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Shivangi, F.

Publications and source records attributed to Shivangi, F..

2 recordsLinked to original sources

The Quantification of Drug Accumulation within Gram-Negative Bacteria

Intrabacterial drug accumulation, mediated by the bacterial permeability barrier, efflux, and intrabacterial drug metabolism, is of general significance to the interaction between small molecules and bacteria. For example, the ability of a small molecule to accumulate within a bacterium influences its ability to serve as a chemical probe of an intracellular protein target and/or its efficacy as an antibacterial drug discovery entity. A general method to quantitatively interrogate both intrabacterial drug accumulation and metabolism (IBDM) is presented for Gram-negative bacteria and exemplified with Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa in both single-compound and high-throughput formats. The liquid chromatography-mass spectrometry based platform does not depend on drug labelling and its utility is highlighted through the demonstrated correlation of drug accumulation with drug minimum inhibitory concentration (MIC) in both wild type and efflux deficient strains of E. coli and a matched pair of K. pneumoniae clinical and laboratory strains of varying degrees of drug resistance. Furthermore, an investigation of drug synergy implicates the selective enhancement of the accumulation of one drug by its partner therapy. Finally, a high-throughput format is validated and deployed which provides a readily adaptable approach to screening assays. We anticipate the further applications of this platform to both the translational and the fundamental studies of the interactions of small molecules with bacteria.

microbiology↗

Identification of Antituberculars with Favorable Potency and Pharmacokinetics through Structure-Based and Ligand-Based Modeling

Drug discovery is inherently challenged by a multiple criteria decision making problem. The arduous path from hit discovery through lead optimization and preclinical candidate selection necessitates the evolution of a plethora of molecular properties. In this study, we focus on the hit discovery phase while beginning to address multiple criteria critical to the development of novel therapeutics to treat Mycobacterium tuberculosis infection. We develop a hybrid structure- and ligand-based pipeline for nominating diverse inhibitors targeting the {beta}-ketoacyl synthase KasA by employing a Bayesian optimization-guided docking method and an ensemble model for compound nominations based on machine learning models for in vitro antibacterial efficacy, as characterized by minimum inhibitory concentration (MIC), and mouse pharmacokinetic (PK) plasma exposure. The application of our pipeline to the Enamine HTS library of 2.1M molecules resulted in the selection of 93 compounds, the experimental validation of which revealed exceptional PK (41%) and MIC (19%) success rates. Twelve compounds meet hit-like criteria in terms of MIC and PK profile and represent promising seeds for future drug discovery programs.

microbiology↗