9.2 Dose-response assessment

Dose-response assessment is a crucial part of toxicology. It helps scientists figure out how much of a substance can cause harm. By studying the relationship between dose and effect, toxicologists can set safe exposure limits for chemicals in our environment, workplaces, and products.

This topic covers key concepts like dose-response curves, threshold effects, and methods for extrapolating data. It explains how scientists use animal studies and statistical models to predict human health risks. Understanding these principles is essential for making informed decisions about chemical safety and regulation.

Fundamentals of dose-response assessment

Importance in toxicology

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• Dose-response assessment is a critical component of toxicological risk assessment that establishes the relationship between the dose of a substance and the observed adverse effects
• Provides a quantitative basis for determining safe exposure levels and setting regulatory limits for chemicals in various contexts (environmental, occupational, consumer products)
• Helps to identify the most sensitive endpoints and susceptible populations for a given substance, informing risk management decisions

Key terminology and concepts

• Dose: the amount of a substance administered to or taken up by an organism, typically expressed as a concentration or a total mass per unit body weight
• Response: the biological effect or outcome resulting from exposure to a substance, which can be measured at various levels of organization (molecular, cellular, organ, individual, population)
• Dose-response relationship: the mathematical function that describes how the magnitude or incidence of a response changes with increasing dose
• Potency: a measure of the strength of a substance's effect, often expressed as the dose required to produce a specific level of response (e.g., ED50, the dose that produces an effect in 50% of the exposed population)
• Hazard: the inherent property of a substance to cause harm under specific conditions of exposure

Dose-response curves

Typical curve shapes

• Sigmoidal (S-shaped) curve: the most common shape for dose-response relationships, characterized by a gradual increase in response at low doses, followed by a steep rise and eventual leveling off at high doses
• Linear curve: a straight-line relationship between dose and response, often observed for non-threshold effects such as genotoxicity and some types of cancer
• Hormetic curve: a biphasic dose-response characterized by a low-dose stimulatory effect and a high-dose inhibitory effect, sometimes referred to as a U-shaped or J-shaped curve

Threshold vs non-threshold effects

• Threshold effects: adverse outcomes that occur only above a certain dose level, below which no significant biological response is observed
  • Threshold doses are often used to derive health-based guidance values for non-cancer endpoints
• Non-threshold effects: adverse outcomes that are assumed to have some level of risk at any dose above zero, typically associated with genotoxic carcinogens and some developmental toxicants
  • Non-threshold effects are often assessed using linear extrapolation models to estimate low-dose risks

Factors influencing curve shape

• Mode of action: the specific biochemical or physiological processes by which a substance exerts its toxic effects can influence the shape of the dose-response curve
  • For example, receptor-mediated toxicity often exhibits a sigmoidal dose-response, while genotoxic effects may follow a linear relationship
• Exposure duration and frequency: the timing and pattern of exposure can affect the shape and position of the dose-response curve
  • Acute, high-dose exposures may produce steeper curves than chronic, low-dose exposures
• Biological variability: differences in susceptibility among individuals or species can lead to variations in dose-response relationships
  • Genetic polymorphisms, age, sex, and health status can all contribute to variability in response

Quantitative dose-response metrics

NOAEL and LOAEL

• No-observed-adverse-effect level (NOAEL): the highest dose or exposure level at which no statistically or biologically significant adverse effects are observed in the exposed population
• Lowest-observed-adverse-effect level (LOAEL): the lowest dose or exposure level at which statistically or biologically significant adverse effects are observed
• NOAEL and LOAEL are derived from experimental animal studies or human epidemiological data and are used as points of departure for establishing health-based guidance values

BMD modeling approach

• Benchmark dose (BMD) modeling: a statistical approach that fits a mathematical model to the dose-response data to estimate the dose associated with a specific benchmark response (BMR) level
  • The BMR is typically set at 5% or 10% extra risk above background for quantal data or a 1 standard deviation change from the control mean for continuous data
• The lower 95% confidence limit of the BMD (BMDL) is often used as a point of departure for risk assessment, as it accounts for the uncertainty in the dose-response data

Point of departure (POD)

• The point of departure is the dose or exposure level derived from the dose-response data that is used as the starting point for low-dose extrapolation and the derivation of health-based guidance values
• PODs can be based on the NOAEL, LOAEL, or BMDL, depending on the available data and the specific approach used in the risk assessment
• The choice of POD can have a significant impact on the final risk estimates and regulatory decisions

Low-dose extrapolation methods

Uncertainty factors

• Uncertainty factors (UFs) are numerical values used to account for various sources of uncertainty and variability when extrapolating from experimental animal data or human studies to the general population
• Common UFs include:
  • Interspecies UF (UFA): accounts for differences in sensitivity between animals and humans (default value of 10)
  • Intraspecies UF (UFH): accounts for variability in sensitivity within the human population (default value of 10)
  • Subchronic-to-chronic UF (UFS): accounts for differences in exposure duration between subchronic and chronic studies (default value of 10)
  • LOAEL-to-NOAEL UF (UFL): accounts for the use of a LOAEL instead of a NOAEL as the point of departure (default value of 10)
  • Database UF (UFD): accounts for deficiencies or gaps in the available toxicity data (default value of 1-10)
• The total UF is the product of all individual UFs and is applied to the POD to derive the health-based guidance value

Interspecies extrapolation

• Interspecies extrapolation involves the use of animal toxicity data to predict human health risks, based on the assumption that biological responses are similar across species when scaled appropriately
• Allometric scaling: a method that adjusts for differences in body size and metabolic rate between species using a power function of body weight
  • For example, the default scaling factor for oral exposures is (body weight)^0.75
• Dosimetric adjustment factor (DAF): a more refined approach that accounts for species-specific differences in pharmacokinetics and target tissue dosimetry
  • DAFs are derived using physiologically based pharmacokinetic (PBPK) models or chemical-specific data on tissue concentrations or biomarkers of exposure

PBPK modeling applications

• PBPK models are mathematical representations of the absorption, distribution, metabolism, and excretion (ADME) processes that determine the concentration of a substance in various tissues and organs over time
• PBPK models can be used to:
  • Extrapolate across species, routes of exposure, and exposure durations
  • Predict internal dose metrics (e.g., peak concentration, area under the curve) that may be more directly related to toxicity than external dose
  • Evaluate the impact of population variability and sensitive subgroups on internal dose and risk
  • Support the development of chemical-specific adjustment factors (CSAFs) for use in risk assessment

Chemical-specific adjustment factors

Use in risk assessment

• Chemical-specific adjustment factors (CSAFs) are data-derived values that replace default uncertainty factors when sufficient chemical-specific data are available
• CSAFs can be derived for interspecies differences in toxicokinetics (AKAF) and toxicodynamics (ADAF), as well as for human variability in toxicokinetics (HKAF) and toxicodynamics (HDAF)
• The use of CSAFs can reduce the overall uncertainty in risk assessment and provide more accurate estimates of safe exposure levels

Comparison to default approaches

• Default uncertainty factors are intended to be conservative and protective in the absence of chemical-specific data, but they may overestimate or underestimate the actual differences in sensitivity between species or individuals
• CSAFs are considered more scientifically robust and defensible than default UFs, as they are based on quantitative data specific to the chemical and endpoint of interest
• However, the development of CSAFs requires extensive data on the ADME properties and mechanism of action of the chemical, which may not be available for many substances
• In practice, a combination of default UFs and CSAFs may be used in risk assessment, depending on the quality and quantity of available data

Dose-response for different endpoints

Cancer vs non-cancer effects

• Dose-response assessment for cancer and non-cancer endpoints often relies on different assumptions and approaches
• Non-cancer effects are typically assumed to have a threshold below which no adverse effects occur, and the goal is to identify a dose that is likely to be without appreciable risk (e.g., RfD, ADI)
• Cancer effects are often assumed to have no threshold, and the goal is to estimate the excess lifetime risk associated with a given level of exposure using linear extrapolation models (e.g., slope factor, unit risk)

Acute vs chronic exposures

• Acute exposures are single or short-term events that can lead to immediate or delayed adverse effects, while chronic exposures are long-term, repeated exposures that may result in cumulative toxicity
• Dose-response assessment for acute exposures focuses on identifying a dose that is unlikely to cause adverse effects in a single exposure event (e.g., acute reference dose, ARfD)
• Chronic dose-response assessment aims to establish a dose that is safe for continuous or repeated exposure over a lifetime (e.g., chronic reference dose, RfD)

Local vs systemic toxicity

• Local toxicity refers to adverse effects that occur at the site of contact or entry into the body (e.g., skin irritation, respiratory tract inflammation), while systemic toxicity involves effects on distant organs or tissues after absorption and distribution
• Dose-response assessment for local toxicity may involve different exposure metrics (e.g., concentration, surface area dose) and endpoints (e.g., irritation scores, histopathology) than systemic toxicity
• Systemic dose-response assessment typically relies on internal dose metrics (e.g., blood or tissue concentrations) and endpoints that reflect the most sensitive target organ or biological response

Population variability considerations

Sensitive subpopulations

• Certain subgroups within a population may be more susceptible to the adverse effects of a substance due to intrinsic or extrinsic factors
• Examples of sensitive subpopulations include:
  • Infants and children, who may have higher exposure levels and immature detoxification systems
  • Elderly individuals, who may have reduced physiological function and co-morbidities
  • Pregnant women and the developing fetus, which may be more vulnerable to certain toxicants
  • People with pre-existing health conditions or compromised immune systems
• Dose-response assessment should consider the potential for increased sensitivity in these subgroups and apply appropriate uncertainty factors or adjustments to ensure their protection

Genetic polymorphisms impact

• Genetic polymorphisms are naturally occurring variations in gene sequences that can influence an individual's susceptibility to the adverse effects of a substance
• Polymorphisms in genes encoding enzymes involved in the metabolism and detoxification of xenobiotics (e.g., cytochrome P450s, glutathione S-transferases) can lead to differences in internal dose and toxicity risk
• For example, individuals with slow acetylator phenotypes for the enzyme N-acetyltransferase 2 (NAT2) may be more susceptible to the carcinogenic effects of aromatic amines

Incorporating variability in assessments

• Population variability can be incorporated into dose-response assessment through the use of probabilistic methods, such as Monte Carlo simulation, which propagate distributions of exposure and sensitivity parameters to characterize the range of risks across a population
• Physiologically based pharmacokinetic (PBPK) models can be used to evaluate the impact of population variability in physiological and biochemical parameters on internal dose and risk
• Chemical-specific adjustment factors (CSAFs) for human variability in toxicokinetics and toxicodynamics can be derived from population data and applied in lieu of default uncertainty factors
• Sensitive subpopulations can be explicitly considered in dose-response assessment by using data from the most susceptible individuals or by applying additional uncertainty factors to account for their potential increased risk

Dose-response in risk assessment

Deriving health-based guidance values

• Health-based guidance values are numerical values derived from dose-response assessment that represent the amount of a substance that can be ingested or inhaled over a specified period without appreciable health risk
• These values are used to set regulatory limits, establish safe exposure levels, and guide risk management decisions
• The derivation of health-based guidance values involves the application of uncertainty factors or chemical-specific adjustment factors to the point of departure (POD) from the dose-response assessment
• The choice of uncertainty factors and the level of protection (e.g., 95th percentile, 99th percentile) can have a significant impact on the final guidance value

Acceptable daily intakes (ADIs)

• The acceptable daily intake (ADI) is the amount of a substance that can be consumed daily over a lifetime without appreciable health risk
• ADIs are typically derived for substances intentionally added to food, such as food additives, pesticides, and veterinary drugs
• The ADI is calculated by dividing the point of departure (e.g., NOAEL, BMDL) by the total uncertainty factor
  • ADI = POD / (UF1 × UF2 × ... × UFn)
• ADIs are usually expressed in units of mg/kg body weight/day

Reference doses (RfDs) and concentrations (RfCs)

• Reference doses (RfDs) and reference concentrations (RfCs) are similar to ADIs but are derived for substances not intentionally added to food, such as environmental contaminants and industrial chemicals
• RfDs are expressed in units of mg/kg body weight/day and represent the daily oral exposure that is likely to be without appreciable risk over a lifetime
• RfCs are expressed in units of mg/m³ and represent the continuous inhalation exposure that is likely to be without appreciable risk over a lifetime
• Like ADIs, RfDs and RfCs are derived by dividing the point of departure by the total uncertainty factor or chemical-specific adjustment factor
• These values are used to set cleanup levels, establish permissible exposure limits, and guide environmental risk assessment and management decisions