CYP2C9 genotype vs. metabolic phenotype for individual drug dosing--a correlation analysis using flurbiprofen as probe drug.

Currently, genotyping of patients for polymorphic enzymes responsible for metabolic elimination is considered a possibility to adjust drug dose levels. For a patient to profit from this procedure, the interindividual differences in drug metabolism within one genotype should be smaller than those between different genotypes. We studied a large cohort of healthy young adults (283 subjects), correlating their CYP2C9 genotype to a simple phenotyping metric, using flurbiprofen as probe drug. Genotyping was conducted for CYP2C9*1, *2, *3. The urinary metabolic ratio MR (concentration of CYP2C9-dependent metabolite divided by concentration of flurbiprofen) determined two hours after flurbiprofen (8.75 mg) administration served as phenotyping metric. Linear statistical models correlating genotype and phenotype provided highly significant allele-specific MR estimates of 0.596 for the wild type allele CYP2C9*1, 0.405 for CYP2C9*2 (68 % of wild type), and 0.113 for CYP2C9*3 (19 % of wild type). If these estimates were used for flurbiprofen dose adjustment, taking 100 % for genotype *1/*1, an average reduction to 84 %, 60 %, 68 %, 43 %, and 19 % would result for genotype *1/*2, *1/*3, *2/*2, *2/*3, and *3/*3, respectively. Due to the large individual variation within genotypes with coefficients of variation ≥ 20 % and supposing the normal distribution, one in three individuals would be out of the average optimum dose by more than 20 %, one in 20 would be 40 % off. Whether this problem also applies to other CYPs and other drugs has to be investigated case by case. Our data for the given example, however, puts the benefit of individual drug dosing to question, if it is exclusively based on genotype.

Statistical model to estimate a threshold dose and its confidence limits for the analysis of sublinear dose-response relationships, exemplified for mutagenicity data.

Strongly sublinear dose-response relationships (slope increasing with dose) raise the question about a putative threshold dose below which no biologically relevant effect would be expected. A mathematical threshold with a break in the curve at the threshold dose is generally rejected for consequences of genotoxicity such as mutation, because proportionality between low dose and the rate of DNA-adduct formation is a reasonable hypothesis. In view of an increasing database for distinct deviation from linearity for mutagenicity, we offer a statistical model to analyze continuous response data and estimate a threshold dose together with its confidence limits, thereby taking data quality and degree of sublinearity into account. The simplest mathematical threshold model is a hockey stick defined by a low-dose part with slope zero at background level a to a theoretical break point at threshold dose td, followed by a linear increase above td with slope b. The function is y (dose d)=a+bx(d-td)x1([d>td]). Using the free statistics software package "R", we make a procedure available to estimate the parameters a, b, and td. Confidence intervals are calculated for all parameters at a significance level that can be defined by the user. If the lower limit of the confidence interval for td is >0, linearity is rejected. The procedure is illustrated by two examples. A small data set with three replicates per dose group, indicating a threshold for the induction of thymidine kinase mutants in L5178Y tk(+/-) mouse lymphoma cells treated with methyl methanesulfonate, did not achieve significance. On the other hand, the large data set reported in this issue (Gocke et al.) on lacZ mutants in bone marrow cells of transgenic mice treated with ethyl methanesulfonate strongly favoured the hockey stick model. The question of a theoretically expected linear dose-related increase below the threshold dose is addressed by linear regression of the data below the break point and estimation of an upper limit of the slope. The question of biological relevance of the resulting slope is discussed against the normal variation of background measures in the control group.

Metabolite profiling in human urine by LC-MS/MS: method optimization and application for glucuronides from dextromethorphan metabolism.

Analysis of human urine for specific compounds or metabolites is an established method for biomonitoring occupational or environmental exposures. Modern liquid chromatography-tandem mass spectrometry is not limited to single compounds but can simultaneously analyze whole classes of urine constituents with both high sensitivity and specificity. Individual differences in the composition of urine are very large in humans, which raises a number of problems that are not encountered in animal experimentation. In this report, we investigated whether analysis of glucuronides as a class could reflect differences between human individuals regarding the polymorphic activity of the cytochrome P450 enzyme CYP2D6. From a group of 152 students that had been classified for CYP2D6 activity, urine of 12 "poor metabolizers" and 35 "extensive metabolizers" was collected 90 min after ingestion of 10mg of the antitussive drug dextromethorphan (DEX) and analyzed for glucuronides. Methods development included the following aspects: adjustment of urine samples to equal creatinine concentration to avoid differences between samples in retention times and ion suppression; on-line enrichment of low-level analytes by column switching; precursor ion scan vs. theoretical multiple reaction monitoring; use of quality control samples to check for reproducibility in large sample series; peak extraction and handling of null entries to build the data matrix; logarithmic data transformation and different scaling procedures; principal component analysis (PCA) vs. discriminant analysis. Our results show that an optimized procedure not only identified the known DEX metabolites as predictors of CYP2D6-specific metabolic pathways but also indicated the presence of additional, so far unknown path-specific glucuronide metabolites. We conclude that metabolite profiling of urine and other biofluids by modern mass spectrometric methodology may help characterize individual differences and become useful in drug development and personalized pharmacotherapy.

Metabolic profiling of glucuronides in human urine by LC-MS/MS and partial least-squares discriminant analysis for classification and prediction of gender.

Mass spectrometry (MS) is increasingly being used for metabolic profiling, but detection modes such as constant neutral loss or multiple reaction monitoring have not often been reported. These modes allow focusing on structurally related compounds, which could be advantageous for situations in which the trait under investigation is associated with a particular class of metabolites. In this study, we analyzed endogenous glucuronides excreted in human urine by monitoring characteristic transitions of putative steroid glucuronides by LC-MS/MS for discrimination of females from males. Two methods for data extraction were used: (i) a manual procedure based on visual inspection of the chromatograms and selection of 23 peaks and (ii) a software-supported method (MarkerView) set to extract 100 peaks. Data from 10 female and 10 male students were analyzed by principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) using software SIMCA. With PCA, only the manual peak selection resulted in clustering males and females. With PLS-DA, the manual method provided full separation on the basis of one single discriminant; the software-supported approach required a two-component model for complete separation. Loading plots were analyzed for their ability to reveal peaks with high discriminating power, that is, potential biomarkers. The PLS-DA models were validated with urine samples collected from five new females and five new males. Gender was correctly assigned for all. Our results indicate that inclusion of biological criteria for variable selection coupled to class-specific MS analysis and data extraction by appropriate software may constitute a valuable addition to the methods available for metabolomics.

Dose-incidence relationships derived from superposition of distributions of individual susceptibility on mechanism-based dose responses for biological effects.

Dose-response relationships for incidence are based on quantal response measures. A defined effect is either present or not present in an individual. The dose-incidence curve therefore reflects differences in individual susceptibility (the "tolerance distribution"). At low dose, only the more susceptible individuals manifest the effect, while higher doses are required for more resistant individuals to be recruited into the affected fraction of the group. Here, we analyze how such dose-incidence relationships are related to mechanism-based dose-response relationships for biological effects described on a continuous scale. As an example, we use the quantal effect "cell division" triggered by occupancy of growth factor receptors (R) by a hormone or mitogenic ligand (L). The biologically effective dose (BED) is receptor occupancy (RL). The dose-BED relationship is described by the hyperbolic Michaelis-Menten function, RL/Rtot = L / (L + K(D)). For the conversion of the dose-BED relationship to a dose-cell division relationship, the dose-BED curve has to be combined with a function that describes the distribution of susceptibilities among the cells to be triggered into mitosis. We assumed a symmetrical sigmoid curve for this function, approximated by a truncated normal distribution. Because of the supralinear dose-BED relationship due to the asymptotic saturation of the Michaelis-Menten function, the composite curve that describes cell division (incidence) as a function of dose becomes skewed to the right. Logarithmic transformation of the dose axis reverses this skewing and provides a nearly perfect fit to a normal distribution in the central 95% incidence range. This observation may explain why dose-incidence relationships can often be described by a cumulative normal curve using the logarithm of the administered dose. The dominant role of the tolerance distribution for dose-incidence relationships is also illustrated with the example of a linear dose-BED relationship, using adducts to protein or DNA as the BED. Superimposed by a sigmoid distribution of individual susceptibilities, a sigmoid dose-incidence curve results. Linearity is no longer observed. We conclude that differences in susceptibility should always be considered for toxicological risk assessment and extrapolation to low dose.

Dose-incidence modeling: consequences of linking quantal measures of response to depletion of critical tissue targets.

In developing mechanistic PK-PD models, incidence of toxic responses in a population has to be described in relation to measures of biologically effective dose (BED). We have developed a simple dose-incidence model that links incidence with BED for compounds that cause toxicity by depleting critical cellular target molecules. The BED in this model was the proportion of target molecule adducted by the dose of toxic compound. Our modeling approach first estimated the proportion depleted for each dose and then calculated the tolerance distribution for toxicity in relation to either administered dose or log of administered dose. We first examined cases where the mean of the tolerance distribution for toxicity occurred when a significant proportion of target had been adducted (i.e., more than half). When a normal distribution was assumed to exist for the relationship of incidence and BED, the tolerance distribution based on administered dose for these cases becomes asymmetrical and logarithmic transformations of the administered dose axis lead to a more symmetrical distribution. These linked PK-PD models for tissue reactivity, consistent with conclusions from other work for receptor binding models (Lutz et al., 2005), indicate that log normal distributions with administered dose may arise from normal distributions for BED and nonlinear kinetics between BED and administered dose. These conclusions are important for developing biologically based dose response (BBDR) models that link incidences of toxicity or other biological responses to measures of BED.

Different types of combination effects for the induction of micronuclei in mouse lymphoma cells by binary mixtures of the genotoxic agents MMS, MNU, and genistein.

Distinction between dose addition and response addition for the analysis of the toxicity of mixtures may allow differentiation of the components regarding similar versus independent mode of action. For nonlinear dose responses for the components, curves of dose addition and response addition differ and embrace an "envelope of additivity." Synergistic or antagonistic interaction may then be postulated only if the mixture effect is outside this surface. This situation was analyzed for the induction of micronuclei in L5178Y mouse lymphoma cells by the two methylating agents methyl methanesulfonate (MMS) and N-methyl-N-nitrosourea (MNU) and the topoisomerase-II inhibitor genistein (GEN). All three chemicals reproducibly generated sublinear (upward convex) dose-response relationships. For the analysis of mixture effects, these genotoxic agents were investigated in the three binary combinations. Statistical testing for dose addition along parallel exponential dose responses was performed by linear regression with interaction based on the logarithm of the number of cells that contain micronuclei. For MMS+MNU, the mixture effect was compatible with dose addition (i.e., significantly larger than calculated for the addition of net responses). For MMS+GEN, the measured effect was larger than for response addition but smaller than for dose addition. For MNU+GEN, the measured effect was below response addition, indicative of true antagonism. In the absence of knowledge on the sublinear dose-response relationships for the individual components, a synergistic effect of MMS on both MNU and GEN would have been postulated erroneously. The observed difference between MMS and MNU when combined with GEN would not have been predicted on the basis of a simplistic interpretation of DNA methylation as the mode of action and may be due to differences in the profile of DNA methylations and/or epigenetic effects. We conclude that knowledge of nonlinearities of the dose-response curves of individual components of a mixture can be crucial to analyze for synergism or antagonism and that an in-depth mechanistic knowledge is useful for a prediction of similarity or independence of action.

Comparative assessment of the inhibition of recombinant human CYP19 (aromatase) by azoles used in agriculture and as drugs for humans.

Azoles (imidazoles and triazoles) are used as antifungal agents in agriculture and in medicine, and also for antiestrogen therapy, e.g., for breast cancer treatment. Antifungal activity is based on inhibition of fungal CYP51 (lanosterol 14alpha-demethylase), and estrogen biosynthesis reduction is due to azole inhibition of CYP19 (aromatase). Inhibition of aromatase by antifungal agents is usually an unwanted side effect and may cause endocrine disruption. A fluorimetric assay based on human recombinant CYP19 enzyme with dibenzylfluorescein as a substrate was used to compare the inhibitory potency of 22 azole compounds. Dose responses were established and duplicate datasets were analyzed with a nonlinear mixed-effects model with cumulative normal distribution for the logarithm of concentration. IC50 values (50% inhibitory concentration) of 13 fungicides used in agriculture ranged more than 700-fold, starting from 0.047 microM. The potency of seven human drugs spanned more than 7000-fold, starting from 0.019 microM. Most potent fungicides included prochloraz, flusilazole, and imazalil, and most potent medicinal antifungals were bifonazole, miconazole, and clotrimazole. These in vitro data indicate that the top-ranking azoles used as antifungal agents or drugs are as potent inhibitors of aromatase as are antiestrogen therapeutics used to treat breast cancer. These putative effects of azole agents and drugs on steroid biosynthesis and sex hormone balance should be considered when used in human subjects and also in wildlife exposed to azole fungicides used in agriculture.

LC-MS/MS analysis of dextromethorphan metabolism in human saliva and urine to determine CYP2D6 phenotype and individual variability in N-demethylation and glucuronidation.

In order to establish a fast screening method for the determination of the CYP2D6 metabolic phenotype a sensitive LC-MS/MS assay to quantify dextromethorphan (DEX) and its O-demethylated metabolite dextrorphan (DOR) in human saliva was developed with limits of quantitation of 1 pmol/ml. Saliva was provided by 170 medical students 2h after oral ingestion of 30 mg (81 micromol) dextromethorphan hydrobromide. Individual ratios of the concentrations DEX/DOR (metabolic ratio, MR(DEX/DOR)) varied more than 25,000-fold (0.03-780). Two groups comprising 156 'Extensive' and 14 'Poor Metabolizers' were clearly distinguished. For the investigation of individual differences in N-demethylation and glucuronidation, four additional metabolites of DEX, 3-methoxymorphinan (MOM), 3-hydroxymorphinan (HOM), and the two O-glucuronides (DORGlu and HOMGlu) were measured by LC-MS/MS analysis of 6-h urine of 24 volunteers. The N-demethylation reactions DEX-to-MOM and DOR-to-HOM defined by the respective MR were significantly correlated. The same holds for the glucuronidation pathways (MR(DOR/DORGlu) versus MR(HOM/HOMGlu)). The three poor CYP2D6 metabolizers excreted relatively high amounts of the parent compound DEX (up to 7 micromol), but only low amounts of glucuronides (DORGlu: 0.4-1.0 micromol; HOMGlu: 0.2-0.7 micromol). For the 21 'Extensive Metabolizers', the two glucuronides were the most abundant, with relatively little interindividual variation (DORGlu: 10-44 micromol; HOMGlu: 5-17 micromol). For the excretion of the glucuronides, two normal distributions provided the best fit, indicating that the determination of the glucuronides alone could allow assignment of the CYP2D6 metabolic phenotype.

Statistical procedures to test for linearity and estimate threshold doses for tumor induction with nonlinear dose-response relationships in bioassays for carcinogenicity.

Sublinear shapes of the dose-response curve in the low-dose range of toxicity testing are often postulated to be indicative of a no-effect threshold. We present statistical procedures to test sublinear dose responses in bioassays for carcinogenicity against the hypothesis of linearity and estimate a lower confidence limit for the dose at the postulated breakpoint. First, a control tumor incidence of 0 is assumed. Tumor incidence at dose 1 is allowed to range from 0 to 4 tumor-bearing animals (TBAs) in groups of 50 animals, dose 2 is assumed to result in a tumor incidence of 5-25 TBAs. The null hypothesis of a linear dose response is tested by (i) the likelihood ratio (LR) test and (ii) the minimum chi(2) (MC) method. Validation by simulation showed the MC method to be more conservative than the LR test. At the 5% level with MC, the following observed numbers of TBAs for the dose sequence 0-1-2 resulted in rejection of the hypothesis of linearity: 0-0-6, 0-1-10, 0-2-13, 0-3-16, 0-4-18. Second, the analysis was adapted to allow for a control tumor incidence of 0-4 TBAs/50 and a tumor incidence of 0-10 TBAs/50 at dose 1, and the minimum number of TBAs at dose 2 to reject linearity at the 5% level was calculated. Third, a program is made available to analyze data derived from protocols that include nonstandard dose span and group size. Internet access to the respective statistics software and source file is provided. Examples for nasal tumor induction by formaldehyde and for the induction of renal adenocarcinoma by ochratoxin A are shown. The proposed analysis may be useful to test sublinear sections of the dose response for the possibility of a threshold for carcinogens and to define dose levels that could be used as a starting point for setting exposure standards.