Pharmacogenomics (PGx) is the science of understanding the influence of a person’s genome (i.e., their complete set of DNA and genes) on drug treatment outcomes. PGx testing can help providers treat patients precisely by predicting drug response and informing appropriate prescribing. These tests fall under the umbrella of precision medicine, which uses multiple data sources to treat patients effectively and proactively. Scientists and researchers have discovered that patients metabolize certain drugs differently, in large part due to genetic variations. PGx helps providers prescribe targeted medications that improve outcomes by understanding a patient’s gene-drug interactions.

Pharmacogenomics can help pharmaceutical companies develop drugs more efficiently, providers better target patients who would benefit from a given therapy, and patients better understand their risk for and avoid adverse drug reactions.

The push towards pharmacogenomics is particularly important right now because it holds the promise of contributing to smart drug development. By studying a drug only in people likely to benefit from it, drug companies might be able to speed up the drug’s development and maximize its therapeutic benefit. This would allow some individuals to receive potentially lifesaving medicines that might otherwise have difficulty gaining approval because of the risk they pose for other patients. Pharmacogenomics can play a major role in developing new therapeutics for rare diseases that would fall under the ultra high-cost drug umbrella.

Areas of disruption

Care delivery

PGx can disrupt the way physicians prescribe drugs by introducing another round of lab testing between diagnosis and prescribing. However, this enables clinicians to tailor care to what is most likely to work for individuals, reducing adverse drug reactions, trial-and-error prescribing patterns, and avoidable costs in disease management.

There are also workforce implications when implementing pharmacogenomics. Because genetic testing requires an addition of another round of testing to be managed by the care team, this may pose both a staffing and administrative burden.

Cost/payment models

Pharmacogenomics isn’t applicable to all marketed drugs, and thus will need to undergo rigorous evaluation to justify costs. This will need to be on a case-by-case basis so that payers are able to justify investing resources in research and development of pharmacogenomics and make reimbursement decisions based on the evidence.

Although reimbursement is an evolving landscape for PGx, it is most commonly accepted for specific therapeutic areas- care in oncology, CV, and mental health where cost-effective evidence is most abundant. There will be difficulty in developing payment models for pharmacogenomic based therapies in therapeutic areas outside of these.

Additionally, genetic testing is expensive, and plans are still figuring out when paying for these tests is worthwhile.

Clinical evidence

Currently, coverage can vary greatly and depends mostly on drug-gene evidence. The large amounts of genomic data that is collected will need more rigorous review, standardization, and protocols in place for every step in the process, from data collection to processing, analysis, and interpretation. This can place challenges on workforce shortages, burnout, and more.

Another challenge is that although pharmacogenomics holds the promise to enhance patient care by enabling treatments tailored to genetic make-up and lowering risk of serious adverse events, the evidence is still new and clinicians may not fully understand the ripple effects of looking into a patient’s genome. Rates of genomic literacy among should be improved upon, so that there isn’t a widening gap in knowledge when translating this to the patient.

A note on health equity

PGx has the potential to support health equity efforts if there is adequate representation in clinical trial data and evidence.

However, some new therapies or tests may be taken up less quickly and in lower numbers in more underserved and minority populations due to barriers in access, availability, ability to pay privately, and lack of understanding of healthcare information. This may result in studying drugs in groups on specific populations, as a result, frustrate health equity efforts.