How can human genetics data help to improve drug discovery success rates?

As recent data demonstrates a declining number of drugs are receiving approval by the U.S. Food and Drug Administration (FDA) for every billion dollars spent on R&D annually. In fact, research conducted over the last few years currently suggests that only 9.6% of new compounds progress from Phase I clinical trials to approval by the U.S. FDA. Couple that with the fact that R&D costs continue to increase year-on-year, and it paints a rather gloomy picture of what the industry’s future could look like.

So, how could this situation be improved? Well, one approach that could increase the chances of developing new drugs with significant therapeutic benefits would be to more widely leverage the wealth of genomic data generated by researchers across the world. These scientists are working to better connect genetic traits with disease incidence, progression, severity and resolution. To-date, however, human genetics data haven’t yet delivered on the promise of enabling the medical miracles we had hoped for. As highlighted in a recent paper, association signals for complex traits tend to be spread across most of the genome, meaning that there are many genes without an obvious connection to a specific disease.

Nevertheless, it’s already been demonstrated that when properly and carefully used, genomic data could guide the identification and prioritisation of new therapeutic targets and associated drug candidates that are likely to offer improved efficacy and safety (thereby helping to reduce the chances of failure during pre-clinical testing and human clinical trials). But, what could we do to ensure we’re taking full advantage of the available data? In a presentation delivered during ELRIG’s 2017 Drug Discovery conference in October 2017, Dr Maya Ghoussaini, Team Leader, Genetics Core Team at Open Targets, explained that the way to unlock the full potential of human genetics data is by implementing a systematic strategy that will allow us to integrate the numerous disease-associated genetic variants that scientists have identified with diverse functional genomic and drug data sets.

(As a quick aside, more insights on this and other related topics are set to be presented at this year’s Drug Discovery event. Find out more here).

How genome-wide association studies can help improve drug discovery success

Although human genetics data haven’t led to the discovery of a breakthrough treatment just yet, research results over the years have set the foundation for uncovering the root factors of various diseases. Since their inception more than a decade ago, genome-wide association studies (GWAS) have enabled the discovery of several variants, genes and biological pathways associated with a growing number of diseases. This has greatly improved our understanding of the genetic architecture of complex traits and disease epidemiology. For example, back as early as 2005, GWAS findings revealed that people carrying a histidine variant within the complement factor H gene (CFH) on chromosome 1 were at higher risk of developing age-related macular degeneration. These results highlighted the potential offered by the approach for teasing out some of the biological factors influencing a range of highly debilitating and heritable diseases, ultimately leading to the discovery of new therapeutics.

As of today, numerous GWAS have been performed, leading to the discovery of hundreds of thousands of genetic variants that predispose carriers to multiple Mendelian and complex diseases, including cardiovascular, metabolic, immune system and neurological disorders. Taking a disease-centric workflow approach, GWAS make it possible to identify and prioritise multiple therapeutic targets associated with a specific disease to drive the development of safe and effective medicines. Still, some question marks remain around how useful GWAS data are for informing and improving drug development programmes.

To help answer this question, researchers in a 2015 study investigated whether human genetics data could be used to effectively predict drug mechanisms. Their findings suggested that selecting genetically-supported targets could double success rates in clinical development and that such data should be used to choose which indications and targets to focus on.

Taking a target-centric approach through phenome-wide association studies

In addition to GWAS, drug discovery can also benefit from the data generated through phenome wide association studies (PheWAS). Contrary to GWAS, PheWAS is a target-centric approach, which uses electronic medical records (EMRs) to explore how a single target is linked to multiple diseases. Since 2010 when the first PheWAS study was published, this approach has made it possible to discover genetic associations related to various immunological diseases. For example, the HLA-B genotype has been associated with spondylopathies, uveitis and variability in platelet count. There is also some evidence that it could be linked with other diseases such as mastoiditis.

The key benefit of PheWAS is that once the underlying common biology of the various diseases is understood, it can present a number of drug-repurposing opportunities. This underlying knowledge of disease mechanisms could also enable the development of more targeted treatments and open up the possibility of a more personalised approach to treating specific patients.

Using a systematic strategy to unlock the full potential of genomic data

As more in-depth, disease-specific data become available, they could be used to guide drug target identification and prioritisation, fuelling the drug discovery pipelines and likely leading to the development of new, more promising treatments. By implementing the systematic strategy that Dr Ghoussaini described in last year’s Drug Discovery event, it could become possible to better understand disease susceptibility and mechanisms of action, opening the path for more efficient medicines. For some diseases, an improved understanding of their underlying pathways may even help with identifying prevention or early intervention mechanisms. The same systematic approach could also allow us to predict drug effects, thus informing efficient drug repositioning. To learn more about how this approach will improve our understanding of disease pathogenesis and guide drug discovery for complex traits, download Dr Ghoussaini’s presentation today.

View Dr Ghoussaini’s presentation