The ability to predict the diffusional release parameters of therapeutic/bioactive compounds is critical in the development of novel pharmaceuticals/functional foods with controlled delivery mechanisms. Current mathematical modelling of diffusive behaviour in semi to high solid, network forming systems, makes an excellent account of variables such as the geometry, volume, surface area pore size, boundary conditions, excipient-bioactive coupling/decoupling etc., to effectively predict diffusive release [1,2,3]. However, in liquid like systems, i.e., in the absence of a three dimensional structure, there is a gap in the understanding of how interactions between a receptor and a diffusant may be exploited to control the diffusive release of a bioactive. Recent work, using a combined in silico and benchtop approach, has effectively demonstrated that receptor - bioactive interactions may have a non-negligible effect on diffusive behaviour in dilute systems, with diffusion rate being impeded by a factor relative to the protein-bioactive interaction strength [4].
The present work expands on these findings, proposing that this effect is likely due to the smaller ligand, taking on an effective radius of the combined host – ligand complex for the duration of a binding interaction, hence reducing its diffusion rate in accordance with the Stokes – Einstein equation. Given this, effective modelling of ligand diffusion rate must take into account the reduced diffusion rate of the molecules which are bound to the host. To do so a modified version of the Stokes – Einstein equation is developed and tested on a model bovine lactoferrin – polysaccharide system. Dynamic analysis of the system is performed using guided pulling simulations and umbrella sampling techniques in the presence of multiple protein molecules. Binding constants derived from these in silico techniques are in good agreement with benchtop results obtained via intrinsic fluorescence quenching.
Incorporation of the binding constants into the modified Stokes – Einstein equation demonstrates an improved fit to the experimentally determined diffusive release of the ligand. This work provides a model to account for molecular interactions in relation to bioactive diffusion kinetics that may be applied to a range of pharmaceutically relevant excipient protein systems. Thus, helping to address some of the complex issues faced by the pharmaceutical and nutraceutical industry, who require more robust predictive models for the development of novel, therapeutics.
References
1Siepmann, J., & Siepmann, F. (2012). Modeling of diffusion controlled drug delivery. Journal of Controlled Release, 161(2), 351–362. https://doi.org/10.1016/j.jconrel.2011.10.006
2Panyoyai, N., & Kasapis, S. (2016). A free-volume interpretation of the decoupling parameter in bioactive-compound diffusion from a glassy polymer. Food Hydrocolloids, 54, 338–341. https://doi.org/10.1016/j.foodhyd.2015.10.019
3Teimouri, S., & Kasapis, S. (2022). Mechanistic interpretation of vitamin B6 transport from swelling matrices of genipin-crosslinked gelatin, BSA and WPI. Food Hydrocolloids, 123, 107195. https://doi.org/10.1016/j.foodhyd.2021.107195
4Condict, L., Elliott, S., Hung, A., Ashton, J., & Kasapis, S. (2024). Interfacing β-casein – Phenolic compound interactions via molecular dynamics simulations with diffusion kinetics in delivery vehicles. Food Chemistry, 435, 137595. https://doi.org/10.1016/j.foodchem.2023.137595