Face it. Despite our best intentions, bad stuff happens on occasion, as seen in the medical field. In hopes of a positive outcome, a patient undergoes treatment, but has a negative reaction. In the pursuit of “first do no harm,” more safeguards are put into place to minimize the chances of a negative outcome. As technology improves, however, we may soon have an equivalent to the bubble wrap suit, particularly in the development of new medical treatments.

The Food and Drug Administration (FDA) – the federal gatekeeper for new drugs – has established numerous criteria that drug candidates must meet before being sold in the U.S. marketplace (FDA, 2017a). After testing the drug candidate in animals, the clinical evaluation begins. The first phase involves looking at how the drug candidate is processed in a few healthy people and figuring out common side effects. If things look good and the drug is considered to be not too toxic, the investigation focuses on both the safety and the effectiveness of the drug candidate for treating the disease/condition in a large number of people. If all goes well with the testing and review, the FDA decides whether to approve the drug. Further monitoring of drug safety still happens after the approval, but success depends on medical professionals and insurance companies (FDA, 2017b; FDA, 2018).

Who foots the bill for all the testing done during the clinical phases? Well, usually the company or other parties who wishes to have the drug approved. You can imagine that this process is not cheap. It is the worst nightmare imaginable when something goes horribly wrong, particularly in later stages. Despite all the safety precautions taken, this nightmare becomes reality more often than anyone would like to see.

The unfathomable sadly materialized in the clinical testing of Pfizer’s drug candidate torcetrapib, designed to treat high cholesterol (Berenson, 2006). The safety barriers put in place failed. The initial biomarkers selected to monitor the drug recipients did not work to alert doctors that something was about to go horrifically wrong. Pfizer lost about a billion dollars in the development of the drug, and sons and daughters lost their lives (Williams et al., 2018).

In a retrospective study, researchers applied a new proteomic technology (SOMAscan platform) to blood samples collected from participants of the failed study (Williams et al., 2018). The data from this proteomic investigation illuminated unknown deep physiological effects of the experimental drug that were also quite pervasive, and which probably would not have been picked up by technologies available at the time the clinical trial was conducted. The effects were especially prevalent in the bodily functions of immunity and inflammation. The insights provided by the SOMAscan proteomic analysis offered probable explanations for the deaths attributed to taking the experimental drug.

Although the SOMAscan technology was not available at that time, the odds are very good that the problems with torcetrapib would have surfaced early enough to make a difference. It is important to note that during the clinical trials, clinicians used instead the best available measurement, the Framingham risk score (a score to assess the likelihood of having heart problems), to monitor the patients, and observed a decreased risk of heart problems for the experimental drug users (Williams et al., 2018). In the retrospective SOMAscan study, the proteomic data showed the opposite result; the experimental drug users were actually at an elevated risk (Williams et al., 2018). This is not the first-time proteomics beat the Framingham risk score, a gold standard for assessing cardiovascular event risks. In an earlier and separate retrospective study, the proteomic assessment using the SOMAscan platform outperformed the Framingham risk score in determining the patients most likely to suffer a heart attack (Ganz et al., 2016).

While it is too late to help those who suffered from the unexpected effects of torcetrapib, we now have an opportunity to spare others from unintended harm. Applying the proteomic bubble wrap during clinical trials may indeed prevent unnecessary deaths. Also, the SOMAscan platform may better identify patients who would benefit the most from the experimental drug or who should avoid it all costs. Already, we know that the inclusion of biomarkers in the stratification of patients to receive experimental treatment can improve the success rate for FDA approval (Wong, Siah, & Lo, 2018). However, we need to use every tool at our disposal to monitor the right things at the right time, to avoid the kind of outcomes seen with the torcetrapib trial.

 

References

Berenson, A. (2006, December 4). End of Drug Trial Is a Big Loss for Pfizer. The New York Times. Retrieved from http://www.nytimes.com/2006/12/04/health/04pfizer.html.

Food and Drug Administration. (2017a, November 24). The FDA’s Drug Review Process: Ensuring Drugs Are Safe and Effective. Retrieved on March 20, 2018 at https://www.fda.gov/Drugs/ResourcesForYou/Consumers/ucm143534.htm

Food and Drug Administration. (2017b, November 17). FDA’s Sentinel Initiative – Background. Retreived on March 20, 2018 at https://www.fda.gov/Safety/FDAsSentinelInitiative/ucm149340.htm

Food and Drug Administration. (2018, February 21). Questions and Answers on FDA’s Adverse Event Reporting System (FAERS). Retrieved on March 20, 2018 at https://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/default.htm.

Ganz, P., Heidecker, B., Hveem, K., Jonasson, C., Kato, S., Segal, M. R., . . . Williams, S. A. (2016). Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients With Stable Coronary Heart Disease. JAMA, 315(23), 2532-2541. doi:10.1001/jama.2016.5951

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

Wong, C. H., Siah, K. W., & Lo, A. W. (2018). Estimation of clinical trial success rates and related parameters. Biostatistics. doi:10.1093/biostatistics/kxx069