Refining the impact of genetic evidence on clinical success, 2024, Eric Vallabh Minikel et al

[Mij]

Abstract
The cost of drug discovery and development is driven primarily by failure, with only about 10% of clinical programmes eventually receiving approval.

We previously estimated that human genetic evidence doubles the success rate from clinical development to approval.

In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery.

These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.

LINK

 

[Mij]

 

[ME/CFS Science Blog]

Chris Ponting pointed to this study following the publication of DecodeME to argue that small effect size in SNPs do not indicate what the potential effect of a drug would be.

Preprint Post in thread 'Initial findings from the DecodeME genome-wide association study of myalgic encephalomyelitis/chronic fatigue syndrome, 2025, DecodeMe Collaboration'

Monday at 8:12 AM

Hi. I've posted this elsewhere on Science for ME, but think it's important to leave here too.
About the "only a 1% difference between [the frequencies of] those with ME and controls". This small "effect size" does not matter if you're focused on drug discovery. This is because the success rate from clinical development to approval "is largely unaffected by genetic effect size", see https://www.nature.com/articles/s41586-024-07316-0. The reason for this is subtle but important. Even when DNA variation tweaks biology only slightly, this variation highlights what...

• Chris Ponting

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[ME/CFS Science Blog]

RS stands for the relative probability of success (expressed relative to drug targets without genetic support). The authors extracted data on
drug development from Citeline Pharmaprojects for monotherapy programmes added since 2000.

The interesting figure is 1D. showing that RS is not influenced by effects size (odds ratio or Beta).



Here are some quotes about this from the paper:

There were no statistically significant associations with estimated effect sizes (P = 0.90 and 0.57, for quantitative and binary traits, respectively; Fig. 1d and Extended Data Fig. 2h) or minor allele frequency (P = 0.26; Fig. 1d). That ever larger GWASs can continue to uncover support for successful targets is also illustrated by two recent large GWASs in type 2 diabetes (T2D)18,19(Extended Data Fig. 4)

Although there has been anecdotal support—such as the HMGCRexample—to argue that genetic effect size may not matter in prioritizing drug targets, here we provide systematic evidence that small effect size, recent year of discovery, increasing number of genes identified or higher associated allele frequency do not diminish the value of GWAS evidence to differentiate clinical success rates. One reason for this is probably because genetic effect size on a phenotype rarely accounts for the magnitude of genetic effect on gene expression, protein function or some other molecular intermediate

Our results argue for continuing investment to expand GWAS-like evidence, particularly for many complex diseases with treatment options that fail to modify disease.

 

[ME/CFS Science Blog]

They give the example of HMGCR, an enzyme involved in cholesterol synthesis. Variants in the HMGCR gene identified by GWAS have only a small effect (I've read SNPs had an OR ≥ 0.9).

However, drugs such as statins, which inhibit HMGCR, produce large reductions in cholesterol land cardiovascular risk.