Self-Titrating Anticoagulant Nanocomplexes That Restore Homeostatic Regulation of the Coagulation Cascade
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Bhatia_Self-titrating.pdf
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Author(s)
Lin, Kevin Yu-Ming
Lo, Justin H.
Consul, Nikita
Kwong, Gabriel A.
Bhatia, Sangeeta N.
Date Issued
August 2014
Journal
ACS Nano
Publisher
American Chemical Society (ACS)
Citation
Lin, Kevin Y., Justin H. Lo, Nikita Consul, Gabriel A. Kwong, and Sangeeta N. Bhatia. “Self-Titrating Anticoagulant Nanocomplexes That Restore Homeostatic Regulation of the Coagulation Cascade.” ACS Nano 8, no. 9 (September 23, 2014): 8776–8785. © 2014 American Chemical Society
Version
Final published version
Abstract
Antithrombotic therapy is a critical portion of the treatment regime for a number of life-threatening conditions, including cardiovascular disease, stroke, and cancer; yet, proper clinical management of anticoagulation remains a challenge because existing agents increase the propensity for bleeding in patients. Here, we describe the development of a bioresponsive peptide–polysaccharide nanocomplex that utilizes a negative feedback mechanism to self-titrate the release of anticoagulant in response to varying levels of coagulation activity. This nanoscale self-titrating activatable therapeutic, or nanoSTAT, consists of a cationic thrombin-cleavable peptide and heparin, an anionic polysaccharide and widely used clinical anticoagulant. Under nonthrombotic conditions, nanoSTATs circulate inactively, neither releasing anticoagulant nor significantly prolonging bleeding time. However, in response to life-threatening pulmonary embolism, nanoSTATs locally release their drug payload and prevent thrombosis. This autonomous negative feedback regulator may improve antithrombotic therapy by increasing the therapeutic window and decreasing the bleeding risk of anticoagulants.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Koch Institute for Integrative Cancer Research at MIT
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1021/nn501129q
