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In the present study, label-free SERS spectroscopy is applied as a useful analytical technique for white wine characterization. 180 samples of three white wines varieties from northeastern Italy, Sauvignon Blanc, Ribolla Gialla and Friulano, collected from three different Italian producers from 2016 vintage, have been analyzed using Ag citrate-reduced colloids and a portable Raman instrument with a 785 nm laser. A PCA of SERS spectra showed that discrimination between wines and wineries is possible. Main spectral differences are due to adenine, carboxylic acids and glutathione, with their ratio changing among different wine types and producers. A robust version of the Soft Independent Modelling of Class Analogy (SIMCA) method was used to model the class space of each wine and to perform the classification among the different categories, yielding overall efficiencies between 87 and 93%. These results are extremely encouraging and open the way to the application of this SERS protocol as a wine identification assay.

Gingival crevicular fluid (GCF) is an interesting biofluid reflecting the physiological and pathological states of a single dental element. Due to this unique feature, in recent years, metabolomic analysis of GCF has gained attention as a biometric tool for the diagnosis and therapy of periodontal disease. Traditional methods are, however, too slow, cumbersome and expensive for a health-care routine. Surface enhanced Raman scattering (SERS) can offer rapid and label-free detailed molecular fingerprints that can be used for biofluid analysis. Here we report the first SERS characterization of GCF using an easy and quick sample preparation. The dominant features in the SERS spectrum of GCF are ascribed to very few metabolites, in particular to uric acid, hypoxanthine, glutathione and ergothioneine. Additionally, we succeeded in differentiating between the SERS signal of GCF collected from healthy volunteers and the one collected from patients with periodontal disease.

Raman hyperspectral imaging is an emerging practice in biological and biomedical research for label free analysis of tissues and cells. Using this method, both spatial distribution and spectral information of analyzed samples can be obtained. The current study reports the first Raman microspectroscopic characterisation of colon tissues from patients with Coeliac Disease (CD). The aim was to assess if Raman imaging coupled with hyperspectral multivariate image analysis is capable of detecting the alterations in the biochemical composition of intestinal tissues associated with CD. The analytical approach was based on a multi-step methodology: duodenal biopsies from healthy and coeliac patients were measured and processed with Multivariate Curve Resolution Alternating Least Squares (MCR-ALS). Based on the distribution maps and the pure spectra of the image constituents obtained from MCR-ALS, interesting biochemical differences between healthy and coeliac patients has been derived. Noticeably, a reduced distribution of complex lipids in the pericryptic space, and a different distribution and abundance of proteins rich in beta-sheet structures was found in CD patients. The output of the MCR-ALS analysis was then used as a starting point for two clustering algorithms (k-means clustering and hierarchical clustering methods). Both methods converged with similar results providing precise segmentation over multiple Raman images of studied tissues.

To date, in spite of their toxicity, the plasmatic concentration of most chemotherapeutic drugs is difficult to monitor in oncological patients, because their quantitative determination is expensive and time consuming. This contribution reports a first attempt for the direct quantitative determination of a chemotherapeutic drug in human serum samples by means of Surface Enhanced Raman Spectroscopy (SERS). In this study, SERS substrates constituted by Au nanoparticles deposited on paper by a simple dipping method have been used for rapid (few minutes) analysis of diluted human serum spiked with different concentrations of methotrexate, MTX. The drug concentrations were chosen in a range designed to cover typical therapeutic plasmatic values (from nanomolar to millimolar) in oncological patients, and the pertinent calibration was obtained by Partial Least-Squares Regression (PLSR). Stability selection was employed to evaluate the capability of the PLSR model to accurately predict and extract spectral variations correlated to MTX concentration. Such a quantitative determination is crucial for frequent, and hence adherent, therapeutic drug monitoring, TDM, of chemiotherapic drugs, given their heavy side effects. Its low cost, rapid response and the possibility of obtaining spectra with simple and compact instruments, make SERS particularly apt for implementing effective TDM. The promising results obtained in the analytical validation indicate which steps are to be taken on the way toward a clinical validation with real samples from oncological patients, for MTX as well as for other chemotherapeutic drugs. © 2016 The Royal Society of Chemistry.

Therapeutic drug monitoring (TDM) for anticancer drug imatinib has been suggested as the best way to improve the treatment response and minimize the risk of adverse reactions in chronic myelogenous leukemia (CML) and gastrointestinal stromal tumor (GIST) patients. TDM of oncology treatments with standard analytical methods, such as liquid chromatography-tandem mass spectrometry (LC-MS/ MS) is, however, complex and demanding. This paper proposes a new method for quantitation of imatinib in human plasma, based on surface enhanced raman spectroscopy (SERS) and multivariate calibration using partial least-squares regression (PLSR). The best PLSR model was obtained with three latent variables in the range from 123 to 5000 ng/mL of imatinib, providing a standard error of prediction (SEP) of 510 ng/mL. The method was validated in accordance with international guidelines, through the estimate of figures of merit, such as precision, accuracy, systematic error, analytical sensitivity, limits of detection, and quantitation. Moreover, the feasibility and clinical utility of this approach have also been verified using real plasma samples taken from deidentified patients. The results were in good agreement with a clinically validated LC-MS/MS method. The new SERS method presented in this preliminary work showed simplicity, short analysis time, good sensitivity, and could be considered a promising platform for TDM of imatinib treatment in a point-of-care setting.

Surface-Enhanced Raman Spectroscopy (SERS) is a label-free technique that enables quick monitoring of substances at low concentrations in biological matrices. These advantages make it an attractive tool for the development of point-of-care tests suitable for Therapeutic Drug Monitoring (TDM) of drugs with a narrow therapeutic window, such as chemotherapeutic drugs, immunosuppressants, and various anticonvulsants. In this article, the current applications of SERS in the field of TDM for cancer therapy are discussed in detail and illustrated according to the different strategies and substrates. In particular, future perspectives are provided and special concerns regarding the standardization of self-assembly methods and nanofabrication procedures, quality assurance, and technology readiness are critically evaluated.

Richard Dluhy opened a general discussion of the paper by Duncan Graham: In your example of a heterogeneous solution-based assay for multicomponent analysis, what is the concentration of the target fungal ssPCR DNA that is used, and how do you manage the kinetics of the reaction such that the target reaches the probe in a time frame appropriate for a clinical assay?

A frequent quantification of drugs concentrations in plasma of patients subject to chemotherapy is seldom performed, mostly because the standard methods (Gas or Liquid Chromatography coupled with Mass Spectroscopy) are expensive and time consuming. In this paper we report the approach pursued in one of the research units of the EU project RAMAN4CLINICS to tackle the problem of a low cost, time adherent quantification of drugs used for oncological patients using a Surface Enhanced Raman Scattering (SERS) spectroscopy. More specifically, the issues concerning the repeatability of the nanostructured substrates will be presented and some promising results to increase the selectivity of the measures toward specific drugs will be discussed, with examples concerning one cytotoxic agent, Irinotecan and one kinase inhibitor, Sunitinib.