Machine learning-powered antibiotics phenotypic drug discovery.

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.

Molecular Phenotyping Combines Molecular Information, Biological Relevance, and Patient Data to Improve Productivity of Early Drug Discovery.

Today, novel therapeutics are identified in an environment which is intrinsically different from the clinical context in which they are ultimately evaluated. Using molecular phenotyping and an in vitro model of diabetic cardiomyopathy, we show that by quantifying pathway reporter gene expression, molecular phenotyping can cluster compounds based on pathway profiles and dissect associations between pathway activities and disease phenotypes simultaneously. Molecular phenotyping was applicable to compounds with a range of binding specificities and triaged false positives derived from high-content screening assays. The technique identified a class of calcium-signaling modulators that can reverse disease-regulated pathways and phenotypes, which was validated by structurally distinct compounds of relevant classes. Our results advocate for application of molecular phenotyping in early drug discovery, promoting biological relevance as a key selection criterion early in the drug development cascade.

Epithelial cells as active player in fibrosis: findings from an in vitro model.

Kidney fibrosis, a scarring of the tubulo-interstitial space, is due to activation of interstitial myofibroblasts recruited locally or systemically with consecutive extracellular matrix deposition. Newly published clinical studies correlating acute kidney injury (AKI) to chronic kidney disease (CKD) challenge this pathological concept putting tubular epithelial cells into the spotlight. In this work we investigated the role of epithelial cells in fibrosis using a simple controlled in vitro system. An epithelial/mesenchymal 3D cell culture model composed of human proximal renal tubular cells and fibroblasts was challenged with toxic doses of Cisplatin, thus injuring epithelial cells. RT-PCR for classical fibrotic markers was performed on fibroblasts to assess their modulation toward an activated myofibroblast phenotype in presence or absence of that stimulus. Epithelial cell lesion triggered a phenotypical modulation of fibroblasts toward activated myofibroblasts as assessed by main fibrotic marker analysis. Uninjured 3D cell culture as well as fibroblasts alone treated with toxic stimulus in the absence of epithelial cells were used as control. Our results, with the caveats due to the limited, but highly controllable and reproducible in vitro approach, suggest that epithelial cells can control and regulate fibroblast phenotype. Therefore they emerge as relevant target cells for the development of new preventive anti-fibrotic therapeutic approaches.