Multiscale, mechanistic model of Rheumatoid Arthritis to enable decision making in late stage drug development.

Rheumatoid Arthritis (RA) is a chronic autoimmune inflammatory disease that affects about 0.1% to 2% of the population worldwide. Despite the development of several novel therapies, there is only limited benefit for many patients.

Tumor dynamics in patients with solid tumors treated with pembrolizumab beyond disease progression.

While many patients are treated beyond progression (TBP), the magnitude and duration of clinical benefit in these patients have not been fully quantified. Data from 799 patients with melanoma (n = 176), non-small cell lung cancer (NSCLC; n = 146), gastric cancer (GC; n = 87), head and neck squamous cell carcinoma (HNSCC; n = 112), clear-cell renal cell carcinoma (ccRCC; n = 51), and urothelial carcinoma (UC; n = 227) TBP were included. Patients had received pembrolizumab beyond confirmed progressive disease (PD) per RECIST v1.

Can a propensity score matching method be applied to assessing efficacy from single-arm proof-of-concept trials in oncology?

As a result of the escalating number of new cancer treatments being developed and competition among pharmaceutical companies, decisions regarding how to proceed with phase III trials are frequently based on findings from either single-arm phase I expansion cohorts or phase II studies that compare the efficacy of the study drug to a standard-of-care benchmark derived from historical data. However, even when eligibility criteria are matched, differences in the distribution of baseline patient features may influence the outcome of single-arm trials in real-world scenarios. Therefore, novel methods are needed to enhance the accuracy of efficacy prediction from current cohorts relative to historical data.