COVID-19 and the Evolving Science of Pharmacogenomics

As the novel coronavirus (COVID-19) pandemic continues to threaten and take lives around the world, scientists globally are working urgently to develop vaccines and therapeutic treatments.¹ Meanwhile, healthcare professionals are doing the best they can with severe equipment shortages and limited condition guidance to help patients. 

Already, data has emerged showing certain individuals – older adults and people who have serious underlying medical conditions (including chronic lung disease, moderate to severe asthma, serious heart conditions are immunocompromised, severely obese, diabetic, chronic kidney disease or liver disease) – are at higher risk of severe illness. Research is underway to investigate how the disease might manifest differently depending on patient characteristics and identify variables that help predict patients most likely at high risk of respiratory failure and in need of mechanical ventilation.

For example, a recent study² looked at patients hospitalized between February 29 to March 27 with polymerase chain reaction (PCR) proven symptomatic COVID-19 infection. While most of these patients experienced only mild symptoms, a relevant proportion developed severe disease progression with increasing hypoxia up to acute respiratory distress syndrome. The patients (32.5%, or 13 out of 40) requiring mechanical ventilation did not differ in age, comorbidities, radiological findings, respiratory rate or qSofa score. However, elevated interleukin-6 (IL-6) was strongly associated with the need for mechanical ventilation.

Another study³ done in March 2020, resulted in the development of a machine learning-based prognostic model, using clinical data collected from Tongji hospital, Wuhan, predicts with an over 90% accuracy the survival rate of patients. Significantly, this model only requires three key clinical features: lactic dehydrogenase (LDH), lymphocyte and High-sensitivity C-reactive protein (hsCRP) – all gene related.

More research is needed to fully understand the novel coronavirus, how to prevent it and how to treat it. But these initial studies point to how genetic variations can impact disease states, and help illustrate the growing importance of genomic medicine, e.g. using genetic information to inform medical care or predict risk of disease. Genomic medicine is transforming healthcare, including how pharmaceuticals are being developed and prescribed. Through the emerging field of pharmacogenomics in which the science of drugs is combined with the study of genes and their functions, biomedical research is increasingly revealing how medication dosing might need to be altered based on a patient’s individual genes and/or gene mutations.

How genes affect response to drugs

The term pharmacogenomics⁴ was coined in the 1950s and captures the idea that large effect size DNA variants contribute importantly to variable drug actions in an individual. Today, the term is widely used to describe the idea that multiple variants across the genome can differ across populations and affect drug response. (Note: pharmacogenomics, which is the study of variations of DNA and RNA characteristics as related to drug response, is slightly different from pharmacogenetics, which is the study of variations in DNA sequence as related to drug response.⁵)

How a drug metabolizes in a body differs among patients. For some, the drug will be effective, for some it will be ineffective, and for others it might cause adverse drug reactions (ADRs). Some conditions that affect a person’s response to certain drugs include clopidogrel resistance, warfarin sensitivity, warfarin resistance, malignant hyperthermia, and thiopurine S-methyltransferase deficiency. Even common medications like ibuprofen, anti-seizure medicines or antibiotics can cause severe ADRs, such as Stevens-Johnson syndrome or, even worse, toxic epidermal necrolysis.

ADRs can range in severity, but they can be fatal. For reference, in the 1990s, a large survey suggested that ADRs occurring in hospitals were the fourth-to-sixth leading cause of in-hospital mortality in the U.S.⁶ – a follow-up survey in 2010 showed no improvement. Furthermore, even the treatment of common diseases, such as hypertension, arrhythmias, or depression, often involves a series of therapeutic trials among different drugs or classes of drugs, and the healthcare burden imposed by the lack of efficacy during these periods of trial and error can be considerable. 

Genetics keys to treatment

Whether fighting a unique and fast-spreading disease like the novel coronavirus or treating a well-known common condition, genomics is increasingly being used to inform more personalized and cost-effective strategies for drug development and use. For example, in 2007, the FDA revised the label on the common blood-thinning drug warfarin (Coumadin) to explain that a person's genetic makeup might influence response to the drug. Some physicians have since begun using genetic information to adjust warfarin dosage. Still, more research is needed to conclusively determine whether warfarin dosing that includes genetic information is better than the current trial-and-error approach.

Cancer is another active area of pharmacogenomic research. Chemotherapy drugs – gefitinib (Iressa) and erlotinib (Tarceva) – work much better in lung cancer patients whose tumors have a certain genetic change⁷. On the other hand, research has shown that the chemotherapy drugs cetuximab (Erbitux) and panitumumab (Vecitibix) do not work very well in the 40 percent of colon cancer patients whose tumors have a genetic change⁷.

Pharmacogenomics also may help to quickly identify the best drugs to treat people with certain mental health disorders. Consider, while some patients with depression respond to the first drug they are given, many do not, and doctors must try another drug. Because each drug takes weeks to take its full effect, patients' depression may grow worse during the time spent searching for a drug that helps. Recently, researchers identified genetic variations that influence the response of depressed people to citalopram (Celexa), which belongs to a widely used class of antidepressant drugs called selective serotonin re-uptake inhibitors (SSRIs). Clinical trials are now underway to learn whether genetic tests that predict SSRI response can improve patients' outcomes.

Precision medicine is the goal to strive for, but the life sciences industry is only at the beginning stages of building the infrastructure for one of the most complex projects in scientific history. Pharmacogenomics, a cornerstone of precision medicine, is proving successful for many conditions. FDA data show that more than 260 therapeutic agents now have information on how genes affect them in their drug labelling. That number continues to grow as new research emerges. 

What’s next with pharmacogenomics?

The life sciences industry has come a long way in genomics. The first human genome cost about $3 billion to sequence. A whole human genome can now be sequenced for under $1,000, and even less to read targeted parts of the genome (panels) or protein codes (exomes). In cancer, genomic profiling of tumors is becoming routine to predict response to therapies. In fact, molecular biomarkers for precision medicine were included in 39% of global oncology trials in 2018.⁸

And the industry has moved swiftly when faced with a fast-spreading outbreak like the novel coronavirus. Thanks to the more recent predictive science methodologies and study, the industry has made tremendous progress in understanding the COVID-19 virus. Several companies are trying to come out with effective preventive measure through vaccines, and the FDA recently approved the first COVID-19 antibody test, among other milestones. While both new and current medications are being studied, we will see more and more pharmacogenomic tests to predict patient’s response to these various medications.

Even so, the practice of using pharmacogenomics in daily medicine is not there yet for many reasons. First, the challenge of standardization must be addressed in order to fully realize the transformative potential of genomic medicine, particularly in the generation of clinical and genomic data and analytics and mainstreaming across multidisciplinary healthcare teams. This will require ongoing investment in technology, health infrastructure, and workforce capacity. This in turn will lead to more advances in the science of “precision medicine” with fewer ADRs, the ability to match the right medication to the patient for more efficacy, finding the right dose, reducing economic burden for inefficacy, and improving clinical outcomes several fold, including during times of crises. 

Mark J. Stevens is Partner - Life Sciences at Guidehouse.

References

1. National Center for Immunization and Respiratory Diseases (NCIRD), Division of Viral Diseases, Center for Disease Control and Prevention (CDC), Coronavirus Disease 2019 (COVID-19).

2. Tobias Herold III, Vindi Jurinovic, Chiara Arnreich, Johannes C Hellmuth, Michael Bergwelt-Baildon, Matthias Klein, Tobias Weinberger, Level of IL-6 predicts respiratory failure in hospitalized symptomatic COVID-19 patients, medRxiv, 2020.

3. Li Yan 1 et al, A machine learning-based model for survival prediction in patients with severe COVID-19 infection, medRxiv, March 2020.

4. Wise AL Manolio TA Mensah GA et al.Genomic medicine for undiagnosed diseases. The Lancet (published online August 5, 2019).

5. Peterson JF Roden DM Orlando LO Ramirez AH Mensah GA Williams MS, Building evidence and measuring clinical outcomes for genomic medicine. The Lancet (published online August 5, 2019).

6. Lazarou J, Pomeranz BH, Corey PN. Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA 1998; 279: 1200–05. 

7. National Human Genome Research Institute, The Future of Healthcare: Take Advantage of New Technologies. December 2018.

8. Wise AL Manolio TA Mensah GA et al.Genomic medicine for undiagnosed diseases. The Lancet (published online August 5, 2019).