Quality Management Principles in Drug Discovery R&D Projects

Nataliia Koval

Citation: Nataliia Koval, "Quality Management Principles in Drug Discovery R&D Projects", Universal Library of Innovative Research and Studies, Volume 02, Issue 04.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The study examines quality management principles relevant to modern drug discovery and development, with an emphasis on the economic and managerial consequences of inadequate quality in early-stage projects. The research novelty lies in the transfer of pharmaceutical quality system concepts, traditionally focused on manufacturing, into the upstream phases of target identification, hit and lead generation, and preclinical candidate selection. The article describes the main elements of pharmaceutical quality systems, including quality by design, quality risk management, and digital quality management platforms, and analyzes their applicability to discovery workflows. Particular attention is given to AI-enabled decision support, lifecycle-based risk management, and data-driven quality metrics. The objective of this work is to develop an integrated conceptual framework that links quality principles with portfolio decisions and early economic evaluation in drug discovery R&D. To achieve this objective, a narrative review, comparative analysis of regulatory guidance, and conceptual modeling methods are employed. The conclusion outlines managerial implications for pharmaceutical companies and research organizations. The article targets R&D managers, quality professionals, and project leaders working in pharmaceutical and early drug discovery settings.


Keywords: Quality Management, Drug Discovery, Pharmaceutical R&D, Quality By Design, Quality Risk Management, ICH Q9, Pharmaceutical Quality System, Digital Quality Systems, Artificial Intelligence, R&D Portfolio Management.

Download doi https://doi.org/10.70315/uloap.ulirs.2025.0204020