The effect of AZD9567 vs. prednisolone on glycaemic control in patients with type 2 diabetes mellitus: Results from a phase 2a clinical trial.

Aims: Corticosteroids are the treatment of choice for many inflammatory diseases but often lead to adverse effects, including hyperglycaemia. This study investigated the mechanisms driving differential effects on glucose control for AZD9567, an oral nonsteroidal selective glucocorticoid receptor modulator vs. prednisolone in 46 patients with type 2 diabetes mellitus.

AZD9567 versus prednisolone in patients with active rheumatoid arthritis: A phase IIa, randomized, double-blind, efficacy, and safety study.

Oral corticosteroid use is limited by side effects, some caused by off-target actions on the mineralocorticoid receptor that disrupt electrolyte balance. AZD9567 is a selective, nonsteroidal glucocorticoid receptor modulator. The efficacy, safety, and tolerability of AZD9567 and prednisolone were assessed in a phase IIa study.

Clinical Pharmacokinetics, Pharmacodynamics, and Immunogenicity of Anifrolumab.

The type I interferon (IFN) signaling pathway is implicated in the pathogenesis of systemic lupus erythematosus (SLE). Anifrolumab is a monoclonal antibody that targets the type I IFN receptor subunit 1. Anifrolumab is approved in several countries for patients with moderate to severe SLE receiving standard therapy.

Nonlinear Population Pharmacokinetics of Anifrolumab in Healthy Volunteers and Patients With Systemic Lupus Erythematosus.

We characterized the population pharmacokinetics of anifrolumab, a type I interferon receptor-blocking antibody. Pharmacokinetic data were analyzed from the anifrolumab (intravenous [IV], every 4 weeks) arms from 5 clinical trials in patients with systemic lupus erythematosus (SLE) (n = 664) and healthy volunteers (n = 6). Population pharmacokinetic modeling was performed using a 2-compartment model with parallel linear and nonlinear elimination pathways.

Exposure-response modeling improves selection of radiation and radiosensitizer combinations.

A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept.