Next-Generation Phenotypic Screening in Early Drug Discovery for Infectious Diseases
Résumé
Cell-based phenotypic screening has proven to be valuable, notably in recapitulating relevant biological conditions, for example, the host cell/pathogen niche. However, the corresponding methodological complexity is not readily compatible with high-throughput pipelines, and fails to inform either molecular target or mechanism of action, which frustrates conventional drug-discovery roadmaps. We review the state-of-the-art and emerging technologies that suggest new strategies for harnessing value from the complexity of phenotypic screening and augmenting powerful utility for translational drug discovery. Advances in cellular, molecular, and bioinformatics technologies are converging at a cutting edge where the complexity of phenotypic screening may no longer be considered a hinderance but rather a catalyst to chemotherapeutic discovery for infectious diseases. Phenotypic Screening for Infectious Disease Drug Discovery The term 'phenotype' (Figure 1) was coined originally in 1903 by the Danish botanist Wilhelm Johannsen [1-3] and emerged as foundational in experimental, theoretical, and fundamental biology juxtaposed with 'genotype'. However, unlike genotype, for which a definition arises from tangible minimal information and molecular typing, the term phenotype lacks clear definition [4], relying on semantic description, for example, 'morphology', 'behavior', 'appearance', 'structure' etc. Indeed, there is an entire literature on the meaning of 'phenotype' but, more than 100 years after its conceptualization, it was only in recent years that the discipline and tools enabling ontology (see Glossary) for high-throughput and high-dimensional phenotyping began to emerge [4-7]. Specifically, for microbiology (MicroO [6]), there are efforts to align with other communitybased efforts establishing standards for ontology in genetics (GO i), phenotype (PATO ii), smallmolecule chemical entities of biological interest (ChEBI iii), and PubChem iv. Indeed, such efforts are invigorated, in part, by the realization that the performance of powerful natural language processing with neural-networks, machine-learning, and deep-learning methods is wholly dependent on the underlying means for data extraction that are linked to such ontologies, and ultimately the ability to relate them [7]. For the purposes of the current article we use the term 'phenotype' in the restrictive context of phenotypic screening [8,9].
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