U.S. Environmental Protection Agency's revised guidelines for carcinogen risk assessment: evaluating a postulated mode of carcinogenic action in guiding dose–response extrapolation

There are new opportunities to using data from molecular and cellular studies in order to bring together a fuller biological understanding of how chemicals induce neoplasia. In 1996, the Environmental Protection Agency (EPA) published a proposal to replace its 1986 Guidelines for Carcinogen Risk Assessment to take advantage of these new scientific advances in cancer biology. The analytical framework within the new guidelines focuses on an understanding of the mode of carcinogenic action. Mode of action data come into play in a couple of ways in these new guidelines. For example, such information can inform the dose–response relationship below the experimental observable range of tumours. Thus, mode of action data can be useful in establishing more appropriate guidance levels for environmental contaminants. It is the understanding of the biological processes that lead to tumour development along with the response data derived from experimental studies that can help discern the shape of the dose–response at low doses (linear vs. nonlinear). Because it is experimentally difficult to establish “true thresholds” from others with a nonlinear dose–response relationship, the proposed guidelines take a practical approach to depart from low-dose linear extrapolation procedures when there is sufficient experimental support for a mode of action consistent with nonlinear biological processes (e.g., tumours resulting from the disruption of normal physiological processes).     

Do polyphenols enter the brain and does it matter? Some theoretical and practical considerations

Although several epidemiological and intervention studies suggest that polyphenols (PPs) and PP-rich foods may improve memory and cognition in animals and humans, PPs' mode of action is only poorly understood. To help distinguish between the different modes of action that have been proposed for PPs, it is obviously important to know how much PPs can accumulate in the brain, if any at all. However, reliable data on PP uptake into the brain of animals are limited as many studies failed to report important control procedures during data acquisition. In this paper, we summarize published data on the penetration of PPs into animal brain and review some hypotheses to explain the biological basis of potentially health-beneficial effects of PPs to the brain. Finally, we highlight promising new approaches, especially those of a hormetic dose-response and gut microbiota-brain interaction, which may allow a better understanding of PPs' mode of action in animals and humans.     

Distinguishing graded and ultrasensitive signalling cascade kinetics by the shape of morphogen gradients in Drosophila

Recently, signalling gradients in cascades of two-state reaction-diffusion systems were described as a model for understanding key biochemical mechanisms that underlie development and differentiation processes in the Drosophila embryo. Diffusion-trapping at the exterior of the cell membrane triggers the mitogen-activated protein kinase (MAPK) cascade to relay an appropriate signal from the membrane to the inner part of the cytosol, whereupon another diffusion-trapping mechanism involving the nucleus reads out this signal to trigger appropriate changes in gene expression. Proposed mathematical models exhibit equilibrium distributions consistent with experimental measurements of key spatial gradients in these processes. A significant property of the formulation is that the signal is assumed to be relayed from one system to the next in a linear fashion. However, the MAPK cascade often exhibits nonlinear dose-response properties and the final remark of Berezhkovskii et al. (2009) is that this assumption remains an important property to be tested experimentally, perhaps via a new quantitative assay across multiple genetic backgrounds. In anticipation of the need to be able to sensibly interpret data from such experiments, here we provide a complementary analysis that recovers existing formulae as a special case but is also capable of handling nonlinear functional forms. Predictions of linear and nonlinear signal relays and, in particular, graded and ultrasensitive MAPK kinetics, are compared.     

Dose response problems in carcinogenesis.

The estimation of risks from exposure to carcinogens is an important problem from the viewpoint of protection of human health. It also poses some very difficult dose-response problems. Two dose-response models may fit experimental data about equally well and yet predict responses that differ by many orders of magnitude at low doses. Mechanisms of carcinogenesis are not sufficiently understood so that the shape of the dose-response curve at low doses can be satisfactorily predicted. Mathematical theories of carcinogenesis and statistical procedures can be of use with dose-reponse problems such as this and, in addition, can lead to a better understanding of the mechanisms of carcinogenesis. In this paper, mathematical dose-response models of carcinogenesis are considered as well as various proposed dose-response procedures for estimating carcinogenic risks at low doses. Areas are suggested in which further work may be useful. These areas include experimental design problems, statistical procedures for use with time-to-occurrence data, and mathematical models that incorporate such biological features as pharmacokinetics of carcinogens, synergistic effects, DNA repair, susceptible subpopulations, and immune reactions.     

Modeling convergent ON and OFF pathways in the early visual system

For understanding the computation and function of single neurons in sensory systems, one needs to investigate how sensory stimuli are related to a neuron’s response and which biological mechanisms underlie this relationship. Mathematical models of the stimulus–response relationship have proved very useful in approaching these issues in a systematic, quantitative way. A starting point for many such analyses has been provided by phenomenological “linear–nonlinear” (LN) models, which comprise a linear filter followed by a static nonlinear transformation. The linear filter is often associated with the neuron’s receptive field. However, the structure of the receptive field is generally a result of inputs from many presynaptic neurons, which may form parallel signal processing pathways. In the retina, for example, certain ganglion cells receive excitatory inputs from ON-type as well as OFF-type bipolar cells. Recent experiments have shown that the convergence of these pathways leads to intriguing response characteristics that cannot be captured by a single linear filter. One approach to adjust the LN model to the biological circuit structure is to use multiple parallel filters that capture ON and OFF bipolar inputs. Here, we review these new developments in modeling neuronal responses in the early visual system and provide details about one particular technique for obtaining the required sets of parallel filters from experimental data.     

Steric hindrance effects on surface reactions: applications to BIAcore

Because surface-volume reactions occur in many biological and industrial processes, understanding the rate of such reactions is important. The BIAcore surface plasmon resonance (SPR) biosensor for measuring rate constants has such a geometry. Though several models of the BIAcore have been presented, few take into account that large ligand molecules can block multiple receptor sites, thus skewing the sensogram data. In this paper some general mathematical principles are stated for handling this phenomenon, and a surface-reaction model is presented explicitly. An integro-partial differential equation results, which can be simplified greatly using perturbation techniques, yielding linear and nonlinear integrodifferential equations. Explicit and asymptotic solutions are constructed for cases motivated by experimental design. The general analysis can provide insight into surface-volume reactions occurring in various contexts. In particular, the steric hindrance effect can be quantified with a single dimensionless parameter. This work was supported in part by NIGMS Grant 1R01GM067244-01.     

Reconstructing the metabolic network of a bacterium from its genome

The prospect of understanding the relationship between the genome and the physiology of an organism is an important incentive to reconstruct metabolic networks. The first steps in the process can be automated and it does not take much effort to obtain an initial metabolic reconstruction from a genome sequence. However, such a reconstruction is certainly not flawless and correction of the many imperfections is laborious. It requires the combined analysis of the available information on protein sequence, phylogeny, gene-context and co-occurrence but is also aided by high-throughput experimental data. Simultaneously, the reconstructed network provides the opportunity to visualize the "omics" data within a relevant biological functional context and thus aids the interpretation of those data.     

Cancer risk assessment for 1,3-butadiene: data integration opportunities

The US Environmental Protection Agency recently released its new guidelines for carcinogen risk assessment together with supplemental guidance for assessing susceptibility from early-life exposure to carcinogens. In particular, these guidelines encourage the use of mechanistic data in support of dose-response characterization at doses below those at which an increase in tumor frequency over background levels might be detected. In this context of the utility of mechanistic data for human cancer risk assessment, the International Life Sciences Institute (ILSI) has developed a human relevance framework (HRF) that can be used to assess the plausibility of a mode of action (MoA) described for animal models operating in humans. The MoA is described as a sequence of key events and processes that result in an adverse outcome. A key event is a measurable precursor step that is in itself a necessary element of the MoA or is a bioindicator for such an element. A number of cellular and molecular perturbations have been identified as key events whereby DNA-reactive chemicals can produce tumors. These include DNA adducts in target tissues, gene mutations and/or chromosomal alterations in target tissues and enhanced cell proliferation in target tissues. This type of data integration approach to quantitative cancer risk assessment can be applied to 1,3-butadiene, for example, using data on biomarkers in exposed Czech workers [1]. For this study, an extensive range of biomarkers of exposure and response was assessed, including: polymorphisms in metabolizing enzymes; urinary concentrations of several metabolites of 1,3-butadiene; hemoglobin adducts; HPRT mutations in T-lymphocytes; chromosomal aberrations by FISH and conventional staining procedures; sister chromatid exchanges. Exposure levels were monitored in a comprehensive fashion. For risk assessment purposes, these data need to be considered in the context of how they inform the MoA for leukemia, the tumor type reported to be increased in synthetic rubber workers exposed to 1,3-butadiene. Also, for the HRF it is necessary to establish key events for a MoA in rodents for the induction of tumors by 1,3-butadiene. There is clearly a species difference in sensitivity to tumor induction, with mice being much more sensitive than rats; key events need to explain this difference. For butadiene, the MoA is DNA-reactivity and subsequent mutagenicity and so following the EPA's cancer guidelines, a linear extrapolation is used from the point of departure (POD), unless additional data support a non-linear extrapolation. For the present case, the human bioindicator data are not informative as far as dose-response characterization is concerned. Mouse chromosome aberration data for in vivo exposures might be used for establishing a POD, with linear extrapolation from this POD. The available cytogenetic data from rodent studies appear to be sufficiently extensive and consistent for this to be a viable approach. This approach of using MoA and key events to establish the human relevance can lead to the development of specific informative bioindicators of response that can be used as surrogates to predict the shape of the tumor dose response curve at low doses. Truly informative predictors of tumor responses should be able to provide estimates of human tumor frequencies at low, environmental exposures to 1,3-butadiene.     

Rationale and Procedures for Using the Tissue-Residue Approach for Toxicity Assessment and Determination of Tissue,Water, and Sediment Quality Guidelines for Aquatic Organisms

This article describes a set of procedures for developing tissue, water, and sediment quality guidelines for the protection of aquatic life by using the tissue-residue approach (TRA) for toxicity assessment. The TRA, which includes aspects of the Critical Body Residue (CBR) approach, associates tissue concentrations of chemicals with adverse biological effects in a dose-response fashion that can be used to determine CBRs. These CBRs can then be used to develop tissue quality guidelines (TQGs), which may be translated into water or sediment guidelines with bioaccumulation factors. Not all toxicants are amenable to this type of analysis; however, some appear to exhibit relatively consistent results that can likely be applied in a regulatory framework. By examining tissue residues, variations in toxicokinetics (temporal aspects of accumulation, biotransformation, and internal distribution) are greatly reduced allowing a greater focus on toxicodynamics (action and potency) of the toxicants. The strongest feature of this approach is causality; hence, guidelines based on tissue concentrations are based on data demonstrating a causal relationship between the acquired dose and the biological effect. Because the TRA has utility for assessing the toxicity of contaminant mixtures, an approach is presented here using toxic unit values that can be used to assess the likelihood of observing toxic effects based on tissue residues.     

2D-QSAR model development and analysis on variant groups of anti-tuberculosis drugs

A quantitative structure activity relationship study was performed on different groups of anti-tuberculosis drug compound for establishing quantitative relationship between biological activity and their physicochemical /structural properties. In recent years, a large number of herbal drugs are promoted in treatment of tuberculosis especially due to the emergence of MDR (multi drug resistance) and XDR (extensive drug resistance) tuberculosis. Multidrug-resistant TB (MDR-TB) is resistant to front-line drugs (isoniazid and rifampicin, the most powerful anti-TB drugs) and extensively drug-resistant TB (XDR-TB) is resistant to front-line and second-line drugs. The possibility of drug resistance TB increases when patient does not take prescribed drugs for defined time period. Natural products (secondary metabolites) isolated from the variety of sources including terrestrial and marine plants and animals, and microorganisms, have been recognized as having antituberculosis action and have recently been tested preclinically for their growth inhibitory activity towards Mycobacterium tuberculosis or related organisms. A quantitative structure activity relationship (QSAR) studies were performed to explore the antituberculosis compound from the derivatives of natural products . Theoretical results are in accord with the in vitro experimental data with reported growth inhibitory activity towards Mycobacterium tuberculosis or related organisms. Antitubercular activity was predicted through QSAR model, developed by forward feed multiple linear regression method with leave-one-out approach. Relationship correlating measure of QSAR model was 74% (R(2) = 0.74) and predictive accuracy was 72% (RCV(2) = 0.72). QSAR studies indicate that dipole energy and heat of formation correlate well with anti-tubercular activity. These results could offer useful references for understanding mechanisms and directing the molecular design of new lead compounds with improved anti-tubercular activity. The generated QSAR model revealed the importance of structural, thermodynamic and electro topological parameters. The quantitative structure activity relationship provides important structural insight in designing of potent antitubercular agent.     

Biological invasion as a natural experiment of the evolutionary processes: introduction of the special feature

Although biological invasion has a devastating impact on biodiversity, it also provides a valuable opportunity for natural experiments on evolutionary responses. Alien populations are often subject to strong natural selection when they are exposed to new abiotic and biotic conditions. Native populations can also undergo strong selection when interacting with introduced enemies and competitors. This special feature aims to highlight how evolutionary studies take advantage of biological invasion and, at the same time, emphasizes how studying evolutionary processes deepens our understanding of biological invasions. We hope this special feature stimulates more invasion studies taking evolutionary processes into account. Those studies should provide fundamental information essential for formulating effective measures in conserving native biodiversity, as well as valuable empirical tests for evolutionary theories.     

Metabolomics: a tool for early detection of toxicological effects and an opportunity for biology based grouping of chemicals-from QSAR to QBAR

BASF has developed a Metabolomics database (MetaMap(?) Tox) containing approximately 500 data rich chemicals, agrochemicals and drugs. This metabolome-database has been built based upon 28-day studies in rats (adapted to OECD 407 guideline) with blood sampling and metabolic profiling after 7, 14 and 28 days of test substance treatment. Numerous metabolome patterns have been established for different toxicological targets (liver, kidney, thyroid, testes, blood, nervous system and endocrine system) which are specific for different toxicological modes of action. With these patterns early detection of toxicological effects and the underlying mechanism can now be obtained from routine studies. Early recognition of toxicological mode of action will help to develop new compounds with a more favourable toxicological profile and will also help to reduce the number of animal studies necessary to do so. Thus this technology contributes to animal welfare by means of reduction through refinement (2R), but also has potential as a replacement method by analyzing samples from in vitro studies. With respect to the REACH legislation for which a large number of animal studies will need to be performed, one of the most promising methods to reduce the number of animal experiments is grouping of chemicals and read-across to those which are data rich. So far mostly chemical similarity or QSAR models are driving the selection process of chemical grouping. However, "omics" technologies such as metabolomics may help to optimize the chemical grouping process by providing biologically based criteria for toxicological equivalence. "From QSAR to QBAR" (quantitative biological activity relationship).     

White-noise analysis of nonlinear behavior in an insect sensory neuron: Kernel and cascade approaches

A functional expansion was used to model the relationship between a Gaussian white noise stimulus current and the resulting action potential output in the single sensory neuron of the cockroach femoral tactile spine. A new precise procedure was used to measure the kernels of the functional expansion. Very similar kernel estimates were obtained from separate sections of the data produced by the same neuron with the same input noise power level, although some small time-varying effects were detectable in moving through the data. Similar kernel estimates were measured using different input noise power levels for a given cell, or when comparing different cells under similar stimulus conditions. The kernels were used to identify a model for sensory encoding in the neuron, comprising a cascade of dynamic linear, static nonlinear, and dynamic linear elements. Only a single slice of the estimated experimental second-order kernel was used in identifying the cascade model. However, the complete second-order kernel of the cascade model closely resembled the estimated experimental kernel. Moreover, the model could closely predict the experimental action potential train obtained with novel white noise inputs.     

Intraspecific trait variation across scales: implications for understanding global change responses

Recognition of the importance of intraspecific variation in ecological processes has been growing, but empirical studies and models of global change have only begun to address this issue in detail. This review discusses sources and patterns of intraspecific trait variation and their consequences for understanding how ecological processes and patterns will respond to global change. We examine how current ecological models and theories incorporate intraspecific variation, review existing data sources that could help parameterize models that account for intraspecific variation in global change predictions, and discuss new data that may be needed. We provide guidelines on when it is most important to consider intraspecific variation, such as when trait variation is heritable or when nonlinear relationships are involved. We also highlight benefits and limitations of different model types and argue that many common modeling approaches such as matrix population models or global dynamic vegetation models can allow a stronger consideration of intraspecific trait variation if the necessary data are available. We recommend that existing data need to be made more accessible, though in some cases, new experiments are needed to disentangle causes of variation.     

Pharmacogenetics in drug regulation: promise, potential and pitfalls

Pharmacogenetic factors operate at pharmacokinetic as well as pharmacodynamic levels-the two components of the dose-response curve of a drug. Polymorphisms in drug metabolizing enzymes, transporters and/or pharmacological targets of drugs may profoundly influence the dose-response relationship between individuals. For some drugs, although retrospective data from case studies suggests that these polymorphisms are frequently associated with adverse drug reactions or failure of efficacy, the clinical utility of such data remains unproven. There is, therefore, an urgent need for prospective data to determine whether pre-treatment genotyping can improve therapy. Various regulatory guidelines already recommend exploration of the role of genetic factors when investigating a drug for its pharmacokinetics, pharmacodynamics, dose-response relationship and drug interaction potential. Arising from the global heterogeneity in the frequency of variant alleles, regulatory guidelines also require the sponsors to provide additional information, usually pharmacogenetic bridging data, to determine whether data from one ethnic population can be extrapolated to another. At present, sponsors explore pharmacogenetic influences in early clinical pharmacokinetic studies but rarely do they carry the findings forward when designing dose-response studies or pivotal studies. When appropriate, regulatory authorities include genotype-specific recommendations in the prescribing information. Sometimes, this may include the need to adjust a dose in some genotypes under specific circumstances. Detailed references to pharmacogenetics in prescribing information and pharmacogenetically based prescribing in routine therapeutics will require robust prospective data from well-designed studies. With greater integration of pharmacogenetics in drug development, regulatory authorities expect to receive more detailed genetic data. This is likely to complicate the drug evaluation process as well as result in complex prescribing information. Genotype-specific dosing regimens will have to be more precise and marketing strategies more prudent. However, not all variations in drug responses are related to pharmacogenetic polymorphisms. Drug response can be modulated by a number of non-genetic factors, especially co-medications and presence of concurrent diseases. Inappropriate prescribing frequently compounds the complexity introduced by these two important non-genetic factors. Unless prescribers adhere to the prescribing information, much of the benefits of pharmacogenetics will be squandered. Discovering highly predictive genotype-phenotype associations during drug development and demonstrating their clinical validity and utility in well-designed prospective clinical trials will no doubt better define the role of pharmacogenetics in future clinical practice. In the meantime, prescribing should comply with the information provided while pharmacogenetic research is deservedly supported by all concerned but without unrealistic expectations.