Muphy-Benenato, Kerry E., Bhagunde, Pratik R., Chen, April, Davis, Hajnalka E., Durand-Reville, Thomas F., Ehmann, David E., Galullo, Vincent, Harris, Jennifer J., Hatoum-Mokdad, Holia, Jahic, Haris, Manjunatha, M.R., Manyak, Erika L., Mueller, John, Patey, Sara, Quiroga, Olga, Rooney, Michael, Sha, Li, Shapiro, Adam B., Sylvester, Mark, Tan, Beesan, Tsai, Andy S., Uria-Nickelsen, Maria, Wu, Ye, Zambrowski, Mark and Zhao, Shannon X. (2015) Discovery of Efficacious Pseudomonas aeruginosa-Targeted Siderophore-Conjugated Monocarbams by Application of a Semi-Mechanistic PK/PD Model. Journal of Medicinal Chemistry, 58 (5). pp. 2195-2205. ISSN 0022-26231520-4804

Abstract

In order to identify new agents for the treatment of Pseudomonas aeruginosa infections to address the serious threat to society posed by the evolution of multi-drug resistant P. aeruginosa, we focused on the well established family of Beta-lactams antibiotics. There is evidence they are effective against the target pathogen and their resistance profiles and pharmacology are well established. To address the major resistance mechanisms to other Beta-lactam antibiotics we studied siderophore-conjugated monocarbams. This class of monocyclic Beta-lactams is stable to metallo Beta-lactamases and they have excellent P. aeruginosa activities due to their ability to exploit the iron uptake machinery of the Gram-negative bacteria. Our medicinal chemistry plan focused on identifying a molecule with optimal potency and physical properties and activity for in vivo efficacy. We examined modifications to the monocarbam linker, the siderophore, and the oxime portion of the molecules. Through these efforts we identified a series of pyrrolidinone-based monocarbams which have good P. aeruginosa cellular activity (P. aeruginosa MIC90 = 2 g/ml), excellent free fraction levels (> 20 % free) and good hydrolytic stability (t1/2 ≥ 100 h). In order to differentiate our compounds and enable prioritization for future in vivo studies, we developed a robust mechanistic PK/PD model which enables prediction of in vivo efficacy from in vitro data.

Item Type:	Article
Date Deposited:	12 Oct 2016 00:45
Last Modified:	12 Oct 2016 00:45
URI:	https://oak.novartis.com/id/eprint/24735