Model‐based meta‐analysis of non‐small cell lung cancer with standard of care PD‐1 inhibitors and chemotherapy for early development decision making

Abstract

Single‐arm cohorts/trials are often used in early phase oncology programs to support preliminary clinical activity assessments for investigational products, administered alone or in combination with standard of care (SOC) agents. Benchmarking clinical activity of those combinations against other treatments, including SOC, requires indirect comparisons against external trials, which presents challenges including cross‐study differences in trial populations/other factors. To facilitate such nonrandomized comparisons, we developed a comprehensive model‐based meta‐analysis (MBMA) framework to quantitatively adjust for factors related to efficacy in metastatic non‐small cell lung cancer (mNSCLC). Data were derived from 15 published studies assessing key programmed cell death protein‐1 (PD‐1) inhibitors pembrolizumab (n = 8) and nivolumab (n = 7), representing current SOC in mNSCLC. In the first stage, a mixed‐effects logistic regression model for overall response rate (ORR) was developed accounting for effects of various population covariates on ORR. The ORR model results indicated an odds ratio (OR) of 1.02 for squamous versus non‐squamous histology and OR of 1.20 for PD‐ligand 1 tumor proportion score (TPS) per every 10% increase of TPS level. Next, a nonparametric mixed‐effects model for overall survival (OS) was developed with ORR/other clinical covariates as input. Subsequently, MBMA simulations of relevant hypothetical scenarios involving single‐arm trial design predicted OS hazard ratios as a function of ORR with matched patient characteristics. Findings from this MBMA and derived parameter estimates can be generally applied by the reader as a framework for interpreting efficacy data from early phase trials to support ORR‐based go/no‐go decisions and futility rules, illustrated through examples in this report.

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.…”