Quality by Design (QbD) and Experimental Design Strategies in the Development and Optimization of Advanced Drug Delivery Systems: A Comprehensive Review

Abstract

The increasing complexity of advanced drug delivery systems (ADDS), including nanoparticles, liposomes, transferosomes, polymeric micelles, nanoemulsions, dendrimers, hydrogels, and lipid-based carriers, has necessitated the adoption of systematic, science-driven approaches to formulation development and process optimization. Quality by Design (QbD) has emerged as a transformative pharmaceutical development paradigm that emphasizes predefined quality objectives, scientific understanding, and risk-based decision-making throughout the product lifecycle. By integrating critical quality target product profiles (QTPP), identification of critical quality attributes (CQAs), critical material attributes (CMAs), critical process parameters (CPPs), and comprehensive risk assessment tools, QbD enables the development of robust, reproducible, and regulatory-compliant drug delivery systems. Complementing the QbD framework, Design of Experiments (DoE) provides a structured statistical methodology to evaluate the influence of multiple formulation and process variables simultaneously while minimizing experimental burden. Experimental designs such as full and fractional factorial designs, Plackett–Burman design, Box–Behnken design, Central Composite Design (CCD), D-optimal design, mixture designs, and response surface methodology (RSM) have become indispensable for optimizing pharmaceutical formulations and establishing design spaces. This comprehensive review critically examines the fundamental principles of QbD and contemporary experimental design strategies, highlighting their applications across diverse advanced drug delivery platforms. It discusses regulatory perspectives, risk assessment methodologies, multivariate optimization techniques, statistical modeling, process analytical technology (PAT), and lifecycle management. Furthermore, recent advances involving artificial intelligence, machine learning, digital twins, and continuous manufacturing are explored as emerging tools that complement conventional QbD approaches and accelerate pharmaceutical innovation. The review also addresses current challenges, limitations, and future opportunities associated with implementing QbD in industrial and academic settings. Overall, the integration of QbD and experimental design strategies offers a rational framework for developing safe, effective, scalable, and patient-centric advanced drug delivery systems while reducing development time, cost, and regulatory uncertainty, thereby supporting the transition toward modern pharmaceutical manufacturing and precision medicine.