Quality by Design and In Silico Approach in SNEDDS Development: A Comprehensive Formulation Framework

Abstract

Background/Objectives: The Self-Nanoemulsifying Drug Delivery System (SNEDDS) has been widely applied in oral drug delivery, particularly for poorly water-soluble compounds. The successful development of SNEDDS largely depends on the precise composition of its components. This narrative review provides an in-depth analysis of Quality by Design (QbD), Design of Experiment (DoE), and in silico approach applications in SNEDDS development. Methods: The review is based on publications from 2020 to 2025, sourced from reputable scientific databases (Pubmed, Science direct, Taylor and francis, and Scopus). Results: Quality by Design (QbD) is a systematic and scientific approach that enhances product quality while ensuring the robustness and reproducibility of SNEDDS, as outlined in the Quality Target Product Profile (QTPP). DoE was integrated into the QbD framework to systematically evaluate the effects of predefined factors, particularly Critical Material Attributes (CMAs) and Critical Process Parameters (CPP_S), on the desired responses (Critical Quality Attributes/CQA), ultimately leading to the identification of the optimal SNEDDS formulation. Various DoEs, including the mixture design, response surface methodology, and factorial design, have been widely applied to SNEDDS formulations. The experimental design facilitates the analysis of the relationship between CQA and CMA/CPP, enabling the identification of optimized formulations with enhanced biopharmaceutical, pharmacokinetic, and pharmacodynamic profiles. As an essential addition to this review, in silico approach emerges as a valuable tool in the development of SNEDDS, offering deep insights into self-assembly dynamics, molecular interactions, and emulsification behaviour. By integrating molecular simulations with machine learning, this approach enables rational and efficient optimization. Conclusions: The integration of QbD, DoE, and in silico approaches holds significant potential in the development of SNEDDS. These strategies enable a more efficient, rational, and predictive formulation process.

Keywords: SNEDDS; design of experiment; molecular modelling; optimization; quality by design.

Figure 1

Illustration of SNEDDS (created in BioRender https://BioRender.com/e1zgkjm accessed on 23 May 2025).

Figure 2

QbD stages based on ICH Q8 (R2).

Figure 3

Relationship between CMAs, CPPs, and CQAs in SNEDDS optimization.