Modelling intramuscular drug fate in vitro with tissue-relevant biomimetic hydrogels

Parenteral administrations are a mainstay of clinical drug delivery. Intramuscular (IM) injections deposit drug directly into skeletal muscle bellies, providing rapid systemic uptake due to the highly vascularized nature of this site. The potential to inject particulate or non-aqueous materials have also made IM injections useful for long-acting formulations. These attributes have supported a plethora of medicines being approved for IM administration. Despite these many approvals across multiple pharmaceutical categories, mechanisms that control drug release from the injection site, and thus its pharmacokinetic properties, remain poorly understood. Several pre-clinical in vivo animals have been used to model IM drug fate in patients, but these approaches have not consistently predicted clinical outcomes. This lack of a predictive in vivo model and no standardized in vitro tools have limited the options of pharmaceutical scientists to rationally design formulations for IM delivery. Here, we describe a novel, tractable in vitro model informed by dominant extracellular matrix (ECM) components present at the IM injection site. Three charge variants of green florescent protein (GFP) and the impact of three common formulation components were examined in an initial test of this in vitro model. A strongly positively charged GFP was restricted in its release from hydrogels composed of ECM components type I collagen and hyaluronic acid compared to standard and strongly negatively charged GFP. Introduction of commonly used buffers (histidine or acetate) or the non-ionic surfactant polysorbate 20 altered the release properties of these GFP variants in a manner that was dependent upon ECM element composition. In sum, this Simulator of IntraMuscular Injections, termed SIMI, demonstrated distinct release profiles of a protein biopharmaceutical surrogate that could be exploited to interrogate the impact of formulation components to expedite novel drug development and reduce current dependence on potentially non-predictive pre-clinical in vivo models.