Abstract
Identification of novel antibiotics remains a major challenge for drug discovery. The present study explores use of phenotypic readouts beyond classical antibacterial growth inhibition adopting a combined multiparametric high content screening and genomic approach. Deployment of the semi-automated bacterial phenotypic fingerprint (BPF) profiling platform in conjunction with a machine learning-powered dataset analysis, effectively allowed us to narrow down, compare and predict compound mode of action (MoA). The method identifies weak antibacterial hits allowing full exploitation of low potency hits frequently discovered by routine antibacterial screening. We demonstrate that BPF classification tool can be successfully used to guide chemical structure activity relationship optimization, enabling antibiotic development and that this approach can be fruitfully applied across species. The BPF classification tool could be potentially applied in primary screening, effectively enabling identification of novel antibacterial compound hits and differentiating their MoA, hence widening the known antibacterial chemical space of existing pharmaceutical compound libraries. More generally, beyond the specific objective of the present work, the proposed approach could be profitably applied to a broader range of diseases amenable to phenotypic drug discovery.
Highlights
Identification of novel antibiotics remains a major challenge for drug discovery
We investigated this grey chemical matter adopting a combined multiparametric high content screening (HCS) approach using a combination of phenotypic drug discovery (PDD) methods and a robust data analysis pipeline powered by machine learning (ML)
The chemical space of the Roche pharma library is limited in respect to antibacterial-susceptibility
Summary
Identification of novel antibiotics remains a major challenge for drug discovery. The present study explores use of phenotypic readouts beyond classical antibacterial growth inhibition adopting a combined multiparametric high content screening and genomic approach. We demonstrate that BPF classification tool can be successfully used to guide chemical structure activity relationship optimization, enabling antibiotic development and that this approach can be fruitfully applied across species. The BPF classification tool could be potentially applied in primary screening, effectively enabling identification of novel antibacterial compound hits and differentiating their MoA, widening the known antibacterial chemical space of existing pharmaceutical compound libraries. The discovery of novel antibacterial drugs remains challenging. To overcome those difficulties and to move beyond well-known targets, phenotypic drug discovery (PDD) screening methods have been a valid alternative[4]. Though constantly evolving and enriching over time, the compound collections designed for drug development are biased for chemical features optimized to increase permeability in eukaryotic cells, routine targets of pharmaceutical research. Corporate compound libraries have a limited chemical diversity[5] in relation to bacteria drugability[6]
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