Modular Design of Bacterial Lipid Mimics for Next-Generation Antimicrobials

Antimicrobial resistance in bacteria is a growing public health crisis, as common drugs are becoming ineffective against many species of pathogenic bacteria.

This research aims to devise highly specific and stable antimicrobials, which target the amphiphilic component that anchors LPS to Gram-negative bacterial membranes, “Lipid A”, for direct antimicrobial effect and to potentiate other antimicrobials.

Taking inspiration from bacterial lipids, which possess multiple tails and a polybasic headgroup, synthetic cationic lipidoids have the potential to be highly specific bacterial membrane-targeting antimicrobials.

Preliminary results demonstrate that some cationic lipidoids bind and disrupt bacterial lipid assemblies, and significantly inhibit the growth of E. coli at micromolar concentrations.

However, the breadth of potential molecular structures arising from the range of available starting materials makes the search for optimum compounds an insurmountable task.

This proposal outlines an innovative use of statistical software to steer modular synthetic design and expedite the identification of promising new antimicrobials.

Relative to a ""one-factor-at-a-time"" approach, statistical design can quickly uncover correlations between structure and activity, and unexpected interactions between structural variables, thus accelerating the discovery of antimicrobial compounds that would not otherwise be obvious.

In addition to uncovering new compounds selective to bacteria, libraries of lipidoids will be investigated to help uncover design rules for the effect of shape on membrane interactions, and generic mechanisms of membrane-targeting antimicrobial action.

Results could also lead to new means to potentiate obsolete antimicrobials that are impermeable to bacterial membranes, or act as a chaperone for highly effective but relatively unstable antimicrobial peptides.