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Writer's pictureAllison Andreyev
5 min read

Is Personalized Medicine Worth It?

Imagine a world where there are thousands of possible variations of medicine, even for the common cold. Personalized medicine is an innovation in biotechnology that studies the human genome to be able to tailor medicine for maximum effectiveness and efficiency for each specific individual. According to Fernald et al., “Thousands of DNA variants have been identified that are associated with diseases and traits. By combining these genetic associations with phenotypes and drug response, personalized medicine will tailor treatments to the patient's specific genotype.”



With numerous studies and projects regarding personalized medicine emerging over recent years, the widespread adoption of this innovation across Europe and the Americas is not far off. Being able to provide people with medicine that will be most effective will immensely improve health and life rates. Though this seems like a revolutionary concept, there are several downsides to personalized medicine. For example, though access to large omics such as genomics, transcriptomics, proteomics, epigenomic, metagenomics, metabolomics, and nutrigenomics has revolutionized the field of biotechnology and led to a better understanding of numerous biomechanisms, this may also be what sets apart high and low-income countries. The study of personalized medicine requires the power and computational skills to be able to analyze large groups of data. Data storage, processing, and interpretation may be what sets us apart. Though healthcare gaps are already visible in today's world between different countries, implementing and expanding on the study of personalized medicine, and incorporating it into our healthcare industry would make these discrepancies even more prevalent. Considering that people living in the developing world make up 84% of our total population, but only 12% of total healthcare spending, implementing personalized medicine will only grow these disparities.


Distributions of populations and global health expenditure according to WHO 2012


The economic value of omics networks as personalized tests for future disease onset or response to specific treatments/interventions remains largely unknown. In addition, economic investments are an important concept to consider when looking at the transition to personalized medicine as they are needed to be able to make policy decisions and guide investments. However, the costs of implementing personalized medicine in healthcare systems remain largely unclear, we know that only a minuscule part of the program would be cost-saving and would not widen the healthcare equality gap between 1st and 3rd world countries. Another challenge of implementing personalized medicine practices in the United States is our current inability to analyze and interpret genomic data, as our technology may not yet be advanced enough to do so. As of now, a whole genome or a few dozen exomes can be sequenced in <2 weeks with an error rate of ~ 1 error per 100 kb (Drmanac et al., 2010). However, though this number seems small, this means a 3 GB human genome would have ~30 000 erroneous variant calls. According to Alyass et al., “Biological processes are very complex, and this coupled with the noisy nature of experimental data and the limitations of statistical analyses (e.g. false positive associations) poses many challenges.” Bioinformatics requires scientists to be able to be proficient in understanding underlying problems in an omic analysis, the methods of data analysis, and the advantages and disadvantages of various computational platforms to be able to draw inferences and initiate explorations with the data. Moreover, approximately 90% of scientists are self-taught when it comes down to the skills regarding developing software such as task automation, code review, unit testing, and version control. Though having scientists have more knowledge in these intersection fields would be immensely beneficial, it is important to recognize that there is a cap to how much knowledge one individual can acquire. Furthermore, Myere et al., states “Laboratory-hosted servers require investments in informatics support for configuring and using software. Such servers are not only expensive to set up and maintain, but do not meet the dynamic requirements of different workflows for processing omics data, leading to either extravagant or sub-optimal servers.” Implementing the necessary technology to process said data would be very costly, considering that most traditional laboratories currently lack it. Acquiring all the necessary technology to do this would also take years, as thousands of high-end servers and computers would have to be produced. Moreover, developing the necessary applications to study genomics would be a very challenging and labor-intensive task.



The security concerns of using this software would also be prevalent. According to Peter Tarczy-Hornoch, necessary security functions would include: 

  • Availability (ensure that accurate and up-to-date information is available);

  • Accountability (ensure that users are responsible for their access to and use of information);

  • Perimeter (control the boundaries of trusted access to an information system);

  • Role-limited access (restrict access for personnel to information that is essential to their jobs);

  • Comprehensibility and control (ensure that record owners understand different aspects of information control, confidentiality, and access).


Implementing these measures and making sure they are being followed will be a long and difficult process, which will set back the implementation of personalized medicine, even though these measures are essential. Not only is processing this data not cost or time-effective, Turcotte states “Genes may carry out different functions in different cell types/tissues, which adds to the already substantial inter-individual variability. Biological complexity presents a challenge in extracting useful information within high-dimensional data.”, meaning that extracting data from genetics would be difficult. This also ties back to the requirement for individual skill and capacity to successfully implement personalized medicine practices. Moreover, it is important to consider what research can be transferred from the lab to the clinic. Most physicians are not prepared to be able to transfer and incorporate personal genetic testing into their practice, as research is also not advanced enough to be able to improve patient care. Lastly, we should consider the impact on patients. Will the average citizen feel comfortable with labs of scientists actively analyzing and extracting their DNA? Will they trust personalized medicine? Will this be something that patients look forward to, or are scared about? These are all important questions to consider. When implementing personalized medicine into healthcare, we do not want it to have a dystopian-esque twist to it, and we need it to be trusted. Although we may not be ready to incorporate personalized medicine into our healthcare systems just yet, in the future, it may be a worthwhile investment.


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