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Se presenta un estudio empírico de carácter descriptivo que evalúa las Competencias Digitales Docentes (CDD) de 1098 profesores universitarios de los cuales 508 son hombres y 590 mujeres a través del instrumento desarrollado por Tourón et al. (2018) en el marco de referencia del INTEF (2017). Los resultados del análisis estadístico muestran que los profesores se encuentran en un nivel intermedio B2, es decir, pueden desarrollar su competencia digital de manera independiente y avanzado C1, lo que significa que estos docentes pueden guiar a otros en el desarrollo de su competencia digital. Por otra parte, el análisis de varianza indica que el sexo, el nivel de estudio y la generación a que pertenecen los profesores son factores asociados al nivel de CDD por lo que se confirma que existen factores personales y contextuales de los profesores que influyen en el conocimiento y uso de los recursos tecnológicos.

A comprehensive evaluation of the dissipation of a myclobutanil plant protection product was performed in tomato and grape samples. Different temperature conditions (3 and 22 °C) were evaluated. A biphasic kinetic model provided a suitable adjustment (

Rationale Liquid chromatography/mass spectrometry is an essential tool for efficient and reliable quantitative and qualitative analysis and underpins much of contemporary drug metabolism and pharmacokinetics. Data-independent acquisition methods such as MSE have reduced the potential to miss metabolites, but do not formally generate quadrupole-resolved product ion spectra. The addition of ion mobility separation to these approaches, for example, in High-Definition MSE (HDMSE ) has the potential to reduce the time needed to set up an experiment and maximize the chance that all metabolites present can be resolved and characterized. We compared High-Definition Data-Dependent Acquisition (HD-DDA), MSE and HDMSE approaches using automated software processing with Mass-MetaSite and WebMetabase. Methods Metabolite identification was performed on incubations of glucagon-like peptide-1 (7-37) (GLP-1) and verapamil hydrochloride. The HD-DDA, MSE and HDMSE experiments were conducted on a Waters ACQUITY UPLC I-Class LC system with a VION IMS quadrupole time-of-flight (QTOF) mass spectrometer operating under UNIFI control. All acquired data were processed using MassMetaSite able to read data from UNIFI 1.9.4. WebMetabase was used to review the detected chromatographic peaks and the spectral data interpretations. Results A comparison of outcomes obtained for MSE and HDMSE data demonstrated that the same structures were proposed for metabolites of both verapamil and GLP-1. The ratio of structurally matched to mismatched product ions found by MassMetaSite was slightly greater for HDMSE than for MSE , and HD-DDA, thus improving confidence in the structures proposed through the addition of ion mobility based data acquisitions. CONCLUSIONS: HDMSE data acquisition is an effective approach for the elucidation of metabolite structures for both small molecules and peptides, with excellent accuracy and quality, requiring minimal tailoring for the compound under investigation.

Peptide drugs have been used in the treatment of multiple pathologies. During peptide discovery, it is crucially important to be able to map the potential sites of cleavages of the proteases. This knowledge is used to later chemically modify the peptide drug to adapt it for the therapeutic use, making peptide stable against individual proteases or in complex medias. In some other cases it needed to make it specifically unstable for some proteases, as peptides could be used as a system to target delivery drugs on specific tissues or cells. The information about proteases, their sites of cleavages and substrates are widely spread across publications and collected in databases such as MEROPS. Therefore, it is possible to develop models to improve the understanding of the potential peptide drug proteolysis. We propose a new workflow to derive protease specificity rules and predict the potential scissile bonds in peptides for individual proteases. WebMetabase stores the information from experimental or external sources in a chemically aware database where each peptide and site of cleavage is represented as a sequence of structural blocks connected by amide bonds and characterized by its physicochemical properties described by Volsurf descriptors. Thus, this methodology could be applied in the case of non-standard amino acid. A frequency analysis can be performed in WebMetabase to discover the most frequent cleavage sites. These results were used to train several models using logistic regression, support vector machine and ensemble tree classifiers to map cleavage sites for several human proteases from four different families (serine, cysteine, aspartic and matrix metalloproteases). Finally, we compared the predictive performance of the developed models with other available public tools PROSPERous and SitePrediction.

Peptide drugs have been used in the treatment of multiple pathologies. During peptide discovery, it is crucially important to be able to map the potential sites of cleavages of the proteases. This knowledge is used to later chemically modify the peptide drug to adapt it for the therapeutic use, making peptide stable against individual proteases or in complex medias. In some other cases it needed to make it specifically unstable for some proteases, as peptides could be used as a system to target delivery drugs on specific tissues or cells. The information about proteases, their sites of cleavages and substrates are widely spread across publications and collected in databases such as MEROPS. Therefore, it is possible to develop models to improve the understanding of the potential peptide drug proteolysis. We propose a new workflow to derive protease specificity rules and predict the potential scissile bonds in peptides for individual proteases. WebMetabase stores the information from experimental or external sources in a chemically aware database where each peptide and site of cleavage is represented as a sequence of structural blocks connected by amide bonds and characterized by its physicochemical properties described by Volsurf descriptors. Thus, this methodology could be applied in the case of non-standard amino acid. A frequency analysis can be performed in WebMetabase to discover the most frequent cleavage sites. These results were used to train several models using logistic regression, support vector machine and ensemble tree classifiers to map cleavage sites for several human proteases from four different families (serine, cysteine, aspartic and matrix metalloproteases). Finally, we compared the predictive performance of the developed models with other available public tools PROSPERous and SitePrediction.

Summary More than 150 peptide therapeutics are globally in clinical development. Many enzymatic barriers should be crossed by a successful drug to be prosperous in such a process. Therefore, the new peptide drugs must be designed preventing the potential protease cleavage to make the compound less susceptible to protease reaction. We present a new data analysis tool developed in WebMetabase, an approach that stores the information from liquid chromatography mass spectrometry-based experimental data or from external sources such as the MEROPS database. The tool is a chemically aware system where each peptide substrate is presented as a sequence of structural blocks (SBs) connected by amide bonds and not being limited to the natural amino acids. Each SB is characterized by its pharmacophoric and physicochemical properties including a similarity score that describes likelihood between a SB and each one of the other SBs in the database. This methodology can be used to perform a frequency analysis to discover the most frequent cleavage sites for similar amide bonds, defined based on the similarity of the SB that participate in such a bond within the experimentally derived and/or public database. Availability and implementation http://webmetabase.com:8182/WebMetabaseBioinformatics/ Supplementary information Supplementary data are available at Bioinformatics online.

Rationale Cytochrome P450 (CYP450) reaction phenotyping (CRP) and kinetic studies are essential in early drug discovery to determine which metabolic enzymes react with new drug entities. A new semi-automated computer-assisted workflow for CRP is introduced in this work. This workflow provides not only information regarding parent disappearance, but also metabolite identification and relative metabolite formation rates for kinetic analysis. Methods Time-course experiments based on incubating six probe substrates (dextromethorphan, imipramine, buspirone, midazolam, ethoxyresorufin and diclofenac) with recombinant human enzymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) and human liver microsomes (HLM) were performed. Liquid chromatography/high-resolution mass spectrometry (LC/HRMS) analysis was conducted with an internal standard to obtain high-resolution full-scan and MS/MS data. Data were analyzed using Mass-MetaSite software. A server application (WebMetabase) was used for data visualization and review. Results CRP experiments were performed, and the data were analyzed using a software-aided approach. This automated-evaluation approach led to (1) the detection of the CYP450 enzymes responsible for both substrate depletion and metabolite formation, (2) the identification of specific biotransformations, (3) the elucidation of metabolite structures based on MS/MS fragment analysis, and (4) the determination of the initial relative formation rates of major metabolites by CYP450 enzymes. Conclusions This largely automated workflow enabled the efficient analysis of HRMS data, allowing rapid evaluation of the involvement of the main CYP450 enzymes in the metabolism of new molecules during drug discovery. Copyright © 2015 John Wiley & Sons, Ltd.

Interest in using peptide molecules as therapeutic agents due to high selectivity and efficacy is increasing within the pharmaceutical industry. However, most peptide-derived drugs cannot be administered orally because of low bioavailability and instability in the gastrointestinal tract due to protease activity. Therefore, structural modifications peptides are required to improve their stability. For this purpose, several in-silico software tools have been developed such as PeptideCutter or PoPS, which aim to predict peptide cleavage sites for different proteases. Moreover, several databases exist where this information is collected and stored from public sources such as MEROPS and ExPASy ENZYME databases. These tools can help design a peptide drug with increased stability against proteolysis, though they are limited to natural amino acids or cannot process cyclic peptides, for example. We worked to develop a new methodology to analyze peptide structure and amide bond metabolic stability based on the peptide structure (linear/cyclic, natural/unnatural amino acids). This approach used liquid chromatography / high resolution, mass spectrometry to obtain the analytical data from in vitro incubations. We collected experimental data for a set (linear/cyclic, natural/unnatural amino acids) of fourteen peptide drugs and four substrate peptides incubated with different proteolytic media: trypsin, chymotrypsin, pepsin, pancreatic elastase, dipeptidyl peptidase-4 and neprilysin. Mass spectrometry data was analyzed to find metabolites and determine their structures, then all the results were stored in a chemically aware manner, which allows us to compute the peptide bond susceptibility by using a frequency analysis of the metabolic-liable bonds. In total 132 metabolites were found from the various in vitro conditions tested resulting in 77 distinct cleavage sites. The most frequent observed cleavage sites agreed with those reported in the literature. The main advantages of the developed approach are the abilities to elucidate metabolite structure of cyclic peptides and those containing unnatural amino acids, store processed information in a searchable format within a database leading to frequency analysis of the labile sites for the analyzed peptides. The presented algorithm may be useful to optimize peptide drug properties with regards to cleavage sites, stability, metabolism and degradation products in drug discovery.

RATIONALE Analytical methods to assess glutathione (GSH) conjugate formation based on mass spectrometry usually take advantage of the specific fragmentation behavior of the glutathione moiety. However, most methods used for GSH adduct screening monitor only one specific neutral loss or one fragment ion, even though the peptide moiety of GSH adducts shows a number of other specific neutral fragments and fragment ions which can be used for identification. METHODS Nine reference drugs well known to form GSH adducts were incubated with human liver microsomes. Mass spectrometric analysis was performed with a quadrupole time-of-flight mass spectrometer in untargeted accurate mass MSE mode. The data analysis and evaluation was achieved in an automated approach with software to extract and identify GSH conjugates based on the presence of multiple collision-induced neutral losses and fragment ions specific for glutathione conjugates in the high-energy MS spectra. RESULTS In total 42 GSH adducts were identified. Eight (18%) adducts did not show the neutral loss of 129 but were identified based on the appearance of other GSH-specific neutral losses or fragment ions. In high-energy MSE spectra the GSH-specific fragment ions of m/z 308 and 179 as well as the neutral loss of 275 Da were complementary to the commonly used neutral loss of 129 Da. Further, one abundant (yet unpublished) GSH conjugate of troglitazone formed in human liver microsomes was found. CONCLUSIONS A software-aided approach was developed to reliably retrieve GSH adduct formation data out of untargeted complex full scan QTOFMSE data in a fast and efficient way. The present approach to detect and analyze multiple collision-induced neutral losses and fragment ions of glutathione conjugates in untargeted MSE data might be applicable to higher throughput to assess reactive metabolite formation in drug discovery. Copyright © 2014 John Wiley & Sons, Ltd.

Aim: High clearance is a commonly encountered issue in drug discovery. Here we present a centralized metabolic soft spot identification assay with adequate capacity and turnaround time to support the metabolic optimization needs of an entire discovery organization. Methodology: An integrated quan/qual approach utilizing both an orthogonal sample-pooling methodology and software-assisted structure elucidation was developed to enable the assay. Major metabolic soft spots in liver microsomes (rodent and human) were generated in a batch mode, along with kinetics of parent disappearance and metabolite formation, typically within 1 week of incubation. Results & conclusion: A centralized metabolic soft spot identification assay has been developed and has successfully impacted discovery project teams in mitigating instability and establishing potential structure–metabolism relationships.

The importance of transporter proteins for the disposition of drugs has become increasingly apparent during the past decade. A noted drug-drug interaction risk is the inhibition of organic anion-transporting polypeptides (OATPs), key transporters for the liver uptake of the widely used statins. We show here the development of a ligand-based in silico model for interaction with OATP1B1, an important representative of the OATP family. The model is based on a structural overlay of 6 known OATP1B1 inhibitors. A data set of about 150 compounds with published OATP1B1 inhibition data was compared to the resulting "transportophor," and a similarity threshold was defined to distinguish between active and inactive molecules. In addition, using a statistical model based on physicochemical properties of the compounds as prefilter was found to enhance the overall predictivity of the model (final accuracy 0.73, specificity 074, and sensitivity 0.71, based on 126 compounds). The combined model was validated using an in-house data set (accuracy, specificity, and sensitivity were 0.63, 0.59, and 0.78, respectively; 62 compounds). The model gives also a structural overlay to the most similar template enabling visualization of where a change in a given structure might reduce the interaction with the transporter.

The identification of metabolites is almost exclusively done with liquid chromatography/tandem mass spectrometry (LC/MSMS) and despite the enormous progress in the development of these techniques and software for handling of data this is a time-consuming task. In this study the use of quadrupole time-of-flight (QTOF)-generated MSE and MS/MS data were compared with respect to rationalization of metabolites. In addition Mass-MetaSite, a semi-automated software for metabolite identification, was evaluated. The program combines the information from MS raw data, in the form of collision-induced dissociation spectra, with a prediction of the site of metabolism in order to assign the structure of a metabolite. The aim of the software is to mimic the rationalization of fragment ions performed by a biotransformation scientist in the process of structural elucidation. For this evaluation, metabolite identification in human liver microsomes was accomplished for 19 commercially available compounds and 15 in-house compounds. The results were very encouraging and for 96% of the metabolites the same structures were assigned using MSE compared with MSMS acquired data. The possibility of using MSE could considerably reduce the analysis time. Moreover, Mass-MetaSite performed well and the correct assigned structure, compared to manual inspection of the data, was picked in the first rank in ∼80% of the cases. In conclusion MSE could be successfully used for metabolite identification in order to reduce time of analysis and Mass-MetaSite could alleviate the work of a biotransformation scientist and decrease the workload by assigning the structure for a majority of the metabolites. Copyright © 2010 John Wiley & Sons, Ltd.

N-dealkylation is a commonly observed metabolic reaction for drugs containing secondary and tertiary amines. On searching the literature, it is obvious that this reaction is far less common among cytochrome P450 2D6 catalyzed reactions compared with other cytochromes P450. The CYP2D6 pharmacophore and characteristic features in the active site cavity suggest a favored substrate orientation that prevents N-dealkylation from occurring. In this study, the literature was searched for N-dealkylated and non-N-dealkylated CYP2D6 substrates. The hypothesis that was suggested and confirmed demonstrated that N-dealkylation occurs by this enzyme when the preferred site of metabolism is blocked toward other oxidative metabolic pathways. An interesting observation was also that addition of stable groups at preferred sites of metabolism generally improved the metabolic stability but also resulted in retained or increased inhibition of the enzyme. In addition, the effect of pH on N- and O-dealkylation of dextromethorphan was shown to be consistent with the hypothesis that an ionized amino function favored substrate dockings resulting in O-dealkylation.

Type II cytochrome P450 (CYP) ligands cause inhibition by direct coordination to the heme iron atom. This interaction usually leads to high inhibitory potential, which can cause drug-drug interaction. The approach to design compounds with diminished CYP inhibition is different depending on whether the compound binds (type II ligand) or not (type I ligand) to the iron atom of the heme group. In this study, the structural characteristics of nitrogen-containing compounds, which bind to the iron atom in two CYP isoforms (CYP2C9 and CYP3A4), were investigated. The in vitro assays applied were fluorescence inhibition assay, difference spectra measurements, and K s determination. Computational modeling as an alternative method to difference spectra measurements to distinguish between type I and type II ligands was also explored. Since two CYP isoforms were used, information about the isoform specificity of type II ligands was also analyzed. The in silico method developed in this study applied together with the information gained from the experimental measurements may result in better decisions during the drug discovery process.

The cytochrome P450 (CYP) family is composed of a large group of monooxygenases that mediate the metabolism of xenobiotics and endogenous compounds. CYP2C9, one of the major isoforms of the CYP family, is responsible for the phase I metabolism of a variety of drugs. The aim of the present investigation is to use rational design together with MetaSite, a metabolism site prediction program, to synthesize compounds that retain their pharmacological effects but that are metabolically more stable in the presence of CYP2C9. The model compound for the study is the nonsteroidal anti-inflammatory drug celecoxib, a COX-2 selective inhibitor and known CYP2C9 substrate. Thirteen analogs of celecoxib were designed, synthesized, and evaluated with regard to their metabolic properties and pharmacologic effects. The docking solutions and the predictions from MetaSite gave useful information leading to the design of new compounds with improved metabolic properties.

Cytochrome P450 (CYP450) reaction phenotyping (CRP) and kinetic studies are essential in early drug discovery to determine which metabolic enzymes react with new drug entities. A new semi-automated computer-assisted workflow for CRP is introduced in this work. This workflow provides not only information regarding parent disappearance, but also metabolite identification and relative metabolite formation rates for kinetic analysis.Time-course experiments based on incubating six probe substrates (dextromethorphan, imipramine, buspirone, midazolam, ethoxyresorufin and diclofenac) with recombinant human enzymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) and human liver microsomes (HLM) were performed. Liquid chromatography/high-resolution mass spectrometry (LC/HRMS) analysis was conducted with an internal standard to obtain high-resolution full-scan and MS/MS data. Data were analyzed using Mass-MetaSite software. A server application (WebMetabase) was used for data visualization and review.CRP experiments were performed, and the data were analyzed using a software-aided approach. This automated-evaluation approach led to (1) the detection of the CYP450 enzymes responsible for both substrate depletion and metabolite formation, (2) the identification of specific biotransformations, (3) the elucidation of metabolite structures based on MS/MS fragment analysis, and (4) the determination of the initial relative formation rates of major metabolites by CYP450 enzymes.This largely automated workflow enabled the efficient analysis of HRMS data, allowing rapid evaluation of the involvement of the main CYP450 enzymes in the metabolism of new molecules during drug discovery.

Structural information on metabolites obtained in relevant biological systems can have considerable impact on the design of new drug candidates. However, with demanding turnaround times, the amount of available structural information may become rate limiting.The workflow for metabolite identification used in our laboratory was compared to a workflow using a software tool built for computer-assisted metabolite identification. The present study covered the in vitro metabolism of a diverse set of 65 in-house compounds. The compounds were profiled across three liver-based systems, 17 compounds were tested in human liver microsomes (HLM), 12 in rat hepatocytes (RHEP), and 36 in human hepatocytes (HHEP).For 92% of the metabolites reported, the exact match or Markush representations were in agreement between the two workflows. The major specific biotransformations in hepatocytes which formed the metabolites were aromatic or aliphatic hydroxylations (33%), N-dealkylations (15%) and glucuronidations (12%).The software was shown to perform well for structural elucidation of metabolites from both phase I and phase II metabolism where the focus was on quickly understanding the rate-limiting metabolic step(s).

The aim of this work is to present the anchor−GRIND methodology. Anchor-GRIND efficiently combines a priori chemical and biological knowledge about the studied compounds with alignment-independent molecular descriptors derived from molecular interaction fields. Such descriptors are particularly useful for series of ligands sharing a common scaffold but with very diverse substituents. The method uses a specific position of the molecular structure (the “anchor point”) to compare the spatial distribution of the molecular interaction fields of the substituents. The descriptors produced are more detailed and specific than the original GRIND while still avoiding the bias introduced by the alignment. Three data sets have been studied to demonstrate the usefulness of the anchor−GRIND methodology for 3D-QSAR modeling. The two first data sets respectively include congeneric series of the hepatitis C virus NS3 protease and of the acetylcholinesterase inhibitors. The third data set discriminates between factor Xa inh...

A conformer- and alignment-independent three-dimensional structure-activity relationship (3D-QSAR) model has been derived that is based on flexible molecular interaction fields calculated in GRID and the subsequent description of these fields by use of alignment-independent descriptors derived in ALMOND. The training set consisted of 22 diverse and flexible competitive inhibitors of the drug-metabolizing enzyme CYP2C9 and generated a model with r(2) of 0.81 and q(2) of 0.62. The predicitive capacity of the model was externally evaluated with a test set of 12 competitive inhibitors and 11 out of 12 were predicted within 0.5 log unit. The most relevant points of interaction in the model correlated well to the amino acids involved in CYP2C9-substrate/inhibitor binding in the active site of a CYP2C9 homology model, further validating the mechanistic sense of our model. This approach offers the possibility to derive predicitve 3D-QSAR models without the need for an alignment rule for chemically diverse ligands and in the absence of target protein crystal structure information.

The aim of the present study is to develop a method for predicting the site at which molecules will be metabolized by CYP 2C9 (cytochrome P450 2C9) using a previously reported protein homology model of the enzyme. Such a method would be of great help in designing new compounds with a better pharmacokinetic profile, or in designing prodrugs where the compound needs to be metabolized in order to become active. The methodology is based on a comparison between alignment-independent descriptors derived from GRID Molecular Interaction Fields for the CYP 2C9 active site, and a distance-based representation of the substrate. The predicted site of metabolism is reported as a ranking list of all the hydrogen atoms of each substrate molecule. Eighty-seven CYP 2C9-catalyzed oxidative reactions reported in the literature have been analyzed. In more than 90% of these cases, the hydrogen atom ranked at the first, second, or third position was the experimentally reported site of oxidation.

Purpose. The purpose of this study was to investigate the pH-dependent passive and active transport of weakly basic drugs across the human intestinal epithelium. Methods. The bidirectional pH-dependent transport of weak bases was studied in Caco-2 cell monolayers in the physiologic pH range of the gastrointestinal tract. Results. A net secretion of atenolol and metoprolol was observed when a pH gradient was applied. However, the bidirectional transport of both compounds was equal in the nongradient system. Hence, at lower apical than basolateral pH a change in passive transport caused by an imbalance in the concentration of the uncharged drug species resulted in a “false” asymmetry (efflux ratio). Furthermore, a mixture of pH-dependent passive and active efflux was found for the P-glycoprotein (P-gp, MDR1, ABCB1) substrates, talinolol and quinidine, but not for the neutral drug, digoxin. However, the clinically important digoxin-quinidine interaction depended on the presence of a pH gradient. Hence, the degree of interaction depends on the amount of quinidine available at the binding site of the P-gp. Conclusions. Active efflux of weak bases can only be accounted for when the fraction of unionized drug species is equal in all compartments because the transport is biased by a pH-dependent passive component. However, this component may take part in vivo and contribute to drug-drug interactions involving P-gp.

ChemGPS, the chemical global positioning system, is a tool that combines rules (equivalent to dimensions) and objects (chemical structures) to provide a consistent chemical space map (Oprea, T. I.; Gottfries, J. J. Comb. Chem. 2001, 3, 157-166.). Rules included, initially, general properties such as size, lipophilicity, and hydrogen bond capacity, while objects include "satellites", intentionally placed outside the druglike space, as well as "core" objects, mostly orally available drugs. ChemGPS molecules (objects) were used in conjunction with the VolSurf (http://www.moldiscovery.com) descriptors (rules), which are relevant for ADME (absorption, distribution, metabolism, and excretion) properties. The combination of ChemGPS and VolSurf, GPSVS, was investigated with respect to the biopharmaceutics classification system, which is recommended by the Food and Drug Administration (FDA) (http://www.fda.gov/cder/OPS/BCS_guidance.htm), in particular with respect to permeability and solubility. The first GPSVS principal component correlates, with no further training, to passive transcellular permeability, as illustrated for the Caco-2, ghost erythrocyte, and blood-brain barrier datasets, respectively. The second GPSVS principal component correlates, without prior training, to solubility, as shown for the octanol-water partition and intrinsic solubility datasets, respectively. Although derived from principal component analysis, the two property axes rotate and form an angle of approximately 43 degrees, thus being no longer orthogonal. GPSVS can be used to map the chemical space with respect to permeability and solubility, as recommended by FDA's biopharmaceutics classification system.

Analytical methods to assess glutathione (GSH) conjugate formation based on mass spectrometry usually take advantage of the specific fragmentation behavior of the glutathione moiety. However, most methods used for GSH adduct screening monitor only one specific neutral loss or one fragment ion, even though the peptide moiety of GSH adducts shows a number of other specific neutral fragments and fragment ions which can be used for identification.Nine reference drugs well known to form GSH adducts were incubated with human liver microsomes. Mass spectrometric analysis was performed with a quadrupole time-of-flight mass spectrometer in untargeted accurate mass MS(E) mode. The data analysis and evaluation was achieved in an automated approach with software to extract and identify GSH conjugates based on the presence of multiple collision-induced neutral losses and fragment ions specific for glutathione conjugates in the high-energy MS spectra.In total 42 GSH adducts were identified. Eight (18%) adducts did not show the neutral loss of 129 but were identified based on the appearance of other GSH-specific neutral losses or fragment ions. In high-energy MS(E) spectra the GSH-specific fragment ions of m/z 308 and 179 as well as the neutral loss of 275 Da were complementary to the commonly used neutral loss of 129 Da. Further, one abundant (yet unpublished) GSH conjugate of troglitazone formed in human liver microsomes was found.A software-aided approach was developed to reliably retrieve GSH adduct formation data out of untargeted complex full scan QTOFMS(E) data in a fast and efficient way. The present approach to detect and analyze multiple collision-induced neutral losses and fragment ions of glutathione conjugates in untargeted MS(E) data might be applicable to higher throughput to assess reactive metabolite formation in drug discovery.

This study demonstrates a selectivity analysis using the GRID/CPCA strategy on four human cytochrome P450 2C homology models (CYP2C8, 2C9, 2C18, and 2C19). Although the four enzymes share more than 80% amino acid sequence identity, the substrate specificity differs. To investigate the selectivity of the enzymes and the amino acids that determine the specificity of each CYP2C enzyme, a selectivity analysis was made using GRID/CPCA. In the GRID calculations 10 probes were used covering hydrophobic, steric, and hydrogen bond acceptor and donor interactions. The selectivity analysis showed that the most important determinants of selectivity among the CYP2C models are the geometrical features of the active sites and the hydrophobic interactions. The selectivity analysis singled out CYP2C8 as the most different of the four CYP2C enzymes with amino acids with distinct properties in positions 114, 205, and 476 (Ser, Phe, and Ile, respectively) compared to the other enzymes. An inverse pharmacophore model for CYP2C9 was constructed from the selective regions, and the model agreed with the docking of diclofenac where the properties of the ligand overlapped with the pharmacophoric points in the model.

Molecular modeling studies on three brassinosteroids analogs having ester and amide function in the side chain have been performed. The activity of such compounds from the structural point of view is discussed in terms of electrostatic potential and ability to form hydrogen bonds.

In the field of brassinosteroids, which are potent plant growth regulators, we have developed a quantitative structure-activity relationship study to develop knowledge from a structural point of view and to find out new requirement definitions. This will help identify other suitable active brassinosteroid derivatives with a good activity/synthetic cost ratio for further application in agriculture. The methodology used to achieve this goal represents a multidisciplinary study involving synthesis, molecular modeling calculations, and bioactivity evaluation. The influence of different molecular properties in the bioactivity of a set of synthetic compounds (i.e., molecular electrostatic potential and the ability to form H bonds) is discussed. The molecular electrostatic potential is expressed in terms of the electrostatic Carbó similarity index (CI) between brassinolide (1) and other brassinosteroids. We have found that the electrostatic charges of the functional groups play an important role in the description of the activity, as evidenced by its good correlation with the CI in most cases. Deviation from this rule could be explained by the H bonding abilities of some of these compounds, which we believe may play an essential role in binding to the natural receptors.

One of the key factors in drug discovery is related to the metabolic properties of the lead compound, which may influence the bioavailability of the drug, its therapeutic window, and unwanted side-effects of its metabolites. Therefore, it is of critical importance to enable the fast translation of the experimentally determined metabolic information into design knowledge. The elucidation of the metabolite structure is the most structurally rich and informative end-point in the available range of metabolic assays. A methodology is presented to partially automate the analysis of this experimental information, making the process more efficient. The computer assisted method helps in the chromatographic peak selection and the metabolite structure assignment, enabling automatic data comparison for qualitative applications (kinetic analysis, cross species comparison).

ACE (angiotensin-1-converting enzyme) is a zinc metallopeptidase that plays a prominent role in blood pressure regulation and electrolyte homeostasis. ACE consists of two homologous domains that despite similarities of sequence and topology display differences in substrate processing and inhibitor binding. The design of inhibitors that selectively inhibit the N-domain (N-selective) could be useful in treating conditions of tissue injury and fibrosis due to build-up of N-domain-specific substrate Ac-SDKP (N-acetyl-Ser–Asp–Lys–Pro). Using a receptor-based SHOP (scaffold hopping) approach with N-selective inhibitor RXP407, a shortlist of scaffolds that consisted of modified RXP407 backbones with novel chemotypes was generated. These scaffolds were selected on the basis of enhanced predicted interaction energies with N-domain residues that differed from their C-domain counterparts. One scaffold was synthesized and inhibitory binding tested using a fluorogenic ACE assay. A molecule incorporating a tetrazole moiety in the P2 position (compound 33RE) displayed potent inhibition (Ki=11.21±0.74 nM) and was 927-fold more selective for the N-domain than the C-domain. A crystal structure of compound 33RE in complex with the N-domain revealed its mode of binding through aromatic stacking with His388 and a direct hydrogen bond with the hydroxy group of the N-domain specific Tyr369. This work further elucidates the molecular basis for N-domainselective inhibition and assists in the design of novel N-selective ACE inhibitors that could be employed in treatment of fibrosis disorders., The present paper reports the development of an N-domain selective ACE inhibitor and the molecular basis for the interaction of key active site residues with the ligand.

Background: The ability to supplement high-throughput metabolic clearance data with structural information defining the site of metabolism should allow design teams to streamline their synthetic decisions. However, broad application of metabolite identification in early drug discovery has been limited, largely due to the time required for data review and structural assignment. The advent of mass defect filtering and its application toward metabolite scouting paved the way for the development of software automation tools capable of rapidly identifying drug-related material in complex biological matrices. Two semi-automated commercial software applications, MetabolitePilot™ and Mass-MetaSite™, were evaluated to assess the relative speed and accuracy of structural assignments using data generated on a high-resolution MS platform. Results/Conclusion: Review of these applications has demonstrated their utility in providing accurate results in a time-efficient manner, leading to acceleration of metabolite identification initiatives while highlighting the continued need for biotransformation expertise in the interpretation of more complex metabolic reactions.

The synthesis of four new brassinosteroid analogs is reported. Two of them elicited high activity as plant growth promoters. Also a new way to define the structural requirements, that can explain the relative high activity of these compounds, is presented.