The pharmacokinetics of levobupivacaine 0.5% after infraorbital or inferior alveolar block in anesthetized dogs

IntroductionData are lacking on the pharmacokinetic profile and safety of levobupivacaine (LB) used for regional anesthesia of the maxilla and mandibles in dogs.MethodsInfraorbital block (n = 10), inferior alveolar block (n = 10) or both infraorbital and inferior alveolar blocks (n = 10) were administered to dogs undergoing dental surgery under isoflurane anesthesia. The dose of LB was calculated as 0.11 ml/kg2/3 for the infraorbital block and 0.18 ml/kg2/3 for the inferior alveolar block. Blood samples were collected before and immediately after administration of the oral blocks, and 3, 4, 7, 12, 17, 32, 47, 62, 92, and 122 min thereafter. Quantification of LB in plasma was performed by LC-MS/MS.Results and discussionThe results are presented as median and interquartile range. In dogs in which all four quadrants of the oral cavity were desensitized with LB, the Cmax was 1,335 (1,030–1,929) ng/ml, the Tmax was 7 (4–9.5) min, and the AUC(0 → 120) was 57,976 (44,954–96,224) ng min/ml. Plasma concentrations of LB were several times lower than the reported toxic concentrations, and no signs of cardiovascular depression or neurotoxicity were observed in any of the dogs, suggesting that the occurrence of severe adverse effects after administration of LB at the doses used in this study is unlikely

Chemical and toxicological characterisation of anticancer drugs in hospital and municipal wastewaters from Slovenia and Spain

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GC-EI-MS datasets of trimethylsilyl (TMS) and tert-butyl dimethyl silyl (TBDMS) derivatives for development of machine learning-based compound identification approaches

In the field of environment and health studies, recent trends have focused on the identification of contaminants of emerging concern (CEC). This is a complex, challenging task, as resources, such as compound databases (DBs) and mass spectral libraries (MSLs) concerning these compounds are very poor. This is particularly true for semi polar organic contaminants that have to be derivatized prior to gas chromatography-mass spectrometry (GC-MS) analysis with electron impact ionization (EI), for which it is barely possible to find any records. In particular, there is a severe lack of datasets of GC-EI-MS spectra generated and made publicly available for the purpose of development, validation and performance evaluation of cheminformatics-assisted compound structure identification (CSI) approaches, including novel cutting-edge machine learning (ML)-based approaches [1].We set out to fill this gap and support the machine learning-assisted compound identification, thus aiding cheminformatics-assisted identification of silylated derivatives in GC-MS laboratories working in the field of environment and health. To this end, we have generated 12 datasets of GC-EI-MS spectra, six of which contain GC-EI-MS spectra of trimethylsilyl (TMS) and six GC-EI-MS spectra of tert-butyldimethylsilyl (TBDMS) derivatives. Four of these datasets, named testing datasets, contain mass spectra acquired by the authors. They are available in full, together with corresponding metadata. Eight datasets, named training datasets, were derived from mass spectra in the NIST 17 Mass Spectral Library. For these, we have only made the metadata publicly available, due to licensing reasons.For each type of derivative, two testing datasets are generated by acquiring and processing GC-EI-MS spectra, such that they include raw and processed GC-EI-MS spectra of TMS and TBDMS derivatives of CECs, along with their corresponding metadata. The metadata contains IUPAC name, exact mass, molecular formula, InChI, InChIKey, SMILES and PubChemID, of each CEC and CEC-TMS or CEC-TBDMS derivative, where available. Eight GC-EI-MS training datasets are generated by using the National Institute of Standards and Technology (NIST)/U.S. Environmental Protection Agency (EPA)/National Institute of Health (NIH) 17 Mass Spectral Library. For each derivative type (TMS and TBDMS), four datasets are given, each corresponding to an original dataset obtained from NIST/EPA/NIH 17 and three variants thereof, obtained after each of the filtering steps of the procedure described below. Only the metadata about the training datasets are available, describing the corresponding NIST/EPA/NIH 17 entires: These include the compound name, CAS Registry number, InChIKey, exact mass, Mw, NIST number and ID number.The datasets we present here were used to train and test predictive models for identification of silylated derivatives built with ML approaches [4]. The models were built by using data curated from the NIST Mass Spectral Library 17 [2] and the machine learning approach of CSI:Output Kernel Regression (CSI:OKR) [2]. Data from the NIST Mass Spectral Library 17 are commercially available from the National Institute of Standards and Technology (NIST)/U.S. Environmental Protection Agency (EPA)/National Institute of Health (NIH) and thus cannot be made publicly available. This highlights the need for publicly available GC-EI-MS spectra, which we address by releasing in full the four testing datasets

Fate and Effects of Anticancer Drugs in the Environment

The book provides current knowledge and research on the presence and effects of anticancer drug residues in the aqueous environment and covers all relevant aspects of the presence of these residues in wastewaters and natural aquatic systems, where numerous analogies between their pharmacokinetics and pharmacodynamics in humans and their effects in the environment can be drawn. This book comprises of 18 chapters and represents the combined work of leading scientists from different research institutions from across the globe. We present the state of the art in the field of anticancer drug residues in the aquatic environment while being cognizant of the many challenges that remain

Aerobic activated sludge transformation of methotrexate: Identification of biotransformation products

This study describes the biotransformation of cytostatic and immunosuppressive pharmaceutical methotrexate. Its susceptibility to microbiological breakdown was studied in a batch biotransformation system, in presence or absence of carbon source and at two activated sludge concentrations. The primary focus of the present study are methotrexate biotransformation products, which were tentatively identified by the ultra-high performance liquid chromatography-quadrupole - Orbitrap-MS. Data-dependent experiments, combining full-scan MS data with product ion spectra were acquired, in order to identify the molecular ions of methotrexate transformation products, to propose the molecular formulae and to elucidate their chemical structures. Among the identified transformation products 2,4-diamino-N10-methyl-pteroic acid is most abundant and persistent. Other biotransformation reactions involve demethylation, oxidative cleavage of amine, cleavage of C-N bond, aldehyde to carboxylate transformation and hydroxylation. Finally, a breakdown pathway is proposed, which shows that most of methotrexate breakdown products retain the diaminopteridine structural segment. In total we propose nine transformation products, among them eight are described as methotrexate transformation products for the first time.a Jožef Stefan Institute, Department of Environmental Sciences, Jamova 39, Ljubljana, Slovenia b Water and Soil Quality Research Group, Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Jordi Girona 18-26, Barcelona, Spain c Catalan Institute for Water Research (ICRA), Scientific and Technological Park of the University of Girona, H2O Building Emili Grahit 101, Girona, SpainPeer reviewe

An effective validation of analytical method for determination of a polar complexing agent

The effectiveness of highly polar agents in cancer treatment is well recognized, but their physicochemical properties make their analytical determination a demanding task. Their analysis requires peculiar sample preparation and chromatographic separation, which heavily impacts the precision of such an analytical method. As a case study, we chose a polar cytotoxic bleomycin, which is a mixture of complexing congeners with relatively high molecular mass, a fact that creates an added challenge in regard to its detection via electrospray mass spectrometry. These issues combined lead to a deprived method performance, so the aim of this study is manifold, i.e., to optimize, validate, and establish quality performance measures for determination of bleomycin in pharmaceutical and biological specimens. Quantification of bleomycin is done at diametrically different concentration levels: at the concentrations relevant for analysis of pharmaceutical dosage forms it is based on a direct reversed-phase HPLC-UV detection, involving minimum sample pretreatment. On the contrary, analysis of bleomycin in biological specimens requires phospholipid removal and protein precipitation followed by HILIC chromatography with MS/MS detection of bleomycin A2 and B2 copper complexes being the predominant species. This study further attempts to solve the traceability issue in the absence of certified reference standards, determines measurement uncertainty, investigates BLM stability and method performance characteristics, and, last but not least, provides an explanatory example of how a method quality assurance procedure should be established in case of an exceedingly complex analytical method

Additional file 3 of Machine learning for identification of silylated derivatives from mass spectra

Additional file 3. Representation of the environmental relevance evaluation