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Advancement of in silico tools would be enabled by the availability of data for metabolic reaction rates and intrinsic clearance CL int of a diverse compound structure data set by specific metabolic enzymes. Our goal is to measure CL int for a large set of compounds with each major human cytochrome P450 P450 isozyme. To achieve our goal, it is of utmost importance to develop an automated, robust, sensitive, high-throughput metabolic stability assay that can efficiently handle a large volume of compound sets.

The substrate depletion method in vitro half-life t 1 2 method was chosen to determine CL int. The assay 384-well format consisted of three parts 1 a robotic system for incubation and sample cleanup; 2 two different integrated, ultraperformance liquid chromatography mass spectrometry UPLC MS platforms to determine the percent remaining of parent compound, and 3 an automated data analysis system. The CYP3A4 assay was evaluated using two long t 1 2 compounds, carbamazepine and antipyrine t 1 2 30 minutes ; one moderate t 1 2 compound, ketoconazole 10 30,000 measurements per isozyme 5000 test compounds additional control samples six time points.

Thus, it was of utmost importance to use an automated, robust, sensitive, high-throughput metabolic stability method that could rapidly handle this large volume of samples. Attempts have been made to automate microsomal stability assays; however, these attempts have reported modest success. These assays typically use 96-well technologies for incubations and sample preparation and ultraperformance liquid chromatography mass spectrometry UPLC MS for data acquisition Korfmacher et al.2003which would require more than 50 separate analyses for each enzyme.

More recently, high-resolution mass spectrometers have been used for analysis; however, data extraction and data analysis remain cumbersome O Connor et al.2006; Shui et al.1999; Di et al. These existing methods, although useful, are semiautomated at best and have their limitations. A higher-throughput analytical method was needed for this project. In this article, we discuss the development of 1 a fully automated procedure for microsomal incubation and sample cleanup, 2 two separate automated UPLC MS methods for screening of large sample sets, and 3 an automated software that extracts data and performs regression analysis using different combinations of data points from which the analyst can choose the most pertinent combination.

This method can benefit drug research and possibly be used to measure metabolic lability in diverse matrices e.microsomes, S9 fractions, cytosol fractions. Albendazole, antipyrine, buspirone, ketoconazole, loperamide, and propranolol were purchased from Sigma-Aldrich St. Water, acetonitrile ACNand formic acid, all UPLC MS grade, were purchased from Thermo Fisher Waltham, MA. Human CYP3A4 supersomes and NADPH Solution A B were purchased from BD Gentest Woburn, MA.

Test compounds were provided by NCATS Compound Management after verification of identity and purity. Unless specified, all other materials were purchased from Sigma-Aldrich. Incubation Method. The substrate depletion method in vitro t 1 2 method to determine CL int was chosen. Disappearance of the parent compound over time was measured with amount of drug at time zero as the reference.

Incubation and liquid handling were carried out using a Tecan EVO 200 robotic system equipped with a 96-channel head, EVOware software version 3. 2a shaking Inheco heating block, and an Inheco cooling block Inheco, Munich, Germany Fig. The heating block was calibrated beforehand using a thermocouple inserted in incubation matrix solution, and a setting of 45 C produced a solution temperature of 37 C.

Supersomes and NADPH solution A B were diluted in 100 mM potassium phosphate buffer pH 7. A solution of albendazole internal standard, IS in ACN was prepared by adding 20. 0 μ l of 10 mM albendazole in dimethylsulfoxide DMSO to 722 ml of ACN and henceforth is called ACN IS. Tecan liquid handler deck layout for the high-throughput metabolic stability assay.

The 384-well plate received from NCATS Compound Management included control duplicates and test compounds at a 10 mM concentration in DMSO. These compounds were diluted to 50 μ M in ACN using the robot in a secondary plate. In the first step, 82. 73 μ l of diluted supersomes 3 pmol was transferred to the incubation plate 384-well, 250 μ l Waters, Milford, MA on the Inheco heating block. During this preincubation period, 2. Pipette tips 50 μ l and 200 ml were purchased from Tecan Morrisville, NC and reservoirs low-profile; RES-SW384-LP and high-profile; RES-SW384-HP for the incubation were purchased from Axygen Woburn, MA.T 0 ; Waters; 100 μ l.

After 5 minutes of preincubation, 2. 27 μ l of compound 50 μ M in ACN was added to the incubation plate, and 7. 43 μ l of NADPH solution A B 1 μ M and 40 μ l of chilled ACN IS were aspirated in a fresh, time 0 plate i. The final concentration of the test compound was 1 μ M. 5 μ l of this mixture was added to the T 0 plate. After the T 0 plate was prepared, 25 μ l of NADPH regenerating solution A B was added to the incubation plate.

Two minutes before each subsequent time point, 40 μ l of chilled ACN IS was added to a fresh 100 μ l plate. 92 μ l of the incubation mixture was sampled at 5, 10, 15, 30, and 60 minutes and opções binárias para iniciantes iq option to the respective plates containing chilled ACN IS. An aliquot of 9. After each time point, the plates were heat-sealed with foil plate sheets Thermo Fisher and centrifuged for 20 minutes at 3000 rpm 6 C. Each automated run produced six 384-well plates, with six time points for each of the 384 compounds.

This would mean that the sample acquisition time would be very long, even with a short UPLC method. To reduce data acquisition time, adjacent wells were pooled, thus combining six plates into three to cut the acquisition time by half. Data Acquisition. Two separate data acquisition methods were developed one using a triple quadrupole MS and the other using a high-resolution MS. The rationale behind developing two methods was to offer alternatives for various laboratory setups.

They were both validated for data quality, operation time, and ease of acquiring data. Method 1 Triple Quadrupole MS. UPLC method. The Waters Acquity UPLC system consisted of a Waters Acquity Binary Solvent Manager, Column Manager and 2777 autosampler along with QuanOptimize software. 1 50 mm equipped with a Waters Acquity UPLC BEH Shield RP18 VanGuard precolumn 1. The mobile phases were A water with 0.

Chromatography used a Waters Acquity UPLC BEH Shield RP18 column 1. 1 formic acid and B ACN with 0. 1 formic acid. The flow rate was 0. 6 ml min, with a gradient of 99 A 1 B isocratic for 0. 1 minutes, to 80 A 20 B over 0. 3 minutes, to 1 A 99 B over 0. 5 minutes, and held at 1 A 99 B for 0. The column re-equilibration time was 0. The cycle time was 2. Sample plates were held at 7 C in the 2777 autosampler until injected. 0 minutes from injection to injection. Triple-quadrupole MS method.

MS data were acquired on a Waters Xevo TQ-S triple quadrupole mass spectrometer equipped with MassLynx version 4. Multiple reaction monitoring MRM methods were automatically developed by the instrument for each compound using the QuanOptimize application described later herein. The samples were injected in the following order 60 minutes, 30 minutes, 15 minutes, 10 minutes, 5 minutes, and 0 minute to minimize carryover effects. An aliquot of 3. 0 μ l containing 50 µ M drug from the secondary plate was diluted into 75 μ l of 1 2 ACN H 2 O to get the QuanOptimize plate.

The QuanOptimize plate was covered with a heat seal and transferred to the UPLC MS MS. An aliquot of 2 μ l of solution, prepared for QuanOptimize, was injected twice in a loop injection without a UPLC column. The flowrate for QuanOptimize was 0. 3 ml min of 50 A 50 B. The first injection determined the optimum ion source cone voltage for the MH precursor ion, and the second injection determined the optimum collision voltage and product ion.

QuanOptimize then built an MRM analytical method for the compound and the IS for each compound set and applied these MRM conditions to the respective samples in the sample list. Sample analysis. For each pooled sample, 2. 0 μ l was injected onto the BEH Shield column with BEH Shield precolumn using the 2777 autosampler. One precursor-product ion pair, with a dwell time of 0. 030 seconds, was used for each compound.

The retention times of the test compounds were determined by reinjecting 2 μ l of the QuanOptimize solution for analysis under the same UPLC chromatography as the samples, using MS2 scanning analysis at mass-to-charge m z 50 1300 at a scan rate of 0. 25 seconds per scan. The retention times of each analyte were determined by manual evaluation of the chromatograms. The peak area under the respective MRM signal for each test compound in the respective pooled samples was integrated at its retention time using Waters TargetLynx.

The output TargetLynx comma delimited text data file was input to the Validator software Bioinformatics, NCATS. The integration of every pooled sample component was manually checked and, in some cases, reintegrated after evaluation. The Validator then produced plots of percent remaining versus time, and Ln response versus time and calculated t 1 2 and CL int using equations 1 and 2 Obach et al.

Method 2 High-Resolution MS. The Thermo Ultimate 3000 UPLC comprised an HPG-3400 binary rapid separation pump and the WPS-3000 autosampler. The column was an Acquity UPLC BEH C18, 2. 1 50 mm, particle size 1. 1 formic acid at a flow rate of 0. The UPLC conditions were 5 B at 0 0. 2 minutes, a linear gradient from 5 95 B from 0.

7 minutes, followed by 95 B for 0. The column effluent was directed to the high-resolution mass spectrometer. High-resolution MS method. The instrument was equipped with a heated electrospray ionization source, and the analysis was performed in positive ionization mode. MS data were acquired on a benchtop QExactive mass spectometer Thermo Fisher Scientific, San Jose, CA. The operating parameters were as follows ion transfer tube temperature 400 C, sheath gas 80, auxiliary gas 30, and spray voltage 3.

A full-scan MS method with mass ranging from 50 to 1000 m z and resolution of 35,000 was used. The instrument was calibrated using the positive ion calibration solution, which comprised a mixture of caffeine, MRFA peptide, Ultramark 1621, and n-butylamine in an ACN-methanol-acetic acid solution. This calibration was performed before acquiring data for each 384-compound batch; the same external calibration was applied throughout each batch. The samples were injected in the following order 60 minutes, 30 minutes, 15 minutes, 10 minutes, 5 minutes, and 0 minute, to minimize carryover effects.

Data analysis by TraceFinder 3. The TraceFinder method developed contained all the necessary opções binárias para iniciantes iq option to run the instruments data acquisition as well as the parameters required for processing, data review, and reporting as an automated workflow. Before each acquisition, parent molecular formulae for the entire batch of compounds were imported into TraceFinder. The software automatically calculated the exact m z of the M H ion.

Parent compounds were identified by their m z values with a mass precision of 5 ppm. The signal-to-noise ratio was set above 10 to eliminate interference peaks. For each batch, 1152 output Excel files were obtained. The Validator software extracted the response data for each compound and produced the following results plots of percent remaining versus time, and Ln versus time; regression analysis of various combinations of data points by the utility and ranked by quality of the fit r 2root mean squared error and calculated t 1 2 and CL int.

Validator Software. To facilitate the calculation of CL int from the response data generated by TargetLynx and TraceFinder software, we developed the IQC Validator software to perform automated fitting and ranking of calculated CL int values. The ranking serves an important function in that it allows the user to quickly validate the fitted data with minimal effort. For a given set of time points in minutes T and the corresponding response values, all 57 possible combinations of T are used to perform Ln linear regression fit.

Each fit in turn is evaluated based on the scoring scheme in eq. 3 3where N is the number of time points, is the Pearson s correlation, and is the root mean square error. The best possible score i. The score is the normalized score that is used in the final ranking, with 1 being the best possible fit. This scoring scheme, when sorted in descending order, identified the most likely fit and calculated t 1 2 and CL int.

The IQC Validator has been implemented in the Java programming language as a desktop client. Figure 8 shows a brief overview of its main user interface. A simple workflow is as follows the user loads in a data file, in either Excel or text format, of time points and response values.t 1 2score, etc. TraceFinder automatically detected and integrated peaks from each raw file and provided an Excel output file, which included IS response, target compounds response, retention times, chromatograms, and sample details.

For each loaded sample, the user selects the best possible fit by any combination of visual inspection and or calculated parameters e. The selections made by the user are saved to a relational data base management system and can be accessed at a later time. All experiments were performed with both data acquisition methods, and the results were very similar t 1 2 values 10. Results for the UPLC High-resolution mass spectrometer HRMS method are described.

Method Validation. Five commercial compounds with different t values were selected as controls to test the qualitative and quantitative performance of developed method. Calibration curves for these control compounds were prepared, and peak area ratios compound IS versus their nominal concentrations were plotted.

The calibration curves were linear over the concentration range of 1 to 5 1000 nM for the control compounds Table 1. The intraday and interday precision for QC samples were below 7 and 11 for all control compounds. Intraday precision and accuracy were determined by measuring three different quality control QC concentrations 10 nM, 50 nM, and 500 nM three times in one day, and the interday precision opções binárias para iniciantes iq option accuracy were determined by measuring concentrations of three QC samples over 5 days.

The intraday and interday inaccuracies were below 8 and 13. Reproducibility assessment for control samples. Half-life values of the control samples across nine 384-well plates based metabolic stability assays were measured by UPLC HRMS. Half-life categories 10 30 min high. The robustness of the UPLC HRMS method was determined by comparing peak responses of the IS across three batches i.three 384-well plates or 3456 sample injections.

5 which were within acceptable limits data not shown. The response was consistent within the same batch as well as across different batches Fig. The sensitivity of the HRMS instrument in detecting peaks for test compounds with 98 turnover is shown in Fig. Instrument calibration was performed before each batch analysis and mass accuracy 2 ppm was sustained throughout each batch run without need for recalibration or use of an internal reference Fig.

These results indicated that the UPLC HRMS method developed was reliable, sensitive, and robust. Peak response for albendazole. Peak area of the IS, albendazole, was plotted A across three batches i.three 384-well test plates. The mean and S. for the three batches B. Sensitivity of the HRMS instrument A 0-minute chromatogram and B 60-minute chromatogram for buspirone 98 turnover generated from full-scan data acquired with the Thermo QExactive.

Mass accuracy of the QExactive. Mass deviation of the IS, albendazole, across nine test plates was measured by comparing the theoretical m z value to the observed m z value. The mass deviation was measured twice, once at the beginning of the batch with the first sample and once at the end of the batch with the last sample i.sample 1152.

The reproducibility of the liquid handler system was investigated by comparing the t 1 2 values of control compounds, included twice in each 384-well plate across multiple plates. Ln response over time of the control compounds across three experiments were plotted to demonstrate the reproducibility between and within experiments Fig. The results show excellent reproducibility within plates and between plates Fig.

The percent coefficient of variation for the t values of buspirone, loperamide, and ketoconazole between experiments was between 15 and 25which is significantly below the 2-fold acceptable limits. Since antipyrine and carbamazepine are stable compounds, no SD was reported. Drug concentration-time profiles for control samples Ln response of control samples were plotted against time.

Letters a and b in the legend correspond to duplicate samples within the same 384-well test plate. Automated Assay Workflow and Throughput Speed. The total preparation and incubation time for each 384-well plate experiment was 2 hours. The automated assay workflow for the high-throughput metabolic stability assay is summarized in Fig. The automated liquid handler system increased efficiency, reduced error, and increased walk-away time for the scientist.

Each incubation plate produced six 384-well plates, with six time points 0 60 minutes for each of the 384 compounds. Adjacent wells were combined from each plate, thus converting six plates into three, which reduced the UPLC HRMS acquisition time by half and further increased the efficiency of the method without compromising the quality of the data. A typical extracted ion chromatogram for a sample that contains two test compounds and the IS is shown in Fig.

Under optimal conditions, two 384-well incubation plates can be assayed in a week by using one robot and one UPLC MS instrument. The UPLC HRMS acquisition was allowed to run overnight and the time required for each batch 1152 samples was 2 days. Once the acquisition was complete, TraceFinder detected integrated peaks and provided separate output files for each sample. These 1152 files were then imported into the Validator software which automatically extracted data from all samples, generated plots Fig.

8and calculated t 1 2 and CL int. These software tools completely eliminated data extraction time and drastically reduced data analysis time. Workflow schematic for the high-throughput metabolic stability assay. Extracted ion chromatograms of the parent compounds in a single sample containing ketoconazole, loperamide, and the IS, albendazole, based on accurate mass with mass tolerance of 5 ppm.

For each sample, the analyst has the option of selecting the most appropriate regression fit with the help of Ln response or remaining versus time curves as well as fitted and calculated parameters. Overview of user interface of the Validator software. Once the regression fit is assigned, the data can be saved and exported for further analysis modeling and simulation.

Compound Library. About 3000 compounds were tested using the newly optimized high-throughput method. Most of these compounds were a part of NCGC NIH Chemical Genomics Center pharmaceutical collection, which encompasses publically available approved and investigational drugs Huang et al.2011 and contains more than 2400 compounds that have been approved for clinical use by US, Canadian, Japanese and European health regulatory authorities.

The remaining compounds tested were from NCATS annotated collection. Molecular properties of compounds, such as logP, topological polar surface area, molecular weight, and Lipinski rule of 5, were calculated using Chemistry Development Kit descriptors tool The Chemistry Development Kit Chemistry Development Kit download SourceForge. org analytical platform Warr, 2012.

As seen from the plots, a large portion of compounds have t 1 2 values greater than 60 minutes, belong in the 251 500 mol. Figure 9 includes plots of the distribution of molecular properties and the CYP3A4 t 1 2 of our test compounds. range, and most of them do not violate Lipinski rules. We did not find any direct correlation of calculated t 1 2 values with the preceding molecular descriptors. Whereas much microsomal metabolic stability data are available in literature, this is, to our knowledge, the first time that such an extensive compound data base is being tested with an individual isozyme.

A detailed presentation of the data as well as in silico model development will follow once CYP3A4 CL int values for the remaining 2000 compounds have been determined. Distributions of molecular weight, experimental t 1 2logP, topological polar surface area, and rule-of-five RO5 violations of the metabolic stability data set generated using KNIME analytical platform. Discussion and Conclusion. A joint team comprised of members from the IQ Consortium and the NIH National Center for Advancing Translational Sciences undertook the task to measure and publish a data base of CL int values for compounds by major metabolic enzymes, for the benefit of advancing drug-design efforts with regard to metabolic stability.

Advantages include enabling advanced computational human metabolic models for individual metabolic isozymes; improving hit selection by high-throughput and computational screening; improving computational models for predicting human pharmacokinetics and enhancing lead optimization by guiding structure modification. For such data to be generated, a high-density assay format was required.

Therefore, a high-throughput assay using automation, 384-well technology, rapid UPLC separations, high-resolution MS as well as MS MS using MRM quantitation, and an automated data analysis method was developed and successfully applied. The t 1 2 values of control compounds between runs exhibited more than 4-fold variation. Initial pilot experiments with the automated liquid handler produced highly variable results.

Compounds in the peripheral wells of the plate had t 1 2 values slightly different than if the same compounds were plated somewhere in the middle of the plate, a phenomenon known as the edge effect. This problem was rectified by preheating the incubation plate and enclosing the liquid handler system during the experiment to ensure even heat distribution across the entire plate.

Air entrapment in the narrow bottoms of the 384-well plates caused random splashing and mixing in adjacent samples. This issue was completely eliminated by reducing the dispensing speed of the liquid handler. Since DMSO concentration affects enzyme activity Di et al. 1 in the final incubation.2003the final concentration of DMSO was kept below 0. The enzyme was purchased in bulk quantity to completely avoid interbatch variability. Of the 3000 compounds tested, the UPLC HRMS produced reliable data for 2642 compounds with an 88.

1 success rate. There could be several reasons for not obtaining reliable data for the 358 undetected compounds such as weak signal, inefficient ionization and adduct formation. Some compounds that undergo ionization in the positive mode may form M NaM Kor M NH4 adduct ions Ortelli et al.2000; Li et al. TraceFinder can be programmed to identify whether any of these adducts are present for the 358 compounds that were not successfully detected. The method described in this article has several advantages over existing published methods integrating automated incubation, automated data acquisition, and automated data analysis.

The high-throughput high resolution MS method also has several advantages, including 1 4-fold higher capacity 384-well format than existing 96-well formats, 2 efficient testing of large number of compounds with minimal labor and supervision, 3 avoiding individual compound optimization as the same generic method can be used to acquire data, and 4 significantly reduced time for data analysis. Additionally, by using HRMS in scanning mode, it is possible to interrogate the data afterward for a preliminary look at metabolite structure information.

In conclusion, we have successfully established and validated an automated high-throughput metabolic stability assay. This system can be used as a rapid assessment tool for initial screening of novel compounds. Future efforts will focus on developing in silico tools and characterizing additional compounds with the system using CYP2C9, CYP2D6, and other major CYP isozymes. The authors thank Paul Shinn Compound Management, NCATS and the IQ Consortium Drug Metabolism Group members for their inputs in experimental design Drs.

Authorship Contributions. Participated in research design Shah, Kerns, Obach, Wang. Conducted experiments Shah, Kerns. Contributed new reagents and analytic tools Nguyen, Xu. Dennis Dean VertexJim Kerns AstellasPrashant Desai Eli Lillyand Christopher Keefer Pfizer. Performed data analysis Shah, Kerns, Obach. Wrote or contributed to the writing of the manuscript Shah, Kerns, Nguyen, Obach, Wang, Zakharov, McKew, Simeonov, Hop, Xu.

Received June 8, 2016. Accepted July 13, 2016. This work was supported by the Intramural Research Program of the National Center for Advancing Translational Sciences National Institutes of Health. Bursi Rde Gooyer MEGrootenhuis AJacobs PLvan der Louw Jand Leysen D 2001 Q SAR study on the metabolic stability of steroidal androgens. J Mol Graph Model 19 552 556. Di LKerns EHHong YKleintop TAMcConnell OJand Huryn DM 2003 Optimization of a higher throughput microsomal stability screening assay for profiling drug discovery candidates.

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Ito K and Houston JB 2004 Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes. Sci Transl Med 3 80ps16. Pharm Res 21 785 792. Korfmacher WAPalmer CANardo CDunn-Meynell KGrotz DCox KLin CCElicone CLiu Cand Duchoslav E 1999 Development of an automated mass spectrometry system for the quantitative analysis of liver microsomal incubation samples a tool for rapid screening of new compounds for metabolic stability.

Rapid Commun Mass Spectrom 13 901 907. Li XFMa MScherban Kand Tam YK 2002 Liquid chromatography-electrospray mass spectrometric studies of ginkgolides and bilobalide using simultaneous monitoring of proton, ammonium and sodium adducts. Analyst Lond 127 641 646. Linget JM and du Vignaud P 1999 Automation of metabolic stability studies in microsomes, cytosol and plasma using a 215 Gilson liquid handler.

J Pharm Biomed Anal 19 893 901. MacKenzie ARMarchington APMiddleton DSNewman SDand Jones BC 2002 Structure-activity relationships of 1-alkyl-5- 3,4-dichlorophenyl - 5- 2- 3-substituted -1-azetidinyl ethyl -2-piperidones. Selective antagonists of the neurokinin-2 receptor. J Med Chem 45 5365 5377. Nassar AEKamel AMand Clarimont C 2004 Improving the decision-making process in the structural modification of drug candidates enhancing metabolic stability.

Drug Discov Today 9 1020 1028. Nebert DW and Russell DW 2002 Clinical importance of the cytochromes P450. Lancet 360 1155 1162. Niro RByers JPFournier RLand Bachmann K 2003 Opções binárias para iniciantes iq option of a convective-dispersion model to predict in vivo hepatic clearance from in vitro measurements utilizing cryopreserved human hepatocytes. Curr Drug Metab 4 357 369. Obach RSBaxter JGListon TESilber BMJones BCMacIntyre FRance DJand Wastall P 1997 The prediction of human pharmacokinetic parameters from preclinical and in vitro metabolism data.

J Pharmacol Exp Ther 283 46 58. O Connor DMortishire-Smith RMorrison DDavies Aand Dominguez M 2006 Ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry for robust, high-throughput quantitative analysis of an automated metabolic stability assay, with simultaneous determination of metabolic data. Rapid Commun Mass Spectrom 20 851 857. Ortelli DRudaz SCognard Eand Veuthey JL 2000.

Analysis of dihydroartemisinin in plasma by liquid chromatography mass spectrometry. Chromatographia 52 445 450. Sakiyama YYuki HMoriya THattori KSuzuki MShimada Kand Honma T 2008 Predicting human liver microsomal stability with machine learning techniques. J Mol Graph Model 26 907 915. Shen MXiao YGolbraikh AGombar VKand Tropsha A 2003 Development and validation of k-nearest-neighbor QSPR models of metabolic stability of drug candidates.

J Med Chem 46 3013 3020. Shui WLin SZhang AChen YHuang Yand Sanders M 2011 Driving efficiency in a high-throughput metabolic stability assay through a generic high-resolution accurate mass method and automated data mining. Protein Cell 2 680 688. Warr WA 2012 Scientific workflow systems Pipeline Pilot and KNIME. J Comput Aided Mol Des 26 801 804. In this issue. Automated High-Throughput Metabolic Stability Assay Method.

Tweet Widget Facebook Like Google Plus One. Abstract Introduction Materials and Methods Results Discussion and Conclusion Acknowledgments Authorship Contributions Footnotes Abbreviations References. ASPET s Other Journals. Copyright 2020 by the American Society for Pharmacology and Experimental Therapeutics.

Table of Contents Table of Contents PDF About the Cover Index by author Editorial Board PDF Front Matter PDF. Government work not protected by U. Publish date July 10, 2017. An Interview with Famed Value Investor Guy Spier. It was probably due to his book, The Education of a Value Investor. In it he recounts everything from his lunch with Warren Buffett to how he s beat the market for two decades and counting. Guy is an extremely generous person who does not mind sharing his knowledge with others and occasionally enjoys playing chess competitively or to pass the time.

Guy runs the Aquamarine Fund, which is closely modeled on the Buffett partnerships of the 50s. He earned his MBA from Harvard Business School and has been a successful investor for the past 25 years. In his latest annual report, he discusses his decision to pass on investing in Amazon in 2012. I found his analysis fascinating, so reached out to him about an interview.

He responded almost immediately, and the result is a 2 hour interview, which I ve included below. We talk about everything from Warren Buffett, to Amazon, to competitive chess. Topics covered in the Interview. Chess and the area of pattern recognition Warren Buffett and his confidence with the stock market Why the fear pattern is important when it comes to investments and the economy Investing in debt and equities The insiders game who wins and who loses What is indexing and why it matters Value Traps Buying Amazon stock What is the biggest danger value investors have.

Bill Ackman vs. First Union Realty Amazon s purchase of Whole Foods Different types of reading to keep up-to-date Reading physical newspapers vs. obtaining information online from apps, blogs, and social media How to approach the world of investing intelligently Where some of the best investing insights come from Deciding what investors to follow and the reasons why Recommendations of research tools for individual investors Using stock screens to research companies Tony Robbins and the self-help culture The Aquamarine Fund.

Resources Mentioned in the Interview. The Tim Ferriss Show Entrepreneurs on Fire The Internet Chess Club Chess24 The Education of a Value Investor My Transformative Quest for Wealth, Wisdom, and Enlightenment Aquamarine Fund Trello Stratechery The Information Factiva Dataroma Capital IQ Money Master the Game 7 Simple Steps to Financial Freedom Awaken the Giant Within Value X.

Guy Spier Interview Transcript. Guy I ll send you this file once it s done. I know that podcasts are all about the highest quality sound and I know that s a Hiel PR-40 mic and you have a pop-thing on it and it looks so great. Are you using a USB or is that going into a. Rob I use and by the way, I m hearing feedback on my end. Guy What does that mean. Rob I can hear my own voice. I don t know if that s getting picked up on the recording or not.

Guy I have no idea but it s really hot here and we don t have air-conditioning so I don t know. If I put some did that feedback go away now. Rob Yeah, I think it did. Guy It was feeding out of the microphone and into the speakers. Your sound is feeding out of the microphone and into the speakers basically. Rob When I bought this microphone and I know nothing about this stuff I just assumed it would have a USB plug on the end of it and I could just plug it into my computer and I was wrong.

Then I did some research and ended up buying a Steinberg UR-22 which is just a box and you plug the mic into it and then out the end of it there is another cord that converts it to USB and I just plug it into my computer. Anyway, I think what you also have is reflection of the desk so it s not as high-quality audio but I think what I love about the podcast medium is that there s an intimacy to it which is just incredible. You re right up there close with your audience.

Rob That really took me by surprise. I ve been blogging for 10 years. I started the podcast almost four years ago and when I was blogging I d get an email every now and again from someone who had read an article but it wasn t very frequent. I don t recall when I first heard of Guy Spier. When I started podcasting, I got a flood of email. I ve gotten to the point now where I read every single email. I tell people that and it s true, but I don t give substantive responses because I can t. I would spend all day responding to email.

What I do instead is take a lot of the questions people ask me and answer them on the podcast. I ve had meet-ups where we ll meet up at coffee shops here locally in the Washington, DC area. So yeah, there is definitely a connection you make with your listeners that you just don t with the written word.

Guy Yeah, it s quite incredible and I ve thought about podcasting mainly because of the fun of connecting to people and the incredible closeness you get to them. I record a podcast with somebody and two or three hours later somebody is emailing me about it and I m blown away. I was going to ask you, what fills the bank account if you re not selling advertisement which I think is great, by the way. Rob The blog basically drives the business side of it the website.

It s also very it s a medium that s very there s a high longevity. At times I ve thought about advertising on the podcast and I came really, really close just a couple of months ago to signing a deal to run ads and it just didn t seem right. It s not that I ll never the day could come when I decide to advertise on the podcast. I don t want to say never but part of me just thought, you have to know when you have enough. I have enough so I just view the podcast as something I enjoy doing and a way to give back that s not motivated by money.

I really enjoy listening to Tim Ferriss podcasts. They re great. There s an element to which he s pushing an angle where he s kind of just pushing some particular thing. And, there I am with whatever it is I m doing and I suddenly realize I ve listened to a few minutes worth of him plugging something. There s something that s not completely pure about that.

At the end of the day people have to put bread on the table so I guess it s is okay. I don t know what you think of John Lee Dumas, JLD. He, as well, has just got a wonderful podcast and it s really inspiring. Again, this applies to the podcast because he s making money out of them as well. He s getting affiliate fees. Rob The truth is, Guy, I don t listen to a lot of podcasts which is maybe ironic, I don t know.

I ve listened to both of theirs and I joined John Lee Dumas he had a forum for podcasters when I was first getting started. He publishes his income online and is making six-figures a month. I think he and his girlfriend moved to Puerto Rico not long ago. Maybe for some of the tax advantages there but he s obviously built up a tremendous business.

And Tim Ferris, depending on who his guest is, I think his interviews are some of the best out there. So you re based in DC and I m honored that you wanted me to come onto your show. Rob You re honored. I m honored that you said yes. I wasn t sure I would even get a return email when I reached out to you. You emailed me right away and I m grateful.

And look, I ve got a whole card full of questions. One of the things we want to talk about is Amazon and I m sure you saw that they just signed a deal to buy Whole Foods which I may ask you about. But yeah, if it s okay I ll jump right in. Rob I want to start with what I think has got to be the most important topic we ll discuss today and that is whether you still play chess. He keeps asking me for games so I play him. Guy You know, I play with my son.

I have a question mark in my mind though. Every time I play him, I play him as well as I can. I don t want to shield him from how well I play which is not all that well. I want to go online and play but it s such a time-sink. The last time I went online which is probably more than a year ago I got so depressed at my rating I had people at a chess rating of 900 beating me so I don t know that I can say I regularly play chess anymore. Rob When you play online, where do you play.

Guy The Internet Chess Club is the place I ve enjoyed playing. I could open that up opções binárias para iniciantes iq option and start playing. They have a great app which is sitting here on my desktop. It s easy with Blitzin. If you like playing we can play each other. I could pull out a game right now. Rob I d love to actually but I don t know if I m set up with the Internet Chess Club. I play at chess24. Guy And what s your rating. Rob It s funny you should ask. Guy Did you hear that.

Guy I just loaded my Blitzin to see what s going on there. Rob Right now my rating is 2,100 on chess24. Guy You re a very good player. I have never gotten that high. My highest rating ever is about 1,600. Rob Well, if you had asked me a couple of days ago my rating on chess24 could very easily been 1,700 or 1,800.

I tend to go through spurts where I win a lot and then lose a lot so my rating can fluctuate by 400 points or so, on Chess24. I d go and play a bunch of 900 rated guys and play really badly or intentionally lose or make a sacrifice where I had no chance of winning up against and watch my rating go down to about 1,200. Guy Actually, when I playing with people with a 1,500 or 1,600 ratings, something I used to enjoy doing would be to deliberately take my rating down.

Then I d get all of these guys challenging me to a 1,200 rated game and I d smash them really good laughs. So the most satisfying thing for me and I m talking to you now, I have enormous respect for you. To play to 2,100 if I m not mistaken, 2,500 is Grand Master. You ought to be able to beat me at every game we play just based on 2,100 compared to what my rating is.

I love open games. I love breaking open anything no strategy left and just pure tactics. When I was playing a lot I really used to enjoy pawn storms.