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Amsterdam North-Holland. Google Scholar Craik, F. Applying cognitive research to problems of aging.Attention and performance XVII pp. Google Scholar Czaja, S. Factors predicting the use of technology Findings from the Center for Research and Education on Aging and Technology Enhancement CREATE. Psychology and Aging, 21, 333 - 352. Google Scholar Crossref Medline ISI Dingus, T.Mollenhauer, M.Fleischman, R.
Effects of age, system experience, and navigation technique on driving with an Advanced Traveler Information System. Human Factors, 39, 177 - 199. Adult age differences in working memory. Psychology and Aging, 4, 500 - 503. Google Scholar Crossref Medline ISI Evans, L. Traffic safety. BloomfieldMI Science Serving Society. Google Scholar Fisk, A.
Google Scholar SAGE Journals ISI Dobbs, A. Handbook of human factors and the older adult. Designing for older adults Principles and creative human factors approaches. Boca Raton, FL CRC Press. Massive gains in 14 nations What IQ tests really measure. Google Scholar Crossref Flynn, J. Google Scholar Crossref ISI Fozard, J. Person-environment relationships in adulthood Implications for human factors engineering. Google Scholar SAGE Journals ISI Fozard, J.
Special issue preface. Human Factors, 23, 3 - 6. Human Factors23, 7 - 27. Google Scholar SAGE Journals ISI HeW. 65 in the United States 2005 Current Population Rep. Washington, DC Government Printing Office. pdf Google Scholar Hofer, S. Design and analysis of longitudinal studies on aging.Handbook of the psychology of aging 6 th ed. Amsterdam Elsevier Academic Press. Google Scholar Horn, J. The theory of fluid and crystallized intelligence in relation to concepts of cognitive psychology and aging in adulthood.Aging and cognitive processes pp.
Google Scholar Crossref Hultsch, D.MacDonald, S. Variability in reaction time performance of younger and older adults. Journal of Gerontology Psychological Sciences57B, P101 - P115. Google Scholar Crossref ISI Jamieson, B. Age-related effects of blocked and random practice schedules on learning a new technology. Journal of Gerontology Psychological Sciences, 55B, P343 - P353. Google Scholar Crossref ISI Jastrzembski, T. The Model Human Processor and the older adult Parameter estimation and validation within a mobile phone task.
Journal of Experimental Psychology Applied, 13, 224 - 248. Google Scholar Crossref Medline ISI Kelly, P. Anthropometry of the elderly Status and recommendations. Human Factors, 32, 571 - 595. Google Scholar SAGE Journals ISI Kline, T. Visibility distance of highway signs among young, middle-aged, and older observers Icons are better than text. Human Factors, 32, 609 - 619. Google Scholar SAGE Journals ISI Kramer, A.Cassavaugh, N.
Influence of age and proximity warning devices on collision avoidance in simulated driving. Human Factors49, 935 - 949. Google Scholar SAGE Journals ISI Kroemer, K. Extra-ordinary ergonomics How to accommodate small and big persons, the disabled and elderly, expectant mothers, and children. Google Scholar Crossref Kubeck, J. Does job-related training performance decline with age. Psychology and Aging, 11, 92 - 107.
Google Scholar Crossref Medline ISI Manton, K. Changes in morbidity and chronic disability in the U. elderly population Evidence from the 1982, 1984, and 1989 National Long Term Care Surveys. Journal of Gerontology Social Sciences50, S194 - S204. Google Scholar Crossref Medline ISI Marbach, G. Job redesign for older workers. Paris OECD Employment of Older Workers. Google Scholar Marquié, J.Cau-Bareille, D. Working with age. London Taylor Francis.
Google Scholar Matthews, J. The Nursebot Project Developing a personal robotic assistant for frail older adults in the community. Home Health Care Management Practice, 14, 403 - 405. Google Scholar SAGE Journals Meyer, D. A computational theory of executive cognitive processes and multiple-task performance Part 1. Basic mechanisms. Psychological Review, 104, 3 - 65. Google Scholar Crossref Medline ISI Mihailidis, A.
The importance of using contextaware design principles when developing cognitive assistive devices for older adults. Gerontechnology, 2, 173 - 188.Sliwinski, M. Google Scholar Mireles, D. Computational explorations of the influence of structured knowledge on age-related cognitive decline. Psychology and Aging, 17, 245 - 259. Google Scholar Crossref Medline ISI Morrell, R. Older adults, health information, and the World Wide Web. Google Scholar Morrell, R. Effects of labeling techniques on memory and comprehension of prescription information in young and old adults.
Journal of Gerontology, 45, P166 - P172. Google Scholar Crossref Medline Morrow, D. Older and younger adult memory for health appointment information Implications for automated telephone messaging design. Journal of Experimental Psychology Applied, 4, 352 - 374. Google Scholar Crossref ISI Morrow, D.Stine-Morrow, E. Environmental support promotes expertise-based mitigation of age differences on pilot communication tasks.
Psychology and Aging, 18, 268 - 284. Google Scholar Crossref Medline ISI Murrell, K.Powesland, P. A study of pillar-drilling in relation to age. Occupational Psychology, 36, 45 - 52. Google Scholar Nayak, U. Elders-led design. Ergonomics in Design, 3, 8 - 13. Google Scholar Nichols, T. Design for aging. Salvendy Ed.Handbook of human factors and ergonomics 3 rd ed. Hoboken, NJ Wiley. Google Scholar Crossref Norman, D.
Categorization of action slips. Psychological Review, 88, 1 - 15. Google Scholar Crossref ISI Owsley, C. Visual cognitive correlates of vehicle accidents in older drivers. Psychology and Aging, 6, 403 - 415. Google Scholar Crossref Medline ISI Pew, R. Technology for adaptive aging. Google Scholar Rabbitt, P.
The Alan Welford memorial lecture. Ageing and human skill A 40th anniversary. Ergonomics, 40, 962 - 981. Google Scholar Crossref Medline ISI Reason, J. Psychological Bulletin, 101, 171 - 191. Human error. CambridgeUK Cambridge University Press. Google Scholar Reuter-Lorenz, P. Neural recruitment and cognitive aging Two hemispheres are better than one, especially as you age.
Psychological Science, 10, 494 - 500. Google Scholar SAGE Journals ISI Rogers, W.McLaughlin, A. Touch a screen or turn a knob Choosing the best device for the job. Human Factors, 47, 271 - 288. Google Scholar SAGE Journals ISI Salthouse, T. The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403 - 428. Google Scholar Crossref Medline ISI Salvucci, D. Modeling effects of age in complex tasks A case study in driving.
In Proceedings of the 26th Annual Conference of the Cognitive Science Society pp. Google Scholar Schulz, K. Aging and work in the 21st century. Google Scholar Sharit, J. Google Scholar Crossref Sharit, J.Hernandez, M. An evaluation of performance by older persons on a simulated telecommuting task. Journal of Gerontology Psychological Sciences, 59B, 305 - 316.
Google Scholar Crossref ISI Sharit, J. Effects of age, speech rate, and environmental support in using telephone voice menu systems. Human Factors45, 234 - 251. Google Scholar SAGE Journals ISI Smith, L. An investigation into the effect of ageing on expert memory with CHREST. In Proceedings of the United Kingdom Workshop on Computational Intelligence - UKCI07.
net 2438 robo de opções binarias gratis iq option Google Scholar Steenbekkers, L.van Beijsterveldt, C. Design-relevant characteristics of ageing users. Delftthe Netherlands Delft University Press. Google Scholar Stephens, E.Schumacher, M. Evidence for an elder s advantage in the naive product usability judgments of older and younger adults. Google Scholar SAGE Journals ISI Stern, Y. Human Factors, 48, 422 - 433.Alexander, G.Prohovnik, I. Relationship between lifetime occupation and parietal flow Implications for a reserve against Alzheimer s disease pathology.
Neurology, 45, 55 - 60. Google Scholar Crossref Medline ISI Straka, G. Training older workers for and in the years after 2000. Journal of Educational Gerontology, 5, 68 - 78. Google Scholar Taylor, J.Mumenthaler, M. Cognitive ability, expertise, and age differences in following air-traffic control instructions. Psychology and Aging, 20, 117 - 133. Design for people with functional limitations resulting from disability, aging, or circumstance.Handbook of human factors and ergonomics 2 nd ed.
Google Scholar Verhaeghen, P.Salthouse, T. Meta-analyses of age-cognition relations in adulthood Estimates of linear and non-linear age effects and structural models. Psychological Bulletin, 122, 231 - 249. Google Scholar Crossref Medline ISI Wada, K. Social effects of robot therapy in a care house Change of social network of the residents for two months.
In Proceedings of IEEE International Conference on Robotics and Automation WeD8. Piscataway, NJ Institute of Electrical and Electronics Engineers, Inc. Google Scholar Wegman, D. Health and safety needs of older workers Committee on the Health and Safety Needs of Older Workers. Google Scholar Welford, A. Ageing and human skill. OxfordUK Oxford University Press. Google Scholar Welford,A. Signal, noise, performance and age.
Human Factors, 23, 97 - 109. Google Scholar SAGE Journals ISI World population ageing 1950-2050 ST ESA SER. New York Department of Economic and Social Affairs Population Division, United Nations. org esa population publications worldageing19502050 Google Scholar. HFES Member Access. Google Scholar Crossref Medline ISI Vanderheiden, G. Algo Signals Review 2020. What is Algo Signals. Algo Signals is one of the best auto trading software for Forex and Crypto Trading.
It s free software that helps traders place Forex trade as effortlessly as it helps trade cryptocurrencies. Algo Signals is completely licensed and legitimate software. With Algo Signals, the day traders find it very easy to apply various money management methods and trading strategies to earn unexpectedly huge profits. This software gives an extensive opportunity to the traders to widen their trading experience by connecting them directly to the licensed brokersthereby upgrading their account as they progress.
The Algo signals software is also equipped with a demo account feature that allows the novice traders to improve their trading skills by familiarizing themselves with the various trading tools and free signals before they try their hands in real trading using their real money. Once the trader feels confident enough to tackle the real trading mode, they can switch to real trading. Algo Signals Review Overview of Algo Signal. The Algo Signals software is designed in such a way that it benefits the traders of all levels.
Newbies can improve their trading skills and get to learn new and tested strategies, whereas experienced traders can improve their profit earning capabilities. Some of the inherent features of the Algo Signals are discussed hereunder for the traders perusal. This is perhaps the most important feature that the Algo Signals trading robot provides to its users. It allows the traders to invest in Bitcoin on autopilot.
Invest in autopilot. The Algo Signals trading software comes with a stop-loss feature, to mitigate the high risk of losing money, which means that the losses will be minimized to the extent that the traders can afford to take. If the trade does not go in favor of the trader, this trading software automatically closes the trade and does not give the time to the traders to ponder and consider whether they want to continue the trade or not. The Algo Signals trading software is programmed in such a way so as to make as many small trades as possible that mitigates the high risk of losing.
This intuitive interface allows even the newbies to set up this trading software and start investing instantly. Works with multiple CFDs broker. This mitigates the high risk of losing your cryptos. The Algo Signals trading robot works with multiple reputed CFD broker platforms that enable the traders to make money when trading CFDs by connecting them to the best available trading venues in the country.
Trading CFDs also has another advantage that you can get started with just 25 per trade, and a 100 of the minimum deposit. But always remember that investing in cryptocurrencies is not devoid of risks and therefore do not invest more than you can afford to take the losses. Types of Account that Algo signals Provide. As the name sounds, this account is solely for the novice traders who hold a keen interest in online trading.
With a mere deposit of 250, the novice traders can access real trading with three currency players. They can also place three trades simultaneously with multiple brokers and also get access to the Leaderboard Multiplier X1. The demo account is a free account that any trader can get access to before switching over to a real live account.
The demo account features the same tools that are in a real account. Therefore, the traders can get an experience of real trading without the high risk of losing real money. The traders can use the demo account for the initial five days before they get their hands down in real trading with real money. Similar to this, there is one robot in our popular as Big Money Rush. Take a quick glance at Big Money Rush Review to know about it more.
Expert Account. After getting enough experience through a novice account or a demo account, a trader can now switch to the Expert account. But for this, the trader needs to make an initial investment of 500. Here the trader can get to trade with nine currency pairs with an added advantage of getting access to the Leaderboard multiplier X2. Master Account. To unleash the full power of the Algo Signals trading software, a trader needs to switch to the master account by depositing 500 to two brokers at the same time.
This account provides all the benefits of the previous three accounts with an added advantage of the Leaderboard multiplier X3. This account also provides the traders with the advanced settings of all currency pairs along with the VIP customer service. Benefits of using Algo Signals robot. Some of the apparent trading benefits that the Algo Signal offers are. Seamless trading. With Algo Signals trading software, the traders are relieved of the responsibility to scan the market and look for profitable opportunities to place their trade.
In fact, they are no longer required to sit before their computers the whole day to scan the market trends; this trading software does it all on behalf of their users. Live data streaming. Mastering the trends of the financial markets need constant screening of the market trends and movements. The Algo Signal trading software does exactly this to boost up the traders experience. With this vital information at their fingertips, the traders can easily adjust and customize the trading features to meet the market changes that prevails at a particular period of time.
It sends live trading signals through live data streaming, thereby giving the traders real-time access into the financial market. Algo Signals Trading Advantage. Preferred brokers. It is one of the best advantages that the Algo Signals software provides its users to maintain the integrity of online trading. As everyone knows online trading is associated with the ruckus frauds and scams, the Algo Signals robot focuses on reducing these threats.
Customizable settings. In order to maintain the integrity and safety of the traders, this software connects the traders only to the licensed and well-regulated brokers. As every trader has a different perspective towards trade and also has varied trade preferences, this software lets the traders choose their own trade settings. The Algo Signals software understands the need of the traders better than anyone else can.
With this trading robot, the traders have the full liberty to customize the preferred probability for the trading signals that they will receive, thereby eventually controlling their trading experiences. A trading bot cannot be complete unless a robust customer support team backs it. Algo Signals software is backed by an amazing customer support team that is available 24 X 7 at the service of the customers. The traders can any time get in touch with the customer support team via live chat or over the phone and get their queries solved instantly.
Benefits of Algo Signal. Algo-signals robot legit. When it comes to online trading software, most of the people feel skeptical as to whether the software is legit or not. Based on our experience, we found that the Algo Signals are reputed trading software. With numerous scam trading signal services doing rounds, it is very important to verify that you are on the right platform.
The Algo Signals software qualifies to be a reliable trading partner; here is how. It is an award-winning trading platform and can be used only in conjunction with licensed and well-regulated brokers, which is the very first sign of trustworthiness. It provides for maximum customization that enables the traders to set their desired preferences pertaining to the risk exposure levels.
Traders can easily switch off the software and try out different strategies in demo account mode. These features are never available with any scam software providers. In fact, the traders are rendered virtually powerless once trading has been initiated. How does Algo Signals robot work. The computer algorithms analyze the price of Bitcoin or Ethereum or other most popular cryptocurrencies and, based on the technical analysis and indicators, make predictions on short term price movements.
The computer algorithms then place a profitable trade on behalf of the traders. Algo Signals is focused more on short-term trading, which means that the profitable positions are held only for a couple of minutes. As soon as the profit set per trade is reached, the position is closed by the computer algorithm. Creating an account. The account creation process with Algo signals is pretty simple and straightforward.
The users just need to fill in some information pertaining to their full names, email addresses, and phone numbers. After verifying these details, the users are sent an auto generated email confirming their registration with Algo Signals. After this, the users are allowed to log in to the Algo Signals platform by using their login details. Moreover, the Algo Signals software automatically opens an account for the registered traders with the recommended CFD brokers.
Account opening at Algo Signals. To get started with Algo Signals, the users need to deposit a minimum fund to the broker s account. Funds can be deposited either through bank transfers, debit or credit cards, PayPal, or other popular payment methods. In order to comply with the broker regulations, the users also need to complete the KYC process by submitting relevant documents like passport or proof of address to the respective brokers.
Adjusting the Settings. In this final step, the users are allowed to adjust the trade settings as per their trade preferences. They can set their own trading strategies pertaining to the amount of minimum investment, daily stop-loss, or even daily profit targets, and the maximum number of concurrent trades. How much can I make with it. While conducting the Algo Signals review, we found that on placing a trade of around 1500 of virtual currency, on autopilot for approximately 3. 5 hours, a steady profit of 500 can be achieved that too with 75 trade accuracy which is a pretty good outcome.
This is in line with other trading reports that are available on the internet, where people are saying that they were able to generate trading profits between 3,000 and 5,000 per day. Of course, you may not be lucky every day, and sometimes you may also lose. But on average, about 80 of the users confirmed that they were able to generate steady profits with the Algo Signals trading software. In fact, the Algo Signals trading software claims to generate a daily profit of 2.
With this type of return, an account with a mere deposit of 500 can reportedly generate a yearly profit of up to 140,000. Can I withdraw my money from it. Yes, traders can withdraw money at their own discretion by filling up a withdrawal request form with the broker. The broker will go through all the necessary details and process the withdrawal application instantly if everything is found as per the requirements. As all the Algo Signals partner brokers are legit brokers and they operate under strict regulatory requirements, it ensures that they cannot unreasonably withhold any of the traders money.
As there is nothing perfect in this world, the Algo Signals software has some drawbacks. There are some reviews of customers where they mentioned not being satisfied with the speed of customer services, or there were occasional delays in getting trading signals. However, the Algo Signals software is constantly developing and upgrading its trading dashboard to make it more modern and advanced.
Also, as the Algo Signals platform is too simplistic, if you are an experienced trader and looking for a fully customizable software solution, this is perhaps not the best platform for you. Algo Signals Review Our Conclusion. After making this Algo Signals review and honest ratings from the real trader, we concluded that the Algo Signals is legitimate and licensed. It is a software that connects the traders to the brokers.
The traders who are keen to take their interest in online trading to the next level can try this free Algo signals trading software. This software exposes the traders to myriads of trade settings, trading signals, and different types of accounts, depending on their deposits. In turn, this improves their chances of winning by mitigating the high risk of losing money through the Leaderboard system.
Therefore, trading CFDs and Forex is made easier by this Algo Signals robot, that claims to provide a highly profitable Forex and cryptocurrency-CFDs signals. Is Algo Signals software legit. Yes, as per our Algo Signals review, the software appears legit. What is an Auto Signals Leaderboard. It is a program based on a formula that allows the traders to earn huge profits through special points. The leaderboard program is reset every month to give a fair chance to all the traders to win big.
Leaderboard Trading Volume x Multiplier Deposit Withdrawals. How does the Algo Signals Software Work. As the Algo Signals is a web-based software, it can be directly run from the web browser and does not require the traders to download the entire version of the software. The software connects the traders to multiple licensed brokers.
Algo Signals. Algo Signals offers an easy to use interface. It offers High Speed and More Accuracy. Easy Deposits and Fast Withdrawals. Algo Signals has Improved Order Entry and Exit Speed. Lack of Human Control. Need for Constant Monitoring. IQ Option Trading Skills And Strategies. IQ Option is happy to offer you a check list will assist you in evaluating your previous mistakes and potentially enhance you trading skills and strategies. After having several losses in a row, traders might feel that they will continue having a losing day.
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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 added 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 robo de opções binarias gratis iq option 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 information 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 and 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 robo de opções binarias gratis iq option 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 robo de opções binarias gratis iq option 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.
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