Drug discovery and human genetics – a 21st century roller-coaster

As our personalised medicine month comes to an end, Alun McCarthy explores the evolution of drug discovery and human genetics over the past decade.

We can all remember the time in 2001 when the human genome sequence was announced in Nature and Science. The excitement of that historic milestone was not just a celebration of a great technological feat, but also because it promised the prospect of future research opportunities. There was a groundswell of feeling that the genome data would facilitate understanding of both human health and disease, leading to a wide range of beneficial applications.

Among the many potential applications in healthcare, there was an expectation that drug discovery in particular would be revolutionised. The genetic research enabled by the genome sequence would generate a stream of novel targets directly derived from the disease aetiology that would transform the productivity of pharmaceutical R&D: the days of failure to show efficacy leading to attrition in clinical development would be over.

It has simply not happened. Attrition in clinical development has not decreased in the last twelve or so years, and is arguably getting worse, especially in phase 3. Although the number of medicines approved by the FDA has increased in the last year or so, this is at least partly due to a greater focus on rare, orphan diseases where the molecular defect is more easily established and corrected. Attrition in the common, complex diseases remains a major challenge.

“Among the many potential applications in healthcare, there was an expectation that drug discovery in particular would be revolutionised.”

One disease area that has definitely benefited from modern genomics is oncology. While there are a number of genetic lesions in any given tumour, evidence suggests that in many cases, the tumour growth is driven by a somatic mutation in a key enzyme or receptor, which is largely responsible for driving the disease progression. The ability to target treatments to the appropriate sub-set of patients whose tumours express specific mutations is having a great impact on survival for these patients.

So why have we not seen the anticipated impact of genetics over the last 12 years in other disease areas. It’s not through lack of investment, as an estimated £200 million has been spent to date on genome wide association studies (GWAS) alone. However, the output from these GWAS has not been as informative as was hoped. Initial results identified a small number of associations, which rarely yielded a coherent view of the dysfunctional pathways. Some more information has only been elucidated by investigators pooling datasets and analysing tens of thousands of subjects. Unlike oncology, where a number of tumours are effectively ‘single-gene’ disorders, the complex diseases seem to be impacted by many gene variants, each with a relatively small impact on disease susceptibility, interacting with a range of environmental variables.

Genetic insights into these complex diseases have been complicated by what has been termed the ‘missing heritability’. Using data from family and twin studies, the contribution of genetics to a disease (the ‘heritability’) can be estimated. However, the output from GWAS however only accounts for a fraction of this heritability, leaving a large part still to be uncovered. There are a number of potential explanations for this missing heritability. For example, it has been proposed that there are many rare variants with strong effects, which would not be discovered in GWAS using chips populated with common variants. This possibility has stimulated the rapid growth in DNA sequencing to look for these rare variants. This sequencing effort has undoubtedly had excellent results in oncology, but there is still much discussion on the role of rare variants in explaining the missing heritability in other disease areas.

“One disease area that has definitely benefited from modern genomics is oncology.”

Another explanation for the missing heritability is the role of gene-gene interactions. According to this proposal, individual genes will not achieve a statistically significance in GWAS on their own, but only in combination with another gene variant. However, these gene-gene interactions are difficult to study on genome-wide scale with conventional analysis methods. In fact, despite the remarkable developments in genotyping technology in the last 10-15 years, the methods for analysing and interpreting these data have not progressed at anything like the same rate. A major challenge is to find novel ways of analysing the vast amounts of genetic data now generated without losing most of the signal to missing heritability.

Despite the somewhat disappointing impact of human genetics on drug discovery over the last dozen years, there is evidence of a renewed interest in the approach. The clearest sign is the purchase of deCODE Genetics by Amgen for £270 million in 2012. The history of deCODE has perhaps reflected the changing fortunes of the impact of genetics in the last decade, finally emerging as a significant addition to Amgen’s R&D efforts. Behind Amgen’s decision was the clear implication that human genetic information is viewed as the best validation of a drug target, and a statement of intent to prioritise targets with such validation over those with a weaker evidence base.

Where next for the great hopes anticipated in 2001? The stubborn way that attrition in pharmaceutical R&D has been unchanged in recent years is a major issue for pharma companies – companies with the current R&D productivity level will not be sustainable in the future, given rising drug development costs, future patent expiries and more commercial pressures on pricing. It is clear that the current paradigm of target identification based on gene expression profiles, in vitro cell systems and in vivo pre-clinical models will need to change. Validation of drug targets must be more firmly entrenched in the human disease aetiology, which means finding a better way to use human genetic information to support drug discovery.



About the author:

Alun McCarthy is President and CEO of PGXIS Ltd, and Chief Scientific officer of CytoPathfinder Inc. PGXIS is a UK-based company which has developed innovative technology to analyse large, complex genomic datasets. This approach – called Taxonomy3 – has a number of benefits over conventional analysis methods. These include: more statistical power to find associations; the ability to identify gene-gene interactions; greater predictive power; and integration of different data types in a single multivariate analysis.


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