The notion of individualism is strong within Western societies. During recent decades the individual with his/her independence and uniqueness has been celebrated and recognized within the media, arts, and sciences. Moreover, the scientific community’s understanding of the individual has been bolstered by the completion of the Human Genome Project. The mapping of the human genome has shown that while mankind’s genetic make-up is 99.1% identical; a small 0.9% inter-individual genetic variability creates and accounts for the vast variability that exists within the human species (Novelli 2010).
The medical community has long recognized the inherent uniqueness of patients as evidenced by the prevalence of specific disease entities within families and ethnicities, variable responses to medications, and diverse manifestations of a single pathology (Novelli 2010). Notwithstanding these observations, medical therapy has generally employed a broad treatment approach to a heterogeneous population rather than a unique treatment approach to the individual patient. This practice is now changing as technological advancements enable practitioners to identify and treat patients based on unique characteristics — a practice known as personalized medicine (PM).
History of personalized medicine
The term personalized medicine first appeared in published works in 1999, however some of the field’s core concepts have been in existence since the early 1960s (Jain 2002). The advent of new technologies has now made personalized medicine a more tangible reality, and has enabled researchers to provide a link between an individual’s molecular and clinical profiles (Ginsburg 2001).
Two key discoveries have allowed for significant progress within the field of personalized medicine — single nucleotide polymorphism (SNP) genotyping and microarray/biochips. (Jain 2002) SNPs are single nucleotide changes in the DNA sequence that are particularly frequent in the population, and account for 90% of all known polymorphisms (Novelli 2010; Jain 2002). The ability to identify these polymorphisms is crucial, as SNPs have been linked to patient susceptibility to various disease processes and drug therapy responsiveness. SNPs have also proven to be an invaluable tool in segregating patients in a multitude of studies and clinical trials.
The invention of the microarray biochip has revolutionized the field of personalized medicine, specifically by creating the ability to store and rapidly analyze a patient’s genome in its entirety. Biochip technology enables researchers and clinicians to conduct SNP genotyping efficiently, and ultimately allows for the rapid development of protein-based diagnostics and therapeutics.
Examples of personalized medicine
Abacavir, a nucleotide reverse transcriptase inhibitor used to treat patients with HIV, is known to cause a hypersensitivity reaction in some patients within six weeks of the onset of therapy. Before the advent of PM this hypersensitivity reaction was made by clinical diagnosis. In 2002, two independent studies demonstrated a possible genetic link between the hypersensitivity reaction and the major histocompatibility complex class I allele HLA-B*57:01 (Mallal 2002; Hetherington 2002). Mallal et al., conducted a follow up study in which they gave abacavir only to patients who were negative for the HLA-B*57:01 gene, and there was no hypersensitivity reaction in that patient population. The results demonstrated that patients that carry the HLA-B*57:01 gene have a 60% chance of developing a hypersensitivity reaction when treated with abacavir, while patients who do not carry the gene do not develop the drug reaction at all (Mallal 2002). This landmark study demonstrated that a patient’s genome can predict response to a specific drug therapy. Moreover, this study was so persuasive that the Food and Drug Administration and the European Medicines Agency altered the abacavir label to advise testing for the HLA-B*57:01 allele prior to initiating abacavir therapy (Novelli 2010).
Besides preventing adverse reactions, PM can assist clinicians in determining ideal drug dosing for their patients. For instance, warfarin, a vitamin K antagonist, is commonly used as a blood thinner. Warfarin is infamous for its narrow therapeutic range, as supratherapeutic dosages place the patient at an increased risk for adverse bleeding, while subtherapeutic levels leave the patient prone to clotting. Initiation of warfarin therapy is truly a guessing game that all clinicians are aware of; in fact, a patient’s dose response can hardly be predicted by the typical patient attributes. Personalized medicine has recently begun to explain this variable patient response. Studies have shown that variation in three genes can explain some of the inter-individual variability of this drug. They are CYP2C9, a gene encoding the enzyme responsible for warfarin metabolism; VKORC1, a gene encoding the enzyme that recycles vitamin K (the drug’s primary target); and CYP4F2, a gene encoding the enzyme responsible for vitamin K metabolism (Novelli 2010). An analysis by Borgiani et al. in 2009 demonstrated that variants of these three genes along with patient age and weight can explain 60.5% of the dosing variability among patients. The quest of personalized medicine to improve warfarin dosing has led to changes in the FDA label to include consideration of genetic variations in drug initiation, and has catalyzed the development of genetic tests that enable clinicians to more accurately conduct warfarin initiation (Borgiani 2009).
Finally, personalized medicine can help predict a patient’s likelihood of developing certain illnesses. Well-known examples are mutations of the BRCA1 and BRCA2 genes that have been implicated in familial breast cancers and the loss of APC gene function in familial adenomatous polyposis. As advancements in personalized medicine continue, clinicians continue to gain new tools to help identify patients at risk for developing debilitating disease. One such disease is age-related macular degeneration (AMD), one of the most common causes of visual impairment in adults over the age of 50. The recent discovery of two genes with a strong association with the development of AMD, CFH and ARMS2, may allow for the development of genetic tests that help physicians identify and provide earlier ophthalmologic screening to at-risk patients (Novelli 2010).
As demonstrated by the three examples above, PM has the potential to immensely influence the practice of medicine.
Future of personalized medicine
Human genome research is the foundation for the future of personalized medicine, and has the ability to eventually customize medical treatments to individual patients through the incorporation of genetics, molecular profiles and clinical characteristics in treatment determination. With the use of risk algorithms, molecular diagnostics, and targeted therapies, the field of personalized medicine is striving to translate research into clinical practice.
Ginsburg and McCarthy state that personalized medicine intersects with the course of a patient’s disease at six major points: predisposition, screening, diagnosis and prognosis, pharmacogenomics, and monitoring (Ginsburg 2001). PM can be used to analyze a patient’s genome to predict his/her likelihood of developing a specific illness, and can direct decision making about preemptive intervention. As a screening tool, PM can identify protein markers of disease long before clinical manifestations, and thus allow for earlier treatment and decreased morbidity and mortality. Similarly, PM can precisely diagnose and prognosticate based on specific genetic characteristics unique to a patient rather than on data collected from a diverse population with a wide array of genetic variables.
Advances within personalized medicine will also increase the efficiency and pace of pharmaceutical development as drug developers can now use toxigenomic markers to screen compounds and improve selection of patients for clinical trials to reduce the number of failures in later stages of drug development. Further, toxigenomic markers predictive of adverse drug reactions can be used to prevent patients from receiving therapy that will harm rather than cure (Ginsburg 2001).
One of the most promising areas within personalized medicine is pharmacogenetics, which evaluates how a patient’s genetic makeup influences treatment effectiveness. Researchers within this field are focused on developing the “Dx-Rx paradigm,” which requires genetic/biomarker testing before prescription writing. This paradigm has been effectively implemented in breast cancer care through the HercepTest/trastuzumab combination, which evaluates tumors in breast cancer patients for the expression of Her2/neu receptors and treats Her2/neu positive patients with trastuzumab — a monoclonal antibody targeting Her2/neu receptors. However, use of this paradigm in treating other pathologies requires that payers understand the value in genetic and molecular diagnostics to manage the use of costly therapies. Consequently, researchers need to develop diagnostic tests that can accurately identify responders from nonresponders to a specific treatment. Results from these tests then need to be made available to key stakeholders in patient care to enable payers to determine which tests are of value in preventing the misuse of costly therapeutics (Ferrara 2007).
Beyond tailored treatment, personalized medicine is moving toward identifying patients within a population who are at an increased risk for developing specific diseases, and is striving to prophylactically treat these people. There are now more than 350 genetic tests available to screen patients for a multitude of diseases, and although most of these tests are for rare, monogenic diseases, research is being directed toward tests for more common, polygenic disorders (Ginsburg 2009). For instance, there are four NIH-funded clinical trials that are focused on diabetes genetic testing (Markowitz 2011). Genetic and biomarker tests hold value in identifying patients susceptible to diseases that can be costly to treat if caught at later stages. However, it is imperative to correlate early identification through genetic testing with patient outcomes and cost-savings to increase the utility of these tests.
There are three beneficiaries from the advancements of personalized medicine — patients, the pharmaceutical industry, and society. As developments are made in the field of PM, patients will receive safer and more effective treatment; the pharmaceutical industry will gain increased efficiency, productivity, and better product lines; and society will gain from decreased health care expenditures as a consequence of the more precise allocation of limited health care resources. Personalized medicine, once fully matured, will revolutionize the practice of medicine and alter the paradigm of diagnosis-based medical practice that dominates Western medicine (Jain 2002; Ginsburg 2001).
Notwithstanding the proven clinical benefit of personalized medicine, further research is needed to elucidate the effect of this field on health care expenditures, patient outcomes (i.e., morbidity and mortality) and overall societal benefit.
- Borgiani, P., Ciccacci, C., Forte, V., Sirianni, E., Novelli, L., Bramanti, P., et al. CYP4F2 genetic variant (rs2108622) significantly contributes to warfarin dosing variability in the Italian population. Pharmacogenomics. 2009;10(2):261–266.
- Ferrara, J. Personalized medicine: Challenging pharmaceutical and diagnostic company business models. McGill Journal of Medicine : MJM : An International Forum for the Advancement of Medical Sciences by Students. 2007;10(1):59-61.
- Ginsburg, G. S., & Willard, H. F. Genomic and personalized medicine: Foundations and applications. Translational Research : The Journal of Laboratory and Clinical Medicine. 2009;154(6):277-287.
- Ginsburg, G. S., & McCarthy, J. J. Personalized medicine: Revolutionizing drug discovery and patient care. Trends in Biotechnology. 2001;19(12):491-496.
- Hetherington, S., Hughes, A. R., Mosteller, M., Shortino, D., Baker, K. L., Spreen, W., et al. Genetic variations in HLA-B region and hypersensitivity reactions to abacavir. Lancet. 2002;359(9312):1121-1122.
- Jain, K. K. Personalized medicine. Curr Op Mol Ther. 2002;4(6):548-558.
- Mallal, S., Phillips, E., Carosi, G., Molina, J. M., Workman, C., Tomazic, J., et al. HLA-B*5701 screening for hypersensitivity to abacavir. N Eng J Med. 2008;358(6):568-579.
- Mallal, S., Nolan, D., Witt, C., Masel, G., Martin, A. M., Moore, C., et al. Association between presence of HLA-B*5701, HLA-DR7, and HLA-DQ3 and hypersensitivity to HIV-1 reverse transcriptase inhibitor abacavir. Lancet. 2002;359(9308):727-732.
- Markowitz, S. M., Park, E. R., Delahanty, L. M., O'Brien, K. E., & Grant, R. W. Perceived impact of diabetes genetic risk testing among patients at high phenotypic risk for type 2 diabetes. Diabetes Care. 2011;34(3):568-573.
- Novelli, G. Personalized genomic medicine. Int Emerg Med. 2010;5(Suppl 1):S81-90. doi:10.1007/s11739-010-0455-9