A model‐based meta‐analysis of immune‐related adverse events during immune checkpoint inhibitors treatment for NSCLC

Abstract

Immune checkpoint inhibitors (ICIs) have become a vital part of the therapeutic landscape for non-small cell lung cancer (NSCLC) in recent years benefiting from their remarkable efficacy. However, ICIs are associated with potentially lifethreatening immune-related adverse events (irAEs). This study aims to quantify dose dependence and additional influencing factors of both any grade and grade greater than or equal to 3 irAEs in patients with NSCLC treated by ICIs. The triallevel irAE data was collected and pooled from 129 cohorts in 81 clinical studies.A logit-transformed meta-regression model was applied to derive the quantitative relationship of irAE rate and ICI exposure. Programmed cell death-1 (PD-1) or programmed cell death ligand-1 (PD-L1) inhibitors showed no dose dependence in patients with NSCLC, whereas cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) inhibitors exhibited a statistically significant dose dependence when used alone or combined with PD-1 or PD-L1 inhibitors. Besides, therapy line and combination of ICIs with chemotherapy or target therapy were significant covariates. Hopefully, the results of this study can improve clinicians' awareness of irAEs and be helpful for clinical decisions during ICI treatment for NSCLC. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC?Conventional clinical trials and meta-analysis have pooled and compared the incidence of immune-related adverse events (irAEs) induced by diverse immune checkpoint inhibitors (ICIs) in several cancer types. WHAT QUESTION DID THIS STUDY ADDRESS?A latest comprehensive model-based meta-analysis based on multiple types of clinical studies was conducted to quantify dose dependence and additional influencing factors of both any grade and grade greater than or equal to 3 irAEs during ICI treatment for non-small cell lung cancer (NSCLC) up to April 30, 2021.

Application and Quality of Model‐Based Meta‐Analysis in Pharmaceutical Research: A Systematic Cross‐Sectional Analysis and Practical Considerations

“…MBMA has been adopted in pharmaceutical research for a variety of disease areas, particularly in nervous system diseases, endocrine/nutritional/metabolic diseases, and neoplasms. For such types of diseases, multiple treatment strategies were developed and tested in many trials, allowing MBMA to estimate the relative effects of different treatments 9,20–24 . For most chronic diseases, such as diabetes and hypertension, which require long‐term management strategies, MBMA is particularly useful in predicting long‐term results based on short‐term data through time event or effect model 25 …”

“…For such types of diseases, multiple treatment strategies were developed and tested in many trials, allowing MBMA to estimate the relative effects of different treatments. 9,[20][21][22][23][24] For most chronic diseases, such as diabetes and hypertension, which require long-term management strategies, MBMA is particularly useful in predicting long-term results based on short-term data through time event or effect model. 25 Although MBMA has been increasingly used over time, our study suggested that the methodological and reporting quality of MBMA studies remained suboptimal.…”