Dataset Information

Identification of the Raman Salivary Fingerprint of Parkinson's Disease Through the Spectroscopic- Computational Combinatory Approach.

ABSTRACT: Despite the wide range of proposed biomarkers for Parkinson's disease (PD), there are no specific molecules or signals able to early and uniquely identify the pathology onset, progression and stratification. Saliva is a complex biofluid, containing a wide range of biological molecules shared with blood and cerebrospinal fluid. By means of an optimized Raman spectroscopy procedure, the salivary Raman signature of PD can be characterized and used to create a classification model. Raman analysis was applied to collect the global signal from the saliva of 23 PD patients and related pathological and healthy controls. The acquired spectra were computed using machine and deep learning approaches. The Raman database was used to create a classification model able to discriminate each spectrum to the correct belonging group, with accuracy, specificity, and sensitivity of more than 97% for the single spectra attribution. Similarly, each patient was correctly assigned with discriminatory power of more than 90%. Moreover, the extracted data were significantly correlated with clinical data used nowadays for the PD diagnosis and monitoring. The preliminary data reported highlight the potentialities of the proposed methodology that, once validated in larger cohorts and with multi-centered studies, could represent an innovative minimally invasive and accurate procedure to determine the PD onset, progression and to monitor therapies and rehabilitation efficacy.

SUBMITTER: Carlomagno C

PROVIDER: S-EPMC8576466 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Identification of the Raman Salivary Fingerprint of Parkinson's Disease Through the Spectroscopic- Computational Combinatory Approach.

Carlomagno Cristiano C Bertazioli Dario D Gualerzi Alice A Picciolini Silvia S Andrico Michele M Rodà Francesca F Meloni Mario M Banfi Paolo Innocente PI Verde Federico F Ticozzi Nicola N Silani Vincenzo V Messina Enza E Bedoni Marzia M

Frontiers in neuroscience 20211026

Despite the wide range of proposed biomarkers for Parkinson's disease (PD), there are no specific molecules or signals able to early and uniquely identify the pathology onset, progression and stratification. Saliva is a complex biofluid, containing a wide range of biological molecules shared with blood and cerebrospinal fluid. By means of an optimized Raman spectroscopy procedure, the salivary Raman signature of PD can be characterized and used to create a classification model. Raman analysis wa ...[more]

PMID: 34764849

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Project description:Phosphodiesterases (PDEs) are vital in signal transduction, specifically by hydrolyzing cAMP and cGMP. Within the PDE family, PDE10A is notable for its prominence in the striatum and its regulatory function over neurotransmitters in medium-spiny neurons. Given the dopamine deficiency in Parkinson's disease (PD) that affects striatal pathways, PDE10A inhibitors could offer therapeutic benefits by modulating D1 and D2 receptor signaling. This study was motivated by the successful history of quinazoline/quinazoline scaffolds in the inhibition of PDE10A. This study involved detailed in silico evaluations through docking followed by pharmacological, pharmacophoric, and pharmacokinetic analyses, prioritizing central nervous system (CNS)-active drug criteria. Seven cyclic peptides, those featuring the quinazoline/quinazoline moiety at both termini, exhibited notably enhanced docking scores compared to those of the remaining alkaloids within the screened library. We identified 7 quinolines and 1 quinazoline including Lepadin G, Aspernigerin, CJ-13536, Aurachin A, 2-Undecyl-4(1H)-quinolone, Huajiaosimuline 3-Prenyl-4-prenyloxyquinolin-2-one, and Isaindigotone that followed the standard CNS active drug criteria. The dominant quinoline ring in our study and its related quinazoline were central to our evaluations; therefore, the pharmacophoric features of these scaffolds were highlighted. The top alkaloids met all CNS-active drug properties; while nonmutagenic and without PAINS alerts, many indicated potential hepatotoxicity. Among the compounds, Huajiaosimuline was particularly significant due to its alignment with lead-likeness and CNS-active criteria. Aspernigerin demonstrated its affinity for numerous dopamine receptors, which signifies its potential to alter dopaminergic neurotransmission that is directly related to PD. Interestingly, the majority of these alkaloids had biological targets primarily associated with G protein-coupled receptors, critical in PD pathophysiology. They exhibit superior excretion parameters and toxicity end-points compared to the standard. Notably, selected alkaloids demonstrated stability in the binding pocket of PDE10A according to the molecular dynamic simulation results. Our findings emphasize the potential of these alkaloids as PDE10A inhibitors. Further experimental studies may be necessary to confirm their actual potency in inhibiting PDE10A before exploring their therapeutic potential in PD.

Dataset Information

Identification of the Raman Salivary Fingerprint of Parkinson's Disease Through the Spectroscopic- Computational Combinatory Approach.

Publications

Identification of the Raman Salivary Fingerprint of Parkinson's Disease Through the Spectroscopic- Computational Combinatory Approach.

Similar Datasets

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