Modern drug discovery and trials too often rely on data drawn from predominantly white populations. That limits how well conclusions generalize to the majority of humanity. Differences in genetics, environment, socioeconomic contexts, and comorbidities mean that treatments effective in one group may fail or cause harm in another.

Consider statins: multiple studies have shown underuse, different efficacy, and varied side-effects across ethnic groups. In the U.S., Black and Hispanic individuals are less likely to be prescribed statins for primary prevention, despite higher cardiovascular risk.  Also, East Asian populations sometimes show greater sensitivity to statin dosing, highlighting pharmacokinetic variation by ancestry.  Because trial cohorts remain largely white, our dosing guidelines may inadvertently neglect or overshoot in many populations.

Another example is sickle cell disease (SCD), a genetic disorder with enormous prevalence in Sub-Saharan Africa and among people of African ancestry. Yet only a small fraction of SCD clinical trials are conducted in those regions.  This mismatch undermines research into disease modifiers, therapeutic response, and genotype–phenotype correlations in the populations most affected.

To address real problems in drug research, we need truly global, diverse, multimodal clinical datasets. Such datasets would help researchers:

1. Discover population-specific biomarkers and drug targets;

2. Calibrate dosing and safety margins for underrepresented ancestry groups;

3. Reduce health disparities by ensuring innovations benefit all;

4. Improve AI & predictive models so they don’t overfit to one demographic.

   
   

In short, diversifying clinical data is not about fairness alone—it represents a fundamental step toward advancing science and improving global health outcomes. When researchers have access to data that reflects the true genetic, cultural, and environmental diversity of humanity, they can design studies that capture meaningful variations in disease patterns, treatment responses, and side-effect profiles. This leads to stronger evidence, more accurate guidelines, and innovations that work for all populations, not just a privileged subset. Ultimately, greater diversity ensures safer, more effective medicine, reduces health disparities, and drives scientific progress that benefits every community worldwide.

References

Jacobs JA, Addo DK, Zheutlin AR, et al. Prevalence of Statin Use for Primary Prevention of Atherosclerotic Cardiovascular Disease by Race, Ethnicity, and 10-Year Disease Risk in the US: National Health and Nutrition Examination Surveys, 2013 to March 2020. JAMA Cardiol. 2023;8(5):443–452.

https://jamanetwork.com/journals/jamacardiology/fullarticle/2802859

Juan Tamargo, Juan Carlos Kaski, Takeshi Kimura, Jack Charles Barton, Ko Yamamoto, Maki Komiyama, Heinz Drexel, Basil S Lewis, Stefan Agewall, Koji Hasegawa, Racial and ethnic differences in pharmacotherapy to prevent coronary artery disease and thrombotic events,

European Heart Journal – Cardiovascular

Pharmacotherapy, Volume 8, Issue 7, November 2022, Pages 738–751, https://doi.org/10.1093/ehjcvp/pvac040

Aygun, B. et al. (2024). Hydroxyurea dose optimization for children with sickle cell anemia in sub-Saharan Africa (REACH): extended follow-up of a multicenter, open-label, phase 1/2 trial. Lancet Hematology, 11(6), E425-E435.

https://www.thelancet.com/journals/lanhae/article/PIIS2352-3026(24)00078-4/abstract