Tag: Human Subjects Research

Person receiving vaccine.

Why Do Differences in Clinical Trial Design Make It Hard to Compare COVID-19 Vaccines?

The Petrie-Flom Center Staff Leave a comment

Cross-posted from Written Description, where it originally appeared on June 30, 2021. 

By Lisa Larrimore Ouellette, Nicholson Price, Rachel Sachs, and Jacob S. Sherkow

The number of COVID-19 vaccines is growing, with 18 vaccines in use around the world and many others in development. The global vaccination campaign is slowly progressing, with over 3 billion doses administered, although the percentage of doses administered in low-income countries remains at only 0.3%. But because of differences in how they were tested in clinical trials, making apples-to-apples comparisons is difficult — even just for the 3 vaccines authorized by the FDA for use in the United States. In this post, we explore the open questions that remain because of these differences in clinical trial design, the FDA’s authority to help standardize clinical trials, and what lessons can be learned for vaccine clinical trials going forward.

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Incidental Findings in Deep Phenotyping Research: Legal and Ethical Considerations

The Petrie-Flom Center Staff Leave a comment

By Amanda Kim, M.D., J.D.‡, Michael Hsu, M.D. ‡, Amanda Koire, M.D., Ph.D. ‡, Matthew L. Baum, M.D., Ph.D., D.Phil. ‡

What obligations do researchers have to disclose potentially life-altering incidental findings (IFs) as they happen in real time?

Deep phenotyping research in psychiatry integrates an individual’s real-time digital footprint (e.g., texts, GPS, wearable data) with their biomedical data (e.g., genetic, imaging, other biomarkers) to discover clinically relevant patterns, usually with the aid of machine learning. Findings that are incidental to the study’s objectives, but that may be of great importance to participants, will inevitably arise in deep phenotyping research.

The legal and ethical questions these IFs introduce are fraught. Consider three hypothetical cases below of individuals who enroll in a deep phenotyping research study designed to identify factors affecting risk of substance use relapse or overdose:

A 51-year-old woman with alcohol use disorder (AUD) is six months into sobriety. She is intrigued to learn that the study algorithm will track her proximity to some of her known triggers for alcohol relapse (e.g., bars, liquor stores), and asks to be warned with a text message when nearby so she can take an alternative route. Should the researchers share that data?

A 26-year-old man with AUD is two years into sobriety. Three weeks into the study, he relapses. He begins arriving to work inebriated and loses his job. After the study is over, he realizes the researchers may have been able to see from his alcohol use surveys, disorganized text messages, GPS tracking, and sensor data that he may have been inebriated at work, and wishes someone had reached out to him before he lost his job. Should they have?

A 35-year-old man with severe opioid use disorder experiences a near-fatal overdose and is discharged from the hospital. Two weeks later, his smartphone GPS is in the same location as his last overdose, and his wearable detects that his respiratory rate has plummeted. Should researchers call EMS? Read More

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Unique Challenges to Informed Consent in Deep Phenotyping Research

The Petrie-Flom Center Staff Leave a comment

By Benjamin C. Silverman

Deep phenotyping research procedures pose unique challenges to the informed consent process, particularly because of the passive and boundless nature of the data being collected and how this data collection overlaps with our everyday use of technology.

As detailed elsewhere in this symposium, deep phenotyping in research involves the collection and analysis of multiple streams of behavioral (e.g., location, movement, communications, etc.) and biological (e.g., imaging, clinical assessments, etc.) data with the goal to better characterize, and eventually predict or intervene upon, a number of clinical conditions.

Obtaining voluntary competent informed consent is a critical aspect to conducting ethical deep phenotyping research. We will address here several challenges to obtaining informed consent in deep phenotyping research, and describe some best practices and relevant questions to consider.

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“Actionability” and the Ethics of Communicating Results to Study Participants

The Petrie-Flom Center Staff Leave a comment

By Patrick Monette

To what end does a physician have a responsibility toward a research participant? Specifically, what data may be considered “actionable” for the physician to disclose to the patient, and when and how might this be done?

In the clinical setting, contemporary medical ethics address a physician’s “fiduciary responsibility.” That is, there is a well-established professional expectation that the physician will place the patient’s interests above their own and advocate for their welfare. This post focuses on an alternative dyad, that of physician and research participant, to explore how the field has broached the topic of actionability in the setting of clinical research. Read More

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Racial Disparities Persist in Human Subjects Research

Beatrice Brown Leave a comment

By Beatrice Brown

Human subjects research has long been plagued by racial inequality. While flagrant abuses have been curtailed, disparities have, unfortunately, persisted.

One area ripe for scrutiny is clinical trial enrollment. A 2018 study by William Feldman, Spencer Hey, and Aaron Kesselheim in Health Affairs documents racial disparities in trials that are exempt from typical requirements for informed consent from study participants.

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Box of Hydroxychloroquine Tablets

Human Subjects Research in Emergencies: The Texas Nursing Home “Study” (Part II)

Jennifer S. Bard Leave a comment

By Jennifer S. Bard

This post is the second in a series about conducting human subjects research in emergencies. These posts are being written in response to a rapidly evolving situation and will reflect the state of knowledge at the time of writing.

In April 2020, Dr. Robin Armstrong, medical director of the Resort, a nursing home in Texas City, Texas, reported “signs of improvement” after he gave hydroxychloroquine, a drug approved by the FDA to treat malaria, to 39 of his nursing home patients who were diagnosed with COVID-19.

At about the same time, information was emerging that now represents the current understanding that hydoxychloroquine isn’t only ineffective in treating COVID-19, but also may cause serious harm to patients. Tensions were raised even higher by the seemingly inexplicable enthusiasm for this treatment by the President and some media outlets.

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Human Subjects Research in Emergencies: An Ethical and Legal Guide (Part I)

Jennifer S. Bard Leave a comment

By Jennifer S. Bard

This post is the first in a series about conducting human subjects research in emergencies. These posts are being written in response to a rapidly evolving situation and will reflect the state of knowledge at the time of writing.

The world is facing a medical emergency in the form of the rapid spread of a new virus, COVID-19, for which there is no known effective treatment and no preventive vaccine.

Without minimizing the need for haste or the significance of the threat, it is still important to remain aware of the risks inherent in rushing to treat patients with anything that might work and simultaneously conducting the research necessary to identify safety and effective interventions.

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Simulated Side Effects: FDA Uses Novel Computer Model to Guide Kratom Policy

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By Mason Marks

FDA Commissioner Scott Gottlieb issued a statement on Tuesday about the controversial plant Mitragyna speciosa, which is also known as kratom. According to Gottlieb, kratom poses deadly health risks. His conclusion is partly based on a computer model that was announced in his recent statement. The use of simulations to inform drug policy is a new development with implications that extend beyond the regulation of kratom. We currently live in the Digital Age, a period in which most information is in digital form. However, the Digital Age is rapidly evolving into an Age of Algorithms in which computer software increasingly assumes the roles of human decision makers. The FDA’s use of computer simulations to evaluate drugs is a bold first step into this new era. This essay discusses the potential risks of basing federal drug policies on computer models that have not been thoroughly explained or validated (using the kratom debate as a case study).

Kratom grows naturally in Southeast Asian countries such as Thailand and Malaysia where it has been used for centuries as a stimulant and pain reliever. In recent years, the plant has gained popularity in the United States as an alternative to illicit and prescription narcotics. Kratom advocates claim it is harmless and useful for treating pain and easing symptoms of opioid withdrawal. However, the FDA contends it has no medical use and causes serious or fatal complications. As a result, the US Drug Enforcement Agency (DEA) may categorize kratom in Schedule I, its most heavily restricted category.

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The Precision Medicine Initiative and Access

Leslie Francis Leave a comment

By Leslie Francis

Persistent differences in participation in clinical trials by race and ethnicity are well known; for example, the 2015 Report of the Working Group on Precision Medicine (PMI) relies on statistics that only 5% of clinical trial participants are African-American and only 1% are Hispanic. A recently-launched website of the FDA, “Drug Trials Snapshots,” confirms this dismal picture.

Designed to “make demographic data more available and transparent,” and to “highlight whether there were any differences in the benefits and side effects among sex, race and age groups,” the website reveals instead an impressive lack of information. Reported on the website are 70 new drug approvals for 78 different indications. These data report only evidence about differences by the census categories for race (White, Black or African-American, Asian, American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, and Unknown). In nine of the reported trials data were considered sufficient to report detected differences in efficacy or side-effects in all racial categories, in two data were considered sufficient to report these differences for African-Americans and Asians, in seven data were considered sufficient to report these differences for Asians, and in two data were considered sufficient to report these differences only for African-Americans. No data are reported about ethnicity, socioeconomic status, disability, or other categories that might be important to the PMI and the benefits data about the planned cohort might bring. Read More