Kode Technology Peptide-Based Kodecyte Diagnostics Using Spirochetes and SARS CoV-2 As Models

aut.embargoNoen_NZ
aut.thirdpc.containsNoen_NZ
dc.contributor.advisorHenry, Steve
dc.contributor.advisorWilliams, Eleanor
dc.contributor.advisorMerien, Fabrice
dc.contributor.authorNagappan, Radhika
dc.date.accessioned2021-12-13T20:48:34Z
dc.date.available2021-12-13T20:48:34Z
dc.date.copyright2021
dc.date.issued2021
dc.date.updated2021-12-13T10:25:35Z
dc.description.abstractDespite advanced developments in molecular testing of disease, antibodies, as biomarkers, play an important role in diagnosing disease, checking immune response and for serological surveillance. There is an increasingly urgent need for rapidly adaptable, sensitive, low-cost antibody diagnostics not only for existing diseases but also for the proper case management and control of emerging and re-emerging infectious diseases, particularly in resource constrained settings. Peptide based diagnostics are a potential alternative to tests that involve recombinant protein antigens, which, while generally effective, have constraints with respect to reproducibility and adaptability. Kode technology is a highly adaptable platform that uses function-spacer-lipid (FSL) constructs to attach epitopes to cells (kodecytes) for use in diagnostic assays. The feasibility of designing more refined synthetic antigens (short peptide epitopes) provides potential for enhanced sensitivity and specificity. Kodecytes can be implemented easily into a simple, rapid, sensitive and relatively less expensive diagnostic using the Kode technology platform. One objective of this study was to develop an algorithm for designing FSL peptide constructs using bioinformatics. This research initially selected two different complex pathogens (T. pallidum and Leptospira) to develop an algorithm and develop Kode technology antibody diagnostics compatible with existing routine serologic platforms. However, with the unprecedented appearance of the COVID-19 (SARS CoV-2) pandemic, this research was extended to create a potential antibody diagnostic assay for COVID-19. Candidate peptides were made for T. pallidum, Leptospira and SARS CoV-2 using the peptide identification and FSL peptide selection algorithm. Validation and the functional prediction of candidate peptides were performed using blood samples and the kodecyte assay. Among the candidate peptides, T. pallidum and SARS CoV-2 had one potential candidate peptide suitable for diagnostics. Assessing the datasets over time will help in further refining the algorithm. The syphilis kodecyte assay and COVID-19 kodecyte assay achieved specificity and sensitivity at least equivalent to an established EIA antibody diagnostic. The Leptospira kodecyte assay was more challenging to validate, as there was minimal access to samples, but the preliminary results were good, and while most of the candidate peptides worked, further validation is required. This research built successful kodecyte assays for three diseases. Kodecyte assays described in this thesis are β versions and will ultimately need to undergo regulatory approval processes and product development trials to be able to be implemented in clinical use. However, despite not yet being optimized, the assays reported are functional and usable.en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/14809
dc.language.isoenen_NZ
dc.publisherAuckland University of Technology
dc.rights.accessrightsOpenAccess
dc.subjectKode diagnosticen_NZ
dc.subjectFSL peptidesen_NZ
dc.subjectSyphilisen_NZ
dc.subjectLeptospiraen_NZ
dc.subjectSARS CoV-2en_NZ
dc.titleKode Technology Peptide-Based Kodecyte Diagnostics Using Spirochetes and SARS CoV-2 As Modelsen_NZ
dc.typeThesisen_NZ
thesis.degree.grantorAuckland University of Technology
thesis.degree.levelDoctoral Theses
thesis.degree.nameDoctor of Philosophyen_NZ
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
-DCT_PhD thesis_Radhika Nagappan_ST9504391__December 2021-.pdf
Size:
5.29 MB
Format:
Adobe Portable Document Format
Description:
Thesis
License bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
889 B
Format:
Item-specific license agreed upon to submission
Description:
Collections