Urinary biomarkers

To develop a method to determine urinary biomarkers for benzoates. The method involves the use of gas chromatography-combustion-isotope ratio mass spectrometry. It is proposed that the procedure developed will allow differentiation of natural benzoates from synthetic ones. 

To select and determine the amount of food colours commonly found in test foods and to develop ELISA methods using 24 hr urinary samples to examine feasibility of urinary biomarkers. 

To investigate whether one or more metabolites of caffeine can be used as a urinary biomarker to assess caffeine intake. 

Wolff MS, Teitelbaum SL, Windham G, Pinney SM, Britton JA, Chelimo C, Godhold J, Biro E, Kushi LH, Pfeiffer CM, Calafat AM (2007) Pilot study of urinary biomarkers of phytoestrogens, phthalates, and phenols in girls. Environ Health Perspect 115 116-121... 

Case Example Pharmacokinetic Calculations to Interpret Phthalate Urinary Biomarker Data. The previous descriptions focused on blood or adipose biomarker concentrations that were converted to body burden to yield estimates of daily dose based on chemical half-life. A modified form of that is conversion of urinary biomarker data to daily exposure dose via simple model calculations as described for phthalates. 

The chlorpyrifos example described in Appendix B illustrates another caveat related to biomarkers that are urinary metabolites. A metabolite can sometimes appear in urine not only as a result of parent-chemical uptake and metabolism but also as a result of uptake of the metabolite from environmental media (Tu et al. 2005 Wilson et al. 2003). Thus, the biomarker for chlorpyrifos, 3,5,6-trichloro-2-pyridinol (TCP), occurs in a wide variety of environmental media, and the concentration in foods surpasses that of the parent chemical (Morgan et al. 2005). If the intake of the metabolite from environmental sources is substantial in comparison with that of the parent chemical, as in the case of chlorpyrifos and TCP, the extrapolation of urinary biomarker concentration to parent-chemical exposure dose is uncertain. 

The risk interpretation of biomonitoring results will tend to have additional uncertainties. That is because, in addition to the standard uncertainties encountered in risk assessment, there is the uncertainty of extrapolating from a blood or urinary concentration to an external dose. There will be variability both in the timing between sample draw and most recent exposure and in the relationship between blood concentration and dose. Those kinds of variability are compounded by uncertainty in the ability of a PK calculation or model to convert biomarker to dose accurately. For example, reliance on urinary biomarker results expressed per gram of urinary creatinine leads to an uncertain calculation of total chemical excretion per day because of the considerable variability in creatinine clearance per day. That complicates an otherwise simple approach to estimating dose. Furthermore, the conversion requires knowledge of fractional excretion via various pathways, which may not be present for a large sample of humans. The uncertainties created by these factors can be bounded via sensitivity and Monte... 

Carlo analysis, but ultimately the variability in fractional excretion and creatinine clearance needs to be understood to characterize population exposure to urinary biomarkers. 

Approaches for less lipid-soluble and nonpersistent chemicals can depend on whether a blood or urinary biomarker is available. 

Urinary biomarkers can be related to exposure dose in a straightforward manner for chemicals that are excreted rapidly in urine. This approach requires the collection of data describing the percentage of dose excreted each day in urine and percentages excreted by different metabolic and elimination pathways. There can be important variability and uncertainty in those factors and in the normalization of the biomarker result (per gram of creatinine). Furthermore, there may be environmental sources of the urinary biomarker that can confound an estimation of parent-chemical dose based on the metabolite in urine. It is also possible that the urinary metabolites may exist as breakdown products in the environment. 

How uncertainties and variability in creatinine clearance can affect urinary biomarker results and their extrapolation to external dose. 

Rigas, M.L., M.S. Okino, and J.J. Quackenboss. 2001. Use of a pharmacokinetic model to assess chlorpyrifos exposure and dose in children, based on urinary biomarker measurements. Toxicol. Sci. 61(2) 374-381. 

The chlorpyrifos and phthalate examples demonstrate that urinary biomarker data can be used within the context of pharmacokinetic modeling to interpret human exposure and risk. For a number of workplace urinary biomarkers, simple approaches have been used to relate biomarker concentration to exposure dose. A prime example is styrene an empirically derived relationship between urinary concentration of the metabolite, man-... 

It is noteworthy that the styrene reference concentration (RfC) in the Integrated Risk Information System is based on the biomarker-response relationship found in workers (Mutti et al. 1984 EPA 1998). The Environmental Protection Agency (EPA) used the relationship of urinary biomarker to ambient-air concentration of workers to develop an RfC that was adjusted for the difference in exposure time between the workplace and the general population. That is a valid approach because it derives a workplace concentration-toxicity relationship in workers, which can then be adjusted for the general population to account for differences in exposure time and can take uncertainty factors into account. It is different from direct adjustment of the styrene BEI to evaluate human population biomonitoring data on styrene metabolites in urine, which would have the uncertainties described above and in Chapter 5. 

An important caveat in interpreting chlorpyrifos metabolite concentrations in urine is that this metabolite (TCP) is widespread in the environment and thus can appear in urine as a result of direct intake as well as from conversion from a parent chemical (Lu et al. 2005 Wilson et al. 2003). For example, the concentration of TCP in foods can be greater than that of chlorypyrifos, and concentrations in house dust can be generally comparable (Morgan et al. 2005). Direct intake of TCP from environmental media makes extrapolation of urinary biomarker concentration to chlorpyrifos exposure dose uncertain. 

In 1991 the UK Committee on Toxicology (COT) set a tolerable daily intake value (TDI) of 0.3 mg/kg body weight/day for DEHA. One year later, in 1992, a urinary biomarker study was reported for DEHA in a limited population exercise in the UK. A skewed distribution was determined with a median value of 2.7 mg/day and this confirmed by an independent route, the earlier estimates of DEHA intake made using dietary survey data. 

De Ruij, B.M. et al., Allylmercapturic acid as urinary biomarker of human exposure to allyl chloride, Occup. Environ. Med, 54, 653-661, 1997. 

Aflatoxins, for example, Bj (36) and their biological markers have been determined using ESI (90,91). Aflatoxins B1 B2, G1 G2 were analyzed in food samples by LC/ESI/MS using a 150 x 2-mm C18 column eluted isocratically with acetonitrile-MeOH-10 mM NH4OAc (2 16 15) (90). The positive ESI spectra were dominated by the protonated molecules, which were used for SIM. The method enabled concentrations down to lppb to be detected in various food materials. LC/ESI/MS/MS has been used for the detection of aflatoxin DNA adducts as urinary biomarkers of exposure (91). 

IchimuraT, Hung CC, Yang SA, et al. Kidney injury molecule-1 a tissue and urinary biomarker for nephrotoxicant-induced renal injury. Am JRenal Physiol 2004 286(3) F552-63. 

Finn WF, Porter GA (1998) Urinary biomarkers and nephrotoxicity. In DeBroe ME, Porter GA, Bennett WM, Verpooten GA (eds) Clinical Nephrotoxins. Kluwer Academic Publishers, Dordrecht, Netherlands, pp 61-99 Finney H, Newman DJ, Gruber W, Merle P, Price CP (1997) Initial evaluation of cystatin C measurement by particle-enhanced immunonephelometry on the Behring nephelemeter systems (BNA, BN II). Clin Chem 43 1016-1022... 

Aleo MD, Navetta KA, Emeigh Hart SG et al. (2002) Mechanism-based urinary biomarkers of aminoglycoside-induced phospholipidosis. Comp Clin Pathol 11 193-194 de Mendoza SG, Kashyap ML, Chen CY, Lutmer RF (1976) High density lipoproteinuria in nephrotic syndrome. Metabolism 25 1143-1149... 

Aleo MD, Navetta KA, Emeigh Hart SG et al. (2002) Mechanism-based urinary biomarkers of aminoglycoside-induced phospholipidosis. Comp Clin Pathol 11 193-194 Aleo MD, Navetta KA, Emeigh Hart SG et al. (2003) Mechanism-based urinary biomarkers of renal phospholipidosis and injury. Toxicol Sci 72 (Suppl) 243 Bandara LR, Kennedy S (2002) Toxicoproteomics - a new pre-clinical tool. Drug Disc Today 7 411—418 Chapman K (2002) The ProteinChip biomarker system from Ci-phergen Biosystems a novel proteomics platform for rapid biomarker discovery and validation. Biochem Soci Trans 30 82-87... 

Price RG, Taylor SA, Chivers I et al. (1996) Development and validation of new screening tests for nephrotoxic effects. Hum Exper Toxicol 15 S10-S19 Price RG, Berndt WO, Finn WF et al. (1997) Urinary biomarkers to detect significant effects of environmental and occupational exposure to nephrotoxins, III. Minimal battery of tests to assess subclinical nephrotoxicity for epidemiological studies based on current knowledge 19 535-552 Price RG (2000) Urinalysis to exclude and monitor nephrotoxicity. Clin Chim Acta 297 173-182 Price RG (2002) Early markers of nephrotoxicity. Comp Clin Pathol 11 2-7... 

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