Clinical Trials Unintentionally Exclude Patients

Clinical trials can offer life saving treatments to patients in need. While most clinical trials fail to meet their intended endpoints, many succeed. However, not all people equally enjoy the opportunity to participate in clinical trials.

Historically, racial and ethnic groups have been underrepresented in clinical trial studies. In 2020, the U.S. Food and Drug Administration reported that 75% of clinical trial subjects were white, while only 6% were Asian, 8% were Black, and 11% were Hispanic.1 Indeed, this disparity of representation extends beyond races and ethnicities to include youth, aging populations, LGBTQIA+, people living with disabilities, people living in rural communities, and people with comorbidities.

The underlying reasons behind this lack of representation are varied and complex. Some factors are sociological, while others are physiological. For example, a recent study found that African and Middle Eastern Americans were excluded from certain cancer clinical trials due to a naturally occurring difference in their blood cell counts. 2

This highlights the urgent need for inclusivity in clinical trials. Without representation, these populations not only lack early access to therapies, but the potential therapeutic value of novel therapies on these populations may not be factored into approval decisions. In some cases, this could preclude access to drugs otherwise beneficial to patient subgroups.

Fortunately, AI can help bring underrepresented patient populations into clinical trials through “virtual patients.” Virtual patients are computer-generated models that mimic the physiological and biological diversity seen in real patients, and they allow us to simulate the inclusion of underrepresented groups into the clinical trial process. These virtual patients are created by using AI to augment existing patient data with synthetic data that fills in the gaps of missing information.

Using advanced algorithms such as Neural Networks (CT-GANs, TVAEs, Gaussian Copula), along with imputation techniques (MICE, KNN, Iterative) and oversampling methods (SMOTE, SVMOTE, Borderline SMOTE), this synthetic data can be generated to closely resemble the characteristics of underrepresented patients, thereby enhancing the representation of these groups. Then, explainable AI models can analyze this data to simulate how various subgroups could react to different treatments, considering factors like age, gender, ethnicity, coexisting conditions, and lifestyle. Furthermore, virtual patients can receive multiple treatments at once, allowing researchers to assess the effectiveness of various interventions on individual patients or groups in a controlled environment.

Artificial intelligence affords researchers an opportunity to address several gaps in the design of clinical trials, as well as their outcomes. Because AI techniques can extrapolate and learn from a wide variety of datapoints incorporating a diversity of patient attributes, AI-driven insights can improve patient representation within trials and provide additional understanding of how results may impact populations not directly included in trials.

Reference
1. Allison K, Patel D, Kaur R. Assessing Multiple Factors Affecting Minority Participation in Clinical Trials: Development of the Clinical Trials Participation Barriers Survey. Cureus. 2022 Apr 23;14(4):e24424. doi: 10.7759/cureus.24424. PMID: 35637812; PMCID: PMC9127181.
2. Hibbs SP, Aiken L, Vora K, et al. Cancer Trial Eligibility and Therapy Modifications for Individuals With Duffy Null–Associated Neutrophil Count. JAMA Netw Open. 2024;7(9):e2432475. doi:10.1001/ jamanetworkopen.2024.32475
  • Jo Varshney
    Jo Varshney

    PhD Founder and CEO VeriSIM Life

    Prior to starting VeriSIM Life—a leader in AIdriven drug discovery and development—Dr. Jo Varshney gained her doctorate in Veterinary Medicine (DVM) and holds a PhD. in Comparative Oncology/Genomics from the University of Minnesota, as well as graduate degrees in Comparative Pathology and Computational Sciences. verisimlife.com/contact

Ads

You May Also Like

Sales Force 2.0: Optimizing the Role of the Sales Force in Integrated Marketing

With the emergence of new technology-based marketing tactics, some pundits have predicted the ultimate ...

AI 2.0: An Entirely New Level of Customer Engagement

In 2010, artificial intelligence (AI) solutions for commercial life sciences were in their infancy. ...

Healthcare’s New World Order: “Plug-in” With Patients

As the healthcare system continues to evolve, the focus has shifted toward providing quality ...