omation mbers - Society for Laboratory Automation and Screening
omation mbers - Society for Laboratory Automation and Screening
omation mbers - Society for Laboratory Automation and Screening
You also want an ePaper? Increase the reach of your titles
YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.
TP072<br />
Burkhard Schaefer<br />
National Institute of St<strong>and</strong>ards <strong>and</strong> Technology<br />
Analytical Chemistry Division<br />
100 Bureau Drive; Building 227; Room A-159<br />
Gaithersburg, Maryl<strong>and</strong> 20899-8394<br />
burkhard.schaefer@nist.gov<br />
Creating the Analytical In<strong>for</strong>mation Markup Language (AnIML)<br />
182<br />
Co-Author(s)<br />
Gary Kramer<br />
Multiple, vendor-specific, proprietary data <strong>for</strong>mats have made it difficult to interchange analytical result data<br />
between instruments <strong>and</strong> applications. When data are interchanged, metadata are rarely included, often<br />
rendering the datasets useless after the particulars of the experiment have been <strong>for</strong>gotten. The few existing<br />
data exchange st<strong>and</strong>ards are not compliant with modern network-based data interchange technologies. A few<br />
years ago NIST began to develop a markup language <strong>for</strong> uv/visible spectrometry data based on the Extensible<br />
Markup Language (XML). The result, SpectroML, showed the power of this approach <strong>and</strong> is now integral to<br />
NIST’s data h<strong>and</strong>ling in its optical filter St<strong>and</strong>ard Reference Materials program.ASTM subcommittee E13.15 on<br />
Analytical Data Management was <strong>for</strong>med in part to develop a general markup language, such as SpectroML, <strong>for</strong><br />
all spectroscopic <strong>and</strong> chromatographic result data. Work has begun on the initial task to develop a core schema<br />
representing the in<strong>for</strong>mation that is common to all analytical techniques. If a general core can be developed <strong>and</strong><br />
adopted, st<strong>and</strong>ard applications can be written to utilize this core <strong>and</strong> accomplish simple functions such as data<br />
visualization, importing data into applications such as spreadsheets, etc. With a simple applet a web browser such<br />
as Microsoft’s Internet Explorer could display any spectral or chromatographic trace. More sophisticated concepts<br />
such as integrating peak areas, leveling baselines, etc. will require more complicated software, but having a single<br />
technique-independent routine to view data, paste the visual image into reports, or import data into other programs<br />
<strong>for</strong> further processing will be of enormous utility. This presentation will describe the current status of the AnIML<br />
development ef<strong>for</strong>ts <strong>and</strong> point out possible uses in various application domains.<br />
TP073<br />
Sadhana Sharma<br />
The Ohio State University<br />
Davis Heart <strong>and</strong> Lung Research Institute (326 HLRI)<br />
473 W. 12th Avenue<br />
Columbus, Ohio 43212<br />
sharma.119@osu.edu<br />
Rapid <strong>and</strong> Sensitive Determination of Cardiovascular Drugs in Mouse Plasma Using<br />
Solid-phase Extraction <strong>and</strong> RP-HPLC<br />
Co-Author(s)<br />
John Shapiro<br />
Stephen C. Lee<br />
Mauro Ferrari<br />
Fast <strong>and</strong> sensitive determination of the drugs is essential <strong>for</strong> pharmaceutical aut<strong>omation</strong> <strong>and</strong> disease management.<br />
Capillary electrophoresis, liquid chromatography–mass spectrometry (LC–MS), immunoassays <strong>and</strong> high-per<strong>for</strong>mance<br />
liquid chromatography (HPLC) are some of the methods used <strong>for</strong> drug analysis. Of these, the HPLC methods<br />
have been used most frequently because of their simplicity, sensitivity, <strong>and</strong> selectivity. Drug analysis from blood/<br />
plasma samples requires sample pretreatment be<strong>for</strong>e actual HPLC analysis. This can be achieved using solidphase<br />
extraction (SPE), protein precipitation <strong>and</strong> SPE, liquid–liquid extraction (LLE), solvent extraction followed by<br />
column-switching, or solvent extraction <strong>and</strong> SPE. Higher number of pretreatment steps decreases efficiency <strong>and</strong><br />
increases analysis time. In addition, most of these HPLC methods require relatively large (a maximum of 0.5–1.0 ml)<br />
biological sample <strong>and</strong> multiple extraction steps. In cases where available sample volumes are in microliters, the<br />
HPLC methods with single LLE are generally not considered sensitive enough due to the presence of endogenous<br />
interferences. Solid phase extraction can be useful <strong>for</strong> such cases. In the present research ef<strong>for</strong>t, a rapid, simple <strong>and</strong><br />
sensitive method <strong>for</strong> the determination of simvastatin in mouse plasma is developed. This method utilizes Waters<br />
OASIS solid-phase extractor <strong>and</strong> Symmetry C8 reversed phase high-per<strong>for</strong>mance liquid chromatography column<br />
<strong>for</strong> micro-sample analysis <strong>and</strong> is useful <strong>for</strong> analysis of the drug released from microfabricated silicon nanoporous<br />
implant in mouse/human.