In silico identification and synthesis of a multi-drug loaded MOF for treating tuberculosis

Abhinav P. Acharya, Kutay B. Sezginel, Hannah P. Gideon, Ashlee C. Greene, Harrison D. Lawson, Sahil Inamdar, Ying Tang, Amy J. Fraser, Kush V. Patel, Chong Liu, Nathaniel L. Rosi, Stephen Y. Chan, Jo Anne L. Flynn, Christopher E. Wilmer, Steven R. Little

Research output: Contribution to journalArticlepeer-review

Abstract

Conventional drug delivery systems have been applied to a myriad of active ingredients but may be difficult to tailor for a given drug. Herein, we put forth a new strategy, which designs and selects the drug delivery material by considering the properties of encapsulated drugs (even multiple drugs, simultaneously). Specifically, through an in-silico screening process of 5109 MOFs using grand canonical Monte Carlo simulations, a customized MOF (referred as BIO-MOF-100) was selected and experimentally verified to be biologically stable, and capable of loading 3 anti-Tuberculosis drugs Rifampicin+Isoniazid+Pyrazinamide at 10% + 28% + 23% wt/wt (total > 50% by weight). Notably, the customized BIO-MOF-100 delivery system cleared naturally Pyrazinamide-resistant Bacillus Calmette-Guérin, reduced growth of virulent Erdman infection in macaque macrophages 10–100-fold compared to soluble drugs in vitro and was also significantly reduced Erdman growth in mice. These data suggest that the methodology of identifying-synthesizing materials can be used to generate solutions for challenging applications such as simultaneous delivery of multiple, small hydrophilic and hydrophobic molecules in the same molecular framework.

Original languageEnglish (US)
Pages (from-to)242-255
Number of pages14
JournalJournal of Controlled Release
Volume352
DOIs
StatePublished - Dec 2022

Keywords

  • Computational MOFs
  • Drug delivery
  • Metal organic frameworks
  • Tuberculosis

ASJC Scopus subject areas

  • Pharmaceutical Science

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