Vandemeulebroecke M, Demin I, Luttringer O, McDevitt H, Ramakrishna R, Sander O
Novartis
Objectives: Quantitative knowledge about the clinical efficacy of approved drugs makes it possible to interpret the efficacy of a candidate drug in light of the competitive landscape. This can be used when setting the desired efficacy profile of a new drug candidate, for example in proof of concept, dose finding or inlicensing. In this context, our objective is to build comprehensive literature-based databases on selected indications and their major treatments, containing longitudinal data and covariates to facilitate dynamic modeling.
Methods: Motivated by a case example in Rheumatoid Arthritis, we present the range of applications to date, and a new generic database infrastructure that was developed to further ramp up these efforts.
Results: Eight drug-disease databases have been built, and two are in development. The range of successful applications spans from dose selection to supporting Go/Nogo milestones. The generic database solution has an easy-to-use front end and allows quick extraction of relevant information, while still retaining the full complexity of a relational database in the background.
Conclusions: Great value can be derived from investing time and effort into building literature-based drug-disease databases.
References:
[1] Demin et al.: Longitudinal model-based meta-analysis in rheumatoid arthritis: an application toward model-based drug development, Clin Pharmacol Ther 2012.
[2] Ito et al.: Disease progression meta-analysis model in Alzheimer’s disease, Alzheimer’s & Dementia 2010.
[3] Mandema et al.: Model-based development of gemcabene, a new lipid-altering agent, AAPS J 2005.
[4] McDevitt et al.: Infrastructure development for building, maintaining and modeling indication-specific summary-level literature databases to support model-based drug development, PAGE 2009.
Reference: PAGE 22 () Abstr 2657 [www.page-meeting.org/?abstract=2657]
Poster: Other Drug/Disease Modelling