Modeling Human Interindividual Variability in Metabolism and Risk: The Example of 4-Aminobiphenyl

We investigate, through modeling, the impact of interindividual heterogeneity in the metabolism of 4‐aminobiphenyl (ABP) and in physiological factors on human cancer risk: A physiological pharmacokinetic model was used to quantify the time course of the formation of the proximate carcinogen, N‐hydro...

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Veröffentlicht in:Risk analysis 1995-04, Vol.15 (2), p.205-213
Hauptverfasser: Bois, Frédéric Yves, Krowech, Gail, Zeise, Lauren
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Zeise, Lauren
description We investigate, through modeling, the impact of interindividual heterogeneity in the metabolism of 4‐aminobiphenyl (ABP) and in physiological factors on human cancer risk: A physiological pharmacokinetic model was used to quantify the time course of the formation of the proximate carcinogen, N‐hydroxy‐4‐ABP and the DNA‐binding of the active species in the bladder. The metabolic and physiologic model parameters were randomly varied, via Monte Carlo simulations, to reproduce interindividual variability. The sampling means for most parameters were scaled from values developed by Kadlubar et al. (Cancer Res., 51: 4371, 1991) for dogs; variances were obtained primarily from published human data (e.g., measurements of ABP N‐oxidation, and arylamine N‐acetylation in human liver tissue). In 500 simulations, theoretically representing 500 humans, DNA‐adduct levels in the bladder of the most susceptible individuals are ten thousand times higher than for the least susceptible, and the 5th and 95th percentiles differ by a factor of 160. DNA binding for the most susceptible individual (with low urine pH, low N‐acetylation and high N‐oxidation activities) is theoretically one million‐fold higher than for the least susceptible (with high urine pH, high N‐acetylation and low N‐oxidation activities). The simulations also suggest that the four factors contributing most significantly to interindividual differences in DNA‐binding of ABP in human bladder are urine pH, ABP N‐oxidation, ABP N‐acetylation and urination frequency.
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The metabolic and physiologic model parameters were randomly varied, via Monte Carlo simulations, to reproduce interindividual variability. The sampling means for most parameters were scaled from values developed by Kadlubar et al. (Cancer Res., 51: 4371, 1991) for dogs; variances were obtained primarily from published human data (e.g., measurements of ABP N‐oxidation, and arylamine N‐acetylation in human liver tissue). In 500 simulations, theoretically representing 500 humans, DNA‐adduct levels in the bladder of the most susceptible individuals are ten thousand times higher than for the least susceptible, and the 5th and 95th percentiles differ by a factor of 160. DNA binding for the most susceptible individual (with low urine pH, low N‐acetylation and high N‐oxidation activities) is theoretically one million‐fold higher than for the least susceptible (with high urine pH, high N‐acetylation and low N‐oxidation activities). 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The simulations also suggest that the four factors contributing most significantly to interindividual differences in DNA‐binding of ABP in human bladder are urine pH, ABP N‐oxidation, ABP N‐acetylation and urination frequency.</description><subject>4-Aminobiphenyl</subject><subject>Acetylation</subject><subject>Aminobiphenyl Compounds - adverse effects</subject><subject>Aminobiphenyl Compounds - metabolism</subject><subject>Aminobiphenyl Compounds - pharmacokinetics</subject><subject>Animals</subject><subject>Bladder</subject><subject>Carcinogens - metabolism</subject><subject>Carcinogens - pharmacokinetics</subject><subject>Computer Simulation</subject><subject>Disease Susceptibility</subject><subject>DNA - metabolism</subject><subject>DNA Adducts - analysis</subject><subject>Dogs</subject><subject>Human</subject><subject>Humans</subject><subject>Hydrogen-Ion Concentration</subject><subject>interindividual variability</subject><subject>Mathematical models</subject><subject>Models, Chemical</subject><subject>Models, Statistical</subject><subject>Monte Carlo Method</subject><subject>Monte Carlo methods</subject><subject>Monte Carlo Simulations</subject><subject>Neoplasms - chemically induced</subject><subject>Neoplasms, Experimental - chemically induced</subject><subject>Oxidation-Reduction</subject><subject>population heterogeneity</subject><subject>Risk</subject><subject>Risk Assessment</subject><subject>toxicokinetics</subject><subject>Urinary Bladder - metabolism</subject><subject>Urination</subject><subject>Urine</subject><issn>0272-4332</issn><issn>1539-6924</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1995</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqVUU1v00AUXCFQCYWfgLTiAheb_fCu415QVDVN1JaPUOC4ems_003XdvDakPx7NkqUIxLv8g4zb95ohpA3nKU8zvt1ypUsEl2ILOVFodLBMiZ5lm6fkMkJekomTOQiyaQUz8mLENaMccZUfkbOclXkQuUTAnddhd61P-libKCly3bA3rWV--2qETz9Dr0D67wbdtS19A4HsJ13oaHQVnTlwuMFvX9AerWFZuORdjXNklnj2s66zQO2O_-SPKvBB3x13Ofk2_zq_nKR3H66Xl7ObpMyGlYJQFaJignAmoOuYcpKNo1uVXRqswKFBc6E1QI1qxlwC5KXnEulsa6QW3lO3h50N333a8QwmMaFEr2HFrsxmEJPeS4yzSPz3T-ZXGvGMi1yGakXB2rZdyH0WJtN7xrod4Yzs-_CrM0-cLMP3Oy7MMcuzDYevz7-GW2D1en0GH7EPxzwP87j7j-UzWr5dSaYigrJQcGFAbcnBegfjc5lrsyPj9fmZvV5_mV-o81C_gXHtajN</recordid><startdate>199504</startdate><enddate>199504</enddate><creator>Bois, Frédéric Yves</creator><creator>Krowech, Gail</creator><creator>Zeise, Lauren</creator><general>Blackwell Publishing Ltd</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>7ST</scope><scope>7T2</scope><scope>7U1</scope><scope>7U2</scope><scope>7U7</scope><scope>C1K</scope><scope>SOI</scope></search><sort><creationdate>199504</creationdate><title>Modeling Human Interindividual Variability in Metabolism and Risk: The Example of 4-Aminobiphenyl</title><author>Bois, Frédéric Yves ; Krowech, Gail ; Zeise, Lauren</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5395-aa4d2d02aef1a6fa80c080055597b49e2ba102b62e60f0a1ba31c11356efde1b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1995</creationdate><topic>4-Aminobiphenyl</topic><topic>Acetylation</topic><topic>Aminobiphenyl Compounds - adverse effects</topic><topic>Aminobiphenyl Compounds - metabolism</topic><topic>Aminobiphenyl Compounds - pharmacokinetics</topic><topic>Animals</topic><topic>Bladder</topic><topic>Carcinogens - metabolism</topic><topic>Carcinogens - pharmacokinetics</topic><topic>Computer Simulation</topic><topic>Disease Susceptibility</topic><topic>DNA - metabolism</topic><topic>DNA Adducts - analysis</topic><topic>Dogs</topic><topic>Human</topic><topic>Humans</topic><topic>Hydrogen-Ion Concentration</topic><topic>interindividual variability</topic><topic>Mathematical models</topic><topic>Models, Chemical</topic><topic>Models, Statistical</topic><topic>Monte Carlo Method</topic><topic>Monte Carlo methods</topic><topic>Monte Carlo Simulations</topic><topic>Neoplasms - chemically induced</topic><topic>Neoplasms, Experimental - chemically induced</topic><topic>Oxidation-Reduction</topic><topic>population heterogeneity</topic><topic>Risk</topic><topic>Risk Assessment</topic><topic>toxicokinetics</topic><topic>Urinary Bladder - metabolism</topic><topic>Urination</topic><topic>Urine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bois, Frédéric Yves</creatorcontrib><creatorcontrib>Krowech, Gail</creatorcontrib><creatorcontrib>Zeise, Lauren</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Environment Abstracts</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Risk Abstracts</collection><collection>Safety Science and Risk</collection><collection>Toxicology Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><jtitle>Risk analysis</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bois, Frédéric Yves</au><au>Krowech, Gail</au><au>Zeise, Lauren</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling Human Interindividual Variability in Metabolism and Risk: The Example of 4-Aminobiphenyl</atitle><jtitle>Risk analysis</jtitle><addtitle>Risk Anal</addtitle><date>1995-04</date><risdate>1995</risdate><volume>15</volume><issue>2</issue><spage>205</spage><epage>213</epage><pages>205-213</pages><issn>0272-4332</issn><eissn>1539-6924</eissn><abstract>We investigate, through modeling, the impact of interindividual heterogeneity in the metabolism of 4‐aminobiphenyl (ABP) and in physiological factors on human cancer risk: A physiological pharmacokinetic model was used to quantify the time course of the formation of the proximate carcinogen, N‐hydroxy‐4‐ABP and the DNA‐binding of the active species in the bladder. The metabolic and physiologic model parameters were randomly varied, via Monte Carlo simulations, to reproduce interindividual variability. The sampling means for most parameters were scaled from values developed by Kadlubar et al. (Cancer Res., 51: 4371, 1991) for dogs; variances were obtained primarily from published human data (e.g., measurements of ABP N‐oxidation, and arylamine N‐acetylation in human liver tissue). In 500 simulations, theoretically representing 500 humans, DNA‐adduct levels in the bladder of the most susceptible individuals are ten thousand times higher than for the least susceptible, and the 5th and 95th percentiles differ by a factor of 160. DNA binding for the most susceptible individual (with low urine pH, low N‐acetylation and high N‐oxidation activities) is theoretically one million‐fold higher than for the least susceptible (with high urine pH, high N‐acetylation and low N‐oxidation activities). The simulations also suggest that the four factors contributing most significantly to interindividual differences in DNA‐binding of ABP in human bladder are urine pH, ABP N‐oxidation, ABP N‐acetylation and urination frequency.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><pmid>7597257</pmid><doi>10.1111/j.1539-6924.1995.tb00314.x</doi><tpages>9</tpages></addata></record>
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subjects 4-Aminobiphenyl
Acetylation
Aminobiphenyl Compounds - adverse effects
Aminobiphenyl Compounds - metabolism
Aminobiphenyl Compounds - pharmacokinetics
Animals
Bladder
Carcinogens - metabolism
Carcinogens - pharmacokinetics
Computer Simulation
Disease Susceptibility
DNA - metabolism
DNA Adducts - analysis
Dogs
Human
Humans
Hydrogen-Ion Concentration
interindividual variability
Mathematical models
Models, Chemical
Models, Statistical
Monte Carlo Method
Monte Carlo methods
Monte Carlo Simulations
Neoplasms - chemically induced
Neoplasms, Experimental - chemically induced
Oxidation-Reduction
population heterogeneity
Risk
Risk Assessment
toxicokinetics
Urinary Bladder - metabolism
Urination
Urine
title Modeling Human Interindividual Variability in Metabolism and Risk: The Example of 4-Aminobiphenyl
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