Personalized Medicine: When one size doesn't fit all
Contributor Ramtin Hakimjavadi
If no two people are the same, why should they be subjected to the same medical treatment? Everyday, physicians encounter patients with a unique set of personal characteristics, needs and preferences.
In fact, as you sit through your genetics lectures, or maybe even the infamous first-year “Haffie Bio” lectures, you may have started to learn a little bit about genetic variances in populations.
Considering this variability, it may seem odd that there ever was, and largely still is, a “one-size-fits-all” approach to the treatment of patients. In other words, if patient A has condition B, then they are invariably prescribed drug C.
In recent years, the increased optimism surrounding what is referred to as “personalized medicine” (PM) shows promise for a transformed healthcare system. As its name implies, it is the movement towards tailoring medical treatment to the individual, not to the masses. By considering information about a patient’s genetic background, and other factors such as age, diet and environmental exposure, physicians are able to administer treatment regimens that complement the unique circumstances of each person.
In 2018, the Government of Canada (along with its partners in various research institutions and private sector companies) is investing $255 million dollars into genomics and precision health research. Focus on these areas would naturally branch out to related disciplines, namely pharmacogenomics. This investment seems to be the corollary of the continually rising costs of healthcare in the country.
The health-to-GDP ratio has only been trending upwards in the last couple of decades, with average spending reaching nearly $6604 per person in 2017. Although hospitals still account for the greatest percentage of overall spending in healthcare (28.3%), drugs are becoming the most rapidly growing expense (currently 16.4%).
The increasing drug-related expenses emphasize the need to move away from the traditional trial-and-error approach to prescribing medication. The fact that different patients can have highly variable drug responses, i.e. being rushed to the ER because you lack a key enzyme required to metabolize a drug, began as early as the 1950s. Phenytoin, a common antiepileptic drug, is known to have high interpatient variability in drug response. Numerous factors such as interactions with other drugs, altered states such as fever or pregnancy and genetic polymorphisms can greatly complicate its use in therapy. Adverse events can be tragic, and in this case may include nystagmus, seizures, and ataxia.
Fortunately, an important component of PM — pharmacogenomics — serves to improve drug therapy outcomes and lower the overall cost of healthcare. By essentially mapping variability in drug response to gene variations, each patient is more precisely matched with the drug that is right for them.
PM is not a novel idea. Doctors may already consider their patient’s medical history before arriving to a decision for a diagnosis or treatment. For example, a history of breast cancer in a woman’s family will most likely prompt a physician to order a screening mammography. However, the recent interest and support that PM has garnered in the clinical sciences can perhaps be attributed to the emerging potential for its intricacy.
As well, the ability to collect and analyze patient-level data has reached new heights, with the massive advances being made in the field of genomics at the centre of it all. Since the completion of the Human Genome Project in 2003, not only have more genomes been sequenced at faster rates and lower costs, but our ability to analyze and understand this vast pool of data has improved.
For a physician, accessing their patient’s genome sequence, as casually as they might examine their bloodwork or MRI scan, is no longer a ridiculous idea. Advanced genome-sequencing technologies and lower price tags have enabled developments in PM to be accelerated.
Although the As, Ts, Gs and Cs of our DNA form the foundation of PM, it is the combination of a patient’s genetic profile, environmental exposure and medical history that paints the overall picture. Moreover, the actual analysis and application of this data is tantamount to its collection.
If we are now inundated with all this patient data, how can we use it to personalize their treatment? The fascinating work of Epidemiology and Biostatistics Professor Dan Lizotte on dynamic treatment regimens exemplifies the potential applications of patient-level data.
Lizotte’s research focuses on an innovative approach to treatment that essentially informs sequential personalized clinical decision making. At each step in the decision-making process, the doctor finds him/herself at a crossroads, and the choice to ultimately take one path over another is informed by up-to-date patient information. His work embodies the movement towards treatments that adapt to the ever-evolving health of each patient.
There is no shortage of success stories, even in the infancy of this movement towards precision medicine. Consider Stephanie Haney, who was diagnosed with stage-4 lung cancer and prescribed drugs that ceased to suppress tumor growth after three years. By taking a series of genetic tests, specific biomarkers in her DNA were recognized. The identification of an “ALK” mutation pointed to a weak spot in her tumor. These weak spots can be targeted to greatly increase the chances of positively responding to certain therapies. In this case, the unique patterns in her DNA informed her doctor that she was eligible to take a drug called Xalkori. Within a few years, the presence of the tumors was almost negligible.
Another example is Caleb Nolan, who was diagnosed with cystic fibrosis as an infant. Cystic fibrosis is among the most common genetic diseases in Canada, but there are over 1900 different known mutations that cause the disease. By identifying the specific mutation in the CFTR gene which led to Caleb’s condition, he was put on Kalydeco, a genetically targeted treatment. Caleb was then able to begin enjoying his childhood outside of hospital bedrooms.
The emergence of PM is exciting, and enthusiasm about its future is certainly warranted, but it is also important to highlight some of its limitations. Despite its promise for significantly improving the quality of healthcare, the road to PM definitely has its speed bumps.
The plethora of genetic testing required for more precise diagnoses and treatment presents an obvious barrier to entry. Although it no longer costs $3 billion dollars to sequence the human genome, it may still be more than what the average person can afford. Moreover, the extent to which supplemental genetic tests will be covered by insurance is unclear.
Other challenges that naturally accompany the act of sequencing a person’s genome are the ethical considerations when it comes to data access. Insurance companies and employers may seek out this information, but it is difficult to decide which third party organizations should have the right to access it. These are important questions to ponder as we enter a time when personalized genome sequencing approaches the mainstream.
Needless to say, exciting careers in PM are certainly in demand. Whether you are studying pharmacogenomics, bioinformatics, epidemiology or other branches of medical research, there is no shortage of opportunity. A future in which decisions regarding the prevention, diagnosis and treatment of a disease consider a patient’s genetic profile and background seems imminent.
Perhaps one day, the overarching principles that match patients to treatments like a jigsaw puzzle will be abandoned, and having patients ask, “is this medicine right for me?” will become the norm.