Inorganic nanoparticles are utilized for therapeutic, diagnostic, or in combination, theranostic purposes. The latter involves simultaneous sensing, imaging, or tracking of drug delivery. Furthermore, these nanoparticles can differ in their morphologies, which affect outcomes such as the effectiveness of hyperthermia, induction, drug loading, circulation time by escaping the body's immune system, imaging modality clarity, and biosensing. However, design of these theranostics is limited by the lack of a method to predict their therapeutic efficacy. Herein, we report a simple and novel computational approach via algebraic and geometric calculations of surface area (SA) to volume (V) ratios (SA:V) which can help predict the efficacy of the inorganic nanoparticles of the investigated morphologies. The approach comprises a coding platform for the program and uses Python 3 on a Windows 10 operating system. Analyses of 29 polyhedral morphologies that inorganic nanoparticles could assume ex silico showed that only particular concave and convex morphologies in this size regime are more productive over the standard sizes as well as a few noted in literature for baseline comparison. Our results provide a method that can aid in predicting the efficacy of inorganic nanoparticles with certain morphology giving rise to their fundamental basis and eventual implementation ex silico.

Inorganic nanoparticles are utilized for therapeutic, diagnostic, or theranostic purposes and the latter involve simultaneous sensing, imaging, or tracking of drug delivery. Further, these nanoparticles differ in their morphologies, which affect outcomes such as the effectiveness of hyperthermia, induction, drug loading, circulation time by escaping the body's immune system, imaging modality clarity, and biosensing. However, design of these theranostics is limited by the lack of a method to predict their therapeutic efficacy. Herein, we report a computational approach involving the surface area (SA) to volume (V) ratios (SA:V), which can help predict the efficacy of the inorganic nanoparticles. The approach comprises a coding platform for the comparator pro-gram and uses a Python 3 on a Windows 10 operating system. Analyses of 22 polyhedral morphologies that inorganic nanoparticles could assume ex silico showed that only particular concave morphologies in this size regime are more productive over the standard sizes. Our results provide a method that can aid in the predicting efficacy of inorganic nanoparticles with certain morphology.