Whole Genome Sequencing (WGS) and other Next Generation Sequencing (NGS) techniques significantly augment the scientific understanding of diseases and how medicines differ in their pharmacological effect from patient to patient. Much like in the clinic, NGS has utility both in clinical research and real world evidence (RWE), particularly within the emerging era of precision medicine. These therapies are designed to target disease sub-populations with the subgroups usually based around specific genetic variants.

WGS provides the complete DNA sequence of a patient that can be compared with a “reference genome” (a representative example of a standardized genome). This allows researchers to identify variants that differ between the reference population and may indicate a higher risk of disease and/or likelihood of responding to a specific treatment.

The cost of genetic sequencing has steadily dropped over time thereby expanding its use, and this cost reduction is expected to continue over the next few years. The increase in “supply” has been matched by an increase in demand as researchers develop new applications for the data generated at a breathtaking pace. Use of genomic data has been identified by some industry observers as one of the most innovative ways to capture and use RWE across the drug development lifecycle. As a result, investors and established businesses around the world have been putting hundreds of millions of dollars into the genomics space over the past few years, which itself is accelerating the pace of change. According to Grand View Research, the global genomics marketing size is estimated to reach $31.1B USD by 2027.

Insights help drug developers and healthcare providers compute

Such insights can help drug developers and physicians identify undiagnosed patients with known variants for a disease or predict differences in rates of disease progression between patient sub-populations. These can also determine which subpopulations of patients are likely to respond to a treatment or those at a higher risk of adverse events due to the patient’s underlying genetic profile.

Genetic sequencing is being used in drug development programs to increase the success of clinical trials by honing eligibility criteria based on biomarkers or genetic indicators. Such an approach ensures that physicians prescribe the right drugs to the right patients resulting in better outcomes the first time around.

Harnessing the right data

While significant value can be gained from genomics, companies must have the right investment mindset to develop the requisite technology and talent to handle the volume and complexity of the data. Even the less complex NGS techniques such as WES will generate millions of data points for every individual patient. To find correlation in such sample sizes requires significant computer power, storage, and expertise.

Pharmaceutical companies also must contend with the quality and accessibility of real world (clinical) datasets that need to be linked to the genetic data in order to make it valuable. Researchers need the ability to compare clinical outcomes between patients who have been stratified based on genetic criteria. However, lack of harmonization between data platforms, inconsistently collected data, and strict data privacy regulations mean that the full value of the data is not being realized today. Pooling datasets across multiple countries is often challenging, which means that patient cohorts of adequate size cannot be easily created for rare diseases.

Despite these challenges, the insights that can be gained from genomic sequencing and validated against real world data (RWD) make these research efforts a valuable addition to a meaningful RWD strategy. The ability to link genetics to treatment outcomes provides pharma companies and physicians insights needed to treat more patients with greater success than before.

Genomics help determine healthcare costs

Numerous life sciences companies have successfully used genetic sequencing to prove efficacy, safety, and cost effectiveness of their drugs. A global biopharma company had a drug for non-small cell lung cancer and wanted to determine which patients were most likely to experience positive outcomes.

Using a genomics database of lung cancer patients, IQVIA analysts parsed patients in the database based on the presence or absence of a genetic mutation. They then compared the genetic results against national medical records and the overall survival, disease progression, and healthcare resource utilization.

The analysis found distinct differences in progression and treatment response among the two groups, which indicated that one population would have significantly lower costs of care using the drug. This allowed the biopharma company to optimize its evidence-based package for regulatory submissions and payer negotiations.

Other companies have used genetic data to hone label claims and to accelerate regulatory approval by focusing first on sub-populations whose disease will progress more rapidly. This allows for targeted follow up and a parallel focus of conducting another study on the entire patient population.

Companies can also use this data to validate internal analyses. Such an important step can help prevent pursuit of ineffective drugs based on false positives, known as Type 1 errors. Consider that some patients with a certain set of genetics may respond well to a particular therapy and have positive outcomes, yet other patients with different genetics may not respond well to the same drug. If you include both of these populations in a trial, the impact of the drug will appear “not effective” overall, because it simply isn’t effective on some of the patients with a certain genetic makeup. However, if you can limit the population to those with the genetic makeup that will be responsive, then you can see that the drug has an impact in that subgroup.

Drugs with genetic support are more likely to be successful - one 2015 study by Matthew R. Nelson, et al., showed that gene target-indication pairs with genetic evidence are approximately twice as likely to progress from Phase I to approval.¹

Putting clinically rich data to work

IQVIA can help analyze and understand the ever-expanding ecosystem of clinically rich data in the context of your organization’s needs. By combining advanced analytics with unparalleled data and scientific expertise, IQVIA provides customers with fit-for-purpose real world evidence approaches to meet stakeholder needs throughout the product lifecycle.

To learn more about how to use genomic data as part of your RWE strategy, contact IQVIA directly.

¹ https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008489