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dc.contributor.authorKwong, Gabriel A.
dc.contributor.authorCarrodeguas, Emmanuel
dc.contributor.authorMazumdar, Eric V.
dc.contributor.authorZekavat, Seyedeh M.
dc.contributor.authorDudani, Jaideep Sunil
dc.contributor.authorBhatia, Sangeeta N
dc.date.accessioned2016-04-19T17:19:44Z
dc.date.available2016-04-19T17:19:44Z
dc.date.issued2015-10
dc.date.submitted2015-04
dc.identifier.issn0027-8424
dc.identifier.issn1091-6490
dc.identifier.urihttp://hdl.handle.net/1721.1/102266
dc.description.abstractAdvances in nanomedicine are providing sophisticated functions to precisely control the behavior of nanoscale drugs and diagnostics. Strategies that coopt protease activity as molecular triggers are increasingly important in nanoparticle design, yet the pharmacokinetics of these systems are challenging to understand without a quantitative framework to reveal nonintuitive associations. We describe a multicompartment mathematical model to predict strategies for ultrasensitive detection of cancer using synthetic biomarkers, a class of activity-based probes that amplify cancer-derived signals into urine as a noninvasive diagnostic. Using a model formulation made of a PEG core conjugated with protease-cleavable peptides, we explore a vast design space and identify guidelines for increasing sensitivity that depend on critical parameters such as enzyme kinetics, dosage, and probe stability. According to this model, synthetic biomarkers that circulate in stealth but then activate at sites of disease have the theoretical capacity to discriminate tumors as small as 5 mm in diameter—a threshold sensitivity that is otherwise challenging for medical imaging and blood biomarkers to achieve. This model may be adapted to describe the behavior of additional activity-based approaches to allow cross-platform comparisons, and to predict allometric scaling across species.en_US
dc.description.sponsorshipMIT Desphande Center for Technological Innovationen_US
dc.description.sponsorshipNational Science Foundation (U.S.). Graduate Research Fellowshipen_US
dc.description.sponsorshipBurroughs Wellcome Fund (Career Award at the Scientific Interface)en_US
dc.language.isoen_US
dc.publisherNational Academy of Sciences (U.S.)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1073/pnas.1506925112en_US
dc.rightsArticle 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.en_US
dc.sourceNational Academy of Sciences (U.S.)en_US
dc.titleMathematical framework for activity-based cancer biomarkersen_US
dc.typeArticleen_US
dc.identifier.citationKwong, Gabriel A., Jaideep S. Dudani, Emmanuel Carrodeguas, Eric V. Mazumdar, Seyedeh M. Zekavat, and Sangeeta N. Bhatia. “Mathematical Framework for Activity-Based Cancer Biomarkers.” Proc Natl Acad Sci USA 112, no. 41 (September 28, 2015): 12627–12632.en_US
dc.contributor.departmentInstitute for Medical Engineering and Scienceen_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technologyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineeringen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentKoch Institute for Integrative Cancer Research at MITen_US
dc.contributor.mitauthorKwong, Gabriel A.en_US
dc.contributor.mitauthorDudani, Jaideep Sunilen_US
dc.contributor.mitauthorCarrodeguas, Emmanuelen_US
dc.contributor.mitauthorMazumdar, Eric V.en_US
dc.contributor.mitauthorZekavat, Seyedeh M.en_US
dc.contributor.mitauthorBhatia, Sangeeta N.en_US
dc.relation.journalProceedings of the National Academy of Sciencesen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsKwong, Gabriel A.; Dudani, Jaideep S.; Carrodeguas, Emmanuel; Mazumdar, Eric V.; Zekavat, Seyedeh M.; Bhatia, Sangeeta N.en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-8102-7958
dc.identifier.orcidhttps://orcid.org/0000-0002-1293-2097
mit.licensePUBLISHER_POLICYen_US


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