. The key to a successful ICI-related treatment lies in the management of irAEs resulting from immune checkpoint inhibitor (ICI) treatment. Although it is difficult to predict irAE, we tried to extract features of irAE expression from analysis of real-world database. This study used data extracted from the Japan Adverse Drug Event Report (JADER) database to assess risk factors associated with serious side effects of irAE, type 1 diabetes (T1DM). The analysis targets were nivolumab, atezolizumab, durvalumab, and pembrolizumab, and the study period was from July 2014 to June 2019. Analysis of Japanese population data confirmed that being women and having melanoma were risk factors for developing ICI-related T1DM. Analysis using this database in combination with information on ICI-related T1DM provides information and guidelines that will help in the safer treatment of ICI in the future. West Asia Organization for Cancer Prevention 2020-06 /pmc/articles/PMC7568867/ /pubmed/32592366 http://dx.doi.org/10.31557/APJCP.2020.21.6.1697 Text en This is an Open Access article distributed under the terms of the Creative Commons Attribution License, (http://creativecommons.org/licenses/by/3.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Takada, Shinya
Hirokazu, Hashishita
Yamagishi, Kayo
Hideki, Sato
Masayuki, Endo
Predictors of the Onset of Type 1 Diabetes Obtained from Real-World Data Analysis in Cancer Patients Treated with Immune Checkpoint Inhibitors
title Predictors of the Onset of Type 1 Diabetes Obtained from Real-World Data Analysis in Cancer Patients Treated with Immune Checkpoint Inhibitors
title_full Predictors of the Onset of Type 1 Diabetes Obtained from Real-World Data Analysis in Cancer Patients Treated with Immune Checkpoint Inhibitors
title_fullStr Predictors of the Onset of Type 1 Diabetes Obtained from Real-World Data Analysis in Cancer Patients Treated with Immune Checkpoint Inhibitors
title_full_unstemmed Predictors of the Onset of Type 1 Diabetes Obtained from Real-World Data Analysis in Cancer Patients Treated with Immune Checkpoint Inhibitors
title_short Predictors of the Onset of Type 1 Diabetes Obtained from Real-World Data Analysis in Cancer Patients Treated with Immune Checkpoint Inhibitors
title_sort predictors of the onset of type 1 diabetes obtained from real-world data analysis in cancer patients treated with immune checkpoint inhibitors
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568867/
https://www.ncbi.nlm.nih.gov/pubmed/32592366
http://dx.doi.org/10.31557/APJCP.2020.21.6.1697
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