Diagnosis of Periodontal Status Using Protein Biomarkers Within Gingival Crevicular Fluid

Web Published:

Non-invasive and Accurate Diagnosis of Periodontal Status Using
Gingival Crevicular Fluid Biomarkers

Princeton Docket # 12-2825


Researchers at Princeton University have developed a novel methodology for the optimal selection of biomarker proteins to diagnose the periodontal status of a patient using gingival crevicular fluid (GCF) samples.  A mixed-integer linear optimization model has been developed and trained on 55 GCF samples from a mixture of periodontally healthy and chronic periodontitis patients, and a high degree of accuracy has been established.  The method was then tested on two different blind sets containing 20 samples and 21 samples, respectively.  Using an optimal combination of 7 human proteins and 3 bacterial proteins, the mathematical model was able to obtain 95% accuracy when predicting the samples as healthy and diseased from both test sets.


The diagnostic potential of GCF has been extensively investigated due to the possibility of non-invasive collection and the complexity of molecules that it contains.  GCF has been shown to be the transudate of gingival tissue interstitial fluid, but during periodontal disease it is transformed into inflammatory exudate which reflects the composition of serum and includes substances derived from the structural tissues of the periodontium and oral bacteria colonizing the gingival pocket.  However, despite the extensive studies in this area, there are no existing chairside tests that can be reliably applied for accurate diagnosis or prognosis in clinical practice.


·         Established Biomarkers in GCF samples

o   for diagnosis of periodontal status

o   as surrogate clinical endpoints

o   for prediction of clinical benefits in therapeutic intervention trials

·          An optimization framework to generate biomarkers applicable to the prognosis or diagnosis of a range of disease.



·         First-to-market potential

·         Non-invasive

·         Site-specific

·         Accurate

·         Point of Care


Intellectual Property Status

A provisional patent application has been filed.   




Laurie Tzodikov
Princeton University Office of Technology Licensing  (609) 258-7256  


Laurie Bagley
Princeton University Office of Technology Licensing   (609) 258-5579   lbagley@Qprinceton.edu





Patent Information:
For Information, Contact:
Laurie Tzodikov
Licensing Associates
Princeton University
Christodoulos Floudas (DECEASED) See Fotini P. Baba
Richard Baliban
Dimitra Sakellari
Zukui Li
Benjamin Garcia