Development of quantitative structure property relationships to support non-target LC-HRMS screening

Κατά την τελευταία δεκαετία, ένας μεγάλος αριθμός αναδυόμενων ρύπων έχουν ανιχνευθεί και ταυτοποιηθεί σε επιφανειακά ύδατα και λύματα, προκαλώντας ανησυχία για το υδάτινο οικοσύστημα, λόγω της πιθανής χημικής τους σταθερότητας. Η τεχνική της υγροχρωματογραφίας - φασματομετρίας μάζας υψηλής διακριτικής ικανότητας (LC-HRMS) αποτελεί μια αποτελεσματική τεχνική για την ανίχνευση αναδυόμενων ρύπων στο περιβάλλον. Η ταυτόχρονη δε ανάλυση των δειγμάτων με τις συμπληρωματικές τεχνικές της υγροχρωματογραφίας αντίστροφης φάσης (RPLC) και της υγροχρωματογραφίας υδρόφιλων αλληλεπιδράσεων (HILIC), συντελεί στην ταυτοποίηση «ύποπτων» ή και άγνωστων ρύπων με ποικίλες φυσικοχημικές ιδιότητες. Για την ταυτοποίηση τους, απαιτείται να πληρούνται συγκεκριμένα κριτήρια, τα οποία αξιολογούνται με βάση τη χρήση διαγνωστικών εργαλείων, όπως η ακριβής πρόβλεψη του χρόνου ανάσχεσης, η in silico θραυσματοποίηση και η πρόβλεψη της συμπεριφορά τους στον ιοντισμό. Στο 3ο κεφάλαιο της παρούσας διδακτορικής διατριβής περιγράφεται η ανάπτυξη μιας ολοκληρωμένης πορείας εργασίας (workflow) για τη διερεύνηση των παραμέτρων που επηρεάζουν τον χρόνο έκλουσης μεγάλου αριθμού ενώσεων που συγκαταλέγονται στους αναδυόμενους ρύπους. Για τον σκοπό αυτό, πάνω από 2.500 αναδυόμενοι ρύποι χρησιμοποιήθηκαν για την ανάπτυξη του μοντέλου πρόβλεψης χρόνου ανάσχεσης για τις 2 υγροχρωματογραφικές τεχνικές (RP- και HILIC-LC-HRMS) και για ηλεκτροψεκασμό τόσο σε θετικό όσο και σε αρνητικό ιοντισμό (+/-ESI). Στη συνέχεια, πραγματοποιήθηκε εφαρμογή του μοντέλου για την υπολογιστική πρόβλεψη του χρόνου ανάσχεσης, για την ταυτοποίηση 10 νέων προϊόντων μετασχματισμού των φαρμακευτικών ενώσεων (tramadol, furosemide και niflumic acid) ύστερα από επεξεργασία με όζον. Στο 4ο κεφάλαιο παρουσιάζεται η ανάπτυξη ενός καινοτόμου γενικευμένου χημειομετρικού μοντέλου το οποίο είναι ικανό να προβλέπει τον χρόνο έκλουσης κάθε πιθανού ρύπου, ανεξαρτήτου υγροχρωματογραφικής μεθόδου που χρησιμοποιείται, συμβάλλοντας σημαντικά στην σύγκριση αποτελεσμάτων από διαφορετικές LC-HRMS μεθόδους. Το συγκεκριμένο μοντέλο χρησιμοποιήθηκε για την ταυτοποίηση «ύποπτων» και άγνωστων ενώσεων σε διεργαστηριακές δοκιμές. Το Κεφάλαιο 5, περιέχει την περιγραφή της ανάπτυξης ενός υπολογιστικού μοντέλου πρόβλεψης τοξικότητας αναδυόμενων ρύπων που ανιχνεύονται στο υδάτινο οικοσύστημα. Το συγκεκριμένο μοντέλο αποσκοπεί στην εκτίμηση του πιθανού περιβαλλοντικού κινδύνου για νέες ενώσεις που ταυτοποιήθηκαν μέσω σάρωσης «ύποπτων» ενώσεων και μη-στοχευμένης σάρωσης, για τις οποίες δεν είναι ακόμα διαθέσιμα πειραματικά δεδομένα τοξικότητας. Τέλος, στο κεφάλαιο 6 παρουσιάζεται ένας αυτοματοποιημένος και συστηματικός τρόπος σάρωσης «ύποπτων» ενώσεων και μη-στοχευμένης σάρωσης σε δεδομένα από LC-HRMS. Η νέα αυτή αυτοματοποιημένη πορεία εργασίας, αποσκοπεί στην λιγότερο χρονοβόρα επεξεργασία των HRMS δεδομένων, και στην εφαρμογή της μη-στοχευμένης σάρωσης ώστε να είναι δυνατή η εφαρμογή τους σε καθημερινούς ελέγχους ρουτίνας ή/και για χρήση από τις κανονιστικές αρχές.Over the last decade, a high number of emerging contaminants were detected and identified in surface and waste waters that could threaten the aquatic environment due to their pseudo-persistence. As it is described in chapters 1 and 2, liquid chromatography high resolution mass spectroscopy (LC-HRMS) can be used as an efficient tool for their screening. Simultaneously screening of these samples by hydrophilic interaction liquid chromatography (HILIC) and reversed phase (RP) would help with full identification of suspects and unknown compounds. However, to confirm the identity of the most relevant suspect or unknown compounds, their chemical properties such as retention time behavior, MSn fragmentation and ionization modes should be investigated. Chapter 3 of this thesis discusses the development of a comprehensive workflow to study the retention time behavior of large groups of compounds belonging to emerging contaminants. A dataset consisted of more than 2500 compounds was used for RP/HILIC-LC-HRMS, and their retention times were derived in both Electrospray Ionization mode (+/-ESI). These in silico approaches were then applied on the identification of 10 new transformation products of tramadol, furosemide and niflumic acid (under ozonation treatment). Chapter 4 discusses about the development of a first retention time index system for LC-HRMS. Some practical applications of this RTI system in suspect and non-target screening in collaborative trials have been presented as well. Chapter 5 describes the development of in silico based toxicity models to estimate the acute toxicity of emerging pollutants in the aquatic environment. This would help link the suspect/non-target screening results to the tentative environmental risk by predicting the toxicity of newly tentatively identified compounds. Chapter 6 introduces an automatic and systematic way to perform suspect and non-target screening in LC-HRMS data. This would save time and the data analysis loads and enable the routine application of non-target screening for regulatory or monitoring purpose

Non-target approach for the determination of novel micropollutants in wastewater using liquid chromatography quadrupole-time of flight mass spectrometry (LC-QTOF-MS)

Wastewaters contain a very large list of micropollutants and transformation products of environmental concern. All these (mostly) synthetic organic chemicals enter the wastewater treatment plants (WWTP) with influents and due to incomplete or zero removal are released in the aquatic environment. Thus, the study of the fate of the emerging pollutants and their transformation products in WWTPs is of paramount environmental importance and can also provide valuable information related to consumption trends. Target screening procedures are limited to a small fraction of these substances, due to the inability to obtain standards for all that substances and the ignorance of the existence of many of them. Recent advances in high resolution mass spectrometry (HRMS) have opened up new windows of opportunity in the field of complex samples analysis. Suspect screening, with suspected substances based on prior information but with no reference standard, is a powerful tool which allows a large increment in the number of compounds to be evaluated. However, in most cases many of the peaks showing greater intensity not correspond to substances included in the target and suspect screening lists. These substances are potentially relevant, due to their high concentration, and their identification is environmentally important. Nevertheless, full identification of unknown compounds is often difficult and there is no guarantee of a successful outcome. The aim of the present work is the development and application of a workflow for the tentative identification of relevant unknown substances (not detected in the previously applied target and suspect methods) using liquid chromatography quadrupole-time-of-flight mass spectrometry (LC–QToF-MS)

Identification of unknowns in real wastewater through the application of a LC-QTOF-MS based workflow

Wastewater contains a high number of organic micropollutants and transformation products of environmental concern. Recent approaches, combining methodologies based on target and suspect screening (for suspected substances based on prior information but with no reference standard) are important for the comprehensive characterization of environmental samples. Nevertheless, samples still contain many chromatographic peaks which do not correspond to substances included in target and suspect screening lists. These substances may be potentially relevant (e.g. due to their concentration or potential effects) and thus the identification of selected non-targets is important. However, full identification of unknown compounds is often difficult and there is no guarantee of a successful outcome. The aim of this work is to show some specific examples on the identification of unknown compounds in real wastewater (collected from the WWTP of Athens). Identifications were conducted using a developed integrated workflow based on LC–QToF-MS to detect formerly unknown organic contaminants in wastewater

Extended suspect and non-target strategies to characterize emerging polar organic contaminants in raw wastewater with LC-HRMS/MS

An integrated workflow based on liquid chromatography coupled to a quadrupole-time-of-flight mass spectrometer (LC-QTOF-MS) was developed and applied to detect and identify suspect and unknown contaminants in Greek wastewater. Tentative identifications were initially based on mass accuracy, isotopic pattern, plausibility of the chromatographic retention time and MS/MS spectral interpretation (comparison with spectral libraries, in silico fragmentation). Moreover, new specific strategies for the identification of metabolites were applied to obtain extra confidence including the comparison of diurnal and/or weekly concentration trends of the metabolite and parent compounds and the complementary use of HILIC. Thirteen of 284 predicted and literature metabolites of selected pharmaceuticals and nicotine were tentatively identified in influent samples from Athens and seven were finally confirmed with reference standards. Thirty four nontarget compounds were tentatively identified, four were also confirmed. The sulfonated surfactant diglycol ether sulfate was identified along with others in the homologous series (SO4C2H4(OC2H4)xOH), which have not been previously reported in wastewater. As many surfactants were originally found as nontargets, these compounds were studied in detail through retrospective analysi

QSAR study of HCV NS5B polymerase inhibitors using the genetic algorithm-multiple linear regression (GA-MLR)

Quantitative structure–activity relationship (QSAR) study has been employed for predicting the inhibitory activities of the Hepatitis C virus (HCV) NS5B polymerase inhibitors. A data set consisted of 72 compounds was selected, and then different types of molecular descriptors were calculated. The whole data set was split into a training set (80 % of the dataset) and a test set (20 % of the dataset) using principle component analysis. The stepwise (SW) and the genetic algorithm (GA) techniques were used as variable selection tools. Multiple linear regression method was then used to linearly correlate the selected descriptors with inhibitory activities. Several validation technique including leave-one-out and leave-group-out cross-validation, Y-randomization method were used to evaluate the internal capability of the derived models. The external prediction ability of the derived models was further analyzed using modified r2, concordance correlation coefficient values and Golbraikh and Tropsha acceptable model criteria's. Based on the derived results (GA-MLR), some new insights toward molecular structural requirements for obtaining better inhibitory activity were obtained

QSRR models to support suspect high resolution mass spectrometric screening of emerging contaminants in environmental samples

Over the last decade, the application of liquid chromatography - high resolution mass spectroscopy (LC-HRMS) has been growing extensively due its ability to identify a wide range of suspect and unknown compounds in environmental samples. However, certain information such as mass accuracy and isotopic pattern of the precursor ion, MS/MS spectra evaluation and retention time plausibility are needed to reach a certain identification confidence. In this context, a comprehensive workflow based on computational tools was developed to understand the retention time behavior of a large number of compounds belonging to emerging contaminants. An extensive dataset was built, containing information for the retention time of 528 and 298 compounds for positive and negative electrospray ionization mode, respectively, to expand the applicability domain of the developed models. Then, the dataset was split into training and test employing k-nearest neighborhood clustering technique so as to build and validate the models’ internal and external prediction ability. The best subset of molecular descriptors was selected using genetic algorithms which is based on the evolutionary computations, and could result in representative selection of descriptors. Multiple Linear Regression, Artificial Neural Networks and Support Vector Machines were used to correlate the selected descriptors with the experimental retention times. Several validation techniques were used, including Golbraikh-Tropsha acceptable model criteria's, Euclidean based applicability domain, r2m, concordance correlation coefficient values, to measure the accuracy and precision of the models. The best linear and non-linear models for each dataset were derived and used to predict the retention time of suspect compounds in a wide-scope survey as the evaluation data set. Overall, the proposed workflow was fast, reliable, and less time consuming which can be employed for identification purposes in environmental sample

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Background The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for “suspect screening” lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide. Results The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA’s CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101). Conclusions The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the “one substance, one assessment” approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/)