Tobacco chemical metabolism can be illustrated with nicotine, and polyaromatic hydrocarbons. Cotinine (alongside the other metabolites of nicotine) is found in urine as a metabolic by-product of nicotine and can be used to measure individual exposure to tobacco products (BoEs, Biomarkers of Exposure) . Metabolism can lead to the production of new and possibly toxic species (bioactivation). For instance, benzo[a]pyrene (B[a]P), an IARC group 1 carcinogen, has first to be metabolically activated to react with DNA (Figure 1) . The examples of nicotine and B[a]P illustrate the key role of metabolite formation and the importance of metabolite identification to help understanding individual exposure and chemical toxicity.
The image below shows the simplified metabolism map for B[a]P. B[a]P is oxidized by P450s (CYP1A1, CYP1A2, CYP1B1, CYP3A4) to form various monohydroxyls and epoxides. Epoxide hydrolysis by EPHX1 generates dihydrodiols that can be further oxidized by P450s to form diol-epoxides or aldo keto reductase (AKR) to yield catechols. Diol-epoxides are reactive towards DNA. Catechols spontaneously convert to O-quinones. Alternatively catechols can be methylated by COMT (catechol-O-methyl-transferase) to yield a stable methoxy group. B[a]P hydroxyl moieties are sulfated or glucuronidated by sulfo- and glucuronosyl-transferases (SULTs, UGTs), while epoxides and O-quinones are conjugated spontaneously or enzymatically (glutathione-S-transferase GSTM1, A1, P1) to glutathione (GSH) .
Many tobacco smoke biomarkers of exposure (BoEs) are metabolites and are derived from normal metabolic activity.
Metabolites can be used as biomarkers. This below schematic illustrates the metabolic route of acrylonitrile, a vapour phase smoke toxicant. Acrylonitrile is metabolized through multiple pathways and 2-cyanoethylmercapturic acid is a urinary metabolite used as biomarker of exposure to acrylonitrile.
BoEs have been introduced in disease epidemiology in order to improve the investigation of health effects linked to exposure to harmful substances such as air pollutants. BoEs are used to assess exposure to toxicants and better understand the underlying dose toxicity mechanisms. BoEs have been used successfully to evaluate the internal dose of some smoke toxicants[3-5]. Furthermore, metabolism can affect BoEs’ formation and smoking behaviour. For instance, gender-associated deletion of UGT2B17, an enzyme involved in NNK detoxification, reduces the excretion of its metabolite NNAL-glucuronide and increases the risk of lung cancer in women . In a population of black-African descent, an inactive polymorphic CYP2A6 allele was identified and was associated with decreased smoking in these individuals [7-8].
Finally, smoke constituent toxicity has been historically assessed on an individual basis, however, bioactivation of toxicants is ultimately the result of a complex series of chemical and metabolic interactions. For instance, using in vitro purified CYP2A13, a human lung cytochrome P450 playing a significant role in the activation of NNK, it was shown that nicotine can inhibit the formation of certain toxic intermediates of NNK .
Therefore, deciphering the metabolism of smoke chemicals is key to understanding toxicity and to developing novel BoEs.
Much useful information on metabolic pathways, enzyme kinetics, and polymorphisms can be found in the literature. This information can be used to support computer generated models of metabolic pathways which forms part of biomarker discovery tools.
Since bioactivation is a key element of smoking related disease, studies of smoke toxicity should ideally be conducted in metabolically active models. Many in vitro and ex vivo models are not metabolically active and therefore selection of the appropriate in vitro model should be based on multiple evidence from multiple sources, including metabolic competency. Typical metabolic enzymes that we are testing include CYP2A6, CYP2A13, CYP2E1, CYP1A1 and CYP3A4 as they are believed to be the major activators of tobacco toxicants [2,10-12].
The picture to the right and graph below show the morphology of cultured human bronchial epithelial cells (HBECs) and CYP2A6 activity measured by coumarin hydroxylation in differentiated HBECs (retinoic acid (RA) added) and undifferentiated HBECs (no retinoic acid). 8-Methoxypsoralen (8-MOP) was used as control inhibitor. This demonstrates that the HBEC cells used within our research programmes are metabolically active at least for CYP2A6.
Smoke toxicants are the result of tobacco combustion/curing; however other volatile tobacco ingredients such as flavours are also absorbed during smoking. Therefore as part of risk characterisation, we also evaluate the metabolic fate of ingredients.
As highlighted in the last TobReg publication , many smoke toxicants have been evaluated individually and those evaluations may not accurately represent the risk linked with exposure to a complex mixture such as tobacco smoke. We are currently investigating some of these interactions.
Many BoEs are metabolites which can be found in biofluids. In addition to the classic hypothesis-driven approach, we are using non-hypothesis driven approaches (metabolomics) to identify potentially new biomarkers of tobacco smoke exposure. One such approach involves metabolomic comparison of urine from smokers and non-smokers and identification of differentially-excreted metabolites.
The below image shows an NMR-Spectra of human smoker urine with the PLS regression scores plotted and coloured by smoker group.
Smokers groups:- Black, Blue, Yellow, Pink correspond respectively to 10mg, 4mg, 1mg ISO tar yields and non-smokers respectively.