Exposure-response modeling using latent variables for the efficacy of a JAK3 inhibitor administered to rheumatoid arthritis patients

• Published in: Journal of pharmacokinetics and pharmacodynamics Vol. 35; no. 2; pp. 139 - 157
• Main Authors: Hutmacher, Matthew M., Krishnaswami, Sriram, Kowalski, Kenneth G.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01-04-2008; Springer Nature B.V
• Subjects: Algorithms; Anti-Inflammatory Agents, Non-Steroidal - therapeutic use; Arthritis, Rheumatoid - drug therapy; Biochemistry; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Dose-Response Relationship, Drug; Endpoint Determination; Humans; Janus Kinase 3 - antagonists & inhibitors; Models, Statistical; Nonlinear Dynamics; Pharmacology/Toxicology; Pharmacy; Randomized Controlled Trials as Topic; Software; Veterinary Medicine/Veterinary Science; Latent variable; Dose-response; Indirect response models; Concentration-response; Ordered-categorical data
• ISSN: 1567-567X; 1573-8744
• DOI: 10.1007/s10928-007-9080-2

Currently, no general methods have been developed to relate pharmacologically based models, such as indirect response models, to discrete or ordered categorical data. We propose the use of an unobservable latent variable (LV), through which indirect response models can be linked with drug exposure. The resulting indirect latent variable response model (ILVRM) is demonstrated using a case study of a JAK3 inhibitor, which was administered to patients in a rheumatoid arthritis (RA) study. The clinical endpoint for signs and symptoms in RA is the American College of Rheumatology response criterion of 20%—a binary response variable. In this case study, four exposure-response models, which have different pharmacological interpretations, were constructed and fitted using the ILVRM method. Specifically, two indirect response models, an effect compartment model, and a model which assumes instantaneous (direct) drug action were assessed and compared for their ability to predict the response data. In general, different model interpretations can influence drug inference, such as time to drug effect onset, as well as affect extrapolations of responses to untested experimental conditions, and the underlying pharmacology that operates to generate key response features does not change because the response was measured discretely. Consideration of these model interpretations can impact future study designs and ultimately provide greater insight into drug development strategies.