Op-Ed: Challenging the Hype over Personalized Medicine

By Reef Aldayafleh, MEng ’20 (BIOE)

We grow up being told to embrace our unique characteristics. We customize our phones, clothes, and drinks to our personal preferences. So why not customize our medicine? Advancement in genomics, data collection and artificial intelligence is spurring a new frontier in healthcare: precision medicine (also known as personalized medicine). President Obama launched the Precision Medicine Initiative in 2015 to move healthcare away from the “one-size-fits-all” approach to a more personalized approach of tailored treatments. Researchers are leveraging patient data to develop precise diagnoses and effective treatments.

However, ongoing challenges in translational research have sparked controversy over how realistic personal medicine is. Human biology and disease are complex. Research is expensive and takes years. Drug companies charge ridiculously high prices. And physicians are slow to adopt new approaches to medicine. Even if proved as a viable treatment, personalized medicine is only beneficial for a small subset of the patient population that can afford it. The current hype over precision medicine may be misleading, and potentially harmful to frustrated individuals searching for alternative therapies. The public needs to be better informed on the realistic impacts and accessibility of precision medicine to avoid the false hopes and promises associated with tailored treatments. Big data collection and analytics enables precision medicine. Physicians can make a more accurate diagnosis or treatment by collecting a person’s diet, exercise, medications, laboratory results and medical history. Researchers have utilized machine learning to increase the analysis rate and data volume of patients and diseases.1 For example, it is common for physicians to strategically combine drugs to treat a patient, but knowing what combination is the most effective and least risky has been challenging. Scientists Rashid et al, at the Cancer Institute of Singapore, developed an artificial-intelligence algorithm that efficiently optimizes drug combination for specific patient samples. A time-consuming task that could have risked a patient’s health can be safely completed by a computer. Utilizing artificial intelligence in healthcare decreases errors in treatments, which can boost doctors’ confidence in decision-making. Artificial intelligence advances drug-development for targeted therapeutics by analyzing patient characteristics. However, physicians have been slow to adopt data-based medical solutions. Machine learning’s nature of producing models without a mechanistic understanding makes physicians skeptical of the generated model predictions. There has been little impact on clinical practice since physicians are not comfortable to shift from the standard care they were taught in traditional medical school. There are multiple stages of technology adoption. The timeline of physicians navigating new platforms for personalized medicine determines the implementation of tailored treatments. Physicians are on the frontiers of healthcare and if they are unable to support artificial intelligence in medicine, personalized medicine will not reach the public anytime soon.

In addition to the lack of support by physicians, personalized medicine is not in favor of the average American. Research in tailored therapeutics is expensive, which contributes to the burden of high healthcare costs. Drug companies are allowed to exploit the sick and charge high out-of-pocket costs to make large profits, causing many people to go into medical debt. Generic drugs were created to lessen this financial burden. Tailored treatments do not have a ‘generic’ version because the drug is specifically catered to the patient. Unless BigPharma has a change of heart and caps drug prices, tailored treatments will be inaccessible to the majority of Americans. Such a lack of accessibility to treatment has been unfortunately common for patients with rare disease. Drug companies exploit rare diseases by charging exorbitant prices since treatment benefits a small group of people and the patient does not have an alternative effective treatment. A similar trend will follow for personalized medicine since tailored treatments give drug companies more power over an individual. The term ‘personalized medicine’ has become a buzzword. The public needs to be better informed over the realistic impacts of personalized medicine. Physicians have yet to implement data-based prediction models for tailored treatments. Individuals seeking effective treatments are left vulnerable without proper health policies to regulate drug companies. A better informed population will push initiatives to adopt data-based medicine and protect patients from exploitation, transforming personalized medicine from fantasy to reality.

About the author:

Reef Aldayafleh has a passion for exploring the wonders of science, where her curiosity shaped her into an interdisciplinary scientist and engineer. She has an extensive background in research from previous work in nanotechnology, medical devices and life sciences. Reef is currently pursuing a Master of Engineering degree in Bioengineering at UC Berkeley. Prior to attending Berkeley, she received her bachelor’s degree in Bioengineering at UC Merced. She is actively seeking opportunities in innovating and enabling scientific discovery and medical advancements. Connect with Reef.

References:

• Fröhlich, Holger et al. “From hype to reality: data science enabling personalized medicine.” BMC medicine vol. 16,1 150. 27 Aug. 2018, doi:10.1186/s12916–018–1122–7
• MacLeod, Katarin & Kraglund-Gauthier, Wendy. (2015). A Case Study of Infusing Technology into Pre-service Secondary Science Teacher Learning: Conceptions and Attitudes While Navigating Changing Digital Landscapes. Journal Of European Education-Avrupa Eğitim Dergisi. 5. 10.18656/jee.24828.
• Rashid, Masturah et al. “Optimizing Drug Combinations Against Multiple Myeloma Using a Quadratic Phenotypic Optimization Platform (qpop).” Science Translational Medicine vol. 10, issue 453. 08 August 2018, doi:10.1126/scitranslmed.aan0941
• Terry, Sharon F. “Obama’s Precision Medicine Initiative.” Genetic Testing and Molecular Biomarkers, Mary Ann Liebert, Inc., Mar. 2015.
• Winslow, Raimond L, et al. “Computational Medicine: Translating Models to Clinical Care.” Science Translational Medicine, U.S. National Library of Medicine, 31 Oct. 2012.

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Op-ed: The need for personalized healthcare was originally published in Berkeley Master of Engineering on Medium, where people are continuing the conversation by highlighting and responding to this story.