Human Observational Data

There are three main sources of evidence for pro-tumorigenic activity of bile acids in the lower gastro-intestinal tract (activity in rodent CRC models, human observational data and mechanistic studies using CRC cells in vitro), which together create a strong case for a role for colorectal mucosal bile acid exposure during human colorectal carcinogenesis. 

Several strands of indirect evidence from human observational studies help put the in vivo rodent data into the context of human colorectal carcinogenesis. 

MNNG ir male/female CD-Fischer rat LCA/tauro-DCA Narisawa et al., 1974  

It is well recognised that the faecal bile acid content of random stool samples is highly variable with marked daily variation.Therefore, studies testing the association between luminal bile acid exposure and the presence of colorectal neoplasia have usually measured serum bile acid levels, which demonstrate less variability and are believed to reflect the total bile acid pool more accurately. Serum DCA levels have been shown to be higher in individuals with a colorectal adenoma compared with individuals without a neoplasm. Only one study has assessed future risk of CRC in a prospective study of serum bile-acid levels. The study was hampered by the small sample size (46 CRC cases). There were no significant differences in the absolute concentrations of primary and secondary bile acids or DCA/CA ratio between cases and controls although there was a trend towards increased CRC risk for those with a DCA/ CA ratio in the top third of values (relative risk 3.9 [95% confidence interval 0.9-17.0 = 0.1]). It will be important to test the possible utility of the DCA/ CA ratio as a CRC risk biomarker in larger, adequately powered studies. A recent study has demonstrated increased levels of allo-DCA and allo-LCA metabolites in the stool of CRC patients compared with healthy controls.  

Additionally, two studies have measured colorectal epithelial cell proliferation and apoptosis in human non-neoplastic mucosa in combination with serum bile acid quantification. Ochsenkuhn et al have reported a positive correlation between serum DCA levels and proliferation measured by flow cytometric cell cycle analysis. However, a more recent study of colorectal adenoma patients failed to detect a correlation between serum DCA and immuno-histochemical Ki-67 antigen labelling. Instead, this latter study revealed a positive correlation between serum DCA and the degree of TUNEL-positive epithelial cell apoptosis.  

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Data from both human and animal exposures are frequently used in the risk assessment of chemical exposures [12]. Most toxicity data are obtained from animal studies. Human data sources are often not recognized, and internationally there is a lack of systematic experimental and clinical (human) observational data. Available data are often of poor comparability and frequently include inadequate follow-up. A number of institutions and services have the ability to collect human health data, and these include poisons information centres, clinical toxicology centres, pre- and post-natal surveillance programs, occupational health services and hospital out-patient services. 

Coupling with its intravenous pharmacokinetic parameters, the extended CAT model was used to predict the observed plasma concentration-time profiles of cefatrizine at doses of 250, 500, and 1000 mg. The human experimental data from Pfeffer et al. [82] were used for comparison. The predicted peak plasma concentrations and peak times were 4.3, 7.9, and 9.3 qg/mL at 1.6, 1.8, and 2.0 hr, in agreement with the experimental mean peak plasma concentrations of... 

System Representation Errors. System representation errors refer to differences in the processes and the time and space scales represented in the model, versus those that determine the response of the natural system. In essence, these errors are the major ones of concern when one asks "How good is the model ". Whenever comparing model output with observed data in an attempt to evaluate model capabilities, the analyst must have an understanding of the major natural processes, and human impacts, that influence the observed data. Differences between model output and observed data can then be analyzed in light of the limitations of the model algorithm used to represent a particularly critical process, and to insure that all such critical processes are modeled to some appropriate level of detail. For example, a... 

The A(A), y(A), and z(A) terms were derived by the CIE from data obtained in visual experiments where observers matched colors obtained by the mixing of the blue, green, and red primary colors. The average result for human observers were defined as the CIE 1931 2° standard observer, and the wavelength dependencies of these color-matching functions are illustrated in Fig. 6. 

For most chemicals, actual human toxicity data are not available or critical information on exposure is lacking, so toxicity data from studies conducted in laboratory animals are extrapolated to estimate the potential toxicity in humans. Such extrapolation requires experienced scientific judgment. The toxicity data from animal species most representative of humans in terms of pharmacodynamic and pharmacokinetic properties are used for determining AEGLs. If data are not available on the species that best represents humans, the data from the most sensitive animal species are used to set AEGLs. Uncertainty factors are commonly used when animal data are used to estimate minimal risk levels for humans. The magnitude of uncertainty factors depends on the quality of the animal data used to determine the no-observed-adverse-effect level (NOAEL) and the mode of action of the substance in question. When available, pharmocokinetic data on tissue doses are considered for interspecies extrapolation. 

Such studies provide important information for a better interpretation of the toxicity observed in animals, and aid in the selection of not only the proposed initial human dose but of the dose-escalation scheme and the frequency of dosing in the clinical trial(s). Further, once such exposure data are available in humans, the data can be used to better correlate the human and animal findings. Toxicity studies should be performed in the same species used to assess exposure. Often, exposure and toxicity are measured in the same study, particularly when nonrodents are used. 

Based on the inadequate evidence for the carcinogenicity of phenol in humans and animals, IARC considers phenol not classifiable as to its carcinogenicity in humans (IARC 1989). Based on a complete lack of human carcinogenicity data, and inadequate animal data, EPA placed phenol in group D, not classifiable as to human carcinogenicity. Therefore, an increase in cancer cases would not be expected to be observed in populations exposed to phenol at concentrations found in the environment or near hazardous waste sites. 

Principal Component Analysis (PCA) is performed on a human monitoring data base to assess its ability to identify relationships between variables and to assess the overall quality of the data. The analysis uncovers two unusual events that led to further investigation of the data. One, unusually high levels of chlordane related compounds were observed at one specific collection site. Two, a programming error is uncovered. Both events had gone unnoticed after conventional univariate statistical techniques were applied. These results Illustrate the usefulness of PCA in the reduction of multi-dimensioned data bases to allow for the visual inspection of data in a two dimensional plot. 

Therefore, information on the toxicological mode(s) of action as well as mechanistic data are essential in establishing the relevance to humans of the toxicological effects observed in experimental animals. The evaluation of the relevance for humans of data from smdies in animals is aided by use of data on the toxicokinetics, including metabolism, of a substance in both humans and the animal species used in the toxicity tests, when they are available, even when they are relatively limited. 

Dose-response assessment today is generally performed in two steps (1) assessment of observed data to derive a dose descriptor as a point of departure and (2) extrapolation to lower dose levels for the mmor type under consideration. The extrapolation is based on extension of a biologically based model (see Section 6.2.1) if supported by substantial data. Otherwise, default approaches that are consistent with current understanding of mode of action of the agent can be applied, including approaches that assume linearity or nonlinearity of the dose-response relationship, or both. The default approach is to extend a straight line to the human exposure doses. 

The first step of the dose-response assessment is the evaluation of the data within the range of observation. If there are sufficient quantitative data and adequate understanding of the carcinogenic process, a biologically based model may be developed to relate dose and response data. Otherwise, as a default procedure, a standard model can be used to curve-fit the data. For each mmor response, a POD from the observed data is estimated to mark the beginning of extrapolation to lower doses. The POD is an estimated dose (expressed in human-equivalent terms) near the lower end of the observed range, without significant extrapolation to lower doses. 

The Human Genome Project went three-dimensional in late 2000. Structural genomics efforts will determine the structures of thousands of new proteins over the next decade. These initiatives seek to streamline and automate every experimental and computational aspect of the structural determination pipeline, with most of the steps involved covered in previous chapters of this volume. At the end of the pipeline, an atomic model is built and iteratively refined to best fit the observed data. The final atomic model, after careful analysis, is deposited in the Protein Data Bank, or PDB (Berman et ah, 2000). About 25,000 unique protein sequences are currently in the PDB. High-throughput and conventional methods will dramatically increase this number and it is crucial that these new structures be of the highest quality (Chandonia and Brenner, 2006). 

In this project, compound A from a potential lead series was a neutral compound of MW 314 with low aqueous solubility (Systemic clearance, volume and AUC following a 0.5mg/kg intravenous dose to rats were well predicted (within twofold) from scaled microsomal clearance and in silica prediction of pKa, logP and unbound fraction in plasma. Figure 10.3a shows the predicted oral profile compared to the observed data from two rats dosed orally at 2mg/kg. The additional inputs for the oral prediction were the Caco-2 permeability and measured human fed-state simulated intestinal fluid (FeSSIF, 92(tg/mL). The oral pharmacokinetic parameters Tmax. Cmax. AUC and bioavailability were well predicted. Simulation of higher doses of compound A predicted absorption-limited... 

In a first step the scaling of intrinsic clearances determined in rat hepatocytes was compared to in vivo clearance. When taking account of non-linearity, the estimated hepatic metabolic clearance values were in reasonable agreement with observed total clearances, which ranged from 7 to 35 mL/min/kg, and it was considered reasonable to estimate the expected clearances in human by a similar scaling of human hepatocyte data. The error around the mean predicted human clearance was based on the variability seen in different batches of human hepatocytes. 

Hydrolyzable tannins are comparatively restricted in the human diet and there are no human metabolic data. Studies in rats have indicated that some 63% of a dose of 1 g/kg commercial tannic acid is excreted unchanged in the feces accompanied by small amounts of gallic acid, pyrogallol, and resorcinol. Plasma after enzymic hydrolysis was found to contain 4-O-methylgallic acid, pyrogallol, and resorcinol. Urine also contained a small amount of gallic acid after enzymic hydrolysis. The most notable observation from this study is the failure of the gut microflora to metabolize the galloylglucoses efficiently, at least at this substantial dose. The viability or composition of the gut microflora was not reported. ... 

The potential benefits of MSC therapy may also be limited in patients suffering from certain disorders, such as arthritis. In a preclinical model of collagen-induced arthritis, it was reported that MSC therapy did not confer any benefit in the treatment of arthritis, indicating a possible contraindication of the therapy [660860]. Although it is not yet known whether such observations would translate into treatment in humans, these data highlight the need to perform careful examinations of the inflammatory environment before considering MSC therapy. 

This is dependent upon the accuracy to which a human observer can read an analog record of the output of the instrument in question, or how closely an indicator marking a particular position on a scale can be read. For example, can a particular pressure gauge be read to the nearest 0.1 kPa, to the nearest 1 kPa, or to the nearest 10 kPa It is important to consider this carefully when presented with data from an instrument with a digital output where the readability will be the same over the whole range of the instrument. In many cases the data obtained are only as precise as those indicated in the analog form. 

Figure 2.21 Relationship between lightness and reflectance. Achromatic papers are arranged on an equally spaced lightness scale by human observers. For each paper, the corresponding reflectance is shown in the graph (data from Land 1974).

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