New methods towards efficient and high-throughput drug-target screening

Supported by:

10 February 2021

Supported by:

10 February 2021


High-throughput genetic screens are an important and widely-used tool in both functional genomics and drug discovery. Our research is focused on developing and applying new technologies to allow larger and more efficient genetic screens to streamline and accelerate the drug discovery process.

A genetic interaction occurs when the simultaneous perturbation of two or more genes produces a phenotype that is not predictable based on the individual perturbation of each gene alone. Screening for genetic interactions is a powerful approach to find new drug targets and has been applied widely in oncology. However, this approach has not realised its full potential because of technical issues. For example, off-target effects, noise or genetic redundancy can make interpretation of results challenging. In addition, identified targets often have inconsistent effects in different model systems and fail to translate to clinical use.

This webinar described the development of multiple new methods that we have developed to systematically address these issues. First, we use a cross-species screening approach to identify targets with the greatest chance of successful translation into complex model systems. Furthermore, we have developed an innovative screening assay, called Variable Dose Analysis (VDA). The VDA method generates a gradient of expression of the target gene across a population of cells and assesses how phenotypes change with expression level. This leads to a dose curve readout for each target gene screened, improving detection of genetic interactions by increasing signal-to-noise ratio and allowing analysis of essential genes. We have already applied these methods to find effective drugs for three tumourigenic diseases and to screen for synergistic drug combinations.

More recently, using an interdisciplinary approach combining laboratory automation, machine learning and improved vector design, we developed two new variants of the VDA method. The first, called high-throughput VDA (htVDA) allows screens to be performed with four-fold increased speed and 10-fold reduced reagent requirements while maintaining high data quality. The second variant, called VDA-plus, enhances data quality by approximately 12-fold compared to standard assays, allowing much more sensitive screens to be performed.

We are now applying these new screening methods to find drug-targets for a range of proliferative and neurodegenerative diseases and continue to develop new solutions to accelerate the drug-discovery process.

Learning outcomes of this webinar:

  • Explore new methods to improve data quality from high-throughput screens
  • Learn about solutions for common problems in drug-target discovery
  • Learn from three case studies where new approaches to screening have identified high-quality candidate drugs for proliferative diseases.


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    Benjamin Housden, Research Fellow, University of Exeter

    Dr Benjamin Housden is a multidisciplinary scientist with over 15 years of experience working in the areas of molecular biology, drug-target discovery and technology development. He performed his PhD under Professor Sarah Bray’s supervision at the University of Cambridge working on the Notch signalling pathway. He then went on to work for six years as a postdoctoral researcher at Harvard Medical School with Professor Norbert Perrimon. During this time, he developed new technologies for the generation of disease models and synthetic lethal screening. 

    In 2017, Ben established his research group at the Living Systems Institute, University of Exeter, where he has developed multiple new technologies for high-throughput genetic screening. This includes methods to improve the transfer of candidate drug targets between model systems and to improve both the efficiency and data quality from genetic screens. Ben is now applying these new methods to identify optimal candidate drug targets to treat a range of tumorigenic and neurodegenerative disorders. 

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