Long-term safety of single-agent ibrutinib in patients with chronic lymphocytic leukemia in 3 pivotal studies

Ibrutinib, a first-in-class once-daily oral Bruton tyrosine kinase inhibitor indicated for chronic lymphocytic leukemia (CLL), is continued until progressive disease or unacceptable toxicity. We conducted an integrated safety analysis of single-agent ibrutinib from randomized phase 3 studies PCYC-1112 (RESONATE, n = 195) and PCYC-1115/1116 (RESONATE-2, n = 135), and examined longer-term safety separately in the phase 1b/2 PCYC-1102/1103 study (n = 94, 420 mg/d). In the integrated analysis (ibrutinib treatment up to 43 months), the most common adverse events (AEs) were primarily grade 1/2; diarrhea (n = 173, 52% any-grade; n = 15, 5% grade 3) and fatigue (n = 119, 36% any-grade; n = 10, 3% grade 3). The most common grade 3/4 AEs were neutropenia (n = 60, 18%) and pneumonia (n = 38, 12%). Over time, prevalence of AEs of interest (diarrhea, fatigue, grade ≥3 infection, bleeding, and neutropenia) trended down; prevalence of hypertension increased, but incidence decreased after year 1. AEs led to dose reductions in 42 (13%) patients and permanent discontinuations in 37 (11%); dose modifications due to AEs were most common during year 1 and decreased in frequency thereafter. The most common AEs (preferred term) contributing to discontinuation included pneumonia (n = 4), anemia (n = 3), and atrial fibrillation (n = 3). With long-term follow-up on PCYC-1102/1103 (ibrutinib treatment up to 67 months), grade 3/4 AEs were generally similar to those in the integrated analysis. Overall, AEs were primarily grade 1/2 and manageable during prolonged ibrutinib treatment in patients with CLL. These trials were registered at www.clinicaltrials.gov as #NCT01578707, #NCT01722487, #NCT01724346, #NCT01105247, and #NCT01109069

Scalable inference of ordinary differential equation models of biochemical processes.

Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability