If you have seen a drug commercial with a long list of potential ominous side effects, you would probably answer “Yes!” In fact, many of the more recent lucrative drugs only successfully helped about 4 to 25% of people treated (Schork, 2015). With such a low chance of the drug actually working, who wants to take on such a high stakes gamble? Do you feel lucky?

Precision medicine heralds the delivery of “the right treatment to the right patient at the right time.” Intuitively, the concept means that patients benefit by being spared from potentially harmful side effects of treatments that will not work for them and healthcare costs could be reduced by not wasting finite funds on treatments that will not benefit the patient.

Despite the promise of precision medicine, opinion pieces have been popping up asking whether or not the world is being misled by its proponents. For example, the author of a recent editorial discussed how some statistical analysis methods may misconstrue the number of patients who respond to treatments (Senn, 2018). The offenders the author identified included, but not limited to the following: arbitrary measurements; subjective cut-off lines for determining a patient who responds from one who does not respond to treatment; patient’s fluctuating physiology; and response rates. Another essay noted how treatments assigned based on genetic findings rarely actually work, but those success stories get overgeneralized and overhyped, which may mislead the public into thinking that the success rates of precision medicine are much higher than they are (Szabo, 2018).

Most of the recent editorial writers are not all doom-and-gloom for precision medicine: They do suggest steps that can be taken. Stephen Senn suggested avoiding arbitrary terms like “responder”, sticking with actual measurements (i.e., not using one’s own judgment to gauge a value), and using N-of-1 clinical trials (clinical trials done with one individual) – although individual clinical trials have their own issues that need to be addressed for them to be successful (Lillie et al., 2011).

But maybe we should take a step back and ask whether we are collecting the “right data” to deliver the “right treatments.” For example, measuring thousands of proteins simultaneously and repeatedly over time could provide more meaningful information about a person’s health status and its changes than other information, such as genomics. With its ability to provide a synopsis of a person’s changing physiology in real-time, the SOMAscan® platform has shown potential in providing a more accurate assessment of patients compared to the “gold standards,” which could have prevented bad outcomes in a clinical trial of a promising new drug (Williams et al., 2018). In other cases, the SOMAscan technology shows potential in outperforming the “gold standards” in offering a more accurate diagnosis, which can lead to being treated with the right treatment sooner (Barbour et al., 2017).

Using the SOMAscan platform in the treatment of patients could alleviate concerns being raised about precision medicine in additional ways. By using measurements that reflect what is happening to the individual at the molecular level, the researchers and clinicians would have the hard-core numbers and insights necessary to determine if patients are responding to medications or other treatments. This could allow for better assignment of responder vs non-responder. The molecular insight would also help visualize fluctuations in physiology, which could be seminal for understanding treatment performance and response rates.

We are just on the cusp of a new age for the practice of medicine. But we have to focus and not get swept up in the hype. There is no doubt that within the next few years we will have a way to determine the health status and the most precise treatment for the individual (right time health).

References

Barbour, C., Kosa, P., Komori, M., Tanigawa, M., Masvekar, R., Wu, T., . . . Bielekova, B. (2017). Molecular-based diagnosis of multiple sclerosis and its progressive stage. Ann Neurol, 82(5), 795-812. doi:10.1002/ana.25083

Lillie, E. O., Patay, B., Diamant, J., Issell, B., Topol, E. J., & Schork, N. J. (2011). The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Per Med, 8(2), 161-173. doi:10.2217/pme.11.7

Schork, N. J. (2015). Personalized medicine: Time for one-person trials. Nature, 520(7549), 609-611. doi:10.1038/520609a

Senn, S. (2018). Statistical pitfalls of personalized medicine. Nature, 563(7733), 619-621. doi:10.1038/d41586-018-07535-2

Szabo, L. (2018, September 11). Are We Being Misled About Precision Medicine? The New York Times. Retrieved on December 5, 2018 from https://www.nytimes.com/2018/09/11/opinion/cancer-genetic-testing-precision-medicine.html

Williams, S. A., Murthy, A. C., DeLisle, R. K., Hyde, C., Malarstig, A., Ostroff, R., . . . Ganz, P. (2018). Improving Assessment of Drug Safety Through Proteomics: Early Detection and Mechanistic Characterization of the Unforeseen Harmful Effects of Torcetrapib. Circulation, 137(10), 999-1010. doi:10.1161/CIRCULATIONAHA.117.028213