Download presentation

Presentation is loading. Please wait.

Published byEzra Rich Modified over 2 years ago

1
2002 David M. Hassenzahl Exploring Carcinogen Risk Analysis Through Benzene Image from Matthew J. Dowd Department of Medicinal Chemistry Virginia Commonwealth University

2
2002 David M. Hassenzahl Objective Use benzene as a case for exploring Toxicology Epidemiology Uncertainty Regulatory Science

3
2002 David M. Hassenzahl Toolbox Building Likelihood Maximization Curve fitting Bootstrapping Z-Scores Relative Risk Dose-Response extrapolation

4
2002 David M. Hassenzahl Overview of benzene Fairly common hydrocarbon –Manufacturing –Petroleum products Strongly suspected human carcinogen –Animal assays –Many epidemiological studies –Leukemia as important endpoint

5
2002 David M. Hassenzahl Benzene structure Image from Matthew J. Dowd Department of Medicinal Chemistry Virginia Commonwealth University

6
2002 David M. Hassenzahl Benzene Data in Should We Risk It? Toxicological Data, p. 175 et seq. Epidemiological Data p 211 – 216 But many other data sets –Other toxicological data (rare) –Chinese workers –Turkish workers

7
2002 David M. Hassenzahl Toxicology Data Set Number of mice Mice with tumors Mouse dose 5000 414 502027 503759 Crump and Allen 1984

8
2002 David M. Hassenzahl What are risks from benzene? Risk as potency times exposure How do we determine potency? –Extrapolate from animal data? –Extrapolate from epidemiological data? –How wrong will we be? What are “real” exposures? –What are effects at these levels?

9
2002 David M. Hassenzahl Toxicology Paracelsus “the dose makes the poison” Regulatory assumptions! This is not Dr. Gerstenberger’s Toxicology!

10
2002 David M. Hassenzahl Reading SWRI Chapter 5 US EPA Proposed guidelines (US EPA 1996)US EPA Proposed guidelines (US EPA 1996) Cox 1996

11
2002 David M. Hassenzahl General idea Applied doses –Greater specificity about exposure than epidemiology Observed effects Artificial control of exposure

12
2002 David M. Hassenzahl Physiologically Based Pharmacokinetics PBPK Investigate flows of materials through bodies System dynamics models More on these in exposure lecture

13
2002 David M. Hassenzahl Studies Animals –Rarely humans Parts –Cell –tissue

14
2002 David M. Hassenzahl Effects Chronic –cancer fatality –increasing interest in other issues –lead and intelligence in children. Acute –Reversible –Irreversible

15
2002 David M. Hassenzahl Crump and Allen Benzene data set Animals at various concentrations Four data points “Designer” mice

16
2002 David M. Hassenzahl Relevance to Humans How to get from high level, lifetime studies of animals to anticipated low dose effects in humans?

17
2002 David M. Hassenzahl Questions about benzene Is benzene a mouse carcinogen? Is benzene a human carcinogen? If so, how potent?

18
2002 David M. Hassenzahl Crump and Allen data set (Crump and Allen 1984) Note: the actual doses are not stated correctly here. See “notes for more information Benzene data set I

19
2002 David M. Hassenzahl Crump and Allen data set. Benzene data set II P(cancer) 0 0.2 0.4 Dose (mg/kg/day) 0255075100 1.0 0.8 0.6

20
2002 David M. Hassenzahl Uncertainty Pervades Often understated Creates (or at least prolongs) conflict Think as we go! (Part of Homework PS 2)

21
2002 David M. Hassenzahl Animal Test Issues Interspecific comparison Statistical uncertainty Heterogeneity Extrapolation Dose Metric

22
2002 David M. Hassenzahl Interspecific comparison Mouse-human –Metabolism as a function of body weight –Dose human = sf Dose mouse –sf = (BW human /BW mouse ) 1-b –b is empirically derived as 0.75 a a. See SWRI page 177.

23
2002 David M. Hassenzahl Interspecific comparison Lifetime of human = lifetime mouse? –Mice age 30 days per human day –Total mouse lifetime is much shorter Analogous organs or processes? –Do mice have cancer points we do not? –Do we have cancer points mice do not? a. See SWRI page 177.

24
2002 David M. Hassenzahl 1. Hallenbeck, 1993 2. Finley et al., 1994 Interspecific comparison

25
2002 David M. Hassenzahl sf = (BW human /BW mouse ) 1-b sf = (70/0.03) 0.25 = 7.0 Dose human = 7.0 Dose mouse Interspecific comparison

26
2002 David M. Hassenzahl Crump and Allen data set, converted to humans Interspecific comparison

27
2002 David M. Hassenzahl Animal Test Issues Interspecies comparison Statistical uncertainty Heterogeneity Extrapolation Dose Metric

28
2002 David M. Hassenzahl Binomial Distribution 50 genetically “identical” mice…binomial distribution? Can use this to generate “likelihood function” to compare the likelihood that any given probability is

29
2002 David M. Hassenzahl Likelihood Maximization More appropriate than Least Squares when you know something about likelihoods “Bootstrapping” method needed We will work through likelihood maximization

30
2002 David M. Hassenzahl Can calculate standard deviation using the binomial Recall that two standard deviations to either side represents a 95% confidence interval, and... Statistical Uncertainty

31
2002 David M. Hassenzahl Crump and Allen data set, applied to humans P(cancer) 0 0.2 0.4 Human Dose (mg/kg/day) 0175350525700 1.0 0.8 0.6 Statistical Uncertainty

32
2002 David M. Hassenzahl Animal Test Issues Interspecies comparison Statistical uncertainty Heterogeneity Extrapolation Dose Metric

33
2002 David M. Hassenzahl Heterogeneity Epidemiology and toxicology Genetically identical mice compared to diverse humans Predictable versus unpredictable susceptibility Male and female differences (observed cancer rates are different)

34
2002 David M. Hassenzahl Heterogeneity Genetic diversity among humans Early insights into cancer mechanism: subpopulation born with one of two “steps” competed Variability as a function of age

35
2002 David M. Hassenzahl Animal Test Issues Interspecies comparison Statistical uncertainty Heterogeneity Extrapolation Dose Metric

36
2002 David M. Hassenzahl Extrapolation Theoretical or “Mechanistic” models: –one-hit –two-hit –two-stage Empirical –Cox “data-driven, model free curve fitting” EPA Proposed Guidelines

37
2002 David M. Hassenzahl Extrapolation Concerns Overestimation Tautological effects Thresholds Hormesis, or “Vitamin” effect Underestimation Saturation Synergistic effects Susceptibility Omission

38
2002 David M. Hassenzahl

39
2002 David M. Hassenzahl After EPA (1996)

40
2002 David M. Hassenzahl Crump and Allen data set, applied to humans P(cancer) 0 0.2 0.4 Human Dose (mg/kg/day) 0175350525700 1.0 0.8 0.6 Statistical Uncertainty

41
2002 David M. Hassenzahl P(cancer) 0 0.2 0.4 Human Dose (mg/kg/day) 0175350525700 1.0 0.8 0.6 LED(10) = 100 mg b /kg/day

42
2002 David M. Hassenzahl If LED(10) = 100 mg/kg/day, then LED(10 -6 ) = 100 10 -6 / 0.1 = 1 10 -4 mg/kg/day Extrapolation

43
2002 David M. Hassenzahl Animal Test Issues Interspecies comparison Statistical uncertainty Heterogeneity Extrapolation Dose Metric

44
2002 David M. Hassenzahl Dose Metric Assumption: exposure is irrelevant to effect Area under the curve/expected value. Lifetime dose leads to average daily dose. Particularly problematic if there are threshold effects or extreme effects

45
2002 David M. Hassenzahl Risk to Humans? Lifetime cancer risk 40 hours per week 50 weeks per year 30 years Average 10 ppm(v) exposure?

46
2002 David M. Hassenzahl Calculate Risk 10ml benzene/liter air 0.313 ml/mg 20m 3 air / day 1000 liters/ m 3 70kg person

47
2002 David M. Hassenzahl Lifetime Cancer Probability is a function of Dose and Potency Assume cumulative dose –Use Daily Dose per kg body weight, averaged over lifetime Potency usually given as q* –Additional risk per unit dose Cancer Risk

48
2002 David M. Hassenzahl Cancer Risk: Exposure Term

49
2002 David M. Hassenzahl Computed Exposure Terms

50
2002 David M. Hassenzahl Computed Exposure Terms

51
2002 David M. Hassenzahl Cancer Risk

52
2002 David M. Hassenzahl “Regulatory Science” Issues Neither a simple question nor a mindless approach –(although often stated this way) “Human health conservative” versus “Heavy hand of conservative assumptions?” –May be overestimates –May be underestimates

53
2002 David M. Hassenzahl Regulatory Toxicology “Real risk” is a reified risk ALL estimates, including central tendencies, are probably wrong More science does not guarantee –“less risk” –“less uncertainty”

54
2002 David M. Hassenzahl Likelihood Maximization A curve fitting technique

55
2002 David M. Hassenzahl Binomial Distribution 50 genetically “identical” mice…binomial distribution? Can use this to generate “likelihood function” for a predicted outcome given an observed outcome

56
2002 David M. Hassenzahl Likelihood Maximization More appropriate than Least Squares when you know something about likelihoods “Bootstrapping” method needed

57
2002 David M. Hassenzahl Can calculate standard deviation using the binomial Recall that two standard deviations to either side represents a 95% confidence interval, and... Statistical Uncertainty

58
2002 David M. Hassenzahl Crump and Allen data set, applied to humans P(cancer) 0 0.2 0.4 Human Dose (mg/kg/day) 0100200300400 1.0 0.8 0.6 Statistical Uncertainty

59
2002 David M. Hassenzahl Counting Rules What is the likelihood of getting 13 heads on 50 flips of a fair coin? We know the EXPECTED value –Expected value is 25 heads

60
2002 David M. Hassenzahl Binomial Developed P(13|50) = 0.000315 P(25|50) = 0.112 P(37|50) = 0.000315 P(24|50) = 0.108 P(50|50) = 8.88 E-16 P(20|50) = 0.0412 Can use function in excel

61
2002 David M. Hassenzahl

62
2002 David M. Hassenzahl Likelihood Given –We’ve tested 50 mice at a dose D i –We found a cancer rate P(D i ) We expect that if we do it again, we will get the same rate We acknowledge that there’s some randomness

63
2002 David M. Hassenzahl Fitting a model We know that our model can’t fit ALL the data points exactly P(100mg/kg/day) = 0.08, etc Let’s get as close to this as we can! Let’s “maximize the likelihood”

64
2002 David M. Hassenzahl Likelihood Function From the binomial, we can derive the likelihood function Likelihood {P * (D i )|P(D i ) is We don’t care the exact likelihood…we just want it as big as possible

65
2002 David M. Hassenzahl Multiple Likelihoods Multiple data points –maximize the multiplied probabilities –gives each equal weight Or, take log –If y = x i –Then ln(y) = ln(x i ) –Maximize sum of logs

66
2002 David M. Hassenzahl Simple Model P*(D) = kD + D 0 Hypothetical data set nDoseP(Cancer|Dose) 5000.02 505000.04 5010000.10 5020000.18

67
2002 David M. Hassenzahl Bootstrap Simple method to fit a model to data Akin to the game “hotter-colder” Optimizes a function –Least squares –Maximum likelihood Varies model parameters –hotter or colder

68
2002 David M. Hassenzahl Bootstrap for benzene data set Create equation where Give known –P(D i ), D i P * (D) = k * D + P * 0 Allow bootstrap to vary k *, P * 0 Maximize sum of log-likelihoods

69
2002 David M. Hassenzahl Epidemiology for Risk Analysis An Introduction

70
2002 David M. Hassenzahl Objective Explore types of epidemiology methods Understand the value and limitations of epidemiology –Bradford-Hill criteria Learn essential epidemiology calculations Address benzene risk using epidemiological data

71
2002 David M. Hassenzahl Overview of epidemiology Exposed human populations Hard to control Rarely addresses causality Common measures –Relative Risk –Z-scores

72
2002 David M. Hassenzahl Pliofilm Cohort Data (SWRI Page 215) Cumulative Exposure ppm-years Leukemia RangeMeanPerson years Observe deaths Expected per pers-yr 0-45113048262.02E-4 45-4001511632062.35E-4 400-1000602466733.39E-4 >1000134191564.81E-4 Total13252584212.30E-4

73
2002 David M. Hassenzahl Two Major Classes Descriptive Population Studies Case Reports Case Series Cross-Sectional Analyses Analytical Intervention Studies Cohort Studies Case-Control studies – Toxicology?

74
2002 David M. Hassenzahl Uncertainty Issues Many toxicology uncertainties apply! Statistical uncertainty Heterogeneity Extrapolation Dose Metric

75
2002 David M. Hassenzahl Population Also called “Correlational” Most of what we call “environmental epidemiology Not controlled No causation Can point us in the right direction Note: this and subsequent slides draw heavily on Gots (1993)

76
2002 David M. Hassenzahl Populations: pros and cons Large samples Can address –major effects –potential causes Low relative risk ratios Study design challenges

77
2002 David M. Hassenzahl Case studies Observed correlation Event and outcome Examples –mobile phones and brain tumors –“Cancer clusters” No control group! A starting point only

78
2002 David M. Hassenzahl Cross-sectional analysis One time deal Bunch of questions or data points

79
2002 David M. Hassenzahl Intervention studies Common in medicine Double-blind Placebo Treatment Some ethical issues

80
2002 David M. Hassenzahl Case-control Retrospective method One group with effect Comparable group without effect Observed differences in possible causes

81
2002 David M. Hassenzahl Cohort studies Retrospective or prospective Look at exposure groups Compare rates of effects

82
2002 David M. Hassenzahl Case-control Pros Rare / long latency outcomes Efficient / small samples Existing data Range of causes / exposures Cons Reconstructed exposure Data hard to validate Confounders Selection of control Can’t calculate rates Causation unknown

83
2002 David M. Hassenzahl Cohort Studies Pros Compares Exposures Multiple outcomes Complete data –Cases –Stages Some data quality control Cons Large samples Long-term commitment –Funding and researchers –Subjects Extraneous factors Expensive Causation rare

84
2002 David M. Hassenzahl Bradford-Hill Criteria (determining causation) Temporality (Chronological relationship) Strength of Association Intensity or duration of exposure Specificity of Association Consistency Coherence and biological plausibility Reversibility

85
2002 David M. Hassenzahl Temporality Chronological relationship Does the presumed cause precede the effect? A cause must precede its effect This does not imply the reciprocal

86
2002 David M. Hassenzahl Strength of Association High relative risk of acquiring the disease Strong p-value (low statistical uncertainty)

87
2002 David M. Hassenzahl Intensity Also duration of exposure As exposure increases Does proposed effect increase?

88
2002 David M. Hassenzahl Specificity of Association. Highly specific case Highly specific exposure Example: –“leukemia from benzene” versus –“cancer from hydrocarbons”

89
2002 David M. Hassenzahl Consistency If multiple findings Do all point the same way? “Meta-analysis” is common (SWRI page 373 - 377

90
2002 David M. Hassenzahl Coherence and biological plausibility Postulate a mechanism Consistent with our understanding of biological processes Better if supporting toxicological data

91
2002 David M. Hassenzahl Reversibility Does removal of a presumed cause lead to a reduction in the risk of ill- health? –MAY strengthen cause-effect relationship May suffer from similar fallacies as temporality

92
2002 David M. Hassenzahl Some Correlation Issues Uncertain dosimetry –very difficult to estimate exposure Latency of effects, especially cancer Confounding factors Bias Representativeness of control group Small numbers

93
2002 David M. Hassenzahl Risk in the Time of Cholera Famous case SWRI 207 to 211 See Gots (1993) and Aldrich and Griffith (1993) …and almost any other epidemiology or statistics text!

94
2002 David M. Hassenzahl Cholera in London, mid1800’s John Snow Drinking water from the Thames High rates of cholera Unknown cause of cholera –Ill humours? –Vapours?

95
2002 David M. Hassenzahl Cholera in London mid 1800’s Many water companies –Southwark and Vauxhall, downstream –Lambeth, upstream –Several others

96
2002 David M. Hassenzahl London Cholera Data 1853-4 Water Company Number of Houses Cholera Deaths Southwark and Vauxhall 40,0461,263 Lambeth26,10798 Rest of London 256,4231,422

97
2002 David M. Hassenzahl Assumptions No confounders, selection problems –Snow did a good job of this, we think Number of people per household –SWRI used 1 per household –Could use other (see whether it makes a difference!)

98
2002 David M. Hassenzahl Relative Risk Risk (or lack thereof) –to exposed group –compared to unexposed group RR = 1 if no effect RR 1 means benefit RR 1 means injury

99
2002 David M. Hassenzahl Relative Risk Caveats Beware when 1 RR x –x = 1.1? 2? 10? Depends on how good the data are –Sample size –Confounders –Other uncertainties

100
2002 David M. Hassenzahl Back to London RR Southwark and Vauxhall versus the rest of London RR = 1263/40,046 / 1520/282,530 RR = 5.86 Expected rate is S and V is the same as the rest of London –p = 1520 / 282,530 = 0.00538

101
2002 David M. Hassenzahl Statistical Test

102
2002 David M. Hassenzahl Risk of Cholera? RR Lambeth versus rest of London is less than one IF Snow found a suitably unbiased, accurate, precise, etc estimator THEN Cholera is probably water-borne!

103
2002 David M. Hassenzahl Benzene and Cancer Given Pliofilm data Is benzene a human carcinogen? Is benzene a human carcinogen at low concentrations? How potent is it? –RR is basically a linear estimator

104
2002 David M. Hassenzahl Pliofilm Data (SWRI Page 215) Cum. Expose ppm- years Leuke- mia RangeMeanPerson years Observe deaths Expected per yr 0-45113048266.16 45-4001511632063.84 400-1000602466731.58 >1000134191560.440 Total132525842112.1

105
2002 David M. Hassenzahl Pliofilm Rubber manufacturer Retrospective cohort study Recreated exposure Many effects Think about potential uncertainties!

106
2002 David M. Hassenzahl Pliofilm Relative Risk Overall RR = 21 / 12.1 = 1.74 Z = 2.56 p = 99.5

107
2002 David M. Hassenzahl Meaning of RR? Is there a threshold? –RR a bit less than one for lowest group –Calculate Z-score (not significant) What is RR excluding lowest group? Is there a non-linear effect?

108
2002 David M. Hassenzahl

109
2002 David M. Hassenzahl

110
2002 David M. Hassenzahl

111
2002 David M. Hassenzahl

112
2002 David M. Hassenzahl What about benzene? Probably a cause of leukemia and other cancers in humans Data suggest a threshold –But maybe not –Or is benzene hormetic? Lots of uncertainty

113
2002 David M. Hassenzahl Conclusions Epidemiology and Toxicology are useful tools We HAVE to make assumptions We don’t know what “X” does –X = benzene, ionizing radiation, Alar… We have to decide what to do about X –Even if that means do nothing

114
2002 David M. Hassenzahl Lessons Learned Managing types and sources of uncertainty Adding toolbox items –Bootstrapping, likelihood maximization, spreadsheet skills, extrapolation If you are better informed but less certain now than several weeks ago, I’ve done my job

115
2002 David M. Hassenzahl References Aldrich, T and Griffith, J., Eds. (1993). Environmental Epidemiology and Risk Assessment, Van Nostrand Reinholt, NY NY. Cox, L.A. (1995). “Reassessing benzene risks using internal doses and Monte-Carlo Uncertainty analysis.” Environmental Health Perspectives 104(Suppl.6):1413-29. Gots, Ronald (1993). Toxic risks : science, regulation, and perception, Boca Raton, Lewis Publishers. Kammen, D.M. and Hassenzahl, D.M. (1999). Should We Risk It? Exploring Environmental, Health and Technological Problem Solving Princeton University Press, Princeton NJ Krump, K.S. and Allen, B.C. (1984). Quantitative Estimates of the Risk of Leukemia from Occupational Exposures to Benzene. Final Report to the OSHA. Ruston, LA: Science Research Systems US EPA (1997) “Proposed Guidelines for Carcinogen Risk Assessment.” Federal Register 61(79) (April 23) 17960-18011.

116
2002 David M. Hassenzahl

117
2002 David M. Hassenzahl

118
2002 David M. Hassenzahl

Similar presentations

OK

CHP400: Community Health Program- lI Research Methodology STUDY DESIGNS Observational / Analytical Studies Case Control Studies Present: Disease Past:

CHP400: Community Health Program- lI Research Methodology STUDY DESIGNS Observational / Analytical Studies Case Control Studies Present: Disease Past:

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on asian continent Ppt on japanese culture Economics ppt on demand and supply Ppt on earth hour video Ppt on going places by a.r.barton Ppt on display advertising A ppt on loch ness monster photos Ppt on beer lambert law problems Ppt on earthquakes and volcanoes Ppt on 4g technology