For decades the implementation of combined chemometric and computational tools has successfully aided scientists in predicting properties of new chemical compounds, based on their molecular structure. Quantitative structure-activity relationship (QSAR) methods, for example, which use linear and non-linear combinations of molecular descriptors, can be utilized to predict physical-chemical and/or biological properties of a molecule. QSAR approaches require well-defined structures of the considered molecules.Similar approaches are envisioned to predict relevant properties of nanoparticles (NPs), which are common form of chemicals used in cosmetics, pharmaceuticals and even food products. Due to their nature, NPs exhibit different characteristics than bulk materials. Nanoparticle samples are typically non-uniform and present various shapes and sizes, which give rise to their properties. Using conventional QSAR techniques is therefore not possible.Our goal is to develop new approaches that will facilitate QSAR-type modeling for nanoparticles. Our strategy is to incorporate new nanoparticle descriptors into existing, proven QSAR methods. We used scanning electron microscopy (SEM) images to obtain valuable visual information based on the morphology and topography of nanoparticles. Using computer vision algorithms, we have analyzed the SEM images and obtained descriptors of regions of interest (i.e. potential NPs), such as shape, size, surface area, roughness etc. These descriptors can be used to group and classify the nanoparticles. Next, we plan to build statistical models correlating nano and microscale parameters with physical-chemical and biological characteristics.We demonstrate applications of our methodology by investigating hydroxyapatite-based bionanomaterials. Hydroxyapatite, (hydroxylapatite, bioapatite, HAp), Ca10(PO4)6(OH)2, is a naturally occurring calcium phosphate mineral and has a wide range of biomedical applications, such as bone implants.

Toward quantitative structure-activity relationship (QSAR) models for nanoparticles

248th National Meeting (2014)

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