Nanoemulsions, composed of lipid surfactants, have gained significant attention as effective adjuvants in various therapeutic applications. They offer advantages such as improved solubility, enhanced bioavailability, and the ability to encapsulate both hydrophobic and hydrophilic drugs. Despite their promising potential, however, not all formulated drug products achieve optimal yield. During drug production, all materials must undergo sterile filtration, during which significant product loss often occurs from filter fouling, leading to process inefficiencies. This issue is compounded by limited understanding of filter fouling phenomena. There has been development of traditional filtration models, such as complete, intermediate, gradual, and cake filtration; yet, these models are overly simplified and frequently do not accurately capture real-world filter fouling behavior. Consequently, current challenges include the lack of standardized filtration protocols across laboratories, time-consuming filter selection processes, difficulties in scaling filtration performance from laboratory to industrial scale, and the absence of shared filtration data. To overcome these challenges, this study aims to develop an advanced predictive model that integrates multiple filtration mechanisms to bridge laboratory-scale experiments and industrial operations. The proposed model will enable better estimation of material requirements, provide deeper insights into fouling mechanisms, and support the development of effective filtration strategies to optimize sterile filtration of nanoemulsion-based therapeutics.
