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

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

J Control Release

Department of Chemical and Petroleum Engineering, University of Pittsburgh, PA 15261, USA; Department of Bioengineering, University of Pittsburgh, PA 15261, USA; Department of Pharmaceutical Sciences, University of Pittsburgh, PA 15261, USA; Department of Ophthalmology, University of Pittsburgh, PA 15261, USA; McGowan Institute for Regenerative Medicine, University of Pittsburgh, PA 15261, USA; Department of Immunology, University of Pittsburgh School of Medicine, PA 15261, USA; Clinical and Translational Science Institute, University of Pittsburgh, PA 15261, USA. Electronic address:

Published: December 2022

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.

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http://dx.doi.org/10.1016/j.jconrel.2022.10.024DOI Listing

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