PubMed · 15987068
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Kim Brøsen. 2005-05-16. [Classical pharmacogenetics].. https://pubmed.ncbi.nlm.nih.gov/15987068/
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A complex mixture of polycyclic aromatic hydrocarbons (PAH) extracted from coal tar, the Standard Reference Material (SRM) 1597, was recently shown to decrease the levels of DNA binding of the 2 strong carcinogens benzo[a]pyrene (BP) and dibenzo[a,l]pyrene (DBP) in the human mammary carcinoma-derived cell line MCF-7 (Mahadevan et al., Chem Res Toxicol 2005;18:224-231). The present study was designed to further elucidate the biochemical mechanisms involved in this inhibition process. We examined the effects of SRM 1597 on the metabolic activation of BP and DBP toward DNA-binding derivatives in Chinese hamster cells expressing either human cytochrome P450 (CYP) 1A1 or CYP1B1. SRM 1597 inhibited BP-DNA adduct formation through the entire exposure time in cells expressing human CYP1A1, while it significantly inhibited adduct formation only up to 48 hr when co-treated with DBP. Conversely, human CYP1B1-expressing cells were unable to catalyze PAH-DNA adduct formation on treatment with SRM 1597 alone, and on co-treatment with BP or DBP. The data obtained from biochemical experiments revealed that SRM 1597 competitively inhibited the activity of both human enzymes as analyzed by 7-ethoxyresorufin O-deethylation assays. While the Michaelis-Menten constant (K(M)) was <0.4 microM in the absence of SRM 1597, this value increased up to 1.12 (CYP1A1) or 4.45 microM (CYP1B1) in the presence of 0.1 microg/ml SRM 1597. Hence the inhibitory effects of the complex mixture on human CYP1B1 were much stronger when compared to human CYP1A1. Taken together, the decreases in PAH-DNA adduct formation on co-treatment with SRM 1597 revealed inhibitory effects on the CYP enzymes that convert carcinogenic PAH into DNA-binding metabolites. The implications for the tumorigenicity of complex environmental PAH mixtures are discussed.
Cigarette smoking increases the risk of numerous health problems, including cancer, cardiovascular and pulmonary disorders, making smoking the leading cause of preventable death in the world. Nicotine is primarily responsible for the highly addictive properties of cigarettes. Although the majority of smokers express a desire to quit, few are successful in doing so. Twin and family studies have indicated substantial genetic contributions to smoking behaviors. One major research focus has been to elucidate the specific genes involved; this has been accomplished primarily through genome-wide linkage analyses and candidate gene association studies. Much attention has focused on genes involved in the neurotransmitter pathways for the brain reward system and genes altering nicotine metabolism. This paper reviews the current state of knowledge for genetic factors implicated in smoking behaviors, and examines how genetic variations may affect therapeutic outcomes for drugs used to assist smoking cessation.
To develop and compare methods that predict individual nicotine (NIC) clearance, which reflects CYP2A6 activity, using random saliva cotinine (COT) and trans 3'-hydroxycotinine (3HC) measurements. COT and 3HC saliva concentrations in smokers were simulated utilizing a mechanistic population pharmacokinetic model of NIC metabolism that was adapted from the one described in a companion paper. Four methods to predict NIC clearance using the metabolites concentrations were compared. The precision bias, and the fraction of predictions that are made with an absolute error below 25% were the performance measures evaluated. Four prediction methods were compared: (M1) reference method, an intercept slope model of the metabolite concentration ratios ([3HC]/[COT]) (M2) an intercept slope model of the natural logarithm of the metabolite ratios (M3) a spline of the logarithm of the metabolite ratios (M4) Maximal Posteriori Bayesian estimate of NIC clearance conditioned on the model, COT and 3HC concentrations. In addition, the effect of smoking patterns on the concentrations of COT and 3HC was evaluated. The precision, accuracy, and the fraction of predictions with an absolute error below 25%, were higher for methods M2-M4 compared to method M1. However, the differences between M2 and M4 were small. Additionally, smoking pattern did not affect the metabolite concentration profiles. Predicting NIC clearance using an intercept slope model of the natural logarithm of the ratio of 3HC to COT appears to be a relatively simple method that is better than using the metabolite ratio directly. This method has a bias of approximately -10%, precision of approximately 60%. The fraction of estimates below an absolute error of 25% is 43%. These results support use of M2 to estimate CYP2A6 activity in smokers in the clinical setting.