Machine learning-powered antibiotics phenotypic drug discovery.
- Zoffmann S Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland. sannah.jensen_zoffmann@roche.com.
- Vercruysse M Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- Benmansour F Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- Maunz A Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- Wolf L Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- Blum Marti R Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- Heckel T Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- Ding H Roche Pharmaceutical Research and Early Development, Roche Innovation Center Shanghai, Shanghai, China.
- Truong HH Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- Prummer M Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- Schmucki R Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- Mason CS Discuva Ltd, part of Summit Therapeutics, Merrifield Centre, Cambridge, UK.
- Bradley K Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- Jacob AI Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- Lerner C Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- Araujo Del Rosario A Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- Burcin M Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- Amrein KE Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- Prunotto M Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
- 2019-03-23
Published in:
- Scientific reports. - 2019
Anti-Bacterial Agents
Bacteria
Drug Discovery
Drug Evaluation, Preclinical
High-Throughput Screening Assays
Humans
Machine Learning
English
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.
- Language
-
- English
- Open access status
- gold
- Identifiers
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- DOI 10.1038/s41598-019-39387-9
- PMID 30899034
- Persistent URL
- https://sonar.ch/global/documents/154574
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