Case Study: Translating from Molecules to Medicine

June 2, 2016
Case Study: Translating from Molecules to Medicine
Inova offers a PGx test for newborns called MediMap™ as part of a standard package of services at Inova Women's Hospital located at Inova Fairfax Medical Campus.

Don Rule and Houda Hachad, PharmD

With $215 million in funding, the federal government’s Precision Medicine Initiative® is expected to generate the scientific evidence needed to move personalized medicine into clinical practice by providing clinicians with the knowledge and tools to determine more precise medical treatment.

One objective of the initiative is to accelerate pharmacogenomics (PGx), which assesses an individual’s unique genomic markers to predict how they will respond to a specific drug in order to help clinicians more closely tailor personalized treatment, and choose safer and more effective therapies.

PGx testing is also important toward identifying gene variations that cause adverse drug reactions, currently estimated by the Food and Drug Administration to cost the U.S. more than $136 billion per year.

Complex Data Overwhelms Cognition

PGx testing provides tremendous clinical value, but the data is nearly useless without proper clinical context. Genetic data is extremely complex, involving the interactions of up to 200 variations of approximately 30 genes that have actionable data for 200 drugs or so. The growing volume of available genetic diagnostic tests, currently estimated to be more than 33,500 at the National Center for Biotechnology Information’s genetic testing registry, makes it challenging for physicians to keep current or manually assess all the co-determinants for an appropriate course of therapy.

With increasingly larger assays, whole ‘ome sequencing, and results that are relevant for multiple purposes, the challenge of translating from molecules to medicine intensifies, and evaluating clinical evidence requires expert curation. It is not just a matter of looking up a gene mutation to determine how a patient will metabolize or react to a given drug. There is a wide range in evidence quality, and many gaps exist in the data and variant databases.

Not all genotypes are straightforward; it is difficult to infer structural variation, and data analysis consumes a great deal of time and expense. Ironically, the large amount of raw, non-actionable data generated by genetic testing, and the challenge to give the data meaning, may actually lower a clinician’s propensity to use genetic tests to advance precision medicine.

Many electronic health record, laboratory, drug interaction checking, and prescribing systems do not incorporate or leverage genetic information, or cannot accurately evaluate cumulative drug-drug-gene variations to help clinicians predict potential interactions and adverse drug events when choosing alternative patient therapies. Another factor complicating the interpretation of genomic data is the often lengthy, overly detailed, complicated reports that do not effectively guide decision making.

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