Toward animal-free approaches in subcutaneous drug delivery: A pain management case study.

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: Strong agreement between ML predictions and in vivo data (specific metrics not provided)

• Primary Outcome: Development of animal-free framework for subcutaneous drug delivery testing

• Nnt Or Sensitivity: Predictive accuracy of machine learning models for PK parameters (specific values not provided)

• Confidence Interval: Not reported in abstract

Clinical Application

Requires specialized equipment (3D printing, computational resources) but potentially more accessible than animal studies Highly relevant to TGA regulatory framework supporting 3Rs principles and Australian pharmaceutical industry reducing animal testing requirements Pharmaceutical researchers and regulatory scientists developing subcutaneous drug delivery systems

Abstract

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.

References

1. 1.Eugster, R., Manten, N., Buttitta, G., Chwatal, G., Schürch, S., Bergadano, A., Aleandri, S., & Luciani, P. (2026). Toward animal-free approaches in subcutaneous drug delivery: A pain management case study. Journal of Controlled Release, 114863. https://doi.org/10.1016/j.jconrel.2026.114863