Candidate screening is an experimental approach that tests multiple different drugs on one system (e.g. a mouse model) to measure their impact on a target biological pathway. 

This laboratory-based method requires a model system of the condition of interest, a collection of drugs that could potentially have an effect, and a way to measure the effect on the pathway (e.g. a change in the production of a protein).                                                  When a group of potential drugs for repurposing have been identified, these can be screened for efficacy at relatively low throughput in the laboratory. 

  • An example of a screening programme for Wolfram syndrome used p21cip (a protein that is underexpressed in the disease) as a biomarker to visualise the effect of the drugs. Compounds known to increase p21cip expression, drugs that were already licenced for Wolfram syndrome and drugs known to cross the blood brain barrier (as underexpression of p21cip in Wolfram causes degeneration of the brain stem) were all screened in a model cell system. In the model, p21cip was tagged with a glowing green protein, such that drugs causing cells to increase expression and thereby increase the intensity of the green glow, were shown to have the most impact on the disease. Sodium valproate, a generic anti-convulsant drug, was revealed to have the biggest impact and has been taken forward to testing in clinical trials.
  • Alternatively, high-throughput candidate screening of compound libraries (collections of stored chemicals) can test a very large number of different drugs in an automated fashion. Collaborating with a contract research organisation, academic group or open innovation platform such as ClinGen’s Consent and Disclosure Recommendations working group, can help you gain access to disease models and compound libraries that can be used for high-throughput screening. For more information on the consent and disclosure recommendations click here 
  • The disadvantages of this strategy are that the process can often generate a lot of potential candidates which can be time consuming to narrow down, and it is not always easy to establish the appropriate tests to determine the product’s efficacy, further exemplifying why collaboration is crucial.