Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

A Computational Method Involving Surface Area to Volume Ratio to Estimate Inorganic Nanoparticle Efficacy

Version 1 : Received: 4 August 2021 / Approved: 5 August 2021 / Online: 5 August 2021 (10:45:07 CEST)
Version 2 : Received: 7 September 2021 / Approved: 7 September 2021 / Online: 7 September 2021 (11:57:23 CEST)

How to cite: Williams, W.A.; Denslow, A.J.; Radulovic, P.W.; Denmark, D.J.; Mohapatra, S.S. A Computational Method Involving Surface Area to Volume Ratio to Estimate Inorganic Nanoparticle Efficacy. Preprints 2021, 2021080138 (doi: 10.20944/preprints202108.0138.v2). Williams, W.A.; Denslow, A.J.; Radulovic, P.W.; Denmark, D.J.; Mohapatra, S.S. A Computational Method Involving Surface Area to Volume Ratio to Estimate Inorganic Nanoparticle Efficacy. Preprints 2021, 2021080138 (doi: 10.20944/preprints202108.0138.v2).

Abstract

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.

Keywords

inorganic nanoparticles; in silico; optimization; theranostic; therapeutic; diagnostic; computation; coding; Python

Comments (1)

Comment 1
Received: 7 September 2021
Commenter: Wesley Williams
Commenter's Conflict of Interests: Author
Comment: Changes for second round of submission:
More solids (more references) added from literature to underscore rationale for using SA:V as an estimate of efficacy.
Language and structure of the entire paper was improved for easier reading and dissemination.
Objects were updated with newer data and improved upon in terms of their resolution.
Coding explanation was updated to be clearer about its purpose.
Focus on less commonly known morpholgies efficacy in literature at the basic, preclinical, and clinical research level.
Statistical testing redone in context to new literature group of solids.
SI was updated in terms of mathematical explanations and adding new solids.
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