Utilization of Real-World Data in Drug Development
Download PDF

Keywords

Real-world data
Drug development
Data mining

DOI

10.26689/par.v8i3.6468

Submitted : 2024-05-21
Accepted : 2024-06-05
Published : 2024-06-20

Abstract

With the rapid development of modern science and technology, traditional randomized controlled trials have become insufficient to meet current scientific research needs, particularly in the field of clinical research. The emergence of real-world data studies, which align more closely with actual clinical evidence, has garnered significant attention in recent years. The following is a brief overview of the specific utilization of real-world data in drug development, which often involves large sample sizes and analyses covering a relatively diverse population without strict inclusion and exclusion criteria. Real-world data often reflects real clinical practice: treatment options are chosen according to the actual conditions and willingness of patients rather than through random assignment. Analysis based on real-world data also focuses on endpoints highly relevant to clinical benefits and the quality of life of patients. The booming big data technology supports the utilization of real-world data to accelerate new drug development, serving as an important supplement to traditional clinical trials.

References

Dagenais S, Russo L, Madsen A, et al., 2022, Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design. Clin Pharmacol Ther, 111(1): 77–89. https://doi.org/10.1002/cpt.2480

Comi E, 2022, Real-World Evidence in Clinical Research: Challenges and Opportunities. Chimica Oggi – Chemistry Today, 40(5), viewed Jan 1, 2024, https://www.teknoscienze.com/tks_article/real-world-evidence-in-clinical-research-challenges-and-opportunities/

Wang SV, Sreedhara SK, Schneeweiss S, et al., 2022, Reproducibility of Real-World Evidence Studies Using Clinical Practice Data to Inform Regulatory and Coverage Decisions. Nat Commun, 13(1): 5126. https://doi.org/10.1038/s41467-022-32310-3

Barrett JS, Azer K, 2023, Opportunities for Systems Biology and Quantitative Systems Pharmacology to Address Knowledge Gaps for Drug Development in Pregnancy. J Clin Pharmacol, 63 Suppl 1: S96–S105. https://doi.org/10.1002/jcph.2265

Bastarache L, Brown JS, Cimino JJ, et al., 2021, Developing Real-World Evidence from Real-World Data: Transforming Raw Data into Analytical Datasets. Learn Health Syst, 6(1): e10293. https://doi.org/10.1002/lrh2.10293

Volansky R, 2022, Real-World Data Confirms Safety, Efficacy of Guselkumab in Plaque Psoriasis. Healio Psoriatic Disease, viewed Jan 1, 2024, https://www.healio.com/news/dermatology/20221019/realworld-data-confirms-safety-efficacy-of-guselkumab-in-plaque-psoriasis

Huang CW, Wu BCY, Nguyen PA, et al., 2023, Emotion Recognition in Doctor-Patient Interactions From Real-World Clinical Video Database: Initial Development of Artificial Empathy. Comput Methods Programs Biomed, 233: 107480. https://doi.org/10.1016/j.cmpb.2023.107480

Wee LE, Tay AT, Chiew C, et al., 2023, Real-World Effectiveness of Nirmatrelvir/Ritonavir Against COVID-19 Hospitalizations and Severe COVID-19 in Community-Dwelling Elderly Singaporeans During Omicron BA.2, BA.4/5, and XBB Transmission. Clin Microbiol Infect, 29(10): 1328–1333. https://doi.org/10.1016/j.cmi.2023.06.016

Bertsimas D, Li ML, Liu X, et al., 2023, Data-Driven COVID-19 Vaccine Development for Janssen. INFORMS Journal on Applied Analytics, 53(1): 1–95. https://doi.org/10.1287/inte.2022.1150

Kouki Y, Okada N, Saga K, et al., 2023, Disproportionality Analysis on Hypothyroidism With Roxadustat Using the Japanese Adverse Drug Event Database. J Clin Pharmacol, 63(10): 1141–1146. https://doi.org/10.1002/jcph.2300

Beck D, Winzenborg I, Gao W, et al., 2022, Interdisciplinary Model-Informed Drug Development for Extending Duration of Elagolix Treatment in Patients with Uterine Fibroids. Br J Clin Pharmacol, 88(12): 5257–5268. https://doi.org/10.1111/bcp.15440