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Identifying an opportunity for drug repurposing and selecting a candidate drug requires several stages of experimentation and preclinical testing.In order to then test your ideas and select a candidate drug to repurpose, there are several routes that can be taken: candidate selection based on laboratory research, candidate screening, or computer-based candidate identification.  

Three boxes show the methods that can be used to identify a drug repurposing candidate. The first box shows a person with a drug in a thought bubble. The heading reads ‘Research-based candidate selection’, which is followed by bullet points that read ‘laboratory research’ and ‘academic/clinician/patient group collaboration’. The second box shows three test tubes in a rack and a heading that reads ‘Candidate screening’. This is followed by bullet points that read ‘screening compound libraries in a laboratory setting’ and ‘testing on a model system’. The third box shows a computer with a molecule on the screen. The heading readings ‘Computer-based candidate identification’, which is followed by bullet points that read ‘literature mining’ , ‘artificial intelligence analysis’ and ‘multi-omics approaches’.

In this lesson we will cover: 

  • Research based candidate selection
  • Candidate screening 
  • Computer based candidate identification 

Research based candidate selection

  • A drug repurposing candidate can be identified based on experiences with patients (e.g. anecdotal evidence), or through ideas that have already been suggested within academiaAs patient groups often have extensive networks in patient and academic communities, it is often possible for them to drive this process by collecting information through these connections.
  • Partnering with clinicians and academic scientists can then allow a patient group to progress the idea into the laboratory, to determine whether it is a suitable candidate. This requires a model system (e.g. a mouse model), on which the impact of the drug on a specific disease pathway can be measured.
  • The Alkaptonuria (AKU) Society’s mission to repurpose nitisinone for use in alkaptonuria is a great example of how a repurposing candidate can be selected. Nitisinone had previously been approved for the treatment of tyrosinemia type 1 which impacts the same biological pathway affected by alkaptonuria. The AKU Society identified a US trial for nitisinone that had failed due to poor endpoint selection, and decided to pursue it as a repurposing project. They then worked in collaboration with academics and clinicians to develop an AKU mouse model on which the drug could be tested, helping to ultimately confirm nitisinone as a suitable repurposing drug candidate. A specialist centre was then secured for AKU that could prescribe nitisinone off-label, and clinical trials were organised with the patent-holding pharmaceutical company and a contract research organisation, to assess its efficacy. Over 140 patients were recruited for the phase III study, which was completed in 2019 with positive results. The consortium are now looking to approach the European Medicines Agency (EMA) to secure marketing authorisation for nitisinone for the treatment of alkaptonuria.

Candidate screening 

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.

Computer based identification

Big data and AI can also be used for in silico (computer-based) identification of drug repurposing candidates. This approach can use algorithms to mine available literature databases and extract information relevant for a particular drug or disease (e.g. disease mechanisms, genetic mutations, small molecules that could affect the disease or a list of authors or institutions looking at the condition). AI can even be used to link published studies, infer connections between them and make predictions about drugs/drug combinations.

Multi-omics approaches (studying the genes and proteins in an individual) can also be used to find repurposing opportunities. For example, if a gene is overexpressed in a disease, a drug that causes its down-regulation could offer a potential therapy.

A series of four bar graphs to illustrate the expression matching approach used by many artificial intelligence approaches to identify repurposing opportunities. Each graph shows a series of different genes as bars on the x-axis (horizontal), and the vertical axis (y-axis) shows the level of gene expression. Bars above the lines represent genes that are over expressed (there is too much made compared to normal), bars below are genes that are under expressed (there is less produced than needed). The first graph shows a series of genes that are over expressed and under expressed for a rare condition. Next to this is a graph showing that a drug can produce an opposite expression patten in a normal cell – the drug causes the genes that are over expressed in the disease to be under expressed by a similar amount. When these are added together – the diseased cell is given the drug – the equal but opposite signals combine, to the final graph, which shows most of the bars are completely gone. This means the cell is now behaving normally, and the disease signature, and hopefully the disease, is removed.

Using this information, repurposing opportunities can be predicted, and new chemical entities modelled and developed using computers.

A successful example of a drug repurposing partnership using AI was exemplified by a collaboration between Healx and the Fragile X Association of America. Using their AI technology to establish which drug expression profiles would counterbalance the aberrant gene expression profiles for the disease, Healx were able to generate 18 candidate drug combinations which had potential to treat fragile X syndrome. Using their fragile X mouse models, the Fragile X Association of America were then able to screen these drug combinations with relatively high throughput and found that 9 of the 18 combinations reversed all four fragile X phenotypes in mice. These candidates will be taken through to clinical trials, which are planned to commence in 2020.