Anti-bacterial resistance is an emerging risk in society. Resistant pathogens are amplifying the virulence of diseases that were considered treatable, and are compromising the efficacy of industrial operations like desalination that require sterility. Chemical research is accelerating to thwart resistant bacteria, however, pathogens will gradually develop resistance to these new organic treatments in an endless arms race between medicinal chemists and biological pathogens. Photodynamic inactivation (PDI), however, offers a sustainable treatment that avoids resistance. PDI exploits photocatalysis to generate reactive oxygen species that oxidize and eventually lyse cellular membranes or capsid proteins. PDI research is nevertheless hindered by, to our knowledge, an absence of predictive models that can guide the development of efficacious PDI systems.<br/>We therefore propose the first computational model of PDI software. Our model is predicated upon first-principles and is open-source – both as an API (PDIpy) and a GUI (iPDI). This software is predicated upon oxidation kinetics of membrane fatty acids, which experimentation supports as being the primary mechanism of cellular lysis via PDI. We exemplify our software with the case study of <i>Staphylococcus aureus</i>, which infamously includes MRSA as an archetype for drug-resistant pathogens. The Python source code for our software is publically available, and we encourage developers to expand upon our framework, and submit edits via our GitHub repository, towards developing a suite of computational tools that can expedite the scientific response to antibiotic resistant organisms with sustainable PDI treatments.

PDIpy: A kinetic model for photodynamic inactivation

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.