Food Toxicology Learning Objectives • Understand the quantitative relationship between toxicant exposure and induced effects. • Describe frequently encountered toxic effects. Dose - Response Relationships • Interpret frequency (normal distribution) and dose - response curves. • Understand threshold Food Toxicology effects with dosage Instructor: Gregory Möller, Ph.D. increase. University of Idaho 2 Food Toxicology Food Toxicology Learning Objectives, 2 What is a Dose? • Understand effective dose, margin-of-safety and • The amount of a substance administered at the relationship of effective vs. toxic dose. one time. • Examine the use of actual data for no observed • Dosage is the amount per unit weight of the effect and lowest observed exposed individual. effect in risk assessments. • Exposure is characterized • Summarize effective, lethal by and toxic doses. – Number of doses • Understand a linearized – Frequency of dosing multi-stage model for – The total period of time non-threshold responses. for the exposure. 3 4 Food Toxicology Food Toxicology Quantifying the Dose Key Concepts • Gram (g) is the standard unit but mg is typical • Dosage - response mathematical relationship of most exposures in toxicology. (positive slope). • Dosage: mg (dose) / kg (bw) / day (duration) • Causal relationship. –mg/kg/d • Observable responses. • Exposures are quantified in relation to the • Statistical management of variability media. of individual responses. – mg/L in water. – mg/kg in food. – Species, genetics, age, sex. –mg/m3 in air. • Variation in units common (ppm, ppb). 5 6 •1 Food Toxicology Food Toxicology Responses (Toxic Effects) Responses (Toxic Effects), 2 • Inflammation. • Lethal synthesis. – Local or systemic response. – Toxicant incorporation into a biochemical pathway. • Necrosis. • Lipid peroxidation. – Cell or tissue death. – Free radical oxidation of fatty acids leading to cell death. • Enzyme inhibition. • Covalent binding. – Biochemical pathway interruption. – Of electrophilic reactive – Competitive; non-competitive. metabolites to nucleophillic • Biochemical uncoupling. macromolecules. – Interference with phosphate molecule synthesis (ATP) 7 8 Ballantyne Food Toxicology Food Toxicology Responses (Toxic Effects), 3 Responses (Toxic Effects), 4 • Receptor interaction. • Neoplasia. – Modification of normal biological effects mediated – Aberrant cell division and tissue growth. by the receptor. • Neoplasms: tumorigenesis, oncogenesis. • Immune-mediated hypersensitivity reactions. • Malignant neoplasms: carcinogenesis. – Antigenic chemicals resulting in allergic reaction. • Immuno-suppression. – Increased susceptibility to infectious agents and tumorigenesis. 9 10 NLM Food Toxicology Food Toxicology Responses (Toxic Effects), 5 Types of Toxic Responses: Idiosyncratic • Genotoxic interaction. • Genetically determined sensitivity or resistance to – Chemical interaction with DNA possibly leading to toxicity heritable change. – Usually lack of enzymes / factor involved in metabolism • Clastogenic (chromosomal) effects. • Primaquine (oxidative anti-malarial drug) - 10% • Mutagenic (base pair) effects. black males / erythrocyte G-6-P dehydrogenase / hemolytic anemia • Developmental and reproductive toxicity. – Glucose-6-phosphate – Adverse effects on dehydrogenase deficiency, conception, and the most common enzyme structure and function deficiency worldwide of the conceptus. • Nitrites - lack NADH-methemoglobin reductase / methemoglobinemia 11 12 •2 Food Toxicology Food Toxicology Types of Toxic Responses: Allergic Dose-Response • Immunological mediated response (memory) • Quantitative analysis of incremental dose increase • Requires sensitizing exposure and occurrence of toxic end effect • May involve chemical/protein complex (hapten) • Responses follow normal frequency distribution • Atypical dose response (gaussian) – Small doses most effective – Large dose tolerance • Ts cells (suppressor T lymphocytes) • Contact dermatitis; anaphylaxis • Pollens, pesticides, sulfur, penicillin 13 14 Food Toxicology Food Toxicology Normal (Gaussian) Distribution Normal Distribution, 2 • Population representation of variability. 15 16 Food Toxicology Food Toxicology Normal Distribution Parameters Dose - Response Curve • Mean + one SD = 68.3 % population Resistant • Mean + two SD = 95.5 % population • Mean + three SD = 99.7 % population • Frequency converted to cumulative gives sigmoid curve Sensitive 17 18 •3 Food Toxicology Food Toxicology Observed Effects Toxic Thresholds 19 20 Food Toxicology Food Toxicology Median Lethal Dose LD50 Shape and Slope Interpretation • Often used to compare toxicity • Only measures lethality • Best for quantal data • Best for acute exposure • Tells nothing about slope • Specific quantifiers 21 22 Food Toxicology Food Toxicology Comparative Toxicity Other Thresholds: ED90 – EC50 – LC10 – TDLo • ED: effective dose – Pharmaceuticals • EC: effective concentration – Pharmaceuticals in vivo • Often blood – Environmental toxicology • LC: lethal concentration – Environmental toxicology •TDLo: Lowest published toxic dose •TCLo: Lowest published toxic concentration 23 24 •4 Food Toxicology Food Toxicology Therapeutic Index - TI Effective Dose • Ratio of dose to produce toxic effect to dose to produce desired effect •TI = LD50/ED50 • The larger the ratio, the greater the safety (e.g. 10) • Slope of dose response important Therapeutic Index (TI) = Toxic Dose/Therapeutic Dose 25 26 Food Toxicology Food Toxicology Margin of Safety Margin of Safety - MOS • Accounts more for slope differences • MOS = LD1/ED99 • Neither TI or MOS works for chemicals with no beneficial effect or repeated doses Margin of Safety (MOS) = LD(01)/ED(99) 27 28 Food Toxicology Food Toxicology Carcinogen Risk Assessment Linearized Multistage Model • Linearized Multistage Model – Assumes non-threshold effect. • Linear extrapolation through zero threshold dose from upper confidence level of lowest dose that caused cancer in animal study. • Analysis results in a cancer slope factor that can be used to predict cancer risk at a specific dose. NLM 29 30 •5 Food Toxicology Food Toxicology Other Models for Risk Assessment Transformation of Variables • One hit model (cancer) • Allows better (simpler) analysis of data at points of interest – Assumes a molecular event with cellular response. such as LD50. • Multi hit model (cancer) • Transformation into an approximate normally distributed variable. – Assumes multiple events prior to cellular activation. •Examples (rj = dead animals; nj = total animals) • Probit model • Probit transformation. – Linearization transformation that – Based on Gaussian (Bell) curve. -1 assumes log normal distribution. – Probit (rj/nj) = Φ (rj/nj) – Useful in acute lethality tests. • PB PK - Physiologically based • Logit transformation. pharmacokinetic model – Log odds of a quantal response. – Uses intensive pharmacokinetic – Logit (rj/nj) = ln [(rj/nj)/1 - (rj/nj)] and mechanistic data. • Weibull transformation. – Exponential model used in modeling multistage processes. 31 32 Food Toxicology Food Toxicology Probit Transformation Probit Transformation, 2 • Probability units → “probits” % Response SD NED Probit • Convert % response to units of deviation from the 0.1 -3 -3 2 mean or “normal equivalent deviations” (NEDs). 2.3 -2 -2 3 • Hence the NED for a 50% response is 0. 15.9 -1 -1 4 • “Probit” approach adds 5 50.0 mean 0 5 to avoid negatives. 84.1 +1 +1 6 97.7 +2 +2 7 99.9 +3 +3 8 33 34 Food Toxicology Food Toxicology Probit Transformation, 3 Log Normal Distribution • Perform log10 transformation of the dose. – Assumes log normal distribution. • Produces an approximately linear relationship. – Allows linear regression analysis. 35 36 •6 Food Toxicology Food Toxicology Probit Unit Transformation Summary: Transformations of D-R Curve • Normal frequency distribution • Arithmetic dose to log dose • Frequency data to cumulative • Probability of response to NED – Standard deviations of mean • NED to probit –NED + 5 37 38 Food Toxicology Dose-Response Curve Summary Major Parameters • Median Lethal Dose - LD50 – Other LDs, TDs or EDs • Slope • Thresholds • System saturations • Comparative toxicity • Risk assessment 39 •7.