Toxicology Mechanisms and Methods, 18:243–250, 2008 c Informa Healthcare USA, Inc. Copyright ISSN: 1537-6516 print; 1537-6524 online DOI: 10.1080/15376510701857189
Combined Computational Metabolite Prediction and Automated Structure-Based Analysis of Mass Spectrometric Data David D. Stranz Sierra Analytics, Inc., 5815 Stoddard Road, Suite 601, Modesto, CA 95356, USA Shichang Miao Amgen, Inc., Department of Pharmacokinetics and Drug Metabolism,1120 Veterans Blvd., South San Francisco, CA 94080; Current address: ChemoCentryx Inc., 850 Maude Ave., Mountain View, CA 94043, USA Scott Campbell and George Maydwell Sierra Analytics, Inc., 5815 Stoddard Road, Suite 601, Modesto, CA 95356, USA Sean Ekins ACT LLC, 601 Runnymede Ave, Jenkintown, PA 19046; Department of Pharmaceutical Sciences, University of Maryland, 20 Penn Street, Baltimore, MD 21201, USA
ABSTRACT As high-throughput technologies have developed in the pharmaceutical industry, the demand for identification of possible metabolites using predominantly liquid chromatographic/mass spectrometry-mass spectrometry/mass spectrometry (LC/MS-MS/MS) for a large number of molecules in drug discovery has also increased. In parallel, computational technologies have also been developed to generate predictions for metabolites alongside methods to predict MS spectra and score the quality of the match with experimental spectra. The goal of the current study was to generate metabolite predictions from molecular structure with a software product, MetaDrug. In vitro microsomal incubations were used to ultimately produce MS data that could be used to verify the predictions with Apex, which is a new software tool that can predict the molecular ion spectrum and a fragmentation spectrum, automating the detailed examination of both MS and MS/MS spectra. For the test molecule imipramine used to illustrate the combined in vitro/in silico process proposed, MetaDrug predicts 16 metabolites. Following rat microsomal incubations with imipramine and analysis of the MSn data using the Apex software, strong evidence was found for imipramine and five metabolites and weaker evidence for five additional metabolites. This study suggests a new approach to streamline MS data analysis using a combination of predictive computational approaches with software capable of comparing the predicted metabolite output with empirical data when looking at drug metabolites. KEYWORDS
Apex; Metabolite; Mass Spectroscopy; MetaDrug; QSAR
INTRODUCTION Received 21 July 2007; accepted 28 August 2007. SE acknowledges GeneGo Inc. Sierra Analytics acknowledges Rocco Falchetto of Novartis AG for initial support for the development of Apex. Address correspondence to Sean Ekins M.Sc., Ph.D, D.Sc., ACT LLC, 601 Runnymede Ave, Jenkintown, PA 19046. E-mail:
[email protected],
[email protected]
Research in metabolism is aimed at answering the questions about how the molecule is metabolized, which enzymes are involved, what are the sites of metabolism, the resulting metabolites, the rate of metabolism (Austel and Kutter 1983), and whether the metabolites are biologically active or potentially toxic (Smith and Obach 2005). It is also important to understand species differences in metabolism and the impact this may have on extrapolation from preclinical species to man. As there have been increases in the throughput of experimental in vitro systems using various enzymes, data sets are being generated that can be used for computational models. As the majority of xenobiotics undergo phase I metabolism via the cytochrome P450 (P450) enzymes primarily in liver, intestine, and kidney (Paine et al. 1997), this family of enzymes has been well studied. These enzymes have therefore been extensively modeled using various computational and structural methods. The history of methods used for the computational prediction of human drug metabolism includes several different approaches such as databases, quantitative structure 243
metabolism relationships (QSMRs)/quantitative structureactivity relationships (QSARs), pharmacophores (de Groot and Ekins 2002), statistical QSAR approaches (Shen et al. 2003; Balakin et al. 2004a, 2004b), rule-based approaches, electronic models (Singh et al. 2003; Korzekwa et al. 2004), homology models, and crystal structures with docking approaches (Jolivette and Ekins 2007). Metabolism knowledge bases (Darvas 1987; Klopman et al. 1994, 1997; Talafous et al. 1994; Boyer and Zamora 2002; Langowski and Long 2002; Borodina et al. 2003, 2004; Button et al. 2003; Erhardt 2003) combine data or rules from many different mammalian species and tend to predict all the metabolic possibilities for a molecule (Nassar and Talaat 2004), rather than the likely metabolites, thus limiting their usefulness in drug discovery. These techniques when used individually have different levels of success and could be combined to improve predictions, perhaps by deploying the most appropriate models based on the molecule neighborhood for the test molecule compared to training sets. P450–substrate/inhibitor recognition interactions have been studied extensively and have generally shown the importance of hydrophobic, hydrogen bonding and ionizable features for both substrates based on Km data and inhibitors using Ki , IC50 , and percent inhibition data (Ekins et al. 2001). Molecular models that account for electronic effects of ligands for P450-mediated metabolism have also been produced (Jones and Korzekwa 1996; Jones et al. 2002) and these have combined aliphatic and aromatic oxidation reactions to generate predictions for metabolic regioselectivities. The combination of approaches may also balance the strengths and weaknesses of each approach and hence the introduction of such hybrid methods is ongoing. For example, a recent technique called MetaSite (Molecular Discovery, Middlesex, UK) generates GRID field descriptors used for determining energetically favorable binding sites on molecules of known structure using crystal structures or homology models for the P450 enzymes and the interaction energy descriptors for the molecules evaluated as substrates (Berellini et al. 2005). A reactivity component is also used in the MetaSite calculation to produce a probability for an atom to be metabolized. This approach has been applied with angiotensin II receptor antagonists predicting the site of metabolism for CYP2C9 and CYP3A4 (Berellini et al. 2005). A recent study has expanded the application to CYP2D6, CYP2C19, and CYP1A2 with 75% to 86% correct predictions (Cruciani et al. 2005). MetaSite has been compared with docking of ligands into crystal structures and homology models of CYP3A4, and MetaSite had 78% overall success compared with 57% for docking (Zhou et al. 2006). MetaSite has a limitation of not being able to predict the absolute or relative amounts of the major and minor metabolites as well as the rate of metabolite formation for a molecule. A second hybrid method, MetaDrug, includes a manually annotated Oracle database of human drug metabolism information including xenobiotic reactions, enzyme substrates, and enzyme inhibitors with kinetic data (Ekins et al. 2005a; Ekins et al. 2005b; Ekins et al. 2005c). The MetaDrug database has been used to predict some of the major metabolic pathways and identify the involvement of P450s using multiple QSAR models enabling the prediction of affinity and rate of metabolism for numerous enzymes (Ekins et al. 2005a, 2006). The user may also upload his or her own QSAR or QSMR data into the software to offer a further level of utility. Steps have been undertaken to provide more confidence in predictions as the QSAR methods also provide Tanimoto D. D. Stranz et al.
similarity as a measure of similarity to training set molecules. Structural alerts for likely reactive metabolites (Li 2002; Uetrecht 2003; Williams and Park 2003) are also provided. MetaDrug has been tested with 66 molecules and captured approximately 79% of first-pass metabolites (Ekins et al. 2006). A more recent application of the underlying metabolism database has been for the creation of Kernel-Partial Least Squares (K-PLS) models for individual metabolic transformations that could be used to rank suggested metabolites (Embrechts and Ekins 2007). In the drug discovery process, it is imperative for medicinal chemists to quickly find out the major metabolic “soft spots” in the lead molecule structure, so that analog compounds can be designed and synthesized to address the metabolic stability problems. The conventional method is to perform in vitro and/or in vivo metabolic studies and identify the structures of the metabolites using spectroscopic methods such as LCMS/MS and NMR. This is often a costly and time-consuming process. With considerable emphasis now on increasing the efficiency of drug discovery, there is interest in using the types of predictive computational approaches (as described above) in as many areas as possible, particularly in early drug discovery. Reliable and rapid metabolite prediction using computational approaches, if achievable, could become a valuable tool for drug discovery. The most powerful tool for analysis of in vivo and in vitro metabolite samples is mass spectrometry, typically coupled with liquid chromatographic separation in an online LC/MS instrument. Mass spectrometry can provide molecular weight information with high accuracy and sensitivity, and with tandem MS/MS instruments, information about molecular structure as well. Confirmation of the likely presence of predicted metabolites using LC/MS and LC/MS/MS is generally a straightforward procedure, but requires expert knowledge in mass spectral interpretation. This manual interpretation is a bottleneck in the metabolite ID process, severely limiting throughput. Mass spectrometer manufacturers have responded to this need by providing some tools for metabolite detection, but to our knowledge none of them explicitly takes the chemical structure of the metabolite into account. Thus, there is always some question where isomers are concerned, and a manual interpretation is required. With recent interest in using metabolite prediction software with mass spectrometry data analysis (Anari et al. 2004; Nassar and Talaat 2004; Shu et al. 2004; Anari and Baillie 2005; Lin et al. 2005), we have used MetaDrug along with a second new software tool, Apex, which can predict the molecular ion spectrum and a fragmentation spectrum that can in turn be used to determine which predicted molecules are present in the analyte. This technology is compatible with all mass spectrometers and hence the combination of MetaDrug (or another predictive metabolism tool) and Apex enables the streamlining of metabolite prediction and mass spectrometry analysis. In silico metabolite prediction typically generates many more potential metabolites than are actually observed. This presents a considerable problem for manual interpretation of the mass spectral data. The analyst must examine each peak detected in the liquid chromatogram and compare the mass spectra under that peak with the spectra expected for the structures. Quite often, many of the predicted metabolites are isomers, requiring a detailed examination of both MS and MS/MS spectra to determine which, if any, are correct. By automating this tedious process, Apex removes the potential 244
for error. Every structure is compared against every spectrum in a fast and comprehensive procedure. Predictions containing tens to thousands of structures are evaluated in a few seconds, without bias, and metabolites that are actually present in the sample are easily confirmed. The purpose of the current study was to illustrate how these independently developed software tools can be utilized to identify likely metabolites (using imipramine as an example), and ultimately frees resources for the identification of other metabolites that are currently not suggested by available metabolism knowledge bases.
EXPERIMENTAL Reagents and Chemicals Rat microsomes were purchased from BD Gentest (Woburn, MA) and were stored at −80◦ C until use. NADPH and imipramine were purchased from Sigma (St. Louis, MO) and Aldrich Chemical Company (Milwaukee, WI), respectively.
In Vitro Incubations Incubations were conducted in 0.5 mL of 0.1 M phosphate buffer (pH 7.4) with 10 µM imipramine, 1.0 mg/mL rat liver microsomes, and 1.3 mM NADPH for 1 h at 37◦ C in open polypropylene tubes in a shaking water bath. The reaction was terminated with two volumes of acetonitrile and centrifuged at 4,000 rpm for 20 min. The supernatant was dried down in a speed vac and the residue was reconstituted in 400 µL watermethanol (2:1) for LCMS injection.
Instrumentation The reconstituted sample was examined in the positive electrospray ionization mode on a Thermo Electron (San Jose, CA) LCQ instrument, using a data-dependent LC/MSn acquisition with six scan events in each scan cycle (event 1: MS full scan; events 2 to 3: MS/MS without and with wide band activation, respectively; Events 4 to 6: MS3 on the three most abundant MS/MS fragment ions from event 3). In this experiment, molecular ion (MS) spectra are continuously recorded during the LC separation. When a molecular ion is observed with intensity above a predetermined threshold, an MS/MS fragmentation spectrum of that ion is automatically generated and recorded (collision energy = 35%; activation Q = 0.25; activation time = 30 ms). Ions observed in the MS/MS spectrum are then isolated and subjected to further stages of fragmentation (MS3 ). The result is a combined MS, MS/MS, and MS3 data set with molecular weight and fragmentation information on all significant species that can be detected under the experimental conditions. For these experiments, 40 µL of sample was injected onto a Shiseido Capcell PAK C18 AQ 3µ 2 × 100 mm reverse phase column (Phenomenex, Torrance, CA) with equilibration at 100% solvent A over 1.5 min, binary gradient elution to 25% solvent B over 20 min, followed by isocratic elution at 100% solvent B for 2 min and re-equilibration at 100% solvent A over 1.5 min for a total run time of 25 min. The flow rate was 400 µL/min throughout. Solvent A was 0.1% formic acid (FA) in 95% water/acetonitrile (ACN); solvent B was 0.08% FA in 245
ACN. The eluent was ionized in the mass spectrometer using electrospray ionization. Spectra were recorded in positive ion mode.
MetaDrug Method for Metabolite Prediction The development of MetaDrug (GeneGo Inc, St. Joseph, MI) has been described in detail previously (Ekins et al. 2005a, 2005d). Approximately 80 human metabolic reaction rules (Ekins et al. 2006) were included in MetaDrug at the time of the current study. The molecular structure of imipramine was used in the mol file format and uploaded into MetaDrug. Using the metabolite rules coded into the software, likely metabolites were predicted. These molecules were then exported as an sdf file.
Data Processing Using Apex The underlying equivalence between chemical structure, chemical formula, and molecular weight is fundamental to the ability of mass spectrometry to distinguish among molecules based on mass (more precisely, the mass-to-charge ratio, or m/z, as is measured by the instrument). Apex (Sierra Analytics, Modesto, CA) is fully structure based and requires a minimum of user expertise in mass spectral interpretation. The software is written using the C++ programming language for the Microsoft Windows operating system. All algorithms for chromatographic and spectral data processing and structure-spectrum correlation were developed in-house by Sierra Analytics. In use, Apex imports the substrate and metabolite structures contained in the sdf file produced by MetaDrug. From these structures, MS and MS/MS spectra are predicted for comparison with the experimental data. The software can take into account multiple adducts, charge states, oligomers, and neutral losses when predicting spectral features. For MS/MS, Apex uses heuristic rules to create a straightforward estimate of the fragmentation of a molecule: from the two-dimensional chemical structure, find all rings and determine the order of all bonds. Then, for each acyclic single bond, cleave the molecule into two parts at that bond, and compute the mass of each part. Additional rules are applied to predict cross-ring cleavages in saturated rings. The fragmentation “spectrum” is the set of all such pairs of masses, where each feature in this “spectrum” has the same intensity. Since the primary discriminant among candidate structures is molecular weight, this simulated spectrum is generally sufficient to distinguish among isomers or isobars by matching one or more predicted and observed fragment ions. Next, the experimental MS data set is imported directly from the MS instrument’s raw format. Apex supports most of the major vendors and MS data system formats. The spectra are peak detected and centroided for comparison with the spectra predicted from the structures. Using a comprehensive scoring and correlation algorithm, the mass spectral data is searched for the presence of each of the predicted structures. Each structure is given a composite score based on the degree of match between the predicted and experimental spectra. The structures are then ranked by correlation score. These three basic mass spectral properties of molecules, their mass, isotopic distribution, and estimated fragmentation, can be used to derive scores for the Metabolite Prediction and Mass Spectrometric Data Analysis
TABLE 1 Metabolites of imipramine identified with MetaDrug and Apex Predicted
Experimentally determined
Yes
Yes
Aromatic hydroxylation (e.g., 2-hydroxylation, 10-hydroxylation) Alkyl hydroxylation
Yes (4 isomers)
Yes
Yes (3 isomers)
Yes
N-oxidation 2-hydroxy desipramine Didesmethylimipramine Iminodibenzyl
Yes (2 isomers) No∗ No∗ Yes
Yes N/A N/A No
Metabolites N-demethylation (desipramine)
∗ These † Apex
Comments Best match overall; all MS/MS features matched Moderate score; isomers are indistinguishable by MS and MS/MS High score, but biased by lack of observed MS/MS fragments Oxidation of dimethylamine most likely
metabolites would be predicted if MetaDrug was used in the sequential mode. cannot determine presence of structures de novo. The only structures that can be determined are those imported from the prediction software.
correlation of a molecule against experimental data. Apex uses each of these properties in several ways to create a set of scores. Each score is in the range of zero (no match) to one (perfect match). (1) Similarity score: Using predicted mass and isotope cluster, compute the “distance” between the predicted features and those observed in a spectrum. If the spectrum contains none of the predicted features, the score is zero; if the spectrum is identical to the prediction, the score is one. A partial match to the prediction, either by mass or relative isotopic abundance, yields a score between zero and one. (2) Purity score: Compute the similarity score, and then weight it by the summed intensity of the matched spectral features divided by the total intensity of the spectrum. A high score results when the isotope cluster is both a good match and is the most intense set of features in the spectrum; the spectrum is pure for that species. (3) Molecular ion cluster (MIC) score: Sum the intensities of the matched spectral features, and then normalize this over all of
the spectra in the data set. (4) MS/MS similarity score: Using the estimated fragmentation spectrum, match the predicted features against those observed and weight according to the intensity of the matched features. The weighting takes into account the expectation that the major features in the spectrum should be accounted for by a predicted fragment. (5) In addition, external signals (such as from an LC/UV or radiochemical detector) can be incorporated as “scores,” where the signal is normalized to a 0–1 range. Each of the structure-based scores is computed for each spectrum in the data set. If a score is plotted for each spectrum in time order, the result is a score chromatogram. Peaks appear in the score chromatogram where the underlying spectra have high score values for that metric. Each individual structure will have a set of score chromatograms, one for each computed score. When a number of potential metabolite structures are presented to Apex, as is the case with MetaDrug, Apex
FIGURE 1 The integrated in silico/in vitro metabolite prediction protocol using MetaDrug and Apex for predicting and verifying metabolites with mass spectrometry data. D. D. Stranz et al.
246
FIGURE 2 Apex identification of imipramine metabolites showing detailed match results for predicted and experimentally observed metabolites, with the correlation score for each of the potential metabolites. 247
Metabolite Prediction and Mass Spectrometric Data Analysis
computes this set of score chromatograms for every predicted structure. A typical metabolite prediction result may contain ten to a hundred or more candidate structures, while a typical chromatographically separated mass spectrometric data set might contain a thousand to several thousand individual mass spectra, and usually several individual scores are combined to yield the final score for any one molecule. The total number of score chromatogram points that must be computed is the product of the number of molecules times the number of scores times the number of individual mass spectra. This total will usually be in the hundreds of thousands to millions of score computations. Apex has been optimized to make this computational process as fast as possible; a typical calculation with 100 structures and 1,000 MS and MS/MS spectra takes a second or two on a 2-GHz Pentium PC.
RESULTS AND DISCUSSION While there has been some interest in using different metabolite prediction software programs with mass spectrometry data analysis (Anari et al. 2004; Nassar and Talaat 2004; Shu et al. 2004; Anari and Baillie 2005; Lin et al. 2005), to our knowledge there has been no detailed discussion of the integration of such approaches. The current study briefly highlights how two separate software products could be used to process a molecular structure, suggest metabolites, and ultimately verify which of these metabolites are observed from experimental data. While we would not suggest that our approach is ideal or in fact the only tool available, we think it is important to bring some of the difficulties inherent in these approaches to the attention of the reader.
Application Example The metabolism of imipramine, a tricyclic antidepressant, has been characterized extensively in vitro and in vivo in human and rat (Strandgarden and Gunnarsson 1994; van Breemen et al. 1998). It is known to undergo N-demethylation, hydroxylation, and N-oxidation. This molecule therefore represents a good example for evaluating the approach taken for metabolite identification in the current study. MetaDrug was used to generate an sdf file containing 15 unique predicted metabolite structures plus the substrate itself (Table 1). The predicted structures and experimental MS data were then imported into Apex (Fig. 1). After selection of appropriate scoring criteria for Apex, the set of structures were correlated against all of the MS and MS/MS spectra generated from the rat liver microsomal samples (Fig. 2). In this example, three scores were computed for each structure: MS similarity (match between actual and predicted molecular ion spectrum), isotopic purity (percentage of the overall spectral intensity due to the structure), and MS/MS similarity (match between actual and predicted fragmentation pattern for the structure). After scoring, an overall correlation for each structure was computed as the product of the three individual scores and a threshold was applied to eliminate structures with low correlation. Of the 16 structures, six had high and five more had moderate correlation scores (Table 1). These included the known primary metabolites: N-demethylation to form desipramine, aromatic 2-hydroxylation of imipramine to form 2-hydroxyimipramine, and imipramine itself. A general scarcity of fragments in the MS/MS spectra made it difficult to D. D. Stranz et al.
distinguish among the alkyl-hydroxylated metabolite isomers. The aromatic-hydroxylated isomers cannot be distinguished due both to lack of observed fragments and because Apex does not predict fragmentation across aromatic rings. In both cases, analysis of bona fide standards of these metabolites would likely be required for a definitive identification.
CONCLUSION Software tools for predicting potential metabolites of small molecule substrates in early drug discovery are important in guiding lead optimization in drug discovery to produce drug candidates with desirable metabolic and toxicological properties. We suggest that the integration of available computational tools for prediction of metabolites such as MetaDrug with MS and software such as Apex for combining the outputs of both represents a method for improving the throughput of metabolite identification studies and builds on earlier studies (Anari et al. 2004; Nassar and Talaat 2004; Anari and Baillie 2005). This combined process will likely become a standard practice in the near future for the pharmaceutical industry. In fact, we are already seeing the development of tools such as Mass Frontier that have more predictive capabilities. Future studies may likely include using additional compounds incubated with human microsomal or other in vitro systems, extending the present work performed with rat microsomes. While we can certainly not claim to have solved the problem of metabolite prediction, there are many limitations to be aware of. Our knowledge base or “training set” from which the metabolite prediction rules have been derived could still be considered tiny compared to chemistry space. Hence, these methods tend to overpredict metabolites. The impact of methods to limit the number of predicted metabolites to those that are most likely and ranking them (Embrechts and Ekins 2007) will need to be assessed to provide further confidence in the predictions. In this previous study K-PLS was used with augmented atom descriptors to generate models to classify whether a metabolite is likely to be produced from a particular starting structure alone. Over 300 molecules, including parent drugs and their primary and secondary (sequential) metabolites, were used to build these models corresponding to individual metabolism rules. Each model was internally validated to assess the capability to classify other molecules. Receiver operator curve statistics models for N-dealkylation and O-dealkylation had AUC values from 0.75 to 0.84 and were able to predict between 61% and 79% of active molecules upon leave-out testing. Other efforts at adding prediction confidence or domains such as Support Vector Machine models for P450s have been described previously (Ekins et al. 2007). The metabolite prediction efforts outlined to date and used for validation of software are relatively simple compared with some of the known metabolic pathways of drugs and endogenous compounds that can involve multiple phase I and phase II reactions. The relative rates of formation of particular metabolites may be important to predict. Some of these metabolites may even be inhibitors for other enzymes involved in the same metabolic pathways and it would be useful to assess or predict this impact. As virtually all computational metabolism prediction efforts have been on human, we should address the considerable amount of metabolism data for mouse and rat, which may enable us to understand species differences (Nedelcheva and 248
Gut 1994). Compilation of metabolite rules for different species will also be important to consider in future software. Four different rule-based methods have been described in a recent review and a comparison of the predictive ability of the methods was limited to seven phenolic compounds (Kulkarni et al. 2005). To be of relevance to the pharmaceutical industry, these rule-based methods need more extensive testing with diverse drug-like structures. An in-depth comparison of the different metabolite prediction algorithms is beyond the scope of this current study but is also needed in order to ensure the optimal selection of software components for the integrated in silico/in vitro metabolite prediction protocol described herein.
REFERENCES Anari, M. R., and Baillie, T. A. 2005. Bridging cheminformatic metabolite prediction and tandem mass spectrometry. Drug Discov. Today 10:711–717. Anari, M. R., Sanchez, R. I., Bakhtiar, R., Franklin, R. B., and Baillie, T. A. 2004. Integration of knowledge-based metabolic predictions with liquid chromatography data-dependent tandem mass spectrometry for drug metabolism studies: application to studies on the biotransformation of indinavir. Anal. Chem. 76:823–832. Austel, V., Kutter, E. 1983. Absorption, Distribution, and Metabolism of drugs. Quantitative Structure-Activity Relationships of Drugs, ed. Topliss, J. G., Academic Press, New York, pp. 437–496 Balakin, K. V., Ekins, S., Bugrim, A., Ivanenkov, Y. A., Korolev, D., Nikolsky, Y., Ivashchenko, A. A., Savchuk, N. P., and Nikolskaya, T. 2004a. Quantitative structure-metabolism relationship modeling of the metabolic N-dealkylation rates. Drug Metab. Dispos. 32:1111– 1120. Balakin, K. V., Ekins, S., Bugrim, A., Ivanenkov, Y. A., Korolev, D., Nikolsky, Y., Skorenko, S. A., Ivashchenko, A. A., Savchuk, N. P., and Nikolskaya, T. 2004b. Kohonen maps for prediction of binding to human cytochrome P450 3A4. Drug Metab. Dispos. 32:1183–1189. Berellini, G., Cruciani, G., and Mannhold, R. 2005. Pharmacophore, drug metabolism, and pharmacokinetics models on non-peptide AT1, AT2, and AT1/AT2 angiotensin II receptor antagonists. J. Med. Chem. 48:4389–4399. Borodina, Y., Rudik, A., Filimonov, D., Kharchevnikova, N., Dmitriev, A., Blinova, V., and Poroikov, V. 2004. A new statistical approach to predicting aromatic hydroxylation sites. Comparison with modelbased approaches. J. Chem. Inf. Comput. Sci. 44:1998–2009. Borodina, Y., Sadym, A., Filimonov, D., Blinova, V., Dmitriev, A., and Poroikov, V. 2003. Predicting biotransformation potential from molecular structure. J. Chem. Inf. Comput. Sci. 43:1636–1646. Boyer, S., and Zamora, I. 2002. New methods in predictive metabolism. J. Comp. Aided Mol. Des. 16:403–413. Button, W. G., Judson, P. N., Long, A., and Vessey, J. D. 2003. Using absolute and relative reasoning in the prediction of the potential metabolism of xenobiotics. J. Chem. Inf. Compu. Sci. 43:1371– 1377. Cruciani, G., Carosati, E., De Boeck, B., Ethirajulu, K., Mackie, C., Howe, T., and Vianello, R. 2005. MetaSite: understanding metabolism in human cytochromes from the perspective of the chemist. J. Med. Chem. 48:6970–6979. Darvas, F. 1987. MetabolExpert: an expert system for predicting the metabolism of substances. In QSAR in Environmental Toxicology, ed. Kaiser, K. L., Reidel Co., Dordrecht, pp. 71–81. de Groot, M. J., and Ekins, S. 2002. Pharmacophore modeling of cytochromes P450. Adv. Drug Del. Rev. 54:367–383. Ekins, S., Andreyev, S., Ryabov, A., Kirillov, E., Rakhmatulin, E. A., Sorokina, S., Bugrim, A., and Nikolskaya, T. 2006. A combined approach to drug metabolism and toxicity assessment. Drug Metab. Dispos. 34:495–503.
249
Ekins, S., Andreyev, S., Ryabov, A., Kirilov, E., Rakhmatulin, E. A., Bugrim, A., and Nikolskaya, T. 2005a. Computational prediction of human drug metabolism. Exp. Opin. Drug Metab. Toxicol. 1:303–324. Ekins, S., Bugrim, A., Nikolsky, Y., and Nikolskaya, T. 2005b. Systems Biology: Applications in Drug Discovery. Drug Discovery Handbook, ed. Gad, S., Wiley, New York, pp. 123–183 Ekins, S., de Groot, M., and Jones, J. P. 2001. Pharmacophore and three dimensional quantitative structure activity relationship methods for modeling cytochrome P450 active sites. Drug Metab. Dispos. 29:936–944. Ekins, S., Embrechts, M. J., Breneman, C. M., Jim, K., Wery J.-P. 2007. Novel applications pf Kernel-partial least squares to modeling a comprehensive array of properties for drug discovery. In Computational Toxicology: Risk Assessment for Pharmaceutical and Environmental Chemicals, ed. Ekins, S., Wiley-Interscience, Hoboken, NJ, pp. 403–432 Ekins, S., Kirillov, E., Rakhmatulin, E., and Nikolskaya, T. 2005c. A novel method for visualizing nuclear hormone receptor networks relevant to drug metabolism. Drug Metab. Dispos. 33:474–481. Ekins, S., Nikolsky, Y., and Nikolskaya, T. 2005d. Techniques: application of systems biology to absorption, distribution, metabolism, excretion, and toxicity. Trends Pharmacol. Sci. 26:202–209. Embrechts, M. J., and Ekins, S. 2007. Classification of metabolites with Kernel-Partial Least Squares (K-PLS). Drug Metab. Dispos. 35:325– 327. Erhardt, P. W. 2003. A human drug metabolism database: potential roles in the quantitative predictions of drug metabolism and metabolismrelated drug-drug interactions. Curr. Drug Metab. 4:411–422. Jolivette, L. J., and Ekins, S. 2007. Methods for predicting human drug metabolism. Adv. Clin. Chem. 43:131–176. Jones, J. P., Korzekwa, K. R. 1996. Predicting the Rates and Regioselectivity of Reactions Mediated by the P450 Superfamily, Academic Press, Inc., New York. Jones, J. P., Mysinger, M., and Korzekwa, K. R. 2002. Computational models for cytochrome P450: a predictive electronic model for aromatic oxidation and hydrogen abstraction. Drug Metab. Dispos. 30:7–12. Klopman, G., Dimayuga, M., and Talafous, J. 1994. META. 1. A program for the evaluation of metabolic transformations of chemicals. J. Chem. Inf. Comput. Sci. 34:1320–1325. Klopman, G., Tu, M., and Talafous, J. 1997. META. 3. A genetic algorithm for metabolic transform priorities optimization. J. Chem. Inf. Comput. Sci. 37:329–334. Korzekwa, K., Ewing, T. J., Kocher, J. P., Carlson, T. J. 2004. Models for cytochrome P450-mediated metabolism. In Pharmaceutical Profiling in Drug Discovery for Lead Selection. Borchardt, R. T, Kerns, E. H., Lipinski, C. A., Thakker, D. R., and Wang, B., (Eds.). Arlington, VA: AAPS Press, pp. 69–80 Kulkarni, S. A., Zhu, J., and Blechinger, S. 2005. In silico techniques for the study and prediction of xenobiotic metabolism: a review. Xenobiotica 35:955–973. Langowski, J., and Long, A. 2002. Computer systems for the prediction of xenobiotic metabolism. Adv. Drug Del. Rev. 54:407–415. Li, A. P. 2002. A review of the common properties of drugs with idiosyncratic hepatotoxicity and the “multiple determinant hypothesis” for the manifestation of idiosyncratic drug toxicity. Chem. Biol. Interact. 142:7–23. Lin, T., Pang, J., Hao, C., Stranz, D., Campbell, S., Mordenti, J., and Pan, L. 2005. Semi-automated metabolite identification using information-dependent LC/MS/MS acquisition, in silico metabolite prediction, and structure based data analysis. Drug Metab. Rev. 37(suppl 2):399. Nassar, A. E., and Talaat, R. E. 2004. Strategies for dealing with metabolite elucidation in drug discovery and development. Drug Discov. Today 9:317–327. Nedelcheva, V., and Gut, I. 1994. P450 in the rat and man: methods of investigation, substrate specificities and relevance to cancer. Xenobiotica 24:1151–1175.
Metabolite Prediction and Mass Spectrometric Data Analysis
Paine, M. F., Khalighi, M., Fisher, J. M., Shen, D. D., Kunze, K. L., Marsh, C. L., Perkins, J. D., and Thummel, K. E. 1997. Characterization of interintestinal and intraintestinal variations in human CYP3Adependent metabolism. J. Pharmacol. Exper. Ther. 283:1552–1562. Shen, M., Xiao, Y., Golbraikh, A., Gombar, V. K., and Tropsha, A. 2003. Development and validation of k-nearest neighbour QSPR models of metabolic stability of drug candidates. J. Med. Chem. 46:3013– 3020. Shu, Y.-Z., Arora, V., Alberts, J., Belcastro, J., and Skiles, G. 2004. Bridging metabolite prediction and metabolite identification- the use of metabolite prediction software for LC/MS data mining of potential metabolites. Drug Metab. Rev. 36(suppl 1):103. Singh, S. B., Shen, L. Q., Walker, M. J., and Sheridan, R. P. 2003. A model for likely sites of CYP3A4-mediated metabolism on drug-like molecules. J. Med. Chem. 46:1330–1336. Smith, D. A., and Obach, R. S. 2005. Seeing through the mist: abundance versus percentage. Commentary on metabolites in safety testing. Drug Metab. Dispos. 33:1409–1417.
D. D. Stranz et al.
Strandgarden, K., and Gunnarsson, P. O. 1994. Metabolism of lofepramine and imipramine in liver microsomes from rat and man. Xenobiotica 24:703–711. Talafous, J., Sayre, L. M., Mieyal, J. J., and Klopman, G. 1994. META. 2. A dictionary model of mammalian xenobiotic metabolism. J. Chem. Inf. Comput. Sci. 34:1326–1333. Uetrecht, J. 2003. Screening for the potential of a drug candidate to cause idiosyncratic drug reactions. Drug Discov. Today 8:832–837. van Breemen, R. B., Nikolic, D., and Bolton, J. L. 1998. Metabolic screening using on-line ultrafiltration mass spectrometry. Drug Metab. Dispos. 26:85–90. Williams, D. P., and Park, B. K. 2003. Idiosyncratic toxicity: the role of toxicophores and bioactivation. Drug Discov. Today 8:1044– 1050. Zhou, D., Afzelius, L., Grimm, S. W., Andersson, T. B., Zauhar, R. J., and Zamora, I. 2006. Comparison of methods for the prediction of the metabolic sites for CYP3A4-mediated metabolic reactions. Drug Metab. Dispos. 34:976–983.
250
