The implications of model-informed drug discovery and development for tuberculosis. Issue 3 (March 2017)

• Record Type: Journal Article
• Authors: Muliaditan, Morris; Davies, Geraint R.; Simonsson, Ulrika S.H.; Gillespie, Stephen H.; Della Pasqua, Oscar
• Is Part Of: Drug discovery today. Volume 22:Issue 3(2017)
• Page Start: 481
• Page End: 486
• Publication Date: 2017-03
• Subjects: Drugs -- Design -- Periodicals; Drugs -- Research -- Periodicals; 615.1
• DOI: 10.1016/j.drudis.2016.09.004
• Languages: English
• ISSNs: 1359-6446

Abstract

Highlights: Dose selection for new combination therapy in tuberculosis has remained empirical. Novel tools are needed for effective translation of preclinical models to humans. A model-based approach provides an opportunity for a better dose rationale and improved trial designs. Abstract : Despite promising advances in the field and highly efficacious first-line treatment, an estimated 9.6 million people are still infected with tuberculosis (TB). Innovative methods are required to effectively transition the growing number of compounds into novel combination regimens. However, progression of compounds into patients occurs despite the lack of clear understanding of the pharmacokinetic–pharmacodynamic (PKPD) relationships. The PreDiCT-TB consortium was established in response to the existing gaps in TB drug development. The aim of the consortium is to develop new preclinical tools in concert with an in silico model-based approach, grounded in PKPD principles. Here, we highlight the potential impact of such an integrated framework on the various stages of TB drug development and on the dose rationale for drug combinations.