New disease targets frequently emerge in literature, but the thorough target validation required to consider these targets for a drug discovery programme is often lacking.
Research in drug discovery and biotechnology increasingly exploits gene editing at industrial scale in order to identify and validate new biological targets for precision medicines. The discovery of CRISPR-Cas9 systems has fuelled a rapid expansion of gene editing adoption with particular interest in the increasingly prevalent area of functional genomic screening.
Early drug discovery used to be conducted mainly in a target-agnostic fashion, focusing on qualitative readouts from a living system - cells, tissue, or organisms - and asking the question, did this entity affect the phenotype?
Identifying functional molecules with the potential to be developed into new therapeutics is often the sole aim of phenotypic drug discovery. However, the approach also provides valuable opportunities to uncover previously unknown, disease-specific drug receptors that can then be exploited through target driven means.
Phenotypic screening is transforming drug discovery as advances are made in development of human-based physiologically-relevant in vitro systems.
Phenotypic drug discovery (PDD) implies screening where the molecular mechanism of action is not assumed and does not require knowledge of the molecular target.
Flow cytometry has many of the technology attributes (eg multi-parameter analysis of cell suspensions) needed when attempting to profile a phenotypic drug response. However, for flow cytometry to be considered the phenotypic drug screening platform of choice it needs to interface with the real screening world, ie be able to support higher throughput and to acquire low sample volumes from high density microplates.
Renewed awareness of the value of phenotypic screening to drug discovery creates many new opportunities to increase drug discovery success and productivity.
The pharmaceutical industry has not seen the hoped-for productivity gains from the various omics datastreams over the last decade. This article discusses how systems biology can exploit the natural interlinkages between these datastreams and put in place a powerful system for modern therapeutic development.