Subcutaneous (s.c.) administration offers practical advantages for long-acting drug delivery, yet its complex tissue environment, lack of standardized models, and limited regulatory guidance pose challenges for predicting pharmacokinetics (PK). Here, we present a complementary set of experimental and computational approaches to reduce reliance on animal testing while maintaining translational relevance. Ex vivo skin models captured the influence of tissue variability, while 3D-printed constructs and molded agarose hydrogels enabled standardized, reproducible in vitro drug release studies. In parallel, machine learning models trained on curated rodent datasets predicted key PK parameters with strong agreement to in vivo data, providing an alternative that bypasses additional animal experiments and resource-intensive assays. Finally, a liposomal carprofen formulation case study demonstrated the translational potential of these methods in veterinary applications. Together, these strategies illustrate routes toward animal-free drug development: simplified experimental systems that mimic s.c. release and computational models that predict systemic PK. By combining them conceptually, we outline a framework that advances the 3Rs principles while supporting mechanistic and predictive understanding of s.c. drug delivery. • Ex vivo, in vitro, and in silico methods combined to study subcutaneous delivery. • 3D-printed and hydrogel models provide reproducible, low-variability drug release. • Machine learning predicts subcutaneous pharmacokinetics and supports 3R principles. • Liposomal carprofen formulation demonstrates translational potential for pain management.
Eugster et al. (Sun,) studied this question.