Expert view: Toward physiological relevance in drug discovery and development

Physiologically relevant screening models have become increasingly important in assay development and the screening of drug candidates.

Phenotypic screening at the cellular and tissue model level is progressing from two dimensional to three dimensional technologies.

Cell-based assays have offered physiological relevance via usage of primary and/or engineered cells. Therefore, depending on the cell type, a number of factors must be considered such as stability, passage viability, protein expression levels, and scalability – particularly with primary cells. In addition, assay parameters, signal-to-background optimisation, and data analysis and management also constitute important considerations.

A variety of detection technologies enabling either singleplex or multiplex readouts, including high content, are available to extract multi-parametric data from a single set of experiments. Reporter gene assays enable sensitive detection of modulation of receptors, kinases, and transcription factors. Calcium mobilisation assays enable the study of physiological processes. Fluorescenceor luminescence-based resonance energy transfer, or bead based proximity assays, enable evaluation of protein-protein interactions. From biochemical/molecular through cell-based assays and high content analysis, there is a continued trend toward miniaturisation and increased throughput with a need for greater sensitivity and reproducibility.

Furthermore, the expansion of 3D cell models is driving the development of supporting assay formats, detection, and data analysis technologies to mimic in vivo biological environment ex vivo. Integration of modular platforms for an end-to-end solution or a holistic approach will be critical to deliver standardised and reproducible formats for complex 3D models. As technology providers such as PerkinElmer and others have evolved from enabling target-based drug discovery strategies to more physiologically relevant cell and tissue-based assay systems, a holistic vision toward managing data generated from both complex biological models and single cell analysis will be critical.

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