Raman Analysis of Saliva as Biomarker of COPD
Chronic Obstructive Pulmonary Disease (COPD) is a debilitating and chronic lung syndrome that causes accelerated lung function decline and death in 20% of cases. The worsening of symptoms, as well as the patient's condition, strictly depends on the identified COPD phenotype, severity stages, exacerbation events, selected drugs, rehabilitation cures and on the adherence of patients to these therapies. Despite the efficient COPD diagnostic procedure, a new fast, sensitive and easily applicable approach must be developed in order to achieve the specific evaluation and monitoring of therapy adherence in COPD patients, stratifying the different COPD phenotypes and foreseeing the exacerbation events in order to optimize the COPD disease management. The application of Raman spectroscopy on saliva has been already proposed for different infective, neurological and cancer diseases, with promising results in the diagnostic and monitoring fields, representing saliva an easy collectable and highly informative biofluid. In this project we propose a combined Raman Spectroscopy - Machine Learning analysis of saliva collected from COPD patients and non-pathological and pathological controls for the development of a multifactorial device able to provide fast and sensitive information regarding COPD phenotypes, exacerbation risks, adherence and effectiveness of pharmacological and rehabilitation therapies, achieving the crucial target of the personalized medicine. Moreover, after the model development, we propose to test the Raman approach in hospital evaluating the creation of a COPD point of care, accompanying the clinicians in the disease management. Starting from FDG preliminary results, the biochemical composition of saliva in patients with diagnosed COPD will be evaluated and statistically compared with the one obtained from age and sex-matched healthy subjects and from patients affected by other respiratory chronic diseases (Asthma). Moreover, an intra-group COPD clustering will be analysed in order to verify a different Raman fingerprint obtained from COPD patients with different phenotypes. The collected Raman data will be processed using a multivariate analysis approach. The classification model will be created using cross-validation and subset validation. Thanks to RS, the overall composition of saliva will be established with minimal sample preparation, providing a comprehensive biochemical fingerprint of the sample. The expected results are I) Identification of the specific COPD Raman fingerprint through the comparison with healthy subjects and patients affected by asthma; II) Monitoring of the therapy adherence through the drug signal and/or biochemical modification in saliva; III) Stratification of the 4 COPD phenotypes on the base of the provided Raman fingerprint; IV) Monitoring of the rehabilitation procedures and effects; V) Association of an high exacerbation risk index to specific COPD patients; VI) Creation of a classification model of the created Raman database.
Start: October 2020