Dose-response relationships biomarkers

Exposure. Measurement of total phenol in the urine is the most useful biomarker following inhalation exposure to phenol (ACGIH 1991). The test is nonspecific and should not be used when workers are exposed to benzene, to household products, or to medications containing phenol. Dermal exposure may also result in overestimation of inhalation exposure. In persons not exposed to phenol or benzene, the total phenol concentration in the urine does not exceed 20 mg/L and is usually <10 mg/L (ACGIH 1991). Phenol can also be measured in the urine after oral exposure, although a dose-response relationship between oral exposure to phenol and phenol in the urine has not been established. Benzene metabolism yields not only phenol, 1,4-dihydroxybenzene, and their sulfates and glucuronides, but also the benzene-... 

The limitations of the use of biomarkers in healthy volunteers must be recognised. For example, although there have been attempts to simulate migraine headache in volunteers, to date none of these models can be considered adequate to serve as a surrogate endpoint. Patients with migraine are not difficult to recruit and are usually healthy apart from their migraine. In this case, it maybe more appropriate to establish tolerability and pharmacokinetics in healthy volunteers and then to select a maximum well-tolerated dose with which to perform a small proof of principle clinical trial in patients. This will need to be followed by larger trials to establish the dose-response relationship. 

An important qualification must be made. While a biomarker may be of proven value in establishing whether a drug has the desired effect in patients or healthy volunteers (see Section 4.6.3) and for evaluation of the dose-response relationship, a biomarker may not be a surrogate for the clinical endpoint. Thus, suppression of testosterone after an initial rise will give an almost immediate endpoint for the effect of GnRH analogues in prostate cancer but the relationship breaks down later in the disease. Measures of blood glucose control are vital... 

Of course, it is not always necessary to rely on biomarkers for rapid evaluation of dose-response relationships in ED. Thus, efficacy of new drugs is readily demonstrated in terms of the clinical endpoint for diseases, such as migraine, inflammatory pain, asthma, psoriasis, glaucoma and many others. 

No biomarkers of effect have been identified for cresols. Studies designed to investigate subtle effects might discern these biomarkers, which would enable finer delineation of the dose-response relationship for an effect and allow better estimation of the levels of cresols to which people could be exposed without risk. Case reports in humans have reported methemoglobinemia and Heinz body formation that may be predictive of hemolytic anemia (Chan et al. 1971 Cote et al. 1984). 

Biomarkers are used at several stages in the risk assessment process. Biomarkers of exposure are important in risk assessment, as an indication of the internal dose is necessary for the proper description of the dose-response relationship. Similarly, biomarkers of response are necessary for determination of the no observed adverse effect level (NOAEL) and the dose-response relationship (see below). Biomarkers of susceptibility may be important for identifying especially sensitive groups to estimate an uncertainty factor. 

An important role for the dose-response relationship and biomarkers is in risk assessment. 

Exposure to toxic chemicals and the effect or response need to be quantitated to define the dose response relationship. These use what are called biomarkers, and new technology is constantly expanding the range of possible measurements. Susceptibility, important in risk assessment, can also be quantitated with biomarkers. 

Group VI biomarkers have the same fundamental properties as those in group IV, so they also share basic uses. But group VI has another important attribute toxicity information is available. It could be the dose-response relationship with the parent chemical in animals or in humans or the rela-... 

Use animal PK modeling to convert the dose-response relationship seen in toxicity studies (applied dose) to a dose-response relationship based on internal dose, using a dose metric derived from human biomonitoring data. This approach fosters the development of a biomarker-response relationship and biomarker-based toxicity values. 

Collect sufficient biomarker data in animals to express the dose-response relationship in key toxicology studies in terms of a biomarker-response relationship, in addition to an applied dose-response relationship. 

Option 2 Use of Animal Pharmacokinetic Modeling to Derive Biomarker-Based Dose-Response Relationship... 

Develop biomarkers suitable for determining internal dose-response or excreted dose-response relationships in animal studies with confirmation of biomarker applicability to humans. 

The best opportunities for communicating health implications of biomonitoring data arise when an unequivocal internal dose-response relationship has been established for humans (by methods discussed in Chapter 5) or when a clinician has data on a person s health that can be used for context-setting. The first case applies primarily to group VII biomarkers (and, with caveats, to some group VI examples) the second case extends to group V biomarkers. 

Toxicologic studies need to be expanded to incorporate collection of biomonitoring data in animals that can be related to humans. Much of the dose-response information used in risk assessments is derived from animal toxicologic studies, and these do not collect information on internal dose. Therefore, dose-response relationships can be expressed only in terms of external dose (such as milligrams per kilogram per day). However, to interpret biomonitoring data, the relationship between internal dose (biomarker concentration) and effect must be understood. 

There is a continuing need for validated biomarkers of exposure that provide information on the frequency, duration, and intensity of an exposure, as well as a better understanding of distribution, metabolism, and excretion within the individual. Likewise, continued development of analytical methods (e.g. Monte Carlo) that provide a broad characterization of exposure and dose-response relationships should be encouraged. 

Swenberg, J.A., Fryar-Tita, E., Jeong, Y.C., Boysen, G., Starr, T. (2008) Biomarkers in toxicology and risk assessment informing critical dose-response relationships. Chemical Research in Toxicology 21 253-225. 

There are currently no reliable data on the dose-response relationship of benzene exposure and chromosomal effects. In light of the data of Ward et al. (1992), further investigation of mutational effects at low doses seems appropriate. Additional data on the quantitative relationship between measured exposures and clastogenic effects might provide an alternative biomarker of effect. 

Cadmium levels in blood are generally recognised as a biomarker of recent exposure to cadmium. It can also be used as biomarker of cumulative internal dose and accumulation of cadmium, buf only when fhere is long-term (decade long) continuous exposure, for example in subsistence farmers consuming their own crops. Cadmium levels in urine are a widely recognised biomarker of cumulative internal dose, kidney and body burden of Cd. Dose-response relationships between urinary Cd and occurrence of kidney effects are described in the subsequent sections of this chapter "Sweden", "Japan", "Belgium", and "Other countries". 

See also American Conference of Governmental Industrial Hygienists Biomarkers, Human Health Biotransformation Dose-Response Relationship Exposure Hazard Identification Medical Surveillance Occupational Safety and Health Administration Psychological Indices of Toxicity Risk Assessment, Ecological Risk Assessment, Human Health. 

See also Absorption Ames Test Animal Models Biomarkers, Human Health Carcinogenesis Clean Air Act Combustion Toxicology Donora Air Pollution Episode Dose-Response Relationship Emergency Response and... 

These models often incorporate intermediate biomarker responses. Consequently, trial simulations driven by PK models, rather than more traditional dose-response relationships, will enable more detailed simulations. For example, exposure differences due to interactions, inclusion of special populations, or from dosing regimen or formulation changes may be explored with the PK models driving PD responses. This will place additional emphasis on the modeler to develop reliable PK models using Phase 1 and 2 data that translate into the patient population. Appropriate consideration of covariates, as discussed later, will be an important part of this development. 

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