Development of innovative analytical methods based on spectroscopic techniques and multivariate statistical analysis for quality control in the food and pharmaceutical fields.

The increasing demand on quality assurance and ever more stringent regulations in food and pharmaceutical fields are promoting the need for analytical techniques enabling to provide reliable and accurate results. However, traditional analytical methods are labor-intensive, time-consuming, expensive and they usually require skilled personnel for performing the analysis. For these reasons, in the last decades, quality control protocols based on the employment of spectroscopic methods have been developed for many different application fields, including pharmaceutical and food ones. Vibrational spectroscopic techniques can be an adequate alternative for acquiring both chemical and physical information related to homogenous and heterogenous matrices of interest. Moreover, the significant development of powerful data-driven methodologies allowed to develop algorithms for the optimal extraction and processing of the complex spectroscopic signals allowing to apply combined approaches for quantitative and qualitative purposes. The present Doctoral Thesis has been focused on the development of ad-hoc analytical strategies based on the application of spectroscopic techniques coupled with multivariate data analysis approaches for providing alternative analytical protocols for quality control in food and pharmaceutical sectors. Regarding applications in food sector, excitation-emission Fluorescence Spectroscopy, Near Infrared Spectroscopy (NIRS) and NIR Hyperspectral Imaging (HSI) have been tested for solving analytical issues of independent case-studies. Unsupervised approaches based on Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC) have been applied on fluorescence data for characterizing green tea samples, while quantitative predictive approaches as Partial Least Squares regression have been used to correlate NIR spectra with quality parameters of extra-virgin olive oil samples. HSI was applied to study dynamic chemical processes which occur during cheese ripening with the aim to map chemical and sensory changes over time. The rapid technical progress in terms of spectroscopic instrumentations has led to have more flexible portable systems suitable for performing measurements directly in the field or in a manufacturing plant. Within this scenario, NIR spectroscopy proved to be one of the most powerful Process Analytical Technologies (PAT) for monitoring and controlling complex manufacturing processes. In this thesis, two applications based on the implementation of miniaturized NIR sensors have been performed for the real-time powder blending monitoring of pharmaceutical and food formulation, respectively. The main challenges in blending monitoring are related to the assessment of the homogeneity of multicomponent formulations, which is crucial to ensure the safety and effectiveness of a solid pharmaceutical formulation or the quality of a food product. In the third chapter of this thesis, tailor made qualitative chemometric strategies for obtaining a global understanding of blending processes and to optimize the endpoint detection are presented

Combining excitation-emission matrix fluorescence spectroscopy, parallel factor analysis, cyclodextrin-modified micellar electrokinetic chromatography and partial least squares class-modelling for green tea characterization

In this study, an alternative analytical approach for analyzing and characterizing green tea (GT) samples is proposed, based on the combination of excitation–emission matrix (EEM) fluorescence spectroscopy and multivariate chemometric techniques. The three-dimensional spectra of 63 GT samples were recorded using a Perkin–Elmer LS55 luminescence spectrometer; emission spectra were recorded between 295 and 800 nm at excitation wavelength ranging from 200 to 290 nm, with excitation and emission slits both set at 10 nm. The excitation and emission profiles of two factors were obtained using Parallel Factor Analysis (PARAFAC) as a 3-way decomposition method. In this way, for the first time, the spectra of two main fluorophores in green teas have been found. Moreover, a cyclodextrin-modified micellar electrokinetic chromatography method was employed to quantify the most represented catechins and methylxanthines in a subset of 24 GT samples in order to obtain complementary information on the geographical origin of tea. The discrimination ability between the two types of tea has been shown by a Partial Least Squares Class-Modelling performed on the electrokinetic chromatography data, being the sensitivity and specificity of the class model built for the Japanese GT samples 98.70% and 98.68%, respectively. This comprehensive work demonstrates the capability of the combination of EEM fluorescence spectroscopy and PARAFAC model for characterizing, differentiating and analyzing GT samples

An analytical approach based on excitation-emission fluorescence spectroscopy and chemometrics for the screening of prostate cancer through urine analysis: A proof–of–concept study

In the present feasibility study, excitation-emission fluorescence spectroscopy has been investigated, as a rapid and accurate analytical method for the development of a tentative model for the early screening of prostate cancer directly through urine analysis in order to provide reliable results while improving patient compliance. Sixty-nine urine samples (46 samples from patients with histologically proven prostate cancer and 23 from healthy donors) were provided, by the University of Pisa, Urology Unit. The excitation-emission fluorescence measurements were performed on centrifugated urine samples at room temperature on a Perkin-Elmer LS55B luminescence spectrometer and the corresponding data array was analysed with parallel factor analysis (PARAFAC). From a synergistic analysis of the obtained results, four main fluorophores, corresponding to four selected PARAFAC factors, were recognizable in the urine excitation-emission matrices (EEMs) and the respective species could be potential markers in the differentiation among healthy and cancer samples. PARAFAC results, in terms of extracted scores, coupled with discriminant algorithms, allowed to develop a first attempt of healthy/cancer discrimination model. The chemometrics models show promising correlation between some of the depicted fluorophores and the disease state. However, considering the limited cohort (not only in terms of number but also of representativeness), this study must be considered as a proof of concept; a more sound and statistically relevant sampling must be performed in order to consider the confounding factors in the cohort treated and to develop an analytical approach applicable in real scenarios

Probing Allosteric Hsp70 Inhibitors by Molecular Modelling Studies to Expedite the Development of Novel Combined F508del CFTR Modulators

Cystic fibrosis (CF) is caused by different mutations related to the cystic fibrosis transmembrane regulator protein (CFTR), with F508del being the most common. Pioneering the development of CFTR modulators, thanks to the development of effective correctors or potentiators, more recent studies deeply encouraged the administration of triple combination therapeutics. However, combinations of molecules interacting with other proteins involved in functionality of the CFTR channel recently arose as a promising approach to address a large rescue of F508del-CFTR. In this context, the design of compounds properly targeting the molecular chaperone Hsp70, such as the allosteric inhibitor MKT-077, proved to be effective for the development of indirect CFTR modulators, endowed with ability to amplify the accumulation of the rescued protein. Herein we performed structure-based studies of a number of allosteric HSP70 inhibitors, considering the recent X-ray crystallographic structure of the human enzyme. This allowed us to point out the main interaction supporting the binding mode of MKT-077, as well as of the related analogues. In particular, cation- and \u2013 stacking with the conserve residue Tyr175 deeply stabilized inhibitor binding at the HSP70 cavity. Molecular docking studies had been followed by QSAR analysis and then by virtual screening of aminoaryl thiazoles (I\u2013IIIa) as putative HSP70 inhibitors. Their effectiveness as CFTR modulators has been verified by biological assays, in combination with VX-809, whose positive results confirmed the reliability of the whole applied computational method. Along with this, the \u201cin-silico\u201d prediction of absorption, distribution, metabolism, and excretion (ADME) properties highlighted, once more, that AATs may represent a chemical class to be further investigated for the rational design of novel combination of compounds for CF treatment