In this blog, Dipender Gill discusses using large-scale genetic association data to inform drug development.

Drug development is inefficient

Less than 10% of drugs entering phase 1 clinical trials eventually enter clinical practice. With the median development cost of every drug that makes it to patients estimated at $1.1 billion, and this process taking approximately 10 years, such a high failure rate clearly has serious implications. Added to the ticking time bomb of an increasingly aged population with growing levels of multi-morbidity, the imperative for more efficient strategies to develop efficacious medicines is greater now than ever before.

Applications of human genetic data will change the drug development paradigm

Human genetic data offers an answer. Genes code for proteins, which make up the majority of drug targets. It follows that naturally occurring genetic variation in the genes coding for drug target proteins can be used to study the effects of their pharmacological perturbation. The random allocation of genetic variants at conception means that such genetic associations are relatively devoid of the environmental confounding and reverse causation bias that can hinder causal inference in traditional epidemiological studies. Thus, used in this ‘Mendelian randomization’ paradigm, genetic data can be used to infer the effects of drug targets. Furthermore, the use of human data offers considerable advantages for clinical translation over animal models.

Retrospective study of Pharmaceutical Research and Development pipelines supports that drug targets with genetic support are more than twice as likely to make it to patient care. In addition, it is estimated that 70% of drugs entering into clinical practice have genetic support. Increasing use of genetic data to guide drug development efforts will likely further improve efficiency.

The time is now

The current era of academic research means that we now have large-scale genome-wide association study summary data, for millions of individuals, freely available on the internet. Many thousands of clinical traits, proteins, and metabolites have been studied. These numbers give only a hint of the level of insight and granularity that can be offered through integration of multi-omics. Coupled with LCP’s existing cloud computing infrastructure and web interface applications to analyse and interpret the data, there is unprecedented potential for gaining pivotal insights to inform disease mechanisms and the effects of drug targets.

Integrated Omics at LCP

We are really excited about the new Integrated Omics offering at LCP.

By leveraging large-scale genetic association data, it is possible to provide causal insights into disease mechanisms, identify therapeutic opportunities, inform drug development, and estimate the health and economic benefits of intervention. A multi-disciplinary approach that brings together expertise across clinical medicine, pharmacology, drug development, statistics, genetic epidemiology, cloud computing and multi-dimensional data is fundamental to solving the drug development paradigm.  

To learn more about the Integrated Omics offering at LCP Health Analytics, please reach out.