Binnian Wei1, June Feng2, Imran J Rehmani3, Sharyn Miller3, James E McGuffey3, Benjamin C Blount3, Lanqing Wang3. 1. Tobacco and Volatiles Branch, Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, United States. Electronic address: bwei@cdc.gov. 2. Tobacco and Volatiles Branch, Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, United States. Electronic address: czf2@cdc.gov. 3. Tobacco and Volatiles Branch, Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, United States.
Abstract
BACKGROUND: Most sample preparation methods characteristically involve intensive and repetitive labor, which is inefficient when preparing large numbers of samples from population-scale studies. METHODS: This study presents a robotic system designed to meet the sampling requirements for large population-scale studies. Using this robotic system, we developed and validated a method to simultaneously measure urinary anatabine, anabasine, nicotine and seven major nicotine metabolites: 4-Hydroxy-4-(3-pyridyl)butanoic acid, cotinine-N-oxide, nicotine-N-oxide, trans-3'-hydroxycotinine, norcotinine, cotinine and nornicotine. We analyzed robotically prepared samples using high-performance liquid chromatography (HPLC) coupled with triple quadrupole mass spectrometry in positive electrospray ionization mode using scheduled multiple reaction monitoring (sMRM) with a total runtime of 8.5 min. RESULTS: The optimized procedure was able to deliver linear analyte responses over a broad range of concentrations. Responses of urine-based calibrators delivered coefficients of determination (R(2)) of >0.995. Sample preparation recovery was generally higher than 80%. The robotic system was able to prepare four 96-well plate (384 urine samples) per day, and the overall method afforded an accuracy range of 92-115%, and an imprecision of <15.0% on average. CONCLUSIONS: The validation results demonstrate that the method is accurate, precise, sensitive, robust, and most significantly labor-saving for sample preparation, making it efficient and practical for routine measurements in large population-scale studies such as the National Health and Nutrition Examination Survey (NHANES) and the Population Assessment of Tobacco and Health (PATH) study. Published by Elsevier B.V.
BACKGROUND: Most sample preparation methods characteristically involve intensive and repetitive labor, which is inefficient when preparing large numbers of samples from population-scale studies. METHODS: This study presents a robotic system designed to meet the sampling requirements for large population-scale studies. Using this robotic system, we developed and validated a method to simultaneously measure urinary anatabine, anabasine, nicotine and seven major nicotine metabolites: 4-Hydroxy-4-(3-pyridyl)butanoic acid, cotinine-N-oxide, nicotine-N-oxide, trans-3'-hydroxycotinine, norcotinine, cotinine and nornicotine. We analyzed robotically prepared samples using high-performance liquid chromatography (HPLC) coupled with triple quadrupole mass spectrometry in positive electrospray ionization mode using scheduled multiple reaction monitoring (sMRM) with a total runtime of 8.5 min. RESULTS: The optimized procedure was able to deliver linear analyte responses over a broad range of concentrations. Responses of urine-based calibrators delivered coefficients of determination (R(2)) of >0.995. Sample preparation recovery was generally higher than 80%. The robotic system was able to prepare four 96-well plate (384 urine samples) per day, and the overall method afforded an accuracy range of 92-115%, and an imprecision of <15.0% on average. CONCLUSIONS: The validation results demonstrate that the method is accurate, precise, sensitive, robust, and most significantly labor-saving for sample preparation, making it efficient and practical for routine measurements in large population-scale studies such as the National Health and Nutrition Examination Survey (NHANES) and the Population Assessment of Tobacco and Health (PATH) study. Published by Elsevier B.V.
Authors: J T Bernert; W E Turner; J L Pirkle; C S Sosnoff; J R Akins; M K Waldrep; Q Ann; T R Covey; W E Whitfield; E W Gunter; B B Miller; D G Patterson; L L Needham; W H Hannon; E J Sampson Journal: Clin Chem Date: 1997-12 Impact factor: 8.327
Authors: Eleanor I Miller; Hye-Ryun K Norris; Douglas E Rollins; Stephen T Tiffany; Diana G Wilkins Journal: J Chromatogr B Analyt Technol Biomed Life Sci Date: 2009-12-22 Impact factor: 3.205
Authors: Peyton Jacob; Christopher Havel; Do-Hoon Lee; Lisa Yu; Mark D Eisner; Neal L Benowitz Journal: Anal Chem Date: 2008-10-08 Impact factor: 6.986
Authors: Baoyun Xia; Benjamin C Blount; Tonya Guillot; Christina Brosius; Yao Li; Dana M Van Bemmel; Heather L Kimmel; Cindy M Chang; Nicolette Borek; Kathryn C Edwards; Charlie Lawrence; Andrew Hyland; Maciej L Goniewicz; Brittany N Pine; Yang Xia; John T Bernert; B Rey De Castro; John Lee; Justin L Brown; Stephen Arnstein; Diane Choi; Erin L Wade; Dorothy Hatsukami; Gladys Ervies; Angel Cobos; Keegan Nicodemus; Dana Freeman; Stephen S Hecht; Kevin Conway; Lanqing Wang Journal: Nicotine Tob Res Date: 2021-02-16 Impact factor: 4.244
Authors: Lanqing Wang; John T Bernert; Neal L Benowitz; June Feng; Peyton Jacob; Ernest McGahee; Samuel P Caudill; Gerhard Scherer; Max Scherer; Nikola Pluym; Mira V Doig; Kirk Newland; Sharon E Murphy; Nicolas J Caron; Lane C Sander; Makiko Shimizu; Hiroshi Yamazaki; Sung Kim; Loralie J Langman; Jeanita S Pritchett; Lorna T Sniegoski; Yao Li; Benjamin C Blount; James L Pirkle Journal: Cancer Epidemiol Biomarkers Prev Date: 2018-05-31 Impact factor: 4.254
Authors: Yu-Ching Cheng; Carolyn M Reyes-Guzman; Carol H Christensen; Brian L Rostron; Kathryn C Edwards; Lanqing Wang; Jun Feng; Jeffery M Jarrett; Cynthia D Ward; Baoyun Xia; Heather L Kimmel; Kevin Conway; Carmine Leggett; Kristie Taylor; Charlie Lawrence; Ray Niaura; Mark J Travers; Andrew Hyland; Stephen S Hecht; Dorothy K Hatsukami; Maciej L Goniewicz; Nicolette Borek; Benjamin C Blount; Dana M van Bemmel Journal: Cancer Epidemiol Biomarkers Prev Date: 2020-01-27 Impact factor: 4.254
Authors: Eric C Donny; Matthew J Carpenter; Tracy T Smith; Joseph S Koopmeiners; Cassidy M White; Rachel L Denlinger-Apte; Lauren R Pacek; Víctor R De Jesús; Lanqing Wang; Clifford Watson; Benjamin C Blount; Dorothy K Hatsukami; Neal L Benowitz Journal: Cancer Epidemiol Biomarkers Prev Date: 2020-02-26 Impact factor: 4.254
Authors: Binnian Wei; K Udeni Alwis; Zheng Li; Lanqing Wang; Liza Valentin-Blasini; Connie S Sosnoff; Yang Xia; Kevin P Conway; Benjamin C Blount Journal: Environ Int Date: 2015-12-12 Impact factor: 9.621
Authors: Arash Etemadi; Hossein Poustchi; Antonia M Calafat; Benjamin C Blount; Victor R De Jesús; Lanqing Wang; Akram Pourshams; Ramin Shakeri; Maki Inoue-Choi; Meredith S Shiels; Gholamreza Roshandel; Gwen Murphy; Connie S Sosnoff; Deepak Bhandari; Jun Feng; Baoyun Xia; Yuesong Wang; Lei Meng; Farin Kamangar; Paul Brennan; Paolo Boffetta; Sanford M Dawsey; Christian C Abnet; Reza Malekzadeh; Neal D Freedman Journal: Cancer Epidemiol Biomarkers Prev Date: 2020-01-08 Impact factor: 4.254