In vitro drug release testing is an important quality control tool for formulation development. However, the literature has evidence that poly-lactide-co-glycolide (PLGA)-based formulations show a slower in vitro drug release than a real in vivo drug release. Much longer in vitro drug release profiles may not be reflective of real in vivo performances and may significantly affect the timeline for a formulation development. The objective of this study was to develop a surfactant mediated accelerated in vitro drug release method for the PLGA nanoparticles (NPs) of a novel chemotherapeutic agent AC1LPSZG, a model drug with a poor solubility. The Sotax USP apparatus 4 was used to test in vitro drug release in a phosphate buffer with a pH value of 6.8. The sink conditions were improved using surfactants in the order of sodium lauryl sulfate (SLS) < Tween 80 < cetyltrimethylammonium bromide (CTAB). The dissolution efficiency (DE) and area under the dissolution curve (AUC) were increased three-fold when increasing the CTAB concentration in the phosphate buffer (pH 6.8). Similar Weibull release kinetics and good linear correlations (R²~0.99) indicated a good correlation between the real-time in vitro release profile in the phosphate buffer (pH 6.8) and accelerated release profiles in the optimized medium. This newly developed accelerated and discriminatory in vitro test can be used as a quality control tool to identify critical formulation and process parameters to ensure a batch-to-batch uniformity. It may also serve as a surrogate for bioequivalence studies if a predictive in vitro in vivo correlation (IVIVC) is obtained. The results of this study are limited to AC1LPSZG NPs, but a similar consideration can be extended to other PLGA-based NPs of drugs with similar properties and solubility profiles.

Design of experiments (DOE) offers a powerful, systematic framework for optimizing nanoparticle (NP) formulations by replacing inefficient one-factor-at-a-time (OFAT) methods. By enabling the simultaneous evaluation of multiple variables, DOE uncovers critical factor interactions and identifies true global optima—critical for quality-by-design approaches. Despite its potential for systematic innovation, DOE remains underutilized in nanomedicine due to its perceived complexity; this review provides a practical roadmap to bridge the gap between statistical theory and robust NP optimization. It provides a practical overview of DOE concepts, including factor selection, design choice, graphical interpretation of results (perturbation/contour plots), model validation (regression analysis and ANOVA), and numerical optimization via desirability function (D). Common pitfalls and best-practice strategies are discussed to support reliable model building and decision-making. A practical case study on poly(lactic-co-glycolic acid) (PLGA) NPs illustrates a multistage workflow: utilizing Taguchi screening to isolate key factors, followed by central composite design (CCD), for precise surface mapping. Numerical optimization using Design-Expert^® software maximized EE% (highest importance) within size/zeta ranges, yielding optimal conditions (5 mg drug amount, 4 mL aqueous volume; D = 0.961). Confirmation runs (EE 41.2%, NP size 124 nm, zeta potential −15 mV) validated predictions (EE 47.6%, NP size 133 nm, zeta potential −17.2 mV), confirming model reliability. Ultimately, by bridging conceptual foundations with practical implementation, this review aims to encourage broader adoption of DOE, particularly among emerging formulation scientists, and serves as a roadmap to accelerate scalable NP development, fostering data-driven innovation and improving efficiency in nanomedicine research. Moreover, future integration of artificial intelligence (AI) and artificial neural networks (ANNs) with DOE will drive a predictive, data-driven approach to NP optimization—accelerating robust, scalable, and regulatory-ready nanomedicine development with fewer experiments.