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Bioorg Med Chem. 2016 Dec 15;24(24):6401-6408. doi: 10.1016/j.bmc.2016.08.034. Epub 2016 Aug 27.

From lead optimization to NDA approval for a new antimicrobial: Use of pre-clinical effect models and pharmacokinetic/pharmacodynamic mathematical modeling.

Author information

1
Institute for Therapeutic Innovation, University of Florida, 6550 Sanger Road, Lake Nona, FL 32827, United States. Electronic address: gdrusano@ufl.edu.

Abstract

Because of our current crisis of resistance, particularly in nosocomial pathogens, the discovery and development of new antimicrobial agents has become a societal imperative. Changes in regulatory pathways by the Food and Drug Administration and the European Medicines Agency place great emphasis on the use of preclinical models coupled with pharmacokinetic/pharmacodynamic analysis to rapidly and safely move new molecular entities with activity against multi-resistant pathogens through the approval process and into the treatment of patients. In this manuscript, the use of the murine pneumonia system and the Hollow Fiber Infection Model is displayed and the way in which the mathematical analysis of the data arising from these models contributes to the robust choice of dose and schedule for Phase 3 clinical trials is shown. These data and their proper analysis act to de-risk the conduct of Phase 3 trials for anti-infective agents. These trials are the most expensive part of drug development. Further, given the seriousness of the infections treated, they represent the riskiest element for patients. Consequently, these preclinical model systems and their proper analysis have become a central part of accelerated anti-infective development. A final contention of this manuscript is that it is possible to embed these models and in particular, the Hollow Fiber Infection Model earlier in the drug discovery/development process. Examples of 'dynamic driver switching' and the impact of this phenomenon on clinical trial outcome are provided. Identifying dynamic drivers early in drug discovery may lead to improved decision making in the lead optimization process, resulting in the best molecules transitioning to clinical development.

KEYWORDS:

Mathematical modeling; Pharmacodynamics; Resistance suppression

PMID:
27612961
DOI:
10.1016/j.bmc.2016.08.034
[Indexed for MEDLINE]

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