SANIST: optimization of a technology for compound identification based on the European Union directive with applications in forensic, pharmaceutical and food analyses

Cristoni, Simone; Dusi, Guglielmo; Brambilla, Paolo; Albini, Adriana; Conti, Matteo; Brambilla, Maura; Bruno, Antonino; Gaudio, Francesca Di; Ferlin, Luca; Tazzari, Valeria; Mengozzi, Silvia; Barera, Simone; Sialer, Carlos; Trenti, Tommaso; Cantù, Marco; Bernardi, L.

doi:10.1002/jms.3895

Cited by 13 publications

(32 citation statements)

References 17 publications

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“…The identification of GSK5182 N‐oxide product was carried out in collaboration with Ion Source & Biotechnologies Srl (Milan, Italy) employing a SACI/ESI source coupled to an Orbitrap mass spectrometer (Thermo Fisher, San Jose, CA, USA) working in positive ion mode . Spectra acquisition was performed in the 40–3500 m/z range.…”

Section: Methodsmentioning

confidence: 99%

Flavin‐Containing Monooxygenase 3 Polymorphic Variants Significantly Affect Clearance of Tamoxifen and Clomiphene

Catucci

Bortolussi

Rampolla

et al. 2018

Basic Clin Pharma Tox

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Human flavin-containing monooxygenase 3 (hFMO3) is a drug-metabolising enzyme that oxygenates many drugs and xenobiotics in the liver. This enzyme is also known to exhibit single nucleotide polymorphisms (SNPs) that can alter the rates of monooxygenation of therapeutic agents. The purpose of this study was to investigate the effect of the three common polymorphic variants of hFMO3 (V257M, E158K and E308G) on the metabolism and clearance of three structurally similar compounds: tamoxifen (breast cancer medication), clomiphene (infertility medication) and GSK5182 (antidiabetic lead molecule). For GSK5182, none of the three variants showed any significant differences in its metabolism when compared to the wild-type enzyme. In the case of clomiphene, two of the variants, V257M and E308G, exhibited a significant increase in all the kinetic parameters measured with nearly two times faster clearance. Finally, for tamoxifen, a mixed behaviour was observed; E158K variant showed a significantly higher clearance compared to the wild type, whereas V257M mutation had the opposite effect. Overall, the data obtained demonstrate that there is no direct correlation between the SNPs and the metabolism of these three hFMO3 substrates. The metabolic capacity is both variant-dependent and substrate-dependent and therefore when testing new drugs or administering already approved therapies, these differences should be taken into consideration.

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Section: Methodsmentioning

confidence: 99%

Flavin‐Containing Monooxygenase 3 Polymorphic Variants Significantly Affect Clearance of Tamoxifen and Clomiphene

Catucci

Bortolussi

Rampolla

et al. 2018

Basic Clin Pharma Tox

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“…Herein, we introduce a new method to increase the prediction efficiency of intestinal dysbiosis detection, based on the antagonist and pathogen bacteria pharmacological activity. Basically, feces molecular profiles are analyzed by liquid chromatography/surface‐activated chemical‐ionization/electrospray ionization (LC/SASI‐MS) and tandem mass spectrometry (MS/MS) in randomized data‐dependent scan acquisition mode . The antagonist and pathogen pharmacological active metabolites are correlated to latent dysbiosis on the basis of literature data.…”

Section: Molecular Fingerprint Potentially Produced By Gut Microbiotamentioning

confidence: 99%

“…Compounds were identified using a first NIST database search screening followed by European Union database match mode (EU directive 2002/657/EC) . The dysbiosis level expressed as a percentage is calculated using Equation 1:

Ci = ((), ((), Fi - 1) / Σj ((), Nt - 1)) * 100

where Ci is the dysbiosis percentage contribution of the i‐th pathogen bacterium, Fi is the frequency associated with antagonist and pathogen metabolic biomarkers and Nt is the total number of detected metabolites associated with all the detected bacteria.…”

Section: Molecular Fingerprint Potentially Produced By Gut Microbiotamentioning

confidence: 99%

“…Analytes were identified using a similarity approach and the NIST algorithm . The identified molecules were confirmed using analytical standards of the compounds and the European Union low recognized database match mode (EU directive 2002/657/EC) . The dysbiosis contribution percentage is calculated for each identified bacterium using Equation 1.…”

Section: Molecular Fingerprint Potentially Produced By Gut Microbiotamentioning

confidence: 99%

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Predicting and preventing intestinal dysbiosis on the basis of pharmacological gut microbiota metabolism

Cristoni

Bernardi

Larini

et al. 2019

Rapid Comm Mass Spectrometry

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The possible relationship(s) between intestinal dysbiosis and various degenerative intestinal-based diseases, e.g. diabetes and obesity, is an ongoing focus of intense investigation. [1][2][3][4] As a consequence of the results of such research, the focus is now on therapeutic strategies for such diseases based on modification of the gut microbiota. 2,4 Fecal transplantation is one such strategy, which was suggested by Zhang and co-workers 2 as a treatment for diabetes mellitus and by Kang and co-workers 4 for treatment of obesity. Kang and co-workers also reported different strategies involving fecal gut microbiota transplantation to treat obesity. Other studies correlated other degenerative diseases to gut microbiota activities and suggested therapeutic strategies to alleviate those disease states. 1 This interesting prospect, i.e., fecal gut microbiota transplantation to combat intestine-related diseases, has led to the development of methods by different research groups to evaluate the gut microbiota status in human subjects. 5-8 These approaches can be classified as:a. Untargeted approaches that search for bacteria responsible for dysbiosis without any prior knowledge of which types of bacterium might be involved. 5,6 b. Targeted approaches that search for specific pathogens and the correlation of these to gut microbiota-related dysbiosis. 7,8 One example of an untargeted approach is that of Pinheiro de Oliveira and co-workers who developed a method that detects thousands of different species using DNA gene amplification. 5 For the aforementioned DNA approaches, it is difficult to determine a correlation between the presence of a bacterial species and its involvement in intestinal dysbiosis without knowledge of how its metabolism affects the intestinal environment. 5,6

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“…Surface Activated Chemical Ionization-Electrospray-NIST Bayesian model database search (SANIST) platform has been recently proposed by us as a powerful tool for personalized medicine ( 28 , 35 ). Basically, it is an open platform based on three core points: Standardized kits for sample preparation to maximize the inter-laboratory data reproducibility.…”

Section: Introductionmentioning

confidence: 99%

Serum Steroid Ratio Profiles in Prostate Cancer: A New Diagnostic Tool Toward a Personalized Medicine Approach

et al. 2018

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BackgroundSerum steroids are crucial molecules altered in prostate cancer (PCa). Mass spectrometry (MS) is currently the elected technology for the analysis of steroids in diverse biological samples. Steroids have complex biological pathways and stoichiometry and it is important to evaluate their quantitative ratio. MS applications to patient hormone profiling could lead to a diagnostic approach.MethodsHere, we employed the Surface Activated Chemical Ionization-Electrospray-NIST (SANIST) developed in our laboratories, to obtain quantitative serum steroid ratio relationship profiles with a machine learning Bayesian model to discriminate patients with PCa. The approach is focused on steroid relationship profiles and disease association.ResultsA pilot study on patients affected by PCa, benign prostate hypertrophy (BPH), and control subjects [prostate-specific antigen (PSA) lower than 2.5 ng/mL] was done in order to investigate the classification performance of the SANIST platform. The steroid profiles of 71 serum samples (31 controls, 20 patients with PCa and 20 subjects with benign prostate hyperplasia) were evaluated. The levels of 10 steroids were quantitated on the SANIST platform: Aldosterone, Corticosterone, Cortisol, 11-deoxycortisol, Androstenedione, Testosterone, dehydroepiandrosterone, dehydroepiandrosterone sulfate (DHEAS), 17-OH-Progesterone and Progesterone. We performed both traditional and a machine learning analysis.ConclusionWe show that the machine learning approach based on the steroid relationships developed here was much more accurate than the PSA, DHEAS, and direct absolute value match method in separating the PCa, BPH and control subjects, increasing the sensitivity to 90% and specificity to 84%. This technology, if applied in the future to a larger number of samples will be able to detect the individual enzymatic disequilibrium associated with the steroid ratio and correlate it with the disease. This learning machine approach could be valid in a personalized medicine setting.

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SANIST: optimization of a technology for compound identification based on the European Union directive with applications in forensic, pharmaceutical and food analyses

Cited by 13 publications

References 17 publications

Flavin‐Containing Monooxygenase 3 Polymorphic Variants Significantly Affect Clearance of Tamoxifen and Clomiphene

Flavin‐Containing Monooxygenase 3 Polymorphic Variants Significantly Affect Clearance of Tamoxifen and Clomiphene

Predicting and preventing intestinal dysbiosis on the basis of pharmacological gut microbiota metabolism

Serum Steroid Ratio Profiles in Prostate Cancer: A New Diagnostic Tool Toward a Personalized Medicine Approach

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