Statistical classification of dynamic bacterial growth with sub-inhibitory concentrations of nanoparticles and implications for disease treatment

Nanoparticles are promising alternatives to antibiotics since nanoparticles are easy to manufacture, non-toxic, and do not promote resistance. Nanoparticles act via physical disruption of the bacterial membrane and/or the development of locally high concentrations of reactive-oxygen species. Physical disruption may be quantified by free energy methods, like extended Derjuan-Landau-Verwey-Overbeek theory predicting initial surface-material interactions. Development of reactive-oxygen species may be quantified using enthalpies of formation to predict minimum inhibitory concentrations.  Neither of these two quantitative structure-activity relations describe the dynamic, in situbehavioral changes in the bacteria’s struggle to survive. Using parameters from Gompertz, underdamped, and diauxic growth models we use principal component analysis and agglomerative hierarchical clustering to classify survival modes across nanoparticle type and concentration. We compare growth parameters of Escherichia coli, Staphylococcus aureus, Methicillin-Resistant Staphylococcus aureus, Staphylococcus epidermidis,Pseudomonas aeruginosa, and Helicobacter pylori, across multiple concentrations of liposomal drug delivery systems, amphiphilic peptide, tellurium and selenium nanoparticles. Clustering reveals bacteria-nanoparticle concentration pairs where nanoparticle induced growth dynamics could potentially spread the infection. While results from liquid cultures on plate readers are not standard for antibiotic analysis, our methods provide rapid screening methods for further testing of antibiotic nanoparticles.