At the Festival of Genomics and Biodata, Paul Nioi (Senior Director, Research, Alnylam Pharmaceuticals) joined us to explore how population genetics is being applied in our effort to discover new drug targets.
Over the last three decades, the cost of drug R&D has increased by ten-fold. Meanwhile, R&D efficiency and productivity has been declining fairly steadily. This trend is known as ‘Eroom’s Law’, which is the opposite of Moore’s Law (which predicts that transistors become exponentially cheaper over time). Consequently, the way in which biopharmaceutical companies are approaching modern drug hunting is changing.
Nioi noted that the major reason why drugs fail in the clinic is due to a lack of efficacy. In other words, the target does not have an effect on the disease. He explained that the core of this problem is a lack of inherent understanding of the key targets involved within the disease. Most of our studies rely on in vitro studies or animal models, such as mice, none of which really mimic the human condition well enough for us to derive valuable targets. Nioi discussed the importance of studying humans and using them as the model organism, specifically through the lens of genetics. He described that having human genetics support is more likely to lead to drug approval.
Exome sequencing of the UK Biobank cohort
Nioi discussed a recent study, published as preprint in medrxiv, as an example to demonstrate the benefits of using genetics within drug discovery. Specifically, a study of new gene discovery for type 2 diabetes (T2D). Here, the team identified associations between rare predicted loss-of-function (pLOF) variants and diabetes-related traits and biomarkers in participants from the UK Biobank.
The team used gene-based collapsing tests to identify genes associated with glucose, HbA1c and T2D diagnosis in 363,977 exome-sequenced participants in the UK Biobank. They found known associations with diabetes including variants in GCK, HNF1A and PDX1. They also identified novel associations between pLOF in GIGFYF1 and increased glucose and HbA1c levels. As no other sources exist that contain the vast amount of data as seen in the UK Biobank, the team replicated their findings using primary care data from the UK Biobank. The team also found an association of pLOF variants within GIGYF1 with T2D diagnosis.
As well as the benefits for efficacy of a drug, Nioi also explored the benefits of using genetics to understand the safety profile of a drug. A PheWAS of GIGYF1 pLOF revealed additional associations, including increased risk of hypothyroidism and decreased cholesterol levels. Nioi noted that unfortunately GIGYF1 (which regulates insulin signalling) has pleiotropic effects and therefore targeting this potentially with a small molecule has complications. However, he noted that targeting GIFYF1’s targets may be a beneficial alternative.
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