Genetic Analysis May Help Lower Drug Costs

By analyzing data on unsuccessful drugs, researchers found that large-scale prospective biobank studies with genetic research could significantly reduce drug costs and improve the drug development pipeline.

The research and development process from conception to randomized trials in humans is expensive and can last quite a long time. It has been estimated that it costs more than $1.2 billion to get a single product to market.

These costs can, in part, be attributed to drugs that have been unsuccessful at different points of development. In a study published in the International Journal of Epidemiology, researchers looked at the drug darapladib, an inhibitor of lipoprotein associated phospholipase A2 (Lp-PLA2).

High levels of Lp-PLA2 is linked to an increased risk of developing cardiovascular disease. However, during 2 large phase 3 trials of darapladib, the drug failed because the biological pathway ended up causing cardiovascular disease.

In the current study, researchers sought to determine whether these poor results could have been predicted by using a genetic variant that mimics the drug’s effect. Investigators analyzed 90,000 patients from the prospective China Kadoorie Biobank (CKB) study to examine the association between the genetic variant and different cardiovascular and non-cardiovascular diseases, also called Mendelian randomization.

“CKB is a powerful resource,” said senior study author and principal investigator professor Zhengming Chen. “Our ongoing research includes measurement of thousands of functional genetic variants which may represent potential drug targets in different biological pathways, and we are using the same approach to assess a number of other important therapeutic targets.”

The results of the study showed that people who had a non-functioning genetic variant did not face a lower risk of cardiovascular and non-cardiovascular disease, a result that upheld the trial findings.

“Our research used genetic variant only found in East Asians, and demonstrates the value of prospective biobank studies with genetic data linked to health record, carried out in different global populations, to predict the potential benefits or harms of new drug targets,” said lead study author Iona Millwood.