Citation :

Rajesh Munirathnam, 2023. "Data Science and Regulatory Affairs: Navigating the Complex Landscape of Drug Approval Processes" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 1, Issue 1: 96-109.

Abstract :

Data science solutions helped modernize the drug approval process and bring fresh approaches for regulation to solve the challenges across modern healthcare areas. As the pharma industry is growing and getting under pressure to cut costs, increase the transparency of the decision-making process and engage in constant innovation, data science provides the necessary tools for better management of the decision-making process, risk evaluation and the speeding up of the approval of safe drugs. This article is devoted to discussing aspects of data science in great detail concerning regulatory functions and their effect on the development of drugs, clinical trials, post-marketing surveillance, and regulatory submissions. Through integrated modern technological elements such as analytic tools, machine learning, and big data, regulatory bodies and entities in the pharmaceutical industry can significantly enhance the efficiency of the drug approval process while cutting costs and conforming to rigorous regulatory measures. This integration has produced more questions than answers and has raised concerns about the urgency of developing a sound legal framework to address the complex revolution in data science. Further, the article reviews examples that outline how data science has effectively impacted regulatory practices and insightful lessons on the related best practices for the future. Data governance, data integrity, and ethical nuances of data science, along with the focus on patients and public safety conundrums while adopting technologies to support regulatory activities, are also highlighted. Based on this coverage, prospective stakeholders of the pharmaceutical and regulatory fields can draw guidelines to unleash data science and, in the process, align the efforts of science and health to bring increasingly superior treatments and patient repercussions.

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Keywords :

Data Science, Regulatory Affairs, Drug Approval Process, Machine Learning, Big Data, Clinical Trials, Risk Assessment, Data Governance.