(341f) Quantitative Correlation for the Stability of Pharmaceutical Nanosuspensions and Their Physicochemical Properties

Recent results from high-throughput screening and combinatorial chemistry have reported that a large portion of viable drug candidates will be poorly water soluble, and as a consequence, exhibit a poor in vivo bioavailability. As a result, a decade of intense research has focused on the production of these poorly soluble drugs into nanosuspensions to increase dissolution rate, bioavailability, and improve overall drug delivery. Unfortunately, the small size of the nano-scale drug crystals causes many unfortunate and undesired consequences including agglomeration, Oswald ripening, and growth, thus destroying the desired nanosuspension properties. Therefore, in order to maintain the desired properties of the nanosuspension, the addition of surfactants and/or polymers are required to introduce steric and electrostatic stability, yet, the current selection and implementation of these additives remains limited to heuristics. As a result, our work has focused on the understanding of molecular interactions between the surface of drug nanoparticles and additives, where the thermodynamic binding energy between the two has been shown to correlate with suspension stability, as well as the ability to quench crystal growth. In this work, a correlation was established for the interaction of additives with the intrinsic properties of the target drug compounds. Nine common, poorly soluble compounds, exhibiting a wide variety of physicochemical properties, including griseofulvin, fenofibrate, itraconazole, azodicarbonamide, ibuprofen, sulfamethozazole, metochlopramide, pyrimethamine, and piroxicam were simulated and correlated with the stabilizer hydroxypropyl methylcellulose (HPMC). HPMC was selected based on our previous work establishing it as an excellent stabilizer for two experimentally validated case studies: griseofulvin and fenofibrate. The newfound correlation accurately establishes the relationship between the simulated binding energy, and the intrinsic properties including molar aqueous solubility, hydrophobicity, melting point, molecular weight, and the supersaturation ratio, where a strong linear correlation was discovered between binding energy, aqueous solubility, and supersaturation ratio. Additionally, these results allow for the intrinsic drug properties to predict and screen for effective surfactants by establishing optimal values for concentration and hydrophobicity. The ultimate goal of this work is to quickly and accurately predict surfactant interactions with nanosuspensions without the need for experimentation.