Professional Patients in Clinical trials: Detecting Truthfulness to Ensure More Meaningful Outcomes.
Developing a drug is a huge undertaking, typically taking 8–10 years and $1 billion or more to get to market and hopefully generate a profit. Further, central nervous system (CNS) diseases tend to be more difficult to treat and therefore potential drugs often take significantly more time and money to develop. A key stage in the process of proving a drug works and meets the criteria of the Food and Drug Association (FDA) are clinical trials. They must be conducted to show safety and efficacy in the patient population. Throughout the course of these trials, several hundred people will be recruited, vetted, managed, tested, and ultimately paid for their participation. This compensation is considered recompense for the patient’s time and risk — the inherent risk of taking experimental drugs– which means they are offered more than just a trivial sum, commonly more than $1,000. The relatively easy way to earn such payments inevitably draws people who will seek to take advantage of the process; these people are known as professional clinical trial patients/subjects.
In 2016, David McCann, Ph.D. et al. at the NIDA Division of Pharmacotherapies and Medical Consequences of Drug Abuse in conjunction with AstraZeneca published the article entitled Medication Nonadherence, “Professional Subjects,” and Apparent Placebo Responders: Overlapping Challenges for Medications Development. This article describes the problem of subjects who in are untruthful in 2 general aspects: [type 1] they lie about having the addiction and continue to lie by positive effects of the drug throughout the trial (destined to succeed) and 2) [type 2] they do have an addiction but do not intend to be treated (destined to fail). Modeling of meta-analytical data from double-blind, CNS drug, clinical trials where subjective results as well as quantitative results (by a measureable readout) were used to determine adherence and essentially the truthfulness of the patient. Both types of professional subjects present challenges to the studies in showing effectiveness, yet an increasing proportion of type 1 patients enrolling in a trial has a greater impact on the outcome. The meta results were compared to the pill count data collected during the clinical trial, further confirming a problem in the truthfulness of the patients who enroll in CNS clinical trials. The data also shows that as the total number of subjects increases the number of untruthful subjects increases proportionately, so merely enrolling more subjects does not improve the trial effectiveness.
This problem is compounded by a few other related factors. In a survey of repeat clinical trial subjects, 25% admit to exaggerating health problems and 14% pretending to have the disorder under the study (Devine et al. 2013). The extent of these issues has led to an increase in the number of placebo response rates in CNS disorder clinical trials (Walsh et al. 2002). Other mechanisms that could be driving high recruitment of untruthful subject may be the excessive use of advertisement to the general public and high compensation for participation.
The challenge to identify deceptive patients before enrollment is a monumental yet essential task in drug trials. Innovation in patient enrollment processes will be necessary to introduce a solution to this multifaceted problem. A global registry of clinical trial patients may be the best solution to this problem. By implementing a system of this kind at the time of patient enrollment the physician can search the patient’s name in the registry. This would allow the physicians (meeting the mandatory approval requirements for obtaining records) to see if the patient is registered as having the affliction being studied, as well as, if they are or have been in enrolled in many clinical trials over time or concurrently (a typical pattern of professional subjects).
Identifying untruthful patients who sucessfully enroll in a study is an aspect of the problem that has seen the most innovation. An effective clinical trial will attempt to replicate a real world situation for a patient who is prescribed therapy. Therefore recruiting these untruthful patients for an in-house study is not ideal and can result in a large increase in cost. Innovative tools have been developed to track patient’s adherence outside of the clinic. The most effective approach is the use of a biomarker in the study drug that can be easily measured via bodily fluids; when samples are taken often enough this can provide a direct and reliable measure of steady levels of a study drug in the patient’s body indicating compliance with the drug regimen. In the clinic the physician would monitor the patient consuming the pill. New technological advances such as Medication Electronic Moderating System (MEMS) (an electronic pill bottle or medication organizer that monitors activity), apps that allows the physician visual observation of pills being consumed, and ingestible sensors that when exposed to the stomach content send a signal to an app on a phone and subsequently to the clinic make monitoring patient adherence a possibility practically everywhere.
The effort to design professional subject resistant clinical trials is believed to be the most effective solution to this overall problem; a design that recruits enough patients despite truthfulness status and is able to ID the deceptive patients early while still conducting the study bias free is ideal. The RAMPUP design subjects all patients who meet the exclusion/inclusion criteria to a run-in period. During the single-blind run-in period a patient has to meet the adherence requirement via biomarker placebo. After this period ALL patients are stratified (by adherence) and randomized (placebo or treatment) into the double blind portion of the study while continuing to monitor adherence. The results of the study are calculated with the knowledge of early adherence incorporated into the analysis. Keeping all patients blind and in the study is beneficial in three ways and an important feature of this design. First, during the run-in period, the researcher wants to capture the true adherence behavior of the study and not influence the patients to be temporarily adherent during the run-in. Second, evaluation of both nonadherent and adherent persons would generate a more ‘real world’ safety profile of the study drug. Finally this approach would evaluate patients who were nonadherent during run-in period for the further development of future professional subject study designs. Following this study design has clear advantages and hopefully sets the basis for the beginning of a new generation of professional subject resistant study designs.
It’s frustrating to speculate that in the past a high potential CNS treatment may have failed FDA approval due to the inclusion of professional subjects. To ensure this does not happen in the future, addressing the problem of clinical trial professional subjects is highly important to the treatment of CNS diseases. Part of the key to solving the professional subject problem may be in the future of wearable tech. Companies like Fitbit and Apple are leading the innovation in this young field. These companies create devices that monitor your body’s vitals such as: activity, exercise, sleep, weight, heart rate and more. In the future one can imagine wearable tech that can measure levels of drugs within your body, detect the presence of diseases including mental health disease, and even dispense therapy. These devices could be integrated into future clinical trial requirements as well as integrated with other future approaches.
Devine EG, Waters ME, Putnam M, et al. Concealment and fabrication by experienced research subjects. Clin Trials. 2013; 10:935–948.
McCann DJ, Petry NM, Bresell A, et al. Medication Nonadherence, “Professional Subjects,” and Apparent Placebo Responders: Overlapping Challenges for Medications Development J Clin Psychopharmacol. 2015 October ; 35(5): 566–573.
Walsh BT, Seidman SN, Sysko R, et al. Placebo response in studies of major depression: variable, substantial, and growing. JAMA. 2002; 287:1840–1847.