How do you describe when you’re sick? Not how you’re feeling, but the actual ‘sickness’ itself? With the increased frequency of conversations regarding ‘sickness’ recently, I noticed an interesting trend. Anecdotally, when someone is infected with an influenza strain they will most likely say they “Caught the flu,” whereas an infection with the common cold would be described as “Caught a cold”. This minor difference in language reflects an interesting discrepancy between how we talk about disease versus what the underlying cause(s) actually are.
Both of these examples, as well as a significant portion of diseases overall, are actually caused by a cohort of different genotypes (the genetic components of an organism) [1-3]. However, the language we use to describe the disease state only conveys the phenotype (the observable characteristics), not the genotype. While this observation may be intriguing, it doesn’t fundamentally demand much attention because for bacterial/viral/fungal infections, the standard treatment for most genotypes is identical. The discrepancy becomes more important in diseases with a strong genetic component (e.g. cancer), where the same disease phenotype may be caused by different genotypes that are not all equally addressed by the same treatment regimen. This nuance in treatment options gets into the emerging field of personalized medicine, in which medical treatments for patients are tailored to their individual characteristics and unique genetic makeup.
Figure 1: All the unique factors, both genetic and environmental, that make you ‘you’ open up avenues for developing personalized medicine. Image source mcmurryjulie at Pixabay
This kind of treatment seems obvious on the surface, and your physician already does aspects of this by tailoring medicine dosage based on weight, body fat percentage, diet, and hormone levels [4-8]. However, personalized medicine goes a step further by accounting for an individual’s genetic variation. Certain genes, for example the liver P450 enzymes (the body’s main detoxifying enzymes), vary substantially even between healthy individuals. The makeup of your liver enzymes directly informs how long certain drugs will be active in your body [9-10]. Therefore, based on a patient’s disease and genetic background a doctor may prescribe a specific compound at a dosage tailored to the patient’s exact physical and genetic characteristics. The personalization of this dosing ensures the patient will have an optimal response to the drug, ideally with minimized side-effects. However, the greatest utility of personalized medicine arises when treating a disease that could be considered truly unique to the patient.
While some genetic diseases do have a singular genotype, meaning the disease phenotype always has the exact same genetic basis, most are actually caused by a diverse set of genotypes. An example of the first case would be sickle cell anemia, where the disease is caused by a single nucleotide mutation (a single spot in the patient’s DNA is changed), altering a native glutamic acid to valine which results in the characteristic sickle-shape of the affected red blood cells . In contrast, for examples such as Fabry or Huntington’s, the disease phenotype encompasses numerous diverse mutations [12-13]. For everyday use there’s no need to capture the nuance in disease genotype. However behind the scenes, these genetic variants have an important impact on how the disease can be treated.
For instance, in Fabry disease, an essential metabolic enzyme (α-galactosidase A) is disrupted leading to the accumulation of its substrate (Globotriaosylceramide) . The exact disease genotype that causes this disruption directly informs which treatment(s) will be effective. Some individuals with the disease completely lack the enzyme needed to break down globotriaosylceramide, meaning they can only be treated by enzyme replacement therapy . However, other individuals have unstable enzyme variants, meaning the enzyme falls apart easily but is theoretically still functional under the right conditions. Both of these genotypes present as Fabry disease, but only the patients with the unstable enzyme can be treated by a novel remedy called a pharmacological chaperone: a drug which binds to and stabilizes the enzyme . This nuance in treatment options represents truly personalized medicine.
Figure 2: All these pills might be identical, but their effects on patients isn’t. Image source pxfuel.
There is a lot to be gained through personalized medicine, especially in very diverse diseases such as cancer. However there are also barriers to this form of treatment such as the protection of patient data . As increasingly comprehensive genetic information is collected, adequate protections must be developed to ensure that information is not used to discriminate against people with a disease or predisposition to a disease. If steps are taken to ensure patient safety, substantial advances can be made in disease treatment.
When developing a drug, clinical trials are the ‘make it or break it’ point. Clinical trials, the process by which the safety and efficacy of drug candidates is evaluated in people, need to account for variation in study participants to ensure only safe and effective drugs are brought to market. Failure to understand how intrinsic differences among patients can impact the safety/efficacy of the drug can lead to dangerous compounds being released to market, such as the common example of thalidomide [17-18]. Thalidomide was originally available as an over-the-counter medicine for anxiety before being adopted as a treatment for morning sickness. However, it was not properly studied in this context and resulted in the substantial occurrence of birth defects in the pregnancies where it was taken. A previous TLS post, Is Science for Women?, covered how sex differences led to poor clinical trial design as women were underrepresented in past clinical trials. Body fat percentage/distribution, hormone levels, enzyme expression and activity all vary among individuals, as well as exhibit variation between sex and race [19-22]. Therefore, traditionally clinical trials must account for all these variations to ensure the drug candidate is safe for broad use. However, what if the drug was to be personalized? Targeting clinical trials based on specific disease genotypes allows for the development of compounds effective for smaller and potentially overlooked groups of people. This is the greatest utility of personalized medicine: not just tailored dosing, but unique drugs that treat the specific genotype causing the disease phenotype in each patient.
As increased fundamental knowledge is gained regarding biology and disease, nuance must be applied for optimal therapeutic strategies. We can step away from drug panaceas to medicine tailored to treat what ails you.
 “Common Cold.” Centers for Disease Control and Prevention, Centers for Disease Control and Prevention, 6 Feb. 2020, www.cdc.gov/antibiotic-use/community/for-patients/common-illnesses/colds.html.
 Intermountain Healthcare. “What’s the Difference between a Cold, the Flu, Seasonal Allergies, and Coronavirus?” What’s the Difference between a Cold, the Flu, Seasonal Allergies and Coronavirus?, Intermountain Healthcare, 10 Apr. 2020, intermountainhealthcare.org/blogs/topics/live-well/2020/03/whats-the-difference-between-a-cold-the-flu-and-coronavirus/.
 “Influenza (Seasonal).” World Health Organization, World Health Organization, 6 Nov. 2018, www.who.int/en/news-room/fact-sheets/detail/influenza-(seasonal).
 Pan, Sheng-dong, et al. “Weight-based dosing in medication use: what should we know?.” Patient preference and adherence 10 (2016): 549.
 Pai, Manjunath P. “Drug dosing based on weight and body surface area: mathematical assumptions and limitations in obese adults.” Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy 32.9 (2012): 856-868.
 Bushra, Rabia, Nousheen Aslam, and Arshad Yar Khan. “Food-drug interactions.” Oman medical journal 26.2 (2011): 77.
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 Gibson G.G., Skett P. (1986) Factors affecting drug metabolism: internal factors. In: Introduction to Drug Metabolism. Springer, Boston, MA
 “Cytochrome p450 - Genetics Home Reference - NIH.” U.S. National Library of Medicine, National Institutes of Health, 9 June 2020, ghr.nlm.nih.gov/primer/genefamily/cytochromep450.
 Nelson, David R., et al. “The P450 superfamily: update on new sequences, gene mapping, accession numbers, early trivial names of enzymes, and nomenclature.” DNA and cell biology 12.1 (1993): 1-51.
 Clancy, Suzanne. “Genetic mutation.” Nature Education 1.1 (2008): 187. https://www.nature.com/scitable/topicpage/genetic-mutation-441/
 “Fabry Disease - Genetics Home Reference - NIH.” U.S. National Library of Medicine, National Institutes of Health, 9 June 2020, ghr.nlm.nih.gov/condition/fabry-disease.
 Sun, Yi-Min, Yan-Bin Zhang, and Zhi-Ying Wu. “Huntington’s disease: relationship between phenotype and genotype.” Molecular neurobiology 54.1 (2017): 342-348.
 Neufeld, Elizabeth. “Chapter 10: Enzyme replacement therapy—brief history.” from Fabry Disease: Perspective from 5 (2006).
 Ringe, Dagmar, and Gregory A. Petsko. “Q&A: What are pharmacological chaperones and why are they interesting?.” Journal of biology 8.9 (2009): 80.
 Brothers, Kyle B., and Mark A. Rothstein. “Ethical, legal and social implications of incorporating personalized medicine into healthcare.” Personalized medicine 12.1 (2015): 43-51.
 “What Is Thalidomide?” Thalidomide, Thalidomide Victims Association of Canada, thalidomide.ca/en/what-is-thalidomide/#:~:text=Thalidomide is a sedative drug,order to relieve pregnancy nausea.
 Fintel, Bara, et al. “THE THALIDOMIDE TRAGEDY: LESSONS FOR DRUG SAFETY AND REGULATION.” HELIX, HELIX, 28 July 2009, helix.northwestern.edu/article/thalidomide-tragedy-lessons-drug-safety-and-regulation.
 Soldin, Offie P., and Donald R. Mattison. “Sex differences in pharmacokinetics and pharmacodynamics.” Clinical pharmacokinetics 48.3 (2009): 143-157.
 Vogenberg, F. Randy, Carol Isaacson Barash, and Michael Pursel. “Personalized medicine: part 1: evolution and development into theranostics.” Pharmacy and Therapeutics 35.10 (2010): 560.
 Schneider, A. L. C., et al. “Liver enzymes, race, gender and diabetes risk: the Atherosclerosis Risk in Communities (ARIC) Study.” Diabetic medicine 30.8 (2013): 926-933.
 Liu, Katherine A., and Natalie A. Dipietro Mager. “Women’s involvement in clinical trials: historical perspective and future implications.” Pharmacy Practice (Granada) 14.1 (2016): 0-0.
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