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Robust and Repeatable Biofabrication of Bacteria-Mediated  Drug Delivery Systems: Effect of Conjugation Chemistry, Assembly Process  Parameters, and Nanoparticle Size
  • +2
  • Ying Zhan,
  • Austin Fergusson,
  • Lacey R. McNally,
  • Richey M. Davis,
  • Bahareh Behkam
Austin Fergusson
Lacey R. McNally
Richey M. Davis
Bahareh Behkam
Author Profile

Abstract

Bacteria-mediated drug delivery systems comprising nanotherapeutics conjugated onto bacteria synergistically augment the efficacy of both therapeutic modalities in cancer therapy. Nanocarriers preserve therapeutics' bioavailability and reduce systemic toxicity, while bacteria selectively colonize the cancerous tissue, impart intrinsic and immune-mediated antitumor effects, and propel nanotherapeutics interstitially. The optimal bacteria-nanoparticle (NP) conjugates would carry the maximal NP load with minimal motility speed hindrance for effective interstitial distribution. Furthermore, a well-defined and repeatable NP attachment density distribution is crucial to determining these biohybrid systems' efficacious dosage and robust performance. Herein, we utilized our Nanoscale Bacteria-Enabled Autonomous Delivery System (NanoBEADS) platform to investigate the effects of assembly process parameters of mixing method, volume, and duration on NP attachment density and repeatability. We also evaluated the effect of linkage chemistry and NP size on NP attachment density, viability, growth rate, and motility of NanoBEADS. We show that the linkage chemistry impacts NP attachment density while the self-assembly process parameters affect the repeatability and, to a lesser extent, attachment density. Lastly, the attachment density affects NanoBEADS' growth rate and motility in an NP size-dependent manner. These findings will contribute to the development of scalable and repeatable bacteria-nanoparticle biohybrids for applications in drug delivery and beyond.
Corresponding author(s) Email:  behkam@vt.edu  

Peer review status:Published

01 Sep 2021Submitted to AISY Interactive Papers
01 Sep 2021Published in AISY Interactive Papers
19 Nov 2021Published in Advanced Intelligent Systems on pages 2100135. 10.1002/aisy.202100135