Molecular approaches to the identification of biomarkers of exposure and effect--report of an expert meeting organized by COST Action B15. November 28, 2003.

In the past, the term biomarker has been used with several meanings when used in human and environmental toxicology as compared to pharmaceutical development. However, with the advent of molecular approaches and their application in the field of drug development and toxicology, the concept of biomarkers has to be newly defined. In the meeting, the experts found consent in defining the term and described the application of biomarkers in toxicology, drug development and clinical diagnostics. Molecular approaches to biomarker identification and selection lead to a large amount of data. Hence, the statistical analysis is challenging and special statistical problems have to be solved in biomarker characterization, of particular interest are attempts aiming at class discovery and prediction. Reliability and biological relevance are to be demonstrated for biomarkers of exposure and effect which is also true for biomarkers of susceptibility. It is envisaged that the application of biomarkers will expand from current use in pre-clinical toxicology to the risk characterization and risk assessment of chemicals and from early clinical phases of drug development to later phases and even into daily clinical use in diagnostics and disease classification.

Association of metabolic gene polymorphisms with tobacco consumption in healthy controls.

Polymorphisms in genes that encode for metabolic enzymes have been associated with variations in enzyme activity between individuals. Such variations could be associated with differences in individual exposure to carcinogens that are metabolized by these genes. In this study, we examine the association between polymorphisms in several metabolic genes and the consumption of tobacco in a large sample of healthy individuals. The database of the International Collaborative Study on Genetic Susceptibility to Environmental Carcinogens was used. All the individuals who were controls from the case-control studies included in the data set with information on smoking habits and on genetic polymorphisms were selected (n = 20938). Sufficient information was available on the following genes that are involved in the metabolism of tobacco smoke constituents: CYP1A1, GSTM1, GSTT1, NAT2 and GSTP1. None of the tested genes was clearly associated with smoking behavior. Information on smoking dose, available for a subset of subjects, showed no effect of metabolic gene polymorphisms on the amount of smoking. No association between polymorphisms in the genes studied and tobacco consumption was observed; therefore, no effect of these genes on smoking behavior should be expected.

CYP1A1, GSTM1 and GSTT1 polymorphisms and lung cancer: a pooled analysis of gene-gene interactions.

Gene-environment interactions have been extensively studied in lung cancer. It is likely that several genetic polymorphisms cooperate in increasing the individual risk. Therefore, the study of gene-gene interactions might be important to identify high-susceptibility subgroups. GSEC is an initiative aimed at collecting available data sets on metabolic polymorphisms and the risks of cancer at several sites and performing pooled analyses of the original data. Authors of published papers have provided original data sets. The present paper refers to gene-gene interactions in lung cancer and considers three polymorphisms in three metabolic genes: CYP1A1, GSTM1 and GSTT1. The present analyses compare the gene gene interactions of the CYP1A1*2A, GSTM1 and GSTT1 polymorphisms from studies on lung cancer conducted in Europe and the USA between 1991 and 2000. Only Caucasians have been included. The data set includes 1466 cases and 1488 controls. The only clear-cut association was found with CYP1A1*2A. This association remained unchanged after stratification by polymorphisms in other genes (with an odds ratio [OR] of approximately 2.5), except when interaction with GSTM1 was considered. When the OR for CYP1A1*2A was stratified according to the GSTM1 genotype, the OR was increased only among the subjects who had the null (homozygous deletion) GSTM1 genotype (OR = 2.8, 95% CI = 0.9-8.4). The odds ratio for the interactive term (CYP1A1*2A by GSTM1) in logistic regression was 2.7 (95% CI = 0.5-15.3). An association between lung cancer and the homozygous CYP1A1*2A genotype is confirmed. An apparent and biologically plausible interaction is suggested between this genotype and GSTM1.

Structure and polymorphisms of human aryl hydrocarbon receptor repressor (AhRR) gene in a French population: relationship with CYP1A1 inducibility and lung cancer.

OBJECTIVE: The aryl hydrocarbon receptor repressor (AhRR) protein may dimerize with the AhR nuclear translocator (ARNT) and may compete with the aryl hydrocarbon receptor (AhR) to bind the xenobiotic responsive elements. The result is a negative feedback mechanism that involves a down regulation of all genes regulated by the AhR transcription factor which positively regulates the expression of the Cytochrome P-4501A1 gene (CYP1A1). METHODS: The structure of the AhRR gene was reconstituted, then the genetic polymorphisms of this gene including the promoter were investigated and the link between these polymorphisms, CYP1A1 inducibility and lung cancer incidence in a French population was examined. Four polymorphisms were found, two in the coding region (609G>C and 1977G>C) and two in the 5'-untranslated region (-96G>A and -869A>T). Among the four polymorphisms, only one, the 609G>C has been previously described. The 609G>C and 1977G>C are localized respectively in exon 6 and 12 and lead to Pro554Ala and Asp641His substitutions, respectively. To evaluate the frequency of these allelic variants, a DNA library of a case-control study of lung cancer (164 controls and 171 patients) was screened. These polymorphisms were detected at the same allele frequency (0.40 for 609C, 0.05 for 1977C, 0.24 for -96A and 0.17 for -869T) in both controls and patients. Statistical analysis did not show any relationship between all the mutations found and CYP1A1 inducibility and lung cancer incidence. CONCLUSION: None of the polymorphisms were found to play a key role in CYP1A1 inducibility or in the susceptibility to develop lung cancer.

CYP1A1 T3801 C polymorphism and lung cancer: a pooled analysis of 2451 cases and 3358 controls.

CYP1A1 is involved in the metabolism of benzopyrene, a suspected lung carcinogen; it is therefore conceivable that genetically determined variations in its activity modify individual susceptibility to lung cancer. The role of the CYP1A1 MspI polymorphism in lung cancer has been widely studied but has not been fully clarified. We have included 2,451 cases and 3,358 controls in a pooled analysis of 22 case-control studies on CYP1A1 and lung cancer risk. We found a clear association between the CYP1A1 homozygous MspI restriction fragment length polymorphism (RFLP) and lung cancer risk in Caucasians (age- and gender-adjusted odds ratio = 2.36; 95% confidence interval 1.16-4.81); other associations were weaker or not statistically significant. The association with the homozygous variant was equally strong for squamous cell carcinomas and adenocarcinomas among Caucasians. We analyzed the risk by duration of smoking: for Caucasian subjects with the MspI RFLP combined variants (homozygotes plus heterozygotes), the increase in the risk of lung cancer was steeper than among the individuals with the homozygous reference allele. Our analysis suggests that Caucasians with homozygous variant CYP1A1 polymorphism have a higher risk of lung cancer. The data were more consistent among Caucasians, with a strong association between the homozygous variant in both squamous cell carcinomas and adenocarcinomas, and a stronger association in men than in women. The analyses were more inconsistent and failed to reach statistical significance in Asians. This observation might be due to design specificities or unknown effect modifiers in the Asian studies.

In silico prediction of ADME and pharmacokinetics. Report of an expert meeting organised by COST B15.

The computational approach is one of the newest and fastest developing techniques in pharmacokinetics, ADME (absorption, distribution, metabolism, excretion) evaluation, drug discovery and toxicity. However, to date, the software packages devoted to ADME prediction, especially of metabolism, have not yet been adequately validated and still require improvements to be effective. Most are 'open' systems, under constant evolution and able to incorporate rapidly, and often easily, new information from user or developer databases. Quantitative in silico predictions are now possible for several pharmacokinetic (PK) parameters, particularly absorption and distribution. The emerging consensus is that the predictions are no worse than those made using in vitro tests, with the decisive advantage that much less investment in technology, resources and time is needed. In addition, and of critical importance, it is possible to screen virtual compounds. Some packages are able to handle thousands of molecules in a few hours. However, common experience shows that, in part at least for essentially irrational reasons, there is currently a lack of confidence in these approaches. An effort should be made by the software producers towards more transparency, in order to improve the confidence of their consumers. It seems highly probable that in silico approaches will evolve rapidly, as did in vitro methods during the last decade. Past experience with the latter should be helpful in avoiding repetition of similar errors and in taking the necessary steps to ensure effective implementation. A general concern is the lack of access to the large amounts of data on compounds no longer in development, but still kept secret by the pharmaceutical industry. Controlled access to these data could be particularly helpful in validating new in silico approaches.

Meta- and pooled analyses of the effects of glutathione S-transferase M1 polymorphisms and smoking on lung cancer risk.

Susceptibility to lung cancer may in part be attributable to inter-individual variability in metabolic activation or detoxification of tobacco carcinogens. The glutathione S-transferase M1 (GSTM1) genetic polymorphism has been extensively studied in this context; two recent meta-analyses of case-control studies suggested an association between GSTM1 deletion and lung cancer. At least 15 studies have been published after these overviews. We undertook a new meta-analysis to summarize the results of 43 published case-control studies including >18 000 individuals. A slight excess of risk of lung cancer for individuals with the GSTM1 null genotype was found (odds ratio (OR) = 1.17, 95% confidence interval (CI) 1.07-1.27). No evidence of publication bias was found (P = 0.4), however, it is not easy to estimate the extent of such bias and we cannot rule out some degree of publication bias in our results. A pooled analysis of the original data of about 9500 subjects involved in 21 case-control studies from the International Collaborative Study on Genetic Susceptibility to Environmental Carcinogens (GSEC) data set was performed to assess the role of GSTM1 genotype as a modifier of the effect of smoking on lung cancer risk with adequate power. Analyses revealed no evidence of increased risk of lung cancer among carriers of the GSTM1 null genotype (age-, gender- and center-adjusted OR = 1.08, 95% CI 0.98-1.18) and no evidence of interaction between GSTM1 genotype and either smoking status or cumulative tobacco consumption.

Can drug-drug interactions be predicted from in vitro studies?

Potential drug-drug interactions as well as drug-xenobiotic interactions are a major source of clinical problems, sometimes with dramatic consequences. Investigation of drug-drug interactions during drug development is a major concern for the drug companies while developing new drugs. Our knowledge of the drug-metabolising enzymes, their mechanism of action, and their regulation has made considerable progress during the last decades. Various efficient in vitro approaches have been developed during recent years and powerful computer-based data handling is becoming widely available. All these tools allow us to initiate, early in the development of new chemical entities, large-scale studies on the interactions of drugs with selective cytochrome P-450 (CYP) isozymes, drug receptors, and other cellular entities. Standardisation and validation of these methodological approaches significantly improve the quality of the data generated and the reliability of their interpretation. The simplicity and the low costs associated with the use of in vitro techniques have made them a method of choice to investigate drug-drug interactions. Promising successes have been achieved in the extrapolation of in vitro data to the in vivo situation and in the prediction of drug-drug interaction. Nevertheless, linking in vitro and in vivo studies still remains fraught with difficulties and should be made with great caution.

In vitro tests for predicting drug-drug interactions: the need for validated procedures.

Over the past decade, the prediction of drug-drug interactions from in vitro studies has become a rapidly expanding field of research. Numerous papers and excellent review articles (Bertz & Granneman 1997; Ito et al. 1998a & b; Lin 2000; Bachmann & Ghosh 2001; Ekins & Wrighton 2001; Weaver 2001) have been published in this area. Yet like any new and fast-growing subject, this one has been developing with some confusion and without any real, efficient organisation. Depending on the drug tested, the models and extrapolation parameters used, etc., results and conclusions may vary widely from study to study (von Moltke et al. 1998; Weaver 2001). Several authors have called for validation of these procedures (Rodrigues et al. 2001; Kummar & Surapaneni 2001; Pelkonen et al. 2001a & b; Kremers 2002), and regulatory authorities intend to require better traceability and reliability (FDA & EMEA guidelines). A systematic and reliable approach is needed also to allow such protocols to be incorporated into early screening for potential drugs and new chemical entities. There is certainly a great need to standardise these studies and to verify their conclusions, but is true validation possible in this field? The main purpose of the present paper is to discuss this issue and to examine what is possible and what is needed to improve the quality of predictions made from in vitro experiments.