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; JoAnne L Flynn; Christopher E Wilmer; Steven R Little

doi:10.1016/j.jconrel.2022.10.024

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

J Control Release. 2022 Dec:352:242-255. doi: 10.1016/j.jconrel.2022.10.024. Epub 2022 Oct 25.

Authors

Affiliations

¹ Department of Chemical and Petroleum Engineering, University of Pittsburgh, PA 15261, USA; Department of Chemical Engineering, School for the Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85281, USA.
² Department of Chemical and Petroleum Engineering, University of Pittsburgh, PA 15261, USA.
³ Department of Microbiology and Molecular Genetics, and Center for Vaccine Research, University of Pittsburgh School of Medicine, PA 15261, USA.
⁴ Department of Chemical Engineering, School for the Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85281, USA.
⁵ Pittsburgh Heart, Lung, Blood Vascular Medicine Institute, Division of Cardiology, Department of Medicine, University of Pittsburgh School of Medicine and University of Pittsburgh Medical Center, Pittsburgh, PA 15261, USA.
⁶ Department of Chemistry, University of Pittsburgh, PA 15261, USA.
⁷ Department of Chemical and Petroleum Engineering, University of Pittsburgh, PA 15261, USA; Department of Electrical and Computer Engineering, University of Pittsburgh, PA 15261, USA; Clinical and Translational Science Institute, University of Pittsburgh, PA 15261, USA.
⁸ 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: srlittle@pitt.edu.

PMID: 36273529
DOI: 10.1016/j.jconrel.2022.10.024

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.

Keywords: Computational MOFs; Drug delivery; Metal organic frameworks; Tuberculosis.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Animals
Antitubercular Agents / therapeutic use
Drug Delivery Systems*
Mice
Pharmaceutical Preparations
Pyrazinamide*

Substances

Pyrazinamide
Pharmaceutical Preparations
Antitubercular Agents