Clinical trials are one significant way in which medicine advances, as clinical studies help to determine the successes, shortcomings, and adverse effects of treatment methods before those treatments are made available to the general population.
However, for clinical trials to be most informative, studies need to involve a range of subjects and different populations. Two Ochsner Journal articles highlight the need for diversity in clinical trials.
The first, a letter to the editor published in the Winter 2019 issue, sheds light on the unequal distribution of clinical trials across the globe. Despite the high prevalence of disease in lower-middle and low-income countries (LMICs), the majority of clinical trials are conducted in high-income countries.
Several barriers have an impact on conducting clinical trials in developing countries. Some governments allocate meager funding and are slow to grant approval, populations may fear exploitation because of religious or cultural beliefs, research-based learning in higher education is frequently insufficient, and those who do have specialized training or experience often choose to work abroad for greater opportunities, thus contributing to the disparity.
Still, the need for clinical trials in developing countries exists. Khoja et al argue that “[g]lobal collaboration among developed countries and LMICs is essential to foster clinical trial research,” and that “[c]linical trials following ethical guidelines that cater to the health needs of people living in LMICs are needed.” The authors acknowledge that such a change will require significant investment in research infrastructure and research-based higher education centers and that governments must reduce approval times and speed regulatory processes to attract funding.
An article in the Ochsner Journal Spring 2020 special issue, Human Subjects Protection in the Era of the Revised Common Rule, highlights another gap in clinical trial inclusion: pregnant subjects. In a review of potential deterrents to enrolling pregnant women in clinical research studies, Dr. Joseph Biggio notes that despite the high number of pregnant women who take medications during pregnancy, “only a fraction of the medications used have been investigated during pregnancy with regard to benefits, risks, and doses.”
Historically, pregnant women have been excluded from clinical research for fear of potential risks. Until recently, federal regulations classified pregnant women as a vulnerable population—similar to children, prisoners, and those with diminished mental capacity. In addition, the complex physiology and changes that occur in a woman’s body during pregnancy can affect drug metabolism and action, making research difficult and expensive. Researchers also have liability concerns, not only for the mother but also for the fetus.
However, the road to including pregnant women in clinical research has not been untraveled. The US Department of Health and Human Services (HHS) revised the Code of Federal Regulations in 2001 to state that pregnant women and their fetuses could be included in research if certain criteria were met. The National Institutes of Health Office of Research on Women’s Health held a scientific forum in 2010 to discuss the challenges associated with including pregnant research subjects and to explore ways to advance research in pregnant women. In the revisions to the Common Rule effective January 2019, the HHS Office for Human Research Protections removed pregnant women from the vulnerable population classification.
Despite the potential risks, complex physiological changes, and liability concerns, Dr. Biggio points out that “the cumulative risk to society is postulated to be lower with scientifically rigorous, carefully monitored clinical studies than with off-label or poorly informed use of medications as often happens in modern obstetric practice.”
These two submissions to the Ochsner Journal highlight significant gaps in clinical trials research and the need to implement strategies to include underrepresented populations in studies to help ensure that data are as widely representative as possible.