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Scientific Progress

OraLiva and partners from the McDevitt laboratory have developed a platform to digitize biology using sensors that learn. This award-winning programmable platform has been adapted for oncology screening applications. Through these efforts, OraLiva is positioned to be the first company to provide a powerful single cell diagnostic platform for various oncological indications suitable for use at the point of care. These highly scalable diagnostic tools have potential to deliver new test and treat capabilities that can help to usher in arrival of precision oncology diagnostic care.

Representative Peer Review Publications 

1. McRae, M. P.; Simmons, G.; Wong, J., and McDevitt, J. T., Programmable Bio-Nanochip Platform: A Point of-Care Biosensor System with the Capacity to Learn. Accounts of Chemical Research 2016, 49 (7), 1359- 6810.1021/acs.accounts.6b00112. ​​

 

Abstract: This publication describes the core science and engineering behind the OraLiva diagnostic engine.  The work was developed in the McDevitt laboratory and includes two microchip sensor configurations that  work within a common microfluidics-based imaging sensor ensemble. As such, this flexible sensor modality  serves as a powerful platform to digitize biology. This paper demonstrates the platform’s flexibility through  the completion of multiplex assays within the single-use disposable cartridges for three clinical applications:  prostate cancer, ovarian cancer, and acute myocardial infarction. This work is published in Accounts of  Chemical Research (impact factor of 23) that is in the top 1% of scientific journals. 

2. McRae M.P., Simmons G.W., Christodoulides N.J., Lu Z., Kang S.K., Fenyo, D., Alcorn, T., Dapkins, I.P., Sharif,  I., Vurmaz, D., Modak S.S., Srinivasan, K., Warhadpande, S., Shrivastav, R., McDevitt J.T. Clinical Decision  Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID 19., Lab on a Chip, 2020, 20, 2075-2085, doi: 10.1039/D0LC00373E (cover feature article). PMID: 32490853;  PMCID: PMC7360344. 

Abstract: This publication describes the efforts of the McDevitt laboratory to develop an integrated point of  care assay system suitable for the profiling of disease severity in COVID-19 patients. This work created one  of the first integrated disease severity tools and as such the work was considered high impact. The  publication was featured on the flagship journal in Lab on a Chip. The work has been followed by a series of  other efforts to use this and related tools in clinical practice. It should be noted that the work in this  publication was completed over a three-week period. The rapid time frame is a testament to the  programmable nature of this diagnostic platform. 

3. Christodoulides N., McRae, M. P., Simmons, G. W., Modak, S. S., McDevitt, J.T., Sensors That  Learn: The Evolution from Taste Fingerprints to Patterns of Early Disease Detection,  Micromachines, 2019, 10(4), 251. doi:10.3390/mi10040251. PMID: 30995728; PMCID:  PMC6523560. 

Abstract: The McDevitt group has sustained efforts to develop a programmable sensing platform that offers  advanced, multiplexed/multiclass chem-/bio-detection capabilities covering a broad range of diagnostic indications. This scalable chip-based platform has been optimized to service real-world biological specimens  and validated for analytical performance. Fashioned as a sensor that learns, the platform can host new  content for the application at hand. These efforts serve to move to the point of big data acquisition alongside  the linkage to machine learning and artificial intelligence. Here, exciting opportunities are afforded by  multiparameter sensing that mimics the sense of taste, overcoming the limitations of salty, sweet, sour,  bitter, and glutamate sensing and moving into fingerprints of health and wellness. This article describes a  platform that digitizes biology and affords clinical decision support tools for a broad range of indications. A  dynamic body of literature and key review articles that have contributed to the shaping of these activities  are also highlighted. This fully integrated sensor promises more rapid transition of biomarker panels into  wide-spread clinical practice yielding valuable new insights into health diagnostics, benefiting early disease detection. 

4. Abram, T.J., Floriano, P.N., James, R., Kerr, A.R., Thornhill, M.H., Redding, S.W., Vigneswaran, N., Raja, R,  McRae, M. P., and McDevitt, J. T., Development of a cytology-based multivariate analytical risk index for  oral cancer. Oral Oncology 2019, 92:6-11. doi: 10.1016/j.oraloncology.2019.02.011. 

Abstract: This is OraLiva’s first product release in in the area of oral lesion adjunct testing. The diagnostic models  that OraLiva now uses for the oral lesion clinical decision making were developed using data acquired from  an international prospective clinical study that was funded by the National Institutes of Health. This unique  clinical study involved recruitment of 999 patients and resulted in development of one of largest cytology  data bases ever created for potentially malignant lesions. These efforts documented the clinical outcomes  of the patients across 6 major diagnostic categories and included over 165 image-based features that were  recorded for over 13M cells all characterized at the single cell level. Dr. McDevitt, the scientific founder for  OraLiva, served as the Principal Investigator for this effort. The study was sponsored by a Grand Opportunity  award (only top 1% of submitted proposals were selected for this distinction).

 

 

5. Speight, P.; Abram, T.; Floriano, P.; James, R.; Vick, J.; Thornhill, M.; Murdoch, C.; Freeman, C.; Hegarty, A.;  D'Apice, K.; Kerr, A.; Phelan, J.; Corby, P.; Khouly, I.; Vigneswaran, N.; Bouquot, J.; Demian, N.; Weinstock,  Y.; Redding, S.; Rowan, S.; Yeh, C.; McGuff, H.; Miller, F.; McDevitt, J., Inter-Observer Agreement in Dysplasia  Grading: Toward an Enhanced Gold Standard for Clinical Pathology Trials. Oral Surgery Oral Medicine Oral  Pathology Oral Radiology 2015, 120 (4), 474-482.10.1016/j.oooo.2015.05.023. 

Abstract: One of the major challenges that exist in the classification of oral lesions is that the gold standard  based on histology and manual pathology examination is complicated by the large disagreement that exists  in pathologist decisions. This is particularly a problem with dysplasia, as dysplasia is about 15 times more frequent than is oral cancer. From a clinical perspective, it is not practical on an individual patient  basis to assemble an expert panel of distinguished pathologists from major academic centers to render a diagnosis. Thus, the development of accurate diagnostic  methods for characterizing the risk of precancer circumstance is essential. This paper describes a 4-stage  adjudication process that was developed to create an enhanced gold standard for the qualification of oral  lesion samples including oral dysplasia. This ‘enhanced gold standard’ here developed served as the guiding  principle to produce the new multivariate diagnostic models that underlie the OraLiva oncology models. 

6. McRae M.P., Kerr A.R., Janal M, Thornhill M.H., Redding S.W., Vigneswaran N., Kang S.K., Niederman, R.,  Christodoulides N.J., Trochesset D.A., Murdoch C., Dapkins I., Bouquot, J., Modak S.S., Simmons G.W.,  McDevitt J.T. Nuclear F-actin Cytology in Potentially Malignant Oral Lesions. Journal of Dental Research,  2021, May;100(5):479-486. doi:10.1177/0022034520973162. Epub 2020 Nov 12. PMID: 33179547.

Abstract: One of the significant outcomes derived from the above-described Grand Opportunity program  was the development of a unique database involving 13M cells profiled at the single cell level. These efforts  led to the bridging between big data analytics and oral lesion diagnostics for the first time. These efforts  also provided fertile grounds for new oncology discoveries some of which are described in this publication.  Here the clinical role of F-actin in malignant transformation in oral lesions was discovered. This paper is  significant in that it includes the most accurate diagnostic models for a broad range of oral lesion disease stages. 

7. McRae, M.P., Modak, S.S., Simmons, G.W., Trochesset, D.A., Kerr, A.R., Thornhill, M.H., Redding, S.W.,  Vigneswaran, N., Kang, S.K., Christodoulides, N.J., Murdoch, C., Dietl, S.J., Markham, R. and McDevitt, J.T.  2020, Point-of-care oral cytology tool for the screening and assessment of potentially malignant oral lesions.  Cancer Cytopathology. doi:10.1002/cncy.22236. 

Abstract: Conversations with key stake holders involved in the treatment and management of oral cancer  patients have resulted in the key insight that oral pathologists must be engaged properly for the development,  optimization, and acceptance of new oral cancer adjunct testing methods. With this perspective in mind,  the McDevitt lab has published a key paper that focusses on the development of a point of care oral cytology  tool for the screening and assessment of potentially malignant oral lesions.