Data analytics-guided rational design of antimicrobial nanomedicines against opportunistic, resistant pathogens.

Nanoparticle carriers can improve antibiotic efficacy by altering drug biodistribution. However, traditional screening is impracticable due to a massive dataspace. A hybrid informatics approach was developed to identify polymer, antibiotic, and particle determinants of antimicrobial nanomedicine activity against Burkholderia cepacia, and to model nanomedicine performance. Polymer glass transition temperature, drug octanol-water partition coefficient, strongest acid dissociation constant, physiological charge, particle diameter, count and mass mean polydispersity index, zeta potential, fraction drug released at 2 h, and fraction release slope at 2 h were highly correlated with antimicrobial performance. Graph analysis provided dimensionality reduction while preserving nonlinear descriptor-property relationships, enabling accurate modeling of nanomedicine performance. The model successfully predicted particle performance in holdout validation, with moderate accuracy at rank-ordering. This data analytics-guided approach provides an important step toward the development of a rational design framework for antimicrobial nanomedicines against resistant infections by selecting appropriate carriers and payloads for improved potency.